Essentials of Nursing Informatics, 7th Edition [7 ed.] 1260456781, 9781260456783

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Essentials of Nursing Informatics

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Notice Medicine is an ever-changing science. As new research and clinical experience broaden our knowledge, changes in treatment and drug therapy are required. The authors and the publisher of this work have checked with sources believed to be reliable in their efforts to provide information that is complete and generally in accord with the standards accepted at the time of publication. However, in view of the possibility of human error or changes in medical sciences, neither the authors nor the publisher nor any other party who has been involved in the preparation or publication of this work warrants that the information contained herein is in every respect accurate or complete, and they disclaim all responsibility for any errors or omissions or for the results obtained from use of the information contained in this work. Readers are encouraged to confirm the information contained herein with other sources. For example and in particular, readers are advised to check the product information sheet included in the package of each drug they plan to administer to be certain that the information contained in this work is accurate and that changes have not been made in the recommended dose or in the contraindications for administration. This recommendation is of particular importance in connection with new or infrequently used drugs.

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Essentials of Nursing Informatics SEVENTH EDITION

Virginia K. Saba, EdD, RN, FAAN, FACMI CEO and President SabaCare, Inc. Arlington, Virginia Distinguished Scholar, Emeritus Georgetown University Washington, District of Columbia Professor, Adjunct Uniformed Services University Bethesda, Maryland

Kathleen A. McCormick, PhD, RN, FAAN, FACMI, FHIMSS Principal/Owner SciMind, LLC North Potomac, Maryland

New York • Chicago • San Francisco • Athens • London • Madrid • Mexico City Milan • New Delhi • Singapore • Sydney • Toronto

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Copyright © 2021 by McGraw Hill. All rights reserved. Except as permitted under the United States Copyright Act of 1976, no part of this publication may be reproduced or distributed in any form or by any means, or stored in a database or retrieval system, without the prior written permission of the publisher. ISBN: 978-1-26-045679-0 MHID: 1-26-045679-X The material in this eBook also appears in the print version of this title: ISBN: 978-1-26-045678-3, MHID: 1-26-045678-1. eBook conversion by codeMantra Version 1.0 All trademarks are trademarks of their respective owners. Rather than put a trademark symbol after every occurrence of a trademarked name, we use names in an editorial fashion only, and to the benefit of the trademark owner, with no intention of infringement of the trademark. Where such designations appear in this book, they have been printed with initial caps. McGraw-Hill Education eBooks are available at special quantity discounts to use as premiums and sales promotions or for use in corporate training programs. To contact a representative, please visit the Contact Us page at www.mhprofessional.com. TERMS OF USE This is a copyrighted work and McGraw-Hill Education and its licensors reserve all rights in and to the work. Use of this work is subject to these terms. Except as permitted under the Copyright Act of 1976 and the right to store and retrieve one copy of the work, you may not decompile, disassemble, reverse engineer, reproduce, modify, create derivative works based upon, transmit, distribute, disseminate, sell, publish or sublicense the work or any part of it without McGraw-Hill Education’s prior consent. You may use the work for your own noncommercial and personal use; any other use of the work is strictly prohibited. Your right to use the work may be terminated if you fail to comply with these terms. THE WORK IS PROVIDED “AS IS.” McGRAW-HILL EDUCATION AND ITS LICENSORS MAKE NO GUARANTEES OR WARRANTIES AS TO THE ACCURACY, ADEQUACY OR COMPLETENESS OF OR RESULTS TO BE OBTAINED FROM USING THE WORK, INCLUDING ANY INFORMATION THAT CAN BE ACCESSED THROUGH THE WORK VIA HYPERLINK OR OTHERWISE, AND EXPRESSLY DISCLAIM ANY WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO IMPLIED WARRANTIES OF MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. McGraw-Hill Education and its licensors do not warrant or guarantee that the functions contained in the work will meet your requirements or that its operation will be uninterrupted or error free. Neither McGraw-Hill Education nor its licensors shall be liable to you or anyone else for any inaccuracy, error or omission, regardless of cause, in the work or for any damages resulting therefrom. McGraw-Hill Education has no responsibility for the content of any information accessed through the work. Under no circumstances shall McGraw-Hill Education and/or its licensors be liable for any indirect, incidental, special, punitive, consequential or similar damages that result from the use of or inability to use the work, even if any of them has been advised of the possibility of such damages. This limitation of liability shall apply to any claim or cause whatsoever whether such claim or cause arises in contract, tort or otherwise.

CONTENTS Contributors ix Foreword xix Preface xxi Acknowledgments xxiii

Part 1 — Nursing Informatics Technologies 1 Carol J. Bickford and Marisa L. Wilson 1 Historical Perspectives of Nursing  Informatics • 3 Virginia K. Saba / Bonnie L. Westra / Juliana J. Brixey

2 Computer Systems Basics—Hardware • 29 Mary L. McHugh

3 Advanced Hardware and mHealth • 45 David J. Whitten / Kathleen G. Charters

4 Computer Systems Basics—Software • 57 Mary L. McHugh

9 Human–Computer Interaction • 153 Gregory L. Alexander

10 Trustworthy Systems for Safe and Private Healthcare • 163 Dixie B. Baker

11 Social Determinants of Health, Electronic Health Records, and Health Outcomes • 181 Marisa L. Wilson / Paula M. Procter

Part 3 — System Life Cycle  193 Denise D. Tyler 12 System Design Life Cycle: A Framework • 195 Susan K. Newbold

13 System and Functional Testing • 219 Theresa (Tess) Settergren / Denise D. Tyler

14 System Life Cycle Tools • 235 Denise D. Tyler

5 Open Source and Free Software • 69 David J. Whitten

6 Data and Data Processing • 101 Irene Joos / Cristina Robles Bahm / Ramona Nelson

Part 2 — System Standards  119 Virginia K. Saba and Joyce Sensmeier 7 Health Data Standards: Development, Harmonization, and Interoperability • 121 Joyce Sensmeier

8 Standardized Nursing Terminologies • 137 Jane Englebright / Nicholas R. Hardiker / Tae Youn Kim

Part 4 — Informatics Theory Standards 251 Virginia K. Saba 15 Healthcare Project Management • 253 Barbara Van de Castle / Patricia C. Dykes

16 The Practice Specialty of Nursing Informatics • 265 Carolyn Sipes / Carol J. Bickford

17 Foundations of Nursing Informatics • 287 Sarah Collins Rossetti / Susan C. Hull / Suzanne Bakken

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Part 5 — Policies and Quality Measures in Healthcare  317 Kathleen Smith 18 Establishing Nursing Informatics in Public Policy • 319 Rebecca Freeman / Allison Viola

19 Quality Measurement and the Importance of Nursing Informatics • 329 Jean D. Moody-Williams

20 Using Six Sigma and Lean for Measuring Quality • 341 Evelyn J. S. Hovenga / Lois M. Hazelton / Sally R. Britnell

21 Informatics Applications to Support Rural and Remote Health • 355 Amy J. Barton

22 Communication Skills in Health IT, Building Strong Teams for Successful Health IT Outcomes • 363 Elizabeth (Liz) Johnson / Karen M. Marhefka

23 Nurse Scheduling and Credentialing Systems • 381 Karlene M. Kerfoot / Kathleen Smith

24 Mastering Skills that Support Nursing Practice • 393 Melissa Barthold †

Part 6 — Nursing Practice Applications  403 Heather Carter-Templeton 25 Translation of Evidence into Nursing Practice • 405 Heather Carter-Templeton

26 Improving Healthcare Quality and Patient Outcomes Through the Integration of Evidence-Based Practice and Informatics • 423 Lynda R. Hardy / Bernadette Mazurek Melnyk

27 Nursing Plan of Care Framework for HIT • 441 Luann Whittenburg / Avaretta Davis

28 Structuring Advanced Practice Knowledge: Curricular, Practice, and Internet Resource Use • 455 Mary Ann Lavin

29 Beyond EMR Implementation: Optimize and Enhance • 481 Ellen Pollack

30 Federal Healthcare Sector Nursing Informatics • 493 Stephanie J. Raps / Margaret S. Beaubien / Christine Boltz / Michael E. Ludwig / Chris E. Nichols / Gerald N. Taylor / Susy Postal

31 Monitoring Interoperability, Device Interface, and Security • 507 R. Renee Johnson-Smith / Jillanna C. Firth

32 Population Health Informatics • 521 Karen A. Monsen

33 Informatics Solutions for Emergency Planning and Response • 535 Elizabeth (Betsy) Weiner / Lynn A. (Slepski) Nash

34 Health Information Technology: Striving to Improve Patient Safety • 553 Patricia P. Sengstack

35 Consumer Patient Engagement and Connectivity in Patients with Chronic Disease in the Community and at Home • 567 Hyeoun-Ae Park / Naoki Nakashima / Hu Yuandong / Yu-Chuan (Jack) Li

Part 7 — Advanced Applications for the Fourth Nursing IT Revolution  583 Kathleen A. McCormick 36 New Models of Healthcare Delivery and Retailers Producing Big Data • 587 Susan C. Hull

37 Artificial Intelligence in Healthcare • 605 Eileen Koski / Judy Murphy

Author deceased

†

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Contents 

38 Telehealth: Healthcare Evolution in the Technology Age • 615 Teresa A. Rincon / Mark D. Sugrue

39 Nursing’s Role in Genomics and Information Technology for Precision Health • 635 Kathleen A. McCormick / Kathleen A. Calzone

40 Big Data Analysis of Electronic Health Record (EHR) Data • 653 Roy L. Simpson

41 Nursing Data Science and Quality Clinical Outcomes • 663

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45 The Evolution of the TIGER Initiative • 725 Toria Shaw Morawski / Joyce Sensmeier

46 Initiation and Management of Accessible, Effective Online Learning • 739 Patricia E. Allen / Khadija Bakrim / Darlene Lacy

47 Social Media Tools in the Connected Age • 757 Diane J. Skiba / Sarah Mattice / Chanmi Lee

48 A Paradigm Shift in Simulation: Experiential Learning in Virtual Worlds and Future Use of Virtual Reality, Robotics, and Drones • 769 E. LaVerne Manos / Nellie Modaress

Lynn M. Nagle / Margaret A. Kennedy / Peggy A. White

42 Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents • 677

Part 9 — Research Applications  791 Veronica D. Feeg

Kaija Saranto / Ulla-Mari Kinnunen / Virpi Jylhä / Pia Liljamo / Eija Kivekäs

49 Computer Use in Nursing Research • 793

43 Global eHealth and Informatics • 693 Hyeoun-Ae Park / Heimar F. Marin

Part 8 — Educational Applications  707 Diane J. Skiba 44 Nursing Curriculum Reform and Healthcare Information Technology • 709 Eun-Shim Nahm / Mary Etta Mills / Marisa L. Wilson

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Veronica D. Feeg / Theresa A. Rienzo / Marcia T. Caton / Olga S. Kagan

50 Information Literacy and Computerized Information Resources • 825 Diane S. Pravikoff / June Levy

Appendix   Clinical Care Classification (CCC) System: Overview, Applications, and Analyses • 843 Virginia K. Saba / Luann Whittenburg

Index • 873

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CONTRIBUTORS Gregory L. Alexander, PhD, RN, FACMI, FAAN Helen Young CUPHSONAA Professor Columbia University School of Nursing New York, New York Chapter 9: Human–Computer Interaction Patricia E. Allen, EdD, RN, CNE, ANEF, FAAN Professor Emeritus, Texas Tech University–Health Science Center School of Nursing Lubbock, Texas Chapter 46: Initiation and Management of Accessible, Effective Online Learning Dixie B. Baker, PhD, MS, MS, BS, FHIMSS Senior Partner, Martin, Blanck and Associates Alexandria, Virginia Chapter 10: Trustworthy Systems for Safe and Private Healthcare Suzanne Bakken, PhD, RN, FACMI, FIAHSI, FAAN The Alumni Professor of Nursing and Professor of Biomedical Informatics Columbia University New York, New York Chapter 17: Foundations of Nursing Informatics Khadija Bakrim, EdD Educational Technologist Texas Tech University Health Science Center School of Nursing Lubbock, Texas Chapter 46: Initiation and Management of Accessible, Effective Online Learning

Cristina Robles Bahm, PhD, MSIS Assistant Professor Program Coordinator Chair Computer Science La Roche University Pittsburgh, Pennsylvania Chapter 6: Data and Data Processing Melissa Barthold,† DNP, MSN, RN-BC, CPHIMSS, FHIMSS Principal Nursing Informatics Consulting Cape Coral, Florida Chapter 24: Mastering Skills that Support Nursing Amy J. Barton, PhD, RN, FAAN, ANEF Professor Daniel & Janet Mordecai Rural Health Nursing Endowed Chair College of Nursing University of Colorado Anschutz Medical Campus Aurora, Colorado Chapter 21: Informatics Applications to Support Rural and Remote Health Margaret S. Beaubien, MS, MSN, RN, CPHIMS Captain, NC, USN (Retired) Napa, California Chapter 30: Federal Healthcare Sector Nursing Informatics Carol J. Bickford, PhD, RN-BC, CPHIMS, FAMIA, FHIMSS, FAAN Senior Policy Advisor American Nurses Association Silver Spring, Maryland Section Editor—Part 1: Nursing Informatics Technologies Chapter 16: The Practice Specialty of Nursing Informatics

Author deceased

†

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Christine Boltz, MA, MS, RN-BC, CPHIMS Captain, NC, USN (Retired)/Online Instructor Excelsior College, School of Nursing Alexandria, Virginia Chapter 30: Federal Healthcare Sector Nursing Informatics Sally R. Britnell, PhD, RN Lecturer Auckland University of Technology Nursing Department School of Clinical Sciences Auckland, New Zealand Chapter 20: Using Six Sigma and Lean for Measuring Quality Juliana J. Brixey, PhD, MPH, RN Associate Professor University of Texas Health Science Center at Houston School of Biomedical Informatics Cizik School of Nursing Houston, Texas Chapter 1: Historical Perspectives of Nursing Informatics Kathleen A. Calzone, PhD, RN, AGN-BC, FAAN Research Geneticist National Institutes of Health, National Cancer Institute, Center for Cancer Research, Genetics Branch Bethesda, Maryland Chapter 39: Nursing’s Role in Genomics and Information Technology for Precision Health Heather Carter-Templeton, PhD, RN-BC, FAAN The University of Alabama Capstone College of Nursing Tuscaloosa, Alabama Section Editor—Part 6: Nursing Practice Applications Chapter 25: Translation of Evidence into Nursing Practice

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Marcia T. Caton, PhD, RN Professor Barbara H. Hagan School of Nursing and Health Sciences Malloy College School of Nursing Rockville Centre, New York Chapter 49: Computer Use in Nursing Research Kathleen G. Charters, PhD, RN Retired Nursing Informatics Consultant Department of Defense Sequim, Washington Chapter 3: Advanced Hardware and mHealth Avaretta Davis, DNP, MHS, RN Deputy Chief Nursing Informatics Officer Veterans Affairs, Veterans Health Administration Washington, District of Columbia Chapter 27: Nursing Plan of Care Framework for HIT Patricia C. Dykes, PhD, RN, FACMI, FAAN Program Director Research, Center for Patient Safety, Research, and Practice Brigham and Women’s Hospital Associate Professor of Medicine, Harvard Medical School Chair, American Medical Informatics Association (AMIA), Board of Directors Boston, Massachusetts Chapter15: Healthcare Project Management Jane Englebright, PhD, RN, CENP, FAAN Senior Vice President & Chief Nurse Executive HCA Healthcare Nashville, Tennessee Chapter 8: Standardized Nursing Terminologies Jillanna C. Firth, RN, BSN Roudebush VA Medical Center CIS/ARK/BCMA Coordinator Indianapolis, Indiana Chapter 31: Monitoring Interoperability, Device Interface, and Security

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Veronica D. Feeg, PhD, RN, FAAN Professor, Associate Dean Barbara H. Hagan School of Nursing Molloy College Rockville Centre, New York Section Editor—Part 9: Research Applications Chapter 49: Computer Use in Nursing Research Rebecca Freeman, PhD, RN, FAAN, FNAP University of Vermont Vice President of Health Informatics Burlington, Vermont Chapter 18: Establishing Nursing Informatics in Public Policy Nicholas R. Hardiker, PhD, RN, FACMI Professor of Nursing and Health Informatics and Associate Dean (Research & Enterprise) School of Human and Health Sciences University of Huddersfield Huddersfield, United Kingdom Chapter 8: Standardized Nursing Terminologies Lynda R. Hardy, PhD, RN, FAAN Director, Data Science & Discovery Associate Professor, Clinical The Ohio State University College of Nursing Columbus, Ohio Chapter 26: Improving Healthcare Quality and Patient Outcomes Through the Integration of Evidence-Based Practice and Informatics Lois M. Hazelton, RN, Dip App Sci (Nurs), B App Sci (Ad Nurs), PhD (Entrepreneurship), FACN Independent Consultant and Researcher Nerrina, Victoria Australia Chapter 20: Using Six Sigma and Lean for Measuring Quality

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Contributors 

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Evelyn J. S. Hovenga, PhD, RN, FACHI, FACS, FACN, FIAHSI CEO, Professor & Director, eHealth Education PTY Ltd Director, Global eHealth Collaborative (GEHCO) East Melbourne, Victoria Australia Chapter 20: Using Six Sigma and Lean for Measuring Quality Susan C. Hull, MSN, RN-BC, NEA, FAMIA Chief Health Information Officer CareLoop, Inc. Boulder, Colorado Co-chair, Alliance for Nursing Informatics Chapter 17: Foundations of Nursing Informatics Chapter 36: New Models of Healthcare Delivery and Retailers Producing Big Data Elizabeth (Liz) Johnson, MS, FAAN, LCHIME, FCHIME, CHCIO, FHIMSS, RN Retired Chief Innovation Officer Tenet Health Dallas, Texas Chapter 22: Communication Skills in Health IT, Building Strong Teams for Successful Health IT Outcomes R. Renee Johnson-Smith, RN, MBA Roudebush VA Medical Center Risk Manager Indianapolis, Indiana Chapter 31: Monitoring Interoperability, Device Interface, and Security Irene Joos, PhD, RN, MSIS Professor, IST Adjunct Professor, Nursing La Roche University Pittsburgh, Pennsylvania Chapter 6: Data and Data Processing

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Virpi Jylhä, PhD, PT Senior Lecturer, Health and Human Services Informatics Department of Health and Social Management University of Eastern Finland Kuopio, Finland Chapter 42: Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents Olga S. Kagan, PhD, RN Adjunct Professor Barbara H. Hagan School of Nursing and Health Sciences Molloy College School of Nursing Rockville Centre, New York Chapter 49: Computer Use in Nursing Research Margaret A. Kennedy, PhD, MS, BScN, RN, CPHIMS-CA, PMP Chief Nursing Informatics Officer and Managing Partner Grevity Consulting Inc Vancouver, British Columbia Canada Chapter 41: Nursing Data Science and Quality Clinical Outcomes Karlene M. Kerfoot, PhD, RN, FAAN Chief Nursing Officer API Healthcare/Symplr Hartford, Wisconsin Chapter 23: Nurse Scheduling and Credentialing Systems Tae Youn Kim, PhD, RN Associate Professor University of California Davis Betty Irene Moore School of Nursing Sacramento, California Chapter 8: Standardized Nursing Terminologies

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Ulla-Mari Kinnunen, PhD, RN Adjunct Professor Senior Lecturer, Health and Human Services Informatics Department of Health and Social Management University of Eastern Finland Kuopio, Finland Chapter 42: Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents Eija Kivekäs, PhD, RN PostDoc Researcher, Health and Human Services Informatics Department of Health and Social Management University of Eastern Finland Kuopio, Finland Chapter 42: Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents Eileen Koski, MPhil, FAMIA Program Director, Health Data & Insights, Center for Computational Health IBM T. J. Watson Research Center Yorktown Heights, New York Chapter 37: Artificial Intelligence in Healthcare Darlene Lacy, PhD, RNC, CNE Associate Professor Texas Tech University Health Sciences Center School of Nursing Lubbock, Texas Chapter 46: Initiation and Management of Accessible, Effective Online Teaching

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Contributors 

Mary Ann Lavin, DSc, RN, ANP-BC (Retired), FNI, FAAN Multidimensional Search and Design Consultant Founder and President, TIIKO, LLC Associate Professor Emerita Saint Louis University Trudy Busch Valentine School of Nursing Saint Louis, Missouri Chapter 28: Structuring Advanced Practice Knowledge: Curricular, Practice, and Internet Resource Use Chanmi Lee, BSN, RN, FNP-C Family Nurse Practitioner Stride Community Health Center Aurora, Colorado Chapter 47: Social Media Tools in the Connected Age June Levy, MLS Vice President CINAHL Information Systems Glendale, California Chapter 50: Information Literacy and Computerized Information Resources Yu-Chuan (Jack) Li, MD, PhD, FACMI, FACHI, FIAHSI Distinguished Professor and Dean, Graduate Institute of Biomedical Informatics College of Medical Science and Technology Taipei Medical University Taipei, Taiwan Chapter 35: Consumer Patient Engagement and Connectivity in Patients with Chronic Disease in the Community and at Home Pia Liljamo, PhD, RN Development Manager Oulu University Hospital Administrative Centre Oulu, Finland Chapter 42: Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents

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Michael E. Ludwig, MSN, RN-BC, CPHIMS U.S. Army Chapter 30: Federal Healthcare Sector Nursing Informatics E. LaVerne Manos, DNP, RN-BC, FAMIA Program Director Interprofessional Master of Science in Health Informatics & Post-Master’s Certificate in Health Informatics Director, Informatics Director, Center for Health Informatics Clinical Associate Professor Kansas University Center for Health Informatics Kansas University School of Nursing Kansas City, Kansas Chapter 48: A Paradigm Shift in Simulation: Experiential Learning in Virtual Worlds and Future Use of Virtual Reality, Robotics, and Drones Karen M. Marhefka, DHA, MHA, RHIA Principal, Impacts Advisors Naperville, Illinois Chapter 22: Communication Skills in Health IT, Building Strong Teams for Successful Health IT Outcomes Heimar F. Marin, RN, MS, PhD, FACMI Alumni Professor, Nursing and Health Informatics Federal University of São Paulo Fellow, American College of Medical Informatics Editor-in-Chief, International Journal of Medical Informatics São Paulo, São Paulo Brazil Chapter 43: Global eHealth and Informatics Sarah Mattice, MS, RN Adjunct Instructor University of Colorado College of Nursing Aurora, Colorado Chapter 47: Social Media Tools in the Connected Age

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Kathleen A. McCormick, PhD, RN, FAAN, FACMI, FHIMSS Principal/Owner SciMind, LLC North Potomac, Maryland Book Editor and Section Editor—Part 7: Advanced Applications for the Fourth Nursing IT Revolution Chapter 39: Nursing’s Role in Genomics and Information Technology for Precision Health

Nellie Modaress, MS Assistant to Online Learning Liaison to School of Nursing University of Kansas Center for Health Informatics Kansas City, Kansas Chapter 48: A Paradigm Shift in Simulation: Experiential Learning in Virtual Worlds and Future Use of Virtual Reality, Robotics, and Drones

Mary L. McHugh Retired Dean of Nursing Los Angeles, California Chapter 2: Computer Systems Basics—Hardware Chapter 4: Computer Systems Basics—Software

Karen A. Monsen, PhD, RN, FAMIA, FAAN Professor and Chair, Population Health and Systems Cooperative University of Minnesota School of Nursing Minneapolis, Minnesota Chapter 32: Population Health Informatics

Bernadette Mazurek Melnyk, PhD, APRN-CNP, FAANP, FNAP, FAAN Vice President for Health Promotion University Chief Wellness Officer Dean and Professor, College of Nursing Professor of Pediatrics & Psychiatry, College of Medicine Executive Director, The Helene Fuld Health Trust National Institute for The Ohio State University Columbus, Ohio Editor, Worldviews on Evidence-Based Nursing Chapter 26: Improving Healthcare Quality and Patient Outcomes Through the Integration of Evidence-Based Practice and Informatics Mary Etta Mills, ScD, RN, NEA, BC, FAAN Professor Organizational Systems and Adult Health University of Maryland School of Nursing Baltimore, Maryland Chapter 44: Nursing Curriculum Reform and Healthcare Information Technology

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Jean D. Moody-Williams, RN, MPP Principal, Transitions, Trust and Triumph: Quality Improvement and Patient Engagement Fulton, Maryland Chapter 19: Quality Measurement and the Importance of Nursing Informatics Toria Shaw Morawski, MSW Sr. Manager, Professional Development Health Information Management Society System (HIMSS) Chicago, Illinois Chapter 45: The Evolution of the TIGER Initiative Judy Murphy, RN, FACMI, LFHIMSS, FAAN Chief Nursing Officer IBM Global Healthcare Minneapolis, Minnesota Chapter 37: Artificial Intelligence in Healthcare

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Contributors 

Lynn M. Nagle, PhD, RN, FAAN Director, Digital Health and Virtual Learning Adjunct Professor Faculty of Nursing University of New Brunswick Adjunct Professor University of Toronto Western University Editor-in-Chief, Canadian Journal of Nursing Leadership President Nagle & Associates Inc. Health Informatics Consulting New Brunswick/Toronto, Canada Chapter 41: Nursing Data Science and Quality Clinical Outcomes Eun-Shim Nahm, PhD, RN, FAAN Professor Organizational Systems and Adult Health University of Maryland School of Nursing Baltimore, Maryland Chapter 44: Nursing Curriculum Reform and Healthcare Information Technology Naoki Nakashima, MD, PhD Professor/Director, Medical Information Center Kyushu University Hospital, Japan Fukuoka City, Japan Chapter 35: Consumer Patient Engagement and Connectivity in Patient with Chronic Disease in the Community and at Home Lynn A. (Slepski) Nash, PhD, RN, PHCNS-BC, FAAN Captain (Retired), U.S. Public Health Service Gaithersburg, Maryland Chapter 33: Informatics Solutions for Emergency Planning and Response Ramona Nelson, PhD, RN, BC, FAAN Professor Emerita Slippery Rock University President Ramona Nelson Consulting Pittsburgh, Pennsylvania Chapter 6: Data and Data Processing

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Susan K. Newbold, PhD, RN-BC, FHIMSS, FAMIA, FAAN Owner Nursing Informatics Boot Camp Franklin, Tennessee Chapter 12: System Design Life Cycle: A Framework Chris E. Nichols, MHA, RN LSSBB Program Manager Enterprise Intelligence & Data Solutions PMO Defense Healthcare Management Systems PEO Falls Church, Virginia Chapter 30: Federal Healthcare Sector Nursing Informatics Hyeoun-Ae Park, PhD, RN, FACMI, FAAN, FIAHSI Professor Seoul National University School of Nursing and Medical Informatics Interdisciplinary Program Seoul, Republic of Korea Chapter 35: Consumer Patient Engagement and Connectivity in Patients with Chronic Disease in the Community and at Home Chapter 43: Global eHealth and Informatics Ellen Pollack, MSN, RN-BC Chief Nursing Informatics Officer UCLA Health Los Angeles, California Chapter 29: Beyond EMR Implementation: Optimize and Enhance Susy Postal, DNP, RN-BC Chief Health Informatics Officer Indian Health Service Rockville, Maryland Chapter 30: Federal Healthcare Sector Nursing Informatics Diane S. Pravikoff, RN, PhD, FAAN Vice President, Research (Retired) CINAHL Information Systems Glendale, California Chapter 50: Information Literacy and Computerized Information Resources

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Paula M. Procter, RN, CertED (FE), MSc, SFHEA, FBCS, CITP, FIMIANI, IAHSI Professor of Nursing Informatics Department of Nursing and Midwifery Sheffield Hallam University Sheffield, United Kingdom Chapter 11: Social Determinants of Health, Electronic Health Records, and Health Outcomes Stephanie J. Raps, MSN, RN-BC Doctoral Candidate Daniel K. Inouye Graduate School of Nursing Uniformed Services University of the Health Sciences Major, U.S. Air Force Bethesda, Maryland Chapter 30: Federal Healthcare Sector Nursing Informatics Theresa A. Rienzo, BSN, RN, MS, MLIS, AHIP Associate Librarian Health Sciences James E. Tobin Library Molloy College Rockville Centre, New York Chapter 49: Computer Use in Nursing Research Teresa A. Rincon, RN, PhD, CCRN-K, FCCM Director of Clinical Ops & Innovation, Virtual Med UMass Memorial Healthcare Assistant Professor, Graduate School of Nursing University of Massachusetts Medical School Worcester, Massachusetts Chapter 38: Telehealth: Healthcare Evolution in the Technology Age Sarah Collins Rossetti, RN, PhD, FACMI, FAMIA Assistant Professor of Biomedical Informatics and Nursing Columbia University Medical Center New York, New York Chapter 17: Foundations of Nursing Informatics

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Virginia K. Saba, EdD, RN, FACMI, FAAN, LL CEO & President Sabacare.com Arlington, Virginia Distinguished Scholar, Adjunct Georgetown University Washington, District of Columbia Professor, Adjunct Uniformed Services University Bethesda, Maryland Book Editor and Section Editor—Part 1: Nursing Informatics Technologies Part 4: Informatics Theory Standards Chapter 1: Historical Perspectives of Nursing Informatics Appendix: Clinical Care Classification (CCC) System: Overview, Applications, and Analyses Kaija Saranto, PhD, RN, FACMI, FAAN, FIAHSI Professor, Health and Human Services Informatics Department of Health and Social Management University of Eastern Finland Kuopio, Finland Chapter 42: Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents Patricia P. Sengstack, DNP, RN-BC, FAAN Associate Professor, Vanderbilt University School of Nursing Nursing Informatics Executive, Vanderbilt University Medical Center Nashville, Tennessee Chapter 34: Health Information Technology: Striving to Improve Patient Safety Joyce Sensmeier, MS, RN-BC, FHIMSS, FAAN Senior Advisor, Informatics Health Information Management Society System (HIMSS) Chicago, Illinois Section Editor—Part 2: System Standards Chapter 7: Health Data Standards: Development, Harmonization, and Interoperability

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Contributors 

Theresa (Tess) Settergren, MHA, MA, RN-BC Director, Nursing Informatics (Retired) University of Minnesota Minneapolis, Minnesota Chapter 13: System and Functional Testing Roy L. Simpson, DNP, RN, DPNAP, FAAN, FACMI Assistant Dean, Technology Management and Clinical Professor Doctoral Program: Doctorate Nursing Practice Nell Hodgson Woodruff School of Nursing  Emory University  Atlanta, Georgia Chapter 40: Big Data Analysis of Electronic Health Record (EHR) Data Diane J. Skiba, PhD, FACMI Professor Emeritus University of Colorado Aurora, Colorado Media Editor Chapter 47: Social Media Tools in the Connected Age Carolyn Sipes, PhD, CNS, APRN, PMP, RN-BC, NEA-BC, FAAN Professor, Core Faculty, PhD Program Walden University Minneapolis, Minnesota Chapter 16: The Practice Specialty of Nursing Informatics Kathleen Smith, MScEd, RN-BC, FHIMSS Managing Partner Informatics Consulting and Continuing Education, LLC Weeki Wachee, Florida Section Editor—Part 5: Policies and Quality Measurement in Health Care Chapter 23: Nurse Scheduling and Credentialing Systems

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Mark D. Sugrue, RN-BC, MSN, FHIMSS Managing Director, Clinical Delivery & Informatics Solutions Commonwealth Medicine University of Massachusetts Medical School Member, HIMSS North America Board of Directors (2019–2020) Chair, HIMSS North America Board of Directors (6/2020–2021) Shrewsbury, Massachusetts Chapter 38: Telehealth: Healthcare Evolution in the Technology Age Gerald N. Taylor, MD, MPH Flight Surgeon, Diplomate American Board of Preventive Medicine Captain, USPHS Coast Guard Chief Medical Informatics Officer Washington, District of Columbia Chapter 30: Federal Healthcare Sector Nursing Informatics Denise D. Tyler, DNP, MSN/MBA, RN-BC Clinical Specialist Visalia, California Section Editor—Part 3: System Life Cycle Chapter 13: System and Functional Testing Chapter 14: System Life Cycle Tools Barbara Van de Castle, DNP, APRN-CNS, OCN, RN-BC Assistant Professor University of Maryland School of Nursing Baltimore, Maryland Nurse Educator Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University Baltimore, Maryland Chapter 15: Healthcare Project Management

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Allison Viola, MBA, RHIA Director, Guidehouse Washington, District of Columbia Chapter 18: Establishing Nursing Informatics Elizabeth (Betsy) Weiner, PhD, RN-BC, FACMI, FAAN Senior Associate Dean for Informatics Centennial Independence Foundation Professor of Nursing Vanderbilt University School of Nursing Nashville, Tennessee Chapter 33: Informatics Solutions for Emergency Planning and Response Bonnie L. Westra, PhD, RN, FAAN, FACMI Associate Professor Emerita University of Minnesota School of Nursing Minneapolis, Minnesota Chapter 1: Historical Perspectives of Nursing Peggy A. White, RN, BA, MN Consultant, Canadian Nurses Association— Canadian Health Outcomes for Better Information and Care Initiative Co-Lead, National Nursing Data Standards Initiative Thornbury, Ontario Canada Chapter 41: Nursing Data Science and Quality Clinical Outcomes David J. Whitten, MSC Medical Informatics Central Regional Hospital Department of Health and Human Services (DHHS) Butner, North Carolina Chapter 3: Advanced Hardware and mHealth Chapter 5: Open Source and Free Software

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Luann Whittenburg, PhD, RN-BC, FNP-BC, CPHIMS, FHIMSS, FAAN Consultant, Health Informatics Fairfax, Virginia Chapter 27: Nursing Plan of Care Framework for HIT Appendix: Clinical Care Classification (CCC) System: Overview, Applications, and Analyses Marisa L. Wilson, DNSc, MHSc, RN-BC, CPHIMS, FAMIA, FAAN Associate Professor Interim Department Chair: Family, Community and Health Systems Health Systems Leadership Pathway Director The University of Alabama at Birmingham School of Nursing Birmingham, Alabama Section Editor—Part 1: Nursing Informatics Technologies Chapter 11: Social Determinants of Health, Electronic Health Records, and Health Outcomes Chapter 44: Nursing Curriculum Reform and Healthcare Information Technology Hu Yuandong, MD Deputy Chief Physician Institute of Chronic Disease Guizhou Provincial Center for Disease Control Guizhou, China Chapter 35: Consumer Patient Engagement and Connectivity in Patients with Chronic Disease in the Community and at Home

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FOREWORD In the years since the previous edition of Essentials in Nursing Informatics was published, U.S. hospitals have achieved nearly universal adoption of electronic health record (EHR) systems. Physician offices are only slightly behind in joining the digital ranks, and health professionals across the nation now utilize EHR systems in daily practice. The “HITECH Act” has forever changed health informatics, and we now face new challenges in improving usability, interoperability, and learning capability of these systems. With the rise of artificial intelligence and a need to reduce documentation burden required by current EHR systems, we see significant opportunity and responsibility for healthcare providers to address these challenges. Nurse informaticists are at the center of this transformational opportunity. Historically, care teams communicated primarily through written notes in the patient chart. Paper represented a technical barrier, as the best patient care is dependent on data availability over time, across locations, and among healthcare team members—including the patient. Access to the most accurate and complete information remains vital, and nursing informaticists are leading much of the work being done to improve the speed, accuracy, and utility of clinical information. We have made progress in gaining nearly instant access to patient data and evidence-based decision support that enables nurses, physicians, and other clinicians to make better decisions about patient care. These technologies, however, require continued optimization of the technology, the interoperability, and the workflow to drive improvement in user experience, reduce documentation burden, and improve patient outcomes. The accelerating demands for gathering and using data to improve patient care and clinical operations have increased awareness of informatics as a core skill, intensifying the need for clinicians to better understand these increasingly ubiquitous technologies. This edition incorporates updated teaching aids to help educators develop more sophisticated users of technology, who are equipped to improve processes and workflows that result in safer, more effective, and efficient patient care. As the specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, and knowledge—and

ultimately, build wisdom—into nursing practice, nursing informatics is uniquely positioned to help lead the optimization journey that will simplify data capture, promote sharing of data in a mobile environment, and create highperforming, patient-centric clinical information systems. The vision for a better future of healthcare is tightly associated with the future of health information technology, and data are the fuel for this journey. Thus, nurse informaticists are critical healthcare leaders for the 21st century, experts in the right place at the right time, bringing the clinical, technical, and leadership skills together to create effective partnerships among their numerous constituencies—leadership teams, clinicians, data scientists, information technologists, and more. Their role is central in advancing value and science-driven healthcare, and so their work in moving healthcare informatics from data management to decision support is essential. In the 15 years since the call for EHRs was made in the 2004 Presidential State of the Union message, we have witnessed rapid evolution of health information technology and its use in healthcare systems. The next 15 years will bring the increasing convergence of data from myriad sources outside of the formal healthcare setting into the context of clinical care. We will move up the analytic hierarchy from descriptive to diagnostic, predictive, and ultimately prescriptive and autonomous systems. Thus, the future of this field promises both challenge and opportunity for prepared participants. Just as the field has evolved, so has nursing informatics. Its practitioners have already provided tremendous energy, insight, and leadership in helping to establish the necessary infrastructure and in driving gains in healthcare technology competency, information literacy, and better healthcare outcomes. Now more than ever, we believe nursing informatics holds great promise to enhance the quality, continuity, value, and experience of healthcare. Jonathan B. Perlin, MD, PhD, MSHA, FACP, FACMI President, Clinical Services Group and Chief Medical Officer HCA Healthcare Jane D. Englebright, PhD, RN, CENP, FAAN Senior Vice President and Chief Nurse Executive HCA Healthcare xix

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PREFACE This seventh edition of Essentials of Nursing Informatics was initiated in response to requests by educators to provide a digital as well as an online version for faculty to use in the development of their course work and by nurses and other users of the sixth edition. We expanded the content to stay current, since the publisher does not plan to generate a Study Guide for this version. To do so, we have added Questions and Answers in each chapter as well as added a Summary in each of the nine parts and one Appendix of the text. Further with the updated ANA Certification Examination, we returned the basic, detailed Fundamental Chapters to update so that the chapters completely address their focus and scope. Each of the nine parts of this edition has had a Section Editor to assist the authors with their content: Part 1: Nursing Informatics Technologies—Carol J. Bickford and Marisa L. Wilson; Part 2: System Standards— Virginia K. Saba and Joyce Sensmeier; Part 3: System Life Cycle—Denise D. Tyler; Part 4: Informatics Theory Standards—Virginia K. Saba; Part 5: Policies and Quality Measures in Healthcare—Kathleen Smith; Part 6: Nursing Practice Applications—Heather Carter-Templeton; Part 7: Advanced Applications for the Fourth Nursing IT Revolution—Kathleen A. McCormick; Part 8: Educational Applications—Diane J. Skiba; and Part 9: Research Applications—Veronica D. Feeg. For this edition, faculty recommended that we write a part summary introducing important concepts in each part. This book was written by experts in nursing and informatics, but when we were editing this book, the most unusual circumstances occurred. The COVID-19 pandemic swept across continents. Nurses in practice were stretched by large volumes of critical care to a large cohort of patients. Unique digital concepts were developed on site, implemented to large groups of healthcare professionals in the ICU, the hospital, nearby local pop-up hospitals, primary care offices, networks of specialty healthcare workers, and skilled nursing facilities and nursing homes. The mandate for interconnected healthcare, telehealth, and digital education quickly became an adopted norm. Several new chapters were added in Part 7: Advanced Applications for the Fourth Nursing IT Revolution (Kathleen McCormick—Section Editor). This part has the following chapters: New Models of Healthcare Delivery

(Chap. 36), Artificial Intelligence in Healthcare (Chap. 37), Telehealth: Healthcare Evolution in the Technology Age (Chap. 38), Nursing’s Role in Genomics and Information Technology for Precision Health (Chap. 39), Big Data Analysis of Electronic Health Record (EHR) Data (Chap. 40), Nursing Data Science and Quality Clinical Outcomes (Chap. 41), Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents (Chap. 42), and Global eHealth and Informatics (Chap. 43). We requested authors to include updates on the digital health requirements, policies, and regulations as a result of the COVID-19 pandemic. The updates in chapters include new references, policies, and skills required by nurses in the field. A complete update and an overview of the Federal Health Care Sector Nursing Informatics are described by experts representing all the federal sectors. The Veteran’s Administration Nursing Plan of Care Framework is described. Instead of an International Section, the nurse authors from Australia, South Korea, Finland, South America, Canada, the United Kingdom, and North America have described their expertise in Six Sigma, Measuring and Evaluating Quality, describing Consumer Patient Engagement and Connectivity in Patients with Chronic Diseases in the Community and in their Home, and Global eHealth initiatives in Nursing Informatics. Their chapters represent the expertise that they bring to Essentials of Nursing Informatics, seventh edition. Our new Media Editor, Diane J. Skiba, is considering including a website for slides, abstracts, and any other materials of interest that the authors determined would support the faculty and/or enhance the educational process. We feel that this new edition will provide the new theories, federal policies, and new content that have impacted the field of Nursing Informatics that are continually changing. Virginia and Kathleen have felt that they have, during the past 20+ years, and their six editions, with their specialist authors in the field, provided the most current and reliable information as this new Nursing Specialty advanced, grew, and changed with the technological advancements that impacted on the changing healthcare processes. We feel honored that this text has been used by the key administrative leaders, educators, and researchers xxi

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xxii 

  P reface

in the field. We feel that this text has helped keep Nursing Informatics in the forefront of our discipline. We hope you will be as pleased with this seventh edition as you have been with the past editions. We hope it will help faculty

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teach the content, assist nurses with the certification requirements, and help us advance Nursing Informatics in the 21st century. Dr. Virginia K. Saba Dr. Kathleen A. McCormick

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ACKNOWLEDGMENTS This seventh edition of Essentials of Nursing Informatics is dedicated to all the section editors, chapter authors, and their co-authors. Each of these prestigious contributors are experts in their respective positions, implementing systems, policies, research, and educational programs to support and advance Nursing Informatics in this country and abroad. We acknowledge our international colleagues in Nursing Informatics. We also acknowledge the McGraw Hill staff Susan Barnes Oldenburg, Richard Ruzycka, and Christina Thomas and their contractors Touseen Qadri from MPS Limited who contributed to the editing of this book, completing the production of this book, as well as supporting the expansion of the book with new resources.

We also want to especially remember those authors and other supporters of Nursing Informatics who have left us last year: Helen Connors, Kathleen (Milholland) Hunter, Margaret Ross Kraft, Melissa Barthold, Julie McAfoos, Andrew McLaughlin, Dr. Donald, Lindberg, and others who have left their mark on the field. The authors also acknowledge their families because without their encouragement and help this book would not be a reality. We thank the Lord for giving us the opportunity to embark on a seventh edition and for the help we received in completing it. Dr. Virginia K. Saba Dr. Kathleen A. McCormick

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part

1

Nursing Informatics Technologies Carol J. Bickford and Marisa L. Wilson

A new feature of the seventh edition of Essentials in Nursing Informatics is a part summary that provides an overview for each of the nine parts of this edition. Each part represents a specific focus on this Nursing Informatics specialty and provides appropriate information in separate chapters. The coronavirus pandemic that occurred during the publishing process also allowed some authors to address its impact on specific practice areas. The informatics nurse uses data to create information and knowledge to support best care practices. The informatics nurse engages the data to information to knowledge process using technology to support patient care, increase efficiency, ensure quality, and improve outcomes. In order to do this, the informatics nurse needs to understand the foundations of computer hardware and software as well as the processes for managing data and information. Understanding how computer hardware and software works is core to fulfilling the tenets of nursing informatics as outlined in the American Nurses Association (ANA) Nursing Informatics: Scope and Standards of Practice, Second Edition. Part 1 content description follows. Chapter 1, entitled Historical Perspectives in Nursing Informatics, authored by Dr. Virginia K. Saba, Dr. Bonnie L. Westra, and Dr. Julie J. Brixey, provides a historical overview of Nursing Informatics (NI) during 10-year period starting in the 1960s with the introduction of computer technology in healthcare. The chapter provides landmark events that influenced the growth of NI as a new nursing specialty. It provides an update on new activities since the previous edition, including new information on where and who were involved in advancing this specialty. It includes the new criteria, established by the NI pioneers, that addressed nursing practice standards, educational content, certification requirements, etc. It also updated the Landmark Events and Pioneers in Computers and Nursing, and Nursing Informatics table with the name of the major NI pioneer involved. Chapter 2, Computer Systems Basics—Hardware, authored by Dr. Mary L. McHugh, provides a helpful overview of basic computer hardware components, their characteristics, and functions. Because computers are ubiquitous in everyone’s personal life and the healthcare industry, an understanding of the operations of such an infrastructure is foundational for the informatics nurse. The five basic types of computers and the associated internal components and diverse peripherals are described, as is the critical connectivity provided by network hardware.

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Chapter 3, developed by Dr. David J. Whitten who updated Dr. Kathleen Charters’ earlier description of Advanced Hardware and mHealth supports innovative mobile healthcare models. This chapter informs the readers of the use of increasingly sophisticated mechanical devices and electronic systems that will allow providers to provide care, consultation, and communications in and across a multitude of settings. This chapter reviews the use of advanced tablets, smartphones, wearables, and implantable or injectable devices made possible by the acceleration of increasingly sophisticated and powerful hardware within an infrastructure that supports these activities. Chapter 4, Computer Systems Basics—Software, authored by Dr. Mary L. McHugh, clarifies the differences and complementary interrelationships of the three basic types of software: system software, utility programs, and applications software. Discussion of the evolution of the generations and levels of programming languages promotes appreciation of the complexity of today’s information systems’ environments and the value of expert programmers, colleagues, and specialists partnering with the informatics nurse to ensure continuity of operations and services. In Chapter 5, Drs. David J. Whitten expanded the previous authors’—Drs. Peter Murray and W. Scott Erdley—focus on an important topic Open Source and Free Software or Free/Libre Open Source Software (FLOSS). Most informatics nurses will work with vendor software products. However, they need to be aware of the resources available for use that are free and open source. The informatics nurse needs to be familiar with the risks and rewards of using this software and the differences between this and proprietary software when making choices. The authors provide a history of FLOSS, along with development models, that dates back to the 1950s. They describe the benefits and issues related to the choice to use FLOSS versus proprietary software. Whitten also provided detailed steps that one would consider when choosing software, whether FLOSS or proprietary. The authors also cover the topic of licensing, which with the FLOSS model encourages sharing of the software to facilitate dissemination. Chapter 6 covers Data and Data Processing. Drs. Irene Joos, Cristina Bahm, and Ramona Nelson offer a broad presentation of this topic. Nelson’s Data to Information to Knowledge to Wisdom Model’s megastructures and concepts underline NI, as well as processes for creating and using knowledge that the data care providers collect. In this chapter, the authors describe important concepts in generating data. They introduce information on big data, data repositories, database management systems, database models, and processes for curating data. The function and processes of interoperability are described. Auditing data, information creation, and interpretation in the form of analytics are explained. Clinical decision support and expert systems are presented as examples of processes using data to support value-based care.

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1 Historical Perspectives of Nursing Informatics Virginia K. Saba / Bonnie L. Westra / Juliana J. Brixey

• OBJECTIVES . Identify a brief historical perspective of nursing informatics. 1 2. Explore lessons learned from the pioneers in nursing informatics. 3. List the major landmark events and milestones of nursing informatics.

• KEY WORDS Computers Computer Literacy Computer Systems Data Standards Electronic Health Records (EHR) Healthcare Information Technology (HIT) Information Systems Internet Nursing Informatics (NI)

OVERVIEW Nursing Informatics is a phrase that evolved from the French word “informatique” which referred to the field of applied computer science concerned with the processing of information such as nursing information. The computer was seen as a tool that could be used in many environments. In the early 1960s, the computer was introduced into healthcare facilities for the processing of basic administrative tasks. Thus the computer revolution in healthcare began and led to today’s healthcare information technology (HIT) and/or electronic health record (EHR) systems. The importance of the computer as an essential tool in HIT systems and the delivery of contemporary healthcare is indisputable. HIT is an all-encompassing term

referring to technology that captures, processes, and generates healthcare information. Computerization and/or electronic processing affect all aspects of healthcare delivery including (a) provision and documentation of patient care, (b) education of healthcare providers, (c) scientific research for advancing healthcare delivery, (d) administration of healthcare delivery services, (e) reimbursement for patient care, (f ) legal and ethical implications, as well as (g) safety and quality issues. Since the inception of the computer, there has been a shift from the use of mainframe, mini, or microcomputers (PCs) to integrating multiple technologies and telecommunication devices such as wireless, handheld, mobile computers, and smart (cell) phones designed to support the continuity of care across healthcare settings and HIT systems. There has also been a shift from storage 3

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4 

  P art 1 • N ursing I nformatics T echnologies

devices to cloud storage. Furthermore, there is less need to develop written instructions for software programs as today’s applications are icon-based, user-friendly, and menu-driven. Additionally, video tutorials are available for many programs. Touch-screen devices are replacing the need for a mouse. Today, computers in nursing are used to manage patient care information, monitor quality, and evaluate outcomes. Computers and networks are also being used for communicating (sending and receiving) data and messages via the Internet, accessing resources, and interacting with patients on the Web. Nurses are increasingly using systems for planning, budgeting, and policy-making for patient care services. Computers are also used to document and process real-time plans of care, support nursing research, test new systems, design new knowledge databases, develop data warehouses, and advance the role of nursing in the healthcare industry and nursing science. Moreover, computers are enhancing nursing education and distance learning with new media modalities. This chapter is an updated and revised version of the chapter “Historical Perspectives of Nursing Informatics” (Saba & Westra, 2015) published in the 6th edition of the Essentials of Nursing Informatics (Saba & McCormick, 2015). In this chapter the significant events influencing the growth of nursing informatics (NI) as a nursing specialty are analyzed according to (1) seven time periods, (2) a view of the newest technological innovations used by nurses, (3) a description of Nursing Informatics Pioneers including a synthesis of lessons learned from videotaped interviews with NI pioneers, (4) electronic health records from a historical perspective, and (5) landmark events in nursing and computers, with Table 1-1 listing those events that influenced the introduction of computers into the nursing profession including key “computer/informatics” nurse that directed the activity. Also, Table 1-2 lists current organizations supporting nursing informatics.

MAJOR HISTORICAL PERSPECTIVES OF NURSING AND COMPUTERS Seven Time Periods Computers were introduced into the nursing profession over 40 years ago. Major milestones of nursing are interwoven with the advancement of computer and information technologies, the increased need for nursing data, development of nursing applications, and changes making the nursing profession an autonomous discipline. The key activities and events for each decade are presented to provide a background and the sequence of events to

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demonstrate nursing’s commitment to Computers and Nursing including Information Technology. Prior to 1960s Computers were first developed in the late 1930s to early 1940s. As computers have evolved, computing power has increased. This was attributed to the increasing number of transistors or chips placed in an integrated circuit. In the mid-1960s Gordon Moore noted that the number doubled approximately every two years. This argument has become known as Moore’s law (Techopedia, 2019). Use of computers in the healthcare industry did not occur until the 1950s and 1960s. During this time, there were only a few experts nationally and internationally who formed a cadre of pioneers that adapted computers to healthcare and nursing which was undergoing major changes. Several professional advances provided the impetus for the profession to embrace computers—a new technological tool. Computers were initially used in healthcare facilities for basic office, administrative, and financial accounting functions. These early computers used punch cards to store data and card readers to read computer programs, sort, and prepare data for processing. Computers were linked together and operated by paper tape using teletypewriters to print their output. As computer technology advanced, healthcare technologies also advanced. The major advances are listed chronologically in Table 1-1. 1960s  During the 1960s the uses of computer technology in healthcare settings began to be explored. Questions such as “Why use computers?” and “What should be computerized?” were discussed. Nursing practice standards were reviewed, and nursing resources were analyzed. Studies were conducted to determine how computer technology could be utilized effectively in the healthcare industry and what areas of nursing should be automated. The nurses’ station in the hospital was viewed as the hub of information exchange; therefore, numerous initial computer applications were developed and implemented in this location. By the mid-1960s, clinical practice presented nurses with new opportunities for computer use. Increasingly complex patient care requirements and the proliferation of intensive care units (ICUs) required that nurses become super users of computer technology as nurses monitored patients’ status via cardiac monitors and instituted treatment regimens through ventilators and other computerized devices such as infusion pumps. A significant increase in time spent by nurses documenting patient care, in some cases estimated at 40%, as well as a noted rise in medication administration errors prompted the need to investigate emerging hospital computer-based information systems (Sherman, 1965; Wolkodoff, 1963).

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  TABLE 1.1   

Landmark Events and Pioneers in Computers and Nursing, and Nursing Informatics

Year(s)

Title/Event

Sponsor(s)

Coordinator/Chair/NI Representative(s)

1973

First Invitational Conference: Management Information Systems (MISs) for Public and Community Health Agencies

National League for Nursing (NLN) and Division of Nursing, Public Health Service (DN/PHS), Arlington, VA

Goldie Levenson (NLN)

1974 to 1975

Five Workshops in USA on MISs for Public and Community Health Agencies

NLN and DN/PHS, selected US Cities

Goldie Levenson (NLN)

1976

State-of-the-Art Conference on Management for Public and Community Health Agencies

NLN and DN/PHS, Washington, DC

1977

First Research: State-of-the-Art Conference on Nursing Information Systems

University of Illinois College of Nursing, Chicago, IL

Harriet H. Werley (UIL)

1977

First undergraduate academic course: Computers and Nursing

The State University of New York at Buffalo, Buffalo, NY

Judith Ronald (SUNY, Buffalo)

1979

First Military Conference on Computers in Nursing

TRIMIS Army Nurse Consultant Team, Walter Reed Hospital, Washington, DC

Dorothy Pocklington (TRIMIS Army)

1980

First Workshop: Computer Usage in Healthcare

University of Akron, School of Nursing, Continuing Education Department, Akron, OH

Virginia Newbern (UA/SON)

Virginia K. Saba (DN/PHS)

Virginia K. Saba (DN/PHS) Goldie Levenson (NLN) Virginia K. Saba (DN/PHS) Margaret Grier (UIL)

Linda Guttman (ANC)

Virginia K. Saba (DN/PHS)

1980

First Computer Textbook: Computers in Nursing

Nursing Resources, Boston, MA

Rita Zielstorff, Editor

1981

First Special Interest Group Meeting on Computers in Nursing at SCAMC

Annual SCAMC Conference Event, Washington, DC

Virginia K. Saba, Chair (DN/PHS)

1981 to 1991

First Nursing Papers at Fifth Annual Symposium on Computer Applications in Medical Care (SCAMC)

Annual SCAMC Conference Sessions, Washington, DC

Virginia K. Saba (DN/PHS)

Four National Conferences: Computer Technology and Nursing

NIH Clinical Center, TRIMIS Army Nurse Consultant Team, and DN/PHS NIH Campus, Bethesda, MD

Virginia K. Saba (DN/PHS)

1981 to 1984

Coralee Farlee (NCHSR) Ruth Carlson and Carol Romano (CC/NIH) Dorothy Pocklington and Carolyn Tindal (TRIMIS Army) Transport Research and Innovation Monitoring and Information System Army

Early academic course on Computers in Nursing (NIH/CC)

Foundation for Advanced Education in Sciences (FAES) at NIH, Bethesda, MD

Virginia K. Saba (DN/PHS)

1982

Study Group on Nursing Information Systems

University Hospitals of Cleveland, Case Western Reserve University, and National Center for Health Services Research (NCHSR/PHS), Cleveland, OH

Mary Kiley (CWS)

Kathleen A. McCormick (NIH/PHS) Gerry Weston (NCHSR) (continued)

 5

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1981

Chapter 1 • Historical Perspectives of Nursing Informatics  

Dorothy Pocklington (TRIMIS Army)

Landmark Events and Pioneers in Computers and Nursing, and Nursing Informatics (continued) Title/Event

Sponsor(s)

Coordinator/Chair/NI Representative(s)

1982 to 2013

Initiated Annual International Nursing Computer Technology Conference

Rutgers, State University of New Jersey, College of Nursing, CE Department, selected cities

Gayle Pearson (Rutgers)

1982

First International Workshop: The Impact of Computers on Nursing

London Hospital, UK and IFIP-IMIA, Harrogate, UK

Maureen Scholes (UK)

1982

First Newsletter: Computers in Nursing

School of Nursing, University of Texas at Austin, Austin, TX

Gary Hales (UT)

1982/1984

Two Boston University (BU) Workshops on Computers and Nursing

Boston University School of Nursing, Boston, MA

Diane Skiba (BU)

1982

PLATO IV CAI Educational Network System

University of Illinois School of Nursing, Chicago, IL

Pat Tymchyshyn (UIL)

1982

Capital Area Roundtable in Informatics in Nursing (CARING) Founded

Greater Washington, DC

Founding Members: Susan McDermott P.J. Hallberg Susan Newbold

1983 to Present (Every 3 Years)

Initiated nursing papers at MEDINFO World Congress on Medical Informatics, International Medical Informatics Association (IMIA)

1983—Amsterdam, NL

Elly Pluyter-Wenting, First Nursing Chair

Jean Arnold (Rutgers) Mary Anne Rizzolo Barry Barber (UK)

1986—Washington, DC, USA 1989—Singapore, Malaysia 1992—Geneva, Switzerland 1995—Vancouver, Canada 1998—Seoul, South Korea 2001—London, United Kingdom 2004—San Francisco, CA, United States 2007—Brisbane, Australia 2010—Capetown, South Africa 2013—Copenhagen, Denmark 2015—São Paulo, Brazil 2017—Hangzhou, China 2019—Lyon, France 2021—Sydney, Australia

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1983

Second Annual Joint SCAMC Congress and IMIA Conference

SCAMC and IMIA, San Francisco, CA and Baltimore, MD

Virginia K. Saba, Nursing Chair

1983

Early Workshop: Computers in Nursing

University of Texas at Austin, Austin, TX

Susan Grobe (UT—Austin)

1983

First Hospital Workshop: Computers in Nursing Practice

St. Agnes Hospital for HEC, Baltimore, MD

Susan Newbold

1983

First: Nursing Model for Patient Care and Acuity System

TRIMIS Program Office, Washington, DC

Karen Rieder (NNC) Dena Nortan (NNC)

  P art 1 • N ursing I nformatics T echnologies

Year(s)

6 

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  TABLE 1.1   

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1983 to 2012

2008 to 2014 2014 to Present (Q 2 Years)

1983—Amsterdam, Netherlands

1983—Maureen Scholes, First Chair

1985—Calgary, Canada

1985—Kathryn J. Hannah and Evelyn J. Guillemin

Renamed: IMIA Nursing Informatics, Special Interest Group (IMIA/NI-SIG)

1988—Dublin, Ireland

Noel Daley and Maureen Scholes

1991—Melbourne, Australia

Evelyn S. Hovenga and Joan Edgecumbe

1994—San Antonio, TX, USA

Susan Grobe and Virginia K. Saba

1997—Stockholm, Sweden

Ulla Gerdin and Marianne Tallberg

2000—Auckland, New Zealand

Robyn Carr and Paula Rocha

2003—Rio de Janeiro, Brazil

Heimar Marin and Eduardo Marques

2006—Seoul, Korea

Hyeoun-Ae Park

2009—Helsinki, Finland

Anneli Ensio and Kaija Saranto

2012—Montreal, Canada

Patricia Abbott (JHU)

2014—Taipei, Taiwan

Polun Chang

2016—Geneva, Switzerland

Patrick Weber

2018—Guadlajara, Mexico 2021—Sidney, Australia

Diane Skiba. Judy Murphy

1984

American Nursing Association (ANA) Initiated First Council on Computer Applications in Nursing (CCAN)

ANA

Harriet Werley, Chair First Exec. Board: Ivo Abraham Kathleen McCormick Virginia K. Saba Rita Zielstorff

1984

First Seminar: Microcomputers for Nurses

University of California at San Francisco, College of Nursing, San Francisco, CA

William Holzemer, Chair

1984

First Nursing Computer Journal: Computers in Nursing CIN, Renamed Computers, Informatics, Nursing

JB Lippincott, Philadelphia, PA

Gary Hales (UT Austin) First Editorial Board: Patricia Schwirian (OSU) Virginia K. Saba (GT) Susan Grobe ( UT) Rita Zielstorff (MGH Lab)

1984 to 1995

First Annual Directory of Educational Software for Nursing

Christine Bolwell and National League for Nursing (NLN)

Christine Bolwell (continued)

 7

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Chapter 1 • Historical Perspectives of Nursing Informatics  

Initiated International Symposium: Nursing Use of Computers and Information Science, International Medical Informatics Association (IMIA) Working Group 8 on Nursing Informatics (NI).

8 

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Landmark Events and Pioneers in Computers and Nursing, and Nursing Informatics (continued)

Year(s)

Title/Event

Sponsor(s)

Coordinator/Chair/NI Representative(s)

1985

NLN initiated First National Forum: Computers in Healthcare and Nursing

National League for Nursing, New York City, NY

Susan Grobe, Chair First Exec. Board: Diane Skiba Judy Ronald Bill Holzemer Roy Simpson Pat Tymchyshyn

1985

First Annual Seminar on Computers and Nursing Practice

NYU Medical Center, NY, NY

Patsy Marr New York University (NYU)

1985

First Invitational Nursing Minimum Data Set (NMDS) Conference

University of Illinois School of Nursing, Chicago, IL

1985

Early academic course: Essentials of Computers, in Undergraduate and Graduate Programs

Georgetown University School of Nursing, Washington, DC

Virginia K. Saba (GU)

1985 to 1990

Early 5-year Project: Continuing Nursing Education: Computer Technology, Focus: Nursing Faculty

Southern Regional Education Board (SREB), Atlanta, GA

Eula Aiken (SREB)

1985

First Test Authoring Program (TAP)

Addison-Wesley Publishing, New York, NY

William Holzemer (UCSF)

1985

First Artificial Intelligence System for Nursing Two early Microcomputer Institutes for Nurses

Creighton On-line Multiple Medical Education Services (COMMES), Creighton University Georgetown University, School of Nursing, Washington, DC

Sheila Ryan, Dean and Faculty Professor, Steven Evans, Developer

University of Southwest Louisiana Nursing Department, Lafayette, LA

Diane Skiba (BU)

Christine Bolwell and Stewart Publishing, Alexandria, VA

Christine Bolwell, Editor

1986

1986

Established first nurse educator’s newsletter: Micro World

Janet Kelly (NYU) Harriet Werley (UIL) Norma Lang (UM)

Virginia K. Saba (GU) Dorothy Pocklington (USL)

1986

CIN First Indexed in MEDLINE and CINAHL

J. B. Lippincott Publisher, Philadelphia, PA

Gary Hales, Editor

1986

First NI Pyramid—NI Research Model

Published in CIN Indexed in MEDLINE and CINAHL

Patricia Schwirian (OSU)

1987

Initiated and Created Interactive Videodisc Software Programs

American Journal of Nursing, New York, NY

Mary Ann Rizzolo (AJN)

1987

International Working Group Task Force on Education

IMIA/NI Working Group 8 and Swedish Federation, Stockholm, Sweden

Ulla Gerdin (NI)

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Kristina Janson Jelger and Hans Peterson (Swedish Federation)

  P art 1 • N ursing I nformatics T echnologies

  TABLE 1.1   

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Videodisc for Health Conference: Interactive Healthcare Conference

Stewart Publishing, Alexandria, VA

Scott Stewart, Publisher

1988

Recommendation #3: Support Automated Information Systems.

National Commission on Nursing Implementation Project (NCNIP), Secretary’s Commission on Nursing Shortage

Vivian DeBack, Chair

1988

Priority Expert Panel E: Nursing Informatics Task Force

National Center for Nursing Research, National Institutes of Health, Bethesda, MD

Judy Ozbolt, Chair

1988

First Set of Criteria for Vendors

ANA (American Nurses Association/Council on Nursing Science)/CANS

Mary McHugh Chair Rita Zielstorff Jacqueline Clinton

1989

Invitational Conference: Nursing Information Systems, Washington, DC

National Commission on Nursing Implementation Project (NCNIP), ANA, NLN, and NIS Industry

Vivian DeBack, Chair

1989 to Present

Initiated First Graduate Programs with Specialty in Nursing Informatics, Master’s and Doctorate

University of Maryland School of Nursing, Baltimore, MD

Barbara Heller, Dean

1989 to 2009

Virtual Learning Resources

Fuld Institute for Technology in Nursing Education (FITNE)

Julie McAfoos, Pioneer Software Developer.

1989

ICN Resolution Initiated Project: International Classification of Nursing Practice (ICNP)

International Council of Nurses Conference, Seoul, Korea

Fadwa Affra (ICN)

1990 to 1995

Annual Nurse Scholars Program

(HBO) Healthcare Technology Company and HealthQuest Corporation

Roy Simpson (HBO)



1987

Program Chairs: Carol Gassert, Patricia Abbott, Kathleen Charters, Judy Ozbolt, and Eun-Shim Nahm

Chapter 1 • Historical Perspectives of Nursing Informatics  

Diane Skiba (BU) Judith Ronald (SUNY Buffalo)

1990

ANA House of Delegates Endorsed: Nursing Minimum Data Set (NMDS) to Define Costs and Quality of Care

ANA House of Delegates

Harriet Werley (UM)

1990

Invitational Conference: State-of-the-Art of Information Systems

NCNIP, Orlando, FL

Vivian DeBack, Chair

1990

Renamed ANA: Steering Committee on Databases to Support Nursing Practice

ANA, Washington, DC

Norma Lang, Chair

1990

Task Force: Nursing Information Systems

NCNIP, ANA, NLN, NIS Industry Task Force, Project Hope, VA

Vivian DeBack, Chair

Kathy Milholland Hunter (ANA)

First Annual European Summer Institute

International Nursing Informatics Experts

Jos Aarts and Diane Skiba (USA)

First Nursing Informatics Listserv

University of Massachusetts, Amherst, MA

Gordon Larrivee

1991

Formation of Combined Annual SCAMC Special Nursing Informatics Working Group and AMIA NIWG

AMIA/SCAMC Sponsors, Washington, DC

Judy Ozbolt, First Chair

(continued)

 9

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1991 to 2001 1991

Landmark Events and Pioneers in Computers and Nursing, and Nursing Informatics (continued)

Year(s)

Title/Event

Sponsor(s)

Coordinator/Chair/NI Representative(s)

1991/1992

Two WHO Workshops: Nursing Informatics

World Health Organization and US PHS, Washington, DC, and Geneva, Switzerland

Marian Hirschfield (WHO) Carol Romano (PHS)

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1991 to Present

Initiated Annual Summer Institute in Nursing Informatics (SINI)

University of Maryland School of Nursing (SON), Baltimore, MD

Program Chairs: Carol Gassert, Mary Etta Mills, Judy Ozbolt, and Marisa Wilson

1992

ANA Approved Nursing Informatics as a New Nursing Specialty

ANA Database Steering Committee, Washington, DC

Norma Lang, Chair

1992

Formation of Virginia Henderson International Nursing Library (INL)

Sigma Theta Tau International Honor Society, Indianapolis, IN

Judith Graves, Director

1991/1992

ANA “Recognized” Four Nursing Terminologies: Clinical Care Classification (CCC) System, OMAHA System, NANDA (North American Nursing Diagnosis Association) and (NIC) Nursing Intervention Classification

ANA Database Steering Committee, Washington, DC

Norma Lang, Chair

1992

Read Clinical Thesaurus Added Nursing Terms in UMLS/NLM

Read Codes Clinical Terms, Version 3

Ann Casey (UK)

1992

Canadian Nurses Association: Nursing Minimum Data Set Conference

Canadian Nurses Association, Edmonton, Alberta, Canada

Phyllis Giovannetti, Chair

1992

American Nursing Informatics Association (ANIA) Founded

Southern, CA

Melodie Kaltenbaugh Constance Berg

1993

Four ANA “Recognized” Nursing Terminologies Integrated into UMLS

ANA Database Steering Committee and NLM

Norma Lang, Chair

1993

Initiated Virginia Henderson Electronic Library Online via Internet

Sigma Theta Tau International Honor Society, Indianapolis, IN

Carol Hudgings, Director

1993

Initiated AJN Network Online via Internet

American Journal of Nursing Company, New York, NY

Mary Ann Rizzolo, Director

1993

ANC Postgraduate Course: Computer Applications for Nursing

Army Nurse Corps, Washington, DC

Army Nurse Corps (ANC)

1993

Formation: Nursing Informatics Fellowship Program

Partners Healthcare Systems, Wellesley, MA

Rita Zielstorff, Director

1993

Alpha Version: Working Paper of (ICNP) International Classification of Nursing Practice

International Council of Nurses, Geneva, Switzerland

Fadwa Affara (ICN)

Betsy Humphreys (NLM)

10    P art 1 • N ursing I nformatics T echnologies

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  TABLE 1.1   

ch01.indd 11



1993

Formed: Denver Free-Net

University of Colorado Health Sciences Center, Denver, CO

Diane Skiba (UC), Developer

1993

Priority Expert Panel E: Nursing Informatics Report: Nursing Informatics: Enhancing Patient Care

National Center for Nursing Research (NCNR/NIH), Bethesda, MD

Judy Ozbolt, Chair

1994

ANA-NET Online

American Nurses Association, Washington, DC

Kathy Milholland (ANA)

1994

Four Nursing Educators Workshops on Computers in Education

Southern Council on Collegiate Regional Education and University of Maryland, Washington, DC; Baltimore, MD; Atlanta, GA; Augusta, GA

Eula Aiken (SREB)

1994

Next Generation: Clinical Information Systems Conference

Tri-Council for Nursing and Kellogg Foundation, Washington, DC

Sheila Ryan, Chair

1994, 2008, and 2014

First Nursing: Scope and Standards of Nursing Informatics Practice

ANA Database Steering Committee

Kathy Milholland (ANA), Carol Bickford (ANA)

1995

First International NI Teleconference: Three Countries Linked Together

International NI Experts:

Sue Sparks (USA)

Nursing Informatics, USA

Evelyn Hovenga (AU)

Health Information System, Australia

Robyn Carr (NZ)

Mary Etta Mills (UMD)

1995

First Combined NYU Hospital and NYU SON: Programs on Nursing Informatics and Patient Care: A New Era

NYU School of Nursing and NYU Medical Center, New York, NY

Barbara Carty, Chair

First Weekend Immersion in NI (WINI)

CARING Group, Warrenton, VA

Susan Newbold (CARING)

Janet Kelly, Co-Chair

Carol Bickford (ANA) Kathleen Smith (USN Retired) 1995

First CPRI Davies Recognition Awards of Excellence Symposium

Computer-Based Patient Record Institute, Los Angeles, CA

Intermountain Healthcare, Salt Lake City, UT Columbia Presbyterian MC, New York, NY Department of Veterans Affairs, Washington, DC

1995

Initiated CARING Web site

CARING

Susan Newbold (CARING) Marina Douglas

1995

First ANA Certification in Nursing Informatics

ANA Credentialing Center (ANCC)

Rita Zielstorff (Chair)

1996

ANA Established Nursing Information and Data Set Evaluation Center (NIDSEC)

ANA Database Steering Committee, Washington, DC

Rita Zielstorff, Chair Carol Bickford Connie Delaney, Co-Chair

  11

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(continued)

Chapter 1 • Historical Perspectives of Nursing Informatics  

Nursing informatics, New Zealand 1995

Landmark Events and Pioneers in Computers and Nursing, and Nursing Informatics (continued)

Year(s)

Title/Event

Sponsor(s)

Coordinator/Chair/NI Representative(s)

1996

First Online Nursing Informatics Course

Wichita State University, Wichita, KS

Mary McHugh Associate Professor

1996–1997

Nightingale Project—Health Telematics Education, Three Workshops and Two International Conferences

University of Athens, Greece, and European Union

John Mantas, Chair (Greece) Arie Hasman, Co-Chair (Netherlands) Consultants: Virginia K. Saba (USA) Evelyn Hovenga (AU)

1996

Initiated TELENURSE Project

Danish Institute for Health and Nursing Research and European Union

Randi Mortensen, Director Gunnar Nielsen, Co-Director

1996

First Harriet Werley Award for Best Nursing Informatics Paper at AMIA

AMIA-NI Working Group (NIWG), Washington, DC

Rita Zielstorff (MGH Computer Lab) First Awardee

1997

Invitational National Nursing Informatics Workgroup

National Advisory Council on Nurse Education and Practice and DN/PHS

Carol Gassert, Chair

1997

ANA Published NIDSEC Standards and Scoring Guidelines

ANA Database Steering Committee

Rita Zielstorff, Chair

1997

National Database of Nursing Quality Indicators (NDNQI®)

American Nurses Association

Nancy Dunton, PI

1997

Initiated Nursing Informatics Archival Collection

NLM—History Collection

Virginia K. Saba, Chair (GT)

1998

Initiated NursingCenter.com Web site

JB Lippincott, New York, NY

Maryanne Rizzalo, Director

1999

Beta Version: ICNP Published

International Council of Nurses, Geneva, Switzerland

Fadwa Affara (ICN)

1999 to 2008

Annual Summer Nursing Terminology Summit

Vanderbilt University, Nashville, TN

Judy Oxbolt, Chair Suzanne Bakken, Program Chair

1999

Convergent Terminology Group for Nursing

SNOMED/RT International, Northbrook, IL

Suzanne Bakken, Chair (NYU), and Debra Konicek (CAP)

1999 and 2004

United States Health Information Knowledgebase (USHIK) Integrated Nursing Data

Department of Defense (Health Affairs), CMS, CDC, AHRQ

Luann Whittenburg (OASD/HA)

Inaugural Virtual Graduation: Online PostMasters: ANP Certificate Program

GSN, Uniformed Services University

Faye Abdellah (USU)

VA Tele-Conference Network, Bethesda, MD

Virginia K. Saba (USU)

Eight Nationwide VA MCs

Charlotte Beason (VA)

1999

Connie Delaney, Co-Chair

Glenn Sperle (CMS) M. Fitzmaurice (AHRQ)

12    P art 1 • N ursing I nformatics T echnologies

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  TABLE 1.1   

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ch01.indd 13

Pan American Health Organization (PAHO), Washington, DC Healthcare Information Management System Society (HIMSS)

Roberto Rodriquez (PAHO) Heimar Marin (Brazil) Joyce Sensmeier

2000

ICNP Programme Office Established

International Council of Nurses, Geneva, Switzerland

Amy Coenen, First Nursing Director

2000

Computer-Based Patient Record Institute (CPRI) 2000 Conference

CPRI, Los Angeles, CA

Virginia K. Saba, Nursing Chair

2001

AMIA’s Nursing Informatics Leaders

University of Wisconsin, Madison, WI

Pattie Brennan, President

Columbia University, New York, NY

Suzanne Bakken, Program Chair

2002

ICNP Strategic Advisory Group Established

ICN, Geneva, Switzerland

Amy Coenen, Director

2002

Conference: Strategy for Health IT and eHealth Vendors

Medical Records Institute (MRI), Boston, MA

Peter Waegemann, President

2002

AAN Conference: Using Innovative Technology

American Academy of Nursing, Washington, DC

Margaret McClure, Chair

2002

Initiated AAN Expert Panel on Nursing Informatics

American Academy of Nursing Annual Conference, Naples, FL

Virginia K. Saba, Co-Chair

2003

Finnish Nursing Informatics Symposium

Finnish Nurses Association (FNA) and Siemens Medical Solutions, Helsinki, Finland

Kaija Saranto (FN)



2000

First Meeting: Nursing Data Standards Project for Central Organization (PAHO) and Brazil First, Director of Professional Services

1999

Linda Bolton, Co-Chair Nellie O’Gara, Co-Chair

Anneli Ensio (FN) Rosemary Kennedy (Siemens)

2003

First ISO-Approved Nursing Standard: Integrated Reference Terminology Model for Nursing

IMIA/NI-SIG and ICN, Oslo, Norway

Virginia K. Saba, Chair (NI/SIG) Kathleen McCormick, Co-Chair (NIWG) Amy Coenen, Co-Chair (ICN) Evelyn Hovenga, Co-Chair (NI/SIG) Susanne Bakken, Chair, Tech. Group

First ICN Research and Development Centre

Deutschsprachige ICNP, Freiburg, Germany

Peter Koenig, Director

2004 to Present

Initiated Annual Nursing Informatics Symposium at (HIMSS) Health Information Management Systems Society Conference and Exhibition

HIMSS Annual Conference, Orlando, FL

Joyce Sensmeier, First Chair

2004

Initial Formation of Alliance for Nursing Informatics (ANI)

AMIA/HIMSS

Connie Delaney, Chair

2004 to 2012

First Nurse on (NCHVS) National Center for Vital Health Statistics Standards Subcommittee

NCVHS, Washington, DC

Joyce Sensmeier, Co-Chair Judy Warren, KUMC (continued)

  13

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2004

Chapter 1 • Historical Perspectives of Nursing Informatics  

Ida Androwich, Co-Chair

Landmark Events and Pioneers in Computers and Nursing, and Nursing Informatics (continued)

Year(s)

Title/Event

Sponsor(s)

Coordinator/Chair/NI Representative(s)

2004

Office of the National Coordinator for Health Information Technology (ONC) Established

National Coordinators

First Coordinator: Dr. David Brailer Dr. Robert Kolodner Dr. David Blumenthal Dr. Farzad Mostashari Dr. Karen DeSalvo, Nominated Directors.

2004

Technology Informatics Guiding Education Reform (TIGER)—Phase I

National Members

Marion Ball, Chair

Online Teleconferences

Diane Skiba, Co-Chair

First TIGER Summit

100 Invited Representatives from 70 Healthcare Organizations; Summit Held at USU, Bethesda, MD

Marion Ball, Chair

Revitalized NI Archival Collection—Initiated Solicitation of Pioneer NI Documents

AMIA/NIWG Executive Committee

Kathleen McCormick, Chair

2005/2008/2009

ICNP Version 1.0, Version 1.1, and Version 2

ICN, Geneva, Switzerland

Amy Coenen, Director

2006/2008

Symposium on Nursing Informatics

Brazil Medical Informatics Society

Heimar Marin, Chair

2007/2008

First National Nursing Terminology Standard: Clinical Care Classification (CCC) System

(ANSI-HITSP)American National Standards Institute’ and ‘Healthcare Information Technology System Panel: Bio-surveillance Committee HITSP Recommended and HHS Secretary Approved

Virginia K. Saba and Colleagues, HITSP Committee Developers

2007 to Present

ANIA/CARING Joint Conferences

Las Vegas, Washington, DC

Victoria Bradley, Initial Chair

2009 to Present

American Recovery and Reinvestment Act of 2009—Health Information Technology for Economic and Clinical Health (HITECH Act of 2009); ONC Formed Two National Committees, Each with One Nurse

ONC National Health Information Technology Committee:

Focus on Hospital HIT/EHR (Healthcare Information Technology/Electronic Healthcare Records) Systems Integrated and Interoperable Terminology Standards

Health Policy Committee

Judy Murphy, (Aurora Health Systems, MI)

Health Standards Committee

Connie Delaney (UMN):

2006

2006

Diane Skiba, Co-Chair

Bonnie Westra, Co-Chair Virginia K. Saba, Consultant

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2009

ICNP recognized by WHO as First International Nursing Terminology

ICN and WHO, Geneva, Switzerland

Amy Coenen, Director

2010 to Present

Nursing Informatics Boot Camp

Hospitals, Schools of Nursing, Vendors, Healthcare Member Organization

Susan K. Newbold, President

14    P art 1 • N ursing I nformatics T echnologies

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  TABLE 1.1   

ch01.indd 15



2010

Initiated: Doctor of Nursing Practice Specialty in Informatics

University of Minnesota, Minneapolis, MN

Connie Delaney, Dean SON, UMN

2010

American Nursing Informatics Association (ANIA and CARING) Merged

ANIA and CARING

Victoria Bradley, First President

2011

Tiger Initiative Foundation Incorporated

TIGER Initiative

Patricia Hinton Walker, Chair

2011

First Nurse as Deputy National Coordinator for Programs & Policy at the Office of the National Coordinator for Health IT (ONC)

Office of the National Coordinator (ONC)

Judy Murphy

2012 to Present

ANIA New Re-Named and First Annual ANIA Conference

ANIA with merged CARING

Victoria Bradley, President

2013

First NI Nurse to be President of IMIA

IMIA

Hyeoun-Ae Park

2013 to 2019 (Q2 years)

Clinical Care Classification (CCC) System User Meetings

Hospital Corp of America (HCA) Nashville, TN

Virginia K. Saba Chair Jane Englebright Co-Chair

2013 to Present

Nursing Knowledge Big Data Science Initiative Annual Conferences

University of Minnesota School of Nursing

Connie Delaney, Chair

Collection of NI Artifacts Donated to McGovern Historic Center First NI Nurse Director of National Library of Medicine (NLM)

University of Texas School of Biomedical Informatics National Library of Medicine

Juliana and Jack Brixey

2016-2020 2019

IMIA/NI-SIG Chair Transfer CCC System to Hospital Corp of America (HCA)

NI Expert - UC Nashville, TN

Diane Skiba, Chair Virginia K. Saba, CCC CNO & President ‘to’ Jane Englbright Senior VP & Chief Nursing Executive , HCA Jane Englebright, Senior VP & Chief Nursing Executive, HCA

2020

7th Edition: Essentials of Nursing Informatics

McGraw-Hill Publishing

Virginia K. Saba, Co-Author Kathleen McCormick, Co-Author

2021

MEDINFO / Combined IMIA and IMIA/ NI-SIG)

Sidney, Australia

Australian NI

Bonnie Westra, Chair

Patricia Sengstack, President (2013/2014) Seoul National University, Seoul, Korea

2016

Patricia Brennen

  15

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Chapter 1 • Historical Perspectives of Nursing Informatics  

2014

Bonnie Westra Co-Chair

16    P art 1 • N ursing I nformatics T echnologies

  TABLE 1.2    Nursing Knowledge Big Data Science Workgroups Trendw

Explanation/Example

Care Coordination

Identify shareable and comparable data across settings to support care coordination activities and improve patient outcomes.

Clinical Data Analytics

Demonstrate the value of sharable and comparable nurse-sensitive data to support practice and translational research for transforming health care and improving patient quality and safety.

Context of Care

Develop the test Big Data set using the Kruchten 4+1 Model and Unified Modeling Language (UML) to introduce an integrating and harmonizing framework for sharable and comparable nurse data across the care continuum that incorporates the Nursing Minimum Management Data Set (NMMDS) and links key Workgroup activities.

Education

Strengthen informatics education at the graduate and specialty levels and the ability of educators who teach informatics to nursing student so that we can achieve the outcomes of shareable and comparable nursing data through the work of nurses at the point of care.

Encoding and Modeling

Develop and disseminate LOINC and SNOMED Clinical Terms for electronic health record nursing assessments, and incorporate them into a framework and repository for dissemination.

Engage and Equip All Nurses in Health IT Policy

Equip nurses with education, tools, and resources and engage them as knowledgeable advocates for health IT policy efforts important to nursing.

E-Repository

Develop and implement a repository designed to collect nursing informatics best evidence in the form of documents, surveys, instruments, algorithms, for example.

Mobile Health

Explore the use of mobile health tools and data for nurses including both nursing-generated data and patient-generated data. Identify and support activities and resources to address unmet needs and create opportunities to utilize mHealth data within nursing workflows.

Nursing Value

Measure the value of nursing care as well as the contribution of individual nurses to clinical outcomes and cost. Develop big data techniques for secondary data analysis that will provide metrics to monitor quality, costs, performance, effectiveness, and efficiency of nursing care.

Social Determinants of Health

Support the inclusion, interoperability, and data exchange of Social Determinants of Health (SDOH) data in electronic health records, personal and m-health tools, and community and public health portals across care settings. Empower nurses (practice, education, research, policy) to use SDOH data as context for planning care. Develop a roadmap to engage nurses to improve population health through large-scale adoption of SDOH.

Transforming Documentation

Explore ways to decrease the nursing documentation burden and serve up the information already in the electronic health record at the right time in the workflow to support evidence-based and personalized care. Elevate purpose-driven, role-based, patient-centric, evidence-informed documentation transformation to capture nurse observations and interventions, and drive purposeful secondary-use and precision nursing.

ch01.indd 16

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Chapter 1 • Historical Perspectives of Nursing Informatics  

1970s  During the late 1960s through the 1970s, hospitals began developing computer-based information systems which initially focused on computerized physician order entry (CPOE) and results reporting; pharmacy, laboratory, and radiology reports; information for financial and managerial purposes; and physiologic monitoring systems in the intensive care units; and a few systems started to include care planning, decision support, and interdisciplinary problem lists. While the content contained in early hospital information systems frequently was not specific to nursing practice, a few systems did provide a few pioneer nurses with a foundation on which to base future nursing information systems (Blackmon et al., 1982; Collen, 1995; Ozbolt & Bakken, 2003; Romano, McCormick, & McNeely, 1982). Regardless of the focus, which remained primarily on medical practice, nurses often were involved in implementing health information technology (HIT) systems. Interest in computers and nursing began to emerge in public health, home health, and education during the 1960s to 1970s. Automation in public health agencies began as a result of pressure to standardize data collection procedures and provide state-wide reports on the activities and health of the public (Parker, Ausman, & Ovedovitz, 1965). In the 1970s, conferences sponsored by the Division of Nursing (DN), Public Health Service (PHS), and the National League for Nursing (NLN) helped public health and home health nurses understand the importance of nursing data and their relationship to new Medicare and Medicaid legislation requirements passed in 1966. The conferences provided information on the usefulness of computers for capturing and aggregating home health and public health information. Additional government-sponsored conferences focused on educational uses of computers for nurses (Public Health Service, 1976). Simultaneously, hospitals and public health agencies embarked on investigating computers and nursing; the opportunity to improve education using computer technology commenced. Bitzer (1966) reported on one of the first uses of a computerized teaching system called PLATO, which was implemented to teach classes in offcampus sites as an alternative to traditional classroom education. The early nursing networks, which were conceived at health informatics organizational meetings, helped to expand nursing awareness of computers and the impact HIT could have on practice. The state of technology initially limited opportunities for nurses to contribute to the HIT design, but as technology evolved toward the later part of the 1970s and as nurses provided workshops nationally, nurses gained confidence that they could use computers to improve practice. The national nursing organization’s

ch01.indd 17

  17

federal agencies (Public Health Service, Army Nurse Corps) and several university schools of nursing provided educational conferences and workshops on the state-ofthe-art regarding computer technology and its influence on nursing. During this time, the Clinical Center at the National Institutes of Health implemented the Technicon Data System (TDS) system; one of the earliest clinical information systems (called Eclipsys & Allscripts) was the first system to include nursing practice protocols. In addition to the use of computers, advancement was underway for other technologies and/or devices used by nurses. For example, the first point-of-care blood glucose monitor became available for use in the clinical setting in 1970 (Clarke & Foster, 2012). The devices became smaller and more widespread in the 1980s. 1980s  In the 1980s, the field of nursing informatics exploded and became visible in the healthcare industry and nursing. Technology challenged creative professionals in the use of computers in nursing. As computer systems were implemented, the needs of nursing took on a cause-and-effect modality; that is, as new computer technologies emerged and as computer architecture advanced, the need for nursing software evolved. It became apparent that the nursing profession needed to update its practice standards and determine specific data standards, vocabularies, and classification schemes that could be used for the computer-based patient record systems. In the 1980s, the microcomputer or personal computer (PC) emerged. This revolutionary technology made computers more accessible, affordable, and usable by nurses and other healthcare providers. The PC brought computing power to the workplace and, more importantly, to the point of care. Also, the PCs served as dumb terminals linked to the mainframe computers and as stand-alone systems (workstations). The PCs were user-friendly and allowed nurses to design and program their own applications. The influence of computer technology extended to the introduction of devices to improve patient safety. For example, the automated dispensing cabinets (ADCs) were introduced in the 1980s (Grissinger, 2012). The computercontrolled ADCs replaced medication carts and drug floor stock. Tracking of medications occurred at the point of care. The use of ADCs in the clinical setting has resulted in the reduction of medication errors. Starting in 1981, national and international conferences and workshops were conducted by an increasing number of nursing pioneers to help nurses understand and get involved in this new emerging nursing specialty. Also during the 1980s, invitational conferences were conducted to develop nursing data sets and vocabularies as well as numerous workshops were conducted at

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18    P art 1 • N ursing I nformatics T echnologies universities to introduce this new specialty into nursing education. During this period, many mainframe healthcare information systems (HISs) emerged with nursing subsystems. These systems documented several aspects of the patient record, namely, provider order entry and results reporting, the Kardex reporting, vital signs, and other systems-documented narrative nursing notes using word-processing software packages. Discharge planning systems were developed and used as referrals to community, public, and home healthcare facilities for the continuum of care. Nurses began presenting at multidisciplinary conferences and formed their own working groups within HIT organizations, such as the first Nursing Special Interest Group on Computers which met for the first time during SCAMC (Symposium on Computer Applications in Medical Care) in 1981. As medical informatics evolved, nursing began focusing on what was unique about nursing within the context of informatics. Resolutions were passed by the American Nurses Association (ANA) regarding computer use in nursing and in 1985, the ANA approved the formation of the Council on Computer Applications in Nursing (CCAN). One of the first activities the CCAN executive board initiated was to solicit several early pioneers to develop monographs on the status of computers in nursing practice, education, research, and management. The CCAN board developed a yearly Computer Nurse Directory on the known nurses involved in the field, conducted computer applications demonstrations at the ANA annual conferences, and shared information with their growing members in the first CCAN newsletter Input-Output. During this time NI newsletters, journals, and several books, such as the first edition of this book Essentials of Computers for Nurses published in 1986, were used for educational courses introduced in the academic nursing programs, and workshops conducted on computers and nursing. The CCAN became a very powerful force in integrating computer applications into the nursing profession. In 1988, the CCAN commissioned three NI experts to prepare a set of criteria on the integration of nursing practice for EHR vendors to follow (Zielstorff, McHugh, & Clinton, 1988). In 1989, the ANA renamed the CCAN to the Steering Committee on Databases to Support Clinical Nursing Practice, which later became the Committee for Nursing Practice Information Infrastructure (CNPII). The purpose of the CNPII was to support development and recognition of national health data standards (Coenen et al., 2001). 1990s  By the 1990s, large integrated healthcare delivery systems evolved, further creating the need for information across healthcare facilities within these large systems to standardize processes, control costs, and assure the

ch01.indd 18

quality of care (Shortliffe, Perreault, Wiederhold, & Pagan, 2003). Advances in relational databases, client-server architectures, and new programming methods created the opportunity for better application development at lower costs. Legislative activity in the mid-1990s paved the way for EHRs through the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (public-law 104-191), emphasizing standardized transactions, and privacy and security of patient-identifiable information (Gallagher, 2010). The complexity of technology, workflow analysis, and regulations shaped new roles for nursing. In 1992, the ANA recognized nursing informatics as a new nursing specialty with a separate Scope of Nursing Informatics Practice Standards, and also established a specific credentialing examination for it (ANA, 2008). Numerous local, national, and international organizations provided a forum for networking and continuing education for nurses involved with informatics (Sackett & Erdley, 2002). The demand for NI expertise increased in the healthcare industry and other settings where nurses functioned, and the technology revolution continued to impact the nursing profession. The need for computer-based nursing practice standards, data standards, nursing minimum data sets, and national databases emerged concurrently with the need for a unified nursing language, including nomenclatures, vocabularies, taxonomies, and classification schemes (Westra, Delaney, Konicek, & Keenan, 2008). Nurse administrators started to demand that the HITs include nursing care protocols and nurse educators continued to require the use of innovative technologies for all levels and types of nursing and patient education. Also, nurse researchers required knowledge representation, decision support, and expert systems based on standards that allowed for aggregated data (Bakken, 2006). In 1997, the ANA developed the Nursing Information and Data Set Evaluation Center (NIDSEC) to evaluate and recognize nursing information systems (ANA, 1997). The purpose was to guide the development and selection of nursing systems that included standardized nursing terminologies integrated throughout the system whenever it was appropriate. There were four high-level standards: (a) inclusion of ANA-recognized terminologies; (b) linkages among concepts represented by the terminologies retained in a logical and reusable manner; (c) data included in a clinical data repository; and (d) general system characteristics. The Certification Commission for Health Information Technology (CCHIT) had similar criteria for the EHR certification, which was later adopted by the Office of the National Coordinator for Health Information Technology (ONC); however, nursing data was no longer included. ANA was ahead of its time in its thinking and

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Chapter 1 • Historical Perspectives of Nursing Informatics  

development. The criteria are now under revision by the ANA to support nurses to advocate their requirements for the emerging HIT systems. Technology rapidly changed in the 1990s, increasing its use within and across nursing units as well as across healthcare facilities. Computer hardware—PCs—continued to get smaller and computer notebooks were becoming affordable, increasing the types of computer technology available for nurses to use. Linking computers through networks both within hospitals and health systems as well as across systems facilitated the flow of patient information to provide better care. By 1995, the Internet began providing access to information and knowledge databases to be integrated into desktop computer systems. It revolutionized information technologies. The Internet moved into the mainstream social milieu with electronic mail (e-mail), file transfer protocol (FTP), Gopher, and Telnet, and World Wide Web (WWW) protocols greatly enhanced its usability and user-friendliness (Saba, 1996; Sparks, 1996). The Internet was used for high-performance computing and communication (HPCC) or the “information superhighway” and facilitated data exchange between computerized patient record systems across facilities and settings over time. The Internet led to improvements in networks, and a browser, WWW, allowed organizations to communicate more effectively and increased access to information that supported nursing practice. The WWW also became an integral part of the HIT systems and the means for nurses to browse the Internet and search worldwide resources (Nicoll, 1998; Saba, 1995). 2000s  A change occurred in the new millennium as more and more healthcare information became digitalized and newer technologies emerged. In 2004 an executive order 13335 established the ONC and issued a recommendation calling for all healthcare providers to adopt interoperable EHRs by at least 2014 or 2015 (http://healthhit.gocv/topic/ about-onc). This challenged nurses to get involved in the design of systems to support their workflow as well as in the integration of information from multiple sources to support nurses’ knowledge of technology. In the late 2000s, as hospitals became “paperless,” they began employing new nurses who had never charted on paper. Technological developments that influenced healthcare and nursing included data capture and data sharing technological tools. Wireless, point of care, regional database projects, and increased IT solutions proliferated in healthcare environments, but predominately in hospitals and large healthcare systems. The use of bar coding and radiofrequency identification (RFID) emerged as a useful technology to match the “right patient with the right medication” to improve patient safety. A barcode medication

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administration (BCMA) system was first implemented in 2000 in an acute care hospital to electronically capture medication management (Wideman, Whittler, & Anderson, 2005). The RFID also emerged to help nurses find equipment or scan patients to assure that all surgical equipment is removed from inside patients before surgical sites are closed (Westra, 2009). Smaller mobile devices with wireless or Internet access such as notebooks, tablet PCs, personal digital assistants (PDAs), and smart cellular telephones increased access to information for nurses within hospitals and in the community. The development and subsequent refinement of voice over Internet protocol (VoIP) provided voice cost-effective communication for healthcare organizations. The Internet which appeared in 1995 provided a means for the development of clinical applications. Also, databases for EHRs could be hosted remotely on the Internet, decreasing costs of implementing EHRs. Remote monitoring of multiple critical care units from a single site increased access for safe and effective cardiac care (Rajecki, 2008). Home healthcare increasingly partnered with information technology for the provision of patient care. Telehealth applications, a recognized specialty for nursing since the late 1990s, provided a means for nurses to monitor patients at home and support specialty consultation in rural and underserved areas. The NI research agenda promoted the integration of nursing care data in HIT systems that would also generate data for analysis, reuse, and aggregation. A historical analysis of the impact of the Nursing Minimum Data Set (NMDS) demonstrated that continued consensus and effort was needed to bring to fruition the vision and implementation of minimum nursing data into clinical practice (Hobbs, 2011). The NMDS continues to be the underlining focus in the newer HIT systems. A new NI research agenda for 2008–2018 (Bakken, Stone, & Larson, 2012) emerged as critical for this specialty. The new agenda is built on the agenda originally developed and published by the National Institute for Nursing Research (NINR) in 1993 (NINR, 1993). The authors focused on the new NI research agenda on “3 aspects of context— genomic health care, shifting research paradigms and social (Web2.0) technologies” (p. 280). A combination of the economic recession along with the escalating cost of healthcare resulted in the American Recovery and Reinvestment Act (ARRA) of 2009 and the Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009 with funding to implement HIT and/or EHR systems, support healthcare information exchange, enhance community and university-based informatics education, and support leading edge research to improve the use of HIT (Gallagher,

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20    P art 1 • N ursing I nformatics T echnologies 2010). One of the first ONC initiatives was the Healthcare Information Technology Standards Panel (HITSP) which was designed to determine what coding systems were used to process patient care data from admission to discharge. It was found that the nursing care data was missing in the existing EHRs. Initially, in 2005–2007, the Clinical Care Classification (CCC) System met the established standards as interoperable and was accepted and approved as the free, coded, nursing taxonomies that could be used for assessing and documenting nursing care to generate quality outcomes (Saba & Whittenburg, 2015). This project ended in 2009 when the HITECH Act of 2009 emerged. 2010s  During 2010, the ONC convened two national committees, (a) National Committee on Health Policy and (b) National Committee on Health Standards, which outlined and designed to address the HITECH Act of 2009. The committees designed the “Meaningful Use” (MU) program which was to be implemented in three stages of legislation consisting of regulations which built onto each other with the ultimate goal of implementing a complete and interoperable EHR and/or HIT system in all U.S. hospitals. For each stage, regulations were proposed by the national committees, developed and reviewed by the public before they were finalized, and submitted to Centers for Medicare and Medicaid (CMS) and the healthcare facilities for implementation. In 2011–2012, MU Stage 1 was initiated focusing primarily on the CPOE initiative for physicians. Hospitals that implemented this MU regulation successfully received federal funds for their HIT systems. In 2013–2015, MU Stage 2 was introduced focusing primarily on the implementation of quality indicators that required electronic data to be collected as an integral component in the HIT systems. The quality indicators would be used to guide hospitals in patient safety and if not implemented used as indicators subject to financial penalties. The proposed MU Stage 3 that focused on care quality outcome measures was not implemented but replaced by the Medicare Access and CHIP Reauthorization Act (MACRA) of 2015. The MACRA legislation created a new Medicare Quality Payment Program that prioritized the value of healthcare received by Medicare beneficiaries and revised Medicare’s reimbursement to eligible providers. The legislation consolidated components of the Physician Quality Reporting System (PQRS), Value-Based Payment Modifier (VBM), and the Medicare Electronic Health Record (EHR) Incentive program into the Merit-Based Incentive Payment System (MIPS). The purpose of the MIPS program, initiated in 2017, was to establish Medicare payment to healthcare professional’s performance score based on a value-based healthcare model. As a result, the

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CMS began to focus reimbursement on a quality payment program that addressed quality outcome measures (MIPS, 2019). It is anticipated that this initiative will also minimize the payment burden on the clinicians by aggregating their patients’ health information from multiple providers into a single electronic application. As of 2018, the CMS proposed policies to strengthen Interoperability. The ONC and CMS rebranded MU to Promoting Interoperability. It is anticipated that the initiative will make use of new technologies and for patients to aggregate their health information from multiple providers into a single electronic application. In 2019, the ONC continued to implement the latest legislation as well as Interoperability through summits, webinars, and public comment. Nurses have always been involved with all phases of MU as well as all other legislation, from the implementation of systems to assuring usage and adaptation to the evolving health policy affecting the HIT and/or EHR systems. Thus, the field of nursing informatics (NI) continues to grow due to the MU regulations which continue to impact on every inpatient hospital in the country. To date, the majority of hospitals in the country has established HIT departments and has employed at least one nurse to serve as a NI expert to assist with the implementation of MU requirements. As the MU requirements changed they also impacted on the role of the NI experts in hospitals and ultimately on the roles of all nurses in the inpatient and outpatient facilities making NI an integral component of all professional nursing services. An example of nursing involvement is the implementation of the CCC System nursing terminologies for documenting nursing practice in the Hospital Corp of American (HCA) healthcare facilities (Saba & Whittenburg, 2015).

Electronic Health Record Systems from a Historical Perspective In 1989, the Institute of Medicine (IOM) of the National Academy of Sciences convened a committee and asked the question, “Why is healthcare still predominantly using paper-based records when so many new computerbased information technologies are emerging?” (Dick & Steen, 1991). The IOM invited representatives of major stakeholders in healthcare and asked them to define the problem, identify issues, and outline a path forward. Two major conclusions resulted from the committee’s deliberations. First, computerized patient record (CPR) is an essential technology for healthcare and is an integral tool for all professionals. Second, the committee after hearing from numerous stakeholders recognized that there

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Chapter 1 • Historical Perspectives of Nursing Informatics  

was no national coordination or champion for CPRs. As a result, the IOM committee recommended the creation of an independent institute to provide national leadership. The Computer-Based Patient Record Institute (CPRI) was created in 1992 and given the mission to initiate and coordinate the urgently needed activities to develop, deploy, and routinely use CPRs to achieve improved outcomes in healthcare quality, cost, and access. A CPRI work group developed the CPR Project Evaluation Criteria in 1993 modeled after the Baldridge Award. These criteria formed the basis of a self-assessment that could be used by organizations and outside reviewers to measure and evaluate the accomplishments of CPR projects. The four major areas of the initial criteria— (a)management, (b) functionality, (c) technology, and (d) impact—provided a framework through which to view the implementation of computerized records. The criteria, which provided the foundation for the Nicholas E. Davies Award of Excellence Program, reflect the nation’s journey from paper-based to electronic capture of health data. The Davies Award of Excellence Program evolved through multiple revisions and its terminology updated from the computerized patient record to the electronic medical record (EMR), and more recently to the electronic health record (EHR). Today, under HIMSS management, the Davies Award of Excellence Program is offered in four categories: (1) Enterprise (formerly Organizational or Acute Care), first offered in 1995; (2) Ambulatory Care, started in 2003; (3) Public Health, initiated in 2004; and (4) Community Health Organizations (CHO), first presented in 2008 (http://apps.himss.org/davies/index.asp).

Usability The usability of EHRs has received increasing attention due to the widespread implementation in both inpatient and ambulatory care. As such, the positive and negative aspects of EHR usability has been identified. Usability is defined as “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency and satisfaction in a specified context of use” (Aydin & Beruvides, 2014; ISO, 2010). The National Institute of Standards and Technology (NIST) has been at the forefront of the initiative to establish a framework that describes and evaluates health information technology (NIST, n.d.). The initiative is in collaboration with the ONC and Agency for Healthcare Research and Quality (AHRQ). The Health Information Management Systems Society (HIMSS, 2019) has identified nine usability principles to be used in the development and evaluation of an EHR. It is imperative that usability principles are used in the design of EHRs. Furthermore, end users of doctors,

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professional nurses, and other healthcare professionals must be involved in the design and upgrades of EHRs. Determination of “usability” does not end with design or implementation but should continue into the post-implementation evaluation process. The data collected can be used to inform future usability upgrades to the EHR. Consumer-Centric Healthcare Systems Another impact of the escalating cost of healthcare is a shift toward a consumer-centric healthcare system. Consumers are encouraged to be active partners in managing their personal health. A variety of technologies have evolved to enable consumers to have access to their health information and choose whether to share this across healthcare providers and settings. Personal health records emerged as either stand-alone systems or those tethered to EHRs. Consumers are increasing in healthcare information literacy as they demand to become more involved in managing their own health.

Patient Portal A feature of the EHR is the patient portal which replaced the personal health record. The portal is a secure online site where a patient accesses his or her health information as well as communicates with his or her team of healthcare providers. Access to the patient portal requires a user name and password. The portal is accessible at anyplace and anytime that the patient has access to the Internet. From the portal, the patient can review a provider visit, laboratory and radiology results, medications, and allergies. Furthermore, a provider can message the patient with reminders for medical screenings, upcoming appointments, medication refills, and billing information. An emerging feature of the patient portal is the e-visit. Using an e-visit, the patient can consult with a provider regarding a non-emergent health issue. The patient portal and personal health record are sometimes used interchangeably. There is an important difference. The personal health record is patient controlled. No one can access including providers unless given permission. In contrast, the patient portal is accessible to providers to upload information.

Wearable Technology in Healthcare The explosion of sensors has influenced the development of consumer wearable products to track health and fitness parameters. Sensors are embedded in wearable devices or fabrics to record heart rate and rhythm, respiration, oxygen saturation, body temperature, hours of sleep, glucose levels, and fitness activities. The consumer receives

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22    P art 1 • N ursing I nformatics T echnologies real-time feedback for the device. The wearable may be synched with the consumer’s smartphone. Data collected by the device is consumer controlled and shared. Data can be shared with other consumers or with healthcare professionals. There has not been widespread acceptance to store consumer-collected data in the EHR. The number of wearable devices continues to increase with the progress in research and development activities of technology companies and universities.

Nursing Knowledge Big Data Science Initiative Nursing has a long history in informatics, developing HIT systems and data standards for capturing the practice of nursing; however, there is a dearth of IT systems that incorporate nursing data standards due to the lack of regulatory requirements and financial incentives. In 2013, the University of Minnesota School of Nursing initiated a national collaborative, bringing together nurses from practice, education, research, software vendors, informatics organizations, and other professional and governmental agencies. Over the past 7 years, annual think-tank conferences were held to report out and plan activities for 11 virtual working groups to accomplish a national agenda to achieve sharable and comparable nursing data to ensure the timely adoption of big data methodologies across all of nursing’s domains (Delaney & Weaver, 2018). Table 1-2 describes the purpose of the 11 virtual workgroups. Proceedings and all supporting documents of over the past 7 years can be found at http://z.umn.edu/bigdata. The ANA approved a position statement that all settings in which nurses work should adopt a free, coded, standardized nursing terminology such as the CCC System; however, for interoperability the terminology standard should be mapped to SNOMED-CT (Systematized Nomenclature of Medicine–Clinical Terms) and LOINC (Logical Observation Identifiers Names and Codes). The 2019 ONC Interoperability Standard supports this position, laying the foundation for future regulatory requirements (ONC, 2019).

NURSING INFORMATICS PIONEERS This section provides information specific to an ongoing project describing the early pioneers that influenced and impacted on the integration of NI into the nursing profession including their significance.

History Project In 1995, Saba initiated a history of NI at the National Library of Medicine that consisted of the collection of

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archival documents from the NI pioneers. The history project was initiated based on a recommendation by Dr. Morris Collen who published the history of medical informatics in 1995 (Collen, 1995). However, it was not until 2001 that the Nursing Informatics Working Group (NIWG) of the American Medical Informatics Association (AMIA) became involved and the NI History Committee was established to lead the project. The committee solicited archival material from the identified NI pioneers for a history of NI to be housed in the NLM as part of its history collection. Beginning in 2004, the rich stories of pioneers in NI were captured through a project sponsored by the AMIANIWG. The AMIA-NIWG History Committee developed an evolving list of pioneers and contributors to the history of NI. Pioneers were defined as those who “opened up” a new area in NI and provided a sustained contribution to the specialty (Newbold & Westra, 2009; Westra & Newbold, 2006). Through multiple contacts and review of the literature, the list grew to 145 pioneers and contributors who shaped NI since the 1950s. Initially, each identified pioneer was contacted to submit their nonpublished documents and/or historical materials to the National Library of Medicine (NLM) to be indexed and archived for the Nursing Informatics History Collection. Approximately 25 pioneers submitted historical materials that were catalogued with a brief description. Currently, the catalogued document descriptions can be searched online: https://www.nlm.nih.gov/hmd/ manuscripts/accessions.html. The documents can be viewed by visiting the NLM. Eventually, each archived document will be indexed and available online in the NI History Collection. Also from the original list, a convenience sample of pioneers was interviewed over a 4-year period at various NI meetings. Videotaped stories from 33 pioneers were recorded and are now available on the AMIA Web site: https://www.amia.org/working-groups/ nursing-informatics/history-project. Backgrounds  The early pioneers came from a variety of backgrounds as nursing education in NI did not exist in the 1960s. Almost all of the pioneers were educated as nurses, with a few exceptions. A limited number of pioneers had additional education in computer science, engineering, epidemiology, and biostatistics. Others were involved with anthropology, philosophy, physiology, and public health. Their unique career paths influenced the use of technology in health care. Some nursing faculty saw technology as a way to improve education. Others worked in clinical settings and were involved in “roll-outs” of information systems. Often these systems were not designed to improve nursing work, but the pioneers had a vision that technology

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Chapter 1 • Historical Perspectives of Nursing Informatics  

could make nursing practice better. Other pioneers gained experience through research projects or working for software vendors. The commonality for all the pioneers is they saw various problems and inefficiencies in nursing, and they had a burning desire to use technology to “make things better.” Videotaped Interviews  The AMIA Nursing Informatics History page contains a wealth of information. The 33 videotaped interviews are divided into two libraries. The full interviews are available in Library 1: Nursing Informatics Pioneers. For each pioneer, a picture, short biographical sketch, transcript of the interview, and an MP3 audio file are included in addition to the videotaped interview. In Library 2: Themes from Interviews, selected segments from the interviews are shared for easy comparison across the pioneers. The themes include the following:

• • • • • • • • •

Nursing informatics—what it is, present, future, what nursing brings to the table Significant events that have shaped the field of nursing informatics Pioneers’ paths—careers that lead up to involvement in (nursing) informatics When nurses first considered themselves informatics nurses Pioneers’ first involvement—earliest events they recall Informatics—its value, pioneers’ realizations of the value of informatics, how they came to understand the value of informatics Demography of pioneers including names, educational backgrounds, and current positions Personal aspirations and accomplishments, an overall vision that guided the pioneers’ work, people the pioneer collaborated with to accomplish their visions, and goals Pioneers’ lessons learned that they would like to pass on

The Web site also provides “use cases” for ideas about how to use the information for teaching and learning more about the pioneers. These resources are particularly useful for courses in informatics, leadership, and research. They also are useful for nurses in the workforce who want to learn more about NI history. Lessons Learned Pioneers have generously shared lessons learned from their work and use of technology in

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health care. Pioneers by definition are nurses who forged into the unknown and had a vision of what was possible, even if they did not know how to get there. One of the pioneers advised, “Don’t be afraid to take on something that you’ve never done before. You can learn how to do it. The trick is in finding out who knows it and picking their brain and if necessary, cornering them and making them teach you!” Another said, “Just do it, rise above it [barriers], and go for it ‘you are a professional’, and you have to be an advocate for yourself and the patient.” Many of the pioneers described the importance of mentors, someone who would teach them about informatics or computer technology, but it was still up to them to apply their new knowledge to improve nursing. Mentors were invaluable by listening, exchanging ideas, connecting to others, and supporting new directions. Networking was another strong theme for pioneers. Belonging to professional organizations, especially interprofessional organizations, was key for success. At meetings, the pioneers networked and exchanged ideas, learning from others what worked and, more importantly, what didn’t work. They emphasized the importance of attending social functions at organizational meetings to develop solid relationships so they could call on colleagues later to further network and exchange ideas. Nursing informatics did not occur in a vacuum; a major effort was made to promote the inclusion of nurses in organizations affecting health policy decisions such as the ONC’s Technology Policy and Standards Committees. The nursing pioneers influenced the evolution of informatics as a specialty from granular-level data through health policy and funding to shape this evolving and highly visible specialty in nursing.

Recent History Committee Activities The NI History Project continues as an activity committee of the AMIA-NIWG. For more than 5 years, a new committee was formed with a new Chair—Juliana J. Brixey. Their monthly meetings have led to several new initiatives to advance to the field. The committee develops yearly goals to align with AMIA-NIWG. Notable goals achieved include the following: reference list of keystone articles written by pioneers, a list of abstract biographies for 11 pioneers not videotaped, an additional video library, Computers in Nursing Series, and a biographical abstract of Dr. Harriett Werley for the Web site. Moreover, the committee members are dedicated to the dissemination of knowledge through publications and oral presentations to nurses in various venues. For example, Newbold has provided an overview of the pioneers at each of her nursing informatics boot camps and has been

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24    P art 1 • N ursing I nformatics T echnologies an invited speaker for numerous nursing events. Also, Newbold and Brixey have presented posters at various national and international IT and NI conferences. In 2014, a significant accomplishment by the history project committee was the creation of the Nursing Informatics History Collection sponsored by the University of Texas School of Biomedical Informatics at Houston (SBMI) for the John P. McGovern Historical Collections and Research Center at the Texas Medical Center Library. The collection is accessible to those interested in exploring the history of the pioneers. The collection continues to grow through the generous donations from the pioneers in nursing informatics. The library accepts books, journals, and other publications but also technological materials such as CDs, DVDs, as well as microfiche, correspondence, professional meeting materials, and any other invaluable material that represents the advancement of nursing and computers including IT.

SUMMARY Computers, and subsequently IT, emerged during the past five decades in the healthcare industry. Hospitals began to use computers as tools to update paper-based patient records. Computer systems in healthcare settings provided the information management capabilities needed to assess, document, process, and communicate patient care. As a result, the “human–machine” interaction of nursing and computers has become a new and lasting symbiotic relationship (Blum, 1990; Collen, 1994; Kemeny, 1972). The history of informatics from the perspective of the pioneers has been described in this chapter. The complete video, audio, and transcripts can be found on the AMIA Web site (www.amia.org/niwg-history-page). Over the last 50 years, nurses have used and contributed to the evolving HIT or EHR systems for the improved practice of nursing. Innumerable organizations sprang up in an attempt to set standards for nursing practice and education, standardize the terminologies, create standard structures for EHRs, and attempt to create uniformity for the electronic exchange of information. This chapter highlighted a few key organizations. The last section focuses on landmark events in nursing informatics, including major milestones in national and international conferences, symposia, workshops, and organizational initiatives contributing to the computer literacy of nurses, as listed in Table 1-1. The success of the conferences and the appearance of nursing articles, journals, books, and other literature on this topic demonstrated the intense interest nurses had in learning more about computers and information technologies. These

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advances confirmed the status of NI as a new ANA specialty in nursing and provided the stimulus to transform nursing in the twenty-first century. This chapter highlights the historical dates of events as well as the major pioneer(s) involved which helps put in perspective when and how the Information Technology and/or Nursing Informatics were integrated into the nursing profession (Table 1-1). They were also categorized and described in the chapter “Historical Perspectives of Nursing Informatics” (Saba & Westra, 2015) published in the sixth edition of Essentials of Nursing Informatics (Saba & McCormick, 2015). In this edition, the major landmark milestones have been updated in Table 1-1. The milestone events are listed in chronological order including for the first time the key NI pioneer or expert involved in the event as well as the first time the key event occurred and which may be ongoing. Many other events may have occurred but this table represents the most complete history of the NI specialty movement. There are currently several key events in which the NI community participates, and many of them are held annually. The conferences, symposia, institutes, and workshops provide an opportunity for NI novices and experts to network and share their experiences. They also provide the latest information, newest exhibits, and demonstrations on this changing field shown in Table 1-3.

  TABLE 1.3   Major Nursing Informatics Events and Conferences for Nursing Informaticians. A. Conferences and Workshops

•• American Medical Informatics Association (AMIA) Annual Symposium:

◦◦ Nursing Informatics Workshop ◦◦ Nursing Informatics Working Group (NIWG) ◦◦ Harriet Werley Award ◦◦ Virginia K. Saba Award

•• HIMSS Annual Conference and Exhibition ◦◦ Nursing Informatics Symposium ◦◦ Nursing Informatics Task Force ◦◦ Nursing Informatics Leadership Award

•• Summer Institute in Nursing Informatics (SINI) at University of Maryland, Baltimore, MD. (Annual) •• Annual American Academy of Nursing (Annual) ◦◦ Panel of Nursing Informatics Experts •• Sigma Theta Tau International: Bi-Annual Conference ◦◦ Virginia K. Saba Nursing Informatics Leadership Award (Bi-Annual)

◦◦ Technology Award; Information Resources (Annual) (Continued)

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Chapter 1 • Historical Perspectives of Nursing Informatics  

  TABLE 1.3   Major Nursing Informatics Events and Conferences for Nursing Informaticians. (Continued)

•• Nursing Informatics Special Interest Group of the •• •• •• ••

International Medical Informatics Association (IMIA/ NI-SIG): Tri-Annual Conference ◦◦ Starting 2014 Bi-Annual Conference International Medical Informatics Association (IMIA): Triennial Congress ◦◦ Nursing Sessions and Papers Nursing Knowledge Big Data Science Initiative Annual Conferences at University of Minnesota, Minneapolis, MN New England Nursing Informatics Annual Symposium New England Nursing Informatics Consortium (NENIC)

B.   Professional Councils and/or Committees

•• American Nurses Association (ANA) ◦◦ Nursing Informatics Database Steering Committee •• National League for Nursing (NLN) ◦◦ Educational Technology and Information Management Advisory Council (ETIMC)

•• American Academy of Nursing (AAN) ◦◦ Expert Panel of Nursing Informatics •• American Medical Informatics Association

◦◦ Nursing Informatics Working Group (NIWG)

C. Credentialing/Certification/Fellowship

•• American Nurses Association (ANA); American Nurses Credentialing Center (ANCC)

◦◦ Informatics Nursing Certification

•• HIMSS

◦◦ Certified Professional in Healthcare Information and Management Systems (CPHIMS)

TEST QUESTIONS 1. What was considered to be the first major clinical information system used for documenting nursing practice in the 1970s? A. Technicon Data System (TDS) B. Metathesaurus of the NLM

C. PLATOU.S. ANC Educational Workshop E. All of the above

2. Which event was the most significant milestone for nursing and computers and/or nursing informatics in the 1980s? A. Internet emerged for public use.

B. U.S. Army conducted an International Computer Conference in Europe.

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C. ANA approved the formation of the Council on Computer Applications in Nursing (CCAN).A Physiological Monitoring System emerged for documenting nursing practice.All of the above 3. Which event was the most significant milestone for informatics in the 1990s? A. Nursing presented first nursing papers at SCAMC. B. HITECH Act was passed.

C. ANA recognized the first four nursing terminologies. F. NLN approved NI standards.

H. All of the above. 4. Which event in 2000s became a significant milestone for healthcare informatics? A. Bar coding technology emerged. B. RFID technology emerged. D. ONC was established E. MU was initiated.

F. All of the above. 5. Which is one of the most significant event(s) that emerged from the NI History Project. A. Video-taping of ANPs.

B. NLM’s NI Pioneers’ History Collection. C. NLN’s Interactive NI Educational CDs. D. NI mentorship standards.

E. All of the above 6. What was learned from the NI Pioneers’ interviews?

A. A vision that NI could make nursing practice visible. B. Learned NI primarily through hands-on experiences.

C. NI needed to be involved in the development of healthcare systems. D. NI was a new nursing specialty.

E. All of the above. 7. What does the ANA Scope of NI Practice address? A. Nursing Process

B. Standards of Nursing Research, Administration, Education, and Practice D. NI Educational Requirements E. NI Terminologies F. All of the above

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26    P art 1 • N ursing I nformatics T echnologies 8. What historical event initiated the need for a CPR Institute?IOM proposed the need for a CPR Institute. A. ANA proposed that CPRI be established.

B. NCVHS proposed NI technology standards. C. Criteria for Davies Award of Excellence. D. All of the above.

9. What nursing software is recommended that should be integrated in an EHR? A. SNOMED-CTApproved HHS Nursing Terminology B. Nursing Process Framework

C. Sample Nursing Plan of Care D. All of the above

10. What became available for the emerging NI educational programs? A. NI textbooks

B. ANA NI certification criteria C. NI standards

D. NLN interactive videos and/or CDs E. All of the above

Test Answers 1. Answer: A 2. Answer: C 3. Answer: C

4. Answer: A, C 5. Answer: B 6. Answer: E

7. Answer: A 8. Answer: A 9. Answer: A 10. Answer: E

ACKNOWLEDGMENT The authors wish to acknowledge Patricia B. Wise for her authorship of the original 5th edition Chapter 3: “Electronic Health Records from a Historical Perspective” from which content has been integrated into this chapter.

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REFERENCES American Association of Colleges of Nursing. (2008, October). The essentials of baccalaureate education for professional nrsing practice. Retrieved from http://www. aacn.nche.edu/education/essentials.htm. American Nurses Association. (1997). NIDSEC standards and scoring guidelines. Silver Springs, MD: ANA. Retrieved from http://ana.nursingworld.org/ DocumentVault/NursingPractice/NCNQ/meeting/ANAand-NIDSEC.aspx. American Nurses Association. (2008). Scope and standards of nursing informatics practice. Washington, DC: ANA. American Association of Nurses. (2010). Nursing informatics: scope & standards of practice. Washington, DC: ANA. Aydin, B., & Beruvides, M. G. (2014). Development of a decision tool for usability cost justification. Proceedings of the 2014 Industrial and Systems Engineering Research Conference. Retrieved from: https://publons.com/journal/35982/ proceedings-of-the-2014-industrial-and-systems-eng Bakken, S. (2006). Informatics for patient safety: A nursing research perspective. Annual Review of Nursing Research, 24, 219–254. Bakken, S., Stone, P. W., & Larson, E. L. (2012). A nursing informatics research agenda for 2008–2018: Contextual influence and key components. Nursing Outlook, 60, 28–29. Bitzer, M. (1966). Clinical nursing instruction via the Plato simulated laboratory. Nursing Research, 15(2), 144–150. Blackmon, P. W., Mario, C. A., Aukward, R. K., Bresnahan, R. E., Carlisle, R. G., Goldenberg, R. G., & Patterson, J. T. (1982). Evaluation of the medical information system at the NIH Clinical Center. Vol 1, Summary of findings and recommendations (Publication No. 82-190083). Springfield, VA: NTIS. Blum, B. I. (1990). Medical informatics in the United States, 1950–1975. In B. Blum, & K. Duncan (Eds.), A History of Medical Informatics (pp. xvii). Reading, MA: Addison-Wesley. Burchan, A., Mario G., & Beruvides, M. G. (2014). Development of a decision tool for cost justification of software usability. International Journal of Information Technology and Business Management, 24–28, 45–73. Retrieved from http://www.jitbm.com/JITBM%2028%20 VOLUME.html Clarke, S. F., & Foster, J. R. (2012). A history of blood glucose meters and their role in self-monitoring of diabetes mellitus. British Journal of Medical Science, 69(2), 83–93. Coenen, A., McNeil, B., Bakken, S., Bickford, C., & Warren, J. J. (2001). Toward comparable nursing data: American Nurses Association criteria for data sets, classification systems, and nomenclatures. CIN: Computers, Informatics, Nursing, 19, 24–28. Collen, M. F. (1994). The origins of informatics. Journal of the American Medical Informatics Association, 1(2), 91–107.

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Chapter 1 • Historical Perspectives of Nursing Informatics  

Collen, M. F. (1995). A history of medical informatics in the United States, 1950 to 1990. Bethesda, MD: American Medical Informatics Association. Delaney, C.W., & Weaver, C.A. (2018, Dec.). Nursing knowledge and the 2017 Big Data Science Summit: Power of partnership. CIN: Computers, Informatics, Nursing, 35(2), 615–616. Dick, R. S., & Steen, E. B. (Eds.). (1991). The computer-based patient record: an essential technology for healthcare. Washington, DC: National Academy Press. Gallagher, L. A. (2010). Revising HIPAA. Nursing Management, 41(4), 34–40. Grissinger, M. (2012). Safeguards for using and designing automated dispensing cabinets. Pharmacy & Theraputics, 37(9), 490–491, 530. Retrieved from https://www.ncbi. nlm.nih.gov/pmc/articles/PMC3462599/ The Health Information Management Systems Society (HIMSS). (2019). What is EHR usability: EHR usability 101. Retrieved from https://www.himss.org/ what-ehr-usability Hobbs, J. (2011). Political dreams, practical boundaries: The case of the Nursing Minimum Data Set, 1983–1990. Nursing History Review, 19(2011), 127–155. Kemeny, J. G. (1972). Man and the computer. New York, NY: Charles Scribner. Merit-Based Incentive Payment System (MIPS). (2019). What’s the Merit-based Incentive Payment System (MIPS). Retrieved from https//www.aafp.org/practicemanagement/payment/medicare-payment/mips.html National Institute of Standards and Technology (NIST). (n.d.). Health IT usability. Retrieved from https://www. nist.gov/programs-projects/health-it-usability Newbold, S. K., Berg, C., McCormick, K. A., Saba, V. K., & Skiba, D. J. (2012). Twenty five years in nursing informatics: A SILVER pioneer panel. In P. A. Abbott, C. Hullin, S. Bandara, L. Nagle, & S. K. Newbold (Eds.), Proceedings of the 11th International Congress on Nursing Informatics, Montreal, QC, Canada. Retrieved from http://knowledge.amia.org/amia-55142-cni2012-1.129368?qr=1 Nicoll, L. H. (1998). Computers in nursing: Nurses’ guide to the Internet (2nd ed.). New York, NY: Lippincott. NINR Priority Expert Panel on Nursing Informatics. (1993). Nursing informatics: enhancing patient care. Bethesda, MD: U.S. Department of Health and Human Services, U.S. Public Health Services, National Institutes of Health. Office of the National Coordinator for Health Information Technology (ONC). (2019). 2019 Interoperability standards advisory. Retrieved from https://www.healthit.gov/ isa/section-i-vocabularycode-setterminology-standardsand-implementation-specifications Ozbolt, J. G., & Bakken, S. (2003). Patient-care systems. In E. H. Shortliffe, L. E. Perreault, G. Wiederhold, & L. M. Pagan (Eds.), Medical informatics computer applications in healthcare and biomedicine series: Health informatics (2nd ed., pp. 421–442). New York, NY: Springer.

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Parker, M., Ausman, R. K., & Ovedovitz, I. (1965). Automation of public health nurse reports. Public Health Reports, 80, 526–528. Public Health Service. (1976). State of the art in management information systems for public health/community health agencies. Report of the Conference. New York, NY: National League of Nursing. Rajecki, R. (2008). eICU: Big brother, great friend: Remote monitoring of patients is a boon for nurses, patients, and families. RN, 71(11), 36–39. Romano, C., McCormick, K., & McNeely, L. D. (1982). Nursing documentation: A model for a computerized data base. Advances in Nursing Science, 4(2), 43–56. Saba, V. K. (1995). A new nursing vision: The information highway. Nursing Leadership Forum, 1(2), 44–51. Saba, V. K. (1996). Developing a home page for the World Wide Web. American Journal of Infection Control, 24, 468–470. Saba, V. K., & McCormick, K. A. (2015). Essentials of nursing informatics (6th ed.). New York, NY: McGraw-Hill. Saba, V. K., & Westra, B. L. (2011). Historical perspectives of nursing and the computer. In V. K. Saba, & K. A. McCormick (Eds.), Essentials of nursing informatics (6th ed., pp. 11–29). New York, NY: McGraw-Hill. Saba, V. K., & Westra, B. L. (2015). Historical perspectives of nursing and the computer. In V. K. Saba & K. A. McCormick (Eds.), Essentials of nursing informatics (6th ed., pp. 3–22). New York, NY: McGraw-Hill. Saba, V. K., & Whittenburg, L. (2015). Appendix A: Overview of the Clinical Care Classification (CCC) System. In V. K. Saba, & K. A. McCormick, (Eds.), Esstentials of nursing informatics. (6th ed., pp. 833–853). New York, NY: McGraw-Hill. Sackett, K. M., & Erdley, W. S. (2002). The history of healthcare informatics. In S. Englebardt, & R. Nelson (Eds.), Healthcare informatics:an interdisciplinary approach (pp. 453–476). St. Louis, MO: Mosby. Sherman, R. (1965). Computer system clears up errors, lets nurses get back to nursing. Hospital Topics, 43(10), 44–46. Shortliffe, E. H., Perreault, L. E., Wiederhold, G., & Pagan L. M. (Eds.). (2003). Medical informatics computer applications in healthcare and biomedicine (2nd ed.). New York, NY: Springer. Sparks, S. (1996). Use of the Internet for infection control and epidemiology. American Journal of Infection Control, 24, 435–439. Techopedia. (2019). Moore’s Law. Retrieved from https:// www.techopedia.com/definition/2369/moores-law Westra, B. L. (2009). Radio frequency identification—Will it reach a tipping point in healthcare? American Journal of Nursing, 109(3), 34–36. Westra, B. L., Delaney, C. W., Konicek, D., & Keenan, G. (2008). Nursing standards to support the electronic health record. Nursing Outlook, 56, 258.e1–266.e1. Westra B. L., & Newbold S. K. (2006). American Medical Informatics Association Nursing Informatics History

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28    P art 1 • N ursing I nformatics T echnologies Committee. CIN: Computers, Informatics, Nursing, 24:113–116. Wideman M. V., Whittler M. E., & Anderson T. M. (2005). Barcode medication administration: Lessons learned from an Intensive Care Unit implementation. In K. Henriksen, J. B. Battles, & E. S. Marks, et al. (Eds.), Advances in patient safety: From research to implementation (Vol 3: Implementation issues). Rockville, MD: Agency for Healthcare Research and Quality. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK20569/ Wolkodoff, P. E. (1963). A central electronic computer speeds patient information. Hospital Management, 96, 82–84. Zielstorff, R., McHugh, H., & Clinton, J. (1988). computer design criteria for systems that support the nursing process. Kansas City, KS: American Nurses’ Association Council on Computer Applications in Nursing.

BIBLIOGRAPHY American Association of Colleges of Nursing. (2006, October). The essentials of doctoral education for advanced nursing practice. Retrieved from http://www. aacn.nche.edu/education/essentials.htm American Association of Colleges of Nursing. (2011). The essentials of master’s education for professional nursing practice. Retrieved from http://www.aacn.nche.edu/ education-resources/MastersEssentials11.pdf Branchini, A. Z. (2012). Leadership of the pioneers of nursing informatics: A multiple case study analysis. Doctoral Dissertations. Paper AAI3529472. Retrieved from http:// digitalcommons.uconn.edu/dissertations/AAI3529472

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Hammond, W. E. (1994). The role of standards in creating a health information infrastructure. International Journal of Bio-Medical Computing, 34, 29–44. Humphreys, B. L., & Lindberg, D. A. B. (1992). The unified medical language system project: A distributed experiment in improving access to biomedical information. In K. C. Lun, P. DeGoulet, T. E. Piemme, & O. Reinhoff (Eds.), MEDINFO 92: Proceedings of the Seventh World Congress of Medical Informatics (pp. 1496–1500). Amsterdam: North-Holland. International Council of Nursing & International Health Terminology Standards Development Organisation. (2010, June). Harmonization [Press Release]. ICN Bulletin, p. 2. International Standards Organization (ISO). (2010). Ergonomics of Human-System Interaction – Part 210: Human-centered Design for Interactive Systems. ISO 9241-210. Retrieved from https://www.nist.gov/ programs-projects/health-it-usability Newbold, S., & Westra, B. (2009). American Medical Informatics Nursing Informatics History Committee update. CIN: Computers, Informatics, Nursing, 27, 263–265. Saba, V. K. (1998). Nursing information technology: Classifications and management. In J. Mantas (Ed.), Advances in health education: A nightingale perspective. Amsterdam: IOS Press. Van Bemmel, J. H., & Musen, M. A. (Eds.). (1997). Handbook of medical informatics. Germany: Springer-Verlag. World Health Organization. (1992). ICD-10: international statistical classification of diseases and health related problems. Geneva: WHO.

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2 Computer Systems Basics—Hardware Mary L. McHugh

• OBJECTIVES . Identify the essential hardware components of a computer. 1 2. List key peripherals attached to most computers. 3. Describe the four basic operations of the central processing unit (CPU). 4. Explain how power is measured for computers. 5. Describe common computer input, output, and storage devices. 6. List the names for six types of computers and describe how they are different. 7. Describe computer network hardware devices and their functions.

• KEY WORDS CPU (central processing unit) Motherboard Memory Peripherals Hardware

INTRODUCTION This chapter covers various aspects of computer hardware: the components and their functions which allow computers to do their work, and the various classes of computers and their characteristics. Basic computer concepts, and devices and media used to communicate, store, and process data are addressed .To understand how a computer processes data, it is necessary to examine the component parts and devices that comprise computer hardware. A computer is a machine that uses electronic components and instructions to the components to perform calculations and repetitive and complex procedures, process text, and manipulate data and signals. Today, computer processors are encountered in most areas of people’s lives. From the grocery store to a community’s power grid, from nuclear power plants to a State’s voting machines,

from infusion pumps to physiological monitors, and from patient record systems to radiology machines and other diagnostic devices, computer processors are employed so widely that today’s society could not function without them. In fact, so dependent has society become on ­computer processing that a major concern of national agencies focused on combatting terrorism is the vulnerability of American society’s infrastructure (Derene, 2019). Not only are power grids (that control the flow of electricity to all communities in the country) controlled largely by ­computer systems, but also are water, sewage treatment facilities, financial networks, gas and oil pipelines, much of the military (including nuclear warhead targeting), and virtually all American industry, including healthcare facilities. Perhaps the best example of the influence of computers on our lives is the deep concern many have expressed 29

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30    P art 1 • N ursing I nformatics T echnologies

Dragonian DigitalVision Vectors/Getty Images.

that foreign countries could use—or may have already used—intrusions into voting machines to affect elections in the United States. Given the essential nature of computers in maintaining society, nurses today should know the basics of computer parts and how they work.

HARDWARE COMPONENTS Computer hardware is defined as all of the physical components of a computer. The basic hardware of a computer composes the computer’s architecture, and includes the electronic circuits, microchips, processors, random access memory (RAM), read-only memory (ROM), the BIOS chip, and graphic and sound cards. These are attached to a component called a motherboard. The motherboard is the “guts” of a computer. It is a square or rectangular board made of a nonconducting material such as fiberglass or heat-resistant plastic. The motherboard consists of layers of the material that have been sealed together with resin and “printed” with copper tracts (Oettinger, 2016; Padilla, 2019). The copper tracts look a bit like threads and are/ interconnected so that electric impulses can be sent throughout the motherboard. The threads of copper create a system of circuits (routes for the impulses to travel) which maximize the speed with which electric impulses can be carried to various components that are soldered onto the motherboard. Devices that may be inside the computer case but are not part of the architecture include the main storage device which is usually an internal hard drive, optical drive or solid state drive, the cooling system (including heatsinks and fans), a modem, Ethernet connectors, universal serial bus (USB) connectors, and multi-format media card readers. In addition, devices attached or linked to a computer that are peripheral to (outside) the main computer box are part of the system’s hardware. These include input and output devices including the keyboard, monitor (with or without a touch screen), mouse, printer, scanner, and Fax. They also include storage components such as external data storage devices, thumb drives, floppy drives, and tape drives. Most personal computer systems also have sound and video systems, including microphones, speakers, subwoofers, earphones, and a video camera. Typically, computer systems are composed of many different component parts that enable the user to communicate with the computer, and with other computers to produce work. The group of required and optional hardware items that are linked together to make up a computer system is called its configuration. When computers are sold, many of the key components are placed inside a rigid plastic housing or case, which is called the box. What can typically be seen from the outside is the box (Fig. 2.1) containing

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•  FIGURE 2.1.  Desktop Computer. the internal components, and the peripherals such as a ­keyboard, mouse, speakers, monitor, and printer. Computer hardware advances during the later 1900s and continuing now in this century have made possible many changes to the healthcare industry. The first business processes to be modified consisted of special administrative functions such as finance, payroll, and billing. Later, computers and associated software programs were developed to assist with hospital bed assignment, nurse staffing and scheduling support, and computer-based charting. Today, many of the hospital’s communication processes are computer based, including programs that support patient communication with the system (often called patient portals), ordering from labs, radiology, pharmacy, and dietary, and all the other services that are ordered to support patient care. Major advances in the fields of miniaturization and computer imaging allowed incredible changes in the department of radiology, allowing noninvasive visualization not only of internal structures, but even of metabolic and movement functions (Cammilleri et al., 2019; Falke et al., 2013; Gropler, 2013; Hess, Ofori, Akbar, Okun, & Vaillancourt, 2013; Ishii, Fujimori, Kaneko, & Kikuta, 2013; Modesti, 2018; O’Neill et al., 2018; Suff & Waddington, 2017). Computer-enhanced surgical instruments enabled surgeons to insert endoscopy tools that allow for both visualization and precise removal of diseased tissues, leaving healthy tissues minimally damaged and the patient unscarred (Botta et al., 2013; Gumbs et al., 2009; Roner et al., 2019; Vilmann et al., 2019). Virtual reality programs and robotics in surgery have greatly enhanced the scope and complexity of surgeries that are now amenable to much less invasive surgeries (Quero et al., 2019). As a result, massive damage to skin, subcutaneous tissues, muscles, and organs have been eliminated

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Chapter 2 • Computer Systems Basics—Hardware 

from many procedures. Today, millions of patients who formerly would have needed weeks in the hospital for recovery from major surgery to organs and bones are now able to be released from the hospital the day of their surgery, or in a day or two more at most. Computers are pervasive throughout the healthcare industry. Their applications are expected to continue to expand and thereby improve the quality of healthcare while at the same time reducing some costs. Most important, the applications of computers to healthcare will greatly expand the diagnostic and therapeutic abilities of practitioners and broaden the diagnostic and treatment options available to recipients of health care. Computers allow for distance visualization and communication with patients in remote areas. Telemedicine is now being used to reduce the impact of distance and location on accessibility and availability of healthcare (Baroi, McNamara, McKenzie, Gandevia, & Brodi, 2018; Evans, Medina, &

Dwyer, 2018; Raikhelkar & Raikhelkar, 2019). And during the Corona Virus-19 epidemic, patient and healthcare provider safety has been enhanced through telemedicine medical appointments when the patient did not have to be physically in the presence of the provider. None of these changes could have happened without tremendous advances in the hardware of computers.

REQUIRED HARDWARE COMPONENTS OF A COMPUTER

Gavin Roberts/Official Windows Magazine via Getty Images.

The heart of any computer is the motherboard (Fig. 2.2), a thin, flat sheet made of a firm or flexible nonconducting material on which the internal components—printed circuits, chips, slots, and so on—of the computer are mounted. The motherboard is made of a nonconducting plastic or fiberglass material. Copper (or other metal) conducting lines (circuits) are embedded into the board.

•  FIGURE 2.2.  Motherboard.

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Mediacolors/Construction Photography/Avalon/Getty Images.

32    P art 1 • N ursing I nformatics T echnologies

•  FIGURE 2.3.  Circuit Board. The motherboard has holes or perforations through which components can be affixed so they can transmit data across the circuits (Fig. 2.3). Typically, one side looks like a maze of soldered metal trails with sharp projections (which are the attachments for the chips and other components affixed to the motherboard). The motherboard contains the microchips (including the CPU), and the wiring, and slots for adding components. The specific design of the components on the motherboard—especially the c­ entral processing unit (CPU) and other microprocessors—­ composes the foundation of the computer’s architecture. A key component of a computer is called the BIOS chip, short for Basic Input/Output System. The BIOS itself is a computer program stored on a permanent (nonvolatile) memory chip on the motherboard, and is called the BIOS chip. This chip controls several essential operations of a computer, including start-up, performing a self-test of the system to ensure the operating system can function, and communication with input and output devices. If it malfunctions, the computer will not even “start up” so that the user can enter commands. It might show some kinds of error messages, but the computer will not function because the BIOS is what loads the operating system, and without the operating system, the computer doesn’t function at all. A computer has four basic components, although most have many more add-on components. At its most basic, a computer must consist of communication buses of the printed circuits and slots on the motherboard, a CPU, the input and output controllers, and storage media. The  motherboard’s storage media is called memory. Memory includes the locations of the computer’s internal or main working storage. Memory consists of registers (a small number of very high speed memory locations), RAM, which is the main storage area in which the computer

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places the programs and data it is working on, and cache (a small memory storage area holding recently accessed data).

Memory Memory refers to the electronic storage devices or chips on the motherboard of a computer. There are three key types of memory in a computer. They are random access memory (RAM), read-only memory (ROM), and cache. Random Access Memory  Random access memory (RAM) refers to working memory used for primary storage. It is used as temporary storage by the CPU and other processors for holding data and commands the processors are actively using. Also known as main memory, RAM can be accessed, used, changed, and written on repeatedly. RAM is the work area available to the CPU for all processing applications. When a user clicks on a program icon, such as a word processing program, the computer loads all or part of the program into RAM where it can be accessed very quickly. It saves work done by the user’s programs until the user either saves the work on permanent storage or discards it. RAM is a permanent part of the computer. Because everything in RAM unloads (is lost) when the computer is turned off, RAM is called volatile memory. The computer programs that users install on their computers to do work or play games are stored on media such as on the hard drive. They are not components of the computer itself and may be replaced by users if desired. Running programs from the hard drive would be a very slow process, so parts of the programs are loaded and unloaded as needed from the much faster RAM. They are unloaded when the user closes the program or turns off the computer. The contents of RAM are erased whenever the power to the computer is turned off. Thus, RAM is made ready for new programs when the computer is turned on again. Read-Only Memory  Read-only memory (ROM) is a form of permanent storage in the computer. It carries instructions that allow the computer to be booted (started), and other essential machine instructions. Its programming is stored on the ROM chip by the manufacturer and cannot be changed by the user. This means that data and programs in ROM can only be read by the computer and cannot be erased or altered by users. As a result, ROM chips are called firmware (as opposed to software that can be changed by programmers). ROM generally contains the programs used by the control unit of the CPU to oversee computer functions. In microcomputers, this may also include the software programs used to translate the computer’s high-level programming languages into machine language (binary code). ROM storage is never erased.

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Chapter 2 • Computer Systems Basics—Hardware 

Cache  Cache is a smaller form of RAM. Its purpose is to speed up processing by storing frequently called (used) data and commands in a small, rapid access memory location. To understand how cache works, think of a surgical nursing unit. Prior to the 1980s, many hospitals did not have many volumetric pumps on the nursing unit. The pumps were usually kept in the Central Supply (CS) department—usually far away in the basement. Whenever a nurse needed a pump (which at that time was used only for especially dangerous IV medication infusions), the nurse had to go to the CS department and fetch it. When no longer needed for that patient, the pump was to be returned to the CS department. This slow process is analogous to a system with no cache. This was a good system when pumps were seldom used on any unit because storage space is always limited. Now, however, changes in practice and patient acuity have led to the need for one or more volumetric pumps used for every patient. The new plan is to have at least one at every bed and extra volumetric pumps in a storage area in the nursing unit so there are always machines nearby. This system is much more efficient for the nurses. Having pumps at the bedside (and a space to store the pumps nearby) greatly reduces the time needed for nurses to get a pump whenever needed. Rarely used equipment is still often kept in the CS department, but frequently needed items must be kept in a nearby storeroom for quick and efficient retrieval. This is similar to cache. Prior to the development of cache, all information had to be fetched from the hard drive or even from a floppy disc and then stored in RAM. To handle all the work, the processor had to move information into and out of RAM (and back to the hard drive) in order to manage all the data from programs and their output. Given that RAM is large, it takes the computer more time to search RAM to find just the pieces needed. Cache is much smaller than RAM, and thus fetching from cache takes much less time than from RAM. Keeping information that will be used frequently in cache greatly reduces the amount of time needed to move data around among the memory locations. It is a relatively inexpensive way to increase the speed of the computer.

Input Devices These devices allow the computer to receive information from the outside world. The most common input devices are the keyboard and mouse. Others commonly seen on nursing workstations include the touch screen, light pen, microphone, and scanner. A touch screen is actually both an input and output device combined. Electronics allow the computer to “sense” when a particular part of the screen is pressed or touched. In this way, users input information into the computer. The touch screen displays information back to the user, just as does any computer monitor. A light pen is a device attached to the computer that has special software that allows the computer to sense when the light pen is focused on a designated part of the screen. It allows smaller screen location discriminations than does a touch screen. For both the touch screen and light pen, software interprets the meaning of the useridentified screen location to the program. Many other input devices exist. Some devices are used for security and can detect users’ fingerprints, retinal prints, voiceprints, or other personally unique physical characteristics that identify users who have clearance to use the system. In healthcare computing, many medical devices serve as input devices. For example, the electrodes placed on a patient’s body provide input into the computerized physiologic monitors. The oximetry device placed on a patient’s finger uses light waves to detect impulses which are sent to a computer and then interpreted as oxygen levels in the blood. Voice systems allow the nurse to speak into a microphone (which is the input device) to record data, submit laboratory orders, or request information from the computer. In radiology, most machines today input digital images from the X-Ray machines to a computer rather than storing them on radiographic film. In fact, the most advanced imaging machines, such as computerized axial tomography (CAT) scans and medical resonance imaging (MRI) machines, could not exist without computer technology.

Input and Output To do work, the computer must have a way of ­receiving commands and data from the outside and a way of reporting out its work. The motherboard itself cannot communicate with users. However, it has slots and circuit boards that allow the CPU to communicate with the outside world. Input and output devices are wired to a controller that is plugged into the slots or circuit boards of the computer. Some devices can serve as both input and output devices to receive and store information as well as send their programs to the computer itself.

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Output Devices  These devices allow the computer to report its results to the external world. Output devices are defined as any equipment that translates the computer information into something usable by people or other machines. Output can be in the form of text, data files, sound, graphics, or signals to other devices. The most obvious output devices are the monitor (display screen) and printer. Other commonly used output devices include storage devices such as the USB drive (also known as flash or thumb drive) and optical media. In healthcare settings, a variety of medical devices serve as output devices. Heart monitors are output devices recording and displaying heart rhythm patterns and initiating alarms when certain conditions are met. Volumetric infusion pump outputs includes both fl ­ uids infused into

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34    P art 1 • N ursing I nformatics T echnologies the patient’s body and images displayed on a screen. The pump delivers a specific volume of IV fluids based on commands that the nurse enters so the ordered fluid volume will be infused in the correct time period.

Storage Media Storage includes the main memory but also external devices on which programs and data are stored. The most common storage device is the computer’s hard drive. Other common media include external hard drives, flash drives, and read/write digital versatile disks (DVDs), and compact disks (CDs). The hard drive and diskettes are magnetic storage media. DVDs and CD-ROMs are a form of optical storage. Optical media are read by a laser “eye” rather than a magnet. Hard Drive The hard drive is a peripheral component that has very high speed and high density (Fig. 2.4). That is, it is a very fast means of storing and retrieving data as well as having a large storage capacity in comparison with some other types of storage. The hard drive is the main storage device of many personal computers and is typically inside the case or box that houses other internal hardware. Internal hard drives are not portable; they are plugged directly into the motherboard. The storage capacity of hard drives has increased and continues to increase exponentially every few years. In 2014, most personal computers (PCs) were sold with between 500 gigabytes (GB) and up to about a terabyte of storage; in 1990 the PCs had about 500 megabytes (MB) capacity (Table 2.1). That is approximately a 1000% to 2100% increase. On the biggest computers, Computer hard drive

Spindle

Disk

Encyclopaedia Britannica/Universal Images Group via Getty Images.

Read/write head Saved file

Actuator Arm

Front

Circuit board

•  FIGURE 2.4.  Hard Drive.

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storage is measured in petabytes (see Table 2.1), which is an almost unimaginably huge number. USB Flash Drive  with the rise in demands for higher and higher density transportable storage, the popularity of the USB drive has also risen. A USB flash drive is actually a form of a small, erasable, programmable, read-only memory (EPROM), a bit like the ROM chips in a computer. It functions a bit like a removable hard drive that is inserted into the USB port of the computer. There are many names for it, including pen drive, jump drive, thistle drive, pocket drive, and so forth. This is a device that can store 4 gigabytes (GB) for about $10. Flash drives can be very tiny, only about ½ in. by 1 in. in some cases. They can also be much bigger and can hold 128 GB or more. The flash drive is highly reliable and small enough to transport comfortably in a pants pocket or on a lanyard as a necklace, or on one’s keychain. The device plugs into one of the computer’s USB ports and instead of saving content to the hard drive or CD-ROM or disk, the user simply saves to the flash drive. Since the flash drive can store so much data in a package so much smaller than a CD or DVD, the convenience makes it worth the slightly higher price to many users. Of course, as its popularity increases, prices drop. It should be noted that flash drives are not really used in clinical settings, at least not for business or patient care purposes. However, they are often carried by personnel who may plug them into the hospital’s computer to do personal work. There is a danger that these devices can end up being used to compromise patient or company confidentiality. Nurses should not save confidential patient or company information onto their personal flash drive (or any other personal storage devices). It is too easy to lose the drive itself, and then confidential information could end up anywhere! While working with hard copy medical records, a person had to laboriously copy confidential information onto a piece of paper to create a major risk to confidentiality. With electronic media, it is perilously easy to copy confidential information and breach the security of that information. All nurses are responsible for protecting confidential patient and company information because of personal and company policies and the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule (https://www. hhs.gov/hipaa/for-professionals/privacy/index.html). Optical Media Optical media include compact disks, digital versatile disks, and Blu-Ray. CD-ROMs and DVDs are rigid disks that hold a higher density of information and have higher speed. Until the late 1990s, CD-ROMs were strictly input devices. They were designed to store sound and data, held about 737 MB of information (see Table 2.1), and large laser writers were required to store

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Chapter 2 • Computer Systems Basics—Hardware 

  35

  TABLE 2.1    Meaning of Storage Size Terms Number of Bytes

Term

Formula (≈ Means “Approximately”)

1,024

1 kilobyte (K)

210 ≈ 1,000

One-third of a single-spaced typed page

1,048,567

1 megabyte (M or MB)

220 ≈ 1,0242

600-page paperback book or 30 s of low-definition video

1,073,741,824

1 gigabyte (G or GB)

230 ≈ 1,0243

An encyclopedia or 90 min of lowdefinition video

1,099,511,627,776

1 terabyte (T or TB)

240 ≈ 1,0244

200,000 photos or Mp3 songs, 10 TB equals Library of Congress body of print material

1,125,899,906,842,624

1 petabyte (PB)

250 ≈ 1,0245

Approximately 1 quadrillion bytes

1,152,921,504,606,846,976

1 exabyte (EB)

260 ≈ 1,0246

Approximately 1 quintillion bytes

1,180,591,620,717,411,303,424

1 zettabyte (ZB)

270 ≈ 1,0247

Approximately 1 sextillion bytes or 1 billion terabytes

1,000,000,000,000,000,000,000,000

1 yottabyte (YB)

280 ≈ 1,0278

Approximately 1 septillion bytes or 1,000 zettabytes or 1 trillion terabytes

1 followed by 27 zeros

1 xenottabyte (XB)

290 ≈ 1,0279

So big it feels like infinity

Other Storage Device   As computers became more standard in offices during the 1990s, more and more corporate and individual information was stored solely on computers. Even when a hard copy backups was kept, loss of information on the hard drive was usually inconvenient at the least and a disaster at worst. Diskettes could not store large amounts of data, so people began to search for economical and speedy ways to back up the information on their hard drive. Zip drives, which were mini magnetic tape devices, were a form of relatively fast (in their time) backup storage for people’s data. Thumb (USB) and external hard drives were faster than tape media and replaced it as the backup media of choice. Today, many people purchase services that allow them to

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back up their data online, which means it gets stored on commercial computers that themselves have backup facilities. Cloud Storage  An extension of the online storage ­service offered by individual vendors is cloud storage. Data stored “in the Cloud” is still stored on commercial computers called servers. However, “cloud” refers to a distributed system of many commercial, networked servers that communicate through the Internet and work together so closely that they can essentially function as one large system. Enormous numbers of servers that store data are physically located in many warehouse-sized buildings (Fig. 2.5). These data storage sites are called data centers. Multiple data centers are linked together to create cloud storage. The advantage

rev606/Getty Images.

data on them. Thus, they were read-only media. However, technology developed in the 1980s by Philips Corporation permitted the development of a new type of CD that could be written on by the user. Those are called CD-RW for Compact Disc Read-Write. As technology advanced and people wanted to store motion pictures on computer-readable media, DVDs were developed and held approximately 4.3 GB of information, which handled a regular 2 h movie. They were originally too limited to handle high definition movies and movies longer than 2 hours, and thus media moved to the even higher storage capacity of Blu-Ray discs. Double-layer Blu-Ray discs can store 54 GB or 4.5 h of highdefinition motion picture media. But the technology has advanced to four-layer discs storing 128 GB of media. The name is derived from the blue color of the laser that writes on the media and ray for the optical ray that reads the media.

Approximate Size in Typed Pages or Other Comparison

•  FIGURE 2.5.  Cloud Storage Center.

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to the customer is safety of the stored data. All personally owned storage devices will fail at some point and it is difficult for people to remember to regularly back up their data. As a result, many people have suffered loss of valued personal data. The cloud storage solution provides greater data security because of multiple security and backup facilities. A key factor in cloud storage is redundancy. The s­ torage vendors must maintain multiple copies of the data they store. If one server in a data center fails (becomes ­inoperable), copies of the data on that server are stored elsewhere and thus the data are not lost. They can be retrieved from another server. There are quite a few vendors who offer individuals free cloud storage space for their c­ ustomers’ personal files, such as photos, music, and the like. They may also offer storage for a modest monthly or yearly fee. Some continuously back up data, others back up data at specified times, and typically the user can order files to be backed up whenever he or she wishes. Cloud storage is far more secure and reliable than a personal hard drive or backup drives. Most users of smartphones, tablet computers, and other portable devices store their data in the Cloud, not only because of the security of the data, but also because storage in small devices is somewhat limited. The Cloud allows more data storage than most individuals need for personal use.

MAJOR TYPES OF COMPUTERS The computers discussed so far are general purpose machines, because the user can program them to process all types of problems and can solve any problem that can be broken down into a set of logical sequential instructions. Special purpose machines designed to do only a very few different types of tasks have also been developed. A category of special purpose computers includes the tablet computers, personal digital assistants (PDAs), and smartphones. Today, five basic types of computers are generally recognized. Each type of computer was developed as the computer industry evolved and for a different purpose. The basic types of computers include the supercomputer, the mainframe, the microcomputer, the handheld, and PDAs. They differ in size, composition, memory and storage capacity, processing time, and cost. They generally have different applications and are found in many different locations in the healthcare industry.

Supercomputers The largest type of computer is the supercomputer (Fig 2.6). First developed by Semour Cray in 1972, the early supercomputer research, development, and production ­ were carried out by Cray Corporation or one of its affiliates (Cray Corp, 2014). A supercomputer is a computational-oriented computer specially designed for scientific applications

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LeRoy N. Sanchez/Los Alamos National Laboratory.

36    P art 1 • N ursing I nformatics T echnologies

•  FIGURE 2.6.  Supercomputer Mainframe.

requiring a gigantic amount of calculations which, to be useful, must be processed at superfast speeds. The supercomputer is truly a world-class “number cruncher.” Designed primarily for analysis of scientific and engineering problems and for tasks requiring millions or billions of computational operations and calculations, they are huge and expensive. Supercomputers are used primarily in such work as defense and weaponry, weather forecasting, advanced engineering and physics, and other mathematically intensive scientific research applications. The supercomputer also provides computing power for the high-performance computing and communication (HPCC) environment.

Mainframes The mainframe computer is the most common fast, large, and expensive type of computer used in large businesses (including hospitals and other large healthcare facilities) for processing, storing, and retrieving data. It is a large multiuser central computer that meets the computing needs of large- and medium-sized public and private organizations. Virtually all large- and medium-sized hospitals (300 beds and up) rely on mainframe computers to handle their business office operations. They may have the hospital’s electronic medical record (EMR) on that computer as well, or they may subcontract mainframe computing from a professional computer system support vendor. Mainframes are used for processing the large amount of repetitive calculations involved in handling ­billing, payroll, inventory control, and business operations computing. For example, large volume sales businesses like grocery store chains and the “big box” stores have mainframe computers tracking all sales transactions. In fact, the machines and software that process transactions in high-volume businesses are known as transaction processing systems (TPS). The information nurses chart on patients in inpatient care

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facilities can be viewed as transactions. For ­example, every time a nurse charts a medication, that charting records use of one or more drugs. That charting in turn is transmitted to the pharmacy so that one item of that drug in inventory can be decreased. Typically, when the count of remaining inventory drops to a certain level, the TPS automatically initiates an order to a pharmacy supply house for more of the drug. Operations such as charting a patient’s vital signs goes into that person’s medical record and might trigger a warning to the nurse should any of the vital signs be out of range for that patient. For example, if the blood pressure is too high or too low, the system might be programmed to signal a warning alert so the nurse is advised to assess the patient and take appropriate action. Given the number of these kinds of “transactions” in clinical facilities, a powerful computer is needed to handle them all, and therefore, the hospital’s EMR and other clinical applications are often handled through a mainframe computer. Mainframes always have very high processing speeds (calculated in millions of processes per second, or MIPS, or in floating point operations per second, or FLOPS). In earlier times (prior to the year 2000), mainframes were often defined almost entirely by their high processing speed. However, computer processing speed changes so rapidly that today’s mainframes are more defined by the following characteristics than merely processing speed:

and its patient location system, the pharmacy department, and the central supply department’s inventory control system. Sometimes clinical monitoring systems such as cardiac and fetal monitors, and surgery information systems may be housed on the mainframe, although these systems may reside on their own separate computer hardware. Today the average sized or large acute care hospital has a HIT system with hardware configuration of a mainframe that may be located on-site (physically located at the hospital) or it might be located somewhere else. In some cases, the mainframe is not owned by the hospital but by a computer service vendor who provides mainframe computing power to multiple customers. In that case, the hospital’s information is processed and stored on the vendor’s computer systems. A mainframe is capable of processing and accessing billions (GB) of characters of data or mathematical calculations per second. Mainframes can serve a large number (thousands) of users at the same time. In some settings, hundreds of workstations (input and output devices that may or may not have any processing power of their own) are wired directly to the mainframe for processing and communication speeds faster than can be achieved with wireless communications. Typically, there are also wireless and telephone linkages into the computer so that remote users can gain access to the mainframe. As compared with a desktop PC, a mainframe has an extremely large memory capacity, fast operating and processing time, and it can process a large number of functions (multiprocessing) at one time.

1. Extensive input and output capabilities to support their multi-user environment

2. Complex engineering to support long-term stability with high reliability, allowing these machines to run uninterrupted for decades 3. Ability to process the massive throughput needed for high-volume business transactions and business office operations.

In hospitals, mainframe computers are often used to support the entire Hospital Information Technology (HIT) system, also known as the Hospital Information System (HIS), purchased from one of the large HIT vendors. The HIT not only includes business and nursing operations components, but also supports many clinical systems. As previously mentioned, the applications nurses use in hospitals and other large healthcare facilities to document patient care, obtain laboratory and radiology results, record medication orders and administration records, and perform many other nursing record-keeping and information retrieval tasks typically involve use of a hospital mainframe computer. Virtually all general hospital departments need large amounts of computer support. A partial listing of departments that typically have their systems on the hospital’s mainframe computer includes the laboratory and radiology systems, the dietary department, the admissions department

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Microcomputers (Personal Computers or PCs) While mainframe computers provide critical service to the healthcare industry, much smaller computers are also an essential part of healthcare computing systems. Computers designed to support a single user are called microcomputers or personal computers (PCs). Much smaller and less powerful than a mainframe, PCs were designed to be used by one person at a time. In hospitals, PCs are used for an increasing number of independent applications as well as serving as an intelligent link to the programs of the mainframe. Hospital nursing departments use PCs to process specific applications such as patient classification, nurse staffing and scheduling, and personnel management applications. Microcomputers are also found in educational and research settings, where they are used to conduct a multitude of special educational and scientific functions. Desktops are replacing many of the mainframe attributes. Desktops can serve as stand-alone workstations and can be linked to a network system to increase their capabilities. This is advantageous, since software multiuser licensing fees are usually less expensive per user than having each user purchase his or her own copy. Computer size has steadily decreased since their invention,

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38    P art 1 • N ursing I nformatics T echnologies while at the same time power has grown exponentially. The components of desktop computers are typically housed in a hard case. While the size of the case can vary considerably, one common size is 2 feet long by 6 to 10 in. wide. The case is most typically connected via wire or wireless technology to a network, keyboard, monitor, mouse, and printer. Microcomputers are also available as portable or laptop computers, notebooks, tablets, and handheld computers. Laptop computers are highly portable because they are much smaller than the standard desktop microcomputer. Many are less than 2 in. deep. There is huge variation in the length and width, but if a 15 in. viewing screen is used, the case is usually about 16 in. by 12 in. Notebook computers are a bit smaller and lighter, although the line between a laptop and a notebook computer is thin. Notebooks tend to have a bit less computing power and are about 12 in. by 8.5 in. and very light.

Desktop and laptop computer systems with wireless connectivity to the hospital’s computer network are often placed on a rolling cart for use of the nursing staff in recording nursing notes, ordering tests and treatments, looking up medications, and other computer work in inpatient and clinic settings. These computers on carts are often referred to as “WOWs” for workstation on wheels, or “CABs” for computer at bedsides. Many nurses find these rolling workstations to be much more useful than fixed computers at patient bedsides for a variety of reasons. Additionally, one workstation can be assigned to a nurse to use with his or her assigned patients, thus eliminating the need for a separate computer for every bed. This solution allows nurses to adjust screen height and location of the mouse on the WOW for their physical comfort that day rather than having to readjust a separate computer at every bedside (Box 2.1).

BOX 2.1  HOME COMPUTER SUGGESTIONS Today, fewer people need a computer in the home because they can do so much with their smartphones. However, many nurses want to have a PC (either a desktop or laptop) in their homes, and need advice on what to buy for a home system to meet their needs. A good rule of thumb is to think of the home computer as a system because much more than the basic hardware is needed by most users. In addition to the CPU, memory, hard drive, and graphics cards, computers in the home should have the following components to meet most people’s needs: a printer, monitor screen, keyboard, and mouse. The multi-function printer should be able to print in both black and white and color at the very least. A better machine can also allow the user to scan pictures and documents, make copies in black and white, or color, and provide fax capability. These multifunction printers are called “all-in-one” printers that can print-fax-scan-copy. They often come with a price tag not much more than a simple black and white printer. Of course the user must have a mouse, keyboard, and monitor screen for basic input and output. While many laptops come with a built-in video camera and microphone, desktop computers often don’t. Fortunately, a basic video camera with microphone can be bought for as little as $30, and that device allows the user to have video-linked conversations with family, friends, and business partners. Although most laptops and desktop computers come with operating systems and basic word processing software, some don’t. For those, the user must also budget for purchasing essential software such as an operating system and security software. Most people will also want good software for writing documents, creating graphics, and editing photos and video, and may want other applications. The operating system is the most basic software that must be purchased. Most come with a Web browser, which is a program that allows the user to access the Internet. There are several excellent free Web browsers that can be downloaded from the Internet if the one that comes with the operating system is not preferred. In addition to Microsoft’s Internet Explorer that comes with the Windows operating system, and the Safari browser that comes with the Mac’s operating system, some very popular free Web browsers include Google Chrome, Mozilla Firefox, and Opera. Many use their home computer to do work at home and need office productivity software packages that include powerful word processors, a spreadsheet, and a presentation graphics program; the productivity package may also include a database management system. Once the buyer has budgeted for the essential peripherals and software, the rest of the budget should buy the most powerful processor and biggest memory and cache the buyer can afford. The processor and cache size are what are going to become obsolete, because applications programs will have updates every few months (many are automatic with the software) and they always consume more processor power and memory storage. Within about 5 years, an average computer will become very slow because its processor, memory, and cache will no longer be big enough to handle the programs the buyer wants to run. Worse, the operating system may become outdated and no longer be fast enough to run some of the updated programs.

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Handheld Computers

that can help identify drug facts such as actions and dosages, and drug interactions, and remind the nurse of potential complications to watch for as well as special nursing actions to take with various medications. Reminders can help the nurse avoid forgetting to perform a treatment or give a medication on time. Wisely used, tablet computers, smartphones, and other PDA technology have the potential to support patient care safety and quality in all settings of care. As wonderful as these handheld devices can be, there are pitfalls for nurses in their use in clinical areas. They come with both still photo and video capabilities. It can be very easy to forget the legal requirement for permission to photograph anything on or about a patient, including a photo of a patient’s face when the technology is so available. Smartphones are incredibly easy media on which to store information. It is quite a simple matter to upload information stored on a smartphone onto the Internet. A few nurses have found themselves in serious trouble when they forgot that social media, such as Facebook, Twitter, and LinkedIn, are not private spaces and they uploaded photographs of patients or confidential patient information on social media. Nurses must remember that most information in their workplace has confidentiality requirements that can be protected only with sophisticated technical barriers to unauthorized access. Those barriers are typically not available in an individual’s smartphone.

Handheld computers are small, special function computers, although a few “full function” handheld computers were introduced in the late 1990s. Even though of smaller size than the laptop and notebook microcomputers, some have claimed to have almost the same functionality and processing capabilities. However, they are limited in their expansion possibilities, their ability to serve as full participants in the office network, and the peripherals they can support. More popular are the palm-sized computers, including personal digital assistants (PDAs), which are the smallest of the handheld computers. The PDA is a very small special function handheld computer that provides calendar, contacts, and notetaking functions, and may provide word processing, spread sheet, and a variety of other functions (Anonymous, 2019). Originally sold as isolated devices, today PDAs have mostly been supplanted by smartphones which combine limited computing power with telephone functionality. Smartphones are ubiquitous and owned by a huge number of people worldwide, from the slums of South Africa to business people in the most developed countries. Smartphones have replaced wristwatches, pocket calendars, and other personal items people used to keep their lives organized. They feel indispensable to many people who might be more comfortable leaving home without a coat in winter than without their smartphone. These devices provide constant connectivity and access to Internet and telephone functions. They are particularly useful in that they can synchronize with other technology and provide automatic support for such things as the user’s electronic calendar. The processors for most smartphones, tablet computers, and other small but powerful devices are made by several companies, such as Apple, Samsung (Exynos), Qualcomm (Snapdragon), and Huawei (HiSilicon Kirin) (Ferrare-Herrmann, 2019; Miller, 2018). There are two major hardware platforms and operating systems for smartphones and tablet computers. They are the Apple Corporation’s iPhone and iPad using the iOS operating system, and smartphones and tablets using the Android operating system (including the Samsung products). There are thousands of software applications (called apps) developed for all these platforms, many of them free or sold at a very low price. In general, the apps work on only the platform for which they were developed, but quite a few will work on both smartphones and tablets using that platform. For example, many apps that work on the iPad tablet will also work on the iPhone. Smartphone clinical applications can allow the nurse to obtain assessments such as electrocardiograms, heart and respiratory rate, hearing acuity, oxygenation, and blood pressure. There are calculators that can make drug dosage calculations safer. There are programs

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CONNECTIVITY, COMPATIBILITY, AND INCOMPATIBILITY ISSUES Communication among various hardware devices cannot be assumed. Given that departments within a single organization have often bought small systems designed to support their work, a single hospital may have literally hundreds of different computers and applications on those computers. Simply wiring incompatible machines so that power can flow between them accomplishes nothing. Often, computers cannot transfer data meaningfully among themselves. Think about the need to make sure an application is compatible with one’s smartphone—and the fact that an android application will not work on an iPhone. Multiply this incompatibility by about 20 different types of systems that could exist in one hospital. The incompatibility problem makes it difficult to create a comprehensive medical record for individual patients. Thus, information stored somewhere in the facility may not be available for providers who need the information to make good patient care decisions elsewhere. As greater attention is placed on patient safety, quality improvement, and analysis of performance data for planning and evaluation, there is a need to acquire and

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40    P art 1 • N ursing I nformatics T echnologies combine data from multiple patient care operations’ computers and systems. Unfortunately, different computers have different architectures, hardware configurations, and storage schemes. Software must be specifically designed to communicate with another program for the two to communicate and exchange data and information. Therefore, systems not designed specifically to work together cannot communicate information and processes to each other without the addition of complex translation programs (that usually don’t exist); that is, they are not interoperable. As a result of the interoperability problems, it may not be economically feasible to move data across different computers and programs. The interoperability problem limits hospitals’ and clinicians’ ability to obtain, combine, and analyze data they need to provide high-quality, safe patient care. Organizationally, progress and performance are hampered when data and information are not available to perform the analysis required to identify problems, opportunities for improvement, safety risks, and to make projections about future needs. Interoperability is necessary to meet the requirements of the Medicare and Medicaid EHR Incentive Programs (which provide financial incentives for the “meaningful use” of certified EHR technology) as part of the HITECH Act of 2009. Interoperability usually requires interoperable software programs using standards such as SNOMED CT, LOINC, and so on (see Chapter 4 “Computer Systems Basics—Software).

COMPUTER POWER The terms bits and bytes refer to how the machine stores information at the lowest, or “closest to machine registers and memory,” level. Computers do not process information as words or numbers. They handle information in bytes. A byte is made up of 8 bits.

Bits and Bytes A bit (binary digit) is a unit of data in the binary numbering system. Binary means two, so a bit can assume one of two positions. Effectively, a bit is an ON/OFF switch—ON equals the value of 1 and OFF equals 0. Bits are grouped into collections of 8, which then function as a unit. That unit describes a single character in the computer, such as the letter A or the number 3, and is called a byte. A byte looks something like this: 0

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0

0

0

1

1

0

0

There are 255 different combinations of 0 and 1 in an 8-character (or 1-byte) unit. That forms the basic limit to the number of characters that can be directly expressed in the computer. Thus, the basic character set hardwired into most PCs contains 255 characters. In the early days of PCs, this was a problem because it severely limited the images that could be produced. However, with the advent of graphics cards and the additional character sets and graphics that graphics cards allow, virtually any character or image can be produced on a computer screen or printed on a printer. Even without graphics cards, additional character sets can be created by means of programming techniques. The size of a variety of computer functions and components is measured by how many bytes they can handle or store at one time (see Table 2.1). Main memory, which includes the ROM on the motherboard in today’s computers, is very large as compared with that of just a few years ago and continues to increase every year with new computers. Since the size of memory is an important factor in the amount of work a computer can handle, large main memory is another key measure in the power of a computer. In the mid-1970s, the PCs on the market were typically sold with a main memory of between 48 and 64 K. By 2019, the size of main memory in computers sold to the public had risen exponentially and most computers in 2019 were advertised with between 8 and 32 GB of main memory. Cache has also become an important variable in computer power and thus in advertising the power of computers. Another important selling point of a computer is the size of the installed hard drive or solid-state drive. The first hard drives sold for microcomputers in the 1970s were external devices that stored about 1500 kilobytes and cost about half as much as the computer itself. At that time, home computers were not sold with internal hard drives. When users turned on the computer, they had to be sure the operating system (OS) diskette was in the disk drive, or the computer could not work. This architecture severely limited the size and functionality of programs. Therefore, consumer demand for internal storage for programs and data was such that hard drive size grew exponentially while at the same time the cost of that storage decreased exponentially. By late 1999, home computers typically had between 6 and 20 GB of space on the hard drive and in 2014 the typical laptop computer was sold with a 300 to 500 GB hard drive. Desktops often came with hard drives that offered a terabyte or more of storage. By 2019, most desktop computers were advertised with between 500 GB to 2 terabyte solid state (or hard disk) drives. Internal storage size may increase, but as more users come to rely on cloud storage, the demand for ever larger internal storage may focus more on the

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need to store programs and applications than user data. Applications programs have become so large that both the main memory and the hard drive storage space have had to increase exponentially as well.

have legitimate access to the information on the network. WANs support geographically dispersed facilities, such as the individual grocery stores in a national chain. A subset of WANs includes the metropolitan area networks (MANs) that support and connect the many buildings of local governmental agencies or university campuses. The most important components of network hardware are the adapter or interface card, cabling, and servers. The role of hardware in a network is to provide an interconnection between computers. For a computer to participate on a network, it must have at least two pieces of hardware:

COMPUTER SPEED The basic operations of the CPU are called cycles, and the four types of cycles, or operations of a CPU, include fetch, decode, execute, and store. Each of these operations may be referred to as a “cycle.” It takes time for the computer to perform each of these functions or cycles. The CPU speed is measured in cycles per second, which are called the clock speed of the computer. One million cycles per second is called 1 megahertz (MHz) and a billion cycles per second is called 1 gigahertz (GHz). CPU speeds are very fast, but because computers may perform many billions of cycles per second, they can be slow if their processors have insufficient speed for the work they are required to process. Clock speeds, like most other components, have greatly improved over time. For example, the original IBM PC introduced in 1981 had a clock speed of 4.77 MHz (4.77 million cycles per second). In 2010, home computers commonly had about 1.8 GHz speeds. In 2019, one of the Intel i5 CPUs has the speed of over 3 GHz. In general, the higher the clock speed possessed by the CPU, the faster and (in one dimension) the more powerful the computer. However, clock rate can be misleading, since different kinds of processors may perform a different amount of work in one cycle. For example, general purpose computers are known as complex instruction set computers (CISCs) and their processors are prepared to perform a large number of different instruction sets. Therefore, a cycle in a CISC computer may take longer than that for a specialized type of computer called a reduced instruction set computer (RISC). Also, the amount of RAM and cache memory can affect computer speed. Nonetheless, clock speed is one important measure of the power of a computer.

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1. Network adapter or network interface card. A network interface card (NIC) is a computer circuit board or card that is installed in a computer so that it can be connected to a network. PCs and workstations on LANs typically contain a NIC specifically designed for the LAN transmission technology, such as Ethernet. NICs provide a dedicated, full-time connection to a network. Most home and portable computers connect to the Internet through modems on an as-needed dial-up connection. The modem provides the connection interface to the Internet service provider. The oldest network interface (or “adapter card”) is an Ethernet card. But wireless network modems are used more often today. Other options include arcnet, serial-port boards, and so on. Most of the time, the choice of NIC depends on the communication medium. (a) Communication medium (cabling). The “communication medium” is the means by which actual transfer of data from one site to another takes place. Commonly used communication media include twisted pair cable, coaxial cable, fiberoptics, telephone lines, satellites, and compressed video. Most of the time, the choice of a communication medium is based on the following:

NETWORK HARDWARE

(b) Distance. Relatively short distances are required for wireless, compressed video, and coaxial cable systems. For much longer distances, fiber-optics, telephone lines, and satellite transmission are used.

A network is a set of cooperative interconnected computers for the purpose of information interchange. The networks of greatest interest include local area networks (LANs), wide area networks (WANs), and the Internet, which is a network of networks. A LAN usually supports the interconnected computer needs of a single company or agency. The computers are physically located close to each other, and generally, only members of the company or agency

(c) Amount of data transfer. Large amounts of data (especially video) are best handled with coaxial cables and compressed video and through satellite communications (satellite and compressed video are very expensive). Smaller amounts of data or serial (nonvideo) streams are best handled through the other wire types, such as twisted pair copper wire and optical fiber, and are less expensive.

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42    P art 1 • N ursing I nformatics T echnologies (d) How often the transfer is needed. Coaxial works best for locally wired networks that are used constantly by a very limited number of users. Telephone wires work well for the relatively high usage public networks (like the Internet) but are more likely to get overloaded when many users try to use the system at the same time. Consider, for example, the busy Internet or phone lines getting clogged up when a tornado or hurricane has struck a community. (e) Availability. Availability depends on cost, transmission speed, number of users (who might clog up the system), weather conditions (satellites), and so on.

CONCLUSION The computer is generally described in terms of several major characteristics of its hardware. The speed is determined by how many cycles per second can be processed, the size of its main memory, its cache, and its hard drive. All these factors combine to determine how many programs and data can be permanently stored on the hard drive and how fast the computer can run programs. In turn, these factors determine what kinds of work the user can do with the computer. Playing online games is one activity that takes a large amount of computing power. As a result, “gaming computers” are known to have a lot of computer power. The physical components of the computer itself and its peripheral hardware constitute the architecture of the computer, and these factors determine how it can be used. A great deal of work and playing on computers today involves interactions with other people and machines. Thus, multiple computers must be able to be connected or networked with each other. All the work performed and games played with computers require essential components, including a motherboard, printed circuits, a CPU, other processors, memory chips, controllers, and peripheral devices. This chapter introduced the fundamental hardware of computers and networks.

Definitions Architecture.  Computer architecture BIOS chip.  BIOS stands for basic input/output system. The BIOS itself is a computer program stored on a nonvolatile memory chip on the motherboard, and is called the BIOS chip. This chip is a component of personal computers that controls several essential operations of a computer, including start-up, performing a self-test of the

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system to ensure the operating system can function, and communication with input and output devices. Chip.  A computer chip is a small wafer of semiconductor material with printed circuits on it. The integrated circuits on a chip perform key functions of the computer, such as the CPU chip which is the “brain” of the computer, and math coprocessors which allow for highly complex mathematical operations. Clock.  The internal clock of a computer is a quartz crystal on the motherboard that vibrates at a constant speed when electricity is applied. Depending on the thickness of the crystal, it may vibrate in millions of cycles per second (megahertz or MHz) and billions of cycles per second (gigahertz or GHz), and other speeds are also possible.

Configuration CPU.  Also known as the “brain” of the computer, the central processing unit (CPU) performs the basic four functions of a computer. Heatsink.  A heatsink is a thermal conductor. That is, it absorbs heat generated by the computer processors into projections on the surface of the heatsink. The projections allow for a much larger surface area to collect heat than a flat surface could, much like the many folds in the brain allow for more brain surface area. The heatsink dissipates the heat away from the processors by absorbing it. A fan is required to then blow the heat from the heatsink to the outside of the computer. Motherboard.  A thin plastic rectangle onto which thin metal threads or lines are printed. These lines are called circuits. These circuits allow various essential components of the computer to communicate electronically as electric impulses travel from component to component along the circuits. Peripherals.  Items added to a computer to enhance its usability and usefulness, and to allow users to enter and retrieve information from a computer. Peripherals include keyboards, monitors, printers, the mouse or joystick, and many other possible accessories. RAM.  Random access memory is a type of volatile memory chip on the motherboard used for temporary storage of data and commands needed by the CPU and other processors to do their work. Information in RAM disappears when the computer is turned off.

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ROM.  Read-only memory is a type of nonvolatile m ­ emory chip on the motherboard used for permanent storage of critical programs that start up the computer and perform other essential functions. As the name implies, n ­ othing can be written on ROM; the computer can only read the information permanently burned onto this chip at the factory,

5. Which of the following medical departments has been MOST changed by computerization? A. Radiology B. Internal Medicine C. Dermatology

Volatile memory. Volatile memory in a computer is memory that saves data only while the computer is ON. When the computer is shut down, the information in volatile memory is lost. However, it is used only by the computer processors to store data and commands while carrying out work, so the user’s files are never stored in volatile memory.

6. Which of the following parts of the hardware is most likely to speed up the operation of the computer? A. The type of plastic used to construct the motherboard B. The hard drive C. ROM chips

Test Questions

7. Which of the following types of computers is most useful in weather forecasting? A. Supercomputer B. Mainframe C. Desktop

1. Which of the following components is essential for a machine to be a computer? A. Operating system

B. Central processing unit C. Video card D. Monitor

2. Where would the BIOS chip be located in a computer? A. Plugged into a USB port B. On the video card

C. Placed into one of the slots

D. On a nonvolatile chip on the motherboard 3. Which of the following are the four essential ­functions of the CPU?

A. Input/Output, Word Processing, Arithmetic, Fetch

D. Gastroenterology

D. Cache

D. Handheld computer

8. Which of the following is the most serious concern about nursing’s use of computers in hospitals? A. Nurses not knowing how to use the system for charting B. Inadvertent violation of patient confidentiality C. Loss of all data on a patient’s record D. A nurse making a mistake and crashing the whole computer system 9. Two nurses are planning to open a consulting b ­ usiness in which they will need a substantial amount of computer power (speed and capability of the machine). Which of the following features is MOST important for them to consider when buying the computer?

B. Arithmetic, Store, Input/Output, Execute

A. Number and clock speed of co-processors

D. Fetch, Decode, Calculate, Store

C. Quality of the printer used to print reports

C. Store, Decode, Fetch, Execute

4. Which of the following parts are considered part of the computer’s basic architecture? (Select all that apply.) A. Motherboard B. Printer

C. Central processing unit

D. Read-only memory (ROM)

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  43



B. Size of the hard disk to store programs and data D. Size of the read-only memory (ROM) chips 10. Which of the following is NOT an essential ­component of an International Network system? A. Network adapter card B. Cabling

C. Satellites

D. Dual monitor screens

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44    P art 1 • N ursing I nformatics T echnologies

Test Answers 1. Answer: B

2. Answer: D 3. Answer: C

4. Answer: A, C, D 5. Answer: A

6. Answer: D 7. Answer: A 8. Answer: B

9. Answer: A

10. Answer: D

REFERENCES Anonymous. (2019). Personal Digital Assistant. Science Direct. Elsevier. Retrieved from https://www.sciencedirect.com/topics/computer-science/personal-digitalassistant. Accessed on April 16, 2019. Baroi, S., McNamara, R., McKenzie, D., Gandevia, S., & Brodi, M. (2018). Advances in remote respiratory assessments for people with chronic obstructive pulmonary disease: A ­systematic review. Telemedicine and eHealth, 24(6), 415–424. Botta, L., Cannata, A., Fratto, P., Bruschi, G., Trunfio, S., Maneggia, C., & Martinelli, L. (2013). The role of the minimally invasive beating heart technique in reoperative valve surgery. Journal of Cardiac Surgery, 27(1), 24–28. Cammilleri, S., ArnaudLe, S., Chagnaud, T., Mattei, J., Bendahan, D., & Guis, S. (2019). Knee psoriatic enthesitis assessed using positron emission tomography (PET)— FNA merged to ultrahigh field magnetic resonance imaging (UHF-MRI). Joint Bone Spine, 86(3), 387–388. Cray Corp. (2014). Cray History. Retrieved from: http://www. cray.com/About/History.aspx. Accessed on March 20, 2014. Derene, G. (2019). How vulnerable is U.S. infrastructure to a major cyber attack? Popular Mechanics. Retrieved from https://www.popularmechanics.com/military/ a4096/4307521/. Accessed on February 27, 2019. Evans, C., Medina, M., & Dwyer, A. (2018). Telemedicine and telerobotics: from science fiction to reality. Updates in Surgery, 70(3), 357–362. Falke, K., Krüger, P., Hosten, N., Zimpfer, A., Guthoff, R., Langner, S., & Stachs, O. (2013). Experimental differentiation of intraocular masses using ultrahigh-field magnetic resonance imaging. PLoS ONE, 8(12), e81284. doi:10.1371/ journal.pone.0081284. Retrieved from http://www. plosone.org/article/info%3Adoi%2F10.1371%2Fjournal. pone.0081284. Accessed on December 12, 2013. Ferrare-Herrmann, E. (2019). What’s the fastest Smartphone processor in 2019? Retrieved from https://www.androidpit.com/fastest-smartphone-processors. Accessed on April 16, 2019.

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Gropler, R. (2013). Recent advances in metabolic imaging. Journal of Nuclear Cardiology, 20(6), 1147–1172. Gumbs, A., Fowler, D., Milone L., Evanko., J., Ude, A., Stevens, P., & Bessler, M. (2009). Transvaginal natural orifice translumenal endoscopic surgery. Annuals of Surgery, 249(6), 908–912. Hess, C., Ofori, E., Akbar, U., Okun, M., & Vaillancourt, D. (2013). The evolving role of diffusion magnetic resonance imaging in movement disorders. Current Neurology and Neuroscience Reports, 13(11), 400–416. Ishii, M., Fujimori, S., Kaneko, T., & Kikuta, J. (2013). Dynamic live imaging of bone: opening a new era of ‘bone histodynametry’. Journal of Bone and Mineral Metabolism, 31(5), 507–511. Miller, M. (2018). Mobile Processors of 2018: The Rise of Machine Learning Features. PC Mag. Retrieved from https://www.pcmag.com/article/359986/mobile-processors-of-2018-the-rise-of-machine-learning-fea. Accessed on April 16, 2019. Modesti, M. (2018). Fluorescent labeling of proteins. Methods of Molecular Biology, 1665, 115–134. Oettinger, R. (2016). How are printed circuit boards made? Streamline Circuits. Retrieved from http://streamlinecircuits.com/2016/10/printed-circuit-boards-made/. Accessed on February 27, 2019. O’Neill, B., Hochhalter, C., Carr, C., Strong, M., & Ware, M. (2018). Advances in neuro-oncology imaging techniques. Ochsner Journal, 18(3), 236–241. Padilla, J. (2019) How are motherboards made: understanding the process of motherboard manufacturing. WePC. Retrieved from https://www.wepc.com/tips/how-aremotherboards-made-manufacturing/. Accessed on March 27, 2019. Quero, G., Lapergola, A., Soler, L., Shabaz, M., Hostettler, A., Collins, T., Marescaux, J., Mutter, D., Diana, M., & Pressaux, P. (2019). Virtual and augmented reality in oncologic liver surgery. Surgical Oncology Clinics, 28(1), 31–44. Raikhelkar, J., & Raikhelkar, J. K. (2019). Advances in telecardiology. In: M. Koenig (Ed.), Telemedicine in the ICU. New York, New York: Springer. Roner, S., Bersier, P., Fürnstahl, P., Vlachopoulos, L., Schweizer, A., & Wieser, K. (2019). 3D planning and surgical navigation of clavicle osteosynthesis using adaptable patient specific instruments. Journal of Orthopedic Surgery and Research, 14(1), 115. Suff, N., & Waddington, S. (2017). The power of bioluminescence imaging in understanding host-pathogen interactions. Methods, 127, 69–78. Vilmann, A., Norsk, D., Svendsen, M., Reinhold, R., Svendsen, L., Park, Y., & Kongel, L. (2019). Computerized feedback during colonoscopy training leads to improved performance: a randomized trial. Gastrointestinal Endoscopy, 88(5), 869–876.

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3 Advanced Hardware and mHealth David J. Whitten / Kathleen G. Charters

• OBJECTIVES . Identify standards that provide the framework for communication. 1 2. Identify Internet protocols. 3. Identify standards that provide the framework for interoperability. 4. Describe the enabling technologies for collaborative care. 5. List three hardware elements advancing mHealth. 6. List two examples of the use of collaborative tools.

• KEY WORDS Advanced hardware Bluetooth HL7 mHealth Mobile device TCP/IP Wi-Fi

INTRODUCTION Healthcare computing technology depends upon ­hardware—the silicon, metal, and plastic portion of the “hardware–software–human” triangle. When forwardthinking professionals think about advances in new hardware and create new care models which require ­ them, they can produce healthcare innovations that positively affect patients and the ways nurses use to describe and deliver healthcare. “eHealth”—the practice and use of information and communication technology—is shaped by new hardware, and also helps to drive increases in the sophistication of mechanical devices and electronic systems. Health professionals and the general public develop new services and provide information which inform new models of disease and practice. This activity pushes existing development while leveraging and advancing new ways of

using hardware. Since mobile devices such as smartphones are integrated into the daily lives of patients, the practice of healthcare and advanced public health depends upon these mobile devices as foundational to delivering targeted healthcare. Three key hardware elements work together to enable mobile health (mHealth) to create a more powerful synergistic whole. In order, they are (1) convenient physical device size, (2) ubiquitous wireless network access, and (3) longer battery life. Progress in mHealth has accelerated in recent years, supported by the increasing prevalence of sophisticated infrastructure and the capability and capacity of internal computers in mobile devices. This capability is usually seen in smartphones, advanced tablets, and wearable/implantable/injectable devices. These devices include quicker processing power and memory storage, and more power applications are enabled through 45

ch03.indd 45

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46    P art 1 • N ursing I nformatics T echnologies large-capacity storage both on the local mobile device and through offline storage of large volumes of information that is made available through cloud computing services. Long-life batteries form the last supporting leg of this triangle.

Hardware There are three extant trends in computer hardware advancement: (1) readily available, conviently packaged information processors which are able to deliver highimpact programs and are accessible at hand to the care providers, (2) extensive communication infrastructure such as electronic networks and mobile telecommunication systems and the cloud to which the local mHealth devices can be connected, and (3) multiple powerful central processing centers that enhance the local mHealth devices. This combination of local machines, an efficient use of electronic infrastructure and powerful cloud services, allow innovations in software and user interfaces that target the special needs of healthcare and nursing. For example, tablets used to be less powerful than laptop computers, but could still act as a bridge between a stationary desktop computer and the internal computers in smartphones. This linkage allows more information to be provided, and leverages higher density displays to quickly present information in a timely way. Tablets are used to run different programs than laptops or desktop computers but can communicate with computer programs. This means care can be supported by specialized programs on easily carried tablets while distant computers can provide significant computing resources which require more computing power, electrical stability and centralized storage for the information generated.. A smartphone is a powerful hand-held computer with an operating system and the ability to access the Internet. Wearable devices, in multiple physical forms, such as watches, are comparable in size to that of a piece of jewelry. These wearable devices are able to provide specialized equipment used to collect physiological measures such as heart rate and rhythm, respiration, sleep cycles, and even rapid blood analysis (Zhu, 2013), as well as other information that requires physical proximity to the wearer. After collecting the information, the device doesn’t need to have the capability of long-term storage and analysis. That is provided by the computers which collect the data sent wirelessly from smartphones via the Internet. Implantable devices, such as an internal cardioverter-defibrillator, provide methods of intervention, as well as the ability to monitor physiological responses. Since many medical conditions are time sensitive, having these devices implanted lessens the need to have them quickly available externally, and allows more flexibility in

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patient’s lifestyle and improves quality of life. Injectable microcircuitry is the focus of active research where many privacy, security, and ethical issues are addressed. Massive amounts of data, available in large-capacity redundant storage, also allows steadily improving faulttolerant designs which are not limited by portability. These machines can be bulky and organized into redundant arrays of independent disks (RAIDs) for replicating and sharing data. The intentional duplication among disks makes it possible to store larger chunks of information than a single storage device can handle, and allows specialized circuits that check that the information is not lost or corrupted. Genomic data and machine learning is facilitated through the combination of accessibility and capacity, as both are essential in recognizing patterns which are only evident when examining large data sets. Multiple patterns in genomics data and historical records require targeted algorithms and neural networks. Organizing these patterns in a way that makes the results accessible through the Internet requires significant multiples of computer processing capacity and storage beyond the limited local storage and computing power within mobile devices. Cloud computing is the short-hand way of expressing a mobile device’s ability to access a large number of computers connected through a communication network and thereby run a program or application on a parallel platform of concurrent computer resources. This allows the user of a smartphone to take photos, edit the photos, and annotate them with clinically relevant context before sharing them. This is a common example of leveraging mobile device access to cloud services without the requirement of leaving the patient to go back to a desktop machine that has the capacity to allow the clinician to edit and share. A limiting factor for mobile computing is the length of time a mobile device can work independently before being connected to a non-mobile power source. Modern rechargeable batteries enhance the device’s accessibility to the patient’s location, whether at bedside or in a more active care locale. Many people complain about battery capacity as this effectively limits the portability of the mobile device. The enhanced need for background processing in the mobile device to support activities creates problems because when there is a high level of background activity, the internal computer and memory use significant amounts of power, but which isn’t evident to the user. For example, running multiple interactive mobile applications (apps) in the background will each drain power, and will shorten the amount of time the device can be used before having to recharge the battery (Schmier, Lau, Patel, Klenk, & Greenspon, 2017). Use of mobile data and video is rapidly expanding (Moore, 2011), driving research on ways to deliver vastly improved power density (Williams, 2013).

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Wireless Communication The foundation of mobile computing and mHealth is a mobile device’s ability to connect with networks in multiple ways. Technology used to wirelessly communicate with a mobile device includes mobile telecommunications technology, Wi-Fi, Bluetooth, and RFID. Mobile telecommunications technology continues to evolve (Federal Communications Commission, 2012). Fifthgeneration (5G) networks that provide faster performance and more capabilities are replacing fourth-generation (4G) and third-generation (3G) networks. Much like them, a 5G network supports all Internet Protocol (IP) communication. It uses new technology to transfer data at very high bit rat5es, significantly improving both the speed and volume of data transfer than was possible with the previous network technology (Nikolich, 2017). The InternationalTelecommunications Union-Radio (ITU-R) communications sector sets the standards for International Mobile Telecommunications-Advanced (IMT-Advanced) technology. The peak speed requirements for 4G service are 100 megabits per second for high mobility communication (e.g., communications while traveling by car or train) and 1 gigabit per second for low mobility communication (e.g., communications while walking or standing still). Technologies that do not fulfill 4G requirements but represent the forerunners to that level of service by providing wireless broadband access include Worldwide Interoperability for Microwave Access (Mobile WiMAX) and Long-Term Evolution (LTE), a standard for wireless communication of high-speed data for mobile phones. (Although the standards-setting body is international, due to different frequencies and bands used by different countries, only multi-band phones will be able to use LTE in all countries where LTE is supported.) Wi-Fi is intended for general local network access— generally within a single building or other limited area. The limited area of access is called a wireless local area network (WLAN). Wi-Fi is a technology that allows an electronic device to exchange data or connect to the Internet wirelessly using (in the United States) 2.4 GHz Ultra High Frequency (UHF) waves and 5 GHz Super High Frequency (SHF) waves. Advanced hardware makes this connection through a wireless network access point, or hotspot. Wi-Fi is based on the Institute of Electrical and Electronics Engineers (IEEE) 802.11 standards. To provide a level of security for the wireless connection, various encryption technologies are used, such as Wi-Fi Protected Access (WPA) and Wi-Fi Protected Access II (WPA2) security protocols. To ensure that devices can interoperate with one another, a type of Extensible Authentication Protocol (EAP) is used. Wi-Fi security concerns are covered in the

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Chapter 3 • Advanced Hardware and mHealth    47 National Institute of Standards and Technology (NIST) Guidelines for Securing Wireless Local Area Networks (NIST Special Publication 800-153) (2012b). Bluetooth is intended for a wireless personal area network (WPAN). Wi-Fi and Bluetooth are complementary. Wi-Fi is access point-centered, with all routed through the access point which is typically a modem or router which brings the Internet into the building from an Internet service provider. The router allows several computers, tablets, and other devices to connect to the Internet through a single access point—the router). Bluetooth is used for symmetrical communication between 2 to 7 Bluetooth devices. Bluetooth permits the connected devices to transfer information among the connected devices. The devices must be physically close to each other because the connection is low-bandwidth situations. For example, a user might connect a smart phone to the radio in an automobile so coversations take place safely through the (handsfree) radio rather than the user trying to drive and handle a phone at the same time. Typically, several devices are paired with a single device, such as Bluetooth keyboards, mice, activity monitors, and cameras paired to a single desktop, tablet, or smartphone. Protocols covering wireless devices include Wireless Application Environment (WAE), which specifies an application framework, and Wireless Application Protocol (WAP), which is an open standard providing mobile devices access to telephony and information services.Bluetooth is a wireless technology standard for control of and communication between devices, allowing exchange of data over short distances. Bluetooth is used for wirelessly connecting keyboards, mice, light-pens, pedometers, sleep monitors, pulse oximeters, etc. The range is application specific. Bluetooth uses 2.4 to 2.485 GHz UHF radio waves, and can connect several devices. The Bluetooth Special Interest Group (SIG) is responsible for Bluetooth standards. Bluetooth security concerns are addressed in the NIST Guide to Bluetooth Security (NIST Special Publication 800-121) (NIST Special Publication, 2012a). Radio-Frequency Identification (RFID) is a technology that uses radio-frequency electromagnetic fields to transfer data, using tags that contain electronically stored information. Typically, RFID is used for equipment tracking and inventory control. For example, in an operating room, RFID is used to automatically poll equipment in the suite and cross-reference that equipment with inventories showing the equipment is certified, and the date of the most recent service. Tags contain an integrated circuit for storing and processing information, and modulating and demodulating a radio frequency. Tags also contain an antenna for receiving and transmitting the signal. The tag does not need to be in the line of sight of the reader, and

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48    P art 1 • N ursing I nformatics T echnologies may be embedded in the object to be identified. The reader is a two-way radio transmitter-receiver that sends a signal to the tag and reads its response. Advanced hardware uses increasingly miniaturized RFIDs; some chips are dustsized. The International Organization for Standardization (ISO) and the International Electrotechnical Commission (IEC), among others, set standards for RFID. The standards for information technology telecommunications and information exchange between systems are ISO/IEC 18092 and ISO/IEC 21481. (Although the standard-setting bodies are international, frequencies used for UHF RFID in the United States are currently incompatible with those of Europe or Japan.) Security concerns are addressed by using cryptography. RFID security concerns are addressed in the NIST Guidelines for Securing Radio Frequency Identification (RFID) Systems (NIST Special Publication SP 800-98) (NIST Special Publication, 2007).

Standards and Protocols A protocol is a method that allows information to be shared on an information channel. Both the sender and the receiver of information must use the same protocol to be able to communicate. These agreements are usually well-established between vendors in the form of standards and best practices. When they are followed, global and easy access to data or records can leverage networks and networked information so that more equipment can use common networking infrastructure in a standardized way. Since the Internet is global in scope and extent, it requires specific networking models and communications protocols. The information is segmented into information packets which are organized using a method commonly known as Transmission Control Protocol (TCP) and the Internet Protocol (IP) or TCP/IP. This suite of standard ways of communicating provides end-to-end connections, between cooperating software and hardware. TCP/IP specifies intricate details about how data are to be formatted, how various Web sites and equipment can be located (through use of a common address method), specialized transmission methods and means for copying data packets from one computer to another, route information so the computers that are communicating can efficiently find each other, error correction schemas with checksums to guarantee that the information that was sent hasn’t been changed (corrupted) as it passes through multiple machines, and finally, that the destination receives all of the information that was sent without loss and in the correct order. To be fast and accurate requires special devices that all share this common agreement about how information should be organized into the TCP/IP protocol.

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Historically, these protocols are established by the Internet Engineering Task Force (IETF) which maintains the standards for the TCP/IP suite as well as multiple other standards and protocols (IETF RFC Index, 2019). There are also agreements for all commonly needed protocols for Internet user-interface services and support services. For example, to communicate e-mail messages, there is the Simple Mail Transfer Protocol (SMTP). The methods that computers use to find information on a local network are specified through Network File System (NFS). These methods require the use of File Transfer Protocol (FTP starting with RFC238 from 1971) and HyperText Transfer Protocol (HTTP—RFC1945 from 1996), both of which have secure forms (SFTP and HTTPS). Security is provided using encryption standards developed by the IETF. IETF also provides confidentiality and integrity for data sent over the Internet. Electronic mail depends on privacy enhancements that date back to 1987. Cryptographic network protocols to protect data while it is being transmitted between computers are Secure Sockets Layer (SSL) and Transport Layer Security (TLS). Protocols for encrypting data at rest include Pretty Good Privacy (PGP) and GNU Privacy Guard (GPG). (Note: GNU is a name, not an acronym). Building on top of six-level network standards, there are standards that ensure health information is properly identified, transmitted correctly, and complete. The seventh (application) level of standard is called Health Level Seven (HL7) (HL7 International, 2018). The FHIR standard with all its deliberations has been accessible to the public free of cost through the Web site https://build.fhir. org/history.html. The HL7 International (https://www.hl7.org) is an American National Standards Institute (ANSI) accredited Standards Developing Organization that maintains the framework and standards for the exchange, integration, sharing, and retrieval of electronic health information. These standards are the most commonly used in the world for packaging and communicating health information from one party to another using language, structure, and data types that allow seamless integration between systems. A myriad of medical equipment, such as lab testing equipment, pharmacy pill filling machines, patient identification card creators, heart monitors, and imaging equipment, all communicate using the HL7 protocol, both to each other and to electronic medical records systems and electronic health information systems. The HL7 standards support management, delivery, and evaluation of health services and clinical practice (Health Level Seven, 2014).

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There are multiple forms of HL7, some of which look like simple text lines, and others such as the FHIR which are organized as JavaScript Object Notation data structures (HL7 FHIR, 2019) and even blockchain records that encrypt transactions to keep a ledger of healthcare activities for a patient. Clinical Content Object Workgroup (CCOW) is an HL7 standard protocol that enables different applications to synchronize at the user-interface level in real time. This standard allows applications to present information in a unified way. For example, with CCOW enabled, a provider could bring up a patient record in the inpatient electronic record application, and then open the outpatient electronic record in a different application, and CCOW would bring up the same patient in the outpatient application. Evolution and adoption of existing technologies and standards allow users to benefit from advanced hardware without the need for deep knowledge or expertise. For example, you can watch a feature film on a smartphone without knowing how the underlying hardware and software work. These advances in hardware along with virtualization support new care models.

Drivers of Mobile Healthcare The 2012 documentary Escape Fire: The Fight to Rescue Healthcare is an urgent call to think differently about healthcare. Clinicians shifting from a focus on disease management to a focus on ending lifestyle disease may leverage the use of mobile platforms. For example, during an outpatient visit, Dr. Natalie Hodge prescribes an app for health self-management in the same way that medicine or any other intervention would be prescribed (Wicklund, 2014, February 13). According to the mHIMSS Roadmap, “patients and providers are leveraging mobile devices to seek care, participate in, and deliver care. Mobile devices represent the opportunity to interact and provide this care beyond the office walls” (Healthcare Information and Management Systems Society [HIMSS], 2012b). Advancements in technology, federal healthcare policy, and commitment to deliver high-quality care in a costefficient manner have led to new approaches (mHIMSS, 2014b, 2014c). The Affordable Care Act (ACA) leverages innovative technology to bring about “a stronger, better integrated, and more accessible healthcare system” (HIMSS, 2012b). For example, mobile apps allow expansion of telemedicine and telehealth services. The current healthcare focus is on preventive and primary care to reduce hospital admissions and emergency department utilization. Engaging patients in management of their chronic diseases helps them maintain their independence

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Chapter 3 • Advanced Hardware and mHealth    49 and achieve a high quality of life. Patients may make use of collaborative tools such as Secure Messaging or the Patient Portal to communicate with their healthcare team, and may find support through social interactions on a blog.

Technology in Mobile Healthcare Under the ACA, innovative technology is seen as an integral component of an integrated, accessible, outcomedriven healthcare system. Mobile technology may be key to providing more effective preventative care, improving patient outcomes, improving access to specialized medical services, and driving system-wide cost reduction. Services to patients and families at home will be personalized and delivered by providers equipped with apps for smartphones, tablets, and laptops (Powell, Landman, & Bates, 2014). The National Institutes of Health defines mHealth as “the use of mobile and wireless devices to improve health outcomes, healthcare services, and health research” (HIMSS, 2012a). A major component of mHealth includes timely access to clinical information such as the data contained in electronic health records (EHRs), personal health records (PHRs), and patient portals. This information should be securely accessible by clinicians, patients, and consumers over various wireless mediums both inside and outside the traditional boundaries of a hospital, clinic, or practice (HIMSS, 2012b). The iPhone and Android operating systems have accelerated the proliferation of mobile data use. By 2015, mobile data traffic will be some 20 times the 2010 level (Moore, 2011). The concept of mHealth can be traced to the early 1990s when the first 2G cellular networks and devices were being introduced to the market. The bulky handset designs and limited bandwidth deterred growth. Lack of communication standards impeded interoperability, and batteries lasted less than 6 hours. A major standards breakthrough occurred in 1997, enabling Wi-Fi capable barcode scanners to be used in hospital inventory management. This lowered the need for specialized knowledge by practitioners and supported healthcare organization cost-saving measures (Ray, 2018). Shortly thereafter, clinicians began to take an increasing interest in adopting technologies. At this time, nurses began to use personal digital assistants (PDAs) to run applications like general nursing and medical reference, drug interactions, and synchronization of schedules and tasks. This quick rate of adoption was quite notable for clinicians were often considered technology adverse. Increased processing capabilities and onboard memory created an appetite for more advanced applications. Network manufacturers were beginning

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50    P art 1 • N ursing I nformatics T echnologies to offer Personal Computer Memory Card International Association (PCMCIA) wireless devices, creating an environment where retrofitted hospital computers or new laptops allowed nurses to access the Internet without adding network cabling. In 2000, the Federal Communications Commission (FCC) dedicated a portion of the radio spectrum to wireless medical telemetry systems (WMTS); this allowed the wide adoption of specialized devices for remote monitoring of a patient’s health. Fiber-optic networks and other specialized communication equipment increased data transmission rates, making it highly feasible for hospitals to run video or voice applications over their local wireless networks. Connection software with the hospital electronic record and Application-Specific Devices (ASDs) can increasingly be integrated with nurse call systems and medical telemetry so that nurses can receive prompt patient-specific alarms, text messages, and alerts tied to their ongoing care. Many vendors are now beginning to offer the same type of nurse call integration and voiceover Wi-Fi capabilities on popular smartphones (HIMSS, 2012b). Nurses soon became familiar with Computers on Wheels (COWs), which evolved to workstations on wheels (WOWs). More wireless devices were integrated into networks and a greater emphasis was placed on error detection and prevention, medication administration safety, and computerized provider order entry (CPOE). Parallel to Wi-Fi technology evolution has been the growth in cellular technology. In many healthcare organizations, seamless roaming between the two systems is a reality. Nurses now have immediate access to patient data at the bedside.

Infrastructure mHealth is a broad, expanding universe that encompasses a wide variety of user stories (use cases) that range from continuous clinical data access to remote diagnosis and even guest Internet access. The role of video in healthcare is evolving as quickly as the standards themselves. Telemedicine carts outfitted with high-resolution cameras include remote translation and interpretation services for non-native speakers as well as the hearing impaired. In the past, WOWs were mainly used to access clinical data, but these carts have gained such wide acceptance that they are often found in use by clinicians on rounds or at change of shift. Hospital systems and ambulatory practices have also started using products like FaceTime, Skype, Google Hangouts, and other consumer-oriented video-telephony and Voice-Over Internet Protocol (VOIP) software applications for patient consults, follow-up, and care coordination (mHIMSS, 2014a).

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Wi-Fi infrastructure connecting medical devices to centralized record keeping computers has obsoleted specialized overlay networks. These multiple-use networks allow hospitals to leverage economies of scale by using their existing Wi-Fi infrastructure for multiple purposes. This does increase the hospital’s dependency on the Information Technology (IT) department and specialized computer knowledge and personnel to ensure the security of the hospital, clinic, or practice wireless network. It also requires considerable investment in proper planning and design for a robust network, capable of supporting various types of medical devices such as infusion pumps, mobile EKG devices, point-of-care lab devices, mobile X-ray machines, portable ultrasound equipment, and blood gas analyzers on a single cohesive network infrastructure. This also requires testing and monitoring of those devices to limit infectious disease transmission. Their portability and simplicity make these devices highly effective in performing diagnostics near patients in a healthcare facility. They also speed the diagnostic cycle by providing reduced turn-around time when needed. This lowers mortality and morbidity of infectious disease while limiting the impact of institutional risks such as antimicrobial resistance (Bissonnette, 2017) An important and often overlooked aspect of mHealth is patient or guest access to the wireless system. Care must be taken to segregate the guest access wireless network from the health professional networks. Wireless guest access provides a way for patients and their families to access the Internet; it can be a valuable tool for hospitals to engage with patients and guests. In a healthcare setting, organizations generally opt to provide free unencrypted access with a splash page that outlines terms and conditions. This allows the hospital to address liability for the patient’s Internet traffic and allows guests and patients to access the network quickly. Real-time location services (RTLS), a concept dating back to the 1990s, has evolved rapidly over the years. RTLS can be used for location tracking of physical assets using RFID tags as beacons, temperature/humidity monitors, distress alert badges, and they can even be used to track hand washing (mHIMSS, 2014e). A properly configured RTLS system can minimize the task of tracking down medical equipment and show the nurse the current status of the equipment. The wide range of uses for RFID technology enables many innovative practices. Biomedical, pharmacy, security, and other departments in the hospital are using this technology (Table 3.1).

Mobile Devices Smartphones and tablets are ubiquitous in the healthcare setting. What started out as consumer devices are now

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Chapter 3 • Advanced Hardware and mHealth    51



  TABLE 3.1    Major Technology Trends (HIMSS, 2012b) Trend

Explanation/Example

Wireless Patient Monitoring

Technologies that enable remote surveillance of patient vital functions through the use of internally and externally located patient devices. Examples: Wirelessly monitored pacemakers and automatic defibrillators

Mobile System Access

Mobile technologies that enable remote/virtual access to current clinical systems such as Electronic Health Records (EHRs) and Picture Archiving and Communication System (PACS). Examples: Web sites, portals, mobile apps

Medical Devices

Mobile and/or wireless-enabled technologies that capture and track key care compliance and disease ­management data. Examples: Digital glucometers, blood pressure devices, pedometers

Virtual Consultation

Remote connectivity and multimedia solutions that enable virtual care consultation, education, and therapy.

Aging in Place

Remote technologies that enable clinically monitored independent living for aging populations.

Examples: Tele-consultations, mobile video solutions Examples: Personal Emergency Response Systems (PERS), video consultations, motion/activity monitoring, fall detection, aggregation, transport

Reproduced, with permission, from Healthcare Information and Management Systems Society. (2012b). HIMSS mHealth Roadmap. Copyright © 2012 Healthcare Information and Management Systems Society (HIMSS). http://www.himss.org/ResourceLibrary/mHimssRoadmapLanding.aspx? ItemNumber=30480&navItemNumber=30479.

in the hands of almost all clinicians. In a short period of time, mobile device performance has improved dramatically, putting them closer and closer in capability to general computing devices such as laptops and desktops. Battery technology has also improved significantly, with most devices able to go 12 hours or more between charging. Within the palm of a nurse’s hand is a fully capable computing device able to perform complex and powerful operations. Many mobile devices are using high-resolution touch screens. When clinical information systems are designed to display well on smartphones and tablets, these devices will emerge as the primary computing device for clinical users. These devices already support text messaging, voice, and video.

Telehealth BYOD (Bring Your Own Device) is one of the latest trends in healthcare IT. The context is that employees of an organization are able to bring their own device to their workplace and access information which is only available to them because they are employees. Mobile devices, especially smartphones and other products available in the marketplace to consumers today, such as the iPhone, iPad, and similar devices from other vendors, have produced loyal customers who do not want to have multiple mobile communication devices attached to their waistband or ­filling the pockets of their lab coat. They prefer one device,

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the device that they own. In many hospitals the IT department has already ensured that their devices are secure and able to meet government regulations. Back-end IT systems are required to ensure that a given device does not introduce vulnerabilities into the system. Mobile Device Management products provide policy enforcement on end-user devices, remote wipe capability, and endpoint integrity. To implement BYOD, owners of the devices must be willing to abide by the hospital’s mobile device policy and allow their devices to be managed (Brandt, 2014). As the concept of unified communications continues to grow, fed by the challenge to attain work-life balance, BYOD is becoming increasingly attractive in many organizations.

Future of mHealth Inside Healthcare Facilities The nation’s healthcare model is on the path toward consolidated, coordinated, value-based care. Information technology tools, mobile applications, and clinical information systems provide an evolving platform for the effective delivery of clinical services, increased operational excellence, and cost containment. Wireless networking, specifically Wi-Fi, began to be widely adopted in hospitals about 10 years ago. In the beginning, few organizations had 100% Wi-Fi coverage, but steadily increasing demand resulted in the deployment of wall-to-wall Wi-Fi coverage in hospitals and often in adjacent outside areas. Cellular network coverage in hospitals has also grown. Initially, owners of

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52    P art 1 • N ursing I nformatics T echnologies mobile devices were accustomed to spotty coverage and dropped calls or even policies banning mobile phones. In recent years, thanks to investments by cellular carriers, coverage areas have grown, along with the user’s expectation of a quality signal. Distributed Antenna Systems (DAS) are commonly used for providing cellular wireless signals while also providing for two-way radio, paging, and first responder communication systems (HIMSS, 2012b). Still, unified communications (a combination of messaging, video, and voice) has not yet realized full potential in healthcare facilities. The value of an emergency room nurse being able to instantly create a video session with a remote patient is not in doubt. However, the infrastructure to accomplish this is still fledgling. Enterprise communication platform vendors have provided these capabilities with devices that integrate with their vendor-specific devices. Broader integration with common devices such as smartphones and tablets is an ongoing effort.

Considerations for mHealth Planning The role of cellular networks in video and voice applications is expanding rapidly. Advances in 4G technologies are beginning to provide the bandwidth necessary for video conferencing and Video Remote Interpreting (VRI). Patients newly discharged from the hospital will be followed by nurses with devices that allow nurses to see and hear the patient, monitor wound healing, and address family concerns. Early intervention for patients with chronic diseases such as asthma, chronic obstructive pulmonary disease (COPD), heart failure, and diabetes will alert caregivers and prevent hospitalizations. Remote monitoring of patients is increasingly viewed as essential for mHealth planning. It is widely believed that, by 2020, the majority of computing will be edge computing, defined by a constantly changing mix of corporate and privately owned mobile and wireless devices talking to a corporate or enterprise cloud. As a result, healthcare will become more patientcentered, and mobile and health visits will occur in the home, school, and office (mHIMSS, 2014d). Data from home monitoring devices to fitness apps raise questions about which kinds of data will be aggregated, and conventions for meta-tagging the source of that data. Ethical, legal, privacy, and security questions must be addressed. How is the data protected? Who is authorized to use it and for what purposes? How will the data be processed to discover patterns (data mining)?

Setting the Stage for mHealth Adoption Smartphones and tablets offer a new engagement model for patients, their family members, and healthcare providers.

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These devices move with their owners from hospital, to home, and beyond. An Internet search for healthcare applications will yield thousands of results and the list constantly grows larger. With the public’s increasing interest in wellness, and a large fitness industry attempting to grow their business, peripheral devices are becoming smartphone-ready. Sensors can now measure heart rate, pulse, oxygen saturation levels, speed, and distance for exercise regimens. Devices are emerging for daily blood tests, automated weight tracking, and sleep monitoring. EKGs can be registered and transmitted through a device no larger than a Band-Aid. The concept of home health has been a driving factor in the proliferation of remote monitoring devices (HIMSS, 2012b). Thanks to advances in machineto-machine (M2M) technology, patients no longer have to travel to the clinic or hospital for routine monitoring. Patients can check their blood sugar, blood pressure, oxygen levels, and other vital signs at home with their results wirelessly transmitted to their healthcare providers. Providing cellular or Wi-Fi communications to the ambulatory practice and the patient’s home is a technology trend that has seen affiliate physician offices partnering with larger hospital systems for access to the EHR and to leverage corporate IT services to provide Wi-Fi for their offices.

Privacy and Security Security and privacy are essential to developing trust between patients and professionals that work with them in healthcare organizations using computer systems. The mobile environment necessitated by mHealth and its data present a greater challenge to security and data integrity because these data are locally created and locally accessible. The data are also collected on the cloud in stored access facilities and stored behind firewalls. The unique challenges of limiting inappropriate access while making it available when needed by healthcare providers require specialized solutions. Many of the same rules are applied to mHealth and its devices are applied within the physical hospital environment. mHealth must comply with all Health Insurance Portability and Accountability Act (HIPAA) mandates, Food and Drug Administration (FDA) regulations, Office of Civil Rights (OCR) enforcements, and requirements from other governing agencies. Size and capacity are the only differences between a smartphone, a personal computer, and an enterprise server. In a large number of security breaches, the thief simply carried the equipment out of the door or removed it from a car or storage location. This decrease in size cannot play a role in protecting data. Protection requires diligence and attention to detail (CISA, 2016).

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One of an organization’s responsibilities is to secure and verify security using active testing to locate vulnerabilities within systems. Some of the details about this were released by the National Cybersecurity Center of Excellence (NCCoE) in Securing Electronic Health Records on Mobile Devices (NCCoE, 2018). The goal of privacy and security is to provide as much effort as needed to protect patients’ personal health information (PHI) from being accessed by unauthorized people, and thus compromising the relationship between the organization and the patient. The benchmark for privacy must be 100% secure PHI.

Legal and Policy State and national policy and regulations have not kept pace with the rate of technology innovation. The proliferation of mHealth technology creates several fundamental issues related to the custody of medical information: who owns it, who can access it, and under what circumstances? As information becomes more portable, the question raised is to what extent records of other providers should be incorporated into clinical records of the practice, hospital, or specialist. Consider the transmission of digital radiology images from a hospital or freestanding diagnostic center to a provider’s smartphone. Consumers and patients use a multitude of devices to collect wellness data. Should all data be incorporated into the EHR, or just portions of the data? Should data from all devices be incorporated into the record, or data from just one or a few devices? Does having too much data obscure potentially critical information? Under what circumstances is the healthcare provider required to maintain records of these transmissions? If the transmissions are received, must all data be reviewed? What does the record look like for legal purposes? Must the source of the data (e.g., patient-provided, wearable device, etc.) be transparent? Clinical significance is the central consideration in the determination of whether wellness, monitoring, and other data transmitted by consumers to their providers should be incorporated into the patient’s EHR. Incorporating vast amounts of routine data might detract from clinically relevant findings. When data are shared between patient and clinician from such devices, it is desirable to have thorough understandings between the treatment team and the patient about how the data are going to be reviewed, incorporated (or not) into the record, and used in patient care (Harman, 2012). Historically, there has been reluctance to accept any data other than the information collected within the physical boundaries of the hospital or practice, with the exception of routine consultations. Hesitancy to accept outside data is based on the receiving provider’s inability to verify

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Chapter 3 • Advanced Hardware and mHealth    53 the accuracy of the data. Today, however, that paradigm is changing. Healthcare professionals must be engaged in care coordination across the care continuum. Excluding data from other sources may provide an incomplete picture of the patient’s care, resulting in inappropriate or substandard treatment. Social media through websites like Instagram, Facebook, LinkedIn, and Twitter, provides collaborative tools that enable constantly accessible information and communications, available wherever the user needs, at all locations via smartphones. The Federal Trade Commission has received a complaint concerning Facebooks handling of health data (AHIMA, 2019) (Trotter, 2018) Indeed, tweets (messages on Twitter) provide breaking news to the world in many occasions. User content is developed and shared through platforms such as YouTube, and video is shared through services such as Skype and FaceTime. As pleasant as it is to receive a new picture of a loved one, social media also presents several types of legal and regulatory concerns:

• •

•

Professionalism: Because social media is so ubiquitous, healthcare professionals may face new questions such as whether or not it is appropriate to “friend” a patient. Privacy: There have been several widely reported incidents of healthcare professionals posting data related to patients on social media sites. Even if the patient’s name is not revealed, releasing data that is not completely de-identified violates the HIPAA Privacy Standards. Who owns health-related data posted to a social media site? Is ownership relevant?

Great changes are advancing healthcare and empowering healthcare professionals. Mobile computing is an essential technology. The new face of healthcare will be mHealth.

Test Questions 1. What are the three key hardware elements that enable mobile health (mHealth)? A. CPU Size, Ubiquitous Wireless Network Access, & Longer Battery Life

B. Longer Battery Life, Convenient Physical Device Size, & Modem

C. Ubiquitous Wireless Network Access, CPU Size, Longer Battery Life

D. Longer Battery Life, Ubiquitous Wireless Network Access, & Convenient Physical Device Size

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54    P art 1 • N ursing I nformatics T echnologies 2. What are the three key trends in Computer Hardware Advancement?

A. Multiple Powerful Central Processing Centers

B. Convenient Packaged Information Processors. C. Extensive Communication Infrastructure

D. Tablets that bridge between desktop and Cloud Computing E. All but D

F. All of the above 3. What best describes Cloud Computing?

A. Cloud Computing uses a mobile device to access a large number of computers. B. Cloud Computing connects multiple computers through a communication network.

C. Cloud Computing runs a computer program on a parallel platform of concurrent computer resources. D. Cloud Computing is used primarily for storage. E. All of the above. F. All but D.

4. What best describes wireless communication? A. Networks that provide faster performance

A. Bluetooth is intended for a wireless personal area network (WPAN). B. Bluetooth permits the connected wireless to transfer information among the connected devices. C. Bluetooth is a wireless technology standard for control of and communication between devices.

D. Bluetooth is used for wirelessly connecting keyboards, mice, light-pens, pulse oximeters, etc.

E. Bluetooth is used for Radio-Frequency Identification Technology that transfers data. F. All but E`

G. All of the above 7. What best describes HL7 Standard Coverage and/or Protocol? A. HL7 maintains the framework and standards for the exchange, integration, sharing, and retrieval of electronic health information.

B. Networks that support all Internet communications

B. HL7 standards are used to package and ­communicate health information from one party to another.

D. Uses new technology to transfer data at high bit rates

D. HL7 is an American National Standards Institute (ANSI) accredited standard.

F. All but C

F. All but A

C. Uses mobile computing device to connect networks in multiple ways

C. HL7 uses language, structure, and data types that allow seamless integration between systems.

E. All of the above

E. All of the above

5. What best describes the purpose of Wi-Fi? A. Technology used for general local network access

B. Technology that allows an electronic device to exchange data C. Technology that allows an electronic device to connect to the Internet

D. Technology that provides a level of security for wireless connections E. Technology intended for only cloud computing F. All of the above G. All but E

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6. What best describes Bluetooth wireless technology standard?

8. Highlight the scope of mHealth.

A. mHealth is the use of mobile and wireless devices to improve health outcomes, healthcare services, and health research. B. mHealth is the timely access to clinical information such as the data contained in electronic health records (EHRs), personal health records (PHRs), and patient portals. C. mHealth uses the cloud to process data.

D. mHealth provides more effective preventative care, improves patient outcomes, improves access to specialized medical services, and drives system-wide cost down.

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E. mHealth provides services to patients and families at home by providers equipped with apps for smartphones, tablets, and laptops.

F. All of the above G. All but C

9. Which best describes the scope of Standards and/or Protocols? A. Allows information to be shared on an information channel

B. Requires that both sender and receiver of the information use the same protocol to communicate with each other C. Provides end-to-end connectivity between cooperating software and hardware D. Protocols follow organizational structure E. All of the above F. All but D

10. What best highlights the legal implications of mHealth technology?

A. Routine data is irrelevant and does not need to be saved. B. Clinical significance is central consideration for ownership of patient data. C. Data from outside healthcare facilities does not belong to the provider. D. Relevance of access to user data on Facebook. E. Only B and C.

F. All of the above.

Test Answers 1. Answer: D 2. Answer: E 3. Answer: F

4. Answer: DE 5. Answer: G 6. Answer: G 7. Answer: E 8. Answer: F 9. Answer: F

10. Answer: E

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REFERENCES AHIMA. (2019, May 8). Critics argue that Facebook’s new health privacy changes don’t go far enough. Journal of American Health Information Management Association. Retrieved from https://journal.ahima.org/2019/05/08/ critics-argue-that-facebooks-new-health-privacychanges-dont-go-far-enough/. Accessed on May 13, 2019. Bissonnette, L. (2017, March). Expert review of molecular diagnostics: Portable devices and mobile instruments for infectious diseases point-of-care testing. Retrieved from https:// www.researchgate.net/publication/315674152_Portable_ devices_and_mobile_instruments_for_infectious_diseases_ point-of-care_testing. Accessed on May 1, 2019. Brandt, J. (2014, August 18). HIMSS newsletter: Bring your own device (BYOD). Retrieved from https://www.himss. org/bring-your-own-device-byod. Accessed on October 10, 2018. CISA (2016, October 1). Security tip (ST04-017): Protecting portable devices: Physical security. Retrieved from https://www.us-cert.gov/ncas/tips/ST04-017. Accessed February 22, 2019. Coleman, B. (2019, March 1). Point-of-care colorimetric analysis through smartphone video. Sens Actuators B Chem. Retrieved from https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC6391882/. Accessed on April 2, 2020. Federal Communications Commission. (2012). mHealth Task Force: Findings and recommendations. Retrieved from http://transition.fcc.gov/cgb/mhealth/mHealthRecommendations.pdf. Accessed on April 13, 2014. Harman, L. (2012, September). Electronic health records: Privacy, confidentiality, and security (original Virtual Mentor 2012). AMA Journal of Ethics. Retrieved from https://journalofethics.ama-assn.org/article/electronichealth-records-privacy-confidentiality-and-security/2012-09. Accessed on September 27, 2018. Health Level Seven. (2014). Introduction to HL7 standards. Retrieved from https://www.hl7.org/ implement/standards/. Accessed on April 13, 2014. Healthcare Information and Management Systems Society. (2012a). Definitions of mHealth. Retrieved from http:// www.himss.org/ResourceLibrary/GenResourceDetail. aspx?ItemNumber=20221. Accessed on April 13, 2014. Healthcare Information and Management Systems Society. (2012b). mHIMSS roadmap. Retrieved from http://www. himss.org/files/mHIMSS%20Roadmap-all%20pages.pdf. Accessed on March 2, 2014. HL7 FHIR. (2019). Summary – FHIR v4.0.0 HL7 Multiple authors. Retrieved from https://www.hl7.org/fhir/summary.html. Accessed January 12, 2019. HL7 FHIR Current Build. (2018, Dec 27). FHIR version history. Retrieved from https://build.fhir.org/history.html. Accessed February 14, 2019. HL7 Introduction. (2018). Introduction to Health Level Seven—HL7.org. Retrieved from https://www.hl7.org/ permalink/?HL7OrgAndProcessPresentation. Accessed February 12, 2019.

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56    P art 1 • N ursing I nformatics T echnologies IETF RFC Index. (2019, May). Request for Comment (RFC) Index. Internet Engineering Task Force. Multiple authors; detailed information from RFC-1 created in 1969 through the present. Retrieved from https://tools.ietf.org/rfc/ index. Accessed on April 2, 2020. mHIMSS. (2014a, February). Case study: Decreasing costs and improving outcomes through community-based care transitions and care coordination technology. Retrieved from http:// himss.files.cms-plus.com/FileDownloads/User%20Case%20 Study%20Decreasing%20Costs%20and%20Improving%20 Outcomes%20through%20Community-Based%20 Care%20Transitions%20and%20Care%20Coordination%20 Technology.pdf. Accessed on March 2, 2014. mHIMSS. (2014b, February). Case study: Geisinger Health System: Weight management text program. Retrieved from http://himss.files.cms-plus.com/FileDownloads/ Use%20Case%20Study%20Geisinger%20Health%20 System%20Weight%20Management%20Text%20Program. pdf. Accessed on March 2, 2014. mHIMSS. (2014c, February). Case study: Improving quality of care for the underserved. Retrieved from http:// himss.files.cms-plus.com/FileDownloads/User%20 Case%20Study%20Improving%20Quality%20of%20 Care%20for%20the%20Underserved.pdf. Accessed on March 2, 2014. mHIMSS. (2014d, February). Case study: Reducing patient no-shows. Retrieved from http://himss.files.cms-plus. com/FileDownloads/Use%20Case%20Study%20 Geisinger%20Health%20System%20Reducing%20 Patient%20No-Shows.pdf. Accessed on March 2, 2014. mHIMSS. (2014e, February). Case study: Vanderbilt University Medical Center: Hand hygiene monitoring app. Retrieved from http://himss.files.cms-plus. com/FileDownloads/Case%20Study%20Vanderbilt%20 University%20Medical%20Center%20Hand%20 Hygiene%20Monitoring%20App.pdf. Accessed on March 2, 2014. Moore, T. (2011, July 27). Fortune: Spectrum squeeze: The battle for bandwidth. Retrieved from http://tech.fortune. cnn.com/2011/07/27/spectrum-squeeze-battle-forbandwidth/. Accessed on April 9, 2014. National Institute of Standards and Technology. (2007, April). Guidelines for securing radio frequency identification (RFID) systems (NIST Special Publication SP 80098). Retrieved from http://csrc.nist.gov/publications/ nistpubs/800-98/SP800-98_RFID-2007.pdf. Accessed on April 9, 2014. National Institute of Standards and Technology. (2012a, June). Guide to bluetooth security (NIST Special Publication 800-121 revision 1). Retrieved from http:// csrc.nist.gov/publications/nistpubs/800-121-rev1/sp800121_rev1.pdf. Accessed on April 9, 2014.

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National Institute of Standards and Technology. (2012b, February). Guidelines for securing wireless local area networks (NIST Special Publication 800-153). Retrieved from http://csrc.nist.gov/publications/nistpubs/800-153/ sp800-153.pdf. Accessed on April 9, 2014. NCCoE. (2018, July). Securing Electronic Health Records on Mobile Devices (NIST Special Publication SP 1800-1). National Cybersecurity Center of Excellence. Retrieved from https://www.nccoe.nist.gov/sites/default/files/ library/sp1800/hit-ehr-nist-sp1800-1.pdf. Accessed on April 1, 2019. Nikolich, P, Chih-Lin, I., Korhonen, J., Marks, R., Tye, B., Li, G., Ni, J., & Zhang, S. (2017, June). Standards for 5G and beyond: Their use cases and applications. IEEE Tech Focus. Retrieved from https://futurenetworks.ieee.org/ tech-focus/june-2017/standards-for-5g-and-beyond. Accessed on May 1, 2019. Powell, A. C., Landman, A. B., & Bates, D. W. (2014, March 24). In search of a few good apps. Journal of the American Medical Association. Retrieved from https://jama.jamanetwork.com/article.aspx?articleid=1852662. Accessed on April 9, 2014. Ray, B. (2018, February). A breakdown of 7 RFID costs: From hardware to implementation. Retrieved from https:// www.airfinder.com/blog/rfid-cost. Accessed on April 2, 2020. Schmier, J. K., Lau, E. C., Patel, J. D., Klenk, J. A., & Greenspon, A. J. (2017). Effect of battery longevity on costs and health outcomes associated with cardiac implantable electronic devices: A Markov model-based Monte Carlo simulation. Journal of Interventional Cardiac Electrophysiology, 50(2), 149–158. Trotter, F. (2018, December 14). Letter to Division of Privacy and Identity Protection, Federal Trade Commission. Retrieved from https://missingconsent.org/downloads/ SicGRL_FTC_Compliant.pdf. Accessed on May 13, 2019. Wicklund, E. (Ed.). (2014, February 13). A doc’s-eye view of mHealth. mHealth News. Retrieved from http://www. mhealthnews.com/news/docs-eye-view-mhealth?singlepage=true. Accessed on March 2, 2014. Williams, M. (2013, October 1). Battery life hasn’t kept pace with advances in mobile computing—but that could change soon. Techradar. Retrieved from http://www. techradar.com/us/news/phone-and-communications/ mobile-phones/why-are-mobile-phone-batteries-stillso-crap--1162779/2#articleContent. Accessed on April 9, 2014. Zhu, H. (2013, April). Cost-effective and rapid blood analysis on a cell-phone. Lab Chip. Retrieved from https://www. ncbi.nlm.nih.gov/pmc/articles/PMC3594636/. Accessed on May 13, 2019.

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4 Computer Systems Basics—Software Mary L. McHugh

• OBJECTIVES . Identify the three categories of software and their functions. 1 2. Describe four important analytic themes in Information Science. 3. Explain five types of programming languages and their general capabilities. 4. Discuss smartphone applications that can be used as part of physical assessment. 5. Explain the differences among LANs, WANs, and MANs.

• KEY WORDS Software Programs Programming languages Read-only memory (ROM) Random access memory (RAM) Operating systems

INTRODUCTION Software is the general term applied to the instructions that direct the computer’s hardware to perform work. It is distinguished from hardware by its conceptual rather than physical nature. Hardware consists of physical components, whereas software consists of instructions communicated electronically to the hardware. Software is needed for two purposes. First, computers do not directly understand human language, and software is needed to translate instructions created in human language into machine language. At the machine level, computers can understand only binary numbers, not English or any other human language.

Second, packaged or stored software is needed to make the computer an economical work tool. Software packages that perform work are called programs. Theoretically, users could create their own software to use the computer. However, writing software instructions (programming) is extremely difficult, time-consuming, and, for most people, tedious. It is much more practical and economical for one highly skilled person or programming team to develop programs that many other people can buy and use to do common tasks. Software is supplied as organized instruction sets called programs or “apps” (for applications), or more typically as a set of related programs called a package. 57

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58    P art 1 • N ursing I nformatics T echnologies For example, several prominent software companies sell their own version of a package of programs that are typically needed to support an office computer, including a word processing program, a spreadsheet program, a presentation graphics program, and sometimes a database manager. Programs translate operations the user needs into language and instructions that the computer can understand. By itself, computer hardware is merely a collection of printed circuits, plastic, metal, and wires. Without software, hardware performs no functions.

CATEGORIES OF SOFTWARE There are three basic types of software: system software, utility programs, and applications software. System software “boots up” (starts up and initializes) the computer system; it controls input, output, and storage; and it controls the operations of all other software. Utility software consists of programs designed to support and optimize the functioning of the computer system itself. Utility programs help maintain the computer system’s speed, clean up unwanted programs, protect the system against virus attacks, access the World Wide Web (WWW), and the like. Applications software include the programs that perform the business or personal work people use the machine to do. Sometimes it can get confusing as to whether programs are utility programs or system software or applications because system software packages today usually include a variety of utility programs with the basic system software packages, and many utility programs can be purchased as stand-alone programs and run separately by the users.

System Software System software consists of a variety of programs that control the individual computer and make the user’s application programs work well with the hardware. System software consists of a variety of programs that initialize, or boot up, the computer when it is first turned on and thereafter control all the functions of the computer hardware and applications software. System software helps speed up the computer’s processing, expands the power of the computer by creating cache memory, reduces the amount of confusion when multiple programs are running together, “cleans up” the hard drive so that storage is managed efficiently, and performs other such system management tasks. Basic Input/Output System  The first level of system control is handled by the basic input/output system (BIOS)

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stored on a read-only memory (ROM) chip on the motherboard. The software on the BIOS chip is the first part of the computer to function when the system is turned on. It first searches for an operating system (OS) and loads it into the random access memory (RAM). Given that the BIOS consists of a set of instructions permanently burned onto a computer chip, it is truly a combination of hardware and software. Programs on chips are often called firmware, because they straddle the line between hardware and software. For this reason, many computer engineers make a distinction between firmware and software. From that perspective, the OS is actually the first level of system software. Operating System  An operating system (OS) is the overall controller of the work of the computer. The OS is software loaded from the hard drive into RAM as soon as the computer is turned on. While the firmware cannot be upgraded without changing the hardware chip, the OS can be upgraded or entirely changed through software. The user can simply delete one system of OS files from the hard drive and install a new OS. Most users purchase a computer with the OS already installed on the hard drive. However, the OS can be purchased separately and installed by the user. OSs handle the connection between the CPU (central processing unit) and peripherals. The connection between the CPU and a peripheral or a user is called an interface. The OS manages the interfaces to all peripheral hardware, schedules tasks, allocates storage in memory and on disks, retrieves programs and data from storage, and provides an interface between the machine and the user. One of the most critical tasks (from the user’s perspective) performed by the OS involves the management of storage. In the early computers, there were no OSs. Every programmer had to include explicit instructions in every program to tell the CPU exactly where in RAM to locate the lines of program code and data to be used during processing. That meant the user had to keep track of thousands of memory locations, and be sure to avoid writing one line of code over another active line of code. Also, the programmer had to be careful that output of one part of processing did not accidentally get written over output from another part of processing. As can be imagined, the need for management of storage consumed a great deal of time and programming code, and it produced many errors in programs. Since those errors had to be discovered and corrected before the program would run correctly, the lack of an OS made programming enormously timeconsuming and tedious. In comparison, programming today—while still a difficult and time-consuming task—is much more efficient. In fact, with the size of programs,

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Chapter 4 • Computer Systems Basics—Software 

memory, and storage media today, no programmer could realistically manage all the storage. OSs allowed not only more complex programs and systems, but without them, there could be no home computers, except for machines owned by skilled programmers.

data and programs are relocated to better use the available space. These programs can also compress data to free up disk space, partition a disk so that the user has more control on where different types of information are stored, and clean up disks by eliminating unnecessary data and information. Other system management utilities locate and remove “temporary” files created by many programs, search for updates for system and applications programs (and may automatically apply the updates), and clean the registry of outdated, broken, or useless entries. Temporary files can build up and clog the system. Many programs and Internet sites temporarily store information on the hard drive as part of their operations, but when those operations are finished, they don’t clean the temporary files. Such files can consume quite a bit of disk space over time and slow down the computer. Disk cleaners can sometimes free up large amounts of disk space just by eliminating those unneeded “temporary” files. Other system management utilities include diagnostic programs designed to find problems with programs or the OS so that they can be fixed, programs that control who can access the computer or certain files on the computer, test the computer’s memory, and other tasks. Backup utilities serve to help the users back up their data. These backup systems are different from things like external drives and Cloud storage. Applications programs may be backed up, but usually that isn’t necessary because legal copies of programs can be reloaded by the person who bought the license. (Illegal, or pirate, programs are a different issue. The computer owner may not have a backup copy of illegally downloaded programs). Given that any computer component can fail, it is very important for users to back up their data to reduce the chance that saved data could be lost permanently. When a hard drive fails (or crashes), the user who has not backed up that drive is at risk of permanently losing photos, personal and work information, songs, videos, and anything else stored on the computer. Of course, backing up data on the same hard drive isn’t necessarily much protection. A better choice is to back up one’s data to the Cloud or to an external (removable) hard drive or some other backup location. Screen savers are computer programs that either blank the monitor screen or fill it with constantly moving images when the user is away from the computer but does not turn it (and the monitor) completely off. They were originally developed for old technology screens (cathode ray tube [CRT] screens or plasma screens) that would be damaged by having the same image on the screen for a long period of time. Modern computer screens have different technology and so don’t suffer that risk. However, screen savers are often entertaining or beautiful to look at, and

Utility Software Utility programs include programs designed to keep the computer system operating efficiently. They do this by adding power to the functioning of the system software or supporting the OS or applications software programs. As such, utility programs are sort of between system software and applications software, although some writers identify this software as part of the system software category. Six types of utility software can describe the majority of utility programs, although there is no formal categorization system for such programs. The categories include at least security programs, system management utilities, backup for the user’s data, screen savers, archival assistance software, and programming environment support programs. Security software, including primarily anti-virus, firewall, and encryption programs, protects the computer and its data from attacks that can destroy programs and data. Anti-virus utilities serve primarily to guard against malicious programs inadvertently accessed, usually through e-mail or downloads from the Internet. Firewalls are a type of security program that makes it much harder for unauthorized persons or systems to enter the computer and hijack or damage programs or data on the computer. Firewalls can include both additional hardware and utility software. Encryption software encodes the data so that it cannot be read until it is decoded. The HTTPS (Hypertext Transfer Protocol Secure) letters on a Web page address indicate that the site encrypts data sent through that site. The encryption is sufficiently high level that it cannot be decoded without a program at the receiver site. This encryption makes buying and selling via the Internet much safer. Without such encryption, credit card and other very private data would not be safe to use to purchase anything via the Internet. Security can also be a hardware issue because there are devices that can greatly enhance the security of computer systems (Markov, 2019). System management utilities are designed to help the user keep the computer system running efficiently. For example, disk management utilities serve to keep hard disk space clean and efficient. They do this by analyzing use of disk space, defragmenting the drive, and deleting duplicate files if the user so commands. Over time as users store and delete data and programs, information on the disk may become scattered across the disk in an inefficient or fragmented way. The defragmenter moves data around on the disk so that small empty spaces are eliminated and

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60    P art 1 • N ursing I nformatics T echnologies do provide a small measure of privacy because they hide whatever the user is working on when the user steps away from the computer. Unless also linked with a program that requires the user to sign back in to access the regular screen, they don’t provide security because a passing person could simply tap a key to get back to the regular screen. However, most people have the good manners to keep their hands off other people’s computers, and the screen saver hides what might be personal or confidential data from casual roaming eyes. Screen savers sometimes do require users to log back into their computer to turn off the screen saver, and those do have a security function. Typically, screen savers activate automatically if the computer does not receive any input from the user for a preset time period. Archival software usually performs at least two functions. First, it compresses information in files to be archived, and then stores them in a compressed form in some long-term storage device. For Windows, programs such as WinZip and WinRar are well-known archival utilities. When the files are retrieved, software must be used to unpack (or decompress) the data so that it can be read. Terms used to describe the data compression performed by archival software include packing, zipping, compressing, and archiving as well as unpacking, unzipping, de-archiving, and extraction. Compression can sharply reduce the size of a large file such that it can be made small enough to e-mail to another person or location. Programming environment support programs are used by program developers to support their programming work or to run their programs. Computers cannot read or understand English or any other human language. Ultimately, programs must change the language in which developers write programs (the source code) into a machine language the computer can understand (assembler or machine language). The programs that perform these translations are called compilers or interpreters. If a programmer wishes to translate a machine language program into a higher level language a human can understand, the programmer uses a decompiler program. Programming is difficult; not only does the programmer have to detail complex logic, but the commands that comprise the program must be written in a specific syntax. Syntax in this usage refers to a set of very specific rules about words, punctuation, word usage, and word order in a particular computer language. Syntax must be exactly correct for a computer to correctly compile or interpret the code and run the program. Problems with either the logic or syntax will cause the program to fail, or perform incorrectly. These kinds of problems are called “bugs” and correcting them is called “debugging” a program. Utility programs designed to help a programmer debug a

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program are called debugging programs. The most commonly used utility programs for programmers include programming aids and the various types of compilers and debuggers.

Applications Software Applications software includes all the various programs people use to do work, process data, play games, communicate with others, and watch videos and multimedia programs on a computer. Unlike system and utility programs, they are written for system users to make use of the computer. When the user orders the OS to run an application program, the OS transfers the program from the hard drive, or removable media, and executes it. Application programs are written in a particular programming language. Then the program is “compiled” (or translated) into machine language so the computer can understand the instructions and execute the program. Originally, programs were written for a specific computer and could only run on that model machine. However, the science of programming languages and their translation eventually advanced to the point that programs today can generally be “ported” (or translated) across many machines. This advance permitted programmers to develop programs that could be used on a class of machines, such as the Windows type or Mac type computers (the two are still generally incompatible). This advance opened a whole new industry, since programs could be mass marketed as off-the-shelf software packages. By far the most commonly used set of programs are the programs in an office package. The most popular office packages include Microsoft Office, Office 365, Google Docs, Apache OpenOffice, and LibreOffice, but there are many other office program packages. The most useful program in these packages is, of course, the word processing program. But spreadsheets and presentation graphics are also widely used, as are the Database Management System software packages such as Microsoft Access. Many of these products also offer e-mail systems, publisher programs, flowchart software, and various other application programs. Nursing applications programs are typically part of a hospital or healthcare organization’s information system. Hospitals usually have a large information system called a Hospital or Health Information System (HIS) or Hospital or Health Information Technology System (HITS). These systems include most of the business applications needed, such as billing, payroll, budget management, inventory control (for the hospital’s Central Supply department), personnel applications, etc. They also include clinical and semi-clinical systems, such as laboratory, pharmacy,

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Chapter 4 • Computer Systems Basics—Software 

admissions and patient locator, order entry/results reporting, and the electronic medical record (EMR) or electronic health record (EHR), that contain the clinical documentation or hospital charts for patients. There are hundreds of application programs that nurses can use in clinical care, and specialized applications for all the settings of care in which nurses work. For example, nurses work with heart monitors, which have programs that assist with interpretation of heart rhythms and may even provide capabilities for nurses to enter data and notes. Fetal monitors, intravenous volumetric pumps, some surgical tools, and a myriad of other instruments have computer processors and programs which support nurses’ use of these tools in patient care. Software has been written and programmed into these machines. The data from patient monitors (including monitoring of blood pressure, heart rate and rhythm, body temperature, oxygenation levels, blood glucose levels, and other parameters) are gathered, fed into a computer and transmitted to the nurse’s work station for interpretation and possible interventions. Home health nurses may use remote monitoring instruments to monitor the health of patients in their homes.

generations: high-level procedural and nonprocedural languages. The third level (and fifth generation) is natural language. The low-level languages are machine-like. Machine language is, of course, binary. It consists of strings of 0s and 1s and can be directly understood by the computer. As noted, machine language is difficult to use and to edit.

Programming Languages A programming language is a means of communicating with the computer. Actually, of course, the only language a CPU can understand is binary or machine language. While it is certainly possible for programmers to learn to use binary—some highly sensitive defense applications are still written in machine language—the language is painfully tedious to use. For most applications, it is an inefficient use of human resources, and worse, machine language programs are quite difficult to update and debug. Since the invention of computers, users have longed for a machine that could accept instructions in everyday human language. Although that goal largely eludes programmers, applications such as office support programs (i.e. word processors, spread sheets, presentation graphics applications and the like) have become much easier to use with graphical user interface (GUI)-based commands.

Generations and Levels of Programming Languages Programming languages are divided into five generations, or sometimes into three levels. The term level refers to how close the language is to the actual machine. The first level includes the first two generations of programming languages: machine language and assembly (or assembler) language. The second level includes the next two

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Machine Language  Machine language is the true language of the computer. Any program must be translated into machine language before the computer can execute it. The machine language consists only of the binary numbers 1 and 0, representing one ON and OFF “switch” controlled by electrical impulses. A single switch is called a binary digit, and for short named a “bit.” All data—numbers, letters, and symbols—are represented by combinations of binary digits. Eight bits are required to represent a single letter or number in a computer. For example, the number 3 is represented by 8 binary numbers (00000011), and 6 is represented by 00000110. Traditionally, machine languages are machine dependent, which means that each model of computer has its own unique machine language. Assembly Language  Assembly language is more like the English language than is machine language, but it is still close to machine language. One command in machine language is a single instruction to the processor. Assembly language instructions have a one-to-one correspondence with a machine language instruction. Assembly language is still used a great deal by system programmers and whenever application programmers wish to manipulate functions at the machine level. As can be seen from Fig. 4.1, assembly language, while more English-like than machine language, is extremely obscure to the nonprogrammer. Third-Generation Languages  Third-generation languages include the procedural languages and were the beginning of the second level in programming languages. Procedural languages require the programmer to specify both what the computer is to do and the procedure for how to do it. These languages are far more English-like than assembly and machine language. However, a great deal of study is required to learn to use these languages. The programmer must learn the words the language recognizes, and must use those words in a rigid style and sequence. A single comma or letter out of place will cause the program to fail or crash. The style and sequence of a language are called its syntax. FORTRAN (FORmula TRANslation) and COBOL (Common Business Oriented Language) are examples of early third-generation languages. A third-generation language written specifically for use in healthcare settings is MUMPS (Massachusetts General

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PRINT_ASCII PROC MOV DL, 00h DL MOV CX, 255 PRINT_LOOP: CALL WRITE_CHAR INC DL LOOP PRINT_LOOP MOV AH, 4Ch INT 21h ;21h PRINT ASCII ENDP

•  FIGURE 4.1.  Assembly Language Lines of Code. Hospital Utility Multi-Programming System). MUMPS was originally developed to support medical records applications at Massachusetts General Hospital. MUMPS offers powerful tools to support database management systems; this is particularly useful in any setting in which many users have to access the same databases at the same time. Therefore, MUMPS is now found in many different industries such as banks, travel agencies, stock exchanges, and of course, other hospitals. Originally, MUMPS was both a language and a full OS; however, today most installations load MUMPS on top of their own computer’s OS. Today, the most popular computer languages for writing new OSs and other system programs are languages named C, C++, and C# (pronounced C-Sharp). Java is used extensively in programming applications used on the Internet (Anonymous, 2019). Two important late third-generation languages are increasing in importance as the importance of the Internet grows. They include the visual programming languages and Java. Java was developed by Sun Microsystems to be a relatively simple language that would provide the portability across differing computer platforms and the security needed for use on a huge, public network like the Internet. The world community of software developers and Internet content providers has warmly received Java. Java programming skills are critical for any serious Web developer. Visual Programming Languages As the popularity of GUI technology grew, several languages were developed to facilitate program development in graphics-based environments. Microsoft Corporation has marketed two very popular such programs: Visual BASIC (Beginners’ All-purpose Symbolic Instruction Code) and Visual C++. These programs and their cousins marketed by other companies have been used for a variety of applications, especially those that allow users to interact with electronic companies through the Internet.

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Concurrent and Distributed Languages  Another way to categorize programs is whether they were designed to work sequentially or concurrently. Originally, all programming languages were strictly sequential. That is, the central processing unit (CPU) processed one line of code at a time, and the next line was not read until the prior line command had been executed. A lot of calculation work and operations such as payroll and invoice processing do require each part of the process to be completed before the next is started. Mathematical and statistical calculations often must be sequential because the result of each calculation is used by the next calculation to complete the work. One way computer speed was increased was to support the CPU with very specialized processors that handled mathematical functions. However, the CPU would wait for the math processor results to continue with the program. As programming addressed much more complex processes, many parts of programs were not dependent on prior processes. That meant different parts of the program could, at least theoretically, be processed simultaneously. However, a single processor can only process one command at a time. Clock speed improvements have been somewhat limited by the heat produced by faster processing. Originally, computers had only one CPU so they had only one core processor. As programs became more complex, and especially as the Internet advanced into a multimedia environment, the clock speed of a single processor could not keep up. It is extremely slow to wait to load text while pictures are loading, but a single processor cannot do two of those actions at the same time. Those who used computers in the early 1990s may remember that Web pages with lots of images could be impossibly slow to load, and this was at least partly due to personal computers having only a single processor. Even though CPU clock speeds increased steadily, a single processor could not keep up with the video, graphs, and sound demands of Internet pages. According to Igor Markov, “Computer speed is not increasing anymore” (Markov, 2014). Another strategy was needed to improve speed. The solution has been to add more CPU processors, and this solution is called multiprocessing which involves multiple processors working in parallel (parallelism). Around the year 2000, dual core processors became available (Varela, 2013). Although they were expensive, they were essential for people who needed to run complex engineering and scientific programs, and people who liked to play complex online games with sophisticated graphics (these people are called “gamers” and their high power computers are called “gaming computers”). The advantages of multiprocessing were such that by 2014, all personal computers advertised for the home and business

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had two or more processors to speed up the operation of complex and graphics intensive programs. A high-speed, sophisticated graphics card is also necessary to handle the volume of graphics in today’s programs and Web pages. The Intel i7 product had six microprocessors in addition to its graphics card. Program languages designed to take advantage of multiple processors are called concurrent languages. Concurrent languages are designed for programs that use multiple processors in parallel, rather than running the program sequentially on a single processor. C++ is an example of a programming language designed as a concurrent language. Closely associated with the need to run multiple parts of a program at the same time is the need to accommodate multiple users at the same time. This is called multithreading (Oracle, 2019). While multithreading is more of an implementation problem than strictly a programming issue, modern, high-level languages handle multiprocessing and multithreading more easily than older languages. Programming languages like Java from Sun Microsystems and Haskell were designed expressly to handle both multiprocessing and multithreading at the same time. The newer C11 and C++11, as well as other languages, were designed to be used in multiprocessing and multithreading environments. The importance of excellent multithreading programming products was well illustrated when the Affordable Care Act of 2019 (ACA) government Web site could not handle the volume of users trying to access the site at the same time.

numbers provided to the program. That is, the user provides SPSS with a data file and selects the command that executes a chi-square on the selected data. But the user does not have to write code telling the computer which mathematical processes (add, subtract, multiply, divide) to perform on the data in order to calculate the statistic. The formula for chi-square is already an integrated part of the SPSS program. An important fourth-generation language is SQL (Structured Query Language). SQL is a language designed for management and query operations on a relational database. It does far more than simply allow users to query a database. It also supports data insert, data definition, creating of the database schema, update and delete, and data modification. It is not particularly user-friendly for non-programmers, but it is an extremely powerful language for information retrieval.

Fourth-Generation Languages Fourth-generation languages are specialized application programs that require more involvement of the user in directing the program to do the necessary work. Some people in the computer industry do not consider these to be programming languages. Procedural languages include programs such as spreadsheets, statistical analysis programs, and database query languages. These programs may also be thought of as applications programs for special work functions. The difference between these languages and the earlier generation languages is that the user specifies what the program is to do, but not how the program is to perform the task. The “how” is already programmed by the manufacturer of the language/applications program. For example, to perform a chi-square calculation in FORTRAN, the user must specify each step involved in carrying out the formula for a chi-square and also must enter into the FORTRAN program all the data on which the operations are to be performed. In Statistical Package for Social Sciences (SPSS), a statistical analysis program, the user enters a command (from a menu of commands) that tells the computer to compute a chi-square statistic on a particular set of

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Fifth-Generation Languages Fifth-generation or thirdlevel languages are called natural languages. In these types of programs, the user tells the machine what to do in the user’s own natural language or through use of a set of very English-like commands. Ideally, voice recognition technology is integrated with the language so that voice commands are recognized and executed. True fifth-generation languages are still emerging. Natural language recognition, in which any user could give understandable commands to the computer in his or her own word style and accent, was being performed at the beginning of the twenty-first century. However, natural language systems are clearly in the future of personal computing. The great difficulty is, of course, how to reliably translate natural, spoken human language into a language the computer can understand. To prepare a translation program for a natural language requires several levels of analysis. First, the sentences need to be broken down to identify the subject’s words and relate them to the underlying constituents of speech (i.e., parsed). The next level is called semantic analysis, whereby the grammar of each word in the sentence is analyzed to recognize the action described and the object of the action. There are several computer programs that translate natural languages based on basic rules of English. They generally are specially written programs designed to interact with databases on a specific topic. By limiting the programs to querying the database, it is possible to process the natural language terms. An exciting application of natural language processing (NLP) is called biomedical text mining (BioNLP). The purpose is to assist users to find information about a specific topic in biomedical literature. This method of searching professional literature articles in PubMed or another database increases the likelihood that a relevant mention of the topic

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64    P art 1 • N ursing I nformatics T echnologies will be discovered and extracted, thus increasing the probability of a comprehensive information extraction process. One example is a program called DNorm. DNorm detects specific disease names (entered by the searcher) in journal articles or other text documents and also associates them with search terms in MeSH (Medical Subject Headings) terms in PubMed and terms in SNOMED-CT (Leaman, Dogan, & Zhivong, 2014; Omicx, 2020; Przybyla et al., 2016). SNOMED-CT (Systematized Nomenclature Of MEDicineClinical Terms) is a database containing a comprehensive list of clinical terms. Nursing terms from all the major nursing terminologies have been listed in SNOMED-CT. It is owned, maintained, and distributed by the International Health Terminology Standards Development Organisation (IHTSDO) and renamed International SNOMED. Text Formatting Languages  Strictly speaking, text formatters are not true programming languages. They are used to format content, originally text, for visual display in a system. However, the skills required to learn to format text are similar to the skills required to learn a programming language, and informally they are called programming languages. The most famous is HyperText Markup Language (HTML). HTML is used to format text for the WWW and is one of the older formatting languages. These languages specify to the computer how text and graphics are to be displayed on the computer screen. The original markup language is Standardized General Markup Language (SGML) which is actually a meta language and the standard for markup languages. There are many other formatting languages, such as eXtensible Markup Language (XML) which is a restricted version of SGML and used in most word processing programs. HTML and XML adhere to the SGML pattern.

COMMON SOFTWARE PACKAGES FOR MICROCOMPUTERS As noted above, the most common package sold with computers is a standard office package. (The standard office package includes a word processing program, a spreadsheet program, and a presentation graphics program.) The upgraded or professional versions usually add some form of database management system, an e-mail system, and a “publisher” program for preparing flyers, brochures, and other column-format documents. The two most commonly used programs are the e-mail system and the word processor. In fact, some people purchase a computer with only an OS, word processor, and an Internet browser, and sign up for their e-mail account and use little else. Another very common product is a desktop publisher. Most of these

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common programs have to be written in two versions: one for the IBM PC (Personal Computer) platform and one for the Apple Mac. Typically, software packages are sold on DVDs (Digital Versatile Discs) or flash drives. Many software companies are now marketing their products through the Internet and customers download the software directly through the Internet from the vendor’s Web site. Security programs are also an important market product. Given the large number of people seeking to steal identities and otherwise use the computer for criminal or malicious activity, every user who accesses the Internet should have security software.

SOFTWARE PACKAGE OWNERSHIP RIGHTS Protecting ownership rights in software has presented a challenge to the computer software industry. A program sold to one customer can be installed on a very large number of machines. This practice obviously seriously harms the profitability of software development. If programs were sold outright, users would have every right to distribute them as they wished; however, the industry could not survive in such market conditions. As a result, the software industry has followed an ownership model more similar to that of the book publishing industry than to the model used by vendors of most commercial products. When most commercial products like furniture or appliances are sold, the buyer can use the product or resell it or loan it to a friend if so desired. The product sold is a physical product that can be used only by one customer at a time. Copying the product is not feasible. However, intellectual property is quite a different proposition: what is sold is the idea. The medium on which the idea is stored is not the product. However, when the PC industry was new, people buying software viewed their purchase as the physical diskette on which the intellectual property was stored. Software was expensive, but the diskettes were cheap. Therefore, groups of friends would often pool money to purchase one copy of the software and make copies for everyone in the group. This, of course, enraged the software vendors. As a result, copyright laws were extended to software so that only the original purchaser was legally empowered to install the program on his or her computer. Any other installations were considered illegal copies, and such copies were called pirate copies. Purchasers of software do not buy full rights to the software. They purchase only a license to use the software. Individually purchased software is licensed to one and only one computer or sometimes the license explicitly allows several installations. This

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Chapter 4 • Computer Systems Basics—Software 

exception can be made if the individual has both a desktop and a laptop. Fair use allows the purchaser to install the software on all the machines he or she personally owns— provided the computers are for that user’s personal use only. Companies that have multiple computers that are used by many employees must purchase a separate copy for each machine, or more typically, they purchase a “site license.” A site license is a way of buying in bulk. The company and software vendor agree on how many machines the software may be used on, and a special fee is paid for the number of copies to be used. Additional machines over the agreed-on number require either an increase in the allowable sites—and payment of the higher site license fee, or separate copies of the software may be purchased. What is not permitted, and is, in fact, a form of theft, is to install more copies of the software than were purchased.

variety of patient populations and clinical problems (e.g., pediatric pocket consultation, toxicology guide, guide to clinical procedures, laboratory results guides, etc.). Software can now be downloaded onto a PDA to measure heart and respiratory rate, perform ultrasounds on various organs, test hearing, perform a simple EKG, and many other physical assessment parameters. As so many items of healthcare equipment have computer processers today, the nurse may not always realize that software is being used. For example, volumetric pumps control IV flow through computer processors. Heart monitors and EKG and EEG machines all have internal computers that detect patterns and provide interpretations of the patterns. Hospital beds may have processors to detect wetness, heat, weight, and other measures. Most radiology equipment today is computer based. Many items of surgery equipment exist only because computer processors are available to make them operate. Some nursing applications include a handy “dashboard,” which is an application that provides a sort of a menu display of options from which the nurse can choose. Typically, dashboards provide the nurse a quick way to order common output from certain (or all) screens, or may provide some kind of alert that a task is due to be performed.

COMMON SOFTWARE USEFUL TO NURSES In most hospitals, much of the software used by nurses is based in an HIS, a multipurpose program designed to support many applications in hospitals and their associated clinics. The components nurses use most include the electronic medical record for charting patient care, admission-discharge-transfer (ADT) systems that help with patient tracking, medication administration record (MAR) software, laboratory systems that are used to order laboratory tests and report the results, and supplies inventory systems through which nurses charge IVs, dressings, and other supplies used in patient care. There are systems for clinicians to document their clinical orders; quality and safety groups such as the Leapfrog Group consider a computerized provider order entry (CPOE) system to be so important that they list it as a separate item on their quality checklist. Additionally, nurses may have the support of computer-based systems for radiology orders and results reporting, a computerized patient acuity system used to help with nurse staff allocation, and perhaps a hospital e-mail system used for at least some hospital communications. Increasingly, nurses are finding that they are able to build regional, national, and international networks with their nursing colleagues with the use of chat rooms, bulletin boards, conferencing systems, and listservs on the Internet. Given that many people have personal digital assistants (PDAs) as part of their cellular phone, nurses may download any of thousands of software applications (apps) onto their PDA to assist them with patient care. Most are very low cost and some are free. Such programs include drug guides, medical dictionaries, and consult guides for a

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COMPUTER SYSTEMS Every functioning computer is a system; that is, it is a complex entity, consisting of an organized set of interconnected components or factors that function together as a unit to accomplish results that one part alone could not. Computer system may refer to a single machine (and its peripherals and software) that is unconnected to any other computer. However, most healthcare professionals use computer systems consisting of multiple, interconnected computers that function to facilitate the work of groups of providers and their support people in a system called a network. The greatest range of functionality is realized when computers are connected to other computers in a network or, as with the Internet, a system of networks in which any computer can communicate with any other computer. Common types of computer networks are point to point, local area network (LAN), wide area network (WAN), and metropolitan area network (MAN). A pointto-point network is a very small network in which all parts of the system are directly connected via wires or wireless (typically provided by a router in a single building). LANs, WANs, and MANs are sequentially larger and given the number of users, they require communications architecture to ensure all users on the network are served. If the network capacity is too small, some users will experience

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66    P art 1 • N ursing I nformatics T echnologies very long waits or perhaps the system will crash from overload (i.e., stop working and have to be restarted). Computer networks must allocate time and memory space to many users, and so must have a way to organize usage of the network resources so that all users are served. There are a variety of allocation strategies for high-level communication in networks. The most common are token ring (developed by IBM), star (also called multipoint; all communications go through a single hub computer), bus (in which all computers are connected to a single line), and tree. For very large networks, backbone communication technology is increasingly used. The use of systems in computer technology is based on system theory. System theory and its subset, network theory, provide the basis for understanding how the power of individual computers has been greatly enhanced through the process of linking multiple computers into a single system and multiple computer systems into networks.

Information Science Information science is an interdisciplinary field primarily concerned with the analysis, collection, classification, manipulation, storage, retrieval, movement, dissemination, and use of information (ASIS&T, 2019; Stock & Stock, 2013). It is concerned with technologies, strategies, and methodologies for getting the right information to people when it is needed without people getting overwhelmed with irrelevant and unwanted information. All science is concerned with measurement and analysis, and information science is no different. Key themes in information science analysis include optimality, performance, complexity, and structure (Luenberger, 2012). Optimality varies with the situation, but generally refers to achieving an optimum value for some desired outcome. For example, when a nurse wants to obtain information on outcomes of patients that suffered a complication for the purpose of determining whether they were rescued or not, the optimal outcome is that the search facility in the information system finds all patient records for patients who were truly at risk, and does not miss any. Additionally, the system retrieves few if any records of patients who did not suffer a high-risk complication. Optimality may refer to almost any variable that is measured on a numerical scale, such as cost, time (e.g., time to answer patient call lights), and workload. Performance is typically considered in the context of average performance of the information system over a series of communication instances. Averages are better representations of performance than long lists of single instance performance. For example, the average time it takes an

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e-mail to reach the intended recipient is much more useful than a long list of each e-mail and its transmission time. Complexity is a reality with the enormous masses of data and information generated, collected, stored, and retrieved. A typical measure of complexity in informatics is the amount of time it takes to complete a task. The time required is most often a function of the amount of information that must be dealt with to complete the task, but can also be greatly affected by how well the database was structured. Structure means developing a system for ordering and cataloging the data and information, particularly in a database. Excellent structure serves to reduce the amount of time required to perform operations on the database, such as search, retrieve, update, sort, and so forth. When data are well structured and cataloged in a database, complexity can actually be reduced because the system will not have to review all the data to find particular items. Rather, it will have to search only the sectors in which the data are going to be found, and the structure tells the programs that operate on the database which sectors to search. Information science is a rapidly growing field, and much of the progress is based on development and testing of mathematic algorithms related to information management tasks, such as storage and retrieval, database structure, measuring the value of information, and other work involved in increasing the efficiency of using information to make better decisions. In nursing, some key issues include ways nurses use information to make better nursing diagnoses and care decisions. Nursing information science is very concerned with measuring patient care outcomes and what nursing protocols produce the best outcomes. As a relatively new field, information science is only beginning to help people effectively retrieve and use the vast amount of data stored in multiple databases to improve health. In the future, data mining and other technologies designed to harvest information from very large databases are likely to become a major focus of health research and hold great promise for improving healthcare by providing accurate information to decision-makers.

SUMMARY Software is the set of commands to the computer that instruct the system as to what the user wants done. Software is the general term that describes the computer instruction code, and particular items of software are called programs. The three general types of software are systems software, utility software, and applications programs. Systems software controls the operations of the

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Chapter 4 • Computer Systems Basics—Software 

machine and how it works with peripheral equipment and supports utility and applications programs. Utility software supports the efficiency, productivity, and security of the systems software and applications programs. Applications software includes all the programs the user employs to do the work for which the user got the computer. Examples of these types of software include OSs for systems software, anti-virus programs for utility software, and word processing for applications software. A computer cannot naturally “understand” human language. The language of the computer is machine language which consists of a series of zeros and ones (0, 1), which represents an ON/OFF switch. One switch is a binary digit and called a “bit.” Eight bits taken together are needed to represent a single letter or number to a user. Machine language is understandably very difficult for programmers. Therefore, more English-like languages have been developed to facilitate programming. As languages become more like English, they are described in terms of levels, and the higher the level, the more English-like the language. Languages are also described by generations. The first level includes the first two generations of programming languages: machine language and assembly language. The second level includes the third and fourth generations: high-level procedural and nonprocedural languages. The third level (and fifth generation) is natural language. The various languages allow programmers to develop programs to meet many needs of both the workplace and personal users. For nurses, there are a large number of programs available in many workplaces to support clinical documentation, interpreting waveforms such as heart rhythms and breathing rhythms, controlling intravenous drip rates, calculating drug dosages, and many other work functions. There are literally thousands of programs for PDAs for work and personal enjoyment. Every day, more medically directed applications are available to download onto one’s smartphone. There are literally thousands of games too. Computing and software have become pervasive in nurses’ lives, and are tools that nurses use every day to provide high-quality nursing care services.

2. Which of the following list the basic categories of software?

Test Questions

6. What generation language is MUMPS (Massachusetts General Hospital Utility MultiProgramming System)—a language written specifically for use in healthcare settings?

1. Which of the following programs are part of system software? A. Nurse charting system software B. Operating system program C. Anti-virus software

D. Screen Saver software

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A. Applications software, Calculations software, Scheduling software

B. Business operations software, Operating system, Security software C. Systems software, Utility software, Applications software D. Utility software, BIOS software, Programming software

3. Which of the following items of software controls the key processes of the computer? A. Operating System B. BIOS System

C. Input-Output Controller Software D. System Backup Software

4. When a programmer talks about “Compiling” the program, what process is the programmer going to perform? A. Writing the program so that it performs the required functions B. Checking the program for errors (debugging)

C. Merging the program into the hospital information system (HIS) and checking that it does not conflict with other parts of the HIS D. Converting the program written in a high-level language into machine language

5. Which of the following languages can a computer understand directly? A. Java

B. Machine Language

C. Assembler Language D. C++

A. First generation

B. Second generation C. Third generation

D. Fourth generation

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68    P art 1 • N ursing I nformatics T echnologies 7. Angela is an informatics nurse who designs Web pages for the nursing department of the hospital where she works. Which of the following languages will give her the most flexibility in designing and implementing complex Web pages? A. FORTRAN B. COBOL

C. MUMPS D. Java

8. What generation of language is SQL (Structured Query Language)—a language designed for management and query operations on a relational database? A. First generation

B. Second generation C. Third generation

D. Fourth generation 9. Which of the following applications programs are typically included in a HIS for nurses to use in patient care? (Mark all that apply.) A. Medication Administration Record (MAR) B. Vital signs graphics

C. Bibliographic search on a clinical problem D. Patient admission and discharge

10. What is the key factor that defines a system? A. Complexity

B. Multiple, interdependent components C. Multiple distinct parts D. Machine processes

Test Answers 1. Answer: B

REFERENCES Anonymous. (2019). Computer programming languages. Computer Science.org. Retrieved from https://www. computerscience.org/resources/computer-programminglanguages/. Accessed on June 10, 2019. ASIS&T. (2019). What is information science? Association for Information Science and Technology. Retrieved from https://www.asist.org/about/information-science/. Accessed on June 12, 2019. Leaman, R., Dogan, R., & Zhivong, L. (2014). DNorm: Disease name normalization with pairwise learning to rank. National Center for Biotechnology Information (NCBI): National Library of Medicine. Retrieved from http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/Demo/ DNorm/. Accessed on February 15, 2014. Luenberger, D. (2012). Information science. Princeton, NJ: Princeton University Press. Markov, I. (2014). Next-generation chips and computing with atoms. Igor Markov: Material for graduate students. Retrieved from http://web.eecs.umich.edu/~imarkov/. Accessed on February 14, 2014. Markov, I. (2019). Next-generation chips and computing with atoms. Material for Graduate Students. Retrieved from http://web.eecs.umich.edu/~imarkov/. Accessed on June 12, 2019. Omicx. (2020). DNorm. Retrieved from https://omictools. com/dnorm-tool. Accessed on April 12, 2020. Oracle Corporation. (2019). Understanding basic multithreading concepts. Multithreading Programming Guide. Retrieved from https://docs.oracle.com/cd/E1945501/806-5257/6je9h032e/index.html. Accessed on June 10, 2019. Przybyla, P., Shardlow, M., Aubin, S., Bossy, R., Eckart de Castilho, R., Piperidis, S., McNaught, J., & Ananiadou, S. (2016). Text mining resources for the life sciences. Database: Journal of Biological Databases and Curation, 2016, 1–30. Stock, W. G., & Stock, M. (2013). Handbook of information science. Berlin: De Gruyter Saur. Varela, C. (2013). Programming distributed computer systems. Cambridge, MA: MIT Press.

2. Answer: C

3. Answer: A

4. Answer: D 5. Answer: B

6. Answer: C

7. Answer: D 8. Answer: D 9. Answer: C 10. Answer: B

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5 Open Source and Free Software David J. Whitten

• OBJECTIVES 1. Describe the basic concepts of open source software (OSS) and free/libre software (FS). 2. Describe the differences between open source software, free software, and proprietary software, particularly in respect of licensing. 3. Discuss why an understanding of open source and free software is important in a healthcare context, in particular where a choice between proprietary and open source software or free software is being considered. 4. Describe some of the open source and free software applications currently available, both healthcare-specific and for general office/productivity use. 5. Introduce some of the organizations and resources available to assist the nurse interested in exploring the potential of open source software. 6. Create and develop an example of OSS. 7. Describe the organization of health databases. 8. Use Boolean Logic to form query conditions. 9. Understand methods for querying and reporting from databases (VistA FileMan, SQL).

• KEY WORDS Querying databases Boolean Logic Open source software Free software Linux

INTRODUCTION It is estimated that, worldwide, over 78% of companies use open source software (OSS). In 2010, this number was only 42% (Vaughn-Nichols, 2015). More than 350 m ­ illion people are estimated to regularly use these products and

thousands of enterprises and organizations use open source code (Anderson & Dare, 2009) ); free and open source software are increasingly recognized as a reliable alternative to proprietary products. Most nurses use open source and free software (OSS/FS) on a daily basis (Table 5.1), often without even realizing it. When searching

69

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70    P art 1 • N ursing I nformatics T echnologies   TABLE 5.1    Common Acronyms and Terms A number of acronyms are used to denote a combination of free software and open source software. OSS/FS is the term that is used for preference in this chapter; others include the following: OSS: Open source software OSS/FS: Open source software/free software FOSS: Free and open source software FLOSS: Free/libre/open source software GNU: GNU is Not Unix Project (a recursive acronym). This is a project started by Richard Stallman, which turned into the Free Software Foundation (FSF, www.fsf.org), to develop and promote alternatives to proprietary Unix implementations. GNU/Linux or Linux: The complete operating system includes the Linux kernel, the GNU components, and many other programs. GNU/Linux is the more accurate term because it makes a distinction between the kernel—Linux—and much of the software that was developed by the GNU Project in association with the FSF.

the Web, the acronym FLOSS is used approximately 10 times as often as the term OSS/FS; FLOSS stands for free, libre, and open source software. Since dental floss is a common item on the Web, searches should include “open source software” as well as FLOSS. Everybody who sends an e-mail or uses the Web uses FLOSS most of the time, as the majority of the hardware and software that allows the Internet to function (Web servers, file transmission protocol [FTP] servers, and mail systems) are FLOSS. As Vint Cerf, Google’s “Chief Internet Evangelist” who is seen by many as the “father of the Internet,” has stated, the Internet “is fundamentally based on the existence of open, non-proprietary standards” (Openforum Europe, 2008). Many popular Web sites are hosted on Apache (FLOSS) servers, and increasingly people are using FLOSS Web browsers such as Chrome and Firefox. While in the early days of computing software was often free, free software (as defined by the Free Software Foundation [FSF]; Table 5.1) has existed since the mid-1980s, the ‘GNU is Not Unix’ Project (GNU)/Linux operating system (Table 5.1) has been developing since the early 1990s, and the open source initiative (OSI) (Table 5.2) definition of open source software has existed since the late 1990s. It is only more recently that widespread interest has begun to develop in the possibilities of FLOSS within health, healthcare, and nursing, and within nursing informatics (NI) and health informatics. In healthcare facilities in many countries, in both hospital and community settings, healthcare information

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technology (IT) initially evolved as a set of facility-centric tools to manage patient data. This was often primarily for administrative purposes, such that there now exists, in many facilities, a multitude of different, often disconnected, systems, with modern hospitals often using more than 100 different software applications. One of the major problems that nurses and all other health professionals currently face is that many of these applications and systems do not interface well for data and information exchange to benefit patient care. A major challenge in all countries is to move to a more patient-centric system, integrating facilities such as hospitals, physicians’ offices, and community or home healthcare providers, so that they can easily share and exchange patient data and allow collaborative care around the patient. In 2010, 84% of hospitals were keeping paper records versus using software. The healthcare industry is the only industry that needs to be paid to get them to switch to using software to store information—$20 billion spent between 2010 and 2013 to get us to 60% of hospitals storing information electronically (Barrata, 2014). Supporters of FLOSS approaches believe that only through openness, in respect to open standards and access to applications’ source codes, the user is in control of the software and able to adapt the application to local needs, and prevent problems associated with vendor lock-in (Murray, Wright, Karopka, Betts, & Orel, 2009). However, many nurses have only a vague understanding of what free and open software is and how these possible applications are relevant to nursing and nursing informatics. This chapter aims to provide a basic understanding of the issues, as it is only through being fully informed about the relative merits, and potential limitations, of the range of proprietary software and FLOSS, that nurses can make informed choices, whether they are selecting software for their own personal needs or involved in procurements for large healthcare organizations. This chapter will provide an overview of the background to FLOSS, explaining the differences and similarities between open source and free software, and introducing some particular applications such as the GNU/Linux operating system. Licensing issues will be addressed, as they are one of the major issues that exercise the minds of those with responsibility for decision-making, and issues such as the interface of FLOSS and proprietary software, or use of FLOSS components are not fully resolved. Some commonly available and healthcarespecific applications will be introduced, with a few examples being discussed. Some of the organizations working to explore the use of FLOSS within healthcare and nursing, and some additional resources, will be introduced. The chapter will conclude with a case study of what many consider the potential “mother of FLOSS healthcare applications,” Veterans Health Information System and

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  TABLE 5.2   Free Software and Open Source Definitions Free Software The term free software is defined as follows by the Free Software Foundation (FSF) (Version 1.122, 2013, www.gnu.org/philosophy/ free-sw.html, emphasis added): Free software is seen in terms of liberty, rather than price, and to understand the concept, you need to think of “free” as in free speech, not as in free beer. The differences are easier to understand in some languages other than English, where there is less ambiguity in the use of the word free. For example, in French, the use of the terms libre (freedom) software versus gratis (zero price) software. Free software is described in terms of the users’ freedom to run, copy, distribute, study, change, and improve the software. More precisely, it refers to four kinds of freedom for the users of the software: • The freedom to run the program for any purpose (freedom 0). • The freedom to study how the program works, and change it to make it do what you wish (freedom 1). Access to the source code is a precondition for this. • The freedom to redistribute copies so you can help your neighbor (freedom 2). • The freedom to distribute copies of your modified versions to others (freedom 3). By doing this you can give the whole ­community a chance to benefit from your changes. Access to the source code is a precondition for this. A program is free software if users have all of these freedoms. Open Source Software The term open source is defined exactly as follows by the open source initiative (OSI) (www.opensource.org/docs/osd): Introduction Open source does not just mean access to the source code. The distribution terms of open source software must comply with the ­following criteria: 1. Free Redistribution The license shall not restrict any party from selling or giving away the software as a component of an aggregate software distribution containing programs from several different sources. The license shall not require a royalty or other fee for such sale. Rationale: By constraining the license to require free redistribution, we eliminate the temptation to throw away many long-term gains in order to make a few short-term sales dollars. If we did not do this, there would be lots of pressure for ­cooperators to defect.

2. Source Code The program must include source code, and must allow distribution in source code as well as compiled form. Where some form of a product is not distributed with source code, there must be a well-publicized means of obtaining the source code for no more than a reasonable reproduction cost preferably, downloading via the Internet without charge. The source code must be the preferred form in which a programmer would modify the program. Deliberately obfuscated source code is not allowed. Intermediate forms such as the output of a preprocessor or translator are not allowed. Rationale: We require access to unobfuscated source code because you cannot evolve programs without modifying them. Since our purpose is to make evolution easy, we require that modification be made easy.

3. Derived Works The license must allow modifications and derived works, and must allow them to be distributed under the same terms as the license of the original software. Rationale: The mere ability to read source is not enough to support independent peer review and rapid evolutionary selection. For rapid evolution to happen, people need to be able to experiment with and redistribute modifications. 4. Integrity of the Author’s Source Code The license may restrict source code from being distributed in modified form only if the license allows the distribution of “patch files” with the source code for the purpose of modifying the program at build time. The license must explicitly permit distribution of software built from modified source code. The license may require derived works to carry a different name or ­version number from the original software. Rationale: Encouraging lots of improvement is a good thing, but users have a right to know who is responsible for the software they are using. Authors and maintainers have reciprocal right to know what they are being asked to support and protect their reputations.

(continued)

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72    P art 1 • N ursing I nformatics T echnologies   TABLE 5.2   Free Software and Open Source Definitions (continued) Open Source Software Accordingly, an open source license must guarantee that source be readily available, but may require that it be distributed as ­pristine base sources plus patches. In this way, “unofficial” changes can be made available but readily distinguished from the base source. 5. No Discrimination Against Persons or Groups The license must not discriminate against any person or group of persons. Rationale: In order to get the maximum benefit from the process, the maximum diversity of persons and groups should be equally eligible to contribute to open sources. Therefore, we forbid any open source license from locking anybody out of the process. Some countries, including the United States, have export restrictions for certain types of software. An OSD-conformant license may warn licensees of applicable restrictions and remind them that they are obliged to obey the law; however, it may not incorporate such restrictions itself. 6. No Discrimination Against Fields of Endeavor The license must not restrict anyone from making use of the program in a specific field of endeavor. For example, it may not restrict the program from being used in a business, or from being used for genetic research. Rationale: The major intention of this clause is to prohibit license traps that prevent open source from being used commercially. We want commercial users to join our community, not feel excluded from it. 7. Distribution of License The rights attached to the program must apply to all to whom the program is redistributed without the need for execution of an additional license by those parties. Rationale: This clause is intended to forbid closing up software by indirect means such as requiring a nondisclosure agreement. 8. License Must Not Be Specific to a Product The rights attached to the program must not depend on the program’s being part of a particular software distribution. If the ­program is extracted from that distribution and used or distributed within the terms of the program’s license, all parties to whom the program is redistributed should have the same rights as those that are granted in conjunction with the original ­software distribution. Rationale: This clause forecloses yet another class of license traps. 9. License Must Not Restrict Other Software The license must not place restrictions on other software that is distributed along with the licensed software. For example, the license must not insist that all other programs distributed on the same medium must be open source software. Rationale: Distributors of open source software have the right to make their own choices about their own software. 10. License Must Be Technology-Neutral No provision of the license may be predicated on any individual technology or style of interface. Rationale: This provision is aimed specifically at licenses which require an explicit gesture of assent in order to establish a ­ contract between licensor and licensee. Provisions mandating so-called “click-wrap” may conflict with important methods of software distribution such as FTP download, CD-ROM anthologies, and Web mirroring; such provisions may also hinder code reuse. Conformant licenses must allow for the possibility that (a) redistribution of the software will take place over non-Web channels that do not support click-wrapping of the download, and that (b) the covered code (or reused portions of covered code) may run in a non-GUI environment that cannot support pop-up dialogs.

Technology Architecture (VistA) (Tiemann, 2004), and recent moves to develop fully FLOSS versions.

FLOSS—THE THEORY Background While we use the term open source (and the acronym FLOSS) in this chapter, we do so loosely (and, some would argue, incorrectly) to cover several concepts,

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including OSS, FS, and GNU/Linux. Each of these concepts and applications has its own definition and ­attributes (Table 5.2). While the two major philosophies in the FLOSS world, i.e., the free software foundation (FSF) p ­ hilosophy and the open source initiative (OSI) philosophy, are today often seen as separate movements with different views and goals, their adherents frequently work together on specific practical projects (FSF, 2010a). The key commonality between FSF and OSI philosophies is that the source code is made available to the users

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by the programmer. FSF and OSI differ in the restrictions placed on redistributed source code. FSF is committed to no restrictions, so that if you modify and redistribute free software, as a part or as a whole of aggregated software, you are not allowed to place any restrictions on the openness of the resultant source code (Wong & Sayo, 2004). The difference between the two movements is said to be that the free software movement’s fundamental issues are ethical and philosophical, while for the open source movement, the issues are more practical than ethical ones; thus, the FSF asserts that open source is a development methodology, while free software is a social movement (FSF, 2010a). FLOSS is contrasted with proprietary software. FLOSS software can be available for commercial use, commercial development, and commercial distribution. The two terms commercial and proprietary are often conflated but strictly need to be separated. Proprietary software is that on which an individual or company holds the exclusive copyright, at the same time restricting other people’s access to the software’s source code and/or the right to copy, modify, and study the software (Sfakianakis, Chronaki, Chiarugi, Conforti, & Katehakis, 2007). Commercial software is developed by businesses or individuals with the aim of making money from its licensing and use. Most commercial software is proprietary, but there is commercial free software, and there is noncommercial nonfree software. FLOSS should also not be confused with freeware or shareware. Freeware is software offered free of charge, but without the freedom to modify the source code and redistribute the changes, so it is not free software (as defined by the FSF). Shareware is another form of proprietary software, which is offered on a “try before you buy” basis. If the customer continues to use the product after a short trial period, or wishes to use additional features, he or she is required to pay a specified, usually nominal, license fee. Shareware authors make a separate decision whether they want to release their source code.

Free Software Definition Free software is defined by the FSF in terms of four freedoms for software users: to have the freedom to use, study, redistribute, and improve the software in any way they wish. A program is only free software, in terms of the FSF definition, if users have all of these freedoms (see Table  5.2). The FSF believes that users should be free to redistribute copies, either with or without modifications, either gratis or through charging a fee for distribution, to anyone, anywhere without a need to ask or pay for permission to do so (FSF, 2010a). Confusion around the use and meaning of the term free software arises from the multiple meanings of the word

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free in the English language. In other languages, there is less of a problem, with different words being used for the “freedom” versus “no cost” meanings of free, for example the French terms libre (freedom) software versus gratis (zero price) software. The “free” of free software is defined in terms of liberty, not price, thus to understand the concept, the common distinction is in thinking of free as in free speech, not as in free beer (FSF, 2010b). Acronyms such as FLOSS (free/libre/OSS—a combination of the above two terms emphasizing the “libre” meaning of the word free) or FLOSS are increasingly used, particularly in Europe, to overcome this issue (International Institute of Infonomics, 2005).

Open Source Software Definition Open source software is any software satisfying the open software initiative’s definition (OSI, n.d.). The open source concept is said to promote software reliability and quality by supporting independent peer review and rapid ­evolution of source code as well as making the source code of software freely available. In addition to providing free access to the programmer’s instructions to the computer in the programming language in which they were written, many versions of open source licenses allow anyone to modify and redistribute the software. The open source initiative (OSI) has created a certification mark, “OSI certified.” In order to be OSI certified, the software must be distributed under a license that guarantees the right to read, redistribute, modify, and use the software freely (OSI, n.d.). Not only must the source code be accessible to all, but also the distribution terms must comply with 10 criteria defined by the OSI (see Table 5.2 for full text and rationale).

FLOSS Development Models and Systems FLOSS has existed as a model for developing computer applications and software since the 1950s (Waring & Maddocks, 2005); at that time, software was often provided free (gratis), and freely, when buying hardware (Murray et al., 2009). The freedoms embodied within FLOSS were understood as routine until the early 1980s with the rise of proprietary software. However, it was only in the 1980s that the term free software (Stallman, 2002) and in the 1990s that the term open source software, as we recognize them today, came into existence to distinguish them from the proprietary models. Richard Stallman advocates free software as an ethical imperative. He believes the software needed to make a program that is used by someone should always be available. “Free software” adheres to the four software freedoms.

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74    P art 1 • N ursing I nformatics T echnologies These are numbered starting with zero as computers internally do. Freedom 0 ensures that users were free to use software in any way as they saw fit. Freedom 1 users are free to study its source code, and they are free to modify software for their own purposes. Freedom 2 means that a user can redistribute copies to help others. Finally, Freedom 3 means a user is free to share his or her modified program with others (Finley, 2019) (GNU Operating System, 2019). The way that software is developed using models of FLOSS contributes to their distinctions from proprietary software. Shaw et al. (2002) state that as FLOSS is “developed and disseminated in an open forum,” it “revolutionizes the way in which software has historically been developed and distributed.” A similar description, in a UK government report, emphasizes the open publishing of source code and that development is often largely through voluntary efforts (Peeling & Satchell, 2001). While FLOSS is often described as being developed by voluntary efforts, this description may belie the professional skills and expertise of many of the developers. Many of those providing voluntary efforts are highly skilled programmers who contribute time and efforts freely to the development of FLOSS. In addition, many FLOSS applications are coordinated through formal groups. For example, the Apache Software Foundation (www.apache.org) coordinates development of the Apache hypertext transfer protocol (HTTP) server and many other products. FLOSS draws much of its strength from the collaborative efforts of people who work to improve, modify, or customize programs, believing they must give back to the FLOSS community so others can benefit from their work. The FLOSS development model is unique, although it bears strong similarities to the openness of the scientific method, and is facilitated by the communication capabilities of the Internet that allow collaboration and rapid sharing of developments, such that new versions of software can often be made available on a daily basis. The most well-known description of the distinction between FLOSS and proprietary models of software development lies in Eric Raymond’s famous essay, “The Cathedral and the Bazaar” (Raymond, 2001). Cathedrals, Raymond says, were built by small groups of skilled workers and craftsmen to carefully worked out designs. The work was often done in isolation, and with everything built in a single effort with little subsequent modification. Much software, in particular proprietary software, has traditionally been built in a similar fashion, with groups of programmers working to strictly controlled planning and management, until their work was completed and the program released to the world. In contrast, FLOSS development is likened to a bazaar, growing organically from an initial small group of traders or enthusiasts establishing

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their structures and beginning businesses. The bazaar grows in a seemingly chaotic fashion, from a minimally functional structure, with later additions or modifications as circumstances dictate. Likewise, most FLOSS development starts off highly unstructured, with developers releasing early, minimally functional code and then modifying their programs based on feedback. Other developers may then join, and modify or build on the existing code; over time, an entire operating system and suite of applications develops, evolves, and improves continuously. The bazaar method of development is said to have been proven over time to have several advantages, including the following:

•

• •

•

Reduced duplication of efforts through being able to examine the work of others and through the potential for large numbers of contributors to use their skills. As Moody (2001) describes it, there is no need to reinvent the wheel every time as there would be with proprietary products whose source code cannot be used in these ways Building on the work of others, often by the use of open standards or components from other applications Better quality control; with many developers working on a project, code errors (bugs) are uncovered quickly and may be fixed even more rapidly (often termed Linus’ Law, “given enough eyeballs, all bugs are shallow” [Raymond, 2001]) Reduction in maintenance costs; costs, as well as effort, can be shared among potentially thousands of developers (Wong & Sayo, 2004).

CHOOSING FLOSS OR NOT Proposed Benefits of FLOSS FLOSS has been described as the electronic equivalent of generic drugs (Bruggink, 2003; Goetz, 2003; Surnam & Diceman, 2004). In the same way as the formulas for generic drugs are made public, so FLOSS source code is accessible to the user. Any person can see how the software works and can make changes to the functionality. It is also suggested by many that there are significant similarities between the open source ethos and the traditional scientific method approach (supported by most ­scientists and philosophers of science), as this latter method is based on openness, free sharing of information, and improvement of the end result. As FLOSS can be obtained ­royalty free, it is less expensive to acquire than ­proprietary ­alternatives. This means that FLOSS can transform healthcare in developing countries just as the availability of generic drugs have.

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Chapter 5 • Open Source and Free Software 

This is only one of several benefits proposed for FLOSS, with further benefits including lack of the proprietary lock-in that can often freeze out innovation, and with FLOSS projects supporting open standards and providing a level playing field, expanding the market by giving software consumers greater choice (Dravis, 2003). Besides the low cost of FLOSS, there are many other reasons why public and private organizations are adopting FLOSS, including security, reliability, and stability, and developing local software capacity. Many of these proposed benefits have yet to be demonstrated or tested extensively, but there is growing evidence for many of them, and we will address some of them in the next section.

Issues in FLOSS There are many issues in the use of FLOSS that we cannot address here in detail. It is important that nurses who are exploring, using, or intending to use FLOSS receive a basic introduction with pointers to additional resources. Generally, decision-making is facilitated through an active awareness of the issues. This section is intended to support interested nurses in their decision-making. The issues that we introduce include, not necessarily in any order of importance:

• • • • • •

Licensing Copyright and intellectual property Total cost of ownership (TCO) Support and migration Business models Security and stability

Licensing and copyright will be addressed in the next section, but the other issues will be covered briefly here, before concluding the section with a short description of one possible strategy for choosing FLOSS (or other software, as the issues are pertinent to any properly considered purchase and implementation strategy). Total Cost of Ownership.  Total cost of ownership (TCO) is the sum of all the expenses directly related to the ­ownership and use of a product over a given period of time. The popular myth surrounding FLOSS is that it is always free as in free of charge. This is true to an extent, as most FLOSS distributions (e.g., Ubuntu [www.ubuntu. com], Red Hat [www.redhat.com], SuSE [www.opensuse. org], Debian [www.debian.org]) can be obtained at no charge from the Internet; however, copies can also be sold. It is part of the definition of open source/free software that an application can’t charge a licensing fee for

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usage. This means that on a licensing cost basis FLOSS applications are almost always cheaper than proprietary software. However, licensing costs are not the only costs of a software package or infrastructure. Personnel costs, hardware requirements, migration time, changes in staff efficiency, and training costs are among other costs that should be considered as well. Without careful consideration of all of this information, it is impossible to really know which software solutions are the most cost effective. The real costs with FLOSS center around the configuration of the software and support for the software as well as training people to use the software (examples are provided in Wheeler, 2007 and Wong & Sayo, 2004). These costs are also included by proprietary software vendors, but not necessarily detailed as such. Wheeler (2007) lists the main reasons why FLOSS comes out cheaper, including the following:

• • • •

FLOSS costs less to initially acquire, because there are no license fees. Upgrade and maintenance costs are typically far less due to improved stability and security. FLOSS can often use older hardware more efficiently than proprietary systems, yielding smaller hardware costs and sometimes eliminating the need for new hardware. Increasing numbers of case studies using FLOSS show it to be especially cheaper in server environments.

Support and Migration.  It can be costly to make an organization-wide change from proprietary software to open source software. Prudently, there are times the costs will outweigh the benefits. When many FLOSS packages were first created, they do not have the same level of documentation, training, and support resources as their common proprietary equivalents. Depending on the extent of adoption, some FLOSS packages did not fully interface with other proprietary software. This is important as an organization may need to share information with other organizations that depend on proprietary software (e.g., patient data exchange between different healthcare provider systems). In recent years, this situation has changed. Proprietary vendors may not provide backward compatibility with previous versions of their own systems to force consumers to upgrade to the newest version. Once a FLOSS package incorporates methods to access older versions, there is little reason to remove them, so they are more compatible than the original system (Apache Open Office Migration Guide, 2018). Migrating from one platform to another should be handled using a careful and phased approach. The European Commission has published a document entitled the

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76    P art 1 • N ursing I nformatics T echnologies “IDA Open Source Migration Guidelines” (European Communities, 2003) that provides detailed suggestions on how to approach migration. These include the need for a clear understanding of the reasons to migrate, ensuring that there is active support for the change from IT staff and users, building up expertise and relationships with the open source movement, starting with noncritical systems, and ensuring that each step in the migration is manageable. Security and Stability.  While there is no perfectly secure operating system or platform, factors such as development method, program architecture, and target market can greatly affect the security of a system and consequently make it easier or more difficult to breach. There are some indications that FLOSS systems are superior to proprietary systems in this respect, and the security aspect has already encouraged many public organizations to switch or to consider switching to FLOSS solutions. The French Customs and Indirect Taxation authority, for example, migrated to Red Hat Linux largely because of security concerns with proprietary software (International Institute of Infonomics, 2005). Among reasons often cited for the better security record in FLOSS is the availability of the source code (making it easier for vulnerabilities to be discovered and fixed). Many FLOSS have a proactive security focus, so that before ­features are added, the security ­considerations are accounted for and a feature is added only if it is d ­ etermined not to compromise system security. In addition, the strong security and permission structure ­inherent in FLOSS applications that are based on the Unix model are designed to minimize the possibility of users being able to compromise systems (Wong & Sayo, 2004). FLOSS ­systems are well known for their stability and r­eliability, and many anecdotal stories exist of FLOSS ­servers functioning for years without requiring maintenance. However, quantitative studies are more difficult to come by (Wong & Sayo, 2004). Security of information is vitally important within the healthcare domain, particularly in relation to any process that might access a patient record, maintain and store the record, and transmit a patient records between two organizations. The advocates of FLOSS suggest that it can provide increased security over proprietary software, and a report to the UK government saw no security disadvantage in the use of FLOSS products (Peeling & Satchell, 2001). According to the same report, even the US government’s National Security Agency (NSA) supports a number of FLOSS security-related projects. The NSA actually maintains a Web site at https://code.nsa.gov to make its code available. Ghidra, a software reverse engineering (SRE) suite of tools developed by NSA’s Research Directorate in support of the Cybersecurity mission, has also been released (Ghidra, 2019). Stanco (2001) considers that the reason the

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NSA thinks that free software can be more secure is that when anyone and everyone can inspect source code, hiding backdoors into the code can be very difficult. In considering a migration to FLOSS, whether it is for everyday office and productivity uses or for health-specific applications, there are some commonly encountered challenges that one may face. These challenges have traditionally been seen as including the following:

• • •

There is a relative lack of mature FLOSS desktop applications. Many FLOSS tools are not user-friendly and have a steep learning curve. File sharing between FLOSS and proprietary applications can be difficult.

As FLOSS applications have matured in recent years, and the user community grown, many of these challenges have been largely overcome, such that today, many OSS/ FA applications are indistinguishable from proprietary equivalents for many users in terms of functionality, ease of use, and general user-friendliness. Choosing the Right Software: The Three-Step Method for FLOSS Decision-Making.  Whether one is working with FLOSS or proprietary tools, choosing the right software can be a difficult process, and a thorough review process is needed before making a choice. A simple three-step method for FLOSS decision-making can guide organizations through the process and works well for all kinds of software, including server, desktop, and Web applications (Surman & Diceman, 2004). Step 1. Define the needs and constraints. Needs must be clearly defined, including those of the ­organization and of individual users. Other ­specific issues to consider include range of features, ­languages, budget (e.g., for training or ­integration with other systems), the implementation time frame, compatibility with existing systems, and the skills existing within the organization.

Step 2. Identify the options. A short list of three to five software packages that are likely to meet the needs can be developed from comparing software packages with the needs and constraints listed in the previous phase. There are numerous sources of information on FLOSS packages, including recommendations of existing users, reviews, and directories (e.g., OSDir.com and OpenSourceCMS.com.) and software package sites that contain promotional information, documentation, and often demonstration versions that will help with the review process.

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Chapter 5 • Open Source and Free Software  Step 3. Undertake a detailed review. Once the options have been identified, the final step is to review and choose a software package from the short list. The aim here is to assess which of the possible options will be best for the organization. This assessment can be done by rating each package against a list of criteria, including quality, ease of use, ease of migration, software stability, compatibility with other systems being used, flexibility and customizability, user response, organizational buy-in, evidence of widespread use of the software, and the existence of support mechanisms for the software’s use. Hands-on testing is key and each piece of software should be installed and tested for quality, stability, and compatibility, including by a group of key users so as to assess factors such as ease of use, ease of migration, and user response.

Making a Decision.  Once the review has been completed, if two packages are close in score, intuition about the right package is probably more important than the actual numbers in reaching a final decision.

Examples of Adoption or Policy Regarding FLOSS FLOSS has moved beyond the closed world of programmers and enthusiasts. Governments around the world have begun to take notice of FLOSS and have launched initiatives to explore the proposed benefits. There is a significant trend toward incorporating FLOSS into procurement and development policies, and there are increasing numbers of cases of FLOSS recognition, explicit policy statements, and procurement decisions. Many countries, regions, and authorities now have existing or proposed laws mandating or encouraging the use of FLOSS (Wong & Sayo, 2004). A survey from the MITRE Corporation (2003) showed that the US Department of Defense (DoD) at that time used over 100 different FLOSS applications. The main conclusion of their study (The MITRE Corporation, 2003) was that FLOSS software was used in critical roles, including infrastructure support, software development, and research, and that the degree of dependence on FLOSS for security was unexpected. In 2000, the (US) President’s Information Technology Advisory Committee (PITAC, 2000) recommended that the US federal government should encourage FLOSS use for software development for high-end computing. In 2002, the UK government published a policy (Office of the e-Envoy, 2002), since updated, that it would “consider OSS solutions alongside proprietary ones in IT procurements” (p. 4), “only use products for interoperability that support open standards and specifications in all future IT developments” (p. 4), and explore the possibility

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of using FLOSS as the default exploitation route for government-funded research and development (R&D) software. Similar policies have been developed in Denmark, Sweden, and The Netherlands (Wong & Sayo, 2004). European policy encouraging the exploration and use of FLOSS has been consequent on the European Commission’s eEurope2005—An Information Society for All initiative (European Communities, 2004) and its predecessors, such as the i2010 strategy (European Communities, 2005) with their associated action plans. These have encouraged the exchange of experiences and best practice examples so as to promote the use of FLOSS in the public sector and e-government across the European Commission and member states of the European Union (EU). In addition, the EU has funded R&D on health-related FLOSS applications as well as encouraged open standards and FLOSS where appropriate in wider policy initiatives. In other parts of world, Brazil and Peru are among countries whose governments are actively moving toward FLOSS solutions, for a variety of reasons, including ensuring long-term access to data through the use of open standards (i.e., not being reliant on proprietary software that may not, in the future, be interoperable) and cost reduction. The South African government has a policy favoring FLOSS, Japan is considering moving e-government projects to FLOSS, and pro-FLOSS initiatives are in operation or being seriously considered in Taiwan, Malaysia, South Korea, and other Asia Pacific countries.

OPEN SOURCE LICENSING While FLOSS is seen by many as a philosophy and a development model, it is also important to consider it as a licensing model (Leong, Kaiser, & Miksch, 2007; Sfakianakis et al., 2007). In this section, we can only briefly introduce some of the issues of software licensing as they apply to FLOSS, and will include definitions of licensing, some of the types of licenses that exist, and how licenses are different from copyright. While we will cover some of the legal concepts, this section cannot take the place of proper legal counsel, which should be sought when reviewing the impact of licenses or contracts. Licensing plays a crucial role in the FLOSS community, as it is “the operative tool to convey rights and redistribution conditions” (Anderson & Dare 2009, p. 101). Licensing is defined by Merriam-Webster (2010) as giving the user of something permission to use it; in the case here, that something is software. Most software comes with some type of licensing, commonly known as the end-user licensing agreement (EULA). The license may have specific restrictions related to the use, modification, or duplication of the software. The Microsoft EULA, for example, specifically prohibits any kind of disassembly,

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78    P art 1 • N ursing I nformatics T echnologies inspection, or reverse engineering of software (Zymaris, 2003). There are some countries especially in Europe where this is unenforceable, as the laws specifically give this right to all. This law is intended to support interoperability (Directive 20009/24/EC of the European Parliament, 2009). Most licenses also have statements limiting the liability of the software manufacturer toward the user in case of possible problems arising in the use of the software. From this working definition of licensing, and some examples of what can be found in a EULA, we can examine copyright. While licensing gives a person the right to use software, with restrictions in some cases, copyright is described as the exclusively granted or owned legal right to publish, reproduce, and/or sell a work (Merriam-Webster, 2010). The distinctions between ownership of the original work and rights to use it are important, and there are differences in the way these issues are approached for proprietary software and FLOSS. For software, the work means the source code or statements made in a programming language. In general, the person who creates a work owns the copyright to it and has the right to allow others to copy it or deny that right. In some cases the copyright is owned by a company with software developers working for that company, usually having statements in their employment contracts that assign copyright of their works to the company. In the case of FLOSS, contributors to a project will often assign copyright to the managers of the project. While in the case of proprietary software, licensing is generally dealt with in terms of restrictions (i.e., what the user is not allowed to do; for FLOSS, licensing is seen in terms of permissions, rights, and encouraging users to do things). Most software manufacturing companies hold the copyright for software created by their employees. In financial terms,

these works are considered intellectual property, meaning that they have some value. For large software companies, such as Oracle or Microsoft, intellectual property may be a large part of their capital assets. The open source community values software differently, and FLOSS licenses are designed to facilitate the sharing of software and to prevent an individual or organization from controlling ownership of the software. The individuals who participate in FLOSS projects generally do realize the monetary value of what they create; however, they feel it is more valuable if the community at large has open access to it and is able to contribute back to the project. A common misconception is that if a piece of software, or any other product, is made freely available and open to inspection and modification, then the intellectual property rights of the originators cannot be protected, and the material cannot be subject to copyright. The open source community, and in particular the FSF, has adopted a number of conventions, some built into the licenses, to protect the intellectual property rights of authors and developers. One form of copyright, termed copyleft to distinguish it from commercial copyright terms, works by stating that the software is copyrighted and then adding distribution terms. These are a legal instrument giving everyone the rights to use, modify, and redistribute the program’s code or any program derived from it but only if the distribution terms are unchanged. The code and the freedoms become legally inseparable, and strengthen the rights of the originators and contributors (Cox, 1999; FSF, 2010c).

Types of FLOSS Licenses A large and growing number of FLOSS licenses exists. Table 5.3 lists some of the more common ones, while fuller

  TABLE 5.3    Common OSS/FS Licenses GNU GPL: A free software license and a copyleft license. Recommended by FSF for most software packages (www.gnu.org/ licenses/gpl.html). GNU Lesser General Public License (GNU LGPL): A free software license, but not a strong copyleft license, because it permits ­linking with nonfree modules (www.gnu.org/copyleft/lesser.html). Modified BSD License: The original BSD license, modified by removal of the advertising clause. It is a simple, permissive ­noncopyleft free software license, compatible with the GNU GPL (www.oss-watch.ac.uk/resources/modbsd.xml). W3C Software Notice and License: A free software license and GPL compatible (www.w3.org/Consortium/Legal/2002/ copyright-software-20021231). MySQL Database License: (www.mysql.com/about/legal). Apache License, Version 2.0: A simple, permissive noncopyleft free software license that is incompatible with the GNU GPL (www.apache.org/licenses/LICENSE-2.0). GNU Free Documentation License: A license intended for use on copylefted free documentation. It is also suitable for textbooks and dictionaries, and its applicability is not limited to textual works (e.g., books) (www.gnu.org/copyleft/fdl.html). Public Domain: Being in the public domain is not a license, but means the material is not copyrighted and no license is needed. Public domain status is compatible with all other licenses, including GNU GPL. Further information on licenses is available at www.gnu.org/licenses/licenses.html and www.opensource.org/licenses.

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Chapter 5 • Open Source and Free Software 

lists of various licenses and terms can be found in Wong and Sayo (2004). The OSI Web site currently lists over 60 (www.opensource.org/licenses), while the FSF Web site lists over 40 general public license (GPL)-compatible free software licenses (www.gnu.org/licenses/licenselist.html). The two main licenses are the GNU GPL and the Berkeley system distribution (BSD)-style licenses. It is estimated that about 75% of FLOSS products use the GNU GPL (Wheeler, 2010), and this license is designed to ensure that user freedoms under the license are protected in perpetuity, with users being allowed to do almost anything they want to a GPL program. The conditions of the license primarily affect the user when it is distributed to another user (Wong & Sayo, 2004). BSD-style licenses are so named because they are identical in spirit to the original license issued by the University of California, Berkeley. These are among the most permissive licenses possible, and essentially permit users to do anything they wish with the software, provided the original licensor is acknowledged by including the original copyright notice in source code files and no attempt is made to sue or hold the original licensor liable for damages (Wong & Sayo, 2004). Here is an example from the GNU GPL that talks about limitations: 16. Limitation of Liability. In no event unless required by applicable law or agreed to in writing will any copyright holder, or any other party who may modify and/or redistribute the program as permitted above, be liable to you for damages, including any general, special, incidental, or consequential damages arising out of the use or inability to use the program (including but not limited to loss of data or data being rendered inaccurate or losses sustained by you or third parties or a failure of the program to operate with any other programs), even if such holder or other party has been advised of the possibility of such damages. (FSF, 2007, para. 16) Like the Microsoft EULA, there are limitations relating to liability in the use of the software and damage that may be caused, but unlike the Microsoft EULA, the GPL makes it clear what you can do with the software. In general, you can copy and redistribute it, sell or modify it. The restriction is that you must comply with the parts of the license requiring the source code to be distributed as well. One of the primary motivations behind usage of the GPL in FLOSS is to ensure that once a program is released as FLOSS, it will remain so permanently. A commercial software company cannot legally modify a GPL program and then sell it under a different proprietary license (Wong & Sayo, 2004).

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In relation to using FLOSS within a healthcare environment, as with use of any software, legal counsel should be consulted to review any license agreement made; however, in general terms, when using FLOSS there are no obligations that would not apply to using any copyrighted work. Someone cannot legally take a body of work, the source code, and claim it as their own. The licensing terms must be followed as with any other software. Perhaps the most difficult issue comes when integrating FLOSS components into a larger infrastructure, especially where it may have to interface with proprietary software. Much has been said about the “viral” nature of the open source license, which comes from the requirement of making source code available if the software is redistributed. Care must be taken that components utilized in creating proprietary software either utilize FLOSS components in such a way as to facilitate distribution of the code or avoid their use. If the component cannot be made available without all of the source code being made available, then the developer has the choice of not using the component or making the entire application open source. Some projects have created separate licensing schemes to maintain the FLOSS license and provide those vendors that wish to integrate components without making their product open source. MySQL, a popular open source database server, offers such an option (Table 5.3). Licensing is a complex issue; we have only touched on some of the points, but in conclusion, the best advice is always to read the license agreement and understand it. In the case of a business decision on software purchase or use, one should always consult legal counsel; however, one should remember that FLOSS licenses are more about providing freedom than about restricting use.

FLOSS APPLICATIONS Many FLOSS alternatives exist to more commonly known applications. Not all can be covered here, but if one thinks of the common applications that most nurses use on a daily basis, these are likely to include the following:

• • • • •

Operating system Web browser E-mail client Word processing or integrated office suite Presentation tools

For each of these, FLOSS applications exist. Using FLOSS does not require an all or nothing approach (Dravis, 2003) and much FLOSS can be mixed with proprietary software and a gradual migration to FLOSS is an option for many organizations or individuals. However, when using a mixture of FLOSS and proprietary or commercial

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80    P art 1 • N ursing I nformatics T echnologies software, incompatibilities can be uncovered and cause problems whose severity must be assessed. Many FLOSS applications have versions that will run on non-FLOSS operating systems, so that a change of operating system, for example, to one of the many distributions of Linux, is not necessarily needed. Most FLOSS operating systems now have graphical interfaces that look very similar to Windows or Apple interfaces.

Operating Systems: GNU/Linux A GNU/Linux distribution (named in recognition of the GNU Project’s significant contribution, but often just called Linux) contains the Linux kernel at its heart and all the FLOSS components required to produce full operating system functionality. GNU/Linux is a term that is increasingly used by many people to cover a distribution of operating systems and other associated software components. However, Linux was originally the name of the kernel created by Linus Torvalds in 1991, which has grown from a one-man operation to now having over 200 maintainers representing over 300 organizations. A kernel is the critical center program code for an operating system. The kernel controls central processing unit (CPU) usage, memory management, and hardware devices. The

kernel also is necessary to allow communication between different programs which run on the operating system. Since the kernel influences performance and limits or includes the hardware platforms that the FLOSS system can run on, it is important that its source code is freely available. The Linux kernel has been ported to run on a vast variety of hardware, from huge computers that fill special air conditioned rooms such as mainframes and supercomputers, through more common consumer computers such as desktop, laptop, and tablet machines. The Linux kernel has even been converted to run on mobile phones and other mobile devices. The Linux kernel is FLOSS, licensed under the GNU GPL. An interesting development in recent years has been running the open source operating system on physical hardware that has been built using the hardware specification for an Open Processor Core as a form of open source (Katz, 2018) Over time, individuals and companies began distributing Linux with their own choice of FLOSS packages bound around the Linux kernel; the concept of the distribution was born, which contains much more than the kernel (usually only about 0.25% in binary file size of the distribution). There is no single Linux distribution, and many commercial distributions and freely available variants exist, with numerous customized distributions that are targeted to the unique needs of different users (Table 5.4).

  TABLE 5.4    Some Common Linux Distributions Ubuntu: Ubuntu is a Linux-based operating system for desktop, server, netbook, and cloud computing environments. First released in 2004, it is loosely based on Debian OS. Ubuntu now releases updates on a six-month cycle. There are increasing numbers of customized variants of Ubuntu, aimed at, for example, educational use (Edubuntu), professional video and audio editing (Ubuntu Studio), and server editions (www.ubuntu.com). Debian: Debian GNU/Linux is a free distribution of the Linux-based operating system. It includes a large selection of prepackaged application software, plus advanced package management tools to allow for easy installation and maintenance on individual systems and workstation clusters (www.debian.org). Mandriva (formerly Mandrakelinux): Available in multiple language versions (including English, Swedish, Spanish, Chinese, Japanese, French, German, Italian, and Russian). Mandrakelinux was first created in 1998 and is designed for ease of use on ­servers and on home and office systems (www2.mandriva.com). Red Hat (Enterprise): Red Hat Enterprise Linux is a high-end Linux distribution geared toward businesses with mission-critical needs (www.redhat.com). Fedora: The Fedora Project was created in late 2003, when Red Hat Linux was discontinued. Fedora is a community distribution (fedoraproject.org). SuSE: SuSE was first developed in 1992. It is a popular mainstream Linux distribution and is the only Linux recommended by VMware, Microsoft, and SAP (www.suse.com and www.opensuse.org). KNOPPIX: KNOPPIX is a bootable Live system on CD-ROM or DVD, consisting of a representative collection of GNU/Linux software, automatic hardware detection, and support for many graphics cards, sound cards, and peripheral devices. KNOPPIX can be used for the desktop, educational CD-ROM, as a rescue system, or adapted and used as a platform for commercial software product demos. As it is not necessary to install anything on a hard disk, but can be run entirely from CD-ROM or DVD, it is ideal for demonstrations of Linux (www.knoppix.net or www.knoppix.org). Centos: The CentOS Linux distribution is a stable, predictable, manageable, and reproducible platform derived from the sources of Red Hat Enterprise Linux (RHEL) (centos.org). There are many Web sites and organizations that maintain lists of the most used Linux distributions: distrowatch.com/dwres. php?resource=major and en.wikipedia.org/wiki/Comparison_of_Linux_distributions as well as www.linux.com/directory/Distributions.

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While all distributions contain the Linux kernel, some contain only FLOSS materials, while others additionally contain non-FLOSS components, and the mix of FLOSS and other applications included and the configurations supported vary. The Debian GNU/Linux distribution is one of the few distributions that is committed to including only FLOSS components (as defined by the open source initiative) in its core distribution. Ubuntu, Linux Mint, and MX Linux are generally viewed as the easiest distributions for new users who wish to simply test or gain a general familiarity with Linux (MUO, 2018). It’s even possible to access a Linux environment within a Microsoft Windows machine without installing a new operating system on your computer. MS Windows 10 now includes the Windows Subsystem for Linux. This allows compatibility with Linux-only software. There are other Linux systems such as Slackware Linux, Gentoo Linux, and FreeBSD that require a degree of expertise and familiarity with Linux if they are to be used effectively and productively. openSUSE, Fedora, Debian GNU/Linux, and Mandriva Linux are mid-range distributions in terms of both complexity and ease of use. Google released their version of an open source operating system called Android. It is suited for a wide range of devices from mobile devices such as phones or tablets up to and including personal computers. It may be that your smartphone runs Android. Over 86% of smartphones worldwide use Android (Finley, 2019). The market has many devices that claim full compatibility with Android. Since the manufacturers choose what part of Android to include, it is always a good practice to test any software that you want to use before purchase.

Web Browser and Server: Firefox and Apache While for most people the focus may be on their clientend use of applications, many rely on other, server-side applications, to function. Web browsing is a prime example where both server and client-side applications are needed. Web servers, such as Apache, are responsible for receiving and fulfilling requests from Web browsers. A FLOSS application, the Apache HTTP server, developed for Unix, Windows NT, and other platforms, is still currently the most popular Web server with 38% of the market share followed by the NGINX Web server at 28% and the Microsoft Web servers at 19%. Apache has long dominated the public Internet Web server market ever since it grew to become the number one Web server in 1996 (Wheeler, 2007; NetCraft Ltd., 2019). Apache began development in early 1995 and is an example of a FLOSS project that is maintained by a formal structure, the Apache Software Foundation.

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Firefox (technically Mozilla Firefox) is a FLOSS graphical Web browser, designed for standards compliance, and with a large number of browser features. It derives from the Mozilla Application Suite, and aims to continue Netscape Communicator as an open project and is maintained by the Mozilla Organization and employees of several other companies, as well as contributors from the community. Firefox source code is FLOSS, and is tri-licensed, under the Mozilla Public License (MPL), the GNU GPL, and the GNU Lesser General Public License (LGPL), which permit anyone to view, modify, and/or redistribute the source code, and several publicly released applications have been built on it. As of May 2019, Google Chrome had 62.4%, Safari had 14.56%, Firefox had 5.1%, UC Browser had 4.17%, and Opera had 3.13% of worldwide usage share of Web browsers, Thus the most used browser Google Chrome is an example of FLOSS (Wikipedia, 2019a). This does not mean the Chrome executable is completely FLOSS as it is released by Google as proprietary freeware due to inclusion of Google-specific code. The subset of Chrome that is totally open source is named Chromium (Wikipedia, 2019b)

Word Processing or Integrated Office Suite: Open Office (Office Productivity Suite) While FLOSS products have been strong on the server side, FLOSS desktop applications are relatively new and few. Open Office (strictly OpenOffice.org), which is based on the source code of the formerly proprietary StarOffice, is a FLOSS equivalent of Microsoft Office, with most of its features. It supports the ISO/IEC standard OpenDocument Format (ODF) for data interchange as its default file format, as well as Microsoft Office formats among others. As of November 2009, Open Office supports over 110 languages. It includes a fully featured word processor, spreadsheet, and presentation software. One of the advantages for considering a shift from a Windows desktop environment to Open Office is that Open Office reads most Microsoft Office documents without problems and will save documents to many formats, including Microsoft Word (but not vice versa). This makes the transition relatively painless and Open Office has been used in recent high-profile switches from Windows to Linux. Open Office has versions that will run on Windows, Linux, and other operating systems. (Note that the text for this chapter was originally written using OpenOffice. org Writer, the word processing package within the OpenOffice.org suite.) The word PowerPoint has become almost synonymous with software for making presentations, and is even commonly used as a teaching tool. The OpenOffice.org suite

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82    P art 1 • N ursing I nformatics T echnologies contains a presentation component, called Impress, which produces presentations very similar to PowerPoint; they can be saved and run in OpenOffice format on Windows or Linux desktop environments, or exported as PowerPoint versions.

Some Other FLOSS Applications BIND.  The Berkeley Internet Name Domain (BIND) is a domain name system (DNS) server, or in other words, an Internet naming system. Internet addresses, such as www. google.com or www.openoffice.org, would not function without DNS. These servers take these human-friendly names and convert them into computer-friendly numeric Internet protocol (IP) addresses and vice versa. Without these servers, users would have to memorize numbers such as 74.125.19.104 in order to use a Web site, instead of simply typing www.google.com. The BIND server is a FLOSS program developed and distributed by the University of California at Berkeley. It is licensed under a BSD-style license by the Internet Software Consortium. It runs 95% of all DNS servers including most of the DNS root servers. These servers hold the master record of all domain names on the Internet. Perl.  Practical Extraction and Reporting Language (Perl) is a high-level programming language that is frequently used for creating common gateway interface (CGI) programs. Started in 1987, and now developed as a FLOSS project, it was designed for processing text and derives from the C programming language and many other tools and languages. It was originally developed for Unix and is now available for many platforms. Perl modules and addons are available to do almost anything, leading some to call it the “Swiss Army chain-saw” of programming languages (Raymond, 2003). PHP.  PHP stands for PHP Hypertext Preprocessor. The name is an example of a recursive acronym (the first word of the acronym is also the acronym), a common practice in the FLOSS community for naming applications. PHP is a server-side, HTML-embedded scripting language used to quickly create dynamically generated Web pages. In an HTML document, PHP script (similar syntax to that of Perl or C) is enclosed within special PHP tags. PHP can perform any task any CGI program can, but its strength lies in its compatibility with many types of relational databases. PHP runs on every major operating system, including Unix, Linux, Windows, and Mac OS X, and can interact with all major Web servers. Both GT.M and YottaDB are versions of the same database engine. Its scalability has been proven in the largest real-time core processing systems in production at financial

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institutions worldwide, as well as in large, well-known healthcare institutions, but with a small footprint that scales down to use in small clinics, virtual machines, and software appliances. The internal data model is a NoSQL hierarchical associative memory (i.e., multi-dimensional array) that imposes no restrictions on the data types of the indexes and the content—the application logic can impose any schema, dictionary, or data organization suited to its problem domain. MUMPS (Massachusetts General Hospital Utility Multi Programming System). Both GT.M and YottaDB are based on a compiler for the standard M (also known as MUMPS) language, which is the basis for an open source stack for implementation of the VistA Hospital Information System. LAMP.  The Linux, Apache, MySQL, PHP/Perl/Python (LAMP) architecture has become very popular as a way of affordably deploying reliable, scalable, and secure Web applications (the “P” in LAMP can also stand for either PHP or Perl or Python). MySQL is a multithreaded, multiuser, SQL (Structured Query Language) relational database server, using the GNU GPL. The PHP-MySQL combination is also a cross-platform (i.e., it will run on Windows as well as Linux servers) (Murray & Oyri, 2005). Content Management Systems.  Many FLOSS applications, especially modern content management systems (CMS) that are the basis of many of today’s interactive Web sites, use LAMP. A CMS has a flexible, modular framework that separates the content of a Web site (the text, images, and other content) from the framework of linking the pages together and controlling how the pages appear. In most cases, this is done to make a site easier to maintain than would be the case if it was built exclusively out of flat HTML pages. There are now over 200 FLOSS FLOSS content management systems (see php.opensourcecms.com for an extensive list) designed for developing portals and Web sites with dynamic, fully searchable content. Drupal (drupal.org), for example, is one of the most well-known and widely used CMS and is currently used for the official sites of the White House (www.whitehouse.gov) and the United Nations World Food Programme (www.wfp.org), and has been used for the South African Government for their official 2010 FIFA World Cup Web site (www.sa2010.gov.za). MyOpenSourcematrix, a CMS designed for large organizations, has been used by the UK’s Royal College of Nursing to provide a content and communications portal for its 400,000 members (Squiz UK, 2007). A content management system allows easy administration and moderation at several levels by members of an online community. This gives complete control of compliance with the organization’s policy for published material and provides for greater interactivity and sense

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of ownership by online community members. In addition, the workload relating to publication of material and overall maintenance of the Web site can be spread among many members, rather than having only one Webmaster. This secures frequent updates of content and reduces individual workloads, making the likelihood of member participation greater. The initial user registration and redistribution of passwords and access can be carried out automatically by user requests, while assignment to user groups is made manually by the site administrators or moderators. FLOSS applications are gaining widespread use within education sectors, with one example of a widely used e-learning application being Moodle (www.moodle.org). Moodle is a complete e-learning course management system, or virtual learning environment (VLE), with a modular structure designed to help educators create high-quality, multimedia-based online courses. Moodle is translated into more than 30 languages, and handles thematic or topicbased classes and courses. As Moodle is based in social constructivist pedagogy (moodle.org/doc/?frame=philosophy. html), it also allows the construction of e-learning materials that are based around discussion and interaction, rather than static content (Kaminski, 2005).

FLOSS HEALTHCARE APPLICATIONS It is suggested that in healthcare, as in many other areas, the development of FLOSS may provide much-needed competition to the relatively closed market of commercial, proprietary software (Smith, 2002), and thus encourage innovation. This could lead to lower cost and higher quality systems that are more responsive to changing clinical needs. FLOSS could also solve many of the problems health information systems currently face including lack of interoperability and vendor lock-in, cost, difficulty of record and system maintenance given the rate of change and size of the information needs of the health domain, and lack of support for security, privacy, and consent. This is because FLOSS more closely conforms to standards and its source code is open to inspection and adaptation. A significant motive for supporting the use of FLOSS and open standards in healthcare is that interoperability of health information systems requires the consistent implementation of open standards (Sfakianakis et al., 2007). Open standards, as described by the International Telecommunications Union (ITU), are made available to the general public and developed, approved, and maintained via a collaborative and consensus-driven process (ITU, 2009, Sfakianakis et al., 2007). A key element of the process is that, by being open, there is less risk of being dominated by any single interest group.

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Bowen et al. (2009) summarize a number of advantages that OSS offers when compared with proprietary software, including, but not limited to, the following: (1) ease of modification and or customization, (2) the large developer community and its benefits, (3) increased compliance with open standards, (4) enhanced security, (5) increased likelihood of source code availability in the event of the demise of the vendor or company, (6) easier to adapt for use by healthcare students, and (7) the flexibility of source code to adapt to research efforts. The cost-effectiveness of OSS also lends well to communities or organizations requiring such an approach (e.g., long-term care facilities, assisted living communities, clinics [public health and educational venue clinics], and home care). Yellowlees, Marks, Hogarth, & Turner (2008) are among those who suggest that many current electronic health record (EHR) systems tend to be expensive, inflexible, difficult to maintain, and rarely interoperable across health systems; this is often due to their being proprietary systems. This makes clinicians reluctant to use them, as they are seen as no better than paper-based systems. FLOSS has been very successful in other informationintensive industries, and so is seen as having potential to integrate functional EHR systems into, and across, wider health systems. They believe that interoperable open source EHR systems would have the potential to improve healthcare in the United States, and cite examples from other areas around the world. Currently, there is much interest in interoperability testing of systems, not only between proprietary systems, but also among FLOSS systems, and between FLOSS systems and proprietary systems. Integrating the Healthcare Enterprise (IHE) has developed a range of open source interoperability testing tools, called MESA, KUDU, and its next generation tool GAZELLE, to test healthcare interoperability according to the standards profiled by the IHE in its technical frameworks. The Certification Commission for Health Information Technology (CCHIT) has developed an open source program called Laika to test EHR software for compliance with CCHIT interoperability standards. There are, of course, potential limitations regarding open source EHRs. Technology staff may require education in order to be adept with understanding and supporting open source solutions. Open source efforts are more likely to be underfunded, which impacts not only the ability to upgrade but also support of the software. Another limitation is the perception of open source solutions as the forgotten stepchild of certification (at least in the United States). Only recently (mid-2009) did the CCHIT modify requirements to allow for more than just proprietary EHRs to become certified. Additional barriers include limited

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84    P art 1 • N ursing I nformatics T echnologies interoperability, fuzzy ROI, slower uptake by users than proprietary software, personnel resistance to this change, and, as previously alluded, IT employees unfamiliar with OSS. Other barriers to use of FLOSS for implementation of EHRs or health information systems (HIS) have been identified, including resistance to change among users and IT departments, lack of documentation associated with some FLOSS projects, and language barriers in some countries, in particular due to the documentation around many FLOSS developments being in English, without translation (Bagayoko, Dufour, Chaacho, Bouhaddou, & Fieschi, 2010). In the case study, we will look at one project, probably the largest, most sophisticated, and furthest developed— VistA. Here we will provide a brief overview of examples of some of the other projects currently existing, some of which have been in development for over 15 years. Many share commonalities in trying to develop components of EHRs and several have online demonstration versions available for exploration. A useful summary of the known projects and products has been provided by the AMIA OSWG (Valdes, 2008), while a number of Web sites provide catalogues of known FLOSS developments in health (www.medfloss.org). Examples exist of FLOSS electronic medical records (EMRs), hospital management systems, laboratory information systems, radiology information systems, telemedicine systems, picture archiving and communications systems, and practice management systems (Janamanchi, Katsamakas, Raghupathi, & Gao, 2009). A few examples indicate this range, and more extensive lists and descriptions are available at several Web portals, including www. medfloss.org.

Indivo

Dossia Consortium (Bourgeois, Mandl, Shaw, Flemming, & Nigrin, 2009; Mandl, Simons, Crawford, & Abbett, 2007).

SMART Platforms Project (smartplatforms.org/) The SMART Platforms project is an open-source, developer-friendly application programming interface and its extensible medical data representation and standardsbased clinical vocabularies. SMART allows healthcare clients to make their own customizations, and these apps can then be licensed to run across the installed base. As of 2014, SMART works with Cerner Millennium at Boston Children’s Hospital, running the SMART app BP Centiles, with i2b2 (a clinical discovery system used at over 75 US academic hospitals), with Indivo (an advanced personally controlled health record system), with Mirth Results (a clinical data repository system for HIEs), with OpenMRS (a common framework for medical informatics efforts in developing countries), with Think!Med Clinical (an openEHR-based clinical information system), and with WorldVistA (an open source EMR based on the US Department of Veterans Affairs VistA system).

GNUMed (gnumed.de) The GNUmed project builds free, liberated open source EMR software in multiple languages to assist and improve longitudinal care (specifically in ambulatory settings, i.e., multiprofessional practices and clinics). It is made available at no charge and is capable of running on GNU/Linux, Windows, and Mac OS X. It is developed by a handful of medical doctors and programmers from all over the world.

(indivohealth.org) Indivo is the original personal health platform, enabling an individual to own and manage a complete, secure, digital copy of her health and wellness information. Indivo integrates health information across sites of care and over time. Indivo is free and open source, uses open, unencumbered standards, including those from the SMART Platforms project, and is actively deployed in diverse settings. Indivo is a FLOSS personally controlled health record (PCHR) system, using open standards. A PCHR enables individuals to own and manage a complete, secure, digital copy of their health and wellness information. Indivo integrates health information across sites of care and over time, and is actively deployed in diverse settings, for example, in the Children’s Hospital Boston and the

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OpenMRS (openmrs.org) OpenMRS® is a community-developed, open source enterprise EMR system platform (Wolfe et al., 2006). Of particular interest to this project is supporting efforts to actively build and/or manage health systems in the developing world to address AIDS, tuberculosis, and malaria, which afflict the lives of millions. Their mission is to foster self-sustaining health IT implementations in these environments through peer mentorship, proactive collaboration, and a code base equaling or surpassing any proprietary equivalent. OpenMRS is a multi-institution, nonprofit collaborative led by Regenstrief

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Institute, Inc. (regenstrief.org) and Partners In Health (pih. org), and has been implemented in 20 countries throughout the world ranging from South Africa and Kenya to Haiti, India, and China as well as in the United States. This effort is supported in part by organizations such as the World Health Organization (WHO), the Centers for Disease Control and Prevention (CDC), The Rockefeller Foundation, and the President’s Emergency Plan for AIDS Relief (PEPFAR).

Chapter 5 • Open Source and Free Software 

(www.dhis2.org) and promote the use of FLOSS within EU member states and organizations. While many of the earlier initiatives were projects whose outputs were not further developed, or are no longer available, several of them laid the basis for current initiatives, such as the Open Source Observatory and Repository Portal (www.osor.eu). Among the early EU projects are the following:

•

District Health Information System (“http://www.dhis2.org” \t “_blank” www.dhis2.org) District Health Information Software 2 (DHIS2) is open source software, using the web to provide a health management information system (HMIS). Starting in South Africa in the 1990s, it is one of the world’s largest HMIS platforms, in use by 67 low and middle income developing countries With inclusion of NGO-based programs, DHIS2 is in use in more than 100 countries with a total population of over 2.30 billion people. The core DHIS2 software development is managed by the Health Information Systems Program (HISP) at the University of Oslo (UiO) https://www.mn.uio.no/ifi/ english/research/networks/hisp/ They focus on a participatory approach, supporting the local information flow management and the local health care delivery within developing focusing on selected health facilities, districts, and provinces.

OpenEHR

•

•

(www.openehr.org) The openEHR Foundation is an international, not-forprofit organization working toward the development of interoperable, lifelong EHRs. However, it is also looking to reconceptualize the problems of health records, not in narrow IT-implementation terms, but through an understanding of the social, clinical, and technical challenges of electronic records for healthcare in the information society. The openEHR Foundation was created to enable the development of open specifications, software, and knowledge resources for health information systems, in particular EHR systems. It publishes all its specifications and builds reference implementations as FLOSS. It also develops archetypes and a terminology for use with EHRs.

•

•

European Projects and Initiatives The European Union (EU) has funded research and development programs through the European Commission. There have been many projects and initiatives to explore

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SMARTIE sought to offer a comprehensive collection, or suite, of selected medical software decision tools, ranging from clinical calculators (i.e., risk factor scoring) up to advanced medical decision support tools (i.e., acute abdominal pain diagnosis). openECG sought to consolidate interoperability efforts in computerized electrocardiography at the European and international levels, encouraging the use of standards. The project aimed to promote the consistent use of format and communications standards for computerized ECGs and to pave the way toward developing similar standards for stress ECG, Holter ECG, and real-time monitoring. The openECG portal still provides information on interoperability in digital electrocardiography, and one of the project’s outputs, the Standard Communications Protocol for Computer-Assisted Electrocardiography (SCP-ECG), was approved as an ISO standard, ISO/DIS 11073-91064. Open source medical image analysis (OSMIA) at www.tina-vision.net/projects/osmia.php was designed to provide a FLOSS development environment for medical image analysis research in order to facilitate the free and open exchange of ideas and techniques. PICNIC from Minoru Development was designed to help regional healthcare providers to develop and implement the next generation of secure, userfriendly regional healthcare networks to support new ways of providing health and social care. Free/Libre/Open Source Software: Policy Support (FLOSSpols) (www.flosspols.org) aims to work on three specific tracks: government policy toward FLOSS; gender issues in open source; and the efficiency of open source as a system for collaborative problem solving; however, it should be noted that many of these are R&D projects only and not guaranteed to have any lasting effect or uptake beyond the lifespan of the project. The Open Source Observatory and Repository for European public administrations (www.osor. eu) is a major portal that supports and encourages

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the collaborative development and reuse of publicly financed FLOSS applications developments for use in European public administrations. It is a platform for exchanging information, experiences, and FLOSS-based code. It also promotes and links to the work of national repositories, encouraging the emergence of a pan-European federation of open source software repositories. OSOR.eu is financed by the European Commission through the initiative Interoperable Delivery of European eGovernment Services to Public Administrations, Businesses and Citizens (IDABC) and is supported by European governments at national, regional, and local levels. OSOR.eu indexes and describes a number of health-related initiatives, some directly related to providing healthcare and others with lessons that might be applicable across a number of sectors, including healthcare. Among the health-specific initiatives listed are: ◦◦ Health Atlas Ireland (www.hse.ie/eng/about/ Who/clinical/Health_Intelligence/About_us/): A FLOSS application using geographical information systems (GIS), health related data sets, and statistical software. It received the Irish Prime Minister Public Service Excellence Award because of its capacity to innovate and to improve the quality and the efficiency health services. Health Atlas Ireland is an open source application developed to use a Web environment to add value to existing health data; it also enables controlled access to maps, data, and analyses for service planning and delivery, major incident response, epidemiology, and research to improve the health of patients and the population.

Many hospitals and healthcare institutions in the EU are increasing their use of open source software (OSOR. eu). The University Hospital of Clermont Ferrand began using FLOSS to consolidate data from multiple computer systems in order to improve its invoicing. The Centre Hospitalier Universitaire Tivoli in Louvière, Belgium, in 2006 estimated that about 25% of its software was FLOSS, including enterprise resource planning (ERP) software, e-mail applications, VPN software openVPN, and the K-Pacs FLOSS DICOM viewing software. Additionally, many hospitals are moving their Web sites and portals to FLOSS content management systems, such as Drupal. The St. Antonius hospital in the cities of Utrecht and Nieuwegein (The Netherlands) are migrating to an almost

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completely FLOSS IT environment, with 3,000 desktops running Ubuntu GNU/Linux, and using OpenOffice for office productivity tools. Growing numbers of examples of the use of FLOSS for developing hospital and HISs exist, especially in developing countries.

ORGANIZATIONS AND RESOURCES Over the past 10 years a number of organizations have sought to explore and, where appropriate, advocate the use of FLOSS within health, healthcare, and nursing. While some of these are still active, others have struggled to maintain activity due to having to rely primarily on voluntary efforts, which can be difficult to sustain over long periods. As a result, current efforts in promoting and publicizing FLOSS seem to be based around looser collaborations and less formal groups, often working on developing and maintaining information resources. The American Medical Informatics Association (AMIA), International Medical Informatics Association (IMIA), and the European Federation for Medical Informatics (EFMI) all have working groups dealing with FLOSS who develop position papers, contribute workshops and other activities to conferences, and undertake a variety of other promotional activities. Each of these groups have nurses actively involved. National (in all countries) and international health informatics organizations seem to be late in realizing the need to consider the potential impact of FLOSS. The IMIA established an Open Source Health Informatics Working Group in 2002. It aims to work both within IMIA and through encouraging joint work with other FLOSS organizations to explore issues around the use of FLOSS within healthcare and health informatics. The mission of the AMIA-OSWG (www.amia.org/workinggroup/open-source) is to act as the primary conduit between AMIA and the wider open source community. Its specific activities include providing information regarding the benefits and pitfalls of FLOSS to other AMIA working groups, identifying useful open source projects, and identifying funding sources and providing grant application support to open source projects. The AMIA-OSWG produced a White Paper in late 2008 that not only addressed and summarized many of the issues on definitions and licensing addressed in this chapter but also provided a list of the major FLOSS electronic health and medical record systems in use, primarily in the United States, at the time (Valdes, 2008). The AMIAOSWG identified 12 systems, in use in over 2500 federal government and almost 900 non-federal government

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sites, which among them held over 32 million individual patient records (Samuel & Sujansky, 2008; Valdes, 2008). The IMIA-OSWG, in collaboration with several other organizations, including the AMIA-OSWG, organized a series of think-tank meetings in 2004, in Winchester, UK, and San Francisco, USA. The main purpose of these events was to “identify key issues, opportunities, obstacles, areas of work and research that may be needed, and other relevant aspects, around the potential for using open source software, solutions and approaches within healthcare, and in particular within health informatics, in the UK and Europe” (Murray, 2004, p.4). Threequarters of attendees at the first event (UK, February 2004) described their ideal vision for the future use of software in healthcare as containing at least a significant percentage of FLOSS with nearly one-third of the attendees wanting to see an “entirely open source” use of software in healthcare. Similar findings arose from the US meeting of September 2004, which had broader international participation. The emergence of a situation wherein FLOSS would interface with proprietary software within the healthcare domain was seen to be achievable and desirable. Such use was also likely if the right drivers were put in place and barriers addressed. Participants felt the strongest drivers included the following:

• • • •

Adoption and use of the right standards The development of a FLOSS “killer application” A political mandate toward the use of FLOSS Producing positive case studies comparing financial benefits of FLOSS budget reductions

Participants rated the most important issues why people might use or do use FLOSS within the health domain as quality, stability, and robustness of software and data as well as long-term availability of important health data because of not being “locked up” in proprietary systems that limit interoperability and data migration. They felt the two most important areas for FLOSS activity by IMIA-OSWG and other FLOSS groups were political activity and efforts toward raising awareness among healthcare workers and the wider public. There was a feeling, especially from the US meeting, that lack of interaction between FLOSS groups was a barrier to adoption in healthcare. Discussions at meetings in 2008 and 2009, and in particular at the Special Topic Conference of the European Federation for Medical Informatics (EFMI) held in London in September 2008, and at the Medical Informatics Europe (MIE) 2009 conference held in

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Sarajevo, Bosnia, and Herzegovina, reflected back on progress made since 2004 (Murray et al., 2009). It was concluded that many of the issues identified in 2004 remained relevant, and while some progress had been made in raising awareness within health and nursing communities of the possibilities of FLOSS, the same issues were still relevant. To date, few nursing or Nursing Informatics (NI) organizations have sought to address the implications of FLOSS from a nursing-focused perspective. The first nursing or NI organization to establish a group dealing with FLOSS issues was the Special Interest Group in Nursing Informatics of IMIA (IMIA/NI-SIG). Established in June 2003, the IMIA-NI Open Source Nursing Informatics (OSNI) Working Group has many aims congruent with those of the IMIA-OSWG, but with a focus on identifying and addressing nursing-specific issues and providing a nursing contribution within multiprofessional or multidisciplinary domains. However, it has been difficult to maintain specific nursing-focused activity and many members now work within other groups to provide nursing input. Among providers of resources (Table 5.5), the Medical Free/Libre and Open Source Software Web site (www. medfloss.org) provides a comprehensive and structured overview of FLOSS projects for the healthcare domain; it also offers an open content platform to foster the exchange of ideas, knowledge, and experiences about projects. The International Open Source Network (IOSN), funded by the United Nations Development Programme (UNDP), is a center of excellence for FLOSS in the AsiaPacific region. It is tasked specifically with facilitating and networking FLOSS advocates in the region, so developing countries in the region can achieve rapid and sustained economic and social development by using affordable, yet effective, FLOSS solutions to bridge the digital divide. While its work and case studies have a focus on developing countries, and especially those of the Asia-Pacific region, the materials they produce are of wider value. In particular, they publish a series of FOSS primers, which serve as introductory documents to FLOSS in general as well as covering particular topic areas in greater detail. Their purpose is to raise FLOSS awareness, particularly among policy-makers, practitioners, and educators. While there is not currently a health offering, the general lessons from the primers on education, open standards, FLOSS licensing, and the general introductory primer to FLOSS are useful materials for anyone wishing to explore the issues in greater detail (IOSN, n.d.).

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88    P art 1 • N ursing I nformatics T echnologies   TABLE 5.5   Selected Information and Resource Web Sites http://web.archive.org/web/20130829185049/http:// www.ibiblio.org/pjones/wiki/index.php/Open_Source_Software_for_Public_Health Linux Medical News: The leading news resource for health and medical applications of OSS/FS. The site provides information on events, conferences and activities, software development, and any other issues that contributors feel are relevant to the use of OSS/FS in healthcare (www.linuxmednews.com). Medical Free/Libre and Open Source Software: A comprehensive and structured overview of Free/Libre and Open Source Software (FLOSS) projects for the healthcare domain. The Web-based resource also offers an open content platform to foster the exchange of ideas, knowledge, and experiences about the projects (www.medfloss.org). SourceForge: SourceForge is the largest repository and development site for open source software. Many healthcare applications and other OSS/FS applications use it as the official repository of their latest versions (sourceforge.net). Free and Open Source Software (FOSS) for Health Web Portal: The FOSS for Health Web portal aims to be a dynamic, evolving repository and venue for interaction, sharing, and supporting those who are interested in using OSS/FS in health and e-Health. It is part of the Open Source and Standards PCTA (PANACeA Common Thematic Activities) of the PAN Asian Collaboration for Evidence-based eHealth Adoption and Application (PANACeA) (www.foss-for-health.org/portal). FOSS Primers: The IOSN is producing a series of primers on FOSS. The primers serve as introductory documents to FOSS in general, as well as covering particular topic areas in greater detail. Their purpose is to raise FOSS awareness, particularly among policymakers, practitioners, and educators. The following Web site contains summaries of the primers that have been published or are currently being produced (www.iosn.net/publications/foss-primers). OSS Watch: OSS Watch is an advisory service that provides unbiased advice and guidance on the use, development, and licensing of free and open source software. OSS Watch is funded by the JISC and its services are available free-of-charge for higher and ­further education within the United Kingdom (www.oss-watch.ac.uk). The Open Source Observatory and Repository (OSOR): OSOR is a platform for exchanging information, experiences, and FLOSS-based code for use in public administrations (www.osor.eu). FOSS Open Standards/Government National Open Standards Policies and Initiatives: Many governments all over the world have developed policies and/or initiatives that advocate and favor open source and open standards in order to bring about increased independence from specific vendors and technologies, and at the same time accommodate both FOSS and proprietary software (en.wikibooks.org/wiki/FOSS_Open_Standards/Government_National_Open_Standards_Policies_and_Initiatives). Free and Open Source Software Portal: A gateway to resources related to free software and the open source technology movement (UNESCO, www.unesco.org/new/en/communication-and-information/access-to-knowledge/ free-and-open-source-software-foss). The Top 100 Open Source Software Tools for Medical Professionals: www.ondd.org/the-top-100-open-source-software-toolsfor-medical-professionals http://www.ondd.org/the-top-100-open-source-software-tools-for-medical-professionals Open Source Methods, Tools, and Applications; Open Source Downloads: www.openclinical.org/opensourceDLD.html Medsphere OpenVista Project: sourceforge.net/projects/openvista Open Source Software for Public Health: www.ibiblio.org/pjones/wiki/index.php/Open_Source_Software_for_Public_Health Clearhealth: www.clear-health.com VistA Resources VistA Monograph: www.ehealth.va.gov/VistA_Monograph.asp VistA CPRS Demo: www.ehealth.va.gov/EHEALTH/CPRS_demo.asp VistA Documentation Library: www.va.gov/vdl Latest Version of WorldVistA: worldvista.org/Software_Download A Description of the Historical Development of VistA: WorldVista, worldvista.org/AboutVistA/VistA_History; Hardhats, www. hardhats.org/history/HSTmain.html VistApedia—A Wiki about VistA: (vistapedia.net)

SUMMARY FLOSS has been described as a disruptive paradigm, but one that has the potential to improve not only the delivery of care but also healthcare outcomes (Bagayoko et al., 2010). This chapter provides a necessarily brief introduction to FLOSS. While we have tried to explain the underlying philosophies of the two major camps, only an in-depth

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reading of the explanations emanating from each can help to clarify the differences. Many of the issues we have addressed are in a state of flux; therefore, we cannot give definitive answers or solutions to many of them, as debate and understanding will have moved on. As we have already indicated, detailed exploration of licensing issues is best addressed with the aid of legal counsel. Readers wishing to develop a further

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understanding of FLOSS are recommended to read the International Open Source Network’s (IOSN) Free and Open Source Software (FOSS) Primer (Wong & Sayo, 2004). Additional resources are identified in Table 5.5.

CASE STUDY 5.1: VistA (VETERANS HEALTH INFORMATION SYSTEM AND TECHNOLOGY ARCHITECTURE) This case study focuses on the long-standing HIS of the US Department of Veterans Affairs (VA). As outlined above, VistA is an acronym for Veterans Health Information systems and Technology Architecture. Started in the early 1980’s with efforts at electronic record keeping via the Decentralized Hospital Computer Program (DHCP) information system, the Veterans Health Administration disseminated this system country-wide by the early 1990s. The name VistA dates back to 1996, when the project previously known as the DHCP was renamed to VistA (VistA Monograph, 2019). VistA is widely believed to be the largest integrated HIS in the world. Because VistA was originally developed and maintained by the US Department of VA for use in veterans’ hospitals it is in public domain. Its development was based on the systems software architecture and implementation methodology developed by the US Public Health Service jointly with the National Bureau of Standards. VistA is in active use today at hundreds of healthcare facilities across the country, from small outpatient clinics to large medical centers. It is currently used by all VA facilities throughout countries where there is a US military presence, as well as in non-military clinics with both military and civilian focuses. VistA itself is not strictly open source or free software, it is technically a government-developed software. By copyright law, such software is released to, and remains in, the public domain. Because of this free availability it has been promoted by many FLOSS organizations and individuals with some suggesting it is the “mother of FLOSS healthcare applications” (Tiemann, 2004). Over the years VistA has demonstrated its flexibility by supporting a wide variety of clinical settings and medical delivery systems, both for inpatient and outpatient care facilities ranging from small outpatient-oriented clinics to large medical centers with significant inpatient populations and associated specialties, such as surgical care or dermatology. Hospitals and clinics in many countries depend on it to manage such things as patient records, prescriptions, laboratory results, and other medical information. It contains, among other components, integrated

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hospital management, patient records management, medication administration (via barcoding), and medical imaging systems. There are many versions of the VistA system in use in the US Department of Defense Military Health System as the Composite Health Care System (CHCS), the US Department of Interior’s Indian Health Service as the Resource and Patient Management System (RPMS), and internationally, including, for example, the Berlin Heart Institute of Germany (Deutsches Herzzentrum Berlin, Deutschland), and National Cancer Institute of Cairo University in Egypt. Since 2010, VistA has been used for EHRs in the Kingdom of Jordan (EHS, 2019). It is also used by Oroville Hospital in California, and at Central Region Hospital of the North Carolina Department of Health and Human Services. The use of VistA demonstrates the proposed benefits of FLOSS. The costs associated with the acquisition and support of an HIS can indirectly affect the quality of healthcare provided by limiting the availability of timely and accurate access to electronic patient records. One solution is to lower the cost of acquiring an HIS by using a software stack consisting of open source, free software (FLOSS). Since VistA is in the public domain and available through the US Freedom of Information Act (FOIA), software license fees are not an issue with regard to deployment. Several FLOSS organizations associated with, and deriving from, VistA are WorldVistA (worldvista.org), Medsphere OpenVista (medsphere.com), DSS vxVistA (https://www.dssinc.com/news/2016/6/28/dss-increleases-new-version-of-open-source-ehr-vxvista-tohealthcare-it-community), the Open Source Electronic Health Record Software Alliance (osehra.org), and the VISTA Expertise Network (www.vistaexpertise.net). WorldVistA was formed as a US-based nonprofit organization committed to the continued development and deployment of VistA. It aims to develop and support the global VistA community, through helping to make healthcare IT more affordable and more widely available, both within the United States and internationally. WorldVistA extends and improves VistA for use outside its original setting through such activities as developing packages for pediatrics, obstetrics, and other hospital services not used in veterans’ hospitals. WorldVistA also helps those who choose to adopt VistA to learn, install, and maintain the software. WorldVistA advises adopters of VistA, but does not implement VistA for adopters. Other organizations do provide these services. Historically, running VistA has required adopters to pay licensing fees for the systems on which it runs: the programming environment (Massachusetts General Hospital Utility Multi-Programming System [MUMPS])

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90    P art 1 • N ursing I nformatics T echnologies and the operating system underneath (such as Microsoft Windows or Linux or VMS). WorldVistA eliminated these fees by allowing VistA to run on the GT.M programming environment and the Linux operating system, which are both open source and free. By reducing licensing costs, users may spend their money on medicine, medical professionals, and other resources more likely to directly improve patient care. The WorldVistA project effort also transfers knowledge and expertise and builds long-term relationships between adopters and the rest of the worldwide VistA community. The complete WorldVistA package comprises the following:

• •

GNU/Linux operating system GT.M, an implementation of the Standard M programming system, (M = MUMPS)VistA Information on VistA, and WorldVistA and software downloads are available at a number of Web sites, including the following: ◦◦ https://www.va.gov/vdl/—VistA Documentation Library ◦◦ https://www1.va.gov/vista_monograph/—VistA Monograph ◦◦ https://sourceforge.net/projects/worldvistaehr/—recent WorldVistA EHR ◦◦ http://www.vistapedia.net—community and user created documentation about VistA

A description of the historical development of VistA is available at http://worldvista.org/AboutVistA/ VistA_History. The sharing of veterans’ health information between the US Department of Defense (DoD) and the VA has been a continuing effort since the initial installation of the DoD’s CHCS in the 1980s. A Directorate (VA/DoD Health Information Sharing Directorate) administers this effort between both of the agencies regarding interoperability along with other initiatives related to IT, healthcare, and data sharing. Efforts coordinated and supported by this intermediary organization currently include bidirectional health information interoperability exchange (BHIE), clinical and health information repository efforts (Clinical Data Repository/Health Data Repository [CHDR]) initiated in 2006, transition of active personnel to veteran status via the Federal Health Information Exchange (FHIE) initiative between the DoD and the VA, laboratory data sharing (Laboratory Data Sharing Interoperability [LDSI]) not only between the DoD and the VA but also among commercial laboratory vendors, and increased quality of care for polytrauma patients due to data exchange. The

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Directorate also coordinates report generation for the VA, the DoD, and the Office of Management and Budget (OMB). In April 2010, the VA and DoD expanded their Virtual Lifetime Electronic Record (VLER) program to exchange more types of clinical data. In 2013, the iEHR effort between the VA and DoD was initiated by Congress to promote sharing between the agencies. The VA also provides Web access for its veterans through VistA. These include the HealtheVet project, which provides 24 × 7 Web-based access to VA health services and information; and the Compensation and Pension Records Interchange (CAPRI), which enables veterans service organizations (VSOs) to view only a member’s EHR if necessary to assist the individual with benefit claims and drug refills.

Future Direction of VistA The VistA software is constantly being updated with current technologies and enhancements as the practice of medicine changes. New ways of accessing VistA using data and programming languages are always changing. Of great importance is the current shift to an open source, open standards environment along with development efforts to support and advance VistA. On May 17, 2019, the Department of Veterans Affairs acting VA Secretary Robert Wilkie signed signed a ten-year, $10 billion contract with Cerner Government Services to replace about 25 non-veteran specific applications of VistA’s 170 applications. Infrastructure updates and program management are expected to add another $6 billion to the project’s total cost. The same report stated” The VistA system being replaced will continue to operate until the migration to the Cerner system is complete, a process that could take as long as a decade.” In 2020, VA Secretary Robert Wilkie stated the VA remainscommitted to the process. With two major delays on Feb 12, and on April 6, the software has not replaced VistA in any hospitals. An April 21, 2020 VA news release stated “In light of the COVID-19 pandemic, the U.S. Department of Veterans Affairs (VA) Office of Electronic Health Record Modernization (OEHRM) is currently reassessing and revising implementation timelines for its new electronic health record (EHR) system.” As of the beginning of May 2020, the first roll out of Cerner’s proprietary non-open source software has been delayed until at least July 2020.

Example of Creating Software Software used in health settings rarely is completely usable as originally delivered by vendor, whether the software is

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licensed as open source software, as free software, or as proprietary software. The process of adapting the healthcare software to the needs of a particular institution usually consists of developing add-on modulers, or creating specialized reports that only are needed by a particular institution. These changes are the work of people working as consultants, or as employees of the institution. Just as the original software has a license which specifies how it can be used, the changes also have a license. Nursing Information is necessary to make the changes and make sure it makes sense in the context where it is used. While it is possible that these local changes are in a stand-alone form, as files on a disk, many times they must be changed using tools within the software system itself. Some of these changes involve modifying the default settings for the software. Other changes involve creating new values for settings that do not have a default value. A very common way to change software is to create reports about the information stored in the healthcare computer system. This section will review these processes with specific examples from SQL and VistA’s FileMan.

CASE STUDY 5.2: ORGANIZING DATA IN A HEALTH DATABASE A database is a part of the computer software that performs the function of a paper record’s filing room. In paper files, the information about a patient are stored using standard forms, and in notes. In a health database, a similar model is used. In the place of a paper form, a database will have records. In the place of questions that may be answered on the form, the database records will have fields. Just as a patient’s paper records may have multiple forms that specify standard information, the computer database may have multiple kinds of records that are all tied together with a common reference to a particular patient. A common way to visualize the various information about a health record is to think of it as a spreadsheet, whether one on paper or on a computer. This model has columns for different kinds of information that may be stored usually with a description at the top of the column. Each successive row or entry in the spreadsheet corresponds to information about a particular event or person. The database model identifies the various forms in the paper record with various kinds of spreadsheets. These spreadsheets are called Tables in SQL and Files in VistA FileMan. The questions of each kind of paper form are identified with the descriptions at the top of the columns in the spreadsheet. The various rows of the spreadsheet

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correspond to people, and the answers put into the form are the cells where the rows and columns intersect. This row and column organization is easy to visualize and corresponds to the simple database model of SQL. VistA FileMan builds upon this simple model by allowing cells to be further subdivided into sub-columns and sub-rows. The cells inside these sub-spreadsheets can be further subdivided if needed to accurately reflect the organization of the data. VistA FileMan includes the simple model of SQL, but enhances it to allow more complex medical data to be stored in a more natural way. Medical Data normally has a many to one and relational nature. Any given patient might have multiple appointments, with each appointment having multiple diagnoses. An appointment for a patient would have a single start time for a particular clinic and a particular clinician. Viewed from the perspective of the clinic, it might have multiple appointments occurring at the same time, with multiple clinicians involved. There may be multiple diagnoses for a particular patient and date time interval, but they would all be from a standard list of diagnoses. Each diagnosis may have multiple treatment options, including particular medications and procedures performed.

Datatypes for Fields Just as an electronic spreadsheet may have a format for each of the cells, a database will have requirements for what can be stored in its database elements. Usually this kind of information is called the datatype of the element. Generally SQL datatypes are described with a word like INTEGER or TIMESTAMP. VistA FileMan datatypes are described as FREE TEXT or NUMERIC or SET OF CODES. Each of these datatypes correspond to some restrictions because it makes the organization of the database more predictable and efficient. When information that is put into a database has no limits, such as when typing Progress Notes or Discharge Summaries, there are usually very few ways to organize it.

Indexes and Cross-References To help find particular entries (or rows) in a database record, it is common to index part of the entry in a special cross-reference where some fields (or columns) are stored in a sorted order. When retrieving the record, the database will be searched along this cross-reference for the indexed information, and group together all the records which have the same index. When paper records are stored, the tabs on a folder provide the same function. When a field is cross-referenced, the information can be retrieved faster, and any print processes using that field will work faster.

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Example Database

Comparison Operations Form Simple Conditions

Let us use a simple database that has several fields in each PATIENT record. This example database purposely does not have the relational aspects of a true health database as these complicate things, while gaining little in making the process of creating an example report which might have a software license. The name of the Table in SQL and the name of the File in VistA FileMan for our example database would be simply PATIENT. The first field named PATNAME has a datatype as a VARCHAR(60) in SQL or a FREETEXT field in VistA FileMan. This field would hold the name of the patient, with no extra padding. The PATNAME field will be indexed. The second field named DATEOFBIRTH has a datatype of DATE in SQL or DATE/TIME in VistA FileMan. This field will hold the day that the patient was born. The third field will be AGE which has a datatype of SMALLINT in SQL or NUMBER in VistA FileMan. In SQL, the datatype always has the same lower bound such as between zero and 255, whereas in VistA FileMan, the number range is defined specifically for each field, such as from zero to 120. This field will hold the number of years since the patient was born. In VistA FileMan, this field normally will be a COMPUTED field. The fourth field named GENDER would be a CHARACTER value of ‘M’ or ‘F’ or ‘U’ in SQL, and be a SET OF CODES in VistA FileMan using a mapping of ‘M’ to MALE, ‘F’ to FEMALE and ‘U’ to UNKNOWN.

Each datatype has particular ways of comparing values. How you compare values depends upon what is needed for a particular report. A value may be the name of a field such as AGE or PATIENT NAME from our example database. A value might be a constant like 70 or “SMITH.” Comparison operators use two or more values together to produce a Condition. Conditions can be used to include or to filter out rows or entries from the database, again, depending on what the report needs. A numeric datatype will have operators that allow one to test if the field is larger or smaller than another field or a particular number. A listing of some numeric comparison operators is listed in Table 5.6 for an example. All of these comparison operators don’t work on all system. You must test them on the software system you are using to see which apply. A character based datatype might be a VARCHAR or a CHARACTER or a MEMO field in SQL. The FREE TEXT datatype or the SET OF CODES datatype in VistA FileMan is also a character-based datatype. Character datatypes will have operators that look for particular text. The CONTAINS operator is to check if a particular field has some text within it. The LIKE operator and the MATCHES operator both look for patterns, such as wildcards in text or unchanging text in a particular order. Most datatypes allow the NULL operator to be used to check if a field is empty.

  TABLE 5.6    (Case Study 5.2) Numeric Comparison Operators Combining to Make Condition FirstValue > SecondValue

Condition where first value is greater than second value

FirstValue < SecondValue

Condition where first value is less than second value

FirstValue = SecondValue

Condition where first value is equal to second value

FirstValue SecondValue

Condition where first value is not equal to second value

FirstValue != SecondValue

Condition where first value is not equal to second value

FirstValue '= SecondValue

Condition where first value is not equal to second value

FirstValue SecondValue

Condition where first value is less than or equal to second value

FirstValue '> SecondValue

Condition where first value is less than or equal to second value

FirstValue >= SecondValue

Condition where first value is greater than or equal to second value

FirstValue !< SecondValue

Condition where first value is greater than or equal to second value

FirstValue '< SecondValue

Condition where first value is greater than or equal to second value

BETWEEN (FirstValue, SecondValue, ThirdValue)

Condition where the first value is less than the second value and the second value is less than the third value

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Boolean Operators Make Complex Conditions Finally, these conditions can then be combined together using Boolean logic to make a more complex Condition. This logic allows a report writer enough flexibility to include and exclude conditions made up of more than one field and value. Boolean Logic is named after George Boole who worked on it in the 1800s. He started with just two values, such as TRUE and FALSE, and showed a logical system that combined them together in all 16 possible ways. Later in the century, Charles Pierce was able to prove that using only one of the ways, either using NAND or NOR, comprised a sole sufficient operator. Electronic engineers took advantage of this to simplify the process of creating computer circuits. If there are two conditions, there are 16 ways to combine them together. With three conditions, there are 32 ways to do so. As you add more conditions, the number of possible ways double each time. This is why some people feel Boolean Logic can be so complex. The following discussion will try to make this simpler. The two simplest way to combine values together is to ignore the conditions completely and always produce the same answer. Since there are two possible answers, one operator named Contradiction always gives the value of FALSE, and the other named Tautology always gives the value of TRUE. No one purposely will use these operators, but may accidentally do so. The most common way to use the Contradiction Operator is when you use two conditions, each using a comparison operator, but no value can satisfy both of the comparisons at the same time—such as checking for a field value like greater than 70 and less than 10 or getting the order wrong on BETWEEN so you test if it is greater than your highest value and lower than your lowest value. Similarly the Tautology Operator may be accidentally used if you look for Conditions that are always TRUE. Some of the ways to combine Conditions can do this and produce a useless search because nothing is excluded from your search, or everything is included. The AND operator is used when you have a list of conditions that must all be TRUE to include an entry or row. Usually each of the conditions will test different fields, such as testing GENDER = ‘M’ and at the same time looking for AGE > 70. Since AND requires that both of these succeed for the same entry, it effectively filters out any entries where it fails. You can combine multiple conditions together with AND to create easily understandable search conditions that are targeting very specific subsets of the database. The OR operator is used when you have a list of conditions where any of them may be true for a particular entry

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or row. It is common to have multiple conditions which include the same field name, as you are trying to include as many possible entries as you can. Few conditions try to exclude entries, such as testing GENDER = ‘M’ or alternately GENDER = ‘U’. When you combine multiple conditions together with an OR, it creates a superset of all of the conditions and increases the size of the subset of the database. The NOT operator is used on a condition to negate its meaning. If the condition originally would include particular entries, then using NOT will exclude them. If the condition pares down the results, the negated condition will increase the results. For example, the NAND operator is simply the NOT operator applied to the results of an AND operation. The NOR operator is simply NOT applied to the results of an OR operation. If a particular operation yields a larger subset, NOT will produce the dual smaller subset, and vice versa.

Using Boolean Operators to Form a Query In SQL, queries are created using a specific language. Every query will use the SELECT syntax with various optional parts. Every SELECT query has to include the fields (columns) and the table name of the database and possibly some extra syntax to limit which entries (rows) are included. Finally, the results are ordered so the output fits the desired report. In VistA FileMan, queries involve three parts: the SEARCH conditions, the SORT ranges, and the PRINT output. Individual SEARCH comparisons are first stated, then the conditions are combined together to make a total condition. Then entries are organized by specifying what fields are used to group together, allowing for subtotals, or special sorting orders. After this, the data that needs to be output for the report is specified in the PRINT output.

First Example The simplest SQL query just states what columns are needed from a particular table. i.e.: SELECT column1, FROM table_name ;

column2....columnN

Using our example database this would be SELECT PATNAME, DATEOFBIRTH, AGE, GENDER FROM PATIENT;

Since there are no filtering conditions, every patient in the database will be output. The VistA FileMan query would just use PRINT FILE

ENTRIES

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94    P art 1 • N ursing I nformatics T echnologies OUTPUT FROM WHAT FILE: PATIENT// SORT BY: NAME// START WITH NAME: FIRST// FIRST PRINT FIELD: PATNAME THEN PRINT FIELD: DATEOFBIRTH THEN PRINT FIELD: AGE THEN PRINT FIELD: GENDER THEN PRINT FIELD: ] Heading (S/C): PATIENT LIST// STORE PRINT LOGIC IN TEMPLATE:EXAMPLE Are you adding ‘EXAMPLE’ as a new PRINT TEMPLATE? No// YES (Yes)

For VistA FileMan, every patient in the database will be output, but this also adds the list of output fields as a PRINT Template, so we don’t have to tell VistA FileMan every time what list to use. This saves effort, but also makes the process simpler.

Second Example The next example is to print out the subset of the patients that happen to satisfy a condition that we specify. This will use a more complex SQL syntax: SELECT column1, column2....columnN FROM table WHERE CONDITION;

If our Condition is both the AGE must be greater than 70 and that the PATNAME must equal SMITH, we would write that condition in SQL as SELECT PATNAME, DATEOFBIRTH, AGE, GENDER FROM PATIENT WHERE (AGE > 70) AND (PATNAME = ‘SMITH’) ;

The same query in VistA FileMan would use Select OPTION: SEARCH FILE ENTRIES OUTPUT FROM WHAT FILE: PATIENT// -A- SEARCH FOR PATIENT FIELD: NAME -A- CONDITION: = EQUALS -A- EQUALS: SMITH -B- SEARCH FOR PATIENT FIELD: AGE -B- CONDITION: > GREATER THAN -B- GREATER THAN: 70 -C- SEARCH FOR PATIENT FIELD: IF: A&B “SMITH”

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NAME EQUALS (case-insensitive) and AGE GREATER THAN “70”

OR: STORE RESULTS OF SEARCH IN TEMPLATE: EXAMPLE2 Are you adding ‘EXAMPLE2’ as a new SORT TEMPLATE? No// Y (Yes) DESCRIPTION: 1> SORT BY: NAME// START WITH NAME: FIRST// FIRST PRINT FIELD: [EXAMPLE]

Notice that the VistA FileMan uses a dialog to set up the condition and uses the character & to mean AND. It also automatically asks about OR conditions, but SQL requires you to type the word OR as part of the condition. If you wish to use an OR inside the condition in VistA FileMan, you must use the exclamation point ! to do so. Each software system will have differences like this. This section is using two different systems as examples, but you must learn the specific way of writing queries for the system you end up using. Just as the comparison Table 5.6 shows different ways to say the same comparison, each system you use will require specific study.

SUMMARY OF REPORT WRITING Nursing Informatics involves understanding the information in a computer system and how it is organized. Each clinical information system must be learned independently. A flexible attitude when creating reports is the most successful. Formulating queries using Boolean Logic also is useful beyond reporting results as most Clinical Decision Support systems also require this formal way of specifying rules about patient data. With attention to details and persistence, using formal Boolean Logic is actually the simplest way to organize comparisons and conditions.

UNCITED REFERENCES Murray (2003); Murray et al. (2005); Murray et al. (2002); Netmarketshare (2010); Oyri, Murray (2005); U.S. Department of Veterans Affairs (2008); U.S. Department of Veterans Affairs (2010); WorldVista and Hardhats (2019); Williams (2002); Certification Commission for Health Information Technology (CCHIT). (n.d.); WorldVista (n.d.)

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Chapter 5 • Open Source and Free Software 

Test Questions 1. Describe the scope of Free Software (FS).

A. FS is defined by four freedoms: To use, to study, to redistribute, and to improve. B. FS is more practical and proprietary.

C. FS does not allow it to be modified and/or redistributed.

D. FS makes their source code available to all users. E. All of the above. F. Only A and D.

2. What are the most significant developmental differences between Open Source Software (OSS) and Free Software (FS)? A. Developed in secret and disseminated for a licensing fee

B. Revolutionized way in which programming languages were developed

C. Developed by voluntary efforts of highly skilled programmers D. Coordinated through isolated experts E. All of the above F. Only B and C

3. Which represent the proposed benefits of OSS/FS (FLOSS)?

A. OSS/FS is available as royalty free and low or no cost. B. OSS/FS requires permission and at a high cost. C. OSS/FS lacks security and stability. D. OSS/FS lacks resources. E. Only A.

F. All of the above. 4. Which best represents an issue in the use of OSS/FS (FLOSS)? A. Licensing, Resources, & Business Models

B. Copyright/Intellectual Property, Innovation, & Security/Stability C. Copyright /Intellectual Property, Licensing, & Security/Stability

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5. What represents one of the Three-Step Method for OSS/FS (FLOSS) Decision Making? A. Defines License/Copyright problems.

B. Reviews business model and resources.

C. Undertakes a detailed review of hardware selection. D. Identifies the options for software selection. E. All of the above. F. Only C and D.

6. What is a major advantage of OSS/FS (FLOSS) software licensing?

A. Restricts a person’s right to use OSS/FS software B. Does not allow the users to legally sell the software

C. Seen in terms of permission or rights of the users D. A model that restricts the rights and redistribution conditions E. Only C

F. All of the above 7. What are the challenges traditionally seen in migrating to OSS/FS (FLOSS) software?

A. Lack of Desktop Applications, Not User-Friendly, & File Sharing Difficult B. Not User-Friendly, Copyright Issues, & File Sharing Difficult C. File Sharing Difficult, Lack of Desktop Applications, & Copyright Issues

D. Lack of Desktop Applications, Not User-Friendly, & Licensing Costs E. All of the above F. Only A and C

8. What common applications most nurses use on a daily basis?

A. Web Browser, Email, Power Point, & Operating System B. Web Browser, Email, Word Processing, & Operating System C. Power Point, Word Processing, Email, & Operating System

D. Business Models, Security/Stability, & Innovation

D. Email, Operating System, Power Point, & Web Browser

F. Only A and C

F. Only B

E. All of the above

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E. All of the above

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96    P art 1 • N ursing I nformatics T echnologies 9. What is an acceptable OSS/FS (FLOSS) (FLOSS) application? A. List of fees for Access to LANs, WANs, or Internet B. Model of the structure of the Hardware Architecture

C. Operating system such as one for GNU/Linux D. List of software charges and User Fee Licensing E. All of the above F. Only C

10. What conditions are needed for OSS/FS (FLOSS) to interface with proprietary software within the healthcare domain? A. Adoption and Use of the Right Standards, Development of a FLOSS ‘Killer Application, Political Mandate to Use FLOSS, & Proof of Financial Benefits of FLOSS

B. Proof of Financial Benefits of FLOSS, OSS Watch, Adoption and Use of the Right Standards, & Development of a FLOSS ‘Killer Application

C. Development of a FLOSS ‘Killer Application, Adoption and Use of the Right Standards, Proof of Financial Benefits of FLOSS, & OSS Watch

D. Adoption and Use of the Right Standards, OSS Watch, Proof of Financial Benefits of FLOSS, & Development of a FLOSS ‘Killer Application E. All of the above

F. None of the above

Test Answers 1. Answer: F 2. Answer: F

3. Answer: E 4. Answer: F

5. Answer: E 6. Answer: E 7. Answer: E 8. Answer: E 9. Answer: F

10. Answer: E

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REFERENCES Anderson, H., & Dare, T. (2009). Passport without a visa: Open source software licensing and trademarks. International Free and Open Source Software Law Review, 1(2), 99–110. Retrieved from http://www.ifosslr.org/ifosslr/article/view/11. Retrieved 11 April 2019 Apache Open Office Migration Guide. (2018). Sharing in a mixed application environment. Retrieved from https:// wiki.openoffice.org/wiki/Documentation/UserGuide/ Migration_Guide/Sharing_Files. Retrieved 11 April 2019 Bagayoko, C-O., Dufour, J-C., Chaacho, S., Bouhaddou, O., & Fieschi, M. (2010). Open source challenges for hospital information system (HIS) in developing countries: A pilot project in Mali. BMC Medical Informatics and Decision Making, 10(22). doi:10.1186/1472-6947-10-22. Retrieved from https://bmcmedinformdecismak.biomedcentral. com/articles/10.1186/1472-6947-10-22. Retrieved 11 April 2019 Barratta, N. C. (2014, October 31). How to train your doctor … to use open source. Retrieved from https://opensource. com/health/14/10/hospitals-save-using-open-source. Retrieved 11 April 2019 Bourgeois, F.C., Mandl, K.D., Shaw, D., Flemming, D., & Nigrin, D.J. (2009). AMIA Annual Symposium Poceeding, (2009): 2009:65-69. Published online 2009, Nov.14. PMCID: PMC2815447. Acessed, May 20, 2019. Bowen, S., Valdes, I., Hoyt, R., Glenn, L., McCormick, D., & Gonzalez, X. (2009). Open-source electronic Health records: Policy implications. In Open Source EHR public policy wiki. Retrieved from https://www.open-emr.org/wiki/index.php/ Open_Source_EHR_Public_Policy. Bruggink, M. (2003). Open source in africa: Towards informed decision-making. The Hague, The Netherlands: International Institute for Communication and Development (IICD). Retrieved from https://core.ac.uk/ download/pdf/48027535.pdf. Certification Commission for Health Information Technology (CCHIT). (n.d.). Project Laika. Retrieved from http://laika.sourceforge.net/. Cox, A. (1999). The risks of closed source computing. Retrieved from http://www.ibiblio.org/oswg/oswgnightly/oswg/en_US.ISO_8859-1/articles/alan-cox/risks/ risks-closed-source/index.html. Directive 2009/24/EC. (2009). Directive 20009/24/EC of the European Parliament and of the council of 23 April 2009 on the legal protection of computer programs. Retrieved from https://eur-lex.europa.eu/LexUriServ/LexUriServ. do?uri=OJ:L:2009:111:0016:0022:EN:PDF. Dravis, P. (2003). Open source software: Perspectives for development. Washington, DC: Global Information and Communication Technologies Department, The World Bank. Retrieved from http://www.infodev.org/en/ Publication.21.html.

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European Communities. (2003). The IDA open source migration guidelines. Morden, Surrey: Netproject Ltd. and Interchange of Data between Administrations, European Commission. Retrieved from http://ec.europa.eu/idabc/ en/document/2623/5585. European Communities. (2004). e-Europe Action Plan 2005. Brussels: European Commission, DirectorateGeneral Information Society. Retrieved from http:// europa.eu/legislation_summaries/information_society/ l24226_en.htm. European Communities. (2005). i2010: A European information society for growth and employment. Brussels: European Commission, Directorate-General Information Society. Retrieved from http://www.epractice.eu/ node/281014. EHS. (2019). FAQ Electronic Health Solutions. Retrieved from https://ehs.com.jo/faq. Finley, K. (2019). The WIRED guide to open source software. Retrieved from https://www.wired.com/story/ wired-guide-open-source-software/. Free Software Foundation (FSF). (2007). GNU general public license. Version 3, 29 June 2007. Boston, MA: Free Software Foundation. Retrieved from http://www.gnu. org/licenses/gpl.html. Free Software Foundation (FSF). (2010a). Why ‘free software’ is better than ‘open source’. Boston, MA: Free Software Foundation. Retrieved from http://www.gnu.org/philosophy/free-software-for-freedom.html. Free Software Foundation (FSF). (2010b). The free software definition. Version 1.92. Boston, MA: Free Software Foundation. Retrieved from http://www.gnu.org/philosophy/free-sw.html. Free Software Foundation (FSF). (2010c). What is copyleft? Boston, MA: Free Software Foundation. Retrieved from http://www.gnu.org/copyleft/copyleft.html. GNU Operating System. (2019). What is free software ? GNU Operating System. Retrieved from https://www.gnu.org/ philosophy/free-sw.en.html. Goetz, T. (2003). Open source everywhere. WIRED, 11(11), 158–167, 208–211. Retrieved from http://www.wired. com/wired/archive/11.11/opensource.html. Goulde, M., & Brown, E. (2006). Open source software: A primer for healthcare leaders. California Healthcare Foundation/Forrester Research. Retrieved from http://www.chcf.org/publications/2006/03/ open-source-software-a-primer-for-health-care-leaders. Guidra. (2019). Ghidra is a software reverse engineering (SRE) framework. Retrieved from https://www.nsa. gov/resources/everyone/ghidra/ https://ghidra-sre. org/ and https://github.com/NationalSecurityAgency/ ghidra. International Institute of Infonomics. (2005). Free/libre and open source software: Survey and study: FLOSS final report. University of Maastricht, The Netherlands: International Institute of Infonomics. Retrieved from http://flossproject.merit.unu.edu/.

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98    P art 1 • N ursing I nformatics T echnologies Murray, P. J., Oyri, K., & Wright, G. (2005). Osni.info—Using open source tools to build an international community of nurse informaticians. Revista Cubana de Informatica Medica, 2(5). Retrieved from http://www.cecam.sld.cu/ pages/rcim/revista_8/articulo_htm/osni_info.htm. Murray, P., Shaw, N., & Wright, G. (2002). Open source and health informatics: Taking forward the discussions. British Journal of Healthcare Computing and Information Management, 19(5), 14. Murray, P. J., Wright, G., Karopka, T., Betts, H., & Orel, A. (2009). Open source and healthcare in europe—Time to put leading edge ideas into practice. In: K. P. Adlassnig, B. Blobel, J. Mantas, & I. Masic (Eds.), Medical informatics in a united and healthy europe, proceedings of MIE2009 (pp. 963–967). Amsterdam: IOS Press. Retrieved from http://person.hst.aau.dk/ska/MIE2009/papers/ MIE2009p0963.pdf. Netcraft Ltd. (2019). February 2019 Web server survey. Retrieved from https://news.netcraft.com/ archives/2019/02/28/february-2019-web-server-survey. html. Netmarketshare. (2010). Browser market share. Retrieved from http://marketshare.hitslink.com/browser-marketshare.aspx?qprid=0&qptimeframe=M&qpsp=136&q pnp=2. Office of the e-Envoy. (2002). Open source software: Use within uk government, version 1. London: Office of the e-Envoy, e-Government Unit. Retrieved from http:// archive.cabinetoffice.gov.uk/e-envoy/frameworks-osspolicy/$file/oss-policy.pdf. Open Source Initiative (OSI). (n.d.). The open source definition, version 1.9. Retrieved from http://www.opensource. org/docs/osd. Openforum Europe Ltd. (2008). The importance of open standards in interoperability (OFE onepage brief no.1 (31.10.08.)). Retrieved from http://www.openforumeurope.org/library/onepage-briefs/ofe-open-standardsonepage-2008.pdf. Oyri, K., & Murray, P. J. (2005). Osni.info—Using free/ libre/open source software to build a virtual international community for open source nursing informatics. International Journal of medical Informatics, 74, 937–945. Peeling, N., & Satchell, J. (2001). Analysis of the impact of open source software. Farnborough: QinetiQ Ltd. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/dow nload?doi=10.1.1.115.8510&rep=rep1&type=pdf. President’s Information Technology Advisory Panel (PITAC). (2000). Developing open source software to advance high end computing. Arlington, VA: National Coordination Office for Computing, Information, and Communications. Retrieved from http://www.itrd.gov/ pubs/pitac/pres-oss-11sep00.pdf. Raymond, E. S. (2001). The cathedral and the bazaar: Musings on Linux and open source by an accidental revolutionary (Rev. ed.). Sebastopol, CA: O’Reilly and Associates.

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6 Data and Data Processing Irene Joos / Cristina Robles Bahm / Ramona Nelson

• OBJECTIVES 1. Describe the data-related implications for data and database systems within the data to wisdom continuum. 2. Explain database models. 3. Describe the purpose, structures, and functions of database management systems (DBMSs). 4. Describe generating, storing, curating, retrieving, and interpreting data and related issues. 5. Explore concepts and issues related to data warehouses, data marts, data stores, big data, dashboards, and data analytics. 6. Explain knowledge discovery in databases (KDD) including data mining, data analytics, and benchmarking and their relationship to evidence-based practice and value-based patient centric care.

• KEY WORDS Big data Curating Dashboard Data Database Data analytics Data lake Data mining Data warehouse Information Knowledge Knowledge discovery in databases (KDD) Wisdom

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INTRODUCTION In 2012, the White House released a document titled FACT SHEET: Big Data Across the Federal Government that lists big data projects that the Federal Government has undertaken (The White House, 2012). This document described initiatives in a wide range of government agencies from the Department of Veterans Administration, Department of Health and Human Services, Food and Drug Administration, and the National Institutes of Health. There are also a number of other government agencies such as the Department of Defense, Homeland Security, and the Office of Basic Energy Sciences with big data projects that directly or indirectly impact the healthcare community. These projects demonstrate that the Federal Government is using data and especially the big data revolution to advance scientific discovery and innovation in a number of areas including the delivery of quality healthcare and personalized healthcare. Recently the Health Resources and Services Administration (HRSA) opened a Data Web site (https://data.hrsa.gov) “dedicated to making high value health data more accessible to entrepreneurs, researchers, and policy makers in the hopes of better health outcomes for all” (US Department of Health and Human Services, 2018, para 1). This site provides data sets for areas such as maternity health, HIV, transplants, as primary care. It provides interactivity where one can interact with the site through query tools, interactive maps, dashboards, and a few more (Marcus, 2018). There are also several health IT legislative acts directly impacting data, data processing, and data management in healthcare. These generally deal with data security, privacy, transmission, access, data exchanges, and interoperability (Office of National Coordinator for Health Information Technology, 2019). A list of the key legislative initiatives includes the following:

• •

21st Century Cures Act (Cures Act)

•

The Health Information Technology for Economic and Clinical Health (HITECH) Act of 2009

• • •

The Medicare Access and CHIP Reauthorization Act of 2015 (MACRA)

Section 618 of the Food and Drug Administration Safety and Innovation Act (FDASIA) of 2012 The Health Insurance Portability and Accountability Act (HIPAA) of 1996 Affordable Care Act of 2010

Additional information describing how each of these laws directly impacting data, data processing, and data management in healthcare can be accessed at https:/ www.

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h e a l th i t . g o v / to p i c / l aw s - re g u l at i o n - a n d - p o l i c y / health-it-legislation. In modern healthcare, the process of moving from data collection to implementing and evaluating the care provided to individuals, families, and communities is highly dependent on automated database systems and the ability of nurses to effectively use sites such as the HRSA’s Data Web site. The goal of healthcare and the movement to big data and data analytics is to drive quality care at lower costs through reducing overutilization of services, improving coding and billing practices, empowering patients, measuring trends, predicting outcomes, and examining how improved workflow and productivity can influence quality outcomes (Barlow, 2013). This chapter introduces the nurse to basic concepts, theories, models, and issues necessary to understand the effective use of automated database systems and to engagement in dialog regarding data analytics, benchmarks, dashboards, and outcomes.

THE NELSON DATA TO WISDOM CONTINUUM The Data, Information, Knowledge and Wisdom Model (Nelson D-W) depicting the megastructures and concepts underlying the practice of nursing informatics was included for the first time in the 2008 American Nurses Association (ANA) Scope and Standards of Practice for Nursing Informatics (American Nurses Association, 2008). In this document the model was used to frame the scope of practice for nursing informatics. This change meant that the functionality of a computer and the types of applications processed by a computer no longer defined the scope of practice for nursing informatics. Rather the goals of nursing and nurse–computer interactions in achieving these goals defined the scope of practice. In other words, technology does not define the practice, rather the practitioners’ use of technology to meet the goals of nursing care defines the practice. The first version of the model was published in 1989 and included only a brief definition of the concepts (Nelson & Joos, 1989). Since that initial publication there have been three additional versions of the model published. Each revision of the model attempted to better illustrate the overlapping nature of the four concepts of data, information, knowledge, and wisdom and the complex interaction between and within each of these four concepts as well as the environment (Nelson, 2018; Ronquillo, Currie, & Rodney, 2016). The Nelson data to wisdom continuum moves from data to information to knowledge to wisdom with

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Wisdom Understanding, applying, integrating service with compassion

Increasing complexity

Constant flux Knowledge Interpreting, integrating, understanding Information Organizing, interpreting

Data Naming, collecting, and organizing

Increasing interactions and interrelationships

•  FIGURE 6.1.  The Nelson Data to Wisdom Continuum. Revised Data Information Knowledge Wisdom (DIKW) Model-2013 Version (Copyright © 2013 Ramona Nelson, Ramona Nelson Consulting. All rights reserved. Reprinted with permission.) constant interaction within and across these concepts as well as the environment (Joos, Nelson, & Smith, 2014; Nelson, 2018). As shown in Fig. 6.1 data are raw, uninterrupted facts without meaning. For example, the following series of numbers are data, with no meaning: 98, 116, 58, 68, 18. Reordered and labeled as vital signs they have meaning and now represent information: temperature 98.0, pulse 58, respirations 18, and blood pressure116/68. These data provide information on a person’s basic condition. Using the nurse’s knowledge this information is then interpreted. The nurse fits these data into a pattern of prior knowledge about vital signs. For example, if the nurse records these vital signs as part of a physical for a high school athlete, they are in the normal range; however, if these same numbers were part of an assessment on an elderly patient with congestive heart failure, the low pulse and blood pressure could suggest a problem. Context and pattern knowledge allow the nurse to understand the meaning and importance of the data and to make decisions about nursing actions with regard to the information. While data by themselves are meaningless,

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information and knowledge by definition are meaningful. When the nurse uses knowledge to make appropriate decisions and acts on those decisions the nurse exhibits wisdom. In other words, the nurse demonstrates wisdom when the nurse synthesizes and appropriately uses a variety of knowledge types within nursing actions to meet human needs. To place data in context to allow production information, one must process the data. This means one must label or code and organize the data so that one can identify patterns and relationships between the data thereby producing information. When the user understands and interprets the patterns and relationships in the data, knowledge results. Finally, the user applies the knowledge as the basis for making clinical judgments and decisions and choosing nursing actions for implementation. The “data to information to knowledge to wisdom” progression is predicated on the existence of accurate, pertinent, and properly collected and organized data. This means the data must be generated, stored, curated, retrieved, interpreted, and used.

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GENERATING DATA Data Definition—Context Data is “a fact represented as an item or event out of context” (Mullins, 2013, p. 686). Data alone do not provide insights. As noted in the previous section, without context it is difficult to make judgments on data alone. It is because of this that data are presented here as a collection of data processes for the storage, curation, retrieval, and interpretation of data with the end goal being to gain wisdom.

Data States When discussing digital data, it is important to discuss the three states of data—data at rest, data in motion, and data in use (Rouse & Fitzgibbons, 2019). Data states can change quickly and often so it is important to understand these states to ensure that sensitive information is secure. This is especially true in businesses such as healthcare, banking, or businesses with strong compliance requirements. Data at rest generally refer to data on storage devices such as a removable one such as a USB thumb drive, a hard drive, a file server, a cloud sever, or offsite backup servers. This is archived data that rarely change. Patient’s past medical records data are considered data at rest. In today’s cybercrime world, it is important to protect these data from unauthorized access and use. These data are subject to security protocols to protect the confidential nature of these data. Data in use refer to data that the information system is currently updating, accessing, reading, or processing. This is its most vulnerable state as it becomes open to access or change by others. Some of these data may contain sensitive data-like social security numbers, birth dates, health insurance numbers, results of diagnostic tests, and so forth. One can attempt to secure these data in use through passwords and user IDs, but these are only as secure as the person’s ability to keep that information private, and the nature of the encryption technology used. Data in motion are data moving between applications, between locations within a computer system (RAM to hard drive, files are moved or copied from one folder to another) , over the network, or over the Internet. Data in motion are an increasing concern in healthcare because streaming data are now available from sensors, monitoring devices, mobile devices, and so forth. Monitoring activities of patients in their home places these data at risk as the data move from the source to the destination database. Increasingly, healthcare providers require access to data at the point of care through mobile devices. It is important that one encrypts these data before moving and while moving

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to these devices. While data in motion entail security risks, they also provide opportunities that we never imagined. For example, monitoring patients in real time in their homes can lead to improved patient care and compliance.

Data Sources—Including Patient-Generated Data and Population Health Data Data have always been important part of healthcare. Before digitization, handwritten nurses’ and doctors’ notes, charts, and drawings provided insight when making decisions about health and health trends. Dr. John Snow plotted data to create a map of the Cholera epidemic in 1854 that showed that most of the sicknesses were concentrated around a specific pump (Johnson, 2007). The advent of computer technology powerful enough to store and analyze data has changed the way that we gather, curate, analyze, and present data in order to make the best decisions about patient health. People and systems generate data in modern healthcare in a number of ways. From medical imagery to devices such as the Fitbit that use the Internet of Things (IoT) to patient portals and population health data, modern health care professionals have the ability to access patient data from many sources. Table 6.1 lists some examples of data sources (Fry & Mukherjee, 2018; Raghupathi & Raghupathi, 2014).

Data Input Operations Since data come from a variety of sources and devices, it is important to note that one of the most important aspects of data processing is to carefully define the healthcare processes that relate to the input of data. For example,

  TABLE 6.1    Examples of Patient Data Sources Lab Tests

Medical Images

Patient Portals

Genetic Profiles

Medical Claims

Prescription Information

Internet of Things Devices— like Fitbit

Physician’s Handwritten Notes

Electronic Medical Records

Emergency Care Data

Fingerprints

Handwriting Samples

Pulse Readings

Blood Pressure Readings

Socioeconomic

Sensors

Social Media

And many more …

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manually entered data especially in an emergency situation are at much higher risk of random data entry errors. This is the reason there are usually clear step-by-step procedures within healthcare for data entry. Due to the variety of data sources and the nature of these sources, these data are increasingly unstructured. Data input operations, both technical and non-technical, are important because they ensure that the data going into the system are explicitly defined. Reaching a consensus and then communicating that consensus to interested parties is of crucial importance to data systems. Clearly defined definitions become even more of a challenge when dealing with unstructured data. This challenge is the primary driving force for the development of standard languages and codes in healthcare. According to Kemp, “It is this capturing of ever-greater volume, velocity and variety of data that, if harnessed effectively, provides the organization with its Big Data opportunity” (Kemp, 2014, p. 23)

Volume  When speaking about the volume of big data, this means the amount of data created on a given day. It is estimated that 2.5 Quintillion bytes of data are being created each day.

Big Data

Veracity  One of the potential pitfalls of relying on big data is that the veracity of the data is often not verified. As will be discussed in the next section, massive amounts of data are often being collected, but these data are not being cleaned or curated over time.

The term Big Data has gained increasing recognition over the last decade. For several decades, nurses have collected and stored data, but the ability to analyze or “do” anything with data has not come to fruition until recently. But how much is “big” exactly? Table 6.2 summarizes different sizes and examples. It is estimated that patients generate about 80 MB of data per year and that healthcare data is the source of 30% of the world’s data production (Huesch & Mosher, 2017). The industry often defines Big Data in terms of the 4 Vs coined by IBM. They are (1) Volume, (2), Variety, (3) Velocity, and (4) Veracity (IBM, 2018). More recently two more Vs were added—Value and Variability (AndreuPerez, Poon, Merrifield, Wong, & Yang, 2015; Rouse, 2018).

Variety  A second aspect of big data is the variety of data being produced and combined in order to gain insights. In terms of healthcare, this variety of data could be handwritten doctor’s notes that have been digitized, lab results, medical imaging, social media posts, etc. Velocity  The third aspect of big data as defined by IBM is the velocity of data. In short, the velocity aspect of big data describes the trend toward gathering data from sensors or other real-time data sources, such as Fitbits, that are streaming information directly into our data repository.

Value  The fifth aspect of big data is clinically relevant data that bring value to both the patient and healthcare systems. The value of big data is that it can lead to valuebased patient centric care and reduced costs. Variability  Variability addresses the extent and speed that the structure of the data are changing as well as the frequency of the change. In healthcare, seasonal variations in flu strains and outbreaks of epidemics demonstrate the variability of illnesses.

  TABLE 6.2    Different Sizes of Data

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Size

Abbreviation

How Many Bytes?

Example

1 Gigabyte

GB

1024 MB

4.7 GB is the capacity of a DVD disc (Gavin, 2018)

1 Terabyte

TB

1024 GB

1 TB is 2000 five-minute songs (Gavin, 2018)

1 Petabyte

PB

1024 TB

1 PB is about 5 billion text documents with a single page of text data (Gavin, 2018)

1 Exabyte

EB

1024 PB

If we wrote every word ever spoken by mankind in a text document it would be about 5 EB (Gavin, 2018)

1 Zettabyte

ZB

1024 EB

1 ZB is 1 billion TB (Barnett, 2016)

1 Yottabyte

YB

1024 ZB

To hold a yottabyte you would need a million datacenters the size of Delaware and Rhode Island combined. At the time of publication this is still a theoretical amount of data (Jackson, 2011)

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DATA REPOSITORIES (STORING DATA) Database Management Systems A database by definition is an organized collection of data. A database management system (DBMS) is software that contains the database as well as a collection or set of programs for accessing and processing these data in the database thereby identifying relationships between the data. It is important to realize that different databases can manage the same database. A common example of this in healthcare are the many different library-based DBMSs used to access the data in the MEDLINE database. Another obvious example is the variety of electronic health record (EHR) systems that different vendors of healthcare institutions use to manage patient data. Advantages of Database Management Systems  The main advantage of a DBMS is that it imposes a structure onto the data that allows interaction between the end user and the data. In general, a DBMS allows the storage, curation, and retrieval necessary to turn data without context into data that can be used to generate information and knowledge useful in making wise patient care decisions. The two main components of a DBMS are a “front-end” which provides an application in which a user can view, manipulate, and interpret data and a “back-end” which is where the data area stored. Figure 6.2 shows this relationship. One thing to notice is that data flow between both the front end and the back end. This DBMS structure includes the ability to store data in a central repository as well as the ability to manage the data in a central location thereby reducing data redundancy, increasing data consistency, and improving access to data (Mullins, 2013). Data redundancy occurs when one stores the same data in the database more than once or stores it in more than one interrelated database. In healthcare there are many examples of data redundancy. Patients may be

working with several physicians all of whom may store their patient records in their own database that is not accessible by other healthcare providers or healthcare institutions, thereby requiring the patient to either provide that information again or obtain their records from the other doctor or facility. The patient’s active medication list may be in both the electronic medical record that the primary provider maintains, in a pharmacy that fills the medication prescriptions, and in the electronic record at a healthcare institution. A well-designed automated database links these records and updates them in one place, and then allows users access to it from this single location regardless of the location of the end user. Data inconsistency results as each user working with different databases updates or changes the data. For example, when a doctor admits a patient to a hospital, different caregivers will ask the patient to identify medications he or she is taking at home. Sometimes the patient will list only prescription medications; other times the patient will include over-the-counter drugs the patient takes on a routine basis. Sometimes the patient will forget to include a medication. If caregivers record these different lists in different sections of the medical record, inconsistency occurs. In a well-designed integrated automated database, each caregiver is working with the same list each time data are reviewed. An additional problem occurs if one uses different terms for the same data. For example, sometimes one might use a generic name while other times one might use the brand name for that drug. This is why standards such as standard languages (i.e., SNOMED) are key to the design of EHRs. An automated database design that uses recognized standards as well as consistent input and access to data is imperative to creating databases necessary for the efficient and effective delivery of quality healthcare. Client-Server Configuration  Because a DBMS is a software product that allows you to structure and organize your data, there are several organizational systems that developers have developed. Three things to consider when evaluating these systems are as follows: what does the

Front End — Provides the view of the database to the user

Back End — The database itself

•  FIGURE 6.2.  The Front End and the Back End of a DBMS.

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Chapter 6 • Data and Data Processing    107

front end look like, what does the back end look like, and where is the data stored? Most modern DBMSs utilize the client-server model. In this scenario the client contains the front end and talks to the server which houses the data in the back end. The client and the server are often on different computers with the database residing on the server.

•

•

Cloud vs. In-house: One of the biggest developments in the recent past is cloud computing. In a cloud-hosted DBMS the back end is accessed through the Internet, while in an in-house hosted system the server that houses the database is on site. Distributed vs. Centralized: One of the decisions that needs to be made is whether the database is going to be distributed or centralized. A centralized system is one where there is a single, central computer that hosts a database and the DBMS. Many hospitals today are examples of this type of system. The hospital is the “hub” and hosts the system where many users on the network access this database. A distributed system is one where there are multiple database files located at different sites. The main difference between these two options is one of control. In a centralized system, there is a central control mechanism. Conversely, in a distributed system there is no centralized control structure. With the changing direction of healthcare to keeping the patient out of the hospital by monitoring them at home, the digitization of all patient records, patient portals, and so forth, there is a shift to a more distributed system or decentralized system (Wiler, Harish, & Zane, 2017).

Structure of a DBMS  In general, a DBMS consists of data that designers structure into tables and join by relationships. Each table consists of attributes and data points associated to those attributes. Table 6.3 shows a sample of a table. The table is named tblPatientInformation and

shows the information for four patients. For this table, the attributes would be PatientID, PatientFirstname, PatientLastName, PatientAge, and PatientInsurance. Developers will assign these attributes a data type like integer, real, character, string, Boolean, etc. Data types are important as they exert some controls for preventing data entry errors. Relational Database Models  The Relational Database Model is still the most popular form of DBMS, but ­Non-Relational Databases (e.g. MpSQ) are on the rise. In the Relational Database Model, tables are related to each other through a system of keys. Each table has a p ­ rimary key which allows the system to request one record at a time. Tables can be combined in such a way to allow the system to generate reports based on all of tables. The main features of this type of a system are tables, attributes, and keys where attributes are the columns in the tables and keys are what allows us to find one record in the table. The functions they provide include creating, updating, or changing data, deleting data, and querying generally by means of Structured Query Language (SQL) statements. Examples of widely used RDBMS include Oracle, MySQL, Microsoft SQL Server, and DB2. NoSQL Database Models  NoSQL is an agile system that easily processes unstructured data and semi-structured data. It is cloud-friendly and a new way of thinking about databases. NoSQL doesn’t adhere to traditional RDMS structure, has a rich query language, and is easily scalable (MongoDB, 2019). NoSQL includes a range of different database technologies that address the growing need for processing different data types such as unstructured and semi-structure. In a NoSQL Database Model there aren’t traditional primary keys in the system, but rather key-value stores (Mullins, 2013). This can be incredibly useful in Big Data systems where searching through all keys in the database application may take too long.

  TABLE 6.3    Sample Database Table for Patient Information tblPatientInformation

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PatientID

PatientFirstName

PatientLastName

PatientAge

PatientInsurance

1234

John

Smith

32

Highmark

2345

Jacob

Barry

24

UPMC

7812

Micah

Thomas

45

Etna

4587

Lynda

Roberts

72

Coventry

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  TABLE 6.4    Comparison between Relational Data and NoSQL Models Relational Data Model

NoSQL Modes

Model type

Relational

Non-relational

Data Supported

Structured

Unstructured or semi-structured

General Retrieval Speed

Slow

Fast

Reliability

High

Low

Consistency

High

Low

Storage Capacity

Small to Large data sets

Optimized for extremely large sets (big data)

Support

Widely available

Community based, not as easily found

Analytics

Several “out of the box” programs for analytics

Ability to perform analytics often requires technical skills

Examples

SQL Server, Oracle, MySQL, DB2, PostgreSQL

MongoDB, Amazon Firebase, Cassandra, HBase, CouchDB

Entity Relationship Model  In an Entity Relationship (ER) Model the database features the organization of the data around entities (Mullins, 2013). It is a flow chart on how entities—people, objects, and concepts—relate to each other. ER models use symbols and connecting lines to depict interconnectedness entities, their relationships, and attributes. The ER Model is closely tied to the Relational Database Model and often serves as the underlying organization for the tables and relationships. Entities are usually the participants in a system.

While a DBMS provides a structure to data, a data warehouse provides specificity. Many organizations have developed specific systems to meet their needs: these are data warehouses. By definition, data warehousing is “the process of extracting, integrating, transforming, and cleansing data and storing it in a consolidated database” (Mullins, 2013, p. 638).

Although new data may be added to the data warehouse, the data that already exist in the system provide an archive of information to be used by an organization. The development of a data warehouse requires a great deal of time, energy, and money. An organization’s decision to develop a data warehouse is based on several goals and purposes. A warehouse no longer requires healthcare providers to access the lab reporting system to see lab work and use a different application with a different interface to view radiology results. When users are viewing several different applications there are several different “versions of the truth.” These can result from looking at the database at different times as well as the use of different definitions. For example, the payroll database may show a different number of nurses on staff than the automated staffing system. That is because the payroll database would include nurses in administrative positions; however, the staffing system may only include the nurses that you assign to patient care. The developer makes these types of decisions in building the warehouse to provide a more consistent approach to making decisions based on the data. A data warehouse makes it possible to separate the analytical and operational processing. With this separation one provides an architectural design for the data warehouse that supports decisional information needs. The user can slice and dice the data from different angles and at different levels of detail.

Purpose of a Data Warehouse  The purpose of a data warehouse is to provide a place to store multiple forms of data in a lightly summarized way. Once the data is generated, cleaned, and stored in a data warehouse it is not modified.

Function of a Data Warehouse  The function of the data warehouse is to serve as a central information repository. In true data warehouse situations, once the data are curated and stored in the data warehouse, there is no

Graph-Oriented Object Data Model  In a G ­ raph-Oriented Object Data (GOOD) Model the underlying organization is not a system of tables and relationships, but rather a graph representation of the objects in the database. This organization allows different kinds of relationships among the data, not just key relationships (Paredaens, VanGucht, Van Den Bussche, & Gyssens, 1994).

Data Warehouses

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need to search for data anywhere else. In order to do this the data warehouse must be able to extract data from the variety of computers systems and import that data into the warehouse. Healthcare industry has many legacy systems and multivendor applications. Healthcare facilities may use their data warehouses to perform clinical analytics, financial forecasting, etc. This, however, can only happen if the correct data is in the system. For example, the National Database of Nursing Quality Indicators (NDNQI) was developed to provide unit-specific quality indicators that assist in looking at the correlation between staffing ratios, direct patient care, and quality outcomes (Montalvo, 2007). This database deals with 15 quality indicators like falls, pressure injuries, nursing hours per day, etc. The data must be in the system to address quality, outcomes, and processes. The data warehouse must be able to deliver the data in the warehouse back to the users in the form of information. Both managers and direct care givers can use information from a data warehouse for decision support. This information can support clinical and administrative research, education, quality improvement, infection control, and a myriad of other decision-making activities in healthcare institutions. The data hospitals collect are already used to support planning, marketing, and project management as well as reporting to accreditation and regulatory agencies. Having it in a data warehouse would greatly improve the efficiency of developing reports. The development of data ware houses promises to convert clinical information from a resource only available for an individual patient into a resource that can be used for clinical effectiveness review, clinical research, and as a source of new discovery. In these ways, clinical data will ultimately benefit people through improvement in clinical care.

Other Types of Storage In addition to data warehouses, there are other types of storage that a system may use. Data Marts  A data mart is a DBMS that is for a single unit of work and may contain a subset of data stored in a warehouse. For instance, a hospital may have a data warehouse where all information is housed, and a single department may have a data mart. Although the definition and distinction are still not agreed upon (Mullins, 2013), it is important to recognize that the main difference lies in the scope of the people who are accessing the data. The advantages of a data mart include quick response time due to less data than a warehouse, simplicity in implementation, greater cost-effectiveness than a data warehouse, value to specific groups like a unit or department in a hospital, and ease of

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Chapter 6 • Data and Data Processing    109 maintenance. The main disadvantage to a data mart is that it cannot provide the organization with organization-wide data analysis; it can only provide departmental or unitwide analysis (Guru99, 2019). The SPIRIT nursing system is an example of a nursing intelligence system to support quality management of nursing care by reusing nursing documentation data. This system uses secondary nursing data in order to measure quality indicators related to the nursing process and to determine the quality of nursing care. To do this, designers built a data mart from the existing data warehouse. With these data the SPIRIT system permits nurse managers to easily answer the questions of how many, how long, and how often quality indicators occur, thereby showing patterns and trends within the data (Hackl, Rauchegger, & Ammenwerth, 2015). For more complex queries, more advanced methods and machine learning techniques are necessary. Data marts provide easier access to monitoring issues such as utilization review for their department or unit, trends and anomaly detection, drug utilization related to specific conditions, and much more. Data Lakes  A data lake is a freer form of a DBMS where the structure of the data is loose and varied including structured, semi-structured, and unstructured data. Input processing can be in batch, real-time, or one-time loads. Sources of data for a data lake can be databases, social media, monitoring devices, e-mails, and so forth (AWS, 2019). A data lake is often characterized by the flow of data into a central repository, similar to rivers flowing into a lake. Often, data in a data lake is diverse in nature and may include, for example, images as well as sound clips. There is no fixed limit on size or file type. The main distinction between a data lake and a data warehouse or a data mart is that the data in the data lake stays in its original form. It is not extracted, integrated, transformed, cleaned, or processed. Data Stores  Lastly, a data store is a generic term used to describe a DBMS that lumps several diverse data sources together. Since the main advantage of modern DBMSs is to be able to look at diverse sets of data and impose a structure on them, the type of structure that the designer chooses should be tied to the purpose of the data store in the first place.

Selecting a Data Storage Repository When selecting any form of a DBMS it is important to realize that with the advent of cloud computing there are literally hundreds of different configurations, but there are DBMS criteria that might help when choosing a system (Wasson, Wilson, Buch, Salomaki, & Lee, 2018).

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110    P art 1 • N ursing I nformatics T echnologies Compare and Contrast  Most modern-day systems are client server systems. This allows for a shared DBMS that many users can access. When thinking about which solution to choose, it is important to consider several things, some of which are as follows:

• • • • •

Network configuration: What type of network will the system be running on? For example, local area network (LAN), wide area network (WAN), and wireless local area network (WLAN).

Type of data being stored: If there will be a lot of medical images, videos, or sounds, it is important to realize that these need a lot of space. Amount of data: How much data are there? If there is a large amount of data, a system that allows for faster retrieval from the system may be necessary.

Systems interoperability: Are there requirements that the system interface with another system? Budget considerations: How much money is being dedicated to the database project?

Access  Lastly, another consideration discussing database management systems is who will need access to the data. Since most systems are client-server systems, it is important to realize that there is a vulnerability in having the client on a different machine than the server. Generally, in terms of access, it is important to answer the following questions (Mullins, 2013):

• •

Who is it? Who is logging into the system?

• •

Who can see it? Is the system encrypted?

Who can do it? What permissions do they have in the DBMS? Who did it? Are there systems audits that provide a trail of who did what?

CURATING DATA One of the aspects that often gets forgotten in data and data processing is the idea of curating the data. Much like a librarian curates a collection in a library, it is important that healthcare professionals utilizing data ensure the quality of the data they are collecting.

Ensuring Data Quality One must take into account several aspects when thinking about the quality of data. One of the most important aspects is that the quality of data is directly related to

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the type of data that are being allowed into the system. For example, if a nurse is entering blood pressure readings in the system, they need to make sure that the reading is accurate, entered correctly, and explicitly defined. Quality also relates to procedures/policies for data entry. For example, data are entered once in one setting and accessed by many others in different healthcare settings. They must use standard terms or codes as different words having the same or similar meaning will not be interpreted by an automated system with the same meaning. In a data warehouse, data are entered once but can be used many times by many users for a number of different purposes. As a result, the quality of the data on entry takes on a whole new level of importance. In this situation, the concept of data stewardship changes. When dealing with a department information system, the department is usually seen as holding primary responsibility for the quality of that data. For example, one might expect nursing to be responsible for the quality of data in an application capturing nursing documentation. However, when thinking about who is responsible for the data in a data warehouse, the concept of a data steward takes on addition significance. “Health Data stewardship refers to the responsibility of ensuring the appropriate use of personal health data” (Health Data Stewardship, 2009, p. 2). Key practices in health data stewardship requires individual rights, responsibilities of the health data steward, security safeguards and controls, and accountability and enforcement. A good data steward are guardians or caretakers of the data within their domain (Eliason and Anderson, 2016). This means they are very knowledgeable about the data domain. This means there will be multiple data stewards in healthcare. For example, pharmacist, physicians, nurses, lab director, and so forth, each are responsible for their data domain. A data steward does not own the data but ensures its quality. The data steward is the “keeper of the data,” not the “owner of the data.” This means we need to “document things in the right way, in the right place so that it can appropriately be measured and correctly analyzed” (Zaino, 2016, para 5). This means nurses need to use standard nursing language along with nursing sensitive quality care indicators, understand the relationship between data stewardship and quality care, and realize the role nurses have in data governance and policies.

Interoperability (Moving Data between Systems) One of the biggest advantages of the data movement, as well as the use of DBMS in general, is the ability to export data from one system and input it into another. However, data must be in a format that automated systems can use

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before this advantage can be realized. For example, many healthcare settings provide new patients with paper forms for recording their health history. If a hospital is undertaking a project to digitize these records on admission to the setting, it would be important that the system being used for digitization creates a digitized record that can easily be imported into the medical records database. Often, system interoperability requires clean data on both sides. If the data are not clean, it really is questionable as to its usefulness.

Auditing Aspects of Data Another useful feature of DBMSs is the ability to pull reports for auditing purposes. For example, if a healthcare professional is performing an audit in order to check the coding for reimbursements, it is necessary for the data in the system to be correct. In order to ensure that the data are correct, it is necessary that the end-user curates and looks after the quality of the data.

RETRIEVING DATA (PRODUCING INFORMATION) One of the primary reasons for storing and curating data in a system is to be able to retrieve the data at a later date. Data retrieval can come in many forms, but at its simplest, it is the retrieval of data from a system in a systematic way.

Retrieval Examples One example of portals is a patient portal that allows a patient to go in and view his or her medical data on the Internet or through another type of application. When a patient logs in to see his or her data, he or she is not viewing a spreadsheet or raw data. The patient is seeing data in a structured way that allows him or her to process and understand the information. Another example of retrieval is the information desk at a hospital. What view of the data would one expect to see? What information should be available to the person manning the information desk? Should his or her view include patient name and room number only? Should he or she “see” the patient diagnosis? Doctor? Insurance? Address? If there are limitations on who may visit the patient, how should this be handled?

SQL The method of data retrieval is often through the use of SQL queries. SQL (see-kwel) stands for Structured Query

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Chapter 6 • Data and Data Processing    111

  TABLE 6.5    SQL Template and SQL Example Here SQL Template

SQL Example—Select

ACTION attribute list FROM table WHERE criteria

SELECT patientName, patientAge FROM tablePatient WHERE patientAge = 18

*Note: Select queries are used to build reports by report building tools

Language and is the language that most developers use to retrieve raw data from the database to display it on a front end. In the example above, a developer used SQL to interface with the database and insert the data into the application or Web site for the patient to view. SQL is essentially the language of data retrieval. Basic SQL Structure  Basic SQL commands take the form of an ACTION being taken on a table WHERE a piece of criteria is constrained in a defined way. For example, in Table 6.5 you can see that SQL is broken down to a basic template. The second column shows an example where we are SELECTing the data for patients from the table that contains the patient data where the patientAge is 18. This should, in theory, return a list of patient names and ages for patients who are 18. SQL Functions In the previous example, the SELECT function was used. Although the easiest to understand, there are several more functions that a database can handle through SQL. One of the biggest functions is to update a record or report on a record. Common functions in the medical field are to store data, update the data, retrieve the data, or to report on same aspect of the data to answer questions or make decisions.

INTERPRETING DATA (PRODUCING KNOWLEDGE AND MAKING DECISIONS) Data Analysis and Presentations Once data is stored and in a retrievable form, it is important to produce knowledge and present the data in a form for decision-making. Analytics  Once the data has been stored, curated, and retrieved, it is then the responsibility of the end user to go through and perform analytics on the data. Many times healthcare analytics are performed in search of a statistical

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  TABLE 6.6    Additional SQL Functions SQL Template—Update Records

SQL Example—Update

UPDATE table_name SET column_name = value WHERE criteria

UPDATE tablePatient SET patientAge = 18 WHERE patientID = 1234;

SQL Template— Create table and Add Records

SQL Example—Create and Add Records

CREATE TABLE table_name ( Column datatype, ... continue for all columns); INSERT INTO table_name (column_list VALUES (value_list);

CREATE TABLE tablePatient ( patientID Number, patientAge Number patientName text); INSERT INTO tablePatient (patientID, patientAge, patientName) VALUES (1234, 18, ‘John Smith);

  TABLE 6.7    Examples of Dashboard Resources Name

Location

Nursing Dashboard in Excel

http://www.qimacros.com/store/nursing-dashboard/

Datasets and Documentation— Health IT Dashboard

https://dashboard.healthit.gov/index.php

Beyond Nursing Quality Measurement: The Nation’s First Regional Nursing Virtual Dashboard

http://www.ahrq.gov/professionals/quality-patient-safety/safety-2/vol1/AdvancesAydin_2.pdf

Quality of Care Dashboard

https://www.parklandhospital.com/summary-indicators

Medication Safety Dashboard

https://apps.nhsbsa.nhs.uk/MOD/MedicationSafety/atlas.html

Outpatient versus Inpatient Trends

https://www.datapine.com/dashboard-examples-and-templates/healthcare

KPI and Dashboards

https://www.datapine.com/kpi-examples-and-templates/healthcare

outcome. For example, the healthcare provider may want to know which patients over the age of 18 had a flu shot this year; they would be searching for a statistic or a number such as 55% of the patients in this healthcare system over the age of 18 had a flu shot. This number, combined with the expertise of the healthcare provider, would provide information needed for planning the time and location of a clinic administering flu shots. Analytics can be of three types: descriptive, predictive, and prescriptive. The example above is descriptive. An example of predictive could be: where might the best place be to have unused ambulances waiting on New Year’s Eve? Maybe they should not all be in the garage where the organization normally parks ambulances. And lastly, an example of prescriptive is a staffing system that measures patient acuity and prescribes the amount and type of staff necessary on a clinical unit during specific days of the week. More on this under the section “Data Mining.” Data analytics has the potential to (1) improve staffing based on patient predictions, (2) keep patients out of the hospital to avoid the high cost of hospitalization by wearing devices that collect data continuously alerting the

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doctor to potential issues, (3) help prevent opioid abuse, (4) prevent unnecessary ER visits, and (5) reduce fraud and enhance data security and many more (Lebied, 2018). Dashboards  The presentation of data may also take the form of a dashboard. The main characteristic of a dashboard is a snapshot view of several pieces of key metrics at once. The data for the dashboard are usually from diverse data sources and the system is updating the data in real time. The purpose of a dashboard is to make it easy to get a snapshot of the database or key performance indicators in one glance. See Table 6.7 for examples of dashboards resources.

Data Mining Traditional methods of retrieving information from databases no longer work with the sheer amount of data that the healthcare industry is producing. One can generate and store data far faster than one can analyze and understand it. The process of extracting information and knowledge from large-scale databases is known as

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knowledge discovery and data mining (KDD). The definition of data mining is “the process of finding anomalies, patterns and correlations within large data sets to predict outcomes” (SAS, 2019, para 1). In more detail, data mining is the computational process that allows us to use our data in order to “mine” insights that we may not have seen without the assistance of a computer. Usually, data mining is performed on sets of data that are so large there is no way to pick out the patterns by observation alone. In healthcare the purpose is to assist in providing quality care, predicting best treatment choices, and utilizing healthcare resources in a cost-effective manner. A traditional approach to the KDD development includes a seven-step process. These steps are task analysis, data selection, data cleaning, data transformation, data mining, pattern interpretation and evaluation, and deployment. Bagga and Singh (2011) propose a three-step process: preprocessing, data mining, and post-processing. Regardless of the approach, the first task is to define the goal of the process. What is the problem specification? Once this task is complete, one must identify the appropriate data needs, and prepare (clean) and process the appropriate data. This will result in the appropriate data set that is complete and accurate (Bagga & Singh, 2011). The next task is data mining. This means to apply computational techniques to find the patterns and trends. Some examples of data mining techniques include rule set classifiers such as IF conditions, THEN conclusion, decision tree algorithms, logistic regression analysis, neurofuzzy techniques, and memory-based reasoning (Chen & Fawcett, 2016; Cummins, Luangkesorn, & Staggers, 2018). Once the data are mined, the results are visualized. The question here is how to present the results to decisionmakers. What does this mean? How can we use this “new” knowledge? Some questions depending on the goal of the data mining could be: What does this mean for evaluating treatment effectiveness? How might we use this knowledge to impact the management of healthcare? How might this help with detection of fraud and abuse? How might this help predict who is at risk for a certain health problem? How might we provide personalized care and treatment? (Kob & Tan, 2005).

Predictive Analytics One of the main outcomes of data mining is usually predictive analytics. Predictive analytics allow us to analyze and mine the data from the past with the intention of being able to predict the future. Specific to healthcare, predictive analytics could potentially tell us that if a 25-year-old is on a specific medication, he or she will likely also develop kidney failure by the time he or she is 45. This prediction

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Chapter 6 • Data and Data Processing    113 is based on previous research using data mining that demonstrated 90% of adult patients who have taken this medication for 20 years have developed kidney failure.

Benchmarking The process of benchmarking is “the continuous process by which an organization can measure and compare its own processes with those of organizations that are leaders in a particular area” (Benson, 1994, para 1). In terms of healthcare specifically, there are four types of benchmarking— internal, competitive, functional, and generic (Crockett & Eliason, 2017). Internal compares one unit with other units or one department with another department. Competitive compares your organization against other organizations in your area or known as a top-notch organization. Functional compares your organization against those in a different business but along similar metric. And lastly, generic compares your organization against others regarding processes. The purpose of benchmarking is to improve processes that lead to quality care at reduced costs.

Decision Analysis and Decision Support Systems Packaging of data into decision analysis and decision support systems is a critical step in healthcare. The purpose of this system is not to replace the decision-making ability of the healthcare professional, but rather to supplement it by showing trends or by supporting the decision of the professional. For example, a decision support system may note certain abnormal lab work on a specific patient and suggest additional lab work than might provide more insight into the patient’s problem. The healthcare provider will evaluate this suggestion in terms of the clinical picture that the patient presents and decide if the additional lab work is necessary.

USING KNOWLEDGE (PRODUCING WISDOM) The end goal of data and data processing is to gain wisdom in order to make better decisions that result in improved patient outcomes, patient centric care, and value-based healthcare. By understanding the knowledge and the implications of that knowledge, nurses are able to manage a wide range of human health problems. Wisdom also includes the appropriate and ethical use of this knowledge to manage human health problems. See Fig. 6.3, which demonstrates the relations between the concepts of data, information, knowledge, and wisdom as they are related to current automated or computer systems.

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Increasing complexity

114    P art 1 • N ursing I nformatics T echnologies

Knowledge Interpreting, integrating, understanding

Wisdom Understanding, applying, integrating service with compassion

Expert System

Information Organizing, interpreting

Decision Support System

Data Naming, collecting, organizing Information System

Increasing interactions and interrelationships

•  FIGURE 6.3.  Moving from Data to Expert Systems. (Copyright © 2013 Ramona Nelson, Ramona Nelson Consulting. All rights reserved. Reprinted with permission.) Over the years as computers have become increasingly powerful and able to manage larger and larger data sets a controversial question is raised, “Can any aspect of wisdom be automated? A related and equally important question is, “How can we design and implement automated systems especially decision support and expert systems to best support the wisdom of expert nurses while maintaining clear guidelines and standards of practice? And also another question, “What are the barriers to development of expert systems?”

• • •

Provide a solution more quickly than humans Reduce waste and cut costs Improve patient care by sharing the knowledge and wisdom of human experts.

Expert systems have four main components: natural language, knowledge base, a database, and an inference engine as well as a means of capturing expert knowledge and an explanation to the end user as to the rationale behind the recommendation. See Table 6.8 for definitions of these terms.

Expert Systems Expert systems represent the present and future vanguard of nursing informatics. These systems aim to help make the nurse “more intelligent” in providing quality care based on evidence. Expert systems use artificial intelligence (AI) to model the decisions an expert nurse would make. They provide the “best decision” recommendation based on what an expert nurse would do unlike decision support systems that provide several options from which the nurse selects. Some of the advantages of expert systems include the following:

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  TABLE 6.8    Definitions of Components of Expert System Component

Definition

Natural Language

Used to interface and interact with the end user

Knowledge Base

Contains rules for decision-making

Database

Facts specific to the domain focus, i.e., nursing

Inference Engine

Links the knowledge base rules with the database

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Published articles as early as the mid-1980s and early 1990s described the components of expert systems and uses in nursing (Darlington, 1997; Turley, 1993). Some of these early systems included COMMES (Creighton OnLine Multiple Modular Expert System), CANDI (Computer-Aided Nursing Diagnosis and Intervention), and the HELP Patient Care Information System (Petrucci, Petrucci,  & Jacox, 1991). Recently you can see articles published with titles like “Telehealth, mHealth Make ­ Nurses Pivotal Presence in Healthcare” (Wicklund, 2018) and “Clinical Decisions Support App Helps Nurses Diagnose Diseases” (Reardon, 2015), each describing systems that aid in providing quality, patient centric care. Issues that need to be addressed to continually move nursing forward in the development of expert systems include administrative support, data quality such as missing values and standard language, data security, privacy, ethics of data collection and use, responsibility, and data oversight.

Evidence-Based Practice The concept of evidence-based practice is widely accepted across each of the healthcare disciplines; however, the reality of providing evidence-based care at the point of care is often an elusive goal. With the advent of Big Data and data analytics techniques capable of analyzing Big Data along with expert systems capable of analyzing unstructured data and using natural language to present the resulting conclusions, real-time evidence-based practice is a potential reality. But before nursing can achieve this potential reality, nursing data, information, knowledge, and wisdom must consistently be included in building automated healthcare systems.

Value-Based Patient Centric Care Value-based healthcare is a delivery model where the focus is on outcomes. This means that healthcare providers receive payment based on outcomes, not fee for services. The idea is that patients would pay less for achieving better health, patient satisfaction would go up, costs would be reduced for payers, and society becomes healthier (NEJM Catalyst, 2017). In this model, cost goes down while value and quality go up. Douglas, Arob, Colella, and Quadri (2016) detail a value-based care model that describes the four major components, one of which is knowledge management that includes analytics, and technology such as EMR, data analysis, root cause analysis, and informatics. They outline 10 job functions of the advanced practice nurse for value-based projects (p. 53). Some of these functions

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Chapter 6 • Data and Data Processing    115 include analyzing patterns of variance in nursing practice, coordinating development of evidence-based practice standards of care, and collecting, compiling, and analyzing value-based data. This approach to delivering care heavily depends on data, databases, analytics, and expert systems.

SUMMARY This chapter describes data, data processing, and data management. The focus of this chapter is on understanding the concepts and issues necessary to effectively use and manage a database system in delivering healthcare. The chapter begins by setting the stage for why data and what we do with it, which are so critical to delivering healthcare. The data to wisdom continuum is then presented as a framework for moving from data to wisdom. The next concepts presented deal with data processing—generating data, storing data, curating data for data quality, retrieving data to produce information, interpreting data to produce knowledge and make decisions, and using knowledge bases to produce wisdom. The benefits of data management and use are then presented.

Test Questions 1. Data in motion is an increasing concern in healthcare because of: A. Streaming apps

B. Monitoring devices C. Mobile devices

D. All of the above 2. A NoSQL data base model is:

A. A database where tables are related by keys

B. An agile system that easily processes unstructured data and semi-structured data

C. A database that features the organization of the data around entities D. A graph representation of the objects in the database

3. Volume, Variety, Velocity, Veracity, and Value and Variability describe: A. Big data

B. Data analytics C. Benchmarks D. Dashboards

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116    P art 1 • N ursing I nformatics T echnologies 4. The two main components of a DBMS are:

A. The data and the structure to store the data like tables

B. The identification and storage of objects within a database system

C. A “front-end” which provides an application in which a user can view, manipulate, and interpret data and a “back-end” which is where the data are stored D. None of the above

5. Dashboards are being used to:

A. Get a snapshot of the database or key performance indicators in one glance

B. Find anomalies, patterns, and correlations within large data sets to predict outcomes

C. Describe, predict, and prescribe for the purposes of improving staffing, keeping patients out of the hospital, helping prevent opioid abuse, ­preventing unnecessary ER visits, and reducing fraud and enhancing data security D. All of the above

6. Curating data means:

A. Encrypting the data so others can’t read it B. Ensuring the quality of the data

C. Preparing the data for storage in a data warehouse D. Moving the data between systems

7. Which type of data analytics would be involved in this scenario? Make a recommendation of peak ER admissions and adjustments to staffing to reduce length of stay, adjust priorities so staff can see as many patients as possible, and maintain quality of care. A. Descriptive.

B. Prescriptive. C. Predictive.

D. All the above 8. The process of finding anomalies, patterns, and correlations within large data sets to predict outcomes is: A. Benchmarking

B. Patient centric care C. Data mining

D. Data analytics

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9. You just had a meeting with the director of your department who proceeded to show you a dashboard that is on his or her computer. This dashboard is used to assist the director in making decisions. In order to do that, the dashboard provides: A. Key performance indicators in one glance B. Updated data with last week’s metrics C. Collected data from one key source D. All the above

10. Expert systems use artificial intelligence (AI) to model the decisions of expert nurses. The main advantages of expert systems in healthcare are to: A. Provide a solution more quickly than humans

B. Improve patient care by sharing the knowledge and wisdom of human experts C. Both A and B

D. Neither A nor B

Test Answers 1. Answer: D 2. Answer: B

3. Answer: A 4. Answer: C

5. Answer: A 6. Answer: B 7. Answer: B

8. Answer: C

9. Answer: A 10. Answer: C

REFERENCES American Nurses Association. (2008). Nursing informatics: Scope and standards of practice. Silver Spring, MD: Nursesbooks.org. Andreu-Perez, J., Poon, C., Merrifield, R., Wong, S., & Yang, G. (2015). Big data for health. IEEE Journal for Biomedical and Health Informatics, 19(4), 1193–1208. AWS. (2019). What is a data lake? Retrieved from https:// aws.amazon.com/big-data/datalakes-and-analytics/whatis-a-data-lake. Retrieved on May 13, 2020 Bagga, S., & Singh, G. (2011). Three phase iterative model of KDD. International Journal of Information Technology and Knowledge Management, 4(2), 695–697.

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Barlow, R. (2013). Making clinical data analytics count: Does size, volume really matter? Health Management Technology, 43(12), 8–11. Barnett, T. (2016). The zettabyte era officially begins (how much is that?). Retrieved from https://blogs.cisco.com/ sp/the-zettabyte-era-officially-begins-how-much-is-that. Accessed on May 5, 2020. Benson, H. (1994). An introduction to benchmarking in healthcare. Retrieved from https://www.ncbi.nlm.nih. gov/pubmed/10139084. para 1. Accessed on May 5, 2020. Chen, L., & Fawcett, T. (2016). Using data mining strategies in clinical decision making: A literature review. CIN: Computers, Informatics, Nursing, 34(10), 448–454. Crockett, D., & Eliason, B. (2017). What is data mining in healthcare? Retrieved from https://www.healthcatalyst. com/data-mining-in-healthcare Cummins, M. R., Luangkesorn, L., & Staggers, N. (2018). Data science and analytics in healthcare. In: R. Nelson & N. Staggers (Eds.), Health informatics: An interprofessional approach (2nd ed.). St Louis, MOs: Elsevier/Mosby. Darlington, K. (1997). Expert systems in nursing. Retrieved from http://www.bcs.org/upload/pdf/nsg-itin-vol9darlington1.pdf Douglas, C., Arob, D., Colella, J., & Quadri, M. (2016). The hackensackUMC value-based care mode. Nursing Administration Quarterly, 40(1), 51–59. Eliason, B., & Anderson, N. (2016). Why the data steward’s role is critical to sustained outcomes improvement in healthcare. Retrieved from https://www.healthcatalyst. com/why-are-data-stewards-so-important-forhealthcare. Accessed date May 14, 2020. Fry, E., & Mukherjee, S. (2018). Tech’s next big wave: Big data meets biology. Retrieved from http://fortune. com/2018/03/19/big-data-digital-health-tech. Accessed date May 14, 2020. Gavin, B. (2018). How big are gigabytes, terabytes, and petabytes? Retrieved from https://www.howtogeek. com/353116/how-big-are-gigabytes-terabytes-andpetabytes. Accessed date May 14, 2020. Guru99. (2019). Data mart tutorial: What is data mart, types and example. Retrieved from https://www.guru99.com/ data-mart-tutorial.html. Accessed date May 14, 2020. Hackl, W., Rauchegger, F., & Ammenwerth, E. (2015). A nursing intelligence system to support secondary use of nursing routine data. Applied Clinical Informatics, 6(2), 418–428. Retrieved from https://www.ncbi.nlm.nih.gov/ pmc/articles/PMC4493340. Accessed date May 14, 2020. Health Data Stewardship: (2009). What, why, who, how. Retrieved from https://www.ncvhs.hhs.gov/wp-content/ uploads/2014/05/090930lt.pdf Huesch, M., & Mosher, T. (2017). Using IT or losing IT? The case for data scientists inside health care. Retrieved from https://catalyst.nejm.org/ case-data-scientists-inside-health-care/

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Chapter 6 • Data and Data Processing    117 IBM. (2018). The 4 V’s of Big Data. Retrieved from https:// www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed date May 14, 2020. Jackson, N. (2011). Infographic: How big is a yottabyte? Retrieved from https://www.theatlantic.com/technology/ archive/2011/05/infographic-how-big-is-a-yottabyte/239034. Accessed date May 14, 2020. Johnson, S. (2007). The ghost map: The story of london’s most terrifying epidemic--and how it changed science, cities, and the modern world. London: Penguin. Joos, I., Nelson, R., & Smith, M. (2014). Introduction to computers for healthcare professionals. Burlington: MA, Jones & Bartlett Learning. Kemp, R. (2014). Legal aspects of managing big data. Retrieved from http://www.kempitlaw.com//wp-content/ uploads/2014/10/Legal-Aspects-of-Big-Data-WhitePaper-v2-1-October-2014.pdf. p. 23. Accessed date May 14, 2020. Kob, H., & Tan, G. (2005). Data mining applications in healthcare. Journal of Healthcare Information Management, 19(2), 64–72. Lebied, M. (2018). 12 Examples of Big Data analytics in healthcare that can save people. Retrieved from https:// www.datapine.com/blog/big-data-examples-inhealthcare. Accessed date May 14, 2020. Marcus, S. (2018). HRSA launches new open data website. Retrieved from https://healthdata.gov/blog/hrsalaunches-new-open-data-website. Accessed date May 14, 2020. MongoDB. (2019). What is NoSQL? Retrieved from https:// www.mongodb.com/nosql-explained. Accessed on May 5, 2020. Montalvo, I. (September 30, 2007). “The National Database of Nursing Quality IndicatorsTM (NDNQI®)” OJIN: The Online Journal of Issues in Nursing. Vol. 12, No. 3. Mullins, C. S. (2013). Database administration: The complete guide to DBA practices and procedures (2nd ed., pp. 638, 686). Boston, MA: Addison-Wesley. Nelson, R. (September 19, 2018). “Informatics: Evolution of the Nelson Data, Information, Knowledge and Wisdom Model: Part 1” OJIN: The Online Journal of Issues in Nursing Vol. 23, No. 3. Nelson, R., & Joos, I. (1989). On language in nursing: From data to wisdom. PLN Visions, 1(5), 6. Office of National Coordinator for Health Information Technology (ONC). (2019). Health IT legislation. Retrieved from https://www.healthit.gov/topic/lawsregulation-and-policy/health-it-legislation. Accessed date May 14, 2020. Paredaens, J., VanGucht, D., Van Den Bussche, J., & Gyssens, M. (1994). A graph-oriented object database model. Retrieved from http://doi.ieeecomputersociety. org/10.1109/69.298174. Accessed date May 14, 2020. Petrucci, K., Petrucci, P., & Jacox, A. (1991). Expert systems and nursing. Nursing Economics, 9(3), 188–190.

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118    P art 1 • N ursing I nformatics T echnologies Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3. Reardon, S. (2015). Clinical decision support app helps nurses diagnose diseases. Retrieved from https://healthitanalytics.com/news/clinical-decision-support-app-helpsnurses-diagnose-diseases. Accessed date May 14, 2020. Ronquillo, C., Currie, L., & Rodney, P. (2016). The evolution of data-information-knowledge-wisdom in nursing informatics. Advances in Nursing Science, 39(1), E1–E18. Rouse, L., & Fitzgibbons, L. (2019). States of digital data. Retrieved from https://searchdatamanagement.techtarget.com/reference/states-of-digital-data. Accessed date May 14, 2020. Rouse, M. (2018). Big Data. Retrieved from https://searchdatamanagement.techtarget.com/definition/big-data. Accessed date May 14, 2020. SAS. (2019). Data mining what it is and why it matters. Retrieved from https://www.sas.com/en_us/insights/ analytics/data-mining.html. para 1. Accessed date May 14, 2020. The White House. (2012). FACT SHEET: Big Data across the federal government. Retrieved from https://obamawhitehouse.archives.gov/the-press-office/2015/12/04/ fact-sheet-big-data-across-federal-government. Accessed date May 14, 2020. Turley, J. (1993). The use of artificial intelligence in nursing information systems. Retrieved from http://project.net. au/hisavic/hisa/mag/may93/the.htm. Accessed date May 14, 2020.

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US Department of Health and Human Services. (2018). Welcome to HealthData.gov. Retrieved from https:// healthdata.gov/content/about. para 1. Accessed date May 14, 2020. Wasson, M., Wilson, M., Buch, A., Salomaki, T., & Lee, R. (2018). Choose the right data store. Retrieved from https://docs.microsoft.com/en-us/azure/architecture/ guide/technology-choices/data-store-overview. Accessed date May 14, 2020. NEJM Catalyst. (2017). What is value-based healthcare? Retrieved from https://catalyst.nejm.org/what-is-valuebased-healthcare. Accessed date May 14, 2020. Wicklund, E. (2018). Telehealth, mHealth make nurses pivotal presence in healthcare. Retrieved from https://mhealthintelligence.com/features/telehealth-mhealth-make-nursespivotal-presence-in-healthcare https://www.dataversity. net/nurses-embrace-role-data-governance-healthcareorganizations-win. Accessed date May 14, 2020. Wiler, J., Harish, N., & Zane, R. (2017). Do hospitals still make sense? The case for decentralization of health care. Retrieved from https://catalyst.nejm.org/hospitals-casedecentralization-health-care. Accessed date May 14, 2020. Zaino, J. (2016). When nurses embrace their role in data governance, healthcare organizations win. Retrieved from https://www.dataversity.net/nurses-embrace-role-datagovernance-healthcare-organizations-win/#

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part

2

System Standards Virginia K. Saba and Joyce Sensmeier

What an honor to have Joyce Sensmeier write a chapter entitled Health Data Standards: Development, Harmonization, and Interoperability (Chapter 7). Joyce has been leading the development, harmonization, and interoperability nationally and internationally. The basic premise of this chapter is that the healthcare landscape now includes multiple care settings, stakeholders, and different information systems. The foundation to the development, implementation, and exchange of electronic health data within a hospital, across hospitals, to communities, and globally are standards. Joyce stresses that a harmonized set of rules and definitions, at the level of data meaning as well as the technical level of data exchange and access, is needed. Her focus is on the recognition that shared information and incentives for the adoption and implementation of interoperability standards improve health outcomes. This is not only an updated chapter, but it includes a new example of the Integrating the Healthcare Enterprise (IHE) Testing Continuum. This new tool includes specifications to implement various products into healthcare. In 2018, eHealth Exchange became a subsidiary of the Sequoia Project. Another subsidiary of the Sequoia Project described in the chapter is Carequality and CommonWell Health Alliance. The impact of the 21st Century Cures Act, and proposed rules in this Act, has led to the development of application programming interfaces (APIs) and Fast Healthcare Interoperability Resources (FHIR) as a standard for APIS. The chapter further focuses on the National Academy of Medicine report in 2018 that recommends better ­procurement practices that include standards and interoperability of systems to enhance patient care and reduce administrative ­burden to nurses. Standardized nursing terminologies that provide data elements in a standard format that can be combined with other data are described in the new and updated Chapter 8 entitled Standardized Nursing Terminologies, by Drs. Jane Englebright, Nicholas R. Hardiker, and Tae Youn Kim. These authors are experts in national and international standardized nursing terminologies, with experience in integrating nursing data elements with the data of other healthcare ­providers into electronic health records and harmonizing these standards into international standards. The chapter ­provides ­historical background to understand where we are today in understanding standardized terminology and data elements. The chapter goes beyond an overview to consider the current state of the science and provide examples of

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how using nursing terminologies can improve and solve real problems in nursing practice. New concepts in interface technology defined by the Office of National Coordinator for Health Information Technology (ONC) are provided. The use of reference terminologies is clearly described. Valuable examples of using standardized nursing terminology in intravenous care and infection monitoring are provided in this new and updated chapter. Chapter 9 by Dr. Gregory R. Alexander on Human–Computer Interaction provides current definitions of human ­factors. The chapter updates the user experience with human–computer interactions in the areas of health IT design/usability, IT fit to workflow, excessive documentation, interoperability, and lack of information to support care processes. A valuable comparison of human–computer interaction methods for capturing end-user experience is new to this e­ dition. Several important new references have been added to this chapter in this edition to solve the nurse usability problems in health information technology. We are privileged to have privacy/security expert Dr. Dixie B. Baker, who has updated content in Chapter10, Trustworthy Systems for Safe and Private Healthcare. A key point in this updated chapter is that nurses must be able to trust that the critical information they need is accurate and available when needed, and at the same time preserving individual privacy rights. Since the last edition, new to the regulatory framework in healthcare information technology is the 21st Century Cures Act which expands individual privacy, security protection, and assurance that patient health and well-being are not at risk. Dr. Baker provides recent data on breaches of personal information and examples of recent attacks on healthcare systems. New to this edition is the description of unsafe medical devices. The framework includes the same seven layers: Risk Management, Information Assurance Policy, Physical Safeguards, Operational Safeguards, Architectural Standards, Security Technology Safeguards, and Usability Services. Each component is updated with new risks, for example, from insider intrusions and genomic data. New to the seventh edition is Chapter 11, entitled Social Determinants of Health, Electronic Records, and Health Outcomes, by nursing informatics experts Drs. Marissa L. Wilson and Paula M. Procter. They cover the areas of complexity in integrating overlapping social, environmental, and economic structures that contribute to health inequalities. This chapter is introduced to nurse informaticists (NIs) to uncover the inclusion of data into the electronic health record. The issues and challenges of linking individual data to communities include keeping data systematically collected on all patients, structured with valid and reliable tools, and standardized using standardized common datasets. A cross-cultural example is provided in this exciting new chapter.

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7 Health Data Standards: Development, Harmonization, and Interoperability Joyce Sensmeier

• OBJECTIVES . Discuss the need for health data standards. 1 2. Describe the standards development process and related organizations. 3. Delineate the importance of health information exchange and interoperability. 4. Describe current health data standards initiatives. 5. Define the role of the healthcare consumer in accessing health data. 6. Explore the business imperative of health data standards.

• KEY WORDS Application Programming Interface Health Information Exchange Interoperability Knowledge representation Standards Terminology

Today’s healthcare landscape consists of a variety of care settings and stakeholders, which all leverage a number of different information systems in their delivery of care. Standards are foundational to the development, implementation, and exchange of electronic health records (EHRs). The effectiveness of healthcare delivery is dependent on the ability of clinicians to securely access health information when and where it is needed. The capability of exchanging health information across organizational and system boundaries, whether between multiple departments within a single institution or among a varied cast of providers, payers, regulators, and others, is essential. A harmonized set of rules and definitions, both at the level of data meaning as well as at the technical level of data exchange and access, is needed to make this possible. Additionally, there must be a sociopolitical structure in place that recognizes the benefits of shared information

and incentivizes the adoption and implementation of such standards to improve health outcomes. This chapter examines health data standards in terms of the following topic areas: Need for health data standards Standards development process, organizations, and categories Knowledge representation Standards coordination and harmonization Innovation and interoperability Health data standards initiatives Role of the healthcare consumer Business imperative for health data standards 121

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122    P art 2 • S ystem S tandards

INTRODUCTION TO HEALTH DATA STANDARDS The ability to communicate in a way that ensures the message is received and the content is understood is dependent on standards. Data standards are intended to reduce ambiguity in communication so that the actions taken based on data are consistent with the actual meaning of that data. The healthcare transformation that is underway requires data capture and sharing and advanced clinical processes, which will enable improved health outcomes. This ultimate end state can only be achieved through the organized structuring and effective use of information to support better decision-making and more effective care processes, thus improving health outcomes and reducing costs. While current information technology (IT) is able to move and manipulate large amounts of data, it is not as proficient in dealing with ambiguity in the structure and semantic content of that data. The term health data standards is generally used to describe those standards having to do with the structure and content of health information. However, it may be useful to differentiate data from information and knowledge. Data are the fundamental building blocks on which healthcare decisions are based. Data are collections of unstructured, discrete entities (facts) that exist outside of any particular context. When data are interpreted within a given context and given meaningful structure within that context, they become information. The term interoperability describes the ability of different information systems, devices, or applications to connect, in a coordinated manner, within and across organizational boundaries to access, exchange, and cooperatively use data among stakeholders, with the goal of optimizing the health of individuals and populations (HIMSS, 2018). Data standards represent both data and their transformation into information. Data analysis generates knowledge, which is the foundation of professional practice standards. Standards are created by several methods (Hammond, 2005): (1) a group of interested parties comes together and agrees upon a standard; (2) the government sanctions a process for standards to be developed; (3) marketplace competition and technology adoption introduce a de facto standard; and (4) a formal consensus process is used by a standards development organization (SDO). The standards development process typically begins with a use case or business need that describes a system’s behavior as it responds to a request that originates from outside of that system. Technical experts then consider what methods, protocols, terminologies, or specifications are needed to address the requirements of the use case. An open acceptance or balloting process is desirable to ensure that the

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developed standards have representative stakeholder input, which minimizes bias and encourages marketplace adoption and implementation. Legislated, government-developed standards are able to gain widespread acceptance by virtue of their being required by either regulation or in order to participate in large, government-funded programs, such as Medicare. Because government-developed standards are in the public domain, they are available at little or no cost and can be incorporated into any information system; however, they are often developed to support particular government initiatives and may not be as suitable for general, privatesector use. Also, given the amount of bureaucratic overhead attached to the legislative and regulatory process, it is likely that they will lag behind changes in technology and the general business environment. Standards developed by SDOs are typically consensus-based and reflect the perspectives of a wide variety of interested stakeholders. They are generally not tied to specific systems. For this reason, they tend to be robust and adaptable across a range of implementations; however, most SDOs are non-profit organizations that rely on the commitment of dedicated volunteers to develop and maintain standards. This often limits the amount of work that can be undertaken. In addition, the consensus process can be time consuming and result in a slow development process, which does not always keep pace with technological change. Perhaps the most problematic aspect of consensus-based standards is that there is no mechanism to ensure that they are adopted by the industry, since there is usually little infrastructure in place for SDOs to actively and aggressively market them. This has resulted in the development of many technically competent standards that are never implemented. The U.S. Standards Strategy (ANSI, 2005) states, “The goal of all international standards forums should be to achieve globally relevant and internationally recognized and accepted standards that support trade and commerce while protecting the environment, health, safety, and security.” There are a number of drivers in the current standards landscape that are working to accelerate health data standards adoption and implementation through innovative efforts and incentives that address this charge.

STANDARDS CATEGORIES Four broad areas are identified to categorize health data standards (Department of Health and Human Services, 2010). Transport standards are used to establish a common, predictable, secure communication protocol between systems. Vocabulary standards consist of nomenclatures and

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Chapter 7 • Health Data Standards: Development, Harmonization, and Interoperability 

code sets used to describe clinical problems and procedures, medications, and allergies. Content exchange standards and value sets are used to share clinical information such as clinical summaries, prescriptions, and structured electronic documents. Security standards are used to safeguard the transmission of health data through authentication and access control.

Transport Standards Transport standards primarily address the format of messages that are exchanged between computer systems, document architecture, clinical templates, the user interface, and patient data linkage (Committee on Data Standards for Patient Safety, 2004). To achieve data compatibility between systems, it is necessary to have prior agreement on the syntax of the messages to be exchanged. The receiving system must be able to divide the incoming message into discrete data elements that reflect what the sending system wishes to communicate. The following section describes some of the major SDOs involved in the development of transport standards. Accredited Standards Committee X12N/Insurance.  Accredited Standards Committee (ASC) X12N has developed a broad range of electronic data interchange (EDI) standards to facilitate electronic business transactions. In the healthcare arena, X12N standards have been adopted as national standards for such administrative transactions as claims, enrollment, and eligibility in health plans, and first report of injury under the requirements of the Health Insurance Portability and Accountability Act (HIPAA). HIPAA directed the Secretary of the Department of Health and Human Services (HHS) to adopt standards for transactions to enable health information to be exchanged electronically, and the Administrative Simplification Act (ASA), one of the HIPAA provisions, requires standard formats to be used for electronically submitted healthcare transactions. The American National Standards Institute (ANSI) developed these, and the ANSI X12N 837 Implementation Guide has been established as the standard of compliance for claims transactions. Institute of Electrical and Electronic Engineers.  The Institute of Electrical and Electronic Engineers (IEEE) has developed a series of standards known collectively as P1073 Medical Information Bus (MIB), which support real-time, continuous, and comprehensive capture and communication of data from bedside medical devices such as those found in intensive care units, operating rooms, and emergency departments. These data include physiological parameter measurements and device settings.

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IEEE standards for IT focus on telecommunications and information exchange between systems including local and metropolitan area networks. The IEEE 802.xx suite of wireless networking standards, supporting local and metropolitan area networks, has advanced developments in the communications market. The most widely known standard, 802.11, commonly referred to as Wi-Fi, allows anyone with a “smart” mobile device or a computer to connect to the Internet wirelessly through myriad access points. IEEE 11073 standards are designed to help healthcare product vendors and integrators create interoperable devices and systems for disease management, health, and fitness. National Electrical Manufacturers Association.  The National Electrical Manufacturers Association (NEMA), in collaboration with the American College of Radiologists (ACR) and others, formed DICOM (Digital Imaging and Communications in Medicine) to develop a generic digital format and a transfer protocol for biomedical images and image-related information. DICOM enables the transfer of medical images in a multi-vendor environment and facilitates the development and expansion of picture archiving and communication systems (PACS). The DICOM standard is the dominant international data interchange message format in biomedical imaging. World Wide Web Consortium.  The World Wide Web Consortium (W3C) is the main international standards organization for development of the World Wide Web (abbreviated WWW or W3). W3C also publishes XML (Extensible Markup Language), which is a set of rules for encoding documents in machine-readable format. XML is most commonly used in exchanging data over the Internet. XML’s design goals emphasize simplicity, generality, and usability over the Internet, which also makes it desirable for use in cross-enterprise health information exchange. Although XML’s design focuses on documents, it is widely used for the representation of arbitrary data structures such as Web Services. Web Services use XML messages that follow the Simple Object Access Protocol (SOAP) standard and have been popular with traditional enterprises. Other transport protocols include the Representational State Transfer (REST) architectural style, which was developed in parallel with the Hypertext Transfer Protocol (HTTP) used in Web browsers. The largest known implementation of a system conforming to the REST architectural style is the World Wide Web. Communication Protocols.  In telecommunications, a protocol is a system of digital rules for data exchange within or between computers. When data are exchanged through

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124    P art 2 • S ystem S tandards a computer network, the rules system is called a network protocol. Communication systems use well-defined formats for exchanging messages. A protocol must define the syntax, semantics, and synchronization of the communication. Examples of communication protocols include the Transmission Control Protocol/Internet Protocol (TCP/ IP), which is the suite of communication protocols used to connect hosts on the Internet. File Transfer Protocol (FTP) is a standard network protocol used to transfer files from one host to another host over a TCP-based network, such as the Internet. Simple Mail Transfer Protocol (SMTP) is an Internet standard for electronic mail (e-mail) transmission.

Vocabulary Standards A fundamental requirement for effective communication is the ability to represent concepts in an unambiguous fashion between both the sender and the receiver of the message. Natural human languages are incredibly rich in their ability to communicate subtle differences in the semantic content, or meaning, of messages. While there have been great advances in the ability of computers to process natural language, most communication between health information systems relies on the use of structured vocabularies, terminologies, code sets, and classification systems to represent health concepts. Standardized terminologies enable data collection at the point of care, and retrieval of data, information, and knowledge in support of clinical practice. The following examples describe several of the major vocabulary standard systems. Current Procedural Terminology.  The Current Procedural Terminology (CPT) code set, maintained by the American Medical Association (AMA), accurately describes medical, surgical, and diagnostic services. It is designed to communicate uniform information about medical services and procedures among physicians, coders, patients, accreditation organizations, and payers for administrative, financial, and analytical purposes. In addition to descriptive terms and codes, it contains modifiers, notes, and guidelines to facilitate correct usage. International Statistical Classification of Diseases and Related Health Problems: Tenth Revision. The International Statistical Classification of Diseases and Related Health Problems: Tenth Revision (ICD-10) is the most recent revision of the ICD classification system for mortality and morbidity, which is used worldwide. The transition to ICD-10-CM and ICD-10 Procedural Coding System (ICD-10-PCS) in 2015 was anticipated to improve

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the capture of health information and bring the United States in step with coding systems worldwide. Nursing and Other Domain-Specific Terminologies.  The American Nurses Association (ANA) has recognized the following nursing terminologies that support nursing practice: ABC Codes, Clinical Care Classification, International Classification of Nursing Practice, Logical Observation Identifiers Names and Codes (LOINC), North American Nursing Diagnosis Association, Nursing Interventions Classification (NIC), Nursing Outcome Classification (NOC), Nursing Management Minimum Data Set, Nursing Minimum Data Set, Omaha System, Patient Care Data Set (retired), Perioperative Nursing Data Set, and SNOMED-CT. These standard terminologies enable knowledge representation of nursing content. Nurses use assessment data and nursing judgment to determine nursing diagnoses, interventions, and outcomes. In 2015, the ANA (2015) reaffirmed support for the use of recognized terminologies as valuable representations of nursing practice and promoted the integration of those terminologies into information technology solutions. In this position statement, ANA noted that standardized terminologies have become a significant vehicle for facilitating interoperability between different concepts, nomenclatures, and information systems. RxNorm.  RxNorm is a standardized nomenclature for clinical drugs and drug delivery devices produced by the National Library of Medicine (NLM). Because every drug information system follows somewhat different naming conventions, a standardized nomenclature is needed for the consistent exchange of information, not only between organizations but even within the same organization. RxNorm contains the names of prescription and many nonprescription formulations that exist in the United States, including the devices that administer the medications. Unified Medical Language System.  The Unified Medical Language System (UMLS) consists of a large biomedical thesaurus that identifies relationships between concepts across multiple vocabularies, including their meanings, concept names, and relationships. There are specialized vocabularies, code sets, and classification systems for almost every practice domain in healthcare. The NLM supports the development, enhancement, and distribution of clinically specific vocabularies to facilitate the exchange of clinical data to improve retrieval of health information, as the central coordinating body for clinical terminology standards within HHS (National Library of Medicine, 2010).

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Chapter 7 • Health Data Standards: Development, Harmonization, and Interoperability 

Content Standards Content Standards are related to the data content within information exchanges. Information content standards define the structure and content organization of the electronic message’s or document’s information content. They can also define a “package” of content standards (messages or documents). In addition to standardizing the format of health data messages and the lexicons and value sets used in those messages, there is widespread interest in defining common sets of data for specific message types. The concept of a minimum data set is defined as “a minimum set of items with uniform definitions and categories concerning a specific aspect or dimension of the healthcare system which meets the essential needs of multiple users” (Health Information Policy Council, 1983). A related concept is that of a core data element. It has been defined as “a standard data element with a uniform definition and coding convention to collect data on persons and on events or encounters” (National Committee on Vital and Health Statistics, 1996). Core data elements are seen as serving as the building blocks for well-formed minimum data sets and may appear in several minimum data sets. A number of SDOs have been increasingly interested in incorporating domain-specific data sets into their messaging standards. American Society for Testing and Materials.  The American Society for Testing and Materials (ASTM) is one of the largest SDOs in the world and publishes standards covering all sectors in the economy. More than 13,000 ASTM standards are used worldwide to improve product quality, enhance safety, and facilitate trade. The ASTM Committee E31 on Healthcare Informatics has developed a wide range of standards supporting the electronic management of health information. Clinical Data Interchange Standards Consortium. The Clinical Data Interchange Standards Consortium (CDISC) is a global, multidisciplinary consortium that has established standards to support the acquisition, exchange, submission, and archive of clinical research data and metadata. CDISC develops and supports global, platform-independent data standards that enable information system interoperability to improve medical research and related areas of healthcare. Health Level Seven.  Health Level Seven (HL7) is an SDO that develops standards in multiple categories including transport and content. HL7 standards focus on facilitating the exchange of data to support clinical practice both within and across institutions. HL7 standards cover a broad spectrum of areas for information exchange including medical orders, clinical observations, test results, admission/transfer/discharge, document architecture,

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clinical templates, user interface, EHR, and charge and billing information. HL7 messaging standards are widely implemented by the healthcare industry and have been deployed internationally for decades. HL7 FHIR (Fast Healthcare Interoperability Resources) is an emerging standard describing data formats and elements (known as “resources”) and an Application Programming Interface (API) for exchanging health information. The FHIR specification is rapidly being adopted as a next generation standards framework for the exchange of EHR data. SNOMED International. SNOMED International is a not-for-profit organization that owns, administers, and develops SNOMED-CT, the global common language for health terms. SNOMED-CT has been developed collaboratively to ensure it meets the diverse needs and expectations of the worldwide medical profession and healthcare community. In the United States, the NLM distributes SNOMED-CT at no cost in accordance with the member rights and responsibilities. LOINC.  Logical Observation Identifiers Names and Codes (LOINC) is a database and universal standard for identifying medical laboratory observations. It was developed and is maintained by the Regenstrief Institute. The purpose of LOINC is to assist in the electronic exchange and gathering of clinical results (such as laboratory tests, clinical observations, and outcomes management and research). Since its inception, the database has expanded to include not just medical and laboratory code names but also nursing diagnosis, nursing interventions, outcomes classification, and patient care data set. National Council for Prescription Drug Programs. The National Council for Prescription Drug Programs (NCPDP) develops both content and transport standards for information processing in the pharmacy services sector of the healthcare industry. Since the introduction of this standard in 1992, the retail pharmacy industry has moved to 100% electronic claims processing in real time. Electronic prescription transactions are defined as EDI messages flowing between healthcare providers (i.e., pharmacy software systems and prescriber software systems) that are concerned with prescription orders. NCPDP’s Telecommunication Standard Version 5.1 was named the official standard for pharmacy claims within HIPAA, and NCPDP is also named in other U.S. federal legislation titled the Medicare Prescription Drug, Improvement, and Modernization Act. Other NCPDP standards include the SCRIPT Standard for Electronic Prescribing, and the Manufacturers Rebate Standard.

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126    P art 2 • S ystem S tandards National Uniform Claim Committee Recommended Data Set for a Noninstitutional Claim.  Organized in 1995, the scope of the National Uniform Claim Committee (NUCC) was to develop, promote, and maintain a standard data set for use in noninstitutional claims and encounter information. The NUCC was formally named in the administrative simplification section of HIPAA as one of the organizations to be consulted by ANSI-accredited SDOs and the Secretary of HHS as they develop, adopt, or modify national standards for healthcare transactions. As such, the NUCC has authoritative voice regarding national standard content and data definitions for noninstitutional healthcare claims in the United States.

Security Standards HIPAA Security Standards for the Protection of Electronic Health Information at 45 CFR Part 160 and Part 164, Subparts A and C.  The HIPAA Security Rule was developed to protect electronic health information and implement reasonable and appropriate administrative safeguards that establish the foundation for a covered entity’s security program (CMS, 2007). Prior to HIPAA, no generally accepted set of security standards or general requirements for protecting health information existed in the healthcare industry. Congress passed the Administrative Simplification provisions of HIPAA to protect the privacy and security of certain health information, and promote efficiency in the healthcare industry through the use of standardized electronic transactions. ISO/IEC 27002:2013 Standard.  ISO/IEC 27002:2013 provides guidelines for organizational information security standards and information security management practices including the selection, implementation, and management of controls taking into consideration the organization’s information security risk environment(s). It is designed to be used by organizations that intend to:

• • •

Select controls within the process of implementing an Information Security Management System based on ISO/IEC 27001; Implement commonly accepted information security controls; and Develop their own information security management guidelines.

STANDARDS COORDINATION AND HARMONIZATION It has become clear to both public and private sector standards development efforts that no one entity has the resources to create an exhaustive set of health data standards that will meet

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all needs. New emphasis is being placed on leveraging and harmonizing existing standards to eliminate the redundant and siloed efforts that have contributed to a complex, difficult to navigate health data standards environment. Advances are being made in the area of standards coordination through the coming together of industry groups to accelerate and streamline the standards development and adoption process. In addition to the various SDOs described above, the following organizations are working at national and international levels to create synergistic relationships between and across organizations. These emerging organizations are involved in standards development, coordination, and harmonization in all sectors of the economy. Since many of the health data standards issues, such as security, are not unique to the healthcare sector, this breadth of scope offers the potential for technology transfer and advancement across multiple sectors. The following is a brief description of some of the major national and international organizations involved in broad-based standards development, coordination, and harmonization.

American National Standards Institute The American National Standards Institute (ANSI) serves as the U.S. coordinating body for voluntary standards activity. Standards are submitted to ANSI by member SDOs and are approved as American National Standards through a consensus methodology developed by ANSI. ANSI is the U.S. representative to the International Organization for Standardization (ISO), and as such is responsible for bringing forward U.S. standards to that organization for approval.

European Technical Committee for Standardization In 1990, Technical Committee (TC) 251 on medical informatics was established by the European Committee for Standardization (CEN). CEN/TC 251 works to develop a wide variety of standards in the area of healthcare data management and interchange. CEN standards are adopted by its member countries in Europe and are also submitted for harmonization with ISO standards.

Health Information Technology Advisory Committee The Health Information Technology Advisory Committee (HITAC) was established in 2017 in accordance with provisions of the U.S. Federal Advisory Committee Act, and was enacted by the 21st Century Cures Office of the National Coordinator for Health IT (ONC) (2018). The HITAC will recommend to the National Coordinator for Health

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Chapter 7 • Health Data Standards: Development, Harmonization, and Interoperability 

Information Technology, policies, standards, implementation specifications, and certification criteria, relating to the implementation of a health information technology infrastructure, nationally and locally, that advances the electronic access, exchange, and use of health information. This committee replaces both the Health Information Technology Policy Committee and the Health Information Technology Standards Committee, as in existence before the date of the enactment of the 21st Century Cures Act.

Implementation Guides Standards, while a necessary part of the interoperability ecosystem, are not sufficient alone to fulfill the needs of data sharing. Simply using a standard does not necessarily guarantee health information exchange within or among organizations and systems. Standards can be implemented in various ways, so implementation specifications or guides are critical to make interoperability a reality (Sensmeier, 2010). Standard implementation specifications are designed to provide specific configuration instructions or constraints for implementation of a particular standard or set of standards. Figure 7.1 highlights the distinction between standards and implementation guides.

Exchange Standard 1

Integrating the Healthcare Enterprise Integrating the Healthcare Enterprise (IHE) is an international standards profiling organization that provides a detailed framework for implementing multiple standards to address specific use cases, filling the gaps between standards, and their implementations. IHE has published a large body of detailed specifications called integration profiles that are being implemented globally by healthcare providers and regional entities to enable standards-based safe, secure, and efficient health information exchange. Vendors publish IHE integration statements to document the IHE integration profiles supported by their products that were successfully tested at an IHE Connectathon. Users can reference the appropriate integration profiles in requests for proposals, thus simplifying the systems acquisition process. Many national and regional interoperability approaches have gained traction in furthering secure, ubiquitous, interoperable health information exchange by leveraging IHE to build their foundation. IHE’s use case–based approach to profiling standards adds tremendous value in advancing the standards-based interoperability needed to solve real-world problems today.

Vocabulary Standard

Exchange Standard 2

Refine, Focus, Disambiguate

Bind

Bind

Refine, Focus, Disambiguate

Implementation Guide A

  127

Implementation Guide B

•  FIGURE 7.1.  HIMSS Health Information Standards Work Group, 2013. (Reproduced, with permission, from HIMSS Health Information Standards Work Group. (2013). Evaluating HIT standards (p. 3). Chicago, IL: HIMSS. Copyright © 2013 Healthcare Information and Management Systems Society (HIMSS)).

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128    P art 2 • S ystem S tandards

Conformity Assessment Program

International Organization for Standardization

To better differentiate those products that can deliver robust integration capabilities in production systems, IHE has implemented a conformity assessment program with formalized, rigorous, and independent testing and validation of IHE profiles and specifications as implemented in specific versions of products going to market (IHE, 2018). On the basis of the conformity assessment scheme, test laboratories are accredited in accordance with the ISO/CEI 17025 standard. IHE International authorizes designated test laboratories to assess the conformity of products with selected IHE profiles. Products that pass this assessment receive IHE’s mark to assure that they operate as they should in meeting IHE specifications to achieve interoperability. Figure 7.2 describes the testing continuum of the IHE Conformity Assessment Program.

The International Organization for Standardization (ISO) develops, harmonizes, and publishes standards internationally. ISO standards are developed, in large part, from standards brought forth by member countries and through liaison activities with other SDOs. Often, these standards are further broadened to reflect the greater diversity of the international community. In 1998, the ISO Technical Committee (TC) 215 on Health Informatics was formed to coordinate the development of international health information standards, including data standards. This Committee published the first international standard for nursing content titled Integration of a Reference Terminology Model for Nursing. This standard includes the development of reference terminology models for nursing diagnoses and nursing actions with relevant terminology and definitions for implementation.

•  FIGURE 7.2.  Integrating the Healthcare Enterprise Conformity Assessment Scheme Committee, 2018. (Reproduced, with permission, from IHE. (2018). IHE testing continuum. Copyright © 2018 Integrating the Healthcare Enterprise (IHE)).

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Chapter 7 • Health Data Standards: Development, Harmonization, and Interoperability 

In order to address the need for global standards harmonization, the Joint Initiative Council (JIC) was founded as a liaison group under ISO/TC 215. Its goal is to enable common timely health informatics standards by addressing and resolving issues of gaps, overlaps, and counterproductive standardization efforts. Membership in JIC is restricted to international SDOs with a formal relationship to ISO.

Object Management Group While the organizations described thus far are made up of volunteer-based SDOs, the Object Management Group (OMG) is representative of a different approach to standards development. OMG is an international consortium made up primarily of for-profit vendors of information systems technology that are interested in the development of standards based on object-oriented technologies. While its standards are developed by private organizations, OMG has developed a process to lessen the potential problems noted previously with proprietary standards. Standards developed in OMG are required to be implemented in a commercially available product by their developers within one year of the standard being accepted, and the specifications for the standard are made publicly available.

INNOVATION AND INTEROPERABILITY Testing and Certification To accelerate the development, use, maintenance, and adoption of interoperability standards across the industry, and to spur innovation, the ONC has developed tools to facilitate the entire standards life cycle and maximize reuse of concepts and components, including tools and repositories for browsing, selecting, and implementing appropriate standards. The ONC has worked with NIST to provide testing tools to validate that a particular implementation conforms to a set of standards and implementation specifications. A certification process has been established so that organizations can be approved as certifying entities to which vendors may submit their EHR systems for review and certification. The Health Information Technology: Initial Set of Standards, Implementation Specifications, and Certification Criteria for Electronic Health Record Technology (45 CFR Part 170) Final Rule, published in 2010 by HHS, and updated in 2015, identifies the technical standards that must be met in the certification process, and coordinates those requirements with the meaningful use objectives. The 2015 Edition Health IT Certification Criteria (ONC, 2015) builds on past

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rulemakings to facilitate greater interoperability for several clinical health information purposes and enables health information exchange through new and enhanced certification criteria, standards, and implementation specifications. The 2015 Edition Test Method has been constructed in an outcome-focused format with additional companion guide documents (Certification Companion Guides) to aid stakeholder implementation including test procedures, test tools, and test data.

Health Information Exchange and Interoperability Formal entities have been established to provide both the structure and the function for health information exchange efforts at independent and governmental or regional/state levels. These organizations, called health information exchanges (HIEs), are geographically defined entities that develop and manage a set of contractual conventions and terms, and arrange for the governance and means of electronic exchange of information. The Strategic Health Information Exchange Collaborative (SHIEC) was founded in 2015 to enable HIEs to share best practices, promote sustainable business models, offer opportunities for joint ventures, and increase awareness of HIE perspectives among public and private entities. Today, SHIEC is a national collaborative representing more than 70 health information exchanges collectively covering more than 200 million people across the United States, well over half of the American population.

The eHealth Exchange In 2009, the first production exchange began between the Social Security Administration and MedVirginia, followed by the Veterans Health Administration and Kaiser Permanente. This was the beginning of what is now known as the eHealth Exchange. The Sequoia Project assumed stewardship of the nationwide health information network exchange from the ONC in 2012. The eHealth Exchange has grown to be the largest public–private, health information network in the country. While the initiative was incubated by The Sequoia Project, the network quadrupled in size to connect participants across all 50 states and support more than 120 million patients. Recognizing the maturity and sustainability of the network, in 2018 the eHealth Exchange became a subsidiary of The Sequoia Project. Today, the eHealth Exchange is the principal network that connects federal agencies and non-federal organizations, allowing them to work together to improve patient care and public health.

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Carequality Another subsidiary of The Sequoia Project, Carequality, is a network-to-network trust framework developed by a diverse group of representatives from across healthcare to connect existing and future data-sharing networks to each other. This capability allows providers to securely share data with providers who are part of an entirely different network. Examples of disparate networks include vendor networks, payer networks, lab networks, and others, such as the eHealth Exchange network.

CommonWell Health Alliance Launched in 2013, CommonWell Health Alliance services include patient enrollment, record location, patient identification & linking, and data query & retrieval. Today, CommonWell offers interoperability services to its 80 members and more than 11,000 provider sites. In 2018, CommonWell began offering its members access to the Carequality Framework via the CommonWell network. This marks a significant milestone on the path to achieving true nationwide health IT interoperability, advancing the vision to make health data available to individuals and providers with the right to use it, regardless of time and place.

21st Century Cures Act The 21st Century Cures Act, signed December 13, 2016 by President Obama, promotes and funds the acceleration of research into preventing and curing serious illnesses; accelerates drug and medical device development; addresses the opioid abuse crisis; and focuses on improving mental health service delivery. The Act also includes a number of provisions that advocate for greater interoperability and adoption of EHRs. Known primarily for its focus on precision medicine, the Act also contains some provisions to improve healthcare IT, specifically by expanding nationwide interoperability and reducing information blocking. This Act marks a sentinel shift in focus, placing a strong emphasis on the importance of providing patients access to their electronic health information that is “easy to understand, secure and updated automatically.” Subsequently, the ONC and the Centers for Medicare & Medicaid Services (CMS) have each unveiled sweeping and highly anticipated rules aimed at reducing information blocking and allowing patients easier access to their health data. These government efforts are collectively drawing attention to the role of the health consumer in being able to access their own health information in

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a simple and easy-to-use manner. Put simply, “patient data belongs to patients” (CMS, 2019).

APIs and HL7 FHIR Through its proposed rule based on the 21st Century Cures Act, the ONC (HHS, 2019) calls for health IT developers to publish APIs and allow health information from such technology to be accessed, exchanged, and used without special effort through their use. Through these open APIs, a developer must also provide access to all data elements (the United States Core Data for Interoperability [USCDI]) of a patient’s EHR to the extent permissible under applicable privacy laws. The proposed rule goes further to require the use of the HL7 FHIR (Fast Healthcare Interoperability Resources) standard for APIs. As this rule becomes final, patient and consumer engagement in their own health care decision-making will quickly become a reality.

The Role of the Consumer The CARIN Alliance is an organization that is working to advance the ability for consumers and their caregivers to easily get, use, and share their digital health information when, where, and how they want it, and specifically via open APIs (CARIN Alliance, 2019). They envision a future where any consumer can choose any application to retrieve their complete health record and healthcare claims information from any provider or health plan in the country. The Alliance is working closely with other key stakeholders to overcome the policy, cultural, and technological barriers to advancing consumer-directed exchange.

THE BUSINESS IMPERATIVE FOR HEALTH DATA STANDARDS The importance of data standards to enhancing the quality and efficiency of healthcare delivery and improving health outcomes is being recognized by national and international leadership. Understanding the business imperative of defining and using health data standards is critical for driving the implementation of these standards into applications and real-world systems. Having data standards for data exchange and information modeling will provide a mechanism against which deployed systems can be validated (Loshin, 2004). Reducing manual intervention will increase worker productivity and streamline operations. Defining information exchange requirements will enhance the ability to automate interaction with external partners, which in turn will improve efficiency and decrease costs.

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Using a standardized nursing language is necessary so that nursing knowledge can be represented and communicated consistently among nurses and other healthcare providers. Identifying key data elements, defining them consistently, and capturing them in a database will build a library of evidenced-based care that can be measured and validated (Rutherford 2008). Enhanced data collection will contribute to greater adherence to standards of care, assessment of nursing competencies, and evaluation of health outcomes, thus enhancing the visibility and impact of nursing interventions on improving patient care. Realizing the promise of standards will depend on the ability to access and share information across time and location from multiple devices, systems, and organizations. According to a report from the National Academy of Medicine (NAM, 2018), a major barrier to progress is not technical; rather, it is in the failure of organizational demand and purchasing requirements. The report asserts that, in contrast to many other industries, the purchasers of healthcare technologies have not used their purchasing power to drive interoperability as a key requirement. The NAM 2018 report asserts that “better procurement practices, supported by standards-based, compatible interoperability platforms and architecture, will allow for safer patient care; reduced administrative burden for clinicians; and significant financial savings.” The complex and highly fragmented healthcare system in the United States makes it difficult to develop a nationwide approach to value-based healthcare, according to a report published by the Boston Consulting Group that called for better data standards and patient registries. The report looked at efforts to improve health outcomes—while also maintaining or lowering costs—in the health systems of 12 countries: Australia, Austria, Canada, Germany, Hungary, Japan, the Netherlands, New Zealand, Singapore, Sweden, the United Kingdom, and the United States. The report (Soderlund, Kent, Lawyer, & Larsson, 2012) outlined four key success factors: 1. Clinician engagement: The greatest improvement comes when clinicians are responsible not only for collecting and interpreting data but also leading improvement efforts. 2. National infrastructure: It looked for common standards for tracking diagnoses, treatments, outcomes, and costs at the patient level; a limited number of shared IT platforms; and a common legal framework regulating the use of patient data. 3. High-quality data: The most effective way to collect relevant data is through disease registries to promote more effective and cost-efficient care.

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Chapter 7 • Health Data Standards: Development, Harmonization, and Interoperability 

4. Outcome-based incentives: Data-driven incentive measures should spur changes in the way clinicians practice, payers reimburse, and suppliers of drugs and medical devices develop and deliver products and services. HIMSS published a Call to Action (2017) to encourage a comprehensive integrated approach to care that can recognize and build upon the many mature, consensus-based standards and profiles already in place, while allowing innovation to pilot and incorporate new and emerging standards. HIMSS called on the Department of Health and Human Services and the broader health information and technology community to demonstrate the following leadership:

• • • • • •

Demand integration between the interoperability approaches and trusted exchange frameworks for the public good; Educate the community to appropriately implement existing and emerging standards, data formats, and use cases to ensure a comprehensive, integrated approach to care; Ensure stakeholder participation from across the care continuum, including patients and caregivers; Identify the “minimum necessary” business rules for trusted exchange to enhance care coordination; Standardize and adopt identity management approaches; and Improve usability for data use to support direct care and research.

HIMSS asserts that now is the time for bold action. Working together, we can achieve secure, appropriate, and ubiquitous data access and electronic exchange of health information.

SUMMARY This chapter introduces health data standards, the organizations that develop, coordinate, and harmonize them, the process by which they are developed, current activities in innovation and interoperability, and a discussion of the business imperative for implementing health data standards. Four broad areas are described to categorize health data standards. Transport standards are used to establish communication protocols between systems. Vocabulary standards are used to describe clinical problems and procedures, medications, and allergies. Content exchange standards and value sets are used to share clinical information such as clinical summaries,

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132    P art 2 • S ystem S tandards prescriptions, and structured electronic documents. And security standards are those used for authentication, access control, and transmission of health data. Organizations involved in the development, harmonization, and profiling of health data standards are described. The important role of testing, conformity assessment, and certification in validating standards implementations in health information technology systems is emphasized. A discussion of the standards development process highlights the international and sociopolitical context in which standards are developed and the potential impact they have on improving the health of individuals and populations. The increasingly significant role of regulation, legislation, and the federal government in furthering the development, adoption, and use of health data standards to enable health information exchange and data sharing is discussed. Several key initiatives that enable a nationwide health information network are described including HIEs, the eHealth Exchange, Carequality and CommonWell Health Alliance. The increasing role of the healthcare consumer in accessing and sharing their own digital health information is highlighted. Finally, the business imperative and importance of health data standards to improving the quality and efficiency of healthcare delivery and the role their adoption plays in improving health outcomes are emphasized.

Test Questions 1. Which of the following indicates the primary reason that we need health data standards? A. Used to represent a healthcare database

B. Used to describe the structure and content of healthcare information C. Used to code nursing data

D. Used to determine hardware necessary to describe health information 2. Which of the following describes the definition of interoperability?

A. A formal consensus process used by a standards development organization B. An open acceptance process to address the requirements of a use case

C. The extent to which systems and devices can exchange data, and interpret that shared data

D. The coordination of unstructured, discrete entities that exist outside of any particular context

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3. What four broad areas categorize health data standards?

A. Content Exchange, Privacy, Security, and Safety B. Security, Safety, Data, and Privacy

C. Transport, Vocabulary, Security, and Content D. Security, Transport, Privacy, and Exchange

4. Which standardized terminologies are used in EHR Systems? A. CPT, ICD-10, CCC, and RxNorm B. IEEE, CPT, CINAHL and CCC

C. ICD-10, UMLS, RxNorm, and DICOM D. CCC, ICD-10, CINAHL, and UMLS

5. Which SDOs develop domain-specific transport standards? A. LOINC, HL7, CINAHL, and ASTM

B. ASC X12N, IEEE, NEMA, and W3C C. CCC, HL7, LOINC, and CINAHL

D. ASTM, HL7, IHTSDO, and LOINC 6. How do data standards enhance the quality and efficiency of healthcare delivery? A. By decreasing worker productivity

B. By enhancing the ability to automate interaction with external partners C. By requiring additional time and effort to use D. By requiring a standardized terminology for exchange

7. The ability to communicate in a way that ensures the message is received and the content is understood is dependent on: A. Data standards

B. Knowledge representation C. Innovation

D. Harmonization 8. Which of the following types of standards are able to gain widespread acceptance by virtue of their being required by either regulation or in order to participate in large, government-funded programs? A. NEMA transport images

B. SNOMED content standards

C. Legislated, government-developed standards D. W3C XML protocols

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Chapter 7 • Health Data Standards: Development, Harmonization, and Interoperability 

9. Which entity serves as the U.S. coordinating body for voluntary standards? A. U.S. Congress B. ANSI

C. ASTM

D. HITAC 10. Which of the following can be described as a national, formal health information exchange entity? A. eHealth Exchange B. SHIEC

C. 21st Century Cures Act D. CMS

11. Which of the following efforts calls for health IT developers to publish APIs to allow health information to be accessed, exchanged, and used without special effort? A. CARIN Alliance

B. CommonWell Health Alliance C. 21st Century Cures Act D. HL7 FHIR

12. In a recent study, four key success factors were identified to enable improved health outcomes while also maintaining or lowering costs. Which of the following is NOT a key success factor? A. Clinician engagement

B. National infrastructure C. High-quality data

D. Financial incentives

Test Answers 1. Answer: B  The term health data standards is generally used to describe those standards having to do with the structure and content of health information. 2. Answer: C  The term interoperability describes the extent to which systems and devices can exchange data, and interpret that shared data. For two systems to be interoperable, they must be able to exchange data and subsequently present that data such that it can be understood by a user. 3. Answer: C  Four broad areas are identified to categorize health data standards. Transport standards are used to establish a common, predictable, secure communication protocol between systems. Vocabulary standards consist of nomenclatures and

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code sets used to describe clinical problems and procedures, medications, and allergies. Security standards are used to safeguard the transmission of health data through authentication and access control. Content standards and value sets are used to share clinical information such as clinical summaries, prescriptions, and structured electronic documents.

4. Answer: A  CPT is a code set maintained by AMA that describes medical, surgical, and diagnostic services. ICD-10 is the classification system for mortality and morbidity used worldwide. CCC is a nursing terminology recognized by ANA. RxNorm is a standardized nomenclature for clinical drugs and drug delivery devices produced by NLM.

5. Answer: B  ASC X12N standards have been adopted as national standards for such administrative transactions as claims, enrollment, and eligibility in health plans. IEEE standards focus on telecommunications and information exchange between systems including local and metropolitan area networks. NEMA formed DICOM to develop a generic digital format and a transfer protocol for biomedical images and image-related information. W3C is the main international standards organization for development of the World Wide Web. 6. Answer: B  The importance of data standards to enhancing the quality and efficiency of healthcare delivery and improving health outcomes is being recognized by national and international leadership. Reducing manual intervention will increase worker productivity and streamline operations.

7. Answer: A  The ability to communicate in a way that ensures the message is received and the content is understood is dependent on standards. Data standards are intended to reduce ambiguity in communication so that the actions taken based on data are consistent with the actual meaning of that data. 8. Answer: C  Legislated, government-developed standards are able to gain widespread acceptance by virtue of their being required by either regulation or in order to participate in large, government-funded programs, such as Medicare. Because governmentdeveloped standards are in the public domain, they are available at little or no cost and can be incorporated into any information system.

9. Answer: B  The American National Standards Institute (ANSI) serves as the U.S. coordinating body for voluntary standards activity. Standards are submitted to ANSI by member SDOs and are approved as American National Standards through a consensus methodology developed by ANSI.

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134    P art 2 • S ystem S tandards 10. Answer: A  The eHealth Exchange is the principal health information network that connects federal agencies and nonfederal organizations, allowing them to work together to improve patient care and public health. 11. Answer: C  Through its proposed rule based on the 21st Century Cures Act, ONC calls for health IT developers to publish APIs and allow health information from such technology to be accessed, exchanged, and used without special effort.

12. Answer: D  Data-driven outcome-based incentives, not financial incentives, should spur changes in the way clinicians practice, payers reimburse, and suppliers of drugs and medical devices develop and deliver products and services.

REFERENCES American Nurses Association. (2015). Inclusion of recognized terminologies supporting nursing practice within electronic health records. Retrieved from https://www. nursingworld.org/practice-policy/nursing-excellence/ official-position-statements/id/Inclusion-of-RecognizedTerminologies-Supporting-Nursing-Practice-withinElectronic-Health-Records/Accessed on February 21, 2019 ANSI. (2005). The United States standards strategy. New York, NY: American National Standards Institute. CARIN Alliance. (2019). CARIN code of conduct. Retrieved from https://www.carinalliance.com/our-work/ trust-framework-and-code-of-conduct/ Centers for Medicare and Medicaid Services (CMS). (2007). HIPAA security series: Security 101 for covered entities (Vol. 2, paper 1, pp. 1–11). Washington, DC: Centers for Medicare and Medicaid Services. Centers for Medicare and Medicaid Services (CMS). (2019). Interoperability and patient access for Medicare advantage organization and Medicaid managed care plans, state Medicaid agencies, CHIP agencies and CHIP managed care entities, issuers of qualified health plans in the federally-facilitated exchanges and health care providers. Retrieved from https://s3.amazonaws.com/public-­ inspection.federalregister.gov/2019-02200.pdf Accessed on February 21, 2019. Committee on Data Standards for Patient Safety. (2004). Patient safety: Achieving a new standard for care. Washington, DC: Institute of Medicine. Department of Health and Human Services (HHS). (2010). Health information technology: Initial set of standards, implementation specifications, and certification criteria for electronic health record technology. (45 CFR Part 170). Washington, DC: Office of the Secretary. Department of Health and Human Services (HHS). (2019). 21st Century Cures Act: Interoperability, information

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blocking, and the ONC health it certification program. Retrieved from https://www.healthit.gov/sites/default/ files/nprm/ONCCuresActNPRM.pdf Accessed on February 21, 2019. Hammond, W. E. (2005). The making and adoption of health data standards. Health Affairs, 23(5), 1205–1213. Health Information Policy Council. (1983). Background paper: Uniform minimum health data sets. Washington, DC: Department of Health and Human Services. HIMSS. (2017). HIMSS Call to Action: Achieve nationwide, ubiquitous, secure electronic exchange of health information. Retrieved from https://www.himss.org/library/ himss-call-action-achieve-nationwide-ubiquitous-secureelectronic-exchange-health-information Accessed on February 21, 2019. HIMSS. (2018). The definition of interoperability. Retrieved from https://www.himss.org/news/himss-redefinesinteroperability Accessed on February 21, 2019. Integrating the Healthcare Enterprise (IHE). (2018). IHE conformity assessment. Retrieved from https://www. ihe-europe.net/testing-IHE/conformity-assessments Accessed on February 21, 2019. National Academy of Medicine (NAM). (2018). Procuring interoperability: Achieving high-quality, connected, and person-centered care. Retrieved from https://nam.edu/ procuring-interoperability-achieving-high-quality-connected-and-person-centered-care/Accessed on February 21, 2019. National Committee on Vital and Health Statistics (NCVHS). (1996). Report of the National Committee on Vital and Health Statistics: Core health data elements. Washington, DC: Government Printing Office. National Library of Medicine (NLM). (2010). Health information technology and health data standards at NLM. Retrieved from http://www.nlm.nih.gov/healthit.html Office of the National Coordinator for Health IT (ONC). (2015). The 2015 Edition Health IT Certification Criteria. Retrieved from https://www.healthit.gov/topic/certification-ehrs/2015-edition Accessed on February 21, 2019. Office of the National Coordinator for Health IT (ONC). (2018). Recommendations to the National Coordinator for Health IT. Retrieved from https://www.healthit.gov/ topic/federal-advisory-committees/recommendationsnational-coordinator-health-it Accessed on February 21, 2019. Rutherford, M. A. (2008). Standardized nursing language: What does it mean for nursing practice? The Online Journal of Issues in Nursing, 13(1). Sensmeier, J. (2010). The impact of standards and certification on EHR Systems. HIMSS10. Atlanta, GA: Foundations of Nursing Informatics. Soderlund, N., Kent, J., Lawyer, P., Larsson, S. (2012). Progress toward value-based health care: Lessons from 12 countries. Retrieved from https://www.bcgperspectives.com/content/articles/health_care_public_sector_progress_toward_ value_based_health_care/Accessed on February 21, 2019.

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Chapter 7 • Health Data Standards: Development, Harmonization, and Interoperability 

WEB SITES The field of data standards is a very dynamic one with existing standards undergoing revision and new standards being developed. The best way to learn about specific standards activities is to get involved in the process. All of the organizations discussed in this chapter provide opportunities to be involved with activities that support standards development, coordination, harmonization, and implementation. Listed below are the World Wide Web addresses for each organization. Most sites describe current activities and publications available, and many include links to other related sites: Accredited Standards Committee (ASC) X12. www.wpcedi. com American Medical Association (AMA). www.ama-assn.org American National Standards Institute (ANSI). www.ansi.org American Nurses Association (ANA). www.nursingworld.org American Society for Testing and Materials (ASTM). www.astm.org Clinical Data Interchange Standards Consortium (CDISC). www.cdisc.org CommonWell Health Alliance. www.commonwellalliance.org Digital Imaging Communication in Medicine Standards Committee (DICOM). www.nema.org eHealth Exchange. www.ehealthexchange.org European Committee for Standardization Technical Committee 251 Health Informatics (CEN/TC 251). www. cen.eu/cen

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Health Level Seven (HL7). www.hl7.org Institute of Electrical and Electronic Engineers (IEEE). www.ieee.org Integrating the Healthcare Enterprise (IHE). www. iheusa.org International Organization for Standardization (ISO). www.iso.org International Statistical Classification of Diseases and Related Health Problems (ICD-10). www.cdc.gov/nchs Logical Observation Identifiers Names and Codes (LOINC). loinc.org National Council for Prescription Drug Programs (NCPDP). www.ncpdp.org National Electrical Manufacturers Association (NEMA). www.nema.org National Library of Medicine (NLM). www.nlm.nih.gov/ healthit.html National Uniform Claims Committee (NUCC). www. nucc.org Object Management Group (OMG). www.omg.org Office of the National Coordinator for Health Information Technology (ONC). www.healthit.gov RxNorm. www.nlm.nih.gov/research/umls/rxnorm Strategic Health Information Exchange Collaborative (SHIEC). www.strategichie.com The Sequoia Project. www.sequoiaproject.org Unified Medical Language System (UMLS). www.nlm.nih. gov/ research/umls World Wide Web Consortium (W3C). www.w3.org

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8 Standardized Nursing Terminologies Jane Englebright / Nicholas R. Hardiker / Tae Youn Kim

• OBJECTIVES . Define “standardized nursing terminology.” 1 2. Examine the impact of standardized terminologies on nursing. 3. Describe the features of advanced nursing terminology systems. 4. Apply a standardized nursing terminology to nursing services.

• KEY WORDS Classification Concept Data Ontology Terminology Vocabulary

INTRODUCTION The widespread adoption of electronic health records (EHRs) provides data from nursing practice that can be used to drive the continuous advancement of nursing science. The data generated by clinicians at the point of care can be harvested from the EHR, aggregated, combined with other data, and studied. The learnings from these analyses can then be incorporated into the documentation rubric within the EHR to facilitate practice change and generate more data. Achieving this cycle from data to insights to improvement requires adopting a standardized terminology with coded concepts. These terminologies provide data elements in a standard format that can be combined with other data sources to evaluate care delivery and continuously improve practice. Standardized, coded data can be used as quality metrics and research metrics, and can facilitate analysis across different organizations (Englebright, 2014).

A standardized nursing terminology embodies nursing concepts that represent the domain of nursing. These essential building blocks for nursing practice can be integrated with the data of other healthcare disciplines to provide care to individual patients and can be aggregated to gain insights for the care of entire patient populations (McCormick et al., 1994). This chapter provides the background necessary to understand standardized terminologies, concepts, and data elements. The chapter gives an overview of the characteristics of nursing terminologies, how they are ­developed, and how they are used in EHR systems. It considers the current “state of the science” of nursing t­ erminology work and provides an example of how n ­ ursing terminologies can be used to solve real problems in nursing practice (Englebright, 2014). Note that the word “terminology” is used throughout the chapter to refer also to “classification,” “vocabulary,” “taxonomy,” or “nomenclature.” Concepts and terms that 137

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138    P art 2 • S ystem S tandards are coded for computer processing are referred to as “data” or “data elements.”

HEALTH TERMINOLOGIES Healthcare terminologies are widely used in administrative applications. Healthcare facilities have long used the International Classification of Diseases (ICD) to report mortality and morbidity statistics internationally (WHO, 1992). A number of administrations, including the U.S. federal government, have adopted this terminology for payment of healthcare services. Other specialty groups have created additional terminologies for payment of their specific services such as Current Procedural Terminology (CPT) for surgical procedures (AMA, 2014) and Logical Observation Identifiers Names and Codes (LOINC) for laboratory tests and assessments (Regenstrief Institute, 2014). Administrative functions in healthcare, such as billing, were the first to be computerized. The widespread use of standardized, coded terminologies supported the rapid transformation of these functions. As these systems grew in sophistication, the industry and government regulators attempted to use these administrative data for measuring quality of care, patient outcomes, and resource consumption. When computerization came to the clinical functions in healthcare, there was not a widely accepted set of standard, coded clinical terminologies to guide the development of EHRs (Elfrink, Bakken, Coenen, McNeil, & Bickford, 2001). In 2012, the Institute of Medicine (IOM) decried the deplorable state of clinical data, noting that patient care data is poorly captured and managed, and scientific evidence is poorly used. The report called for the capture of clinical care data in real time and at the point of care for better patient care coordination and management. The report also recommended that to be usable the data must be interoperable to support better care across the full continuum of patient care (IOM, 2012).

NURSING TERMINOLOGIES Although the development of nursing terminologies preceded the IOM report, it was motivated by many of the same concerns. There was a need to quantify nursing resources, to effectively use the EHR systems that were entering the care environment, and to enable the application of a growing body of evidence-based nursing practice available in electronic knowledge bases (Saranto, Moss, & Jylha, 2010). As early as 1859, Florence Nightingale named her six canons of care as “what nurses do” in her text Notes on

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Nursing (1859). She considered the six canons to be measures of “good standards” that are essential for the practice of nursing. It took another 80 years for her work to be expanded in the United States when Virginia Henderson published her Textbook of the Principles and Practices of Nursing (1939) in which she delineated her “14 patterns of daily living.” Her works were followed by the works of several nurse-theorists who presented their theories and standards of nursing practice such as King’s “Process of Nursing,” Roger’s “Four Building Blocks,” or Abdellah’s “21 Problems” (Fordyce, 1984). These models were all developed as approaches to patient care; however, none referred to or predicted the use of computers to support the implementation of nursing practice standards (Englebright, 2014). In 1970 the American Nurses Association (ANA) approved the nursing process as the standard of professional nursing practice. The nursing process provides the framework for gathering patient care data, beginning with the assessment phase, through diagnosis, goal designation, planning, and evaluation (Yura & Walsh, 1983, pp. 152–155). In 1989, the ANA’s Steering Committee on Databases to Support Nursing Practice created a process to recognize terminologies and vocabularies that support nursing practice (Table 8.1). ANA (2008) recognizes minimum data sets, interface terminologies, and reference terminologies that support nursing practice (Table 8.2).

Minimum Data Sets The ANA recognizes two minimum data sets, the Nursing Minimum Data Set (NMDS) and the Nursing Minimum Management Data Set (NMMDS). Minimum data sets define an essential set of data elements for describing nursing practice or nursing management. Each data element has a standard definition and code that enables it to be used in a variety of settings and systems, maintaining the same meaning when moved from the originating system into a larger pool of data.

Nursing Minimum Data Set (NMDS) The NMDS identifies essential, common, and core data elements to be collected for all patients/clients receiving nursing care (Werley & Lang, 1988). The NMDS generally includes three broad categories of elements: (a) nursing care, (b) patient or client demographics, and (c) service elements (Table 8.3). Many of the NMDS elements are consistently collected in the majority of patient/client

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Chapter 8 • Standardized Nursing Terminologies 

  TABLE 8.1    Terminology Recognition Criteria Approved by ANA’s Congress on Nursing Practice and Economics (2008) In 2008 the American Nurses Association’s Congress on Nursing Practice and Economics approved the following ­criteria as the framework for the evaluation process for ­recognition of a terminology supporting nursing practice: 1. The terminology supports one or more components of the nursing process.

  TABLE 8.2   Current American Nurses Association (ANA)–Recognized Terminologies and Data Sets Minimum Data Sets

• Nursing Minimum Data Set (NMDS) • Nursing Management Minimum Data Set (NMMDS)

Interface Terminologies

• Clinical Care Classification (CCC) System • International Classification for Nursing Practice (ICNP) • North American Nursing Diagnosis Association International (NANDA-I) • Nursing Interventions Classification (NIC) System • Nursing Outcomes Classification (NOC) • Omaha System • Perioperative Nursing Data Set (PNDS) • Alternative Billing Concepts (ABC) Codes

Reference Terminologies

• Logical Observation Identifiers Names and Codes (LOINC) • SNOMED Clinical Terms (SNOMED-CT)

2. The rationale for development supports this terminology as a new terminology itself or with a unique contribution to nursing/healthcare. 3. Characteristics of the terminology include: • Support of one or more of the nursing domains • Description of the data elements • Internal consistency • Testing of reliability, validity, sensitivity, and specificity • Utility in practice showing scope of use and user population • Coding using context-free unique identifier 4. Characteristics of the terminology development and maintenance process include: • The intended use of the terminology • The centricity of the content (patient, community, etc.) • Research-based framework used for development • Open call for participation for initial and ongoing development • Systematic, defined ongoing process for development • Relevance to nursing care and nursing science • Collaborative partnerships • Documentation of history of decisions • Defined revision and version control mechanisms • Defined maintenance program • Long-term plan for sustainability 5. Access and distribution mechanisms are defined.

  TABLE 8.3   The U.S. Nursing Minimum Data Set (NMDS) Data Elements Nursing

• Care Elements Nursing Diagnosis • Nursing Intervention • Nursing Outcome • Intensity of Nursing Care

Patient or Client Demographic Elements

• • • • •

Service Elements

• Unique facility or service agency identifier • Unique identifier of principal registered nurse provider • Episode admission or encounter date • Discharge or termination date • Disposition of patient or client • Expected payer for most of this bill

6. Plans and strategies for future development are defined. ANA elected to retire its terminology recognition program in 2012 because of the National Committee on Vital and Health Statistics (NCVHS) recommendations for selection of standardized languages for health information technology solutions, the federal government’s actions related to the National Library of Medicine’s procurement of SNOMED-CT licenses, establishment of the Office of the National Coordinator for the Health Information Technology (ONC) and its various departments and committees, and the resultant decisions identifying standardized terminologies requisite for interoperability and data and information reporting and exchange (see http://www.healthit.gov/ policy-researchers-implementers/meaningful-use-stage2-0/standards-hub for additional details). Reproduced, with permission, from Westra B.L. (2010). Testimony Vocabulary Task Force, Standards Committee, Office of the National Coordinator. CIN: Computers, Informatics, Nursing, 28(6), 380–385.

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Personal identification Date of birth Sex Race and ethnicity Residence

records across healthcare settings, especially the patient and service elements. The NMDS is also being worked upon by a number of countries as the International Nursing Minimum Data Set (i-NMDS) (Westra, Matney, Subramanian, Hart, & Delaney, 2010).

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  TABLE 8.4   Hierarchy of Elements within the Nursing Management Minimum Data Set (NMMDS) Environment

01. Facility unique identifiers 02. Type of nursing delivery unit/service 03. Patient/client population 04. Volume of nursing delivery unit/ service 05. C  are delivery structure and outcomes 06. Patient/client accessibility 10. Accreditation/Certification/ Licensure

Nurse Resources

13. Staffing 14. Satisfaction 19. N  urse demographics per unit or service 20. Clinical mental work 21. Environmental Conditions 22. E lectronic Health Record (EHR) implementation stages

Nursing Management Minimum Data Set (NMMDS) Similar to the NMDS, the Nursing Management Minimum Data Set (NMMDS) defines 18 elements that are essential to support the management and delivery of nursing care across all types of settings (Kunkle et al., 2012). The elements are organized into three categories: environment, nursing care resources, and financial resources (Table 8.4) (Werley, Devine, Zorn, Ryan, & Westra, 1991). The NMMDS supports numerous constructed variables as well as aggregation of data, for example, unit level, institution level, and network level. This NMMDS provides the structure for the collection of uniform information that influences quality of patient care, directly and indirectly. The Environment and Nursing Care categories for the NMMDS have been reviewed, normalized to national data definition standards, and incorporated into LOINC (Regenstrief Institute, 2014); whereas the financial categories are excluded.

Interface Terminologies Interface terminologies are designed for use at the point of care. They use terms and concepts that are familiar to practicing nurses. Interface terminologies vary in scope, structure, and content. They were developed by different organizations, with different funding sources, for different purposes, with different foci, and with different

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copyright privileges. Most of the early terminologies were initially developed for paper-based documentation systems. However, over time and with the advancement of technology, all of the nursing terminologies have been adapted for automated data processing and aggregation (ONC, 2017). Some of the interface terminologies are very broad and have applicability to a variety of care settings; others are narrower. For example, CCC started as a home health care system, but has expanded to address both acute and community settings. Omaha began as a rehabilitation system and has expanded to additional settings. The Perioperative Nursing Data Set (PNDS) was developed for procedural areas and has maintained that specialty focus.

Reference Terminologies The ANA also recognizes two reference terminologies, LOINC and SNOMED-CT (Nursing Resources for Standards and Interoperability, 2017). A reference terminology acts as a common reference point that can facilitate cross-mapping between interface terminologies. SNOMED-CT  SNOMED-CT was developed collaboratively by the College of American Pathologists (CAP) and the UK National Health Service (Wang, Sable, & Spackman, 2002). It now falls under the responsibility of SNOMED International. SNOMED-CT possesses both reference properties and user interface terms. SNOMED-CT is considered to be the most comprehensive, multilingual healthcare terminology in the world and integrates concepts from many nursing terminologies. Before SNOMED International acquired SNOMEDCT from CAP, many of the ANA-recognized Interface Terminologies for Nursing were integrated into SNOMEDCT. SNOMED-CT is distributed at no cost in member countries by their national coordinating center such as the NLM in the United States. SNOMED-CT is one of a suite of designated standards for use for the electronic exchange of health information, and also is a required standard in interoperability specifications of the U.S. Health Information Technology Standards Panel (HITSP) (National Library of Medicine, 2019). LOINC  Logical Observation Identifiers Names and Codes (LOINC) was initiated in 1994 by Regenstrief Institute, a non-profit medical research organization associated with Indiana University. LOINC is a universal standard that is comprised of more than 71,000 observation terms primarily used to represent laboratory tests, measurements, and observations. It is also a clinical terminology for laboratory

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test orders and results, clinical measures such as vital signs, and other patient observations (LOINC, 2015). In 1999, LOINC was identified by the Health Level Seven (HL7) Standard Development Organization (SDO) as a preferred code set for laboratory test names in transactions between healthcare facilities, laboratories, laboratory testing devices, and public health authorities. In 2002, LOINC established a Clinical LOINC Nursing Subcommittee to provide LOINC codes primarily for patient assessments. LOINC is available at no cost and is also one of the suites of designated standards for use in U.S. Federal government systems for the electronic exchange of clinical health information (Nursing Resources for Standards and Interoperability, 2015).

Nursing Terminology Challenges There is a movement to harmonize nursing and multidisciplinary terminologies. However, there are two major challenges. First, the existence of multiple, specialized terminologies has resulted in areas of overlapping content, areas for which there was no content, and large numbers of different codes and terms for the same concepts (Chute, Cohn, & Campbell, 1998; Cimino, 1998a). Second, existing terminologies most often were developed to provide sets of terms and definitions of concepts for human interpretation, with computer interpretation only as a secondary goal (Rossi Mori, Consorti, & Galeazzi, 1998). The latter is particularly true for the majority of nursing terminologies that have been designed primarily for direct use by nurses in the course of clinical care (Association of Operating Room Nurses [AORN], 2007; Martin, 2005; Saba & Taylor, 2007). However, EHR systems that support functionality such as decision support may require more granular (i.e., less abstract) data than may be found in today’s interface terminologies (Campbell et al., 1997; Chute, Cohn, Campbell, Oliver, & Campbell, 1996; Cimino, 1998b; Cimino, Hripcsak, Johnson, & Clayton, 1989); mapping interface terminologies to Reference terminologies may provide a solution.

Advanced Terminologies for Nursing Healthcare terminologies suitable for implementation in EHR systems have been studied by numerous experts who have provided an evolving framework that enumerates a number of desirable characteristics. The characteristics apply to any terminology being used in the healthcare industry, including nursing. Advanced terminologies must be concept-oriented (with explicit semantics), rather than based on surface linguistics (Chute et al., 1998; Cimino, 1998b; Cimino et al., 1989). Other recommended criteria

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Chapter 8 • Standardized Nursing Terminologies 

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Thought or reference

Symbolizes

Stands for

Symbol Sun

Refers to

Referent

Soleil

•  FIGURE 8.1.  The Semiotic Triangle Depicts the Relationships among Objects in the Perceivable or Conceivable World (Referent), Thoughts about Things in the World, and the Labels (Symbols or Terms) Used to Represent Thoughts about Things in the World. include domain completeness and a polyhierarchical organization. Additional criteria applying to concepts themselves include being atomic level (a single concept coded as a single data element), nonredundancy (unique identifier), nonambiguity (explicit definition), concept permanence (cannot be duplicated), compositionality (ability to combine concepts to form new unique concepts), and synonymy (a single concept supports multiple terms with same meanings) (de Keizer & Abu Hanna, 2000; Henry & Mead, 1997; Whittenburg, 2011; Zielstorff, 1998). In order to appreciate the significance of concept orientation, it is important to understand the definitions of and relationships among things (objects) in the world, our thoughts (concepts) about things in the world, the labels (terms) we use to represent and communicate our thoughts about things in the world, and the coded data elements needed to represent and be processed by computer (Bakken et al., 2000; Moss, Damtongsak, & Gallichio, 2005). The terminology relationships are depicted by a descriptive model commonly called the semiotic triangle (Fig. 8.1) (Ingenerf, 1995; Ogden & Richards, 1923). The International Organization for Standardization (ISO), International Standard ISO: 1087-1:2000 provides definitions for elements that correspond to each vertex of the triangle (ISO, 2000). Concept (i.e., thought or reference): Unit of knowledge created by a unique combination of characteristics—a characteristic is an abstraction of a property of an object or of a set of objects.

Object (i.e., referent): Anything perceivable or conceivable.

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142    P art 2 • S ystem S tandards “Bladder Irrigation”

  TABLE 8.5   Evaluation Criteria Related to ConceptOriented Approaches Atomic-based—concepts must be separable into constituent components (Chute et al., 1998) Compositionality—ability to combine simple concepts into composed concepts, e.g., “pain” and “acute” = “acute pain” (Chute et al., 1998) Concept permanence—once a concept is defined it should not be deleted from a terminology (Cimino, 1998b) Language independence—support for multiple linguistic expressions (Chute et al., 1998) Multiple hierarchy—accessibility of concepts through all reasonable hierarchical paths with consistency of views (Chute et al., 1998; Cimino, 1998b; Cimino et al., 1989) Nonambiguity—explicit definition for each term, e.g., “patient teaching related to medication adherence” defined as an action of “teaching,” recipient of “patient,” and target of “medication adherence” (Chute et al., 1998; Cimino, 1998b; Cimino et al., 1989) Nonredundancy—one preferred way of representing a concept or idea (Chute et al., 1998; Cimino, 1998b; Cimino et al., 1989) Synonymy—support for synonyms and consistent mapping of synonyms within and among terminologies (Chute et al., 1998; Cimino, 1998b; Cimino et al., 1989)

Term (i.e., symbol): Verbal designation of a general concept in a specific subject field—a general concept corresponds to two or more objects which form a group by reason of common properties (ISO, 2000). As specified by the criteria in Table 8.5 and illustrated in Fig. 8.2, a single concept may be associated with multiple terms (synonym).

Ontologies Terminology models may be formulated and elucidated in an ontology language that represents classes (also referred to as concepts, categories, or types) and their properties (also referred to as relations, slots, roles, or attributes) such as Web Ontology Language (OWL) (Rector, 2004). In this way, ontology languages or terminologies are able to support, through explicit semantics, the formal definition of concepts and their relationships with other concepts (Fig. 8.2); they also facilitate reasoning about those concepts, for example, whether two concepts are equivalent or whether one concept, such as “vital sign,” subsumes (is a generalization of ) another, such as “temperature, pulse,

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Irrigating

actsOn

Bladder

•  FIGURE 8.2.  A Simple Graphical Example of a Formal Representation of the Nursing Activity Concept “Bladder Irrigation.” and respirations (TPR)” (Hardiker, Hoy, & Casey, 2000). Ontology languages are often used to support advanced terminologies. One example is the use of OWL to represent International Classification for Nursing Practice (ICNP).

Web Ontology Language (OWL) Outside the health domain, work in relation to the Semantic Web has resulted in the recognition of OWL as an emerging standard (i.e., a W3C recommendation) (McGuiness & van Harmelen, 2004). OWL is intended for use where applications, rather than humans, process data. OWL builds on existing recommendations such as Extensible Markup Language (XML) (surface syntax for structured documents), Resource Description Framework (RDF) (a data model for resources), and RDF Schema (a vocabulary for describing the properties and classes of resources) by providing additional vocabulary and a formal semantics. Software, both proprietary and open source, is available for (a) managing terminology models or ontologies developed in OWL (e.g., Protégé, 2010) and (b) reasoning on the terminology model (e.g., FaCT++) (Tsarkov, 2009). ICNP is maintained in OWL—it is a compositional standards-based terminology for nursing practice (Hardiker & Coenen, 2007). An OWL representation (in XML) of the nursing activity concept “Bladder Irrigation” is provided in Table 8.6.

Terminology Models A terminology model is a concept-based representation of a collection of domain-specific terms (data elements) that is optimized for the management of terminological definitions. It encompasses both “schemata” and “type definitions” (Campbell, Cohn, Chute, Shortliffe, & Rennels, 1998; Sowa, 1984). Schemata incorporate domain-specific knowledge about the typical constellations of entities, attributes, and events in the real world and, as such, reflect plausible combinations of concepts for naming a nursing

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  TABLE 8.6    Possible OWL Representation (in XML) of the Nursing Activity Concept “Bladder Irrigation”











diagnosis or problem, for example, “pain” may be combined with “acute” or “chronic” to make “acute pain” or “chronic pain” or for naming clinical nursing intervention, for example, “vital signs” may be combined with “teach” to form “teach vital signs.” Schemata may be supported by either formal or informal composition rules (i.e., grammar). Type definitions address obligatory conditions that state only the essential properties of a concept (Sowa, 1984), for example, a nursing activity must have a recipient, an action, and a target. Examples of terminology models that can be used to guide or underpin nursing terminologies include the international technical standard ISO 18104:2003 Integration of a Reference Terminology Model for Nursing (Bakken, Cashen, & O’Brien, 1999; Hardiker & Rector, 1998; International Council of Nurses, 2001; ISO, 2003; Saba, Hovenga, Coenen, & McCormick, 2003) and its successor ISO 18104:2014, Categorical Structures for Representation of Nursing Diagnoses and Nursing Actions in Terminological Systems (ISO, 2014). In 2003 ISO, which is responsible for identifying international standards for Health Informatics, approved ISO 18104:2003, Integration of a Reference Terminology Model for Nursing, which covers two Reference Terminology Models: one for Nursing Diagnoses (Fig. 8.3) and one for Nursing Actions (Fig 8.4) (ISO, 2003). The standard was developed by a group of experts within ISO Technical Committee 215 (Health Informatics) Working

ch08.indd 143

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Chapter 8 • Standardized Nursing Terminologies 

is perspective on

dimension is applied to

focus timing has site

site

is applied to

judgment degree potentiality acuity timing

has subject of information

subject of information

•  FIGURE 8.3.  Reference Terminology Model for Nursing Diagnoses. (This excerpt is from ISO 18104:2014, Figure 1 on page 9, with the permission of ANSI on behalf of ISO. © ISO 2015. All rights reserved.) Group 3 (Semantic Content), including representatives of the International Medical Informatics Association— Nursing Informatics Working Group (IMIA-NI) and the International Council of Nurses (ICN). The model was built on work originating within the European Committee for Standardization (CEN) (European Committee for Standardization, 2000). The development of ISO 18104:2003 and its successor was motivated in part by a desire to harmonize the plethora of nursing terminologies in use around the world (Hardiker, 2004). Another major incentive was to integrate with other evolving terminology and information model standards—the development of ISO 18104:2003 was intended to be “consistent with the goals and objectives of other specific health terminology models in order to provide a more unified reference health model” (ISO, 2003, p. 1). Potential uses identified for the terminology models included to (1) facilitate the representation of nursing diagnosis and nursing action concepts and their relationships in a manner suitable for computer processing; (2) provide a framework for the generation of compositional expressions from atomic concepts within a reference terminology; (3) facilitate the mapping among nursing diagnosis and nursing action concepts from various terminologies; (4) enable the systematic evaluation of terminologies and associated terminology models for purposes of harmonization; and (5) provide a language to describe the structure of nursing diagnosis and nursing action concepts in order to enable appropriate integration with information models (ISO, 2003).

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144    P art 2 • S ystem S tandards

has site site

action timing

has route

target

acts on has means

has subject of care

route

subject of care means

•  FIGURE 8.4.  Reference Terminology Model for Nursing Actions. (This excerpt is from ISO 18104:2014, Figure 2 on page 10, with the permission of ANSI on behalf of ISO. © ISO 2015. All rights reserved.) The standard was not intended to be of direct benefit to practicing nurses. It was intended to be of use to those who develop coding systems, terminologies, terminology models for other domains, health information models, information systems, software for natural language processing, and markup standards for representation of healthcare documents. The ISO 18104:2003 models underwent substantial bench testing, both during their development and through independent research (Hwang, Cimino, & Bakken, 2003; Moss, Coenen, & Mills, 2003; Saba et al., 2003). The current ISO 18104:2014 model which was released in 2014 further clarifies the structure of terminological expressions of nursing concepts in order to ensure the interoperability of standardized terminologies across information systems adopted in healthcare (ISO, 2014). Figures 8.3

and 8.4 show the updated models for nursing diagnoses and nursing actions, respectively.

NURSING TERMINOLOGIES IN USE Nursing terminologies are used in a variety of ways in the practice setting. They can provide conceptual guidance and a data model, and can link concepts from practice to granular data definitions provided by reference terminologies. As the need for sophisticated data analysis has advanced, nurse executives have increasingly looked to include nursing data into these analyses (Englebright & Jackson, 2017). Case Study 8.1 demonstrates how a standardized nursing terminology, in this case Clinical Care Classification (CCC), can be used to create a nursing documentation system that generates data for performance improvement.

CASE STUDY 8.1 A large health system decided to undertake the daunting task of rebuilding an electronic documentation system with the vision to create a patient-centric record to guide and inform the provision of safe, effective and efficient care and produce data to evaluate the care of individual and populations of patients. Reducing documentation burden was a clear goal; however, the need for shareable, comparable data that could be used to drive improvements in care was also critical to extracting value from the clinical hours spent on documentation. The Clinical Care Classification (CCC) was selected to guide the build process and to organize the data objects. Content development was organized around the 21 Care Components and four Action Types defined in the taxonomy. When the work group had addressed each of these elements, the content development was complete. Content displays were organized around the four health care patterns to give a consistent mental model and screen orientation throughout the system. The use of a standard taxonomy facilitated the creation of truly individualized plans of care for each patient. For example, a patient is admitted to the intensive care unit with the medical diagnosis of severe sepsis. The patient is being cared for by a multi-disciplinary team, including several physician specialists. The admitting nurse assesses the patient and identifies four priority problems or nursing diagnoses that the nursing team will champion for the patient. He selects “Confusion” (D07.1), “Infection Risk” (K25.5), “Tissue Perfusion Alteration” (S48.0), and “Family Coping Impairment”

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  145

(E11.2). These selections reflect the essence of nursing care for this patient and create a unique nursing plan of care for the patient. The nursing plan of care contributes to the multidisciplinary plan of care. Goals or expected outcomes are “To Improve” patient function in all these problem areas (D07.1.1, S48.0.1, and E11.2.1) and “To Stabilize” current status by preventing hospital acquired infections (K25.5.2). Interventions for each problem include the four action types of “Assess” (1), “Perform” (2), “Teach” (3), and “Manage” (4). As nursing interventions are documented, data is accumulated in a data warehouse. Inside the warehouse, the data can be combined with administrative, human resources, quality, and labor management data and displayed in dashboards to assist in managing and improving care. For example, as the intervention of “Bathing with chlorhexidine” (43.0) and “Intravenous Care” (79.1) are performed, the outcome of the “Infection Risk” (K25.5) nursing diagnosis can be documented as stabilized (K25.5.2). Inside the data warehouse these documentation elements can be combined with patient demographics, staffing levels, and infection rates (see Figs. 8.5 and 8.6). The resulting insights can be used to improve infection prevention programs in the institution.

All KPIs % Total-Care Compliance

96.21%

0/5

% Compliance of Timely and Necessary Dressing Changes (7 Days)

96.63%

0/5

% Daily Documentation of Device Monitoring

95.78%

0/5

0%

50%

100%

February

April

June

Documentation elements from the EHR are transmitted to the data warehouse; performance levels are calculated and displayed to guide improvement actions.

•  FIGURE 8.5.  Key Performance Indicators for Intravenous Care. (Used with permission of HCA Healthcare)

SUMMARY AND IMPLICATIONS FOR NURSING The developers of nursing and healthcare terminologies, and informatics scientists have made significant progress. From decades of nursing research, there exists an extensive set of terms describing patient problems, nursing interventions and actions, and nursing-sensitive patient outcomes (ANA, 2008; AORN, 2007; Coenen, 2003; Dochterman & Bulechek, 2004; International Council of Nurses, 2009; Martin, 2005; Moorhead, Johnson, & Maas, 2004; NANDA, 2008; Ozbolt, 1998; Saba, 2012, 2019). Terms which are useful for representing nursing-relevant

ch08.indd 145

concepts have been integrated into or linked into large healthcare reference terminologies (Bakken et al., 2000; Bakken et al., 2002; Henry, Holzemer, Reilly, & Campbell, 1994; Lange, 1996; Matney, Bakken, & Huff, 2003). A number of efforts within nursing and the larger healthcare arena are aimed toward the achievement of advanced terminology systems that support semantic interoperability across healthcare information systems. Ontology languages supported by suites of software tools have been developed within the context of terminologies with broad coverage of the healthcare domain (Campbell et al., 1998). Applicability of these tools to the nursing domain has been demonstrated (Hardiker & Rector, 1998; Zingo, 1997).

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146    P art 2 • S ystem S tandards Summary View YTD Goal Progress

Clinical Outcomes

Outcome Measure Rolling 4 Quarters

Monthly

Workforce Engagement

Efficient Delivery

Patient Experience

Quarterly

QTD

Monthly

Monthly

R12

YoY Comparison

PoP Comparison

Goal

Baseline

Progress

CP*

PP*

%∆

CP PY

%∆

CAUTI SIR

Lower is Be..

0.411

0.862

6%

0.833

0.862

–3.4%

0.858

–2.9%

CLABSI SIR

Lower is Be..

0.354

0.874

10%

0.822

0.874

–5.9%

1.011

–18.7%

CDIFF SIR

Lower is Be..

0.497

0.718

---

0.665

0.718

–7.4%

0.796

–16.5%

CAUTI Infections

Lower is Be..

---

74.0

---

73.0

73.0

0.0%

93.0

–21.5%

CLABSI Infections

Lower is Be..

---

67.0

---

60.0

56.0

7.1%

71.0

–15.5%

CDIFF Infections

Lower is Be..

---

847.0

---

781.0

861.0

–9.3%

1126.0

–30.6%

IP Overall Rating..

Higher is Be..

78

71.0

6%

71.5

71.4

0.0%

71.7

–0.3%

ED Overall Ratin..

Higher is Be..

74

70.8

0%

69.5

69.7

–0.3%

69.6

–0.2%

Nurse Leader Ro..

Higher is Be..

93

93.8

100%

94.0

94.2

–0.1%

69.6

0.9%

Comm About Pain

Higher is Be..

---

69.7

---

69.5

69.4

0.2%

78.2

–0.1%

Comm w/Nurses

Higher is Be..

---

78.1%

---

78.2

78.2

0.0%

71.4

–0.1%

How offen staff..

Higher is Be..

---

71.5

---

71.5

71.3

0.3%

74.6

0.2%

Nurses expl in w..

Higher is Be..

---

74.2%

---

74.7

74.4

0.4%

76.5

0.2%

Nurses listen car..

Higher is Be..

---

76.2%

---

76.2

76.2

–0.1%

83.7

–0.4%

Nurses treat wit..

Higher is Be..

---

83.7%

---

83.7

84.0

–0.3%

67.8

0.1%

Staff talk about..

Higher is Be..

---

68.0

---

67.5

67.5

0.0%

93.2

–0.5% –3.5%

% Contract Hours

Lower is Be..

---

4.5%

---

5.2%

5.3%

–2.3%

5.4%

% Overtime Hours

Lower is Be..

---

5.3%

---

4.6%

6.1%

–25.5%

4.5%

0.4%

FTE Variance

Neg. # is Mi..

---

3092.41

---

1395.46

2090.16

–33.2%

1075.66

29.7%

PCT Variance to..

Negative is..

---

.00

---

–326.00

–398.00

18.1%

.00

FTE Variance to..

Positive is ..

---

1293

---

5307

2698

96.7%

3908

Sitters FTEs

Lower is Be..

---

2032.76

---

1935.98

1897.21

2.0%

2073.68

–6.6%

Sitters Hours pe..

Lower is Be..

---

.45

---

.42

.40

5.0%

.47

–10.5%

5 Quarter Trend

35.8%

RNs with BSN or..

Higher is Be..

---

42.3%

---

44.5%

44.6%

–0.4%

38.4%

15.9%

RNs with Certific..

Higher is Be..

---

7.6%

---

8.1%

8.0%

1.4%

5.9%

36.9%

(R12) FT/PT RN T..

Lower is Be..

17%

16.5%

100%

16.4%

16.5%

–0.4%

17.3%

–4.8%

(R12) FT/PT FY R..

Lower is Be..

17%

22.0%

8%

21.5%

21.8%

–1.3%

23.2%

–7.0%

•  FIGURE 8.6.  Data on Infection Results Aggregated into Scorecards for Unit, Hospital, or System Level Performance Monitoring and Improvement. Standardized nursing terminologies are needed to (a) provide valid clinical care data, (b) allow data sharing across today’s EHR systems, (c) support evidence-based decision making, (d) facilitate evaluation of care processes, and (e) permit the measurement of outcomes. Standardized nursing data elements are needed to facilitate aggregation and comparison for clinical, translational, and comparative effectiveness research, as well as for the development of practice-based nursing protocols and evidence-based knowledge, including the generation of healthcare policy (Hardiker, Bakken, Casey, & Hoy, 2002). To support continuity of care and the exchange of data, for example, to implement in the United States the federal regulations for “meaningful use” (MU), standardized nursing concepts

ch08.indd 146

must be interoperable between EHR systems, and across healthcare settings and population groups. Such demands require that the initial standardized nursing terminology concepts be coded in a structure that is suitable for ­computer-based processing. Today, advanced concept-oriented terminology systems are increasingly being considered as essential infrastructure for clinical practice. They (a) provide for nonambiguous concept definitions, (b) facilitate composition of complex concepts from more primitive concepts, and (c) support mapping among terminologies (Campbell et al., 1997; Chute et al., 1996; Cimino, Clayton, Hripcsak, & Johnson, 1994; Henry et al., 1994). A number of benefits are being realized through the use of advanced terminology

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  147



Chapter 8 • Standardized Nursing Terminologies 

systems such as (a) the facilitation of evidence-based practice (e.g., linking of clinical practice guidelines to appropriate patients during the patient-provider encounter); (b) the matching of potential research subjects to research protocols for which they are potentially eligible; (c) the detection and prevention of potential adverse outcomes; (d) the linking of online information resources; (e) the increased reliability and validity of data for quality evaluation; and (f) the data mining of unknown relationships in EHRs for purposes such as clinical research, health services research, or knowledge discovery. In addition, complementary research has focused on examining how terminology models and advanced terminology systems relate to other types of models that support semantic interoperability, such as (a) the Health Level 7 Reference Information Model (RIM) (Goossen et al., 2004), (b) Open EHR Archetypes (Beale, 2003), (c) Detailed Clinical Models (Goossen, 2008), and (d) an ontology for document naming (Dykes, Dadamio, & Kim, 2012; Hyun et al., 2009). Such interoperability is a prerequisite to meeting the information demands of complex healthcare, management, and nursing environments.

3. What type of standardized languages does the ANA recognize?

Test Questions 1. In 1970 the American Nurses Association (ANA) approved the Nursing Process as the standard of professional nursing practice, with its framework that provides: A. Assessment phase B. Diagnosis

C. Goal Designation D. Planning phase

B. Interface Terminologies

C. Reference Terminologies

D. Support nursing practice E. Only B and C

F. All of the above 4. What is the Minimum Data Set? A. An essential set of 18 data elements for describing nursing practice B. A set of 28 data elements with varying definitions and codes C. A set of different concepts that each are unique

D. A set of only minimum management data E. A and D F. Only A

5. What are Interface Terminologies?

A. Terminologies that are used for the point of care documentation B. Terminologies with terms and concepts familiar to practicing nurses C. Terminologies used for documenting nursing education

E. Implementation phase

D. Terminologies for International nursing specialties

G. All of the above

F. All of the above

F. Evaluation phase

H. None of the above 2. What kind of tree structure does a standardized nursing terminology require? A. Data that can be aggregated upward.

E. A and B

6. What are Reference Terminologies? A. A terminology that can serve as a common reference point

B. Data that can be parsed downward.

B. A terminology that can facilitate cross-mapping between interface terminologies

D. Data that represents the Nursing Process framework.

D. A terminology that has multiple purposes.

C. Data elements that are atomic level.

E. All of the above.

F. None of the above .

ch08.indd 147

A. Minimum data sets

C. A terminology that the ANA recognizes such as LOINC and/or SNOMED CT E. All of the above F. A and B

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148    P art 2 • S ystem S tandards 7. Nursing terminologies that have been designed primarily for direct use by nurses in the course of nursing practice may not provide the granularity needed for EHR functionality. A. Granular nursing concepts need not be defined and/or coded.

B. Mapping Interface Terminologies to Reference Terminologies may provide a potential solution.

C. Granular nursing practice documentation is not recorded in EHRs. D. Mapping of granular concepts is not necessary. E. All of the above. F. Only B.

8. What are the criteria for advanced terminologies for nursing? A. Advanced terminologies must be concept-oriented.

B. Advanced terminologies must have domain completeness.

C. Advanced terminologies must have poly-hierarchical organization.

D. Advanced terminologies must have unique definitions and codes. E. All of the above.

F. None of the above. 9. What is the function of Ontology Language?

A. An Ontology Language is a set of classes (also referred to as concepts, categories, or types).

B. An Ontology Language has properties (also referred to as relations, slots, roles, or attributes).

C. An Ontology Language consists of the formal definition of concepts and their relationships with other concepts. D. An Ontology Language facilitates reasoning about those concepts, for example, whether two concepts are equivalent or whether one concept, such as “vital sign,” subsumes (is a generalization of ) another, such as “temperature, pulse, and respirations (TPR)”.

E. All of the above. F. Only D.

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10. What is a Nursing Terminology Model?

A. A Nursing Terminology Model is a concept-based representation of a collection of domain-specific terms (data elements).

B. A Nursing Terminology Model encompasses “schemata” (domain-specific knowledge about the typical constellations of entities, attributes, and events in the real world). C. A Nursing Terminology Model encompasses types of definitions (obligatory conditions that state only the essential properties of a concept).

D. A Nursing Terminology Model must be approved as a standard by the International Organization Standards (ISO). E. All of the above F. Only B and C.

Test Answers 1. Answer: G 2. Answer: E 3. Answer: F 4. Answer: F

5. Answer: E 6. Answer: E 7. Answer: E 8. Answer: E 9. Answer: E 10. Answer: F

REFERENCES American Medical Association. (2014). CPT-2014: Current procedural terminology. Chicago, IL: AMA. American Nurses Association. (2008). Nursing informatics: Scope and standards of practice. Silver Spring, MD: ANA. Association of Operating Room Nurses. (2007). PNDS— Perioperative nursing data set (2nd ed., rev.). Denver, CO: AORN. Bakken, S., Cashen, M., & O’Brien, A. (1999). Evaluation of a type definition for representing nursing activities within a concept-based terminologic system. In N. Lorenzi (Ed.), 1999 American Medical Informatics Association Fall Symposium (pp. 17–21). Philadelphia, PA: Hanley & Belfus.

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Bakken, S., Cimino, J. J., Haskell, R., Kukafka, R., Matsumoto, C., Chan, G. K., & Huff, S. M. (2000). Evaluation of the clinical LOINC (Logical Observation Identifiers, Names, and Codes) semantic structure as a terminology model for standardized assessment measures. Journal of the American Medical Informatics Association, 7(6), 529–538. Bakken, S., Warren, J. J., Lundberg, C., Casey, A., Correia, C., Konicek, D., & Zingo, C. (2002). An evaluation of the usefulness of two terminology models for integrating nursing diagnosis concepts into SNOMED clinical terms. International Journal of Medical Informatics, 68(1–3), 71–77. Beale, T. (2003). Archetypes and the EHR. Studies in Health Technology and Informatics, 96, 238–244. Campbell, J., Carpenter, P., Sneiderman, C., Cohn, S., Chute, C., & Warren, J. (1997). Phase II evaluation of clinical coding schemes: Completeness, taxonomy, mapping, definitions, and clarity. Journal of the American Medical Informatics Association, 4(3), 238–251. Campbell, K., Cohn, S., Chute, C., Shortliffe, E., & Rennels, G. (1998). Scalable methodologies for distributed development of logic-based convergent medical terminology. Methods of Information in Medicine, 37(4–5), 426–439. Chute, C., Cohn, S., & Campbell, J. (1998). A framework for comprehensive terminology systems in the United States: Development guidelines, criteria for selection, and public policy implications. ANSI Healthcare Informatics Standards Board Vocabulary Working Group and the Computer-based Patient Records Institute Working Group on Codes and Structures. Journal of the American Medical Informatics Association, 5(6), 503–510. Chute, C. G., Cohn, S. P., Campbell, K. E., Oliver, D. E., & Campbell, J. R. (1996). The content coverage of clinical classifications. Journal of the American Medical Informatics Association, 3(3), 224–233. Cimino, J. (1998a). The concepts of language and the language of concepts. Methods of Information in Medicine, 37(4–5), 311. Cimino, J. (1998b). Desiderata for controlled medical vocabularies in the twenty-first century. Methods of Information in Medicine, 37(4–5), 394–403. Cimino, J., Hripcsak, G., Johnson, S., & Clayton, P. (1989). Designing an introspective, multi-purpose, controlled medical vocabulary. In L. C. Kingsland III (Ed.), Symposium on Computer Applications in Medical Care (pp. 513–518). Washington, DC: IEEE Computer Society Press. Cimino, J. J., Clayton, P. D., Hripcsak, G., & Johnson, S. B. (1994). Knowledge-based approaches to the maintenance of a large controlled medical terminology. Journal of the American Medical Informatics Association, 1(1), 35–50.

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Coenen, A. (2003). Building a unified nursing language: The ICNP. International Nursing Review, 50(2), 65–66. deKeizer, N. F., & Abu-Hanna, A. (2000). Understanding terminology systems II: Experience with conceptual & formal representation of structure. Methods of Information in Medicine, 39, 22–29. Dochterman, J., & Bulechek, G. M. (2004). Nursing interventions classification (4th ed.). St. Louis, MO: C. V. Mosby. Dykes, P. C., Dadamio, R. R., & Kim, H. E. (2012, June 23). A framework for harmonizing terminologies to support representation of nursing practice in electronic records. Nursing Informatics: Proceedings of the International Congress on Nursing Informatics. 2012 (p. 103). Montreal, Canada. Elfrink, V., Bakken, S., Coenen, A., McNeil, B., & Bickford, C. (2001). Standardization of nursing vocabularies: A foundation for quality care. Seminars in Oncology Nursing, 17(1), 18-23. Englebright, J. (2014). Defining and incorporating basic nursing actions into the electronic health record. Journal of Nursing Scholarship, 46, 50–57. Englebright, J., & Jackson, E. (2017). Wrestling with big data:  How nurse leaders can engage. In C. Delaney, C. Weaver, J. Warren, T. Clancy, & R. Simpson (Eds.), Big dataenabled nursing. Cham, Switzerland: Springer. European Committee for Standardization. (2000). CEN ENV Health Informatics: Systems of concepts to support nursing. Brussels, Belgium: CEN. Fordyce, E. M. (1984). Theorists in nursing. In J. M. Fynn, & P. B. Heffron (Eds.), Nursing from concept to practice (pp. 237–258). Bowie, MD: Brady. Goossen, W., Ozbolt, J., Coenen, A., Park, H., Mead, C., Ehnfors, M., & Marin, H. (2004). Development of a provisional domain model for the nursing process for use within the Health Level 7 reference information model. Journal of the American Medical Informatics Association, 11(3), 186–194. Goossen, WT. (2008). Using detailed clinical models to bridge the gap between clinicians and HIT. Studies in Health Technology and Informatics, 141, 3–10. Hardiker, N. (2004). An international standard for nursing terminologies. In J. Bryant (Ed.), Current perspectives in healthcare computing (pp. 212–219). Swindon, UK: Health Informatics Committee of the British Computer Society. Hardiker, N. R., Bakken, S., Casey, A., & Hoy, D. (2002). Formal nursing terminology systems: A means to an end. Journal of Biomedical Informatics, 35(5–6), 298–305. Hardiker, N. R., Hoy, D., & Casey, A. (2000). Standards for nursing terminology. Journal of American Medical Association, 7(6), 523–528.

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150    P art 2 • S ystem S tandards Hardiker, N. R., & Rector, A. (1998). Modeling nursing terminology using the GRAIL representation language. Journal of the American Medical Informatics Association, 5(1), 120–128. Hardiker, N. R., & Coenen, A. (2007). Interpretation of an international terminology standard in the development of a logic-based compositional terminology. International Journal of Medical Informatics, 76S2, S274–S280. Harmer, B., & Henderson, V. (1939). Textbook of the principles and practices of nursing. New York, NY: McMillan. Henry, S. B., Holzemer, W. L., Reilly, C. A., & Campbell, K. E. (1994). Terms used by nurses to describe patient problems: Can SNOMED III represent nursing concepts in the patient record? Journal of the American Medical Informatics Association, 1(1), 61–74. Henry, S. B., & Mead, C. N. (1997). Nursing classification systems: Necessary but not sufficient for representing “what nurses do” for inclusion in computer-based patient record systems. Journal of the American Medical Informatics Association, 4(3), 222–232. Hwang, J. I., Cimino, J. J., & Bakken, S. (2003). Integrating nursing diagnostic concepts into the medical entities dictionary using the ISO Reference Terminology Model for Nursing Diagnosis. Journal of the American Medical Informatics Association, 10(4), 382–388. Hyun, S., Shapiro, J., Melton, G. B., Schlegel, C., Stetson, P., Johnson, J. B., & Bakken, S. (2009). Iterative evaluation of the Health Level 7—LOINC clinical document ontology for representing clinical document names: A case report. Journal of the American Medical Informatics Association, 16 (3), 395–399. Ingenerf, J. (1995). Taxonomic vocabularies in medicine: The intention of usage determines different established structures. In R. A. Greenes, H. E. Peterson, & D. J. Protti (Eds.). Proceedings: MedInfo ’95 (pp. 136–139). Vancouver, BC: HealthCare Computing and Communications, Canada. Institute of Medicine. (2012). Best care at low cost: The path to continuously learning healthcare in America. Washington, DC: IOM. International Council of Nurses. (2001). International classification for nursing practice (beta 2 version). Geneva, Switzerland: International Council of Nurses. International Council of Nurses. (2009). International classification for nursing practice (version 2). Geneva, Switzerland: International Council of Nurses. Retrieved from http://www.Icn.ch/PillarsPrograms/Internationalclassification-for-nursing-practice-icnpr/. Accessed on June 1, 2014. International Organization for Standardization. (2000). International Standard ISO 1087 1:2000 terminology: Vocabulary: Part 1: Theory and application. Geneva, Switzerland: International Organization for Standardization. International Organization for Standardization. (2003). International Standard ISO 18104:2003 Health Informatics—Integration of a reference terminology

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model for nursing. Geneva, Switzerland: International Organization for Standardization. International Organization Standardization. (2014). International Standard ISO 18104:2014 Health Informatics: Categorical structures for representation of nursing diagnoses and nursing actions in terminological systems. Geneva, Switzerland: International Organization for Standardization. Kunkle, D., Westra, B. L., Hart, C. A., Subramanian, A., Kenny, S., & Delaney, C. W. (2012). Updating and normalization of the nursing management. Minimum data set: Element 6: Patient/Client accessibility. Computers in Nursing, 30(3), 134–141. Lange, L. (1996). Representation of everyday clinical nursing language in UMLS and SNOMED. In J. Cimino (Ed.), 1996 American Medical Informatics Association Fall Symposium (pp. 140–144). Philadelphia, PA: Hanley & Belfus. Logical Observation Identifiers Names and Codes (LOINC) (2015, February, 17). Retrieved from the U.S. National Library of Medicine: https://www. nlm.nih.gov/research/ umls/LOINC_main.html. Accessed on March, 10, 2019 Martin, K. S. (2005). The Omaha System: A key to practice, documentation, and information management. St. Louis, MO: Elsevier. Matney, S., Bakken, S., & Huff, S. M. (2003). Representing nursing assessments in clinical information systems using the logical observation identifiers, names, and codes database. Journal of Biomedical Informatics, 36(4–5), 287–293. McCormick, K., Lang, N., Zielstorff, R., Milholland, K., Saba, V. K., & Jacox, A. (1994). Towards Standard Classification Schemes for Nursing Languages: Recommendations of the American Nurses Association Steering Committee on Databases to Support Clinical Nursing Practice. Journal of the American Medical Information Association, 1(6), 421. McGuiness, D. L., & van Harmelen, F. (Eds.), (2004). OWL Web Ontology Language overview. World Wide Web consortium. Retrieved from www.w3.org/TR/owl-features/. Accessed on July 27, 2010. Moorhead, S., Johnson, M., & Maas, M. (Eds.), (2004). Nursing outcomes classification (3rd ed.). St. Louis, MO: C. V. Mosby. Moss, J., Coenen, A., & Mills, M. (2003). Evaluation of the draft international standard for a reference terminology model for nursing actions. Journal of Biomedical Informatics, 36(4–5), 271–278. Moss, J. A., Damtongsak, M., & Gallichio, K. (2005). Proceedings of 2005 AMIA Annual Symposium (pp. 545– 549). Washington, DC: AMIA. National Library of Medicine. (2019). SNOMED CT. Retrieved from: https://www.nlm.nih.gov/healthit/ snomedct/. Accessed on March, 10, 2019 Nursing Resources for Standards and Interoperability. (2017, February 10). Retrieved from U.S. National Library of Medicine web site: https://www.nlm.nih.gov/research/

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umls/Snomed/nursing_terminology_resources.html. Accessed on March, 10, 2019 National Resources for Standards and Interoperability. (2015, July 28). Retrieved from U.S. National Library of Medicine web site: https://www.nlm.nih.gov/research/ umls/SNOMED/Nursing_terminology_resources.html. Accessed on March, 10, 2019 Nightingale, F. (1859). Notes on nursing. Commemorative edition. Philadelphia, PA: J. B. Lippincott. North American Nursing Diagnosis Association. (2008). NANDA nursing diagnoses 2009–20011: Definitions and classification 2009–2011. Philadelphia, PA: North American Nursing Diagnosis Association. Office of the National Coordinator for Health Information Technology (ONC). (May 15, 2017). Standard nursing terminologies: A landscape analysis. MBL Technologies, Clinovations, Contract # GS35F0475X, Task Order # HHSP2332015004726. https://www.healthit.gov/sites/ default/files/snt_final_05302017.pdf. Accessed April 18, 2019. Ogden, C., & Richards, I. (1923). The meaning of meaning. New York, NY: Harcourt, Brace & World. Ozbolt, J. G. (1998). Ozbolt’s Patient Care Data Set (Version 4.0). Nashville, TN: Vanderbilt University. Protégé. (2010). What is Protégé-OWL? Retrieved from http://protege.stanford.edu/overview/protege-owl.html. Accessed on July 27, 2010. Rector, A. L. (2004). Defaults, context, and knowledge: Alternatives for OWL-indexed knowledge bases. Pacific Symposium on Biocomputing (pp. 226–237), January 6–10, 2004, Hawaii. Regenstrief Institute. (2014). Logical Observation Identifiers Names and Codes (LOINC). Indianapolis, IN: Regenstrief Institute. Rossi Mori, A., Consorti, F., & Galeazzi, E. (1998). Standards to support development of terminological systems for healthcare telematics. Methods of Information in Medicine, 37(4–5), 551–563. Saba, V. (2019). Clinical care classification system. Retrieved from www.sabacare.com. Accessed on June 10, 2010. Saba, V., Hovenga, E., Coenen, A., & McCormick, K. A. (2003, September). Nursing language: Terminology models for nurses. Geneva, Switzerland: ISO Bulletin. Saba, V. K. (2012). Clinical Care Classification (CCC) System, Version 2.5: User’s guide. New York, NY: Springer. Saba, V. K., & Taylor, S. L. (2007). Moving past theory: Use of a standardized, coded nursing terminology to enhance nursing visibility. CIN: Computers, Informatics, Nursing, 25(6), 324–331.

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Saranto, K. A., Moss, J., & Jylha, V. (2010). Medication counselling: Analysis of electronic documentation using the Clinical Care Classification System. In C. Safran, H. Marin, & S. Reti (Eds.), Proceedings of the MEDINFO 2010. The Netherlands: IOS Press. Sowa, J. (1984). Conceptual structures. Reading, MA: Addison-Wesley. Tsarkov, D. (2009). Factplusplus. Retrieved from http://code. google.com/p/factplusplus/. Accessed on July 27, 2010. Wang, A., Sable, J. H., & Spackman, K. (2002). The SNOMED clinical terms development process: Refinement and analysis of content. In I. Kohane (Ed.), 2002 American Medical Informatics Association Fall Symposium (pp. 845–849). Philadelphia, PA: Hanley & Belfus. Werley, H. H., Devine, E. C., Zorn, C. R., Ryan, P., & Westra, B. L. (1991). The Nursing Minimum Data Set: abstraction tool for standardized, comparable, essential data. American Journal of Public Health, 81(4), 421–426. Werley, H. H., & Lang, N. M. (Eds.), (1988). Identification of the Nursing Minimum Data Set. New York, NY: Springer. Westra, B., Matney, S., Subramanian, A., Hart, C., & Delaney, C. (2010). Update of the NMMDS & mapping to LOINC®. In C. Weaver, C. Delaney, P. Weber, & R. Carr (Eds.), Nursing and informatics for the 21st century: An international look at practice, education, and EHR trends (2nd ed., pp. 269–275). Chicago, IL: AMIA & HIMSS. Whittenburg, L. (2011). Postpartum nursing records: Utility of the clinical care classification system. Doctoral Dissertation, Fairfax, VA: George Mason University. World Health Organization. (1992). International statistical classification of diseases and related health problems: ICD-10 (10th ed.). Geneva, Switzerland: WHO. Yura, H., & Walsh, M. B. (1983). The nursing process (4th ed.). Norwalk, CT: Appleton-Century-Crofts Publishing. Zielstorff, R. D. (1998, September 30). Characteristics of a good nursing nomenclature from an informatics perspective. Online Journal of Issues in Nursing, 3(2). Manuscript 4. Retrieved from http://ojin.­nursingworld. org/MainMenuCategories/ANAMarketplace/ ANAPeriodicals/OJIN/TableofContents/Vol31998/ No2Sept1998/CharacteristicsofNomenclaturefrom InformaticsPerspective.html Accessed on March, 10, 2019 Zingo, C. A. (1997). Strategies and tools for creating a common nursing terminology within a large health maintenance organization. In U. Gerdin, M. Tallberg, & P. Wainwright (Eds.), Proceedings: Nursing Information 1997 (pp. 27–31). Stockholm, Sweden: IOS Press.

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9 Human–Computer Interaction Gregory L. Alexander

• OBJECTIVES . Define Human–Computer Interaction. 1 2. Recognize theoretical underpinnings for Human–Computer Interaction. 3. Describe Human–Computer Interaction methods in healthcare.

• KEY WORDS Human–Computer Interaction Information technology Patient care Patient safety System design

INTRODUCTION Human–Computer Interaction (HCI) is broadly defined as an intellectually rich and highly impactful phenomenon influenced by four disciplines: (1) Human Factors and Ergonomics, (2) Information Systems, (3) Computer Science, and (4) Library and Information Science (Grudin, 2012). Aspirations of fledgling HCI researchers and practitioners, over the past few decades, were to develop better menus, enhance use of graphical user interfaces, advance input devices, construct effective control panels, and improve information comprehension (Waterson & Catchpole, 2016). There are few fields, like HCI, which can claim such a rapid expansion and strong influence on the design of ubiquitous technologies including desktops, Web, and mobile devices used by at least 5 billion users around the world (Shneiderman, 2012). In healthcare specifically, digital technologies that are rapidly becoming important in HCI domains are listed in Table 9.1 (Gulliksen, 2017). This chapter provides important information for nurses engaged in HCI efforts to improve healthcare

systems and processes. The purpose of this chapter is to elevate nurses’ understanding of theoretical underpinnings for HCI approaches used to evaluate clinical technologies; to infuse HCI concepts by identifying important HCI approaches, during this time of rapid and continuous change; and finally, to describe how HCI evaluation can lead to improved performance and outcomes in nurse-led systems (Nelson, 2018).

HUMAN FACTORS: A BUILDING BLOCK FOR HUMAN–COMPUTER INTERACTION Human factors is a discipline that tries to optimize relationships between technology and people (Kantowitz & Sorkin, 1983; McCormick & Sanders, 1982). Human factors have been defined in a number of ways by a number of experts (Table 9.2). Human factors experts apply information about human characteristics and behavior to determine optimal design specifications for tools people use, 153

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154    P art 2 • S ystem S tandards

  TABLE 9.1    Examples of Digital Healthcare Technologies Requiring HCI-Focused Methodologies • Technologies for preventative health and self-management: To help people gather information about symptoms and conditions through online knowledge databases and repositories for self-management of chronic conditions. • Technologies to make administration more effective: Increasing extent of IT use in administrative activities may provide better administrative oversight of clinical processes and risk management that might be useful for identifying residents at risk and improving care delivery. • Electronic health records: Sharing clinical information across healthcare organizations (e.g., hospitals and nursing homes) through an interoperable health information exchange to support decisions about admission and discharge needs for patients. • Digital skills for information and communication: Establishing realistic and measureable benchmarks for eHealth literacy levels for consumer health. • Technology supporting synchronous and asynchronous medical decision-making: Access and usability of technologies in remote, rural areas to support synchronous and asynchronous medical diagnosis, treatment, and monitoring of people with chronic conditions. • Sensor technologies and remote monitoring: Use of meaningful interfaces and data visualization techniques from sensor systems that integrate multiple types of clinical information captured from people living in the community, including activities of daily living, fall risk, and vital signs capturing (e.g., heart rate, respirations, bed restlessness). • Computers in trauma and disaster management: Augmented virtual reality systems to rehabilitate and restore function to a person’s body after a traumatic injury. • Medication administration technology: Electronic medication reminder systems for patients or clinical decision support tools for healthcare providers to assure proper medication dosing and management.

such as technology, in their daily life (Johnson & Barach, 2007). The goal of a human factors approach in nurse-led systems is to optimize the interactions between nurses and the tools they use to perform their jobs, minimize error, and maximize efficiency, optimize well-being, and improve quality of life. HCI, concerned with interactions between people and computers, is an area of study concentrated on by human factors experts (Staggers, 2002). HCI is defined as the study of how people design, implement, and evaluate interactive computer systems in the context of users’ tasks and work (Nelson & Staggers, 2014). HCI emerged in the 1980s as an interdisciplinary field incorporating ideals of computer science, cognitive science, and human factors engineering, but since has grown into a science incorporating concepts and approaches from many other disciplines. An excellent history of HCI written by Grudin can be found in Human Computer Interaction Handbook (Grudin, 2012). Some critics believe current descriptions of HCI require broader definitions. Critics suggest that current definitions do not reflect ubiquitous, pervasive, social, embedded, and invisible user-oriented technologies (Shneiderman, 2012). Further, some HCI critics want to move beyond computer use to emphasize other components of HCI including “… user experience, interaction design, emotional impact, aesthetics, social engagement, empathetic interactions, trust building, and human responsibility” (Shneiderman, 2012).

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FRAMEWORKS FOR HCI NURSING RESEARCH Early pioneers in nursing informatics set the stage for development of nursing information systems and their use in storing information, knowledge development, and development of technology in caregiving activities (Graves & Corcoran, 1989; Schwirian, 1986; Turley, 1996; Werley & Grier, 1981). These early models had several limitations including a lack of environmental and task-oriented elements, conceptual differences across frameworks, and a lack of time dimensions; subsequently, nursing frameworks were proposed to illustrate dynamic interactions occurring between nurses, computers, and enabling elements that optimize a user’s ability to process information via computers (Staggers & Parks, 1993). These became the early foundations for incorporating human factors approaches into the design of information technologies used by nurses. However, there were still limitations identified in these early models because they did not explicitly make the patient part of the model and they didn’t define the context or include all elements of nursing’s metaparadigm (Effken, 2003). Effken (2003) proposed the Informatics Research Organizing Model, which emphasized all elements of nursing’s metaparadigm including the system, nurse, patient, and health. Later, Alexander’s Nurse—Patient Trajectory Framework was proposed (Alexander, 2007). Alexander’s framework utilizes nursing process theory, human factors, and nursing and patient

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  TABLE 9.2    Human Factors Definitions Author

Year

Human Factors Definition

Weinger, Pantiskas, Wiklund, and Carstensen Rogers, Lamson, and Rousseau

1998

Human factors is the study of the interrelationships between humans, the tools they use, and the environments in which they live and work.

2000

Lin, Vicente, and Doyle

2001

Gosbee

2002

Bates and Gawande

2003

Potter, Boxerman, Wolf, Marshall, Grayson, Sledge, and Evanoff

2004

A general tenet of human factors design is that safety should be ensured through design of the system. If potential hazards cannot be designed out, then they should be guarded against. If guarding against the hazards is not possible, then an adequate warning system should be developed. This discipline focuses on the interaction between technology, people, and their work context. Human factors have sometimes been narrowly associated with human–computer interaction design guidelines. A discipline concerned with the design of tools, machines, and systems that take into account human capabilities, limitations, and characteristics. Principles of design using human factors suggest it is important to make warnings that are more serious and look different than those that are less serious. The study of human beings and their interactions with products, environments, and equipment in performing tasks and activities.

Boston-Fleischhauer

2008

Sharples, Martin, Lang, Craven, O’Neill, and Barnett

2012

Vincent, Li, and Blandford

2013

Guastello

2014

Wachter and Gupta

2018

Alexander, Frith, and Hoy

2019

The discipline that studies human capabilities and limitations and applies that knowledge to the design of safe, effective, and comfortable products, processes, and systems for the human beings involved. The discipline of human factors has demonstrated that if a device is well designed then this will have positive implications for usability, defined as “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use.” (ISO 9241-11) The application of theory, principles, data, and methods to design in order to optimize human well-being and overall system performance. Although it still stays true to its original concerns about the person–machine interface, it has expanded to include new developments in stress research, accident analysis and prevention, and nonlinear dynamic systems theory (how systems change over time), and some aspects of human group dynamics and environmental psychology. Human factors engineering is an applied science of systems design that is concerned with the interplay between humans, machines, and their work environments. Its goal is to assure that devices, systems, and working environments are designed to minimize the likelihood of error and optimize safety. The scientific discipline concerned with the understanding of the interactions among humans and other elements of a system, and the profession that applies theory, principles, data, and methods to design in order to optimize human well-being and overall system performance.

Alexander, S., Frith, K., & Hoy H. (2019). Applied clinical informatics for nurses (2nd ed.). Burlington, MA: Jones & Bartlett Learning. Bates, D. W. & Gawande, A. A. (2003). Improving safety with information technology. New England Journal of Medicine, 348, 2526–2534. Boston-Fleischhauer, C. (2008). Enhancing healthcare process design with human factors engineering and reliability science, part 1: Setting the context. Journal of Nursing Administration, 38, 27–32. Gosbee, J. (2002). Human factors in engineering and patient safety. Quality and Safety in Health Care, 11, 352–354. Guastello, S. J.(2014). Human factors engineering and ergonomics: A systems approach (2nd ed.). Boca Raton, FL: CRC Press. Lin, L., Vicente, K. J., & Doyle, D. J. (2001). Patient safety, potential adverse drug events, and medical device design: A human factors engineering approach. Journal of Biomedical Informatics, 34, 274–284. Potter, P., Boxerman, S., Wolf, L., Marshall, J., Grayson, D., Sledge, J., & Evanoff, B. (2004). Mapping the nursing process: A new approach for understanding the work of nursing. Journal of Nursing Administration, 34, 101–109. Rogers, W. A., Lamson, N., & Rousseau, G. K. (2000). Warning research: An integrative perspective. Human Factors, 42, 102–139. Sharples, S., Martin, J., Lang, A., Craven, M., O’Neill, S., & Barnett, J. (2012). Medical device design in context: A model of user-device interaction and consequences. Displays, 33, 221–232. Vincent, C. J., Li, Y., and Blandford, A. (2014). Integration of human factors and ergonomics during medical device design and development: It’s all about communication. Applied Ergonomics, 45(3), 413–419. Wachter, R., & Gupta, K. (2018). Human factors and errors at the person–machine interface (Section 2, Chapter 7). Understanding patient safety (3rd ed.). New York, NY: McGraw-Hill. Weinger, M., Pantiskas, C., Wiklund, M. E., & Carstensen, P. (1998). Incorporating human factors into the design of medical devices. Journal of the American Medical Association, 280, 1484.

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156    P art 2 • S ystem S tandards trajectories as components of a framework that can be used to evaluate patient care systems. The midrange framework specifically emphasizes the use of human factors, including HCI, approaches to link patient care processes, nurse and patient trajectories, and nursing and patient outcomes (Fig. 9.1). Examples of HCI design and research focused on user experiences (UX) will be used to achieve the objectives.

DESIGNING FOR HCI The discipline of HCI incorporates proponents of interaction design. Interaction designers are concerned withshaping digital things for people’s use to maximize efficiency and minimize error (Lowgren, 2013). Concepts proposed by HCI experts in healthcare have significant implications for design of pervasive technologies that are being developed and adopted by healthcare providers PATIENT CONTEXT

and patients (Staggers, Elias, Makar, & Alexander, 2018). Interaction designers are characterized as shaping and transforming processes through the use of digital devices; they consider all possible futures for a digital design space; designers frame a problem at the same time they are creating a solution; and finally, designers address instrumental and technical aspects of digital media, but also recognize aesthetical and ethical aspects of designs (Lowgren, 2013).

USER EXPERIENCE (UX) IN HCI UX encompasses all aspects of the end-user experience (Norman & Nielsen, 2019). Current UX pain points in nursing that are impacting clinical practice include health IT design/usability, IT fit to workflow, excessive documentation, interoperability, and lack of information to support care processes (Staggers et al., 2018). These UX pain points among PATIENT ENVIRONMENT

PATIENT NEEDS

NURSE CONTEXT

NURSE ENVIRONMENT NURSING PROCESS ACTIONS

REPORTING

RECORDING

HUMAN FACTORS

PATIENT TRAJECTORY

NURSE TRAJECTORY

OBSERVATION

HUMAN–COMPUTER INTERACTION SUGGESTED USER EXPERIENCE (UX) PAIN POINTS HEALTH IT DESIGN/USABILITY WORKFLOW DOCUMENTATION BURDEN INTEROPERABILITY

NURSING OUTCOMES

PATIENT OUTCOMES

STAFF SATISFACTION PROCESSES OF CARE PATIENT SAFETY

PATIENT SATISFACTION PROCESSES OF CARE PATIENT SAFETY

•  FIGURE 9.1.  Alexander’s Nurse-Patient Trajectory Framework. (From Alexander G.L. (2007). The nurse-patient trajectory framework. Studies in Health Technology and Informatics, 129(2), 910-914. Reproduced with permission from Gregory L. Alexander, PhD.)

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others can be assessed using methods available for designers to assess the usefulness of a piece of medical technology. Table 9.3 provides a comparison of HCI methods that can be used by interested readers for capturing end-user experiences.

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if an interactive system is usable, and if a system is usable, then usability evaluation can determine the extent of usability using hardy usability metrics, and finally, that usability evaluation is an accessible form of analysis and easy enough to learn about in HCI literature (Cockton, 2013).

HEALTH IT DESIGN/USABILITY IN HCI

Contextual Inquiry

Usability evaluation determines the extent that a technology is easy and pleasurable to use by determining if it is well adapted to users, their tasks, and that negative outcomes are minimized as a result of use (Bastien, 2010). Usability evaluation has been a staple of HCI researchers for the past 30 years and continues to grow in prominence as technology advances have been made around the world. HCI designers have proposed that usability is inherently measureable in all electronic technologies, that usability evaluation determines

Contextual inquiry is qualitative in nature. This methodology is derived from ethnography, which focuses on scientific descriptions and illustrations of social groups and systems. Contextual inquiry is usually conducted in the field using extensive, well-designed, systematic observations to capture how people interact with technology in real-world settings. Through this method the researcher becomes immersed in the group or system to understand how interactions take place. This method provides rich data that can be voluminous

  TABLE 9.3    Comparison of Health IT HCI Design Methods Cognitive Task Analysis

Usability Tests

Heuristics

Cognitive Walkthrough

Focus Groups

Delphi Technique

Moderate/ high Moderate/ high Field

Moderate

Moderate

Low

Low

Low

Low

Moderate

Moderate

Low

Low

Moderate

Moderate

Field/lab

Field/lab

Lab

Lab

Office

Stage of product development

Preconcept/ concept

Evaluation

Concept through to evaluation

Evaluation

Evaluation

Conference room All

Type of data

Qualitative

Qualitative

Qualitative and quantitative

Quantitative

Qualitative and quantitative

Qualitative

Quantitative

Type of user

“Real” users

“Real” users

“Real” users and proxies

Proxies

Proxies

“Real” users and proxies

“Real” users

Level of investigator expertise required

Moderate/ high

Moderate/ high

Moderate

Moderate

Moderate

Moderate

Moderate

Information yield

Moderate/ high

Moderate/ high

Moderate/ high

Low/ moderate

Low/ moderate

Low/moderate

Moderate

Method Relative costs Time Setting

Contextual Inquiry

Pre-concept/ concept

Source: Reprinted with permission from Martin, J.L., Norris, B.J., Murphy, E., & Crose, J.A. (2008). Medical device development: The challenge for ergonomics. Applied Ergonomics, 39, 271-283. Copyright © Elsevier.

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158    P art 2 • S ystem S tandards depending on number of settings involved, time spent in the settings, and the number of observations captured in the setting. Typically, sample subjects are key informants who have specialized knowledge, status, or skills which are of interest to the researcher. Oftentimes one, two, or more subjects can be observed individually or in dyads over periods of time to understand how interactions take place or how technology may influence interactions. Decisions about the sample and settings also have impacts on costs of conducting contextual inquiry research, which can be high. Researchers using contextual inquiry methods use their interpretations of observations as a source to answer important questions about social groups or systems. In many studies, other methods are used concurrently with contextual inquiry, such as focus groups in order to validate researchers’ interpretations of phenomenon observed in the field.

Cognitive Task Analysis Cognitive research is used to describe psychological processes associated with the acquisition, organization, and use of knowledge (Hollnagel & Hollnagel, 2003). Cognitive processes in human–machine interactions are complex and involve continuous exchanges of information between operators and the machines they use, which is a type of shared cognition. For example, nurses and physicians work in tandem to deliver optimal care for each patient. The design of human– machine interfaces such as nursing and physician interfaces used for documentation and medical record review must consider the nature of interdisciplinary work. Unfortunately, studies typical of evaluating nursing workflow disruption have not been a focus in similar studies assessing physician workflow; for instance, investigating how nursing roles and activities are affected by physician orders when implementing a clinical information system would provide valuable design input for electronic medical record designs (Lee & McElmurry, 2010). Medical devices are also an important human–machine interface, which are sometimes shared and need to be tested collaboratively by interdisciplinary healthcare teams, but these evaluations are limited. For example, evaluations of computerized provider order entry for pharmacy and medication administration systems should include both pharmacists and nurses. Joint interdisciplinary studies might ensure safer execution of orders and delivery of medications as a result of agreed upon design considerations that benefit both disciplines (Alexander & Staggers, 2009). Cognitive processes in human–machine systems involve the operator providing input to the machine, the machine acting on the input, and displaying information back to the operator; the operator processes information through sensing mechanisms such as visual, auditory, somatosensory, and vestibular systems; and finally, the operator determines if the information from the machine is accurate, providing

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correct communication, decides what actions to take, and provides new input to the machine (Proctor, Proctor, & Gavriel, 2006). Attempts to understand and exploit human capabilities and strengths within the area of human cognitive ability are critical to the safe design of technology. Safe design includes responses to human stress and is an important variable in HCI research. For example, the ability of a nurse to make timely and accurate decisions and to be vigilant of machine alarms during periods of sleep deprivation while working several 12-h night shifts in a row is a common work scenario that is worthy of attention in HCI research. Furthermore, human factors experts in nursing have begun using mapping techniques called link analysis to map the cognitive processes of nursing work to understand what stresses or interruptions nurses encounter during work, which contribute to cognitive delay. For example, nonlinearity of nursing work, which requires frequent shifts in the process of delivering care, results in interruptions and delays in care that contribute to unsafe environments (Potter, Boxerman, & Wolf, 2004). Understanding cognitive abilities of operators in the healthcare sector provides better understanding of physical and operational structures that affect clinical decision-making and clinical reasoning that may lead to potential system failures. Cognitive task analysis (CTA) used to evaluate task load has been used in healthcare settings. These types of analyses are typically qualitative in nature and involve interactions with “real” users to inform the design of new devices, which have usability outcomes already established (Martin, Norris, Murphy, & Crowe, 2008). Examples of CTA, in health care research, include the identification of potential errors performed with computer-based infusion devices used for terbutaline administration in preterm labor; to evaluate cognitive and physical burdens during period of high workload and stress while using computerbased physiological monitoring systems in cardiac anesthesia; and to gain new perspectives in the work of nursing processes to understand how disruptions can contribute to nursing error in acute care environments (Cook & Woods, 1996; Obradovich & Woods, 1996; Potter, et al., 2004).

Usability Tests The International Organization for Standardization’s definition of usability is “The extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use” (Iso/Iec, 1998). Usability evaluations are viewed as a critical component to inform all stages of medical device product development from initial concepts through evaluation. The potential impacts of well-designed usability studies for medical devices are reduced user errors and intensification of patient safety efforts. For example, the National Institute

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of Standards and Technology in the U.S. Department of Commerce issued a report describing why usability of electronic medical records is critical (Lowry, Quinn, & Mala, 2012). In the report, the authors outline a three-step process for electronic health record (EHR) evaluation and human user performance testing including the following: (1) During the electronic health record design stage, users, work settings, and workflows are documented to determine possible system usability problems and to develop a working model that minimizes potential safety risks; (2) conduct an expert analysis comparing the prototype interface designs to rigorously established design standards, such as heuristics discussed next in order to establish estimates of effectiveness, efficiency, and potential risks; and finally (3) examine critical tasks to be performed with real users documenting objective performance measures, such as task completion times, number of errors, and failures. Additionally, subjective measures are important to identify, such as mismatches between user expectations identified during periods when users are encouraged to think aloud about their experiences. Subject measures can be measured with simple usability scales, as the System Usability Scale, which is freely available for researchers. These processes, if conducted in a rigorous way, could be applied to any medical device usability evaluation, not just electronic health records. Usability studies are often conducted in labs during early conceptual and prototype stages of a project. However, usability studies should also be conducted through to final evaluation stages in the field. This is important because field experiments can bring up unintended consequences due to workflows and processes encountered by users during interactions with devices. There is evidence in the literature that field studies are not conducted as often as they should be for crucial medical devices being implemented in patient care settings (Alexander & Staggers, 2009). Reasons for a lack of usability studies in the field could be increased costs and disruptions to services from excessive time commitments required for these types of assessments.

Heuristics This type of HCI evaluation involves a small group of experts, who evaluate quantitatively how well a device meets established design standards, called heuristics (Sharp, Rogers, & Preece, 2008). Procedures for a heuristic evaluation are part of an iterative design process that enables identification and, hopefully, elimination of potential risks that may cause dangerous outcomes. For example, designers of an aging-in-place sensor system supported an extensive heuristic analysis of the sensor data display using three outside experts who were trained in HCI methods (Alexander et al., 2011). The sensor data display was evaluated against 16 heuristics, which included

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96 heuristic criteria in total, during the conceptual phase, before it was deployed in the clinical setting where care coordinators use the sensor interface to coordinate care. The assessment was conducted in a usability lab where the expert reviewers could interact with the sensor interface. Many recommendations were made by reviewers to the design team to improve the sensor data interface, which were incorporated into the subsequent interface designs. The heuristic evaluation resulted in 26 recommendations for design change. Findings were classified according to their importance, the most important being heuristic criteria that were not met. The most important design changes that were not met included flexibility and efficiency of use of the interface due to lack of functional descriptions of interface components provided, help and frequently asked question (FAQ) documentation that were provided to users, lack of the interface to support all skill levels of users, and poor navigational issues related to lack of feedback about where users were located in the system during interactions. Positive ratings were given heuristic criteria related to aesthetic appeal of the interface and minimalist design. Through this process the design team was able to identify weaknesses in system design prior to implementation, responded by redesigning the interface to strengthen support of these established criteria, and reducing the risk of potential negative outcomes of use of the system.

Cognitive Walkthrough Similar to heuristic evaluation, cognitive walkthrough is conducted using expert evaluators who are not necessarily part of the population of end users of a technological device (Martin et al., 2008). Cognitive walkthrough evaluations are task-specific, as compared to heuristic evaluation, which provides a holistic view of the interface and system features. To be successful an investigator conducting a cognitive walkthrough must be aware of who are the systems users, what tasks are to be analyzed, and sequences of tasks to be conducted, and that evaluators must know how the interface functions (Mahatody, Sagar, & Kolski, 2010). This means that evaluators must be familiar with tasks, task composition, how tasks are allocated, and feedback given in response to tasks. Tasks.  Tasks involve interplay between physical and cognitive activities and may be considered to follow a continuum between nearly pure physical tasks, such as transporting a patient to an X-ray to nearly pure cognitive tasks such as assessing hemodynamic status. Tasks tend to describe discrete, detailed behaviors needed to carry out functions and functions tend to describe continuous, macro-level behaviors, such as analyzing or detecting phenomena (Sharit, 1997).

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160    P art 2 • S ystem S tandards Task Composition.   A task or action sequence starts with a goal, then steps are initiated based upon user intentions, followed by the sequence of actions to be performed or intended to be performed, and the steps in the execution of the task. After tasks are executed they are evaluated based on user perception, interpretation, and evaluation of the interpretations of the actions. Task structures may be shallow, narrow, wide, and deep. Most everyday tasks, which occupy most of a human’s time, are considered shallow, narrow structures that are opportunistic in nature, requiring little complexity in analysis and minimal conscious activity. In shallow and narrow structures, humans need only examine alternative actions and act; alternatively, wide and deep structures require a considerable amount of conscious planning and thought, and usually require deliberate trial-and-error functions (Norman, 2002). Feedback.   Conditions that have been found to hinder feedback in healthcare environments include incomplete awareness that system failures have occurred, time and work pressures, delays in action or outcome sequences, case infrequence, deficient follow-up, failed communication, deficient reporting systems, case review biases, shift work, and handoffs (Croskerry, 2000). Feedback is an important element that may be derived from display information in HCIs and is important in the perception, implementation, and evaluation of tasks. Not all system feedback mechanisms are technical in nature, sometimes feedback mechanisms are created through human quality audits, peer reviews, and data mining. Emotional risks associated with the failure to provide feedback include loss of confidence, uncertainty about performance, and increased stress. Feedback mechanisms have been recognized as important components in HCIs. Improvements have been recognized in the visibility and standardization of coordination of care mechanisms in wireless computerized information systems in nursing home information systems. In these settings, improved feedback mechanisms positively affected staff documentation and communication patterns in automated wireless nursing home environments where mobile devices were used by nurse assistants to document activities of daily living as they occurred; simultaneously, nurses were able to see what cares had been completed and outcomes related to the care. This seamless transition resulted in better quality and efficiency of patient care (Rantz, Alexander, & Galambos, 2010). In other reports evaluations of response times to critical laboratory results using automated feedback mechanisms resulted in decreased response times following an appropriate treatment order (Kuperman, Teich, & Tanasijevic, 1999). Information technologies that facilitate transmission of important patient data can improve the quality of care.

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Focus Groups Focus groups are an excellent method to cumulate rich qualitative descriptions of how people interact with technological systems. These are low costs methodologies that require little overhead to conduct, but can provide a significant amount of information about usefulness of technologies, system processes, and satisfaction of people using technology. Focus groups methodologies can be used throughout all product design stages and usually involve small groups of users being interviewed by a researcher who is interested in a specified phenomenon familiar to the users (Krueger, 1994). Focus groups are usually conducted in a controlled environment to avoid distraction, such as a conference room, where people can feel free to share openly about their experiences. Typically, focus groups are conducted using well-thought-out questions, which are used by researchers to maintain some methodological consistency. Taking time to think about these questions ahead of time will enhance reliability and reduce bias that could be introduced by randomly questioning participants. Data from multiple focus groups are usually analyzed for emerging themes that help support the research question. Data are analyzed until data saturation is reached and all themes are realized from the data.

Delphi Technique The Delphi technique is used to gain consensus from experts on a subject. This method uses multiple rounds of data collection from experts, with each round using data from previous rounds. The questions posed focus on the opinions, forecasts, and judgments of experts on a specific topic. Each round of questions completed is analyzed, summarized, and returned to the experts with a new questionnaire. With each round of questions experts look over previous information provided by the group and formulate opinions based on the whole group’s feedback until a consensus is reached. This process of response, feedback, and response is usually repeated at least three times, or until a general group consensus is obtained. Benefits of this methodology include acquiring input from multiple experts who may be geographically dispersed; further, overhead and costs are generally low to conduct the method. Some limitations to this method are that it can be time consuming and cooperation of consensus panel members might be reduced over the length of the study.

OUTCOMES OF HCI Traditional outcomes associated with HCI methods are efficiency, effectiveness, and satisfaction, which, as stated, are highly related to how usable a piece of technology

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is. These outcomes have been a long-standing, central feature of HCI for many years. With current emphasis by national institutes, such as National Institute of Standards and Technology, on incorporating HCI methodologies into the life cycle of developing technologies, HCI principles and methods will continue to grow in importance. Theoretical frameworks or models of clinical care using any form of technology should incorporate HCI outcomes related to both the clinician and the patient. For example, satisfaction with particular technologies will differ depending on whether the interaction being evaluated is between a patient and a computer or a nurse and a computer. As new forms of technologies evolve, traditional HCI outcomes may require updating to keep pace with development.

Test Questions 1. Trajectories that patient’s experience often begin before contact is made with healthcare providers. A. True

B. False

2. It is recommended to use human computer action methods when testing interoperable health information exchange. A. True

B. False

3. Cognitive Task Analysis methods use quantitative data. A. True

B. False

4. Documentation in EHRs is part of the nursing context that should be considered in the IT design studies. A. True

B. False

5. Delphi Techniques primarily use qualitative data to arrive at final decisions. A. True

B. False 6. Any human factors evaluation should consider environmental variables. A. True

B. False 7. Proxies may be used for Cognitive Walkthrough methodologies. A. True

B. False

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8. Human–computer interaction designers typically separate the problem from the solution when designing IT systems. A. True

B. False 9. Contextual inquiry methods are quantitative in nature. A. True

B. False 10. Heuristic design methods are iterative in nature. A. True

B. False

Test Answers 1. Answer: A 2. Answer: A 3. Answer: B

4. Answer: A 5. Answer: B

6. Answer: A 7. Answer: A 8. Answer: B 9. Answer: B

10. Answer: A

REFERENCES Alexander, G. L. (2007). The nurse-patient trajectory framework. Studies in Health Technology and Informatics, 129, 910–914. Alexander, G. L., & Staggers, N. (2009). A systematic review on the designs of clinical technology: Findings and recommendations for future research. Advances in Nursing Science, 32(3), 252–79. Alexander, G. L., Wakefield, B. J., Rantz, M. J., Aud, M. A., Skubic, M., & Erdelez, S. (2011). Evaluation of a passive sensor technology interface to assess elder activity in independent living. Nursing Research, 60(5), 318–325. Bastien, J. M. (2010). Usability testing: a review of some methodological and technical aspects of the method. International Journal of Medical Informatics, 79, e18–e23. Cockton, G. (2013). Usability evaluation. “The Encyclopedia of Human-Computer Interaction.” Retrieved from http:// www.interaction-design.org/encyclopedia/interaction_ design.html Accessed 08/28/2020.

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162    P art 2 • S ystem S tandards Cook, R. I., & Woods, D. D. (1996). Adapting to new technology in the operating room. Human Factors, 38(4), 593–613. Croskerry, P. (2000). The feedback sanction. Academic Emergency Medicine, 7(11), 1232–1238. Effken, E. (2003). An organizing framework for nursing informatics research. Computers Informatics Nursing, 21(6), 316–325. Graves, J. R., & Corcoran, S. (1989). The study of nursing informatics. Image Journal of Nursing Scholarship, 21(4), 227–231. Grudin, J. (2012). A moving target: The evolution of HumanComputer Interaction. In J. Jacko (Ed.), Human-computer interaction handbook. London: CRC Press. Gulliksen, J. (2017). Institutionalizing human-computer interaction for global health. Global Health Action, 10. doi:https://doi.org/10.1080/16549716.2017.1344003 Hollnagel, E. (2003). Prolegomenon to cognitive task design. In: E. Hollnagel (Ed.), Handbook of cognitive task design (pp. 3–15). Mahwah, NJ: Lawrence Erlbaum Associates. Iso/Iec. (1998). Ergonomic requirements for office work with visual display terminals (VDT)s—Part 14 Menu dialogue. International Standards Organization (ISO), ISO 924114. Retrieved from https://www.iso.org/standard/16886. html. Accessed April 30, 2020. Johnson, J. K., & Barach, P. (2007). Clinical microsystems in health care: The role of human factors in shaping the microsystem. In: P. Carayon (Ed.), Handbook of human factors and ergonomics in health care and patient safety (pp. 95–107). Mahwah, NJ: Lawrence Erlbaum Assoc. Kantowitz, B. H., & Sorkin, R. D. (1983). Human factors: Understanding people–system relationships. New York, NY: John Wiley & Sons. Krueger, R. A. (1994). Focus groups: A practical guide for applied research. Thousand Oaks, CA: Sage. Kuperman, G. J., Teich, J. M., Tanasijevic, M. J, et al. (1999). Improving response to critical laboratory results with automation. Journal of the American Medical Informatics Association, 6, 512–522. Lee, S., & McElmurry, B. (2010). Capturing nursing care workflow disruptions: Comparison between nursing and physician workflows. CIN: Computers, Informatics, Nursing, 28(3), 151–159. Lowgren, J. (2013). Interaction design-brief intro. “The Encyclopedia of Human-Computer Interaction.” Retrieved from http://www.interaction-design.org/encyclopedia/ interaction_design.html. Accessed 08/28/2020 Lowry, S. Z., Quinn, M. T., Mala, R., et al. (2012). Technical evaluation, testing, and validation of the usability of electronic health records. Gaithersburg, MD: National Institute of Standards and Technology. Mahatody, T., Sagar, M., & Kolski, C. (2010). State of the art on the Cognitive Walkthrough Method: Its variants and evolutions. International Journal of Human Computer Interaction, 26(8), 741–785. Martin, J. L., Norris, B. J., Murphy, E., & Crowe, J. A. (2008). Medical device development: The challenge for ergonomics. Applied Ergonomics, 39,271–283.

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McCormick, E. J., & Sanders, M. S. (1982). Human factors in engineering and design. New York, NY: McGraw-Hill. Nelson, R. (2018). The evolution of health informatics. In: R. Nelson, & N Staggers (Eds.), Health informatics: a interprofessional approach (2nd ed. pp. 594–611). St. Louis, MO: Elsevier. Nelson, R., & Staggers, N. (2014). Health informatics. St. Louis, MO: Mosby. Norman, D. A. (2002). The design of everyday things. New York, NY: Double Day. Norman, D., & Nielsen, J. (2019). The definition of user experience (UX). Retrieved from https://www.nngroup.com/ articles/definition-user-experience/ Accessed 08/28/2020. Obradovich, J. H., & Woods, D. D. (1996). Users as designers: How people cope with poor HCI design in computerbased medical devices. Human Factors, 38(4), 574–592. Potter, P., Boxerman, S., Wolf, L., et al. (2004). Mapping the nursing process: A new approach for understanding the work of nursing. Journal of Nursing Administration, 34, 101–109. Proctor, R. W., & Proctor, J. D. (2006). Sensation and perception. In: S. Gavriel (Ed.), Handbook of human factors and ergonomics (pp. 53–88). Hoboken, NJ: Wiley. Rantz, M. J., Alexander, G. L., Galambos, C., et al. (2010). Evaluation of the use of beside technology to improve quality of care in nursing facilities: A qualitative analysis. CIN: Computers, Informatics, Nursing, 29(3), 149–156. Schwirian, P. M. (1986). The NI pyramid: A model for research in nursing informatics. Computers in Nursing, 4(3), 134–136. Sharit, J. (1997). Allocation of functions. In: G. Salvendy (Ed.), Handbook of Human factors and ergonomics (pp. 302–337). New York, NY: John Wiley & Sons. Sharp, H., Rogers, Y., & Preece, J. (2008). Interaction Heuristic Evaluation Toolkit. from the Interaction Design: Beyond Human-computer Interaction (3rd ed).White Sussex, UK: Wiley & Sons.. Shneiderman, B. (2012). Expanding the impact of humancomputer interaction. In: J. Jacko (Ed.), Human-computer interaction handbook (pp. xv–xvi). London: CRC Press. Staggers, N. (2002). Human-computer interaction. In: S. Englebardt & R. Nelson (Eds.), Information technology in health care: An interdisciplinary approach: harcourt health science company (pp. 321–345). Staggers, N., & Parks, P. L. (1993). Description and initial applications of the Staggers & Parks nurse-computer interaction framework. Computers in Nursing, 11(6), 282–290. Staggers, N., Elias, B., Makar, E., & Alexander, G. L. (2018). The imperative of solving nurses’ usability problems with health information technology. Journal of Nursing Administration, 48(4), 191–196. doi:10.1097/NNA.0000000000000598. Turley, J. (1996). Toward a model for nursing informatics. Image Journal of Nursing Scholarship, 28(4), 309–313. Waterson, P., & Catchpole, K. (2016). Human factors in healthcare: Welcome progress, but still scratching the surface. BMJ Quality and Safety, 25, 480–484. Werley, H. H., & Grier, M. R. (1981). Nursing information systems. New York, NY: Springer.

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10 Trustworthy Systems for Safe and Private Healthcare Dixie B. Baker

• OBJECTIVES 1. To provide an overview of U.S. law and regulations applicable to the protection of electronic health information. 2. To explain the relationship among security, privacy, and trust and their relevance to nursing professionals. 3. To describe by examples the risk context establishing the need for trustworthy systems. 4. To introduce and describe a trust framework comprising seven layers of protection essential for protecting sensitive and safety-critical health information and services.

• KEY WORDS Health information technology Privacy Security Trustworthy systems

INTRODUCTION For over a decade, the healthcare industry has been undergoing a dramatic transformation that is affecting every aspect of healthcare, from how, to whom, and by whom care is delivered, to how conditions are diagnosed and treated, how biomedical science advances, and how information is collected, protected, and leveraged in both individual and population health. Disruptive changes brought about through legal mandates aimed at lowering the cost and improving the quality of care, advances in information technology, and a more collaborative, engaged consumer population have resulted in an increased focus

on team-based care, value-based reimbursement, and an increasing reliance on health information technology (HIT) across the continuum of care. As noted by former National Coordinator David Blumenthal, MD, MPP, “Information is the lifeblood of modern medicine” and HIT is its circulatory system, without which neither individual healthcare professionals nor healthcare institutions can perform at their best or deliver the highest-quality care (Blumenthal, 2009). To carry Dr. Blumenthal’s analogy one step further, at the heart of modern medicine lies “trust.” For the past 20 years, Gallup poll respondents have ranked nurses the “most trusted profession[al],” based on their honesty and 163

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164    P art 2 • S ystem S tandards ethics (Stone, 2019). To maintain their trustworthiness, nurses must be able to trust that the critical information resources they need will be accurate and available when needed, while preserving individual privacy rights. They must trust that the information in a patient’s electronic health record (EHR) is accurate and complete and that it has not been accidentally or intentionally corrupted, modified, or destroyed. Consumers must trust that their caregivers will keep their most private health information confidential and will disclose and use it only to the extent necessary and in ways that are legal, ethical, and authorized, consistent with the individual’s personal expectations and preferences. Above all else, consumers must trust that their caregivers and the technology they use will “do no harm.” The nursing field is firmly grounded in a tradition of ethics, patient advocacy, care quality, and human safety. The registered nurse has a responsibility to be well indoctrinated on clinical practice that respects personal privacy and that protects confidential information and life-critical information services. The American Nurses Association’s (ANA’s) Code of Ethics for Nurses with Interpretive Statements, the foundation of the nursing profession includes a commitment to “promote, advocate for, and protect the rights, health, and safety of the patient” (ANA, 2015). The International Council of Nurses (ICN) Code of Ethics for Nurses affirms that the nurse “holds in confidence personal information and uses judgment in sharing this information” (ICN, 2012). Fulfilling these ethical obligations is the individual responsibility of each nurse, who must trust that the information technology she relies upon will help and not harm patients and will protect their private information. Recording, storing, using, and exchanging information electronically do indeed introduce risks. As anyone who uses e-mail, texting, or social media knows, very little effort is required to instantaneously make personal information accessible to millions of people (trusted and otherwise) and artificially intelligent technology throughout the world. We also know that nefarious people and their software agents skulk around the Internet and insert themselves into our Web sites, laptops, tablets, and smartphones, eager to capture our passwords, identities, contact lists, and credit card numbers. At the same time, the capability to receive laboratory results within seconds after a test is performed; to virtually work with a patient’s entire care team to continuously monitor conditions wherever the patient and care team members may be; to align treatments with proven, outcomes-based, automated protocols and decision-support software; and to practice personalized medicine tailored to the patient’s condition, family history, and genetics, all are enabled through HIT.

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Regulatory Foundation The foundational law governing healthcare privacy and security within the United States is the Health Insurance Portability and Accountability Act (HIPAA) of 1996 (HIPAA, 1996), and its privacy and security rules (CFR, 2013). The Security Rule requires compliance with a set of administrative, physical, and technical standards, and the Privacy Rule (CFR, 2013) sets forth privacy policies and practices to be implemented. HIPAA regulations establish uniform minimum privacy and security standards. However, HIPAA establishes that any applicable state law that is more stringent than the HIPAA regulations will take precedence over the HIPAA rules. All U.S. states and territories have enacted laws requiring notification when personal information is breached, and 18 states have enacted laws protecting personal information more broadly than HIPAA and other federal regulations (Holzman & Nye, 2019). For any healthcare organizations that provide inperson or virtual services to European residents, the European General Data Protection Regulation (GDPR) may also apply (GDPR, 2019). Because the HIPAA regulations apply only to “covered entities” and their “business associates” and not to everyone who may hold health information, and because more stringent state laws may take precedence over HIPAA, and because some healthcare organizations provide services to Europeans, the privacy protections of individuals and security protections of health information will vary depending on who is holding the information, where the consumer resides, and the location from which services are provided. It is the responsibility of every nursing professional to be informed about and to act in compliance with the laws and regulations that apply within the jurisdiction within which care is provided. The U.S. Health Information Technology for Economic and Clinical Health (HITECH) Act that was enacted in 2009 as part of the American Recovery and Reinvestment Act (USC, 2009) provided major structural changes, funding, and financial incentives designed to significantly expedite and accelerate a transformation of U.S. healthcare to improve efficiency, care quality, and patient safety, and to reduce cost through the digitization of health information. The HITECH Act codified the Office of National Coordinator (ONC) for health information technology (HIT) and assigned it responsibility for developing a nationwide infrastructure that would facilitate the use and exchange of electronic health information, including policy, standards, implementation specifications, and certification criteria. In enacting the HITECH Act, Congress recognized that the meaningful use and

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Chapter 10 • Trustworthy Systems for Safe and Private Healthcare 

exchange of electronic health information was key to improving the quality, safety, and efficiency of the U.S. healthcare system. At the same time, the HITECH Act recognized that as more health information was recorded and exchanged electronically to coordinate care, monitor quality, measure outcomes, and report public health threats, the risks to personal privacy and patient safety would be heightened. This recognition is reflected in the fact that four of the eight areas the HITECH Act identified as priorities for the ONC specifically address risks to individual privacy and information security: 1. Technologies that protect the privacy of health information and promote security in a qualified EHR, including for the segmentation and protection from disclosure of specific and sensitive individually identifiable health information, with the goal of minimizing the reluctance of patients to seek care (or disclose information about a condition) because of privacy concerns, in accordance with applicable law, and for the use and disclosure of limited data sets of such information 2. A nationwide HIT infrastructure that allows for the electronic use and accurate exchange of health information

3. Technologies that as a part of a qualified EHR allow for an accounting of disclosures made by a covered entity (as defined by the HIPAA of 1996) for purposes of treatment, payment, and healthcare operations

4. Technologies that allow individually identifiable health information to be rendered unusable, unreadable, or indecipherable to unauthorized individuals when such information is transmitted in the nationwide health information network or physically transported outside the secured, physical perimeter of a healthcare provider, health plan, or healthcare clearinghouse The HITECH Act resulted in the most significant amendments to the HIPAA Security and Privacy Rules since the rules became law. These included a Breach Notification Rule (CFR, 2009), guidance for rendering protected health information unusable, unreadable, or undecipherable to unauthorized individuals (HHS, 2013), and creation of the “wall of shame” report of major breaches of protected health information (HHS, 2019). The 21st Century Cures Act, enacted in late 2016, clearly established healthcare innovations, improved efficiencies, and patient safety as high priorities for the

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21st  century. The law’s stated mission was to “accelerate the discovery, development, and delivery of 21st century cures, and for other purposes” (USC, 2016). Among those “other purposes” were several provisions specifically addressing individual privacy, security protection, and assurance that protective measures do not put patients’ health and well-being at risk. First, whereas HIPAA focuses on “protected health information” (i.e., a defined subset of identifiable health information exchanged for treatment, payment, and healthcare operations), the 21st Century Cures Law addresses identifiable, sensitive information, defined as: “information that is about an individual and that is gathered or used during the course of research and:

(A) through which an individual is identified; or (B) for which there is at least a very small risk, as determined by current scientific practices or statistical methods, that some combination of the information, a request for the information, and other available data sources could be used to deduce the identity of an individual.” This definition embodies the flexibility required to allow interpretation to evolve as “available data sources” continue to expand and as the power of information analytics continues to intensify. Second, in proposing measures to enable new, beneficial treatment options to be available to patients more quickly, the law included provisions for expediting clinical research while protecting the safety and privacy of research participants. Specifically, the law allowed for waivers of informed consent for clinical testing that is judged to pose no more than minimal risk to the individual subjects involved and that includes appropriate safeguards to protect the individual rights, safety, and welfare of the participants. As the principal research oversight body, Institutional Review Boards (IRBs) hold the decision power over whether the risks associated with a research protocol are “minimal.” To improve the efficiency of the IRB review process, the law called for the elimination of duplicative regulations and activities, and enabled the sharing of IRBs across regulatory agencies. Third, the law acknowledged confusion within the healthcare community regarding the ways in which the HIPAA Privacy Rule allows sharing of information pertaining to patients with mental illnesses and substance abuse disorders. Fearing that this confusion could hinder the communication of information essential for the health and safety of individuals with mental illnesses, the

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166    P art 2 • S ystem S tandards law called for regularity clarification, which the U.S. HHS Office of Civil Rights issued in 2017 (HHS, 2017).

Security, Privacy, and Trust Since the HITECH Act was enacted in 2009, HIT has assumed an increasingly important role in the provision of care and in healthcare decision-making, particularly for the hospitals and healthcare providers who are now required to use certified EHR technology—that is, technology that has been independently certified against national standards of clinical functionality and security protection. As of 2017, nearly 80% of office-based physicians, 99% of large hospitals, and 97% of medium-sized hospitals were using certified HIT (ONC, 2019). It is worth noting that many specialists whose practices lie outside the purview of HITECH are capturing and storing patient information in uncertified electronic records systems or paper files in hanging folders, and are still relying on fax machines for information exchange. Consumers remain a bit cautious about HIT. More than 90% of providers have implemented portals that enable consumers to view their own health data, but fewer than 33% of consumers are using those portals (Heath, 2018). Data collected by the U.S. Office of the National Coordinator for HIT (ONC) revealed that, although the majority of consumers (74%) expressed confidence that their medical records were safe from unauthorized viewing, many consumers (66%) reported concern regarding electronic exchange of their health information. Ten percent of individuals reported that they had gone so far as to withhold information from their healthcare providers due to privacy and security concerns (ONC, 2019). Today’s nurses practicing in these “wired” environments depend upon HIT to provide instantaneous access to accurate and complete health information and validated decision support, with assurance that sensitive data and individual privacy are appropriately protected. Legal obligations, ethical standards, and consumer expectations drive requirements for technological and operational assurances that data and software applications will be available when they are needed; that private and confidential information will be protected; that data will not be modified or destroyed other than as authorized; that technology will be responsive and usable; and that systems designed to perform health-critical functions will do so safely. These are the attributes of trustworthy HIT—technology that is worthy of our trust. The Markle Foundation’s Connecting for Health collaboration identified privacy and security as a technology principle fundamental to trust: “All health information exchange, including in support of the delivery of care and the conduct of research and public health

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reporting, must be conducted in an environment of trust, based on conformance with appropriate requirements for patient privacy, security, confidentiality, integrity, audit, and informed consent” (Markle, 2006). Many people think of “security” and “privacy” as synonymous. Indeed, these concepts are related in that security mechanisms can help protect personal privacy by assuring that confidential personal information is accessible only by authorized individuals and entities. However, privacy is more than security, and security is more than privacy. Healthcare privacy principles were first articulated in 1973 in a U.S. Department of Health, Education, and Welfare report entitled Records, Computers, and the Rights of Citizens as “fair information practice principles” (DHEW, 1973). The Markle Foundation’s Connecting for Health collaboration updated these principles to incorporate new risks created by a networked environment in which health information routinely is electronically captured, used, and exchanged (Markle, 2006). Based on these works, as well as other national and international privacy and security principles focusing on individually identifiable information in an electronic environment (including but not limited to health), the ONC developed a Nationwide Privacy and Security Framework for Electronic Exchange of Individually Identifiable Health Information that identified eight principles intended to guide the actions of all people and entities that participate in networked, electronic exchange of individually identifiable health information (ONC, 2008). These principles essentially articulate the “rights” of individuals to openness, transparency, fairness, and choice in the collection and use of their health information. Whereas privacy deals with the rights of individuals to control access to and use of information relating to themselves, security deals with the protection of valued information assets. Security mechanisms and assurance methods are used to protect the confidentiality, authenticity, integrity, and availability of information and data, including the capture of an accurate record of all accesses to and use of information. While these mechanisms and methods are critical to protecting personal privacy, they are also essential in protecting patient safety, care quality, and institutional integrity— and in engendering trust. For example, if laboratory results are corrupted during transmission, if data in an EHR are overwritten, or if a fraudulent electronic message is received, the nurse is likely to lose confidence that the HIT can be trusted to help provide quality care. If an adversary alters a clinical decision-support rule or launches a denial-of-service attack on a sensor system for tracking wandering Alzheimer’s patients, individuals’ lives are put at risk!

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Chapter 10 • Trustworthy Systems for Safe and Private Healthcare 

Trustworthiness is an attribute of each system component, each software application, and of integrated enterprise systems as a whole—including those components that may exist in “clouds” or in coat pockets. Trustworthiness is extremely difficult to retrofit, as it must be designed and integrated into the system and conscientiously preserved as the system evolves. Discovering that an operational system cannot be trusted usually indicates that extensive—and expensive—changes to the system and its operational environment are needed. In this chapter, we introduce a framework for achieving and maintaining trustworthiness in HIT.

TRUST IN CRISIS—WHEN THINGS GO WRONG Although we would like to be able to assume that our computing technology, networks, clouds, smartphones, and software applications are trustworthy, unfortunately that may not be the case. When computer systems, network infrastructure, and software applications fail to protect personal information and safety-critical data, or are unavailable to provide critical services when needed, our trust is undermined, and personal privacy and safety are imperiled.

Breaches of Personal Information Since the 2009 HITECH regulation, entities covered under HIPAA have been required to report to the HHS breaches affecting 500 or more individuals. HHS maintains a public Web site, frequently referred to as the “wall of shame,” listing the breaches reported. In 2018 (the year before this chapter was written), a total of 353 breaches affecting 13,025,814 individuals were reported—more than twice the number of records exposed in 2017. The 10 largest healthcare breaches in 2018 accounted for nearly 68% of the total number of reported records exposed in that year (HHS, 2019). Perhaps even more significant than the numbers is the dramatic shift in causes to which these breaches are ­attributed. In the years between 2009 and 2017, fewer than half of all reported breaches were attributed to “hacking/ IT” and “unauthorized access” combined. For 2018, 86% of the breaches reported were attributed to these causes. Clearly, as more healthcare information is becoming available electronically, its perceived value as a target for intruders and insiders is increasing. An attack on the University of Connecticut (UCONN) and UCONN Health offers a dramatic example of the potential effects of a data breach. In February 2019, UCONN Health announced that an adversary had gained

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access to several employees’ e-mail accounts through a phishing attack that lured employees into clicking on an e-mail link that appeared to have come from a trusted source, but actually was malware. Sensitive, personal information from 326,000 patients was compromised, including patient names, birth dates, addresses, medical information, and Social Security numbers. A forensic evaluation revealed that the UCONN systems were compromised in August, 2018, but the breach was not discovered until December 24, 2018; patients received notification two months later (Davis, 2019). One of those patients who received a breach notification discovered a fraudulent transaction on her bank account and ascribed it to the UCONN breach. She subsequently filed a classaction lawsuit against UCONN, alleging that, in addition to the fraudulent banking activity detected, the breach increased the plaintiffs’ risk of financial fraud and identity theft for years to come. Note that implementation of an effective intrusion detection system (IDS), as described below, could have detected this threat much earlier.

Denial of System Services Service interruptions may be attributed to a number of factors, including both acts of nature and malicious human activity. Security technologies and operational practices help organizations detect potential threats to critical services, manage emergencies, and recover from service interruptions and outages. Distributed denial-of-service (DDoS) can be particularly difficult to detect and contain. In the Spring of 2014, a hacker collective known as “Anonymous” launched a DDoS attack against a mental-health clinic and ­Boston-area children’s hospitals. The attacks were part of a “campaign” called #OpJustina aimed at bringing public attention to the case of a young girl who was separated from her parents following a misdiagnosis made by Boston Children’s Hospital medical staff. Specifically, the parents had brought their daughter to the hospital for treatment for a digestive problem, claiming she had been diagnosed as having a mitochondrial disorder. The doctors at Boston Children’s Hospital concluded that the child’s symptoms were related to a psychiatric condition and that she possibly had been abused by her parents. The hospital filed abuse charges against the parents, and the girl was sent to a mental-health treatment facility. In retaliation, the Anonymous group launched an attack targeting the facilities’ Web sites and networks, installing malware in over 40,000 network routers. Boston Children’s Hospital and several other hospitals in the Boston area were rendered unavailable. Later in the year, at a HIMSS Privacy and Security Forum, Daniel Nigrin,

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168    P art 2 • S ystem S tandards Boston Children’s Hospital’s Senior Vice President and CIO, presented a detailed account of the attack and lessons learned, among which were the importance of implementing DDoS countermeasures, having an inventory of critical data and services, and having an alternative to e-mail for external communications, such as Short Message Service (SMS) messaging (Ouellette, 2014). In August 2018, a federal jury convicted Martin Gottesfeld of carrying out the attacks (Cimpanu, 2019).

Unsafe Medical Devices In a 2018 report relating to vulnerabilities in medical devices being approved by the FDA, the Office of the Inspector General (OIG) wrote: Cybersecurity is an area of increasing risk to patients and the health care industry as more medical devices use wireless, Internet, and network connectivity. Researchers have shown that networked medical devices cleared or approved by FDA can be susceptible to cybersecurity threats, such as ransomware and unauthorized remote access, if the devices lack adequate security controls. These networked medical devices include hospital-room infusion pumps, diagnostic imaging equipment, and pacemakers (Murrin, 2018).

Just months after this report was published, the U.S. Department of Homeland Security, which oversees the security of critical infrastructure, including medical devices, alerted consumers of serious cybersecurity vulnerabilities in 16 different models of implantable defibrillators manufactured by Medtronic and sold throughout the world. The vulnerabilities would enable a sophisticated attacker to harm patients by altering the software programming in the implanted device. As many as 750,000 individuals were at risk of having the programming in their implanted devices altered or erased by a malicious attacker. The vulnerabilities also affected bedside monitors that read data from the devices in patients’ homes and clinical programming devices. The vulnerabilities were discovered by two teams of security researchers and reported to Medtronic, which investigated the issue and then reported it to authorities (Carlson, 2019).

Total Havok! Perhaps the finest example of what happens when an organization eschews basic security practices, dismisses security warnings, ignores critical software updates, and fails to anticipate or plan for a cyber disaster is the WannaCry ransomware attack of May 2017. Within the course of a

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single weekend, the WannaCry perpetrators held hostage over 300,000 organizations in 150 countries of the world— including about a third of the hospital facilities operated by the United Kingdom’s National Health Service (NHS). By exploiting known security vulnerabilities in outdated Microsoft Windows software implementations, the virus took control of each affected computer, encrypted all data, effectively paralyzing operations, as the attacker demanded ransom payment of $300 to be paid in bitcoin. The response was one of reaction to panic instead of a planned response to emergency. Following a review of the incident, the National Audit Office noted that the impact of WannaCry could have been avoided if basic security practices had been applied (Palmer, 2017). The bottom line is that computer systems, networks, software applications, medical devices, people, and enterprises are highly complex, and the only safe assumption is that “things will go wrong.” Trustworthiness is an essential attribute for the systems, software, devices, services, processes, and people used to manage individuals’ personal health information and to help provide safe, high-quality health care.

HIT TRUST FRAMEWORK Trustworthiness can never be achieved by implementing a few policies and procedures, and purchasing some security technology whose sales representatives portrayed as a “total solution.” Protecting sensitive and safety-critical health information and assuring that the systems, services, and information that nurses rely upon to deliver quality care are available when they are needed, require a complete HIT trust framework that starts with an objective assessment of risk, and that is conscientiously applied throughout the development and implementation of policies, operational procedures, and security safeguards built on a solid system architecture. This trust framework is depicted in Fig. 10.1 and comprises seven layers of protection, each of which is dependent upon the layers below it (indicated by the arrows in the figure), and all of which must work together to provide a trustworthy HIT environment for healthcare delivery. This trust framework does not dictate a physical architecture; it may be implemented within a single medical practice or across multiple institutional sites, and may comprise enterprise, mobile, device, and cloud components.

Layer 1: Risk Management Risk management is the foundation of the HIT trust framework. Objective risk assessment informs decision-making and positions the organization to correct those physical,

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Layer 7: Usability Services Identity Federation

Single Sign-On

Layer 6: Security Technology Safeguards Non-Repudiation Encryption

Malware Defense Transmission Security

Interoperability Availability

Simplicity Process Isolation

Entity Authentication Access Control

Audit Trail Data Integrity

DEPENDENCY

Layer 5: Architectural Safeguards Redundancy and Failover Scalability

Layer 4: Operational Safeguards Evaluation Security and Privacy Operations Personnel Training and Management Business Agreements

Intrusion Detection Insider Threat Detection Incident Response Continuity of Operations

Reliability Fail-Safe Design

Configuration Management Authentication and Authorization Infrastructure Consent Management

Layer 3: Physical Safeguards

Facilities Systems and Networks

Electronic Media Medical Devices

Layer 2: Information Assurance Policy Privacy Policy

Security Policy

Layer 1: Risk Management Security Risk Assessment Privacy Risk Assessment

Applications and Data Criticality Analysis Risk Management Strategy

•  FIGURE 10.1.  The trust framework comprises a layered approach to achieving a trustworthy health system environment. operational, and technical deficiencies that pose the highest risk to the information assets within the enterprise. The identification and valuation of an enterprise’s data and software applications is a critical step in risk assessment. In today’s healthcare environments, ongoing risk assessment is a cornerstone of cybersecurity protection. Objective risk assessment also puts into place protections that will enable the organization to manage any residual risk and liability. Patient safety, individual privacy, and information security all relate to risk, which is simply the probability that some “bad thing” will happen. Risk is always considered with respect to a given context comprising relevant threats, vulnerabilities, and valued assets. Threats can be natural occurrences (e.g., earthquake, hurricane), accidents, or malicious people and software programs. Vulnerabilities are present in facilities, hardware, software, virtual environments (i.e., “clouds”), communication systems, business processes, and people. Valued assets can be anything from reputation to business infrastructure to information to human lives. A security risk is the probability that a threat will exploit a vulnerability to expose confidential information, corrupt or destroy data (i.e., digitized information), or interrupt or deny essential information services. If that risk could result in the unauthorized disclosure of an individual’s

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private health information, the compromise of an individual’s identity, or a denial of an individual’s rights to control information about them, it also represents a privacy risk. If the risk could result in the corruption of clinical data or an interruption in the availability of a safety-critical system, causing human harm or the loss of life, it is a safety risk as well. Information security (sometimes referred to as “cybersecurity”) is widely viewed as the protection of information confidentiality, data integrity, and service availability. Indeed, these are the three areas directly addressed by the technical safeguards addressed in the HIPAA Security Rule (CFR, 2013). Generally, safety is most closely associated with protective measures for data integrity and the availability of life-critical information and services, while privacy is more often linked to confidentiality protection. However, the unauthorized exposure of private health information, or corruption of one’s personal EHR as a result of an identity theft, also can put an individual’s health and safety at risk. Risk management is an ongoing, individualized discipline wherein each individual or each organization examines its own threats, vulnerabilities, and valued assets and decides for itself how to deal with identified risks— whether to reduce or eliminate them, counter them with

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170    P art 2 • S ystem S tandards protective measures, or tolerate them and prepare for the consequences. Risks to personal privacy, patient safety, care quality, financial stability, and public trust all must be considered in developing an overall strategy for managing risks both internal and external to an organization. In bygone times, risk assessment focused on resources within a well-defined physical and electronic boundary that comprised the “enterprise.” In today’s environment, where resources may include virtual “cloud” components, bring-your-own devices (BYOD), telecommuting, and any Wi-Fi device within signal range of a wireless access point, risk assessment is far more complex and requires careful analysis of potential data flows and close examination of service-level agreements (SLAs) for enterprise and cloud services and data-sharing agreements with HIPAA business associates, service subcontractors, and collaborators.

Layer 2: Information Assurance Policy The risk management strategy will identify what security and privacy risks need to be addressed through institutional policies that govern operations, information technology, and individual behavior. These institutional policies comprise high-level rules that guide organizational decision-making and that define behavioral expectations and sanctions for unacceptable actions. These policies (which may be documented separately or together) define rules for protecting the data and software application assets critical to the organization’s operations and patients’ safety and privacy. The rules address the protection of confidential information from unauthorized access, including patients’ personal information and information that is confidential to the enterprise. The rules address the protection of the integrity of critical data and applications, and technical and operational assurances of the continuity of operations, and rules for handling emergencies, including security breaches. Privacy rules address operational protections of the privacy rights of patients, as well as employees, and assurances of choice and transparency with respect to how individuals’ personal information is used and shared. The policies include rules that protect human beings, including patients, employees, family members, and visitors, from physical harm that could result from data disclosure, corruption, or service interruption. Overall, the information assurance policy defines the rules to be enforced to protect the organization’s valued information assets from identified risks to confidentiality, data integrity, and service availability, and to assure that individual privacy rights are upheld. Enforcement is achieved through operational practices and procedures and by rules programmed in authorization technology, such as an OAuth 2.0 authorization services (IETF, 2012).

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Some policy rules are mandated by applicable state, federal, and international laws and regulations (see Introduction section above). Other rules may be dictated by the Accountable Care Organization (ACO) with which the provider is associated. Sharing rules set forth in patient consents also must be enforced. The policy that codifies the nurse’s obligation to protect patients’ privacy and safety is embodied in the ICN Code of Ethics for Nurses (ICN, 2012):

• • •

The nurse holds in confidence personal information and uses judgment in sharing this information. The nurse, in providing care, ensures that use of technology and scientific advances are compatible with the safety, dignity, and rights of people. The nurse takes appropriate action to safeguard individuals, families, and communities when their health is endangered by a co-worker or any other person.

Information assurance policy provides the foundation for the development and implementation of physical, operational, architectural, and security technology safeguards. Nursing professionals can provide valuable insights, recommendations, and advocacy in the formulation of information assurance policy within the organizations where they practice, as well as within their professional organizations and with state and federal governments.

Layer 3: Physical Safeguards Physically safeguarding health information, and the information technology used to collect, store, retrieve, analyze, and exchange health data, is essential to assuring that information needed at the point and time of care is available, trustworthy, and usable in providing safe, high-quality healthcare. Although the electronic signals that represent health information are not themselves “physical,” the facilities within which data are generated, processed, stored, displayed, and used; the media on which data are recorded; the information system hardware used to process, access, and display the data; and the communications equipment used to transmit and route the data are. So are the people who generate, access, and use the information the data represent. Physical safeguards are essential to protecting these assets in accordance with the information assurance policy. The HIPAA Security Rule prescribes four standards for physically safeguarding electronic health information protected under HIPAA: facility-access controls, workstationuse policies and procedures, workstation-security measures, and device and media controls. These protections may

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Chapter 10 • Trustworthy Systems for Safe and Private Healthcare 

appear inapplicable to today’s “Internet of Things (IoT)” environment in which IT is embedded in implanted devices (e.g., pacemakers, defibrillators), patient-monitoring equipment, and home-health devices, and virtualized across “clouds” of services that span multiple physical computing centers. Today, the continuum of care may span multiple healthcare providers, delivery systems, homecare devices, and consumer apps. In this extensive, complex, virtualized environment, the physical protection of computing equipment, network equipment, sensors, media, and people is essential—even when physical boundaries are difficult to define. Healthcare organizations are increasingly choosing to contract for equipment and services provided by one or more third parties, rather than hosting and operating these services within their own facilities. Purchased services include both data-center outsourcing, in which an organization hires a third party to manage their applications and data from a data center operated by the service provider, and cloud services, wherein services are “virtualized” across multiple computers and physical locations. Cloud services are available through four business models:

•

• • •

Software-as-a-service (SaaS), in which the cloud provider provides one or more software applications that a user (client) may access from any Internet-enabled device. This model is particularly well suited for small physician practices, as it enables EHR applications to be available without requiring each practice to have its own application servers, and staff to maintain them. Platform-as-a-Service (PaaS), in which the cloud provider hosts the subscriber’s own software for access from any Internet-enabled device. Network-as-a-Service (NaaS), in which the cloud provider provides a network that the client accesses through a portal, while running applications and storing data locally. Infrastructure-as-a-Service (IaaS), in which the cloud provider makes all the computing hardware, network infrastructure, and storage available to the subscriber at the time they are needed, accessible from any Internet-enabled device. This model is particularly well suited for healthcare organizations that accumulate large volumes of data (e.g., genomic data, imaging), as resources are provided “on demand.”

The HIPAA Security Rule requires that the providers of these services sign a business associate agreement (see “Layer 4: Operational Safeguards”) in which the service providers agree to meet all of the HIPAA security standards, including physical protection.

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Layer 4: Operational Safeguards Operational safeguards are processes, procedures, and practices that govern the creation, handling, usage, disposal, and sharing of health information in accordance with the information assurance policy. The HIT trust framework shown in Fig. 10.2 includes the following operational safeguards. Security and Privacy Operations. HIPAA regulations require that each healthcare organization designate a “security official” and a “privacy official” to be responsible for developing and implementing security and privacy policies and procedures, respectively. The management of services relating to the protection of health information and patient privacy touches every function within a healthcare organization. Every nurse should identify the individuals designated with these responsibilities, as they are the best source of reliable information about an organization’s security and privacy policies and procedures. Personnel Training and Management.  One of the most valuable actions a healthcare organization can take to maintain public trust is to inculcate a culture of safety, privacy, and security. If every person employed by, or associated with, an organization feels individually responsible for protecting the confidentiality, integrity, and availability of health information, and the privacy and safety of patients, the risk for that organization will be vastly reduced! Recognition of the value of workforce training is reflected in the fact that the HIPAA Security and Privacy Rules require training in security and privacy, respectively, for all members of the workforce, including executives at the highest levels within an organization. Formal privacy and security training should be required to be completed at least annually, augmented by simple and frequent reminders. Such training should cover applicable policies and procedures for protecting health information assets, individual responsibilities for protecting privacy and security, procedures for reporting suspected breaches, and institutional sanctions for policy violations. Business Agreements.  Business agreements help manage risk and bound liability, clarify responsibilities and expectations, and define processes for addressing disputes among parties. The HIPAA Privacy and Security Rules require that each business partner (person or organization) that provides to a HIPAA-covered entity services involving protected health information must sign a “business associate” contract obligating the service provider to comply with HIPAA requirements, subject to the same enforcement and sanctions as covered entities. However,

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172    P art 2 • S ystem S tandards if a breach occurs, the covered entity retains primary responsibility for reporting and responding to the breach. So, it is essential that healthcare entities perform due diligence to assure that their business associates understand and are capable of providing the required levels of security protection, activity monitoring, breach detection, and breach reporting. Business associates may deliver services at any location authorized by the agreement, including within the covered entity’s own premises, at facilities operated by the service provider, at a patient’s home, or in a “cloud.” The HIPAA Privacy Rule also requires that, before authorizing a third party to use data from which certain identifiers have been removed (i.e., “limited data set”), the third party must sign a “data use agreement” defining the specific purpose and the period of time for which the data set may be used. Agreements are only as trustworthy as the entities that sign them. Organizations should exercise due diligence in deciding with whom they will enter into business agreements. Configuration Management. Configuration management refers to processes and procedures for maintaining an accurate and consistent accounting of the physical and functional attributes of a system throughout its life cycle. From an information assurance perspective, configuration management is the process of controlling and documenting modifications to the hardware, firmware, software, and documentation involved in the protection of information assets. Authentication and Authorization Infrastructure (AAI).  Arguably the operational process most critical to the effectiveness of technical safeguards is the process used to manage the identities of individuals and software applications, and to assign authorization rules to govern these entities’ use of the system. This process includes procedures for positively establishing the identity of the individuals and software applications to whom rights and privileges are being assigned, procedures for assigning authorizations to those identities, and procedures for modifying or revoking authorizations when the individual changes roles within the organization or leaves the organization, or when the access privileges assigned to an app need to be changed. Many of the technical safeguards (e.g., authentication, access control, audit, digital signature) rely upon and assume the accuracy of the identity that is established when an individual or entity is registered with the system. Identity management begins with verification of the identity of each individual and software application before assigning a credential and authorizing rights and privileges.

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This process, called “identity proofing,” may require a person to present one or more government-issued documents containing the individual’s photograph, such as a driver license or passport. For software applications, this may be accomplished through a registration process with a certification service. Once identity has been positively established, the individual or software app is given a credential to use to prove identity at the time they are seeking access. For individuals, the credential will take the form of something the person possesses (e.g., smartcard), something he knows (e.g., password, private encryption key), or a biometric (e.g., fingerprint). For software apps, the credential usually is a digital certificate containing the app’s public–private encryption key pair. The rights and privileges authorized for the individual or app also are assigned at this time. The AAI life cycle includes the prompt termination of rights and privileges when the individual leaves the organization or otherwise no longer needs the resources and privileges assigned to him, as well as ongoing maintenance of the governance processes that support this life cycle. Consent Management.  Obtaining an individual’s consent prior to taking any actions that involve her physical body or personal information is fundamental to respecting her right to privacy, and a profusion of state and federal laws set forth requirements for protecting and enforcing this right. Medical ethics and state laws require that providers obtain a patient’s “informed consent” before delivering medical care, or administering diagnostic tests or treatment. The Common Rule, designed to protect human research subjects, requires informed consent before using an individual as a participant in a research project (HHS, 2009). The HIPAA Privacy Rule specifies conditions under which an individual’s personal health information may be used and exchanged, including uses and exchanges that require the individual’s express “authorization.” Certain types of information, such as psychotherapy notes and substance abuse records, have special restrictions and authorization requirements (HHS, 2017). Managing an individual’s consents and authorizations and assuring consistent adherence to the individual’s privacy preferences are a complex but essential process to protecting personal privacy. Today, consent management is primarily a manual process, and consents are collected and managed within a single institution. However, as access to health information and biological specimens is shared among multiple institutions, consent management becomes much more complex. At the same time, the risk to personal privacy is heightened. To enable more automation in the management of individual consents, the Health Level Seven (HL7)

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Chapter 10 • Trustworthy Systems for Safe and Private Healthcare 

standards organization has specified a Fast Healthcare Interoperability Resources (FHIR) standard for electronically exchanging patient consents (HL7, 2018) and an implementation guide for segmenting data for special protection (HL7, 2014). Intrusion Detection.  One of the most effective means of detecting external threat attempts to infiltrate an organization’s network or system is through the use of an IDS. An IDS usually comprises hardware and software that continuously collects information about network and system activity and detects suspicious behaviors, unauthorized access attempts, and breaches of sensitive information. Detection algorithms generally are based on either of two complementary models—one that looks for variance from normal (statistical anomalies) and the other that looks for matches with predefined characteristics (signatures). Specifically, statistical anomaly detection uses system and network data to build a model of “normal” activity and then looks for significant deviations from “normal.” Signature-based detection stores predefined characteristics of known threats and then monitors activities for matches to these signatures. The HIPAA Security Rule requires organizations to maintain an audit trail of system activity. Generally, audit trails are system specific, complex, and not amenable to manual review. IDSs are capable of receiving data from multiple systems and networks to provide a comprehensive view of activity across the organization. Most large healthcare organizations use IDSs to detect external intrusions. However, a recent survey of 2,464 security professionals from 680 healthcare provider organizations found that only 7% of the organizations surveyed were using IDS (BB, 2018). Lacking IDS, healthcare organizations rely on manual review of audit logs to detect potential intrusions, putting them at a significant disadvantage for early detection of potential breaches. As more clinical data are generated and exchanged, the sheer volume will overpower system-activity review as a manual operation. Insider Threat Detection. An “insider” is any employee, part-time worker, business associate employee, vendor, or healthcare partner who has access to an organization’s network, applications, systems or data. The insider threat has been characterized as comprising three types (Dtex, 2019): 1. Compromised: Users whose credentials are compromised and leveraged by outsider infiltrators 2. Negligent: Users who introduce insider risk due to careless behavior or human error 3. Malicious: Users who intentionally engage in activity to harm the enterprise

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The 2019 cybersecurity survey conducted by the Health Information Management and Systems Society (HIMSS) asked 239 healthcare security professionals to characterize the threat actor in their organizations’ most recent significant cybersecurity incident. Their responses revealed that 65% were caused by insiders, as defined above—28% compromised, 31% negligent, and 6% malicious insiders (HIMSS, 2019). The primary challenge in detecting the insider threat is determining what “potentially harmful” and “suspicious” activities look like within the context of a population of individuals, each with unique behaviors, work styles, motivations, habits, goals, and values. What is deemed suspicious behavior for one person may represent usual behavior for another. For this reason, the methods used by IDS technology to detect statistically anomalous behaviors are particularly well suited for detecting insider threats. Technologies that look for threat signatures are also applicable here—except that insider threat signatures tend to be different from those of external intruders. For example, active attempts to circumvent organizational policies, such as use of Web-based personal e-mail, copying files to Universal Serial Bus (USB) drives, executing software apps from USB drives, and uploading files to cloud repositories, may signify an insider threat (Dtex, 2019). One of the best defenses against the insider threat is awareness training that emphasizes individual responsibility for helping to assure compliance with security policy. Incident Response. Awareness and training should include a clear explanation of what nurses and other professionals should do if they suspect a security incident, such as a malicious code infiltration, a denial-ofservice attack, or a breach of confidential information. Organizations need to plan their response to an incident report, including procedures for investigating and resolving the incident, notifying individuals whose health information may have been exposed as a result of the incident, and penalizing parties responsible for the incident. Not all security incidents are major or require enterprise-wide response. Some incidents may be as simple as a user accidentally including protected health information in a request sent to the help desk. Incident procedures should not require a user or help-desk operator to make a judgment call on the seriousness of a disclosure; procedures should clearly specify what an individual should do when she notices a potential security incident. As the increasing number of breaches reported to the HHS reveal, within healthcare, data breaches are no longer exceptional but inevitable. Hence, it is crucial that every healthcare organization has a well-articulated plan for responding to a data breach, and that the healthcare

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174    P art 2 • S ystem S tandards organization practices exercising the plan before an actual breach occurs. Immediately following a breach, the HIPAA-covered entity will need to assess the severity of the breach, the number of individuals whose data may have been exposed, and whether the threat has been contained. If more than 500 individuals’ data may have been breached, the HIPAA Breach Notification Rule requires that the breach be reported to the Secretary of the HHS and to a prominent media source serving the state in which the victims are located. Regardless of the number of individuals whose data may have been exposed, the covered entity must send each victim a letter notifying the individual of the breach (CFR, 2009). The Breach Notification Rule makes specific exceptions for portable media that contain PHI encrypted in accordance with guidance issued by the Office of Civil Rights (HHS, 2013). Continuity of Operations.  Unexpected events, both natural and human-produced, do happen, and when they do, it is important that critical health services can continue to be provided. As healthcare organizations become increasingly dependent on electronic health information and HIT, the need to plan for unexpected events, and to develop and exercise operational procedures that will enable the organization to continue to function, becomes more urgent. The HIPAA Security Rule requires that organizations establish and implement policies and procedures for responding to an emergency. Contingency planning is part of an organization’s risk-management strategy, and the first step is performed as part of a risk assessment—identifying those software applications and data that are essential for enabling operations to continue under emergency conditions and for returning to full operations. These businesscritical systems are those to which architectural safeguards such as fail-safe design, redundancy and failover, and availability engineering should be applied. Evaluation.  Periodic, objective evaluation of the operational and technical safeguards in place helps measure the effectiveness of the security management program. A formal evaluation should be conducted at least annually and should involve independent participants who are not responsible for the program. Security evaluation should include resources and services maintained within the enterprise, as well as resources and services provided and managed by business associates—including cloudservices providers. Independent evaluators can be from either within or outside an organization, so long as they can be objective. In addition to the annual programmed evaluation, security technology safeguards should be evaluated whenever changes in circumstances or events occur that affect the risk profile of the organization.

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Layer 5: Architectural Safeguards A system’s architecture comprises its individual hardware and software components, the interrelationships among them, their relationship with the environment, and the principles that govern the system’s design and evolution over time. As shown in Fig. 10.2, specific architectural design principles, and the hardware and software components that support those principles, work together to establish the technical foundation for security technology safeguards. In simpler times, the hardware and software components that comprised an enterprise’s architecture were under the physical and logical control of the enterprise itself, but in an era when an enterprise may depend upon external providers of enterprises services (e.g., a health exchange service, hosting service, SaaS, IaaS), this may not be the case. Still, the design principles discussed below apply whether an enterprise’s architecture is centralized or distributed, physical or virtual. Redundancy and Failover.  Security- and safety-critical system components should be engaged and integrated so that no single point of failure exists. If a given component fails, the system should engage a second, back-up component with no breach of sensitive information, interruption of operations, or corruption of data. Scalability.  As more health information is recorded, stored, used, and exchanged electronically, systems and networks must be able to deal with that growth. The most recent stage in the evolution of the Internet specifically addresses the scalability issue by virtualizing computing resources into “cloud” services. Indeed, the Internet itself was created on the same principle as cloud computing— the creation of a virtual, ubiquitous, continuously expanding network through the sharing of resources (servers) owned by different entities. Whenever a user sends information over the Internet, the information is broken into small packets that are then sent (“hop”) from server to server from source to destination, with all of the servers in between being “public”—in the sense that they probably belong to someone other than the sender or the receiver. Cloud computing, a model for providing “on demand” computing services accessible over the Internet, pushes virtualization to a new level by sharing applications, storage, and computing power to offer scalability beyond what would be economically possible otherwise. Reliability.  Reliability is the ability of a system or component to perform its specified functions consistently, over a specified period of time—an essential attribute of trustworthiness.

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Chapter 10 • Trustworthy Systems for Safe and Private Healthcare 

Fail-Safe Design.  Safety-critical components, software, and systems should be designed so that if they fail, the failure will not cause people to be physically harmed. Note that fail-safe design may indicate that, under certain circumstances, a component should be shut down, or forced to violate its functional specification, to avoid harming someone. The interrelationships among redundancy and failover, reliability, and fail-safe design are complex, yet critical to patient safety. The “break-the-glass” feature that enables an unauthorized user to gain access to patient information in an emergency situation is an example of fail-safe design. If, in an emergency, an EHR system fails to provide a nurse access to the clinical information he or she needs to deliver care, the “break-the-glass” feature will enable the system to “fail safely.” Fail-safe methods are particularly important in research where new treatment protocols and devices are being tested for safety. Interoperability.  Interoperability is the ability of systems and system components to work together. To exchange health information effectively, HIT must interoperate not only at the technical level, but also at the syntactic and semantic levels. The Internet and its protocols, which have been adopted for use both within (wired and wireless) and between enterprises, package and transmit data in small packets (electronic bits) over a network in such a way that upon arrival at their destination, the data appear the same as when they were sent (syntactic interoperability). If the data are encrypted, the receiving system must be able to decrypt the data, and if the data are wrapped in an electronic envelope (e.g., electronic mail message, HL7 FHIR resource), the system must open the envelope and extract the content. Finally, the system must translate the electronic data into health information that the system’s applications and users will understand (symantic interoperability). Open standards, including encryption and messaging standards, and standard vocabulary for coding data for exchange are fundamental to implementing interoperable healthcare systems. Availability.  Required services and information must be available and usable when they are needed. Availability is measured as the proportion of time a system is in a functioning condition. A reciprocal dependency exists between security technology safeguards and highavailability design—security safeguards depend upon the availability of systems, networks, and information, which in turn enable those safeguards to protect enterprise assets against threats to availability, such as denialof-service attacks. Resource virtualization and “cloud” computing are important technologies for helping assure availability.

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Simplicity.  Safe, secure architectures are designed to minimize complexity. The simplest design and integration strategy will be the easiest to understand, to maintain, and to recover in the case of a failure or disaster. While systems, networks, and devices may perform complex functions, they should be built so that they are easy for a user to use correctly and for a trained professional to configure and maintain. Process Isolation.  Process isolation refers to the extent to which processes running on the same system with different authorizations, virtual machines (VMs) running on the same hardware, or applications running on the same computer are kept separate. Process isolation is important to ensuring that if one process, VM, or application misbehaves or is compromised, other processes, VMs, or applications can continue to operate safely and securely. Isolation is particularly important to preserving the integrity of the operating system itself. The operating system is the most critical component in a system because it is responsible for managing, protecting, and provisioning all system resources (e.g., data files, directories, memory, application processes, network ports). Within today’s operating systems, processes critical to the security and reliability of the system execute within a protected hardware state, while untrusted applications execute within a separate state. However, this hardware architectural isolation is undermined if the system is configured so that untrusted applications are allowed to run with privilege, which puts the integrity of the operating system itself at risk. For example, if a user logs into an account with administrative privileges and then runs an application that has been infected by a virus (or opens an infected e-mail attachment), the entire operating system may become infected. Within a cloud environment, the hypervisor is assigned responsibility for assuring that VMs are kept separate so that processes running on one subscriber’s VM cannot interfere with those running on another subscriber’s VM. In general, the same security safeguards used to protect an enterprise system are equally effective in a cloud ­environment—but only if the hypervisor is able to maintain isolation among virtual environments. Another example of isolation is seen in the Apple iOS environment. Apps running on an iPad or iPhone are isolated such that not only are they unable to view or modify each other’s data, but also one app does not even know whether another app is installed on the device. (Apple calls this architectural feature “sandboxing.”) Note that in 2018 a critical flaw in the Intel processor violated the process-isolation principle, resulting in a serious security threat to all systems that use this processor. For more detail, refer to Greenberg (2018).

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Layer 6: Security Technology Safeguards Security technology safeguards are software and hardware services specifically designed to perform security-related functions. Essential technical safeguards are depicted in Fig. 10.1. Entity Authentication.  When an individual or software app requests access to resources within a system, the first action the system takes is to authenticate the identity of the requester. As discussed above, before an individual or software application is registered with a system, AAI processes are used to validate identities, assign credentials, and authorize system rights and privileges. (See Authentication and Authorization Infrastructure (AAI) above.) Whenever the person or software application requires access, it asserts its identity and uses its credential to authenticate that identity. The system then verifies that the “proof ” offered provides the identity evidence required. If so, the system initiates a user session and hands it off to the access control function. Access Control. Access-control services help assure that user sessions are able to gain access only to those resources (e.g., computers, networks, applications, services, data files, FHIR resources) for which the user is authorized, and that they are able to use those resources only within the bounds of the authorization. Accesscontrol mechanisms protect against unauthorized access, use, modification, and destruction of resources, and unauthorized performance of system functions (e.g., privileged actions, application execution). Accesscontrol rules are based on federal and state laws and regulations, the enterprise’s information assurance policy, as well as patient-elected preferences. These rules may be based on the user’s identity, the user’s role, the context of the request (e.g., location, time of day), and/or a combination of the sensitivity attributes of the data and the user’s authorizations. Audit Controls. Security audit controls collect and record information about security-relevant events within a system or network. Audit logs are generated by multiple software components within a system, including operating systems, servers, firewalls, applications, and database management systems. Audit logs are a primary source of information used to detect intrusions and misuse. Data Integrity.  Data integrity services provide assurance that electronic data have not been modified or destroyed except as authorized. While access-control mechanisms help protect the data from unauthorized access and

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modification, data integrity mechanisms help validate that data have not been changed. Cryptographic hash functions are commonly used for validating the integrity of transmitted data. A cryptographic hash function is a mathematical algorithm that takes a large block of data as input and generates a fixed-length “hash value” such that any change to the data will change the hash value that represents it—thus detecting an integrity breach. Operational protections such as data back-up, disaster recovery planning, and storage replication are helpful in protecting the integrity of stored data. Non-Repudiation.  Sometimes the need arises to assure not only that data have not been modified inappropriately, but also that the data are in fact from an authentic source. This proof of the authenticity of data source is often referred to as “non-repudiation” and can be met through the use of digital signatures. Digital signatures use publickey (asymmetric) encryption (see Encryption below) to encrypt a block of data using the signer’s private key. To authenticate that the data block was signed by the entity claimed, the receiver only needs to try decrypting the data using the signer’s public key; if the data block decrypts successfully, its authenticity is assured. Encryption.  Encryption is simply the process of obfuscating information by running the data representing that information through a mathematical algorithm (sometimes called a “cipher”) to render the data unreadable until the data are decrypted by someone possessing the proper encryption “key.” “Symmetric” encryption uses the same key to both encrypt and decrypt data, while “asymmetric” encryption (also known as “public-key encryption”) uses two keys that are mathematically related such that one key is used for encryption and the other for decryption. One key is called a “private” key and is held secret; the other is called a “public key” and is openly published. Which key is used for encryption and which for decryption depends upon the assurance objective. For example, secure e-mail encrypts the message contents using the recipient’s public key so that only a recipient holding the private key can decrypt and view the message, then digitally signs the message using the sender’s own private key so that if the recipient can use the sender’s public key to decrypt the signature, thus gaining assurance that the sender actually sent it (i.e., non-repudiation). Encryption technology can be used to encrypt both data at rest and data in motion. So, it is used both to protect electronic transmissions over networks and to protect sensitive data in storage. Malware Defense.  Malicious software, also called “malware,” is any software application designed to infiltrate a

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Chapter 10 • Trustworthy Systems for Safe and Private Healthcare 

system without the user’s permission, with the intent to damage or disrupt operations, or to use resources to which the application is not authorized to access. Malicious software includes programs commonly called viruses, worms, Trojan Horses, ransomware, and spyware. Protecting against malicious software requires not only technical solutions to prevent, detect, and remove these invasive pests, but also effective user-awareness programs, organizational policy enforcement, and continuous monitoring of trusted sources of information regarding current threats, such as the National Cyber Awareness Center (NCAS, 2019). Transmission Security.  Sensitive and safety-critical electronic data that are transmitted over vulnerable networks, such as the Internet, must be protected against unauthorized disclosure and modification. The Internet protocol provides no protection against the disclosure or modification of any transmissions, and no assurance of the identity of any transmitters or receivers (or eavesdroppers). Protecting network transmissions between two entities (people, organizations, or software programs) requires that the communicating entities authenticate themselves to each other, confirm data integrity using something like a cryptographic hash function, and encrypt the channel over which data are exchanged. Both the Transport Layer Security (TLS) protocol (IETF, 2008) and Internet Protocol security (IPsec) protocol suite (IETF, 1998) support these functions, but at different layers in the Open System Interconnection (OSI) model (ISO, 1996). TLS authenticates the identity of the server at the end point (and optionally authenticates the identity of the connecting client) and then establishes an encrypted channel between the client and the server. TLS operates at the OSI transport layer (layer 4), allowing software applications to exchange information securely. For example, TLS might be used to establish a secure link between a user’s browser and a merchant’s checkout application on the Web. The principal advantages of TLS are that it is widely implemented (every minimally trustworthy server is TLS protected) and that, within a browser, it displays an indicator when a secured channel has been established (by displaying a locked padlock icon before the URL). IPsec establishes a protected channel between two Internet gateways at the OSI network layer (layer 3). The network layer in an execution stack is where the Internet Protocol (IP) itself executes. IPsec first authenticates both gateway end points, and then encrypts the channel, enabling secure exchange similar to an isolated physical network. For example, IPsec might be used to establish a virtual private network (VPN) that allows all hospitals within an integrated delivery system to openly yet securely

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exchange information. Because IPsec is implemented at the network layer, it is less vulnerable to malicious software applications than TLS and also less visible to users (e.g., IPsec does not display the locked padlock icon).

Layer 7: Usability Services The top layer of the trust framework includes services that make life easier for users, thus reducing the urge to attempt to circumvent security controls. “Single signon,” often referred to as a “security service,” actually is a usability service that helps make the authentication process less painful for users. Both single sign-on and identity federation enable a user to authenticate himself or herself once and then to access multiple applications, multiple databases, or multiple enterprises for which she is authorized, without having to reauthenticate herself to each. Single sign-on enables a user to navigate among authorized applications and resources within a single organization. Identity federation enables a user to navigate between services managed by different organizations, and is usually implemented using an OAuth 2.0 profile called OpenID Connect (OIDF, 2014). Both single sign-on and identity federation require the exchange of security attributes. Once the user has authenticated herself to a system, that system can pass the user’s identity, along with other attributes, such as institutional affiliation, role, method of authentication, and time of login, to another entity. The receiving entity then enforces its own access-control rules, based on the identity and attributes passed to it. Neither single sign-on nor identity federation actually adds security protections (other than to reduce the need for users to post their passwords to their computer monitors). In fact, if the original identity-proofing process or authentication method is weak, the risk associated with that weakness will be propagated to any other entities to which the authenticated identity is passed. Therefore, whenever single sign-on or federated identity is implemented, a key consideration is the level-of-assurance provided by the methods used to identity-proof and authenticate the individual.

SUMMARY AND CONCLUSIONS Healthcare is in the midst of a dramatic and exciting transformation that will enable individual health information to be captured, used, and exchanged electronically using interoperable HIT. The potential benefits to the health of individuals and populations are dramatic. Outcomesbased decision support will help improve the safety and quality of health care. The availability of huge quantities

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178    P art 2 • S ystem S tandards of de-identified clinical information and pseudonymized genomic data will help scientists discover the underlying bases for diseases, leading to earlier and more accurate detection and diagnoses, more targeted and effective treatments, and ultimately more personalized medicine. In this chapter we have explained the critical role that trustworthiness plays in HIT adoption and in providing safe, private, high-quality care. We have introduced and described a trust framework comprising seven layers of protection essential for establishing and maintaining trust in a healthcare enterprise. Many of the safeguards included in the trust framework have been codified in HIPAA standards and implementation specifications. Building trustworthiness in HIT always begins with objective risk assessment, a continuous process that serves as the basis for developing and implementing a sound information assurance policy and physical, operational, architectural, and technological safeguards to mitigate and manage risks to patient safety, individual privacy, care quality, financial stability, and public trust.

Test Questions 1. U.S. laws and regulations relating to healthcare information require: A. Security protections B. Privacy protections

C. Patient safety protections D. All of the above

2. Dr. David Blumenthal called health information: A. “the backbone of health”

B. “the lifeblood of modern medicine”

C. “the connective tissue of the human body” D. “the prescription for safe care” 3. Nursing is firmly grounded in:

A. Ethics, camaraderie, and a strong will

B. Ethics, patient advocacy, care quality, and human safety C. Health information technology

D. Care quality, ethics, patient empathy, and safety 4. The terms privacy, safety, and trust are synonymous. A. True

B. False

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5. Trustworthiness in HIT is achieved by implementing: A. A risk-management plan

B. Security and privacy policies

C. Authentication and authorization infrastructure (AAI) D. System audit trail E. All of the above

F. All of the above and more 6. The primary causes of major breaches of healthcare data in 2018 were: A. Stolen laptops

B. Failure to shred patient records

C. Hacking and unauthorized access D. Phishing expeditions

7. Implantable devices and patient monitors are always 100% trustworthy. A. True

B. False 8. A security threat is:

A. The probability that malware will infect a hospital system

B. The probability that a threat will exploit a vulnerability to expose confidential information, corrupt or destroy data, or interrupt or deny essential information services C. The certainty that a breach has occurred

D. The high likelihood that an ex-employee with a grudge is attempting to break into the system 9. “Cloud” services include:

A. Software-as-a-Service B. Platform-as-a-Service C. Network-as-a-Service

D. Infrastructure-as-a-Service E. All of the above

10. Intrusion and misuse detection systems both use: A. Statistical modeling of system activity

B. Detection of signatures of breaches or misuse C. Alarms when breaches are detected D. Audit trails

E. A, B, and C

F. A, B, and D

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Chapter 10 • Trustworthy Systems for Safe and Private Healthcare 

Test Answers 1. Answer: D 2. Answer: B 3. Answer: B 4. Answer: B 5. Answer: F

6. Answer: C 7. Answer: B 8. Answer: B 9. Answer: E 10. Answer: F

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Davis, J. 326,000 patients impacted in UConn Health phishing attack. Health IT Security. February 25, 2019. Retrieved from https://healthitsecurity.com/ news/326000-patients-impacted-in-uconn-healthphishing-attack. Accessed on July 20, 2020. Dtex Systems (Dtex). (2019). Insider threat intelligence report. Retrieved from https://dtexsystems.com/2019-insiderthreat-intelligence-report/. Accessed on April 4, 2019. GDPR Requirements and On-Going Compliance (GDPR). (2019). Absolute. Retrieved from https://www.absolute.com/en/landing/ gdpr?utm_content=294466996307&utm_ medium=cpc&utm_source=google&utm_campaig n=gdpr&gclid=Cj0KCQjw4fHkBRDcARIsACV58_ FE8fFRkO7mfRvq-CN530rotbU5HGvnUUCIjIH2w3_ VRW61sFTBCRcaAhdAEALw_wcB. Accessed on March 28, 2019. Greenberg, A. (2018). A critical Intel flaw breaks basic security for most computers. Wired. January 3, 2018. Retrieved from https://www.wired.com/story/criticalintel-flaw-breaks-basic-security-for-most-computers/. Accessed on April 4, 2019. Health Information and Management Systems Society (HIMSS). (2019). 2019 HIMSS cybersecurity survey. Retrieved from https://www.himss.org/sites/himssorg/ files/u132196/2019_HIMSS_Cybersecurity_Survey_ Final_Report.pdf. Accessed on April 4, 2019. Health Insurance Portability and Accountability Act of 1996. Public Law 104-191. August 21, 1996. Retrieved from http://aspe.hhs.gov/admnsimp/pl104191.htm. Accessed on March 26, 2019. Health Level Seven (HL7). (2014). HL7 Version 3 Implementation Guide: Data segmentation for privacy (DS4P). Release 1. May 2014. Retrieved from http:// www.hl7.org/implement/standards/product_brief. cfm?product_id=354. Accessed on April 3, 2019. Health Level Seven (HL7). (2018). FHIR R4: Resource consent. Retrieved from https://www.hl7.org/fhir/consent. html. Accessed on April 3, 2019. Heath, S. (2018). Patient portal adoption tops 90%, but strong patient use is needed. Patient Engagement HIT. July 31, 2018. Retrieved from https://patientengagementhit. com/news/patient-portal-adoption-tops-90-but-strongpatient-use-is-needed. Accessed on April 10, 2019. Holzman, D., & Nye, J. Healthcare privacy and cybersecurity: Outlook for 2019. CynergisTek. January 31, 2019. Video Retrieved from https://www.youtube.com/ watch?v=ygdzeTg-BQk. Accessed on March 28, 2019. International Council of Nurses (ICN). (2012). The ICN code of ethics for nurses. Geneva, Switzerland. Retrieved from https://www.icn.ch/sites/default/files/inline-files/2012_ ICN_Codeofethicsfornurses_%20eng.pdf. Accessed on March 20, 2019. Internet Engineering Task Force (IETF). (1998). Security architecture for the internet protocol. RFC 2401. November, 1998. Retrieved from http://www.ietf.org/rfc/ rfc2401.txt. Accessed on April 5, 2019.

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180    P art 2 • S ystem S tandards Internet Engineering Task Force (IETF). (2008). The transport layer security (TLS) protocol. Version 1.2. RFC 5246. August, 2008. Retrieved from http://tools.ietf.org/html/ rfc5246. Accessed on April 5, 2019. Internet Engineering Task Force (IETF). (2012). The OAuth 2.0 authorization framework. RFC 6749. Retrieved from https://tools.ietf.org/html/rfc6749. Accessed on April 10, 2019. International Organization for Standardization (ISO). (1996). Information technology—Open systems interconnection— Basic reference model: The basic model. ISO/IEC 74981:1994. Corrected version 1996. Retrieved from https://www. iso.org/standard/20269.html. Accessed on April 19, 2019. Markle Foundation Connecting for Health (Markle). (2006). The common framework: overview and principles. 2006. Retrieved from https://www.markle.org/sites/default/files/ Overview_Professionals.pdf. Accessed on March 25, 2019. Murrin, S. (Murrin). (2018). FDA should further integrate its review of cybersecurity into the premarket review process for medical devices. HHS Office of Inspector General. OEI-09-16-00220. September 2018. Retrieved from https://oig.hhs.gov/oei/reports/oei-09-16-00220.pdf. Accessed on March 28, 2019. National Cyber Awareness Center (NCAS). (2019). U.S. Department of Homeland Security. Retrieved from https://www.us-cert.gov/ncas. Accessed on April 5, 2019. Office of the National Coordinator for Health Information Technology, U.S. Department of Health and Human Services (ONC). (2008). Nationwide privacy and security framework for electronic exchange of individually identifiable health information. December 15, 2008. Retrieved from https://www.healthit.gov/sites/default/ files/nationwide-ps-framework-5.pdf. Accessed on March 25, 2019. Office of the National Coordinator for Health Information Technology. U.S. Department of Health and Human Services (ONC). (2019). Health IT dashboard. Retrieved from https://dashboard.healthit.gov/quickstats/quickstats.php. Accessed on March 20, 2019. Open Identity Foundation (OIDF). (2014). OpenID Connect Core 1.0 incorporating errata set 1. Retrieved from https://openid.net/specs/openid-connect-core-1_0.html. Accessed on April 10, 2019. Ouellette, P. (2014). Boston Children’s CIO talks DDoS threats, lessons learned. Health IT Security. September 16, 2014. Retrieved from https://healthitsecurity.com/ news/boston-childrens-cio-talks-ddos-threats-lessonslearned. Accessed on March 27, 2019. Palmer, D. (2017). WannaCry ransomware: Hospitals were warned to patch system to protect against cyber-attack— but didn’t. ZDNet. October 27, 2017. Retrieved from https://www.zdnet.com/article/wannacry-ransomwarehospitals-were-warned-to-patch-system-to-protectagainst-cyber-attack-but-didnt/. Accessed on April 1, 2019.

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Stone, A. (2019). Nurses ranked ‘most trusted profession’ in 2018. ONS Voice. February 1, 2019. Retrieved from https://voice.ons.org/advocacy/nurses-ranked-mosttrusted-profession-in-2018. Accessed on April 19, 2019. United States Congress, 104th Session (USC). (1996). United States Congress (USC). (2009). American Recovery and Reinvestment Act of 2009 (ARRA). H.R. 1. February 17, 2009. Retrieved from http://frwebgate.access. gpo.gov/cgibin/getdoc.cgi?dbname=111_cong_ bills&docid=f:h1enr.pdf. Accessed on April 5, 2019. United States Congress (USC). (2016). 21st Century Cures Act. Public Law 114-255. December 13, 2016. Retrieved from https://www.congress.gov/114/plaws/publ255/ PLAW-114publ255.pdf. Accessed on March 12, 2019. U.S. Department of Health and Human Services (HHS). (2009). Protection of human subjects. 45 CFR Part 46. January 15, 2009. Retrieved from http://www.hhs. gov/ohrp/policy/ohrpregulations.pdf. Accessed on April 3, 2019.  U. S. Department of Health and Human Services (HHS). (2013). Guidance to render unsecured protected health information unusable, unreadable, or undecipherable to unauthorized individuals. July 26, 2013. Retrieved from https://www.hhs.gov/hipaa/for-professionals/breachnotification/guidance/index.html. Accessed on July 26, 2020. U.S. Department of Health and Human Services (HHS). (2017). HIPAA Privacy Rule and sharing information related to mental health. Retrieved from https://www.hhs. gov/sites/default/files/hipaa-privacy-rule-and-sharing-inforelated-to-mental-health.pdf. Accessed on April 3, 2019. U.S. Department of Health and Human Services (HHS). (2019). Breach portal: notice to the Secretary of HHS breach of unsecured protected health information. Retrieved from https://ocrportal.hhs.gov/ocr/breach/ breach_report.jsf. Accessed on March 26, 2019. U.S. Department of Health, Education, and Welfare (DHEW). (1973). Records, computers and the rights of citizens: Report of the Secretary’s Advisory Committee on Automated Personal Data Systems. July 1973. Retrieved from http://epic.org/privacy/hew1973report/. Accessed on March 25, 2019. Wilkes hospital computer network down due to virus (Wilkes). (2018). Wilkes Journal-Patriot. September 18, 2018. Retrieved from https://www.journalpatriot.com/ news/wilkes-hospital-computer-network-down-due-tovirus/article_cd4b10f0-bc67-11e8-b282-0777ede880b6. html. Accessed on July 26, 2020.

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11 Social Determinants of Health, Electronic Health Records, and Health Outcomes Marisa L. Wilson / Paula M. Procter

• OBJECTIVES 1. Evaluate importance of social determinants of health (SDOH) to patient care and outcomes. 2. Describe governmental momentum toward inclusion of SDOH data. 3. Examine potential sources of SDOH data. 4. Evaluate strategies for incorporating SDOH data.

• KEY WORDS Accountable Health Communities Centers for Medicare and Medicaid Innovation Community Indicators Interoperability Social determinants of health Social determinants of health measurement Standardization

INTRODUCTION In 2010, the Affordable Care Act (ACA) was enacted to bring about a decrease in the number of uninsured Americans, to usher in an improvement in the quality of care received, and to result in a reduction in the overall cost of healthcare through an increase in access to preventive services. The main goal of the ACA was to promote the health of the country in order to improve quality of life and contain cost. The ACA shifted the focus of the healthcare industry away from payment for the volume of services rendered toward accountability for quality of care and health outcomes achieved which does not happen

with medical treatment and services, testing, or pharmaceuticals alone. The ACA placed increased attention on areas outside of traditional medical treatment and care services and onto those factors that affect overall health where we live, work, and play. These factors are the social determinants of health (SDOH) which are those complex, integrated, and overlapping social, environmental, and economic structures that are responsible for most health inequalities. Given that medical care is estimated to account for only 10% to 20% of the modifiable contributors to health outcomes, the SDOH factors have to be considered if the goals are to be achieved (Hood, Gennuso, Swain, & Catlin, 2016).

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182    P art 2 • S ystem S tandards For the informaticist, considering the inclusion of SDOH data along with the pool of data currently collected during medical care and treatment interactions and usually stored in an electronic health record (EHR) or other specialty database brings up several concerns that must be addressed in order for health and quality to be achieved. The concerns are as follows: 1. What are the SDOH factors? 2. What must be considered in the SDOH data ­collection process? 3. Can community-level SDOH data be used? 4. How does the collection of SDOH data impact the people, processes, and technologies that interact in any setting? 5. How does the informatician work with an interprofessional and multiorganizational team to design successful projects to address the SDOH? The aim of this chapter is to introduce the reader to the best evidence and resources that can be used to answer these questions.

BACKGROUND The topic of social determinant’s influence on health is no longer a topic of debate. What is known is that the impact of SDOH factors such as sanitation, food insecurity, housing instability, transportation shortages, environmental issues, interpersonal violence on a patient’s health and well-being, healthcare utilization usage, and overall cost of care has been well established (Brooske, Athens, & Kindig, 2010; Gottlieb, Quinones-Rivera, Manchanda, Wing, & Ackerman, 2017). Landmark documents such as the 2008 Closing the Gap report by the World Health Organization Commission on Social Determinants of Health provides evidence to demonstrate that income, education, social status, and social support are correlated with increased morbidity and premature mortality (British Medical Association, 2011). In the United States, currently, 90% of healthcare dollars are spent on medical treatments that occur within a healthcare setting such as a hospital or provider’s office. However, up to 70% of a person’s overall health is driven by social and environmental factors and the behaviors that influence them, which are external to the medical and health care environment (Schroeder, 2007). Analyses presented by multiple organizations ascribe correlation with health outcomes as follows (Goinvo, 2017): 1. Seven percent by the environment (pollution, location, exposure to firearms, allergens) 2. Eleven percent by medical care

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3. Twenty-two percent by genetics and biology 4. Thirty-six percent by individual behavior (­psychological, mood and affect, risk, physical ­activity, sleep, and diet) 5. Twenty-four percent by social circumstances (social connectedness, social status, culture and tradition, race, ethnicity, sexual orientation, military service, gender identity, incarceration, discrimination, and work conditions) Goinvo, 2017. These structural determinants and conditions not only impact health of people but also contribute to over onethird of the causes of death in the United States every year (Galea, Tracy, Hoggatt, DiMaggio, & Karpati, 2011). There is increased recognition that improving health, reducing costs, and improving quality will require broader approaches than what is currently offered within healthcare settings. Truly, achieving health will require a recognition of and a response to the social, economic, and environmental factors that impact health. Informaticians will need to understand the current evidence related to screening, risk assessment, data transfer, and evaluation of those programs established to address the SDOH factors. Informaticians will need to consider how SDOH data interact with and are considered along with medical data captured within EHRs.

International Recognition of SDOH Note that the concern with the SDOH factors is not only a US centric issue but is being addressed by other countries as well, and this chapter provides a view from the United Kingdom. Beginning in 2011, the World Health Organization (WHO) Commission on Social Determinant of Health, the Rio Political Declaration on Social Determinants, the United Nations (UN) General Assembly, and the World Health Assembly all expressed global political commitment for the implementation of approaches that incorporate SDOH to reduce health disparities by improving access and addressing underlying conditions (Donkin, Goldblatt, Allen, Nathanson, & Marmot, 2017). The United Sates is a rather newcomer to this perspective which has been driven by the relatively poor health status of the US population compared to that of other countries. Despite all that is spent on medical care, the United States lags in infant and maternal mortality, injuries, homicide, adolescent pregnancy, heart disease, obesity, diabetes, chronic lung disease, and drug-related mortality as compared to other like countries (National Research Council [NRC] and Institute of Medicine [IOM], 2013). These comparatively poor outcomes have fostered an interest in understanding the reasons. The fact that the United States has a higher rate of morbidity and mortality

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  183

•  FIGURE 11.1.  Social Determinant of Health Visualization. (Created by GoInvo at https://www.goinvo.com/vision/determinants-of-health/. Licensed under a Creative Commons Attribution 3.0 license.) even though it spends more on healthcare than the other countries in the Organization for Economic Co-operation and Development (OECD) has coined the American Paradox (Bradley & Taylor, 2013). It may be because several OECD countries not only focus on the provision

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of care but also incorporate SDOH information in their provision of care and into their EHRs. The United States spends more on healthcare than other OECD countries and significantly less on social services, while other OECD countries with better outcomes spend more on tracking

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184    P art 2 • S ystem S tandards and addressing social services which could address the SDOH factors and ultimately improve health (PetersonKaiser, 2017). Given that SDOH contributes to poor outcomes and higher cost, it is important to note that there are efforts to address them within the context of current payment reform in the United States. In 2018, Health and Human Services Secretary Azar indicated that the Center for Medicare and Medicaid (CMS) Innovation Center (CMMI) was seriously considering inclusion of the role of social determinants of health and that the thirty-one 2017 Accountable Health Communities are serving as exemplars of how to address SDOH in the context of health (see: https://www.hhs. gov/about/leadership/secretary/speeches/2018-speeches/ the-root-of-the-problem-americas-social-determinantsof-health.html). The Accountable Health Communities models address the gap between clinical care and community services in the current health care delivery system. The selected model leadership and researchers are testing whether screening and identification of SDOH and then referral and navigation to community services will impact costs, improve outcomes, and reduce healthcare utilization (CMS, 2019). Medicaid Managed Care Organizations are increasingly using care management and coordination to target SDOH factors such as housing, behavioral health, substance abuse, and nutrition using screening, data collection, and referral to coordinated partnerships to address risk (Institute for Medicaid Innovation, 2019). Despite some success, there are challenges. Financing, data collection, data sharing, standardization of screening tools, quality metrics, and integration of strategies between systems represent opportunities for nurse leadership to impact the health of the nation.

human rights, and equitable in reducing health disparities (American Nurses Association, 2015). The Code of Ethics also challenges the nurse to identify conditions and circumstances that contribute to illness, injury, and disease, to foster health lifestyles, and to participate in institutional and legislative efforts to protect and promote health (American Nurses Association, 2015). The ANA Code of Ethics applies to the nurse who works in informatics and challenges that nurse to work to incorporate SDOH data within that pool of data that originates from the traditional EHR. The Code of Ethics supports the efforts of that informatics nurse who studies SDOH data, calculates risk to patients and populations, and works with teams to implement and evaluate responses to reduce disparities and poor outcomes.

SDOH: CONTRIBUTORS TO HEALTH The World Health Organization (WHO) defines social determinants of health as the conditions in which people are born, work, grow, live, and age and include the wider set of forces and systems shaping the conditions of daily life (World Health Organization, 2019). The WHO definition leaves wide open the potential list of SDOH data elements (Table 11.1). The Institute of Medicine (IOM) National Academies of Health and Medicine Committee on Recommended Social and Behavioral Domains and Measures for EHRs in a twophase project, took a broad list of 31 SDOH domains identified for possible consideration and applied criteria of strength of association with health outcomes, clinical and population health ­relevance, and research

CODE OF ETHICS There is much work to be done to address SDOH by the Nurse Informatician. Competencies and professional performance expectations directs this nurse to address the data, the causes, and the solutions. In addition, consider that the Nurse Informatician, the Informatics Nurse Specialist, or the Nurse Clinical Analyst is still a nurse and, as such, must consider application of the American Nurses Association (ANA) Code of Ethics with Interpretive Statements in their professional practice. Specifically, provision 8 of the ANA Code of Ethics directs the nurse to collaborate with other health professionals and the public to protect human rights, promote health diplomacy, and reduce health disparities (American Nurses Association, 2015). The ANA Code of Ethics calls for all nurses to be creative and innovative in creating approaches that are ethical, respectful of

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  TABLE 11.1    World Health Organization Determinants of Health Social environment

Education

Transport

Economic environment

Social support

Food and nutrition

Individual characteristics

Genetics

Water

Behaviors

Culture

Waste and pollution

Income

Access and use of service

Radiation

Social status

Gender

Housing

Source: World Health Organization

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Chapter 11 • Social Determinants of Health, Electronic Health Records, and Health Outcomes 

usefulness to cull that list down to 17 domains with 31 measures (The National Academies of Sciences, Engineering, Medicine, 2018). To further refine the domain list, the IOM reviewed that list against criteria of readiness with a standard measure, feasibility of collection; usefulness for inclusion in an EHR; and committee judgment (The National Academies of Sciences, Engineering, Medicine, 2018). The final list of identified specific domains and core measures that capture targeted SDOH were to be included in EHRs as part of Phase 3 of Meaningful Use. The IOM then explored and recommended specific data elements and methods for collection (The National Academies of Sciences, Engineering, Medicine, 2018) (Table 11.2). The IOM made several recommendations to the Office of the National Coordinator for Health Information   TABLE 11.2    Institute of Medicine Recommended Core Domains and Measures Domain

Measure

Alcohol use

3 questions from the Alcohol Use Disorders Identification Test-C (AUDIT-C)

Race and ethnicity

2 questions based off of Census categories

Residential address

1 question from intake

Tobacco use and exposure

2 questions based off of the National Health Interview Survey (NHIS)

Census track median income

Census track Neighborhood and Community Compositional Characteristic

Depression

2 questions from the Patient Health Questionnaire-2 (PHQ-2)

Education

2 questions on educational attainment

Financial resource strain

1 question on overall financial strain

Intimate partner violence

4 questions from Humiliation Afraid Rape Kick (HARK)

Physical Activity

2 questions from Exercise Vital Signs

Social ­connections and social isolation

4 questions from National Health and Nutrition Examination Survey III (NHANES III)

Stress

1 question from Elo, A-L, Leppanen, A., & Jahkola, A. (2003)

Source: Institute of Medicine of the National Academies. (2015). Capturing social and behavioral domains and measures in electronic health records: Phase 2.

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Technology related to the potential inclusion of these SDOH domains and data elements in EHRs. Two of the recommendations were as follows (The National Academies of Sciences, Engineering, Medicine, 2018): 1. The certification process for EHRs should include the standard measures for four social and behavioral domains that are already collected (race/ethnicity, tobacco use, alcohol use, and residential address). 2. The certification process for EHRs should add for inclusion the standard measures for the other eight recommended domains (educational attainment, financial resource strain, stress, depression, physical activity, social isolation, intimate partner violence, and neighborhood median income). Standardization of the data elements within each selected domain would allow vendors to build product that could acquire, store, transmit, and download self-reported data pertinent to the SDOH factors. Standardization would allow for sharable, comparable data across phases of care as well as within locations and systems of care. This would allow advances in understanding the contributions of SDOH factor management to improved outcomes and quality and reduced costs.

SOURCES OF SDOH DATA Given the importance of and focus on SDOH factors on individual and population health, many healthcare systems have begun to explore ways to integrate this data with patients’ clinical data. Medicaid and the Children’s Health Insurance Program (CHIP) payment reform projects are providing financial incentives for bringing the issue of SDOH data collection to a broader audience of providers beyond the community health centers and safety net providers who have traditionally worked to meet these needs among their high-risk populations (Cantor & Thorpe, 2018). EHR vendors have begun to develop tools within their EHRs for capturing and storing SDOH data. The vendors are developing tools that use this data for individual risk assessment and referral and for population health management. However, this work is not necessarily following a recommended strategy or being done using standard questions and answers or uniformly coded data elements, which ultimately thwarts attempts at transmission of data across systems. This work, although welcome for the recognition of the importance of SDOH to outcomes, will present challenges. In addition, SDOH data collection can occur external to an organization or internally during an interaction with care providers.

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Community-Level Data—Community Indicators One of the clearest determinants of health is geography. Where people live and the resulting consequences create barriers to care and exacerbate disparities (Graham, Ostrowski, & Sabina, 2015). Health and longevity are greatly influenced by one’s zip code which can be a stronger predictor of health than other factors such as race and genetic code. With this in mind, it is important to consider the utility of data generated external to a healthcare system. Community-level SDOH data are useful at a system level, can enhance performance of predictive models, and are of interest to researchers looking to determine the influence of community context in health outcomes. Community-level data use often requires the involvement of community members so that all are aware of the indicator development and how they will be used, particularly if the end product is a publicly available community comparison tool. Open source and publicly available data sets reflective of community-level SDOH can be used to create single data sets that are mapped at a census track level for analysis after processing and queries. Some sources of this data include that available from the U.S. Department of Agriculture, the Centers for Disease Control and Prevention, the American Community Survey of the U.S. Census Bureau, and other private o ­ rganizations (Table 11.3).

In 2019, the Agency for Healthcare Research and Quality (AHRQ) launched an application challenge to advance visualization resources of community-level SDOH data. The goal of the challenge will be to support the development of tools that will allow visualization of data clusters to enhance SDOH research and analysis of community-level health services (see: https://www.ahrq. gov/sdoh-challenge/about.html). This challenge will open the door to additional tools for evaluating communitylevel data.

Issues Related to Collection of Community-Level Data Community-level SDOH data are often open and available for public use. However, in order to consider joining this to data from EHRs there are some issues to consider. Initially, the informatician has to determine which data is needed and this decision should be guided by clinical need, standards recommendations such as that from the IOM, availability, and adequacy of the data dictionary description. Following this, consideration must be made to the risk of attributing a factor to any one individual within a community. Consider that an individual can reside in the same census track or neighborhood as others and not be experiencing identical risk to any other resident of that neighborhood. The lowest level of measurement also has to be

  TABLE 11.3    Select Sources of Community-Level SDOH Data Organization

Title

URL

AARP Brookings

AARP Livability Index Metro Monitor

CDC

Behavioral Risk Factors Data

CDC

Chronic Disease Indicators

CDC

Interactive Atlas of Heart Disease and Stroke National Center for HIV/AIDS, Hepatitis, STD, and TB The Social Vulnerability Index Community Health Needs Assessment City Health Dashboard

https://livabilityindex.aarp.org https://www.brookings.edu/research/ metro-monitor-2017/ https://chronicdata.cdc.gov/browse?category=Behavioral +Risk+Factors https://nccd.cdc.gov/cdi/rdPage.aspx?rdReport=DPH_CDI. ExploreByLocation&rdRequestForwarding=Form https://www.cdc.gov/socialdeterminants/data/index.htm

CDC CDC Community Commons Department of Population Health NYU Reinvestment Fund U.S. Census Bureau U.S. National Library of Medicine

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PolicyMap U.S. Census Community Health Maps

https://www.cdc.gov/nchhstp/atlas/index.htm https://svi.cdc.gov https://www.communitycommons.org/board/story/ 2019/03/04/chna/ https://healthitanalytics.com/news/social-determinantsof-health-dashboard-expands-to-500-cities https://www.policymap.com https://www.census.gov https://communityhealthmaps.nlm.nih.gov

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considered. Do you only have state data or city data or can you get data at the census track level? Consider how far you can “zoom” into the data without violating confidentiality. Some conditions may be relatively rare in a community, so there may be a risk of identifying a subject inadvertently. The informaticist will also need to consider his or her skill with big data techniques as these may be required since the velocity, volume, value, variety, and veracity of the data must be accounted for before processing and storing the community-level SDOH data. The informaticist may also need to learn new tools in order to make sense of the data—predictive and visual analytics tools, geocoding, heat mapping may all be new techniques to the informaticist used to working with EHR data.

Individual-Level Data The other and more direct method for obtaining SDOH data is directly from the individual. This can be accomplished through electronic screenings, checklists, or surveys or even by using paper versions of surveys. Collection can be done at the point of care, through a portal or personal health record, or on a tablet or kiosk while the individual waits. An important consideration is that the tool incorporates standardized terminology and data modeling, and that the data elements are encoded to ensure interoperability that will facilitate exchange of health information between phases of care. Vendors have added SDOH screenings into EHRs that include intimate partner violence, social isolation, alcohol and tobacco use, depression, financial resources, and food, transport, and housing insecurity. However, care must be taken to ensure that these screening tools incorporate reliable and valid tools and that the data elements are standardized and encoded. There are examples of individual-level tools that are valid, reliable, and standardized that the nurse informatician may want to review. The Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE) developed by the National Association of Community Health Centers is one such tool (see: http:// www.nachc.org/research-and-data/prapare/about-theprapare-assessment-tool/). PRAPARE is an SDOH assessment tool but also includes an implementation and action toolkit. A compilation of other valid, reliable, and standardized individual-level SDOH data collection tools can be found at the Social Interventions Research and Evaluation Network (SIREN) supported by the University of California, San Francisco (see: https://sirenetwork.ucsf. edu/tools-resources/mmi/screening-tools-comparison). SIREN catalyzes and disseminates research to advance efforts to address SDOH. SIREN houses an evidence library, tools, reports, and resources.

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Comparison of SDOH Individual-Level Tools When selecting a tool to use as an integrated screen within an EHR or as separate data collection process outside of the EHR, there are a few considerations that will impact choice. It is important to decide the intended population. Is this screening for adults, children, pregnant women, everyone? What is the setting in which these data will be collected? What domains will be covered? For example, it may not be best to screen for everything if the site is only focusing on addressing food insecurity. Is the tool valid and reliable? What is the reading level? What is the average completion time, as it will impact workflow? Is there a cost to using the tool? SIREN and PRAPARE offer insights to tool selection that are important to consider. If you are using a tool that has already been built within an EHR, the informaticist should conduct due diligence and determine the source of the question and the standardization of the answers.

Issues Related to Collection of IndividualLevel Data Just as there are issues related to the use of community-level SDOH data, there are issues to consider with individual-level SDOH data collection and use. Some items to consider are related to documentation burden, workflow challenges, clinician engagement in the process, patient refusal to answer, interpreters needed, training needs, operational challenges, and a lack of a closed loop between collection of data, risk calculation, and referral to a community-based resource. A team of researchers implemented a suite of SDOH data tools in three Pacific Northwest Community Health Centers and used a mixed methods approach to assess the adoption of the tools (Gold et al., 2018). Results pointed to barriers for the patient and the providers. As an example, although 97% to 99% of the 1098 screened patients had at least one SDOH need documented, only 19% had a referral documented in the EHR, and only 15% to 21% of the screened patients indicated even wanting help (Gold et al., 2018). Results such as these point to the need for: 1. Consideration of the questions being asked, 2. A detailed review of the workflow, 3. Avoidance of documentation burden by perhaps sharing the data collection between different provider types and support staff, 4. Training of providers, staff, and patients, 5. Report generation for tracking, and

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188    P art 2 • S ystem S tandards 6. Development of a closed loop so that providers can assess that a patient received a referral and subsequently went to the referral site.

“THE TRIPLE S” OF SDOH DATA The Health Information and Technology, Evaluation and Quality (HITEQ) Center, a national training and technical assistance center developed and operated by JSI and Westat, has coined the phrase “The Triple S” for SDOH data (HITEQ, 2017). It is useful for nurse informaticians to consider their recommendations. The recommendations are as follows: 1. Systematic: SDOH data need to be collected on all patients or on all patients in a given target population at the frequencies required. (See the IOM-recommended frequencies as examples.) 2. Structured: SDOH data need to be collected using valid and reliable tools. 3. Standardized: SDOH data need to be collected using standardized common data sets with common definitions and structures such as ICD-10, SNOMED, or LOINC.

Standardization Screening for SDOH is increasingly being done primary and specialty care settings. It is essential that the standardization and harmonization of SDOH data occur so that assessment and risk mitigation are standardized, that data are trackable and interoperable, and so that evidence of impact can be assessed (Olson, Oldfield, & Navarro, 2019). As of this writing, the International Classification of Diseases, Tenth Edition (ICD-10-CM) does include coding for SDOH data in the categories Z55–65 (Table 11.4). To address specific measurements and observations, the Logical Observation Identifiers Names and Codes (LOINC) has also coded responses to panels that are among the IOM-recommended domain sources (Table 11.5).

OPTIMIZING THE COLLECTION OF SDOH DATA People, Process, and Technology Layering onto the usual clinical data collection with SDOH data requires a detailed look at the people, processes, and technologies that are used. Documentation burden; workflow inefficiencies; provider, staff, and patient reluctance

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  TABLE 11.4    ICD-10-CM Codes for SDOH Data Code

Name

Z55

Health literacy (illiteracy and schooling …)

Z56

Employment and unemployment (work environment)

Z57

Occupational exposure (radiation, dust, smoke…)

Z59

Housing and economic circumstances (homeless, inadequate housing …)

Z60

Social environment (life transitions, living alone …)

Z62

Upbringing (inadequate parental supervision, overprotection …)

Z63

Primary support group (family member absence, disappearance, death …)

Z64

Psychosocial circumstances (unwanted pregnancy, discord …)

Z65

Other psychosocial circumstances (convictions, imprisonment, crime …)

Source: American Hospital Association. Resources on ICD-10_CM Coding for Social Determinants of Health. https://www.aha.org/dataset/ 2018-04-10-resource-icd-10-cm-coding-social-determinants-health.

  TABLE 11.5    LOINC Coding Supporting SDOH Panels Panel

Tools

802167-5

2015 Health IT Certification Criteria Patient Health Questionnaire (PHQ-2) Alcohol Use Disorder Identification Test—Consumption (AUDIT-C) Humiliation, Afraid, Risk, and Kick (HARK) National Health and Nutrition Examination Survey (NHANES)

82152-0

Adverse Childhood Events (ACE) Behavioral Risk Factor Surveillance System (BRFSS)

Source: LOINC. Represent Social Determinants of Health with LOINC. https://loinc.org/sdh/.

are all real issues that need to be considered as part of interprofessional planning and decision-making. Whether the data are coming from an external source or are being gathered during a point of care interaction, planning for this process has to occur. Some questions to consider are as follows: 1. Who is the target population? 2. What are the predominant issues driving disparity in that population?

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3. Can community-based data provide answers, or does it have to be directly from the individual? 4. What tools will be used? 5. What analytics techniques will be used? 6. Who will collect the data if the data is being gathered at the point of care? Can this collection be contributed to by different members of the team such as support staff, nursing, physicians, physical and occupational therapy, nutrition, social work, and chaplaincy? 7. What training will occur?

  TABLE 11.6    Important SDOH Project Resources Resources

URL location

Community Tool Box

https://ctb.ku.edu/en/tableof-contents/overview/ models-for-communityhealth-and-development/ social-determinants-ofhealth/main

Institute of Medicine

http://nationalacademies.org/ HMD/Activities/PublicHealth/ SocialDeterminantsEHR.aspx

Addressing the SDOH factors that contribute to poor outcomes is a team endeavor. The team should be engaged in answering these questions.

National Association of Community Health Centers (NACHC) PRAPARE

http://www.nachc.org/ research-and-data/prapare/

CLOSING THE LOOP

Office of Disease Prevention and Health Promotion

https://www.healthypeople.gov

Social Interventions Research and Evaluation Network (SIREN)

https://sirenetwork.ucsf.edu

The American Planning Association

https://www.planning.org/ policy/guides/

If a team is to address the SDOH factors that are driving poor outcomes among their population, the loop must be closed between the provider, the referral agency, and back to the provider. Providers must have access to clinical decision support (CDS) type tools that will use data collected to produce community services that are appropriate for that patient to address their need. As an example, if a patient resides in a specific area, has a family income of a certain amount, and then screens for food insecurity, a CDS tool should be able to offer up appropriate community food resources. If that patient is a newly diagnosed type 2 diabetic, that community-based resource should receive that referral so that when the patient arrives, he or she can choose appropriate foods. The provider then should have some way of knowing via messaging that the patient did, in fact, go to the food resource. EHR vendors are working on these types of resources and the databases needed to support them. In addition, application developers are also working to fill this space. Applications such as NowPow (see: https://www.nowpow.com) and Healthify (see: https://www.healthify.us) are just two of the tools attempting to make those connections and close the loop.

RESOURCES Nurse informaticians are fortunate to have, at their disposal, resources to use as they are making decisions on the incorporation of SDOH data into the information environment whether that data is coming from external or internal sources. These resources are offered by organizations at the forefront of programmatic development and evaluation seeking to address the disparities, poor outcomes, and increased costs brought about by variations in

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social determinants of health factors. Table 11.6 lists some of the most useful and rigorously developed resources.

A VIEW FROM THE UNITED KINGDOM The financial burden of healthcare provision through the National Health Service (NHS) in the United Kingdom raises similar concerns to those in the United States albeit from a different payment/provider structure. The main concern in the United Kingdom, along with most other Western economies, is the change in the demographics of the population and the ever-increasing financial support needed for healthcare. In 2010, the main driver report (The Marmot Review: Fair Society, Healthy Lives, 2010) was published highlighting the role of social determinants of health impact upon patient care and outcomes. Within NHS England the division between community care (21.034 million contacts in 2014–15) and hospital care (16.25 million admissions in 2015–16) is biased toward community/primary care; currently there is a general strategic goal to reduce the costly hospital admission route for care provision. To pay for both the community/primary and hospital care, an expenditure of 9.75% of the GDP in

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190    P art 2 • S ystem S tandards 2016 (UK wide) (NHS Confederation, 2017) was provided through taxation. There are six determinants highlighted from the 2010 report (The Marmot Review: Fair Society, Healthy Lives, 2010) which continue to be monitored in order to direct appropriate healthcare support in an improved manner based upon need. These determinants are as follows:

• • • • • •

The built and natural environment Work and the labor market Vulnerability Income Crime Education

Information is freely available on each of the above determinants through Public Health England (Public Health England, 2019) in a section entitled “Wider Determinants of Health.” As with many other countries, statistical information for the six determinants is available through different governmental departments at national level, for example, figures for vulnerability are collected and published by the Ministry of Housing, Communities and Local Government, with income information primarily provided through the Office for National Statistics. The collective view of social determinants allows for greater coordination of care and targeted release of scarce resources. The NHS gatekeeper for healthcare is the General Practitioner (GP). The GP is based within the local community and as such is well versed with the local inequalities and the potential health needs of the local population. In collaboration with the local Clinical Commissioning Group (CCG) (NHS Clinical Commissioners, 2019) which is responsible for planning and buying (commissioning) healthcare services including hospital care as well as the services people receive in the community through community nurses and allied health professionals, the GP will refer patients to appropriate services which may or may not include hospital care. More recently, the CCGs have been involved in shaping healthy cities and economies through collaborative working with partners outside the “health” boundaries to improve not only the health but also the social and economic well-being of local populations. Stated in the introduction to “Shaping healthy cities and economies,” published in December 2016 (NHSCC, 2010), “Combating health inequalities and social exclusion: By supporting early interventions and addressing the broader social determinants that impact population health, CCGs can help alleviate and prevent poverty, thereby reducing pressures on health and social services”

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(p. 3). This goal is expected to improve health outcomes for individuals and society as a whole while potentially reducing the financial support for healthcare. An extension to the developments spearheaded by the CCGs are integrated care systems where local collaborative initiatives are working toward improving inequalities, one such in the north of England “Health and Care Working Together in South Yorkshire and Bassetlaw” (NHS England, 2019) describes how the integrated care system is made up of 18 NHS organizations (GP/Community Services and Hospitals for acute, mental health and long term conditions) and link with 6 local authorities, voluntary sector, and independent partners. Such integration expands the understanding of the needs of people living in the area and offers opportunities to share the health burden. It certainly appears that all countries are agreed that the wider views of populations are required to reduce inequalities in the 21st century to maintain capabilities to provide affordable healthcare, but the approach may be different based upon the available healthcare system.

Test Questions 1. Social determinants of health factors outcomes include: A. Housing status

B. How much education a person obtains C. Having food or being able to get food D. All of the above

2. Social and behavioral factors are about what percentage of a person’s health? A. Up to 25% B. Up to 40% C. Up to 50% D. Up to 70% 3. Social determinants of health contribute to what proportion of causes of death? A. One-fourth B. One-half

C. One-third

D. All of the above 4. Managing social determinants of health are only an issue in the United States. A. True

B. False

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Chapter 11 • Social Determinants of Health, Electronic Health Records, and Health Outcomes 

5. The ANA Code of Ethics directs the nurse to collaborate with other health professionals and the public to protect human rights, promote health diplomacy, and reduce health disparities. A. True

B. False 6. The Institute of Medicine (IOM) made two recommendations to the Office of the National Coordinator related to the Social Determinants of Health. Choose all that are correct.

A. The certification process for EHRs should include the standard measures for four social and behavioral domains that are already collected (race/ethnicity, tobacco use, alcohol use, and residential address).

B. The certification process for EHRs should add for inclusion the standard measures for the other two recommended domains (educational attainment, financial resource strain). C. The certification process for EHRs should add for inclusion the standard measures for the other eight recommended domains (educational attainment, financial resource strain, stress, depression, physical activity, social isolation, intimate partner violence, and neighborhood median income). D. The certification process for EHRs should include the standard measures for five social and behavioral domains that are already collected (race/ ethnicity, tobacco use, alcohol use, weight, and residential address). 7. Understanding the social determinants of health characteristics of a patient can only be determined by surveying the patient. A. True

B. False 8. Social determinants of health characteristics cannot be applied to a population. A. True

B. False 9. The “Triple S” of Social Determinants of Health data are: A. Systematic, Structured, and Standardized B. Sustained, Structured, and Standardized C. Sustained, Structured, and Systematic D. Systematic, Stable, and Standardized

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10. Some social determinants of health data are standardized and are in ICD-10, SNOMED, and LOINC. A. True

B. False

Test Answers 1. Answer: D 2. Answer: D 3. Answer: C 4. Answer: B

5. Answer: A

6. Answer: A, C 7. Answer: B 8. Answer: B

9. Answer: A 10. Answer: A

REFERENCES American Nurses Association. (2015). Code of ­ethics for nurses with interpretive statements (2nd ed.). Silver Spring, MD: American Nurses Association. Retrieved from https://www.r2library.com/Resource/ Title/1558105999. Accessed on May 5, 2020. Bradley, E. H., & Taylor, L. A. (2013). The American healthcare paradox. Why spending more is getting us less. New York, NY: Public Affairs. British Medical Association. (2011). Social determinants of health: what can doctors do? London: British Medical Association. Brooske, B. C., Athens, J. K., Kindig, D. A., Park, H., & Remington, P. L. (2010). Different perspectives for assigning weights to determinants of health. University of Wisconsin Population Health Institute. Retrieved from http://www.countyhealthrankings.org/sites/default/files/ differentPerspectivesForAssigningWeightsToDeterminantsOfHealth.pdf. Accessed on May 5, 2020. Cantor, M. N., & Thorpe, L. (2018). Integrating data on social determinants of health into electronic health records. Health Affairs, 37(4), 585–590. Centers for Medicare & Medicaid Services [CMS]. (2019). Accountable Health Communities Models. Retrieved from https://innovation.cms.gov/initiatives/ahcm/. Accessed on May 5, 2020. Donkin, A., Goldblatt, P., Allen, J., Nathanson, V., & Marmot, M. (2017). Global action on the social determinants of health. BMJ Global Health, 3:e000603. doi:10.1136/ bmjgh-2017-000603. Elo, A-L., Leppänen, A., Jahkola, A. Validity of a single-item measure of stress symptoms. Scandinavian journal of work, environment & health. 29, 444-51. 10.5271/sjweh.752.

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192    P art 2 • S ystem S tandards Galea, S., Tracy, M., Hoggatt, K. J., DiMaggio, C., & Karpati, M. (2011). Estimated deaths attributable to social factors in the United States. American Journal of Public Health, 101(8), 1456–1465. Goinvo. (2017). Determinants of health. Retrieved from https://www.goinvo.com/vision/determinants-ofhealth/#methodology. Accessed on May 5, 2020. Gold, R., et al. (2018). Adoption of social determinants of health EHR tools by community health centers. Annals of Family Medicine, 16(5), 399–407. Gottlieb, L. M., Quinones-Rivera, A., Manchanda, R., Wing, H., & Ackerman, S. (2017). States influences on Medicaid investments to address patients’ social needs. American Journal of Preventive Medicine, 52(1), 31–37. Graham, G., Ostrowski, M. L., & Sabina, A. (2015). Defeating the zip code health paradigm: data, technology, and collaboration are key. Health Affairs. Retrieved from https://www.healthaffairs.org/do/10.1377/ hblog20150806.049730/full/. Accessed on May 5, 2020. Health Information Technology, Evaluation, and Quality Center [HITEQ]. (2017). Why collect standardized data on social determinants of health? Retrieved from https:// hiteqcenter.org/DesktopModules/EasyDNNNews/ DocumentDownload.ashx?portalid=0&moduleid=865&a rticleid=330&documentid=179. Accessed on May 5, 2020. Hood, C. M., Gennuso, K. P., Swain, G. R., & Catlin, B. B. (2016). County health rankings: relationships between determinant factors and health outcomes. American Journal of Preventive Medicine, 50(2), 129–135. Institute for Medicaid Innovation. (2019). Medicaid plans to tackle social determinants of health bhut barriers remain. Retrieved from https://www.medicaidinnovation.org/ news/item/medicaid-plans-tackle-social-determinantsof-health-but-barriers-remain. Accessed on May 5, 2020. NHS Clinical Commissioners. (2019). Retrieved from https://www.nhscc.org/ccgs/. Accessed on May 5, 2020. NHS Confederation. (2017). NHS statistics, facts and figures. Retrieved from https://www.nhsconfed.org/resources/ key-statistics-on-the-nhs. Accessed on May 5, 2020. NHS England. (2019). Health and Care Working Together in South Yorkshire and Bassetlaw. Retrieved from https:// www.england.nhs.uk/integratedcare/integrated-caresystems/south-yorkshire-andbassetlaw-ics/. Accessed on May 5, 2020.

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NHSCC. Shaping healthy cities and economies. (2010). Retrieved from https://www.nhscc.org/policy-briefing/ shaping-healthy-cities-and-economies/. Accessed on May 5, 2020. Olson, D. P., Oldfield, B. J., & Navarro, S. M. (2019). Standardizing social determinant of heath assessments. Health Affairs. Retrieved from https://www.healthaffairs. org/do/10.1377/hblog20190311.823116/full. Accessed on May 5, 2020. Peterson-Kaiser. (2017). Health Systems Tracker. What do we know about social determinants of health in the U.S. and comparable countries? Peterson Center on Healthcare and Henry J. Kaiser Family Foundation. Retrieved from https://www.healthsystemtracker.org/ chart-collection/know-social-determinants-health-u-scomparable-countries/#item-u-s-highest-rate-years-lifelost-­disability-premature-death-due-firearm-assaults. Accessed on May 5, 2020. Public Health England. (2019). Wider determinants of health. Retrieved from https://fingertips.phe.org.uk/­ profile/wider-determinants. Accessed on May 5, 2020. Research Council [NRC] and Institute of Medicine [IOM]. (2013). U.S. heatlh in international perspective: shorter lives, poorer health. Washington, DC: The National Academies Press. Schroeder, S. A. (2007). We can do better—improve the health of the American people. New England Journal of Medicine, 357, 1221–1228. The Marmot Review: Fair Society, Healthy Lives. (2010). Strategic review of health inequalities in England post 2010. Retrieved from http://www.instituteofhealthequity. org/resources-reports/fair-society-healthy-lives-themarmot-review/fair-society-healthy-lives-full-report-pdf. pdf. Accessed on May 5, 2020. The National Academies of Sciences, Engineering, Medicine. (2018). Recommended social and behavioral domains and measures for electronic health records. Retrieved from http://nationalacademies.org/HMD/Activities/ PublicHealth/SocialDeterminantsEHR.aspx. Accessed on May 5, 2020. World Health Organization. (2019). Social determinants of health. Retrieved from https://www.who.int/social_determinants/en/. Accessed on May 5, 2020.

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part

3

System Life Cycle Denise D. Tyler

Part 3 provides an overview of the System Life Cycle. This is an important skill for the informatics nurse to master. The American Nurses Credentialing Center (ANCC) recognizes the importance of the system life cycle by devoting 28% of the certification exam to the topic 2018 certification exam. Each chapter in this part provides valuable information and practical tools for both novice and experienced informatics nurses. The system life cycle and project management share tasks and functions; the primary difference is that project management has a clear start and stop. In contrast, like the nursing process, the system life cycle is a continuous process. Each chapter in this part has valuable information to use in the workplace and when studying for the certification exam. Chapter 12, System Life Cycle: A Framework, by Dr. Susan K. Newbold introduces the electronic health record (EHR) and the current landscape that provides an opening to the importance of each phase of the system life cycle. The chapter includes detailed information on all aspects of the system life cycle—from system selection through system implementation and maintenance. In addition, she ties the system life cycle into project management, which is covered in Chapter 15, Healthcare Project Management, by Drs. Barbara Van de Castle and Patricia C. Dykes. Dr. Newbold also addresses the growing trend of converting legacy systems, providing valuable tools like a sample cutover plan. Following the system life cycle chapter is Chapter 13 entitled System and Functional Testing by Drs. Theresa (Tess) Settergren and Denise D. Tyler. In this chapter, they provide valuable information on testing that is useful for informatics nurses to use as a guide for what to test and provides examples of tools used for testing. The chapter introduces what a testing plan should include and the different types and phases of testing. The authors provide multiple tables that display what to test and examples of testing worksheets. The chapter contains information on why testing is so critical to the success of EHR implementation or upgrade and the role of the informatics nurse in testing. Part 3 ends with Chapter 14, entitled System Life Cycle Tools, by Dr. Denise D. Tyler used during the system life cycle and project management. Dr. Denise D. Tyler reviews things such as change management techniques, workflow analysis, and quality improvement tools. It also includes examples of metrics to measure the success of the project. The chapter provides many examples of the different tools used by the informatics nurse during the system life cycle.

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12 System Design Life Cycle: A Framework Susan K. Newbold

• OBJECTIVES 1. Describe a methodology and checklist for the phases of a system design life cycle. 2. Describe barriers and critical success factors related to implementation. 3. Describe the Informatics Nurse role in a clinical system’s system design life cycle. 4. Discuss the heightened impact of regulatory and financial requirements on the electronic health record (EHR).

• KEY WORDS Clinical Workflow Analysis Communication Plan Feasibility Study Go Live Plan Issues List Project Scope Request for Information (RFI) Request for Proposal (RFP) Usability Workplan

INTRODUCTION In the past, clinical systems implementation projects were considered successful when implemented on time and within budget. Later, the concepts of end-user perceptions determining project success in conjunction with streamlining clinician workflow–layered clinical systems projects with additional success criteria. In the recent past, the focus for clinical systems implementations has been on systems improving patient safety through evidence-based practice (EBP) while meeting the federal requirements set forth in the Health Information and Technology for Economic and Clinical Health (HITECH) Act of 2009 (HHS.gov, n.d.).

As part of the HITECH Act, the Centers for Medicare & Medicaid Services (CMS) set forth a program providing organizations that demonstrate the meaningful use of an EHR to improve patient safety significant financial incentives. Today, the successful system implementation project must be completed on time and within budget, and offers end users streamlined workflow, with added safety in the delivery of healthcare and qualifying the organization for the financial benefits of meeting the meaningful use requirements. The System Design Life Cycle (SDLC), also known as the System Life Cycle (SLC) outlined in this chapter, discusses the tasks multiple disciplines must accomplish to produce a technically sound, regulatory compliant and 195

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196    P art 3 • S ystem L ife C ycle user-friendly EHR supporting safe, effective, and efficient patient care delivery. The SDLC framework described by the American Nurses Credentialing Center’s Nursing Informatics Test Content Outline (2018) consists of four major phases: (1) planning and analysis; (2) designing and building, (3) implementation and testing, and (4) monitoring, maintaining, supporting, and evaluating. The chapter further defines these phases into the key tasks of a practical clinical systems implementation checklist and high-level work plan used successfully for real-world implementations in the acute care setting. Many examples in this chapter refer to the implementation of an EHR; however, the framework, phases, and tasks discussed can and should be applied to any clinical system or application implementation. There are two text books which are often utilized for the systems analysis and design process for healthcare projects. Dennis, Wixon, and Roth (2015) also use four phases: (1) planning, (2) analysis, (3) design, and (4) implementation. Kendall and Kendall (2014) break the systems development life cycle into seven phases: (1) identifying problems, opportunities, and objectives, (2) determining human information requirements, (3) analyzing system needs, (4) designing the recommended system, (5) developing and documenting software, (6) testing and maintaining the system, and (7) implementing and evaluating the system. Analysts may disagree on the exact number of phases, but they generally agree that an organized approach is needed.

ELECTRONIC HEALTH RECORD The EHR is a longitudinal electronic record of patient health information generated by one or more encounters in any care delivery setting. Included in this information are patient demographics, progress notes, problems, medications, vital signs, past medical history, immunizations, laboratory data, and radiology reports. The EHR automates and streamlines the clinician’s workflow. The EHR has the ability to generate a complete record of a clinical patient encounter as well as to support other care-related activities directly or indirectly via interface. See other chapters in this book including the chapter on computerized provider order entry. The skills required to deliver direct patient care include the ability to understand and coordinate the work of multiple disciplines and departments. As multiple departments work in concert for optimum and safe patient care delivery, the components of an EHR integrate data in a coordinated fashion to provide an organization’s administration and clinicians demographic, financial, and clinical

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information. The SDLC provides a framework to attain a successful implementation.

CURRENT LANDSCAPE The Centers for Medicare & Medicaid Services (CMS) is the single largest payer for healthcare in the United States. Nearly 90 million Americans receive healthcare benefits through Medicare, Medicaid, and the State Children’s Health Insurance Program (CMS, n.d.). As the costs of healthcare increase, both the US population as well as the US government have become more critical of a payerbased health system. Four factors impacting healthcare payments and hospital information systems implementations are the evolution of evidence-based practice, the Federal Meaningful Use requirements set forth in the HITECH Act of 2009 now evolving to Promoting Interoperability, the cost of technology, and the use of project management principles.

Evidence-Based Practice Melnyk and Fineout-Overholt (2015) define evidencebased practice (EBP) as a problem-solving approach that incorporates the best available scientific evidence, clinicians’ expertise, and patients’ preferences and values. The purpose of EBP is to utilize scientific studies to determine the best course of treatment for a patient. Functionality within the EHR can provide access to the studies to understand the recommended treatment while reviewing a patient’s data in real time.

Federal Initiative—HITECH Act 2009 This act seeks to promote the meaningful use of information technology to improve patient safety in healthcare delivery. The initiative requires an organization or provider to demonstrate consistent and appropriate use of information technology. Adoption of the technology is required in stages, with increasing numbers of requirements in each stage. Federal financial incentives are awarded to those meeting each stage. Financial penalties will be levied against organization failing to meet the requirements (HHS.gov, n.d.). The Meaningful Use requirements leverage the results published in studies indicating a marked increase in patient safety when specified functions of an information system are utilized. In addition, standardized terminology criteria permitting comparison of healthcare treatments and outcomes across healthcare facilities are incorporated into the later HITECH Act’s stages. CMS has

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evolved Meaningful Use into Promoting Interoperability in an attempt to encourage a greater level of interoperability among systems (www.cms.gov). Legislative and legal aspects of informatics are discussed in another chapter.

Technology: Cost, Benefit, and Risk Technology costs are high, increasing the risk of significant financial losses from a poor implementation. Vendors deliver the same software to clients; the success of an information system project often rests on a well-planned and well-executed implementation. A well-planned implementation dovetails an organization’s strategic goals and culture, with the introduction of and ability to assimilate technology and workflow changes into the daily practice of healthcare delivery. The SDLC provides a structured implementation approach to accomplish this. Healthcare information systems implementation time lines are often long, spanning 10 to 16 months for a full hospital information system implementation. Most organizations have implemented at least one EHR and are either upgrading their current system or switching to a new system. The SDLC approach is still utilized. Increasing a project’s risk level, a technology generation, now only months in length as opposed to years, can render partial obsolescence of a system by one technology generation before the first productive use of a system is sometimes obtained. A well-planned and executed implementation, on the other hand, provides a high level of risk mitigation and cost containment. It is important to remember that technology is not the best solution to every problem; failure to recognize problems caused by inefficient processes from an information system problem contributes to the risk and potential costs of a system.

Project Management With roots in the construction industry, a significant body of knowledge in the area of planning and tracking large-scale projects has evolved. The Project Management Institute (PMI) has become the central and certifying organization for project management professionals. The Project Management Plan (PMP) developed through PMI’s efforts has migrated to the Information Technology (IT) area and is commonly called a project work plan (Project Management Institute, 2019). It is the main planning document for an IT project and describes how major aspects of the project will be executed and managed. The work plan is a living document, updated continually throughout the project. Nurses have the ability to coordinate and manage multiple diverse care situations; this affords them strong skills to manage complex projects using a Project Workplan as a primary tool.

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Chapter 12 • System Design Life Cycle: A Framework    197 An excellent text with healthcare examples is Information Technology Project Management, by K. Schwalbe (Schwalbe, 2016) (see Chapter 15, “Healthcare Project Management”). A second essential tool for clinical implementations is the project’s “issues list.” As concerns, unusual situations, special education/training needs, programming errors, sequencing concerns impacting workflow, and new regulations are uncovered, they are placed on the issues list. Issues are added to the list and prioritized in relation to other issues and to the project goals and assigned an urgency status. Examples of issue statuses are open, in progress, testing, and closed. The progress of an issue is tracked by the team on a regular basis with short progress notes added to the issue. When a resolution is reached, the resolution is documented in the issues list and the status is updated. The resolution documentation detail helps eliminate the need for the team to revisit the decision. Suggested data elements in an issue’s list are as follows:

• • • • • • •

Issue number

• • • • •

Note date

Status Date added to the list Person identifying the problem/adding it to the list Module/application involved Description of the problem/issue Type of problem (e.g., programmatic, training, process, hardware, network) Notes (e.g., work/efforts to resolve issue) Responsible party Resolution date Resolution description

SYSTEM DEVELOPMENT LIFE CYCLE The System Life Development Life Cycle is defined by the major components of (a) Planning, (b) Analysis, (c) Design/ Develop/Customize, and (d) Implement/Evaluate/Maintain/ Support. While this chapter discusses phases of the SDLC related to an EHR implementation in an acute care setting, it is applicable to many healthcare settings and projects. To continue to meet new regulatory and professional standards, EHRs and software applications must be continuously updated and upgraded in the Maintenance Phase. Regardless of the size or type of the system, any EHR, single application implementation, or upgrade project should address each of the items on the clinical system implementation checklist presented in Fig. 12.1. Though not every project will require interfacing or data

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198    P art 3 • S ystem L ife C ycle

System Life Cycle Phases

Clinical Software Implementation Major Tasks

Planning

Governance Structure Project Purpose Project Scope Document Resource Planning

Analysis

Technical Requirements Functional Design Document System Proposal Document

Design, Develop, and Customize

Design Functional Specifications Technical Specifications Develop Focused Plans Customize System Dictionary Data and Profiles Policies and Procedures

Implement, Evaluate, Support, and Maintain

Implement Plans Policies and Procedures Live Operations Cut Over and Go Live Plans Evaluate—post-live Daily support operations Ongoing maintenance

•  FIGURE 12.1.  SDLC Stages in Relation to Clinical Software Implementation Checklist.

conversion or the addition of new devices, review of the checklist’s steps will assure that essential considerations are not overlooked (Fig. 12.1). The SDLC phases use a problem-solving, scientific approach. Problem-solving begins with observation and understanding of the operations of the current systems or processes, sometimes referred to as the “current state.” The second phase requires an in-depth assessment and definition of the new system’s requirements: defining the “future state.” Designing, developing, and customizing a plan to meet requirements are addressed in the third phase. The fourth phase, implementing, evaluating, supporting, and ongoing maintenance, assures the system is sustainable after implementation. Nurses’ daily use of the Nursing Process, a problemsolving methodology, underlies the successes nurses have achieved in clinical informatics. Countless iterations of the problem-solving methodology are used during the implementation and updating/upgrading of software. Inherent in the implementation process is the need to recognize and manage change and its impact on patient care delivery and clinician work patterns/workflow. Often, finding a balance between the technical data capture criteria and

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the daily workflow of clinicians is required. Details are discussed in Chapter 14, “System Life Cycle Tools.” As noted, vendors supply essentially the same software to clients at the time of purchase. The abilities of the project team members and organization to introduce and assimilate changes into daily practice can determine the success of a project. Literature focusing on the workflow impact of an EHR and the cultural impact on an organization are well documented by the Project Management Institute (PMI) and the Healthcare Information Management Systems Society (HIMSS). Lorenzi, Novak, Weiss, Gadd, and Unerti (2008) all stress the need to manage the change process foundational to an EHR implementation if success is to be attained. Attempting to implement or upgrade a system without reviewing each of the checklist items within the SDLC framework generally results in failure in one or more of the following areas:

• • • •

EHR or application does not meet the stated goal of the project. Failure to gain end-user acceptance. Expenditures exceed budget. Anticipated benefits are unrealized.

In recent years the quality and abundance of online resources specific to clinical systems implementation have grown significantly. Due in large part to the Federal HITECH meaningful-use requirements, the PMI and HIMSS both offer training and certification processes specific to healthcare-related projects. The following online sites provide additional and supporting information: American Health Information Management Association (AHIMA): http://www.ahima.org/ American Medical Informatics Association (AMIA): http://www.amia.org Healthcare Information and Management Systems Society (HIMSS): https://www.himss.org/ Project Management Institute: https://www.pmi.org/ Promoting Interoperability (PI) Programs: https:// www.cms.gov/EHRincentivePrograms The Office of the National Coordinator for Health Information Technology (ONC): https://www. healthit.gov/

PLANNING PHASE The planning phase of the project begins once an organization has determined an existing requirement may be filled or solved by the development or implementation of

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Chapter 12 • System Design Life Cycle: A Framework    199

an EHR or application. Establishing the committee framework to research and making recommendations for the project is an important first step. The key documents created in the planning phase are the following:

• • • • •

Project Governance Structure Gap Analysis Feasibility Study Project Scope Document Development of a high-level work plan and resource requirements

impact of the new system on the culture of the organization and to take active steps to mitigate the effects of change on the organization (Lorenzi et al., 2008). Transition management is a series of “… deliberate, planned interventions undertaken to assure successful adaptation/assimilation of a desired outcome into an organization” (Douglas & Wright, 2003). The informatics nurse often leads the assessment and documentation of the “current state” and development of the desired “future state.” Cognizance of the new system’s impact serves as a visible leader in the transition management efforts.

Steering Committee

Governance Structure and Project Staff The clinical leadership of an organization is highly involved in the establishment of an EHR committee structure. The organization’s strategic goals and priorities must be reviewed and considered. The informatics nurse specialist and information systems management team provide oversight; however, committees work to develop the structure and participate to best guarantee the success of the project. Assigning the appropriate resources, whether financial or personnel, is a critical success factor. Evaluations of both successful and less than successful implementations have stressed the need to anticipate the

Before an EHR is developed or selected, the organization must appoint an EHR steering committee. The EHR steering committee, composed of internal and external stakeholders, is charged with providing oversight guidance to the selection and integration of the organization’s strategic goals relative to the EHR requirements. During the planning phase, the projected return on investment (ROI) is established. The Steering Committee members’ collective knowledge of the organization’s daily operations provides global insight and administrative authority to resolve issues. In most facilities, the Steering Committee has the ultimate authority for decision-making (Fig. 12.2).

CIS/EHR Steering Committee CIO/CFO—Chair Project Team Project Manager—Chair Team Leaders for each major department affected

Department Team Team Leader—Chair Pharmacy

Department Team Team Leader—Chair Nutritional Services

Department Team Team Leader—Chair Radiology

Department Team Team Leader—Chair Finance

Department Team Team Leader—Chair Laboratory

Department Team Team Leader—Chair Registration/Admissions

Department Team Team Leader—Chair Nursing

Department Team Team Leader—Chair Medicine

Department Team Team Leader—Chair Quality Management/ Utilization Review

Department Team Technical Manager—Chair

Department Team Team Leader—Chair Patient Accounting

Department Team Team Leader—Chair Health Information Mgmt

Conversion Team

Hardware and Systems Team

Interface Team

Network Team

•  FIGURE 12.2.  Clinical Information System Steering Committee Structure.

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200    P art 3 • S ystem L ife C ycle

Project Team

Departmental Teams

The project team is led by an appointed project manager (often the Informatics Nurse Specialist) and includes a designated team leader for each of the major departments affected by the system selection, implementation, or upgrade proposed. The objectives of the project team are to (1) understand the technology and technology restrictions of the proposed system, (2) understand the impact of intradepartmental EHR decisions, (3) make EHR decisions at the interdepartmental level, and (4) become the key resource for their application. A stated goal for the selection, implementation, or upgrading of an EHR is to improve patient safety and care; gains made by one department at the expense of another department rarely work to improve overall patient safety and care delivery. The project team’s ability to evaluate multiple departments’ information requirements in light of the capabilities of the proposed system is integral to overall success. Issues unable to be resolved by the Project Team are presented to the Steering Committee for resolution (Fig. 12.3). The project manager is responsible for managing all aspects of the project; this includes software application development, hardware, and network acquisition/readiness, as well as oversight of the interface and conversion tasks. The project manager must possess good communication, facilitation, organizational, and motivational skills when leading a successful implementation. A sound knowledge of healthcare delivery, regulatory requirements, and hospital culture, processes, and politics is essential.

The responsibility of the departmental teams is (1) to thoroughly understand the department’s information requirements and workflow, (2) to gain a full understanding of the software’s features and functions, (3) to complete a gap analysis for the new system’s capabilities with the department’s requirements, (4) to assist in the system testing effort, (5) to participate in developing and conducting end-user education, and (6) to provide a high level of support during the initial activation period of the new system. The team leaders must possess a sound knowledge of the hospital and departmental policies and procedures (both formal and informal), excellent organizational and communication skills, and must be adept at gaining consensus and resolving conflict. Team members may change during the course of a 10to 16-month implementation. Hospital leaders, visionaries, and change agents’ participation must balance the pragmatic bottom line dictated by organizational needs (e.g., Promoting Interoperability incentives vs. patient outcomes).

DEVELOP PROJECT SCOPE During the planning phase, the problem statement and goals of the implementation are defined, committee structures established, and the organization’s requirements are defined for selecting, implementing, or upgrading an EHR or application, including the implications for

Documentation Steering Committee Vice President Nursing – Chair Project Team Informatics Nurse – Project Manager Team Leaders for each major unit/department affected

Legal Counsel

Rehabilitation Team

Psychiatric Unit

Medical Unit

Pediatrics Team

Ambulatory Care

Surgical Unit

Representative

Emergency Services

Intensive Care Units

Technical Team

Home Health

Nutritional Services

Representative

Medical Records Representative

Medical Staff

•  FIGURE 12.3.  Application Implementation Committee Structure.

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Chapter 12 • System Design Life Cycle: A Framework    201

regulatory compliance for safe and quality clinical practice. Commercial software developers and consultants rank this phase as the most critical factor in the selection of a system, even more important than the system itself. Optimal planning takes time and thoughtful consideration. Time spent in developing a sound plan that encompasses all the checklist steps will reduce the amount of time spent in reworking areas not reviewed during the planning phase. Plan the work and then work the plan. The planning phase involves the following tasks:

• • • • •

Definition of committee structure

• •

Development of a high-level work plan

Definition of requirements and/or stated goal Feasibility study Gap analysis Documentation and negotiation of project scope document Allocation of resources

Definition of the Project’s Purpose Definition of the project’s purpose/stated goal is essential and often not readily apparent. According to Schwalbe (2016), a project should have a well-defined objective resulting in a unique product, service, or result. The project definition includes a description of how the system will be evaluated. Establishing the evaluation criteria early in the process supports the successful management philosophy of beginning with the end in mind. The results and improvements expected from implementing the system are described by realistic goals for the system. They might include increased functionality, decreased costs, increased personnel productivity, and meeting Federal Promoting Interoperability requirements. When updating or expanding the EHR or application, the project definition includes the identification of equipment currently available, its age, the degree of amortization, and the need for hardware or operating system software upgrades prior to undertaking an upgrade project.

Feasibility Study A feasibility study is a preliminary analysis to determine if the proposed problem can be solved by the implementation of an EHR or component application. The feasibility study not only clarifies the problem and/or stated goal but also helps identify the information needs, objectives, and scope of the project. The feasibility study helps the EHR steering committee understand the real problem and/or goal by analyzing multiple parameters and by presenting possible

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solutions. It highlights whether the proposed solution will produce usable products and whether the proposed system’s benefits more than justify the costs. Operational issues are reviewed to determine if the proposed solution will work in the intended environment. Technical issues are reviewed to ensure the proposed system can be built and/ or will be compatible with the proposed and/or current technology. Legal and statutory regulations are reviewed to ensure compliance with local and federal law. The feasibility study includes a high-level description of the human resources required and how the selected system will be developed, utilized, and implemented. The feasibility study describes the management controls to be established for obtaining administrative, financial, and technical approvals to proceed with each phase of the project. The feasibility study seeks to answer the following questions:

• • • • • • • • • •

What is the real problem to be solved and/or stated goal to be met? Where does the project fit into the overall strategic plan of the organization? What specific outcomes are expected from the project? What are the measurable criteria for determining project success from the above outcomes? What research and assumptions support the implementation project? What are the known limitations and risks to the project? What is the timing of the remaining phases of the project? Who will be committed to implementing the project? What are the estimated costs in both dollars and personnel time? What is the justification for the project, including the relationship between costs and benefits?

A feasibility study includes the following topic areas. Statement of the Objective  The first step in conducting a feasibility study is to state the objectives for the ­proposed system. These objectives constitute the purpose(s) of the system. All objectives are outcome-oriented and are stated in measurable terms. The objectives identify the “end product” by defining what the EHR will do for the end users. Environmental Assessment  The project is defined in terms of the support it provides to both the mission and the strategic plans of the organization. The project is evaluated

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202    P art 3 • S ystem L ife C ycle relative to the organization’s competition. The impact of legal, regulatory, and ethical considerations is reviewed. Scope Management Plan  The scope of the proposed system establishes system constraints and outlines what the proposed system will and will not produce. Included in the scope management plan is a description of how the team will prepare scope statement, create the work breakdown structure, verify completion of the project deliverables, and control requests for changes to the project scope. Also the criteria by which the success of the project will be judged may be included. The scope document outlines the boundaries of the project, establishes responsibilities for each team members, and sets up procedures as to how completed work will be verified and approved (Schwalbe, 2016). Documentation and Negotiation of a Project Scope Document  A project scope document is drafted by the project team and submitted to the project’s steering committee for acceptance. The project scope document includes the scope of the project, the application level management requirements, the proposed activation strategy for implementing the EHR or application, and the technical management and personnel who will implement and maintain the equipment and programs. The Scope Document is based on the findings of the feasibility study. The project scope document becomes the internal organizational contract for the project. It defines the short- and long-term goals, establishes the criteria for evaluating the success of the project, and expands the work plan to include further detail regarding the steps to be accomplished in the development or implementation of a system or application. Timeline  A project timeline is developed providing an overview of the key milestone events of the project. The projected length of time for each major phase of the project is established. Often called a project work plan, the major steps required for project are outlined in sufficient detail to provide the steering committee background on the proposed development or implementation process. Recommendations  Committees may lose sight of the fact that not all projects are beneficial to the strategic mission of the organization. A decision can be made not only to proceed but also not to proceed with a project. The viability of the project is based on the review of the multiple factors researched in the feasibility study. It is critical to consider whether more personnel or equipment is necessary rather than more computerization. In addition to

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identifying potential hardware and software improvements, the costs and proposed benefits are factored into the project’s viability decision. In upgrading or considering expansion of a system, a concerted effort to maximize use of the current system and to make process improvements in the current management and coordination of existing systems should be undertaken before deciding to procure a new system(s). If, based on the findings of the feasibility study, the project steering committee determines to continue with the project, a project scope agreement is prepared.

Resource Planning An important step in the planning phase is to determine what resources are required to successfully carry out the agreed upon project scope. A firm commitment of resources for development of the entire EHR project includes all phases of implementation and is for the system to fulfill its stated objectives. The following points should be considered when planning for resources:

• •

Present staffing workload

• •

Present cost of operation

• • •

Human resources (i.e., number of personnel, experience and abilities, and percentage of dedicated time to the project) Relationship of implementation events with non-project events (e.g., The Joint Commission accreditation process, state certification inspections, peak vacation and census times, union negotiations, and house staff turnover) Anticipated training costs Space availability Current and anticipated equipment requirements for the project team

Highly successful projects have spent the requisite amount of time to thoroughly complete the planning phase. Further, successful organizations have communicated senior management and administration’s project expectations through dissemination of the project scope document to all departments in the organization.

ANALYSIS PHASE The system analysis phase, the second SDLC phase of developing an EHR, is the fact-finding phase. All data needs related to the requirements are defined in the project scope agreement developed in the Analysis Phase.

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Chapter 12 • System Design Life Cycle: A Framework    203



The collection of data reflecting the existing ­problem or goal is the first step in the system analysis phase. As a result of thorough data collection, refinements to the project scope agreement may occur. Added benefits to the organization may be realized through the small ­refinements. Larger project scope refinements should be carefully researched and evaluated (using the steps outlined in the feasibility study methodology) prior to requesting a major project scope change. Large or small, all changes must continue to support the goal(s) of the project and the strategic plan of the organization. Two important documents are created as a result of data collection. The first is the creation of a workflow document for each major goal or problem to be resolved by the implementation of the new software or system; the second is a functional design document outlining how the new system will resolve the identified goals/problem.

the system will look and function upon completion of the implementation—begins to take shape. The review of requirements of the Americans with Disability Act (www. ada.gov) is done to assure compliance with special needs of staff. Stakeholders review the document to assist in the prioritization of problems/issues to be resolved. Current costs and resources required for processing the organization’s volume of data are compared with estimates for the cost of processing with the new system. If a system is being upgraded or expanded, the current equipment and functions are described. Careful evaluation is undertaken to ensure compatibility with the new system’s requirements and to maximize the use of available equipment as long as possible. Depreciation costs of available equipment and projected budget expenditures are reviewed. The importance of this phase should not be underestimated. Design changes made during the analysis stage often add minimal costs to the project; as the project progresses to the development and implementation phases, the cost of programmatic or design changes increases dramatically. When a project is in the planning phase, the relative cost to make a design change or fix an error is one; in the analysis phase, the relative cost to fix the error/design change is three to six times that of the planning phase. The relative cost to fix an error or change a system design jumps to 40 to 1000 times once the system is operational.

Gap Analysis

Technical Analysis

Drawing on the work completed in the planning phase, the workflow document, and the system proposal document from the analysis phase, a comparison of what is available in the current processes and what is desired in the new system is completed. Often referred to as a gap analysis, the comparison provides the project team with a list of features and functions desired but not immediately available in the new system/application. The departmental teams review the f­ eatures/functions and estimated costs, evaluate alternatives to achieving them, and make recommendations to the Steering Committee. Features/functions may be delayed to a subsequent activation phase of the project, lobbied for inclusion in the current activation plan, or eliminated from the project. Data are collected and analyzed to gain a sound understanding of the current system, “current state,” how it is used, and what is needed from the new system. Process analysis is foundational to the actual system design, since it examines the objectives and project scope in terms of the end-user requirements, the flow of information in daily operation, and the processing of required data elements. Through the analysis effort, the individual data elements, interfaces, and EHR decision points of the project are identified (www.pmi.org). The “future state”—how

A review of the project’s technical requirements is conducted in the analysis phase. Trained/certified technical personnel review the requirements for EHR software to run efficiently. This may include programs to run the EHR software, hardware, and networks. Physical requirements for space, electrical needs, and air-conditioning/cooling are considered. A technical architecture is developed to assure the speed of data transmissions, and sufficient storage capability to meet clinical and financial requirements over time. These requirements in conjunction with costs are evaluated and compiled into technical recommendations for the project.

Key documents created in this phase are the following:

• •

Gap Analysis

• •

Functional Design Document

Technical requirements for hardware, software, networks System Proposal Document

Data Collection

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Determination of Information Needs A needs assessment outlines the high-level information required by multidisciplinary users. Standard terminology use as defined by the CMS and ONC is critical to meeting Federal Promoting Interoperability program requirements. The numbers of federally mandated data elements are significant. Planning for the data collection across an organization’s multiple departments and clinician workflow is imperative to successfully meeting the requirements

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204    P art 3 • S ystem L ife C ycle of the HITECH Act. Workflow review and identification of the information needed clarify what users will expect from the system and how it can be collected in the course of daily operations. Such knowledge is essential in designing the system’s output, input, and processing requirements constituting the basis for the new “future state.”

Workflow Document The workflow document assimilates the data collected into logical sequencing of functions/tasks performed by the end users for each goal or problem area. Departmental standards of care, ordering patterns, procedures, operating manuals, reports (routine, regulatory, and year-end), and forms used in day-to-day operations are collected. Individual data elements required by clinicians in each department are identified and analyzed for continuity and duplication, and cross-referenced to the required HITECH data elements. The workflow document includes the following:

• • • • • • • •

A list of assumptions about the process or work effort A list of the major tasks performed by the user A list of the subtasks and steps the user accomplishes and outlines The determination of optional or required status for each task The frequency of the task being performed The criticality and important factors of the tasks/ subtasks The order of the subtasks The number and frequency of alternate scenarios available to the end user to accomplish a particular task

There are multiple sources of data for completing a workflow document. These include the following:

• • • • • •

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Written documents, forms, and flow sheets Policy and procedure manuals Questionnaires

Functional Design Document The functional design document is the overview statement of how the new system will work. It uses the workflow documents as its base, adding the critical documentation of the integration of each of the workflow documents to create a new system, implement a commercial software application, or upgrade a system. The functional design document, in this phase, outlines the human and machine procedures, the input points, the processing requirements, the output from the data entry, and the major reports to be generated from the new system. The functional design is a description of the functions required from the proposed EHR system or component and describes how tasks will be accomplished. From the functional design document, database structure will be determined. Two data types are often used in databases—free text data (allowing the user to describe a response in his or her own words) and discrete data (structured data presented in application via check boxes or drop-down lists). Discrete data elements with links to standard terminology are the preferred data type. They increase the ability to report on and compare data. Meaningful Use 2014 requirements include the use of structured data linked to standard terminologies such as SNOMED-CT (Structured Nomenclature of Medicine—Clinical Terminology) and LOINC (Logical Observation Identifiers Names and Codes) used for laboratory tests (see Chapter 8, “Standardized Nursing Terminologies”). When new software is being created, the functional design document provides the programmers with a view of screens, linkages, and alternate scenarios to accomplish a task. Initial programming efforts can begin once the functional design is accepted. In the instance where a commercially available system or application is being implemented, the functional design outlines how the end users will use the system’s programs to accomplish their tasks. In some cases, commercial software provides multiple pathways to accomplish a single task; the functional specification may suggest deploying a limited number of available pathways.

Interviews

Data Analysis

Observations

The analysis of the collected data is the second step in the analysis phase. The analysis provides the data for development of an overview of the clinical requirements and/ or stated goal defined in the project scope agreement. Several software tools can be used in the development of the workflow and functional design documents and are discussed in Chapter 13, “System and Functional Testing.”

Development of workflow diagrams utilizing available software is most helpful in documenting the flow of information, people, and processes involved in the “current state.” The graphic representation provided by workflow diagrams allows a clear visualization of the gains proposed in the “future state.”

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Data Review The third step in the analysis phase is to review the data collected in the feasibility study, the workflow documents, and the functional specification and provide recommendations to the project steering committee for the new system. The review focuses on system requirements and/or attaining the project goals outlined in the feasibility study based on the best methods or pathways derived from the workflow documents and the functional design. Recommendations for streamlining workflow are suggested. The success of an EHR implementation project rests on the ability of the departmental and project teams to analyze the data and propose solutions benefiting the total organization without favoring certain departments at the expense of others. The benefits of a thorough structured analysis provide objective data to support the EHR. The careful analysis of end-user requirements and potential solutions has been proved to reduce the cost of design and implementation.

Benefits Identification The overall anticipated benefits from the system are documented in the fourth step in the system analysis process. The benefits are stated in quantifiable terms and become the criteria for measuring the ROI and success of the project.

System Proposal Development The final document created in the system analysis stage is a system proposal document. The proposal is submitted to the project’s steering committee for review and approval. It sets forth the problems and/or goals and the requirements for the new system’s overall design. It outlines the standards, documentation, and procedures for management control of the project, and it defines the information required, the necessary resources, anticipated benefits, a detailed workplan, and projected costs for the new system. The system proposal furnishes the project steering committee with recommendations concerning the proposed EHR or application. The system proposal document answers four questions: 1. What are the major problems and/or goals under consideration? 2. How will the proposed EHR solution correct or eliminate the problems and/or accomplish the stated goals? 3. What are the anticipated costs? 4. How long will it take?

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Chapter 12 • System Design Life Cycle: A Framework    205 The system proposal describes the project in sufficient detail to provide a management-level understanding of the system or application without miring in minutiae. Much of the information required in the system proposal is collected in the earlier phases of the analysis. It has been suggested this proposal is best accepted when presented as a business proposal and championed by a member of the project’s steering committee. The format of the final system proposal includes the following information:

• • • • • • • • • • •

A concise statement of the problem(s) and/or goal(s) Background information related to the problem Environmental factors related to the problem Competition Economics Politics Ethics Anticipated benefits Proposed solutions Budgetary and resource requirements Project timetable

Acceptance of the system proposal by the project steering committee provides the project senior management support. Following acceptance by the project steering committee, it is not unusual for major EHR proposals to be presented to the institution’s governing board for their acceptance and approval and to receive funding. Often the requirement for board approval is dependent on the final cost estimates of the system. Acceptance of the proposal by the project steering committee and the governing board assures not only funding for the project but also critical top-down management and administrative support for the project. The final system proposal is an internal contract between the EHR committees/teams (steering, project, and departmental) and the institution. As noted earlier, the active support and involvement of all senior executives in the development of the feasibility study are essential. The championing of the final system proposal greatly enhances the chances of acceptance of the system proposal.

DESIGNING AND BUILDING PHASE In this phase, the design details to develop the system and the detailed plans for implementing and evaluating the system evolve for both the functional and the technical components. Data dictionaries are populated with entries and

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206    P art 3 • S ystem L ife C ycle project team’s work to assure the functional design supports the clinician and departmental workflows. Policies and procedures are reviewed and updated to reflect the use of the new application/system in the delivery of care. Thorough testing of the new system occurs and detailed plans, developed in this, as well as previous phases, are executed. There are multiple project documents created in this phase:

• • • •

Gap Analysis Functional Specifications Technical Specifications Implementation Workplan containing detailed plans specific to ◦◦ Hardware and Peripheral Devices ◦◦ Interfaces ◦◦ Conversions ◦◦ Testing ◦◦ End-User Training ◦◦ Cut Over Plan

•

◦◦ Go Live Plan Post-Live Evaluation Reports

System Design The project teams receive application training often directly from the vendor. In some cases a limited number of team members attend vendor training with the expectation they will train other team members. The Project Teams determine the best utilization of functionality based upon the identified elements of project goals, scope, software functionality, and the organization’s workflow. The definition of current workflow, documented in the analysis phase, serves as the bases for changes, both programmatic and process-oriented, required to support the new system’s workflow. Functional Specifications The functional specifications use the functional design document developed in the system analysis phase of an EHR and builds on the design by formulating a detailed description of ALL system inputs, outputs, and processing logic required to complete the scope of the project. It further refines what the proposed system will encompass and provides the framework for its operation.

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Commercial software vendors generally provide a detailed functional specification document for their system or application in the form of manuals. The manuals, usually application-specific, include an introduction, a section for each workflow, and a technical section. From the provided documentation, the hospital’s departmental and project teams produce the organization’s functional specification by evaluating the available commercial software’s functions with the organization’s workflow documents and making decisions on the functionality to be used by the institution. The detailed functional specifications are critical to the system’s acceptance; each screen, data flow, and report the user can expect to see are analyzed. The examples incorporate real data into the explanations and drawings. The technical aspects of the HITECH Act and Promoting Interoperability program criteria must be fully understood and followed carefully including the use of both evidencebased practice links and clinical decision support rules for patient safety within clinical workflows. Additional information on ERH adoption can be found at https://www. healthit.gov/playbook/electronic-health-records/. During this step, the departmental teams and users determine what the actual data will look like in its output form, and they gain consensus from the departmental teams for the proposed workflow design. Requirements for meeting the HITECH PI program data collection are integral to completing the functional specifications. In-depth understanding of the federal criteria, layered with thoughtful implementation planning and execution, will lead to staff ’s universal adoption of new data collection and data sharing procedures. There is fluidity between the functional specification and initial programming prototype efforts. The design team creating the new application often works closely with the programmers, making adjustments in the design and specification based on federal requirements, programming logic, newly identified information needs, and/or technologies. As the functional specification matures and major design decisions (e.g., selection of the underlying application technology and database structure) have occurred, a design freeze point is established. This indicates the functional specification is complete and full programming efforts can begin. Once completed, the functional specification provides not only the road map for programming efforts but also the starting point for developing testing and training plans. The advantages of establishing testing plans in concert with the development of the functional specification include a more thorough test plan (workflows are not

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missed), and “what if ” questions often spark the need to develop or allow alternate workflow.

Technical Specifications In the system design phase, technical personnel work closely with the project and departmental teams to ensure the components of the proposed system work in concert with technology and end-user needs and to assist in the development of the implementation plan. A dedicated technical manager is required. He or she is responsible for the coordination of efforts in five major areas: hardware, networks, software, interface application, and legacy system data conversion. Detailed technical specifications are developed for each area. The project’s technical ­manager and team leaders ensure all of the components/ applications of the EHR work in concert with all the other components.

Hardware In the case of new software development, the technical project manager ensures the new software uses the best technology platform available. The ability to operate the new application on multiple hardware platforms is often desired. Technical specifications describing the recommended equipment are developed and tested in the development laboratory. When commercial software is being implemented or upgraded, the technical project manager ensures the physical environment for the new system conforms to the new system’s technical specifications. This may include the need to build a new computer room, establish or upgrade a network, and procure the correct devices for the new system. The types of devices to be used (mobile PCs vs. handheld vs. bedside devices) require dialog and testing with team leaders and department team members. The testing and deployment of the new equipment (terminals, multiple types of printers [e.g., card, label, prescription],  Internet output [e.g., electronic prescription systems], and/or wireless devices) are the responsibility of the technical ­manager. Ongoing maintenance requirements for the new system’s processing unit, operating systems, and network are coordinated by the project’s technical manager. Selecting the correct hardware for the system depends on its design, application, and software requirements. Technical conditions may dictate selection of a mainframe, a minicomputer, a microcomputer, or a combination of the above. Computer hardware is obtained in several different ways. Central processing may be purchased or leased from a hardware vendor for in-house use; however, when

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Chapter 12 • System Design Life Cycle: A Framework    207 cost is a significant factor, timesharing computer processing with other facilities may be considered. Many Internet Web-hosted medical applications are now available also known as cloud-based systems. The technical manager must evaluate such offerings in light of interoperability with the main hospital information system as well as data security during transmissions to and from the Web. Input, output, and processing media, including secondary storage, are selected.

Peripheral Device Plan Knowledge of the many clinical workflows is an important component of the peripheral device plan. There are now many types of devices available to clinicians to support their daily workflow. For some data collection, wireless tablets may work well when full features and functions are needed (e.g., provider/nursing/ancillary rounding). For other data collection, smaller handheld devices may provide connectivity for limited data collection needs. Informatics Nurse Specialists are integral in reviewing the primary needs of each stakeholder, suggesting a limited number of companies/devices to trial, and providing the compilation of the trial evaluation data. Informatics Nurse Specialists and the technical team work together to assure all hardwares is installed and tested at the appropriate time.

Networks Proliferation of Web-based applications and reference/ search engines in addition to the locally based EHR necessitates a thorough review of the current and anticipated volume of transactions (financial and clinical) and high utilization times for accessing the EHR. The EHR no longer resides simply within the walls of a hospital. Health systems composed of inpatient, outpatient, long-term care, home health, and patient access are variables to be considered when determining the size and types of networks.

Application Software The project’s technical manager is responsible for establishing the technical specifications outlining the operational requirements for the new system. The specifications detail the procedures required to maintain the application software on a daily, weekly, and monthly basis. The specifications are compiled as the starting point for determining the operations schedule for the system and/or the institution. The operations plan includes detailed information related to when the system will be scheduled for routine maintenance, plans for operations

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208    P art 3 • S ystem L ife C ycle during system failures, and acceptable periods, if needed, during the week/month for the system to be unavailable to the users. Additional requirements for assuring data reliability and availability following planned and unplanned system downtime as well as procedures outlining data recovery following a downtime are developed. Change control policies and procedures for identifying, tracking, testing, and applying software fixes are established. With the popularity of Web-based systems, Web design, maintenance, and security have added a level of complexity to maintaining a system within a healthcare environment’s security regulations (e.g., Health Insurance Portability and Accountability Act [HIPAA]). Often niche software (e.g., patient portal, secure texting software, e-prescribing, appointment scheduling, preventative healthcare alerts) complements the central hospital information system. Requirements of each must be reviewed and outlined in the technical specifications.

Interface Applications An interface system defines programs and processes that are required to transmit data between disparate systems. The project’s technical manager coordinates all interfacing activities for the new application. While utilization of the industry’s Health Level Seven (HL7) interface standards has greatly reduced the effort required to establish clinical interfaces by providing a standard specification for the transmission of data, the number of clinical interfaces in an EHR has dramatically increased. It is not unusual for an EHR to interface with separate registration, patient billing, ancillary departmental systems (e.g., lab, radiology, pharmacy, ICU systems), as well as multiple types of wireless devices. With the advent of Health Information Exchanges (HIEs), patient data will be sent via interfaces outside a healthcare systems domain. Interface developments advocate the use of an interface engine decreasing the number of individual interfaces to be managed. System security is detailed in Chapter 10, “Trustworthy Systems for Safe and Private Healthcare.” Meaningful Use Stage 2 requirements encompassed the ability to share data electronically with Federal and local agencies as well as with the patient. Implementation and use of an Internet portal by patients is a Meaningful Use Stage 2 requirement. Adherence to the Federal Meaningful Use data and transmission criteria is, therefore, essential. More complex environments may include interfaces to physiological monitors and wireless portable devices, and provide remote access into the healthcare network’s clinical system for physicians and their staff. The interface specification details whether the interface will be one-way or bidirectional. A bidirectional

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interface implies data are flowing both to and from a system. Conversely, a one-way interface may either send data to or receive data from a separate system but does not do both. An important process in development of the interface specification is the comparison of data elements in each system in order to determine the data elements, and their technical format is included in the interface.

LEGACY SYSTEMS DATA CONVERSIONS The conversion of data from legacy systems to a new system is a major area of coordination for the project’s technical manager. Most hospitals currently use automated registration and billing systems; determining the conversion requirements and developing and testing the conversion programs are critical steps in implementing a new system or application. While all steps are important in the implementation of a new system, the interface and conversion design and testing tasks are frequent areas causing project delays. The importance of oversight and communication by the project technical manager to keep the technical tasks on the established timetable should not be underestimated.

Development Multiple plans are developed during this portion of the Implementing and Testing Phases. The detailed implementation work plan encompasses the multiple plans targeting specific aspects of the EHR. Often the appropriate departmental teams are responsible for creating the details for a focused plan. Together with the Project Manager and Team Leaders, the focused plans are incorporated into the implementation work plan. At a minimum the following focused plans are required:

• • • • • •

Communications plan Hardware and Peripheral Devices plan Interface plan Conversion plan Testing plan End-User Training plan

The developed functional and technical specifications define a significant amount of form and substance for the new EHR. The next step is to assess the timeframes established in the final scope document with the development timeframes established during the system design and the interface and conversion requirements to establish a detailed

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Chapter 12 • System Design Life Cycle: A Framework    209

work plan. The work plan identifies a responsible party and a beginning date and end date for each phase, step, task, and subtask. This plan coordinates all tasks necessary to complete the development of new software, implement a new system, and/or upgrade a current system. Many software vendors and consultants provide an implementation work plan template for their systems or applications. The supplied work plans must be reviewed and revised to meet the individual needs and timetables of the organization’s project. Automated work plan software is available to create and monitor a project/implementation plan. The implementation checklist describes the high-level tasks to be included in clinical implementation work plans. It is advisable to take advantage of automated software and existing plans. Figure 12.4 provides an example of detailed work plan based on the checklist. Whether the project involves software development or the implementation or upgrading of a system, the implementation work plan details the following:

• • • • •

Personnel Timeframes Costs and budgets Facilities and equipment required Operational considerations

A successful implementation ensures all checklist items are planned, executed, and tracked by the project manager and project team leaders. Clinical Implementation Workplan Sample PROJECT ADMINISTRATION Identify Initial Project Team Project Coordination Meeting Initiate Target Hardware/Software Delivery Dates Establish Project Control

Project Supervision/Order Management Project Status Meetings Project Steering Committee Meeting REQUIREMENTS AND PLANNING

Network Kickoff / Review Application Network Overview Project Definition Project Definition Planning Session Complete Project Definition Document Complete Workplan for

Develop Project Charter

Determine 3 indicators for quantified benchmarking Develop plan for collection of pre-system indicator data Execute plan for collection of pre-system indicator data Finalize/Approve Project Scope/Workplan

Project Organization -

Organize Implementation Project Team Organize Workflow Design and Project Task Force(s) Organize Data Standardization Task Force

•  FIGURE 12.4.  Sample Clinical Implementation Workplan.

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System Selection In the instance where commercially available software is being considered (which is now more the norm), the key documents completed in earlier phases assist in beginning the system selection process. The task of selecting a new system becomes more objective as a result of the thoughtful evaluation for the functional specification and design document. The process and documents provide the steering committee and project team with information to objectively evaluate commercial system offerings. The system proposal document also assists the institution’s legal team in formulating a contract with the software vendor as well as providing the basis for the development of a formal request for proposal (RFP) or request for information (RFI) to potential vendors.

Request for Proposal (RFP)/Request for Information (RFI) An RFI document created is sent to selected vendors indicating the organization’s interest in gaining knowledge about the vendor’s products. At a high level, the key features desired for the new system are listed. Vendors respond to the RFI with their product’s likely ability to meet the high-level requirements. Additional knowledge about available technical solutions not considered is often gained from the RFI responses. The project team reviews the responses and selects two to four vendors meeting the majority of the high-level requirements. An RFP document is created by the project team and sent to the selected vendors outlining in greater detail the features and functions desired for the new system. Clinical and financial workflow scenarios as well as the desired functions developed by the project team can be included in the RFP. The vendor RFP responses are equally detailed; they are closely evaluated both from the written responses and during subsequent in-person or webinar style demonstrations of their product. A number of system evaluation tools have been published including ones with free use (Health IT.gov). The tool will provide a list of areas to be assessed during the review and evaluation of systems or applications. Preparation activities for project team members evaluating demonstrations of systems or applications for purchase should include a discussion of aspects of the evaluation tool to be used and the definition of the criteria to increase objectivity in the selection process. Figures 12.5 and 12.6 are examples of tools utilized in evaluating the cost and potential usability of prospective vendors’ software.

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210    P art 3 • S ystem L ife C ycle

Information System Application Nursing Documentation Implementation License Hardware Training Support Interfaces Conversions Order Entry Implementation License Hardware Training Support Interfaces Conversions Yearly Total 3 Year Total

Year 1

$

Vendor A Costs Year 2 Year 3

$ $

Year 1

$

Vendor B Costs Year 2 Year 3

$

$ $

$

Year 1

$

Vendor C Costs Year 2 Year 3

$ $

$

•  FIGURE 12.5.  Sample Cost Comparison Worksheet. Reviewer Name: ________________________________________________ Department: ______________________ Date: _________________ Application: ___________________________ Vendor:_________________ SYSTEM ATTRIBUTES

CIRCLE SCORE POOR (1) AVG (2–3) GOOD (4–5)

Consistency and use of standards Visibility of system state Match between system and world Minimalist—without extra distractions Minimize memory load Informative feedback Flexibility Good error messages Error prevention Clear closure Reversible actions User’s language User is in control Help and documentation availability Overall system desirability

1 1 1 1 1 1 1 1 1 1 1 1 1 1

2 2 2 2 2 2 2 2 2 2 2 2 2 2

3 3 3 3 3 3 3 3 3 3 3 3 3 3

Total Average Rating

1

2

3

4 4 4 4 4 4 4 4 4 4 4 4 4 4 4

5 5 5 5 5 5 5 5 5 5 5 5 5 5 5

COMMENTS:___________________________________________________________________ ______________________________________________________________________________

•  FIGURE 12.6.  Sample Demo Ranking Form. (Data from Zhang, J., Johnson, T.R., Patel, V.L., Paige, D.L, & Kubose, T. (2003). Using usability heuristics to evaluate patient safety of medical devices. Journal of Biomedical Informatics, 36, 23-30.)

Communications Plan Healthcare systems or applications often affect more than one department. Results from a laboratory system are reviewed by clinicians; the pharmacy system utilizes creatine results

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to adjust medication dosages for renal impaired patients. Documented nursing observations (e.g., wounds, catheters, psychosocial assessments) are utilized by case management, providers, and insurance companies. New functionality must be planned and communicated to all stakeholders.

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Chapter 12 • System Design Life Cycle: A Framework    211

A communications plan is often created in conjunction with the organization’s public relations department. The plan is developed to promote frequent face-to-face communications among departments, to multiple levels of administration, and to external stakeholders (e.g., regulatory organizations, payers, and the local communities served). Communications to all stakeholders/constituents affected by the project are developed. Segment-targeted communications plans are developed identifying the type, content level, and media for information dissemination to each identified stakeholder. Rarely have staff complained of receiving too much information about a new process or change. More often the complaint is “No one told us!” Multiple communication mediums are utilized, including but not limited to the following:

• • • •

Verbal updates presented at departmental/staff meetings Fact sheets/newsletters/flyers Faxes, e-mail, and Web site posting Social media/blogs

The communication plan, once developed and executed, must be monitored and modified as the implementation progresses. Up to this point, thorough planning and thoughtful design discussions have been held. During Development, the decisions are actualized with the entry of elements into the data dictionaries, and comparison of the clinical and departmental workflow to those created in the system. The functional specification indicating how the departments and clinicians want the system to work and the workflow document describing how processes are carried out are established by populating in the data dictionaries. The plans for Interfaces, Conversions, Testing, Communications, and Training are carried out.

Policies and Procedures Reviews of policies and procedures are conducted, revisions reflecting changes being implemented with the new system/application workflows. It is advisable to complete the policy reviews and complete procedure revisions prior to the start of end-user training.

Workflow, Dictionaries, and Profiles In this portion of the phase, project team members review data requirements and workflow previously documented. Data dictionaries and profiles are populated with entries to established desired new system workflow. This becomes an iterative process of populating data dictionaries with values supporting the workflow design and functional specification; testing the design with the project team;

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evaluating options suggested as a result of testing; and refining/reevaluating the functional specification. As data dictionaries are established, project teams begin to develop clinical decision support functions. Clinical decision support is defined by HIMSS (Healthcare Information and Management Systems Society, n.d.) as “a process for enhancing health-related decisions and actions with pertinent, organized clinical knowledge and patient information to improve health and healthcare delivery. Information recipients can include patients, clinicians and others involved in patient care delivery; information delivered can include general clinical knowledge and guidance, intelligently processed patient data, or a mixture of both; and information delivery formats can be drawn from a rich palette of options that includes data and order entry facilitators, filtered data displays, reference information, alerts, and others.” Two main types of clinical decision support systems exist. One type uses a knowledge base, as systems without a knowledge base rely on machine learning to analyze data. The challenge for the project team is to find the correct balance of the number and types of alerts presented to the clinician. Clinical alert fatigue, caused “... by excessive numbers of warnings about items such as potentially dangerous interaction presented to the clinician and as a result the clinician may pay less attention or even ignore some vital alerts....” (Kesselheim, Cresswell, Phansalkar, Bates, & Sheikh, 2011), is a well-documented phenomenon (see Chapter 26, “Improving Healthcare Quality and Patient Outcomes Through the Integration of EvidenceBased Practice and Informatics”).

Testing The system, whether newly developed or commercially available, must be tested to ensure all data are processed correctly and the desired outputs are generated. Testing verifies the computer programs are written correctly and when implemented in the production (live) environment the system will function as planned. System implementation requires three levels of testing. The first level is often called a functional test. During this round of testing, the departmental teams test and verify the databases (files, tables, data dictionaries), ensuring correct data have been entered into the system. The expected departmental reports are reviewed to assure correctness and accuracy. Multiple iterations of the functional test often occur until the departmental team is confident the system setup and profiles support the work of the department. The second level of testing, integrated systems testing, begins when all departments indicate successful completion of their functional testing. During integrated testing, the total system

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212    P art 3 • S ystem L ife C ycle is tested; this includes interfaces between systems as well as the interplay between applications within the same system. The integrated test must mimic the production (live) environment in terms of the volume of transactions, the number of users, the interfaced systems, and the procedures to be followed to carry out all functions of the system. It is at this point, organization-wide procedures to be instituted when the system is unavailable, often called downtime procedures, are thoroughly tested. Downtime procedures must be taught during end-user training. At the end of integrated testing, the organization makes a formal decision to proceed or postpone activation of the new systems. Often referred to as the “Go-No Go” decision, members of the steering committee, project team, and technical staff review the outstanding issues from both unit and integrated testing to make their decision. The final round of testing occurs during end-user training. As more users interact with the new system, previously unfound problems may surface. Evaluation of the severity of the newly discovered concerns and the corrective action required is an ongoing process during implementation. Significant information on testing processes and tools can be found in Chapter 13, “System and Functional Testing.” End-User Training.  It is essential to train the end users on how to use the system in their daily workflow. An EHR will function only as well as its users understand its operation and the operations streamline their workflow. Two levels of training take place for the implementation of a system. The project team and selected members of the departmental team receive training from the developers or vendor. This training details the databases (files and tables), processing logic, and outputs of all the system’s features and functions. End-user training takes place once the departmental and project teams have finished profiling the system to meet the functional and technical specifications developed and functional testing has been completed. The preparation for end-user training necessitates a mini-work plan often developed and managed by a team led by the Education/Training department. Enduser training stresses how the user will complete his or her workflow using the system features and functionality. All users of the new system or application must receive training. Training on a new system should occur no more than six weeks prior to the activation of the new system. When training occurs for more than six weeks before activation of the system, additional refresher training is often required by the end users. Training takes place before and during the activation of a new system. After system implementation, refresher courses as well as new employee introductory training on

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the use of the system are often provided by the institution. The large number of provider, nursing, and ancillary staff members to be trained necessitates a significant amount of advance planning. Training is most effective when hands-on, interactive instruction is provided. Training guides or manuals explain the system; however, retention of information is increased if the learners are able to interact with the new system in a manner simulating their workflow with the system. Computer-assisted instruction (CAI) can be used to provide hands-on experience. Often the Web-based training provides the user opportunities for self-paced, on-demand learning. End-user training is offered with two perspectives. First perspective provides a general overview of the system, and the second perspective explains how the user will interact with the system to complete his or her daily work. While a training manual is developed for the training sessions, most end users express the desire to have a pocketsize reminder or “cheat sheet” outlining the key functions of the new system or application. Both the user’s manual and the pocket reminders should be available for departmental use. When possible, a training environment on the computer system should be established for the organization. Establishing a training lab as well as providing access to the training environment from the departments and nursing units prior to the activation of the new system provides end users the opportunity to practice at times convenient to their work requirements and reinforces the training.

MONITORING, MAINTAINING, SUPPORTING, AND EVALUATING System Documentation The preparation of documents to describe the system for all users is an ongoing activity, with development of the documentation occurring as the various system phases and steps are completed. Documentation should begin with the final system proposal. Several manuals are prepared: a user’s manual, a reference manual, and an operator’s maintenance manual. These manuals provide guides to the system components and outline how the entire system has been developed. The manuals may be stored online for easier updates, but also need to be accessible during an unplanned downtime.

Implementation—Go Live Few if any healthcare organizations have the luxury to stop operations during an implementation (Lorenzi et al., 2008). Implementation encompasses the cutover plan

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Chapter 12 • System Design Life Cycle: A Framework    213

(data driven) and the implementation plan for the facility to continue to operate (people/processes) during this period. Staffing, patient care delivery, and support of the end user during the “Go Live” period are detailed within the “Go Live” plan. The planning includes assuring patient care functions as smoothly as possible during the time between the cutoff of the old system and the start of processing on the new system. Downtime functions/ processes and forms are reviewed to assure patient care, and processing of data continues and is able to be accurately reflected in the patient’s record. This often includes developing forms streamlining the documentation to be entered when the new system is available (Fig. 12.7).

Sample Cutover Plan Four activation approaches are possible: (1) parallel, (2) pilot, (3) phased-in, and (4) big bang. In the parallel approach, the new system runs parallel with the existing system until users can adjust. In the pilot approach, a few departments or units try out the new system to see how it works and then help other units or departments to use it. In the phased-in approach, the system is implemented by one unit or department at a time. In the bigbang approach, a cutover date and time are established for the organization, the old system is stopped, and all units/ departments begin processing on the newly installed system. At this point big-bang activation seems to be the most popular approach. The timing of conversion activities and the activation of all interfaces require particular coordination between the technical staff and the project teams. The project’s technical manager, in conjunction with the project manager, is

DONE

DATE

2-Nov 30-Nov 30-Nov

30-Nov 30-Nov

30-Nov

START TIME

8:30 12:00

TARGET END TIME

SEQUENCE

30-Nov SMS-1 23:00 CAI-1

responsible for assuring the development of thorough go live plans. A command center is established to coordinate all issues, concerns, and go live help desk functions. A sufficient number of phone lines and cell phones are secured to support the move to the live production environment. Team members and trainers often serve as resources to the end users on a 24-h basis for a period of time postimplementation. Sometimes called “super users,” these team members are available in the departments and on the nursing units to proactively assist users during the first one to two weeks of productive use of the new system or application. The coordination of all activities requires a cohesive team effort. Communication among the team members is foundational; end users are informed of the sequence events, the expected time frames for each event, and the channels established for reporting and resolving issues. Daily meetings of key team members to review issues and chart the progress of the new system are held. Decisions affecting the “Go Live” are made in a timely manner and require a thoughtful and thorough approach when changes to procedures and computer programs are contemplated. The executive team and senior management group are kept as up-to-date as the end users. The goal of most clinical implementations is to improve the delivery of information to the end user. The end-user suggestions and issues, therefore, must be tracked and resolved. Providing timely follow-up to issues and suggestions will be critical to the success of the new system. Often, the informatics nurses are responsible for this follow-up. It is highly recommended to have all end-user logins, passwords, and system devices and printers tested five to seven days before going live. Requests for login and password support comprise the largest number of calls to the

TASK DESCRIPTION Change profile PRFDT to 29 days. Continue changing this profile (minus one day) on a daily basis until 11/30 when it is set to 1. Review/Final configuration changes CHECKPOINT CONFERENCE CALL

14:00 14:00

18:00

16:00 MD-1

Order rewrites by physicians. Nursing staff will hold on to any orders which can be held until 02:00

16:00 Nursing-1

Enter orders for 6 a.m. Lab draw in legacy system

00:00 Nursing-2

Validation of Allergies (including food allergies) and patient factors data on Go Live patient factor validation checklist

TASK DEPENDENCY

none none scheduled

RESP. PERSON

HANDOFF CONTACT

ALL 6 a.m. Lab draws should not wait until 2 p.m. Enter when orders are rewritten. WE ARE NOT REWRITING MEDICATION ORDERS!

none Legacy Lab Up

COMMENTS All areas that enter orders. To prevent future orders to be placed in legacy system with start date greater than conversion date.

Nursing Staff

Nurses Only

Hand off validation checklist

Go Live patient factor validation form checklist created to help with this.

•  FIGURE 12.7.  Sample Cutover Plan.

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214    P art 3 • S ystem L ife C ycle Command Center help desk during the first few weeks of a new system’s use. Clinical and departmental managers are in the best position to assure all staff have logged into the new system and have the appropriate role for their job requirements. Login issues are followed closely by printer issues (e.g., printer offline, printer settings incorrect, output expected to print at a location does not print) during the first weeks of a new system. The hardware team members can troubleshoot issues best if they have been given a script outlining one or two functions resulting in a printed output. Assuring these two areas are addressed completely prior to “Go Live” will dramatically lessen the anxiety of the end user as well as eliminate a large number of calls to the Command Center during the “Go Live” period. With an organized and thorough Integrated Testing period, the actual first productive use of the system and subsequent days of the “Go Live” period are likely to be a “boring” nonevent. Feedback from the end users and administrative staff will help determine how long the Command Center will need to be staffed on a 24-h-a-day basis. The command center is set up and ready to coordinate all issues, concerns, and “Go Live” Help Desk functions. The Command Center has a sufficient number of phone lines and to support the move to the live, production environment. For a period of time, this will include 24 h a day operations. Often, the vendor representatives/consultants of the new software company are on site to assist with “Go Live” support and staffing of the Command Center. The advantages of having designated command center include close proximity of clinical, administrative, technical, and vendor team members to quickly assess and prioritize issues. This close proximity also allows rapid communications and trending of problems in near real-time during the first days of the new system’s use. Team members, trainers, and super users serve as resources to the end users on a 24-h basis during “Go Live,” often a period of one to two weeks. Evaluation Post-Live.  The important tasks of Evaluation are as follows:

• •

Collection of post-live Success Criteria

•

Transitioning end-user support from the Command Center to the Help Desk Closure of the project

•

Completion of a System/Project Evaluation including the results of the Success Criteria

The system is evaluated to determine whether it has accomplished the Project Scope’s stated objectives. It involves a comparison of the working system with its functional

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requirements to determine how well the requirements are met, to determine possibilities for growth and improvement, and to preserve the lessons learned from the implementation project for future efforts. The Post “Go Live” evaluation describes and assesses, in detail, the new system’s performance. Utilizing the criteria established in Planning Phase, the evaluation process summarizes the entire system, identifying both the strengths and weaknesses of the implementation process. Comparison of the pre and post-implementation Success Criteria data provide quantitative data as to the successes obtained with the new system. The evaluation often leads to system revisions and, ultimately, a better system. To evaluate an implemented hospital information system, many principles are important. One authority suggests evaluating duplication of efforts and data entry, fragmentation, misplaced work, complexity, bottlenecks, review/approval processes, error reporting via the Issue Tracking mechanism, or the amount of reworking of content, movement, wait time, delays, set-up, low importance outputs, and unimportant outputs. This evaluation component becomes a continuous phase in total quality management. The system is assessed to determine whether it continues to meet the needs of the users. The totally implemented system will require continuous evaluation to determine if upgrading is appropriate and/or what enhancements could be added to the current system. Formal evaluations generally take place no less than every six months and routinely every two to four years after the systems have been implemented. The formal evaluation can be conducted by an outside evaluation team to increase the objectivity level of the findings. Informal evaluations are done on a weekly basis. Other approaches to evaluating the functional performance of a system exist. The Clinical Information System Evaluation Scale (Gugerty, Miranda, & Rook, 2006) describes a 37-item measurement tool for assessing staff satisfaction with a CIS. Investigating such functions as administrative control, medical/nursing orders, charting and documentation, and retrieval and management reports are used to assess system benefits. Each of these areas is evaluated through time observations, work sampling, operational audits, and surveys. System functional performance can be assessed by examining nurses’ morale and nursing department operations. Documentation of care must be assessed if patient care benefits are to be evaluated. The following questions should be asked:

• • •

Does the system assist in improving the documentation of patient care in the patient record? Does the system reduce patient care costs? Does the system prevent errors and save lives?

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Chapter 12 • System Design Life Cycle: A Framework    215

To evaluate nurses’ morale requires appraising nurses’ satisfaction with the system, the following questions may be considered useful:

• • • • • • •

Does the system facilitate nurses’ documentation of patient care? Does it reduce the time spent in such documentation? Is it easy to use? Is it readily accessible? Are the display “screens” easy to use? Do the displays capture patient care? Does the system enhance the work situation and contribute to work satisfaction?

To evaluate the departmental benefits requires determining if the clinical information system helps improve administrative activities. The following questions must be answered:

• • • •

Does the new system enhance the goals of the department? Does it improve department efficiency? Does it help reduce the range of administrative activities? Does it reduce clerical work?

Other criteria are necessary to evaluate technical performance; these include reliability, maintainability, use, response time, accessibility, availability, and flexibility to meet changing needs. These areas are examined from several different points—the technical performance of the software as well as hardware performance. The following questions must be answered:

• • • • • • • •

Is the system accurate and reliable? Is it easy to maintain at a reasonable cost? Is it flexible? Is the information consistent? Is the information timely? Is it responsive to users’ needs? Do users find interaction with the system satisfactory? Are input devices accessible and generally available to users?

Implementation of a clinical system is a project; by definition a project has a beginning, a middle, and an end (Project Management Institute, 2019). The transitioning of the end-user support functions from the Command

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Center to the Help Desk and submission of the Post-Live Evaluation to the Steering Committee are particularly important events in determining the end of an implementation project and the beginning of the maintenance and growth phases of the new system.

Daily Support Operations Daily support operations begin during the Go Live period. Help Desk functions for recording and tracking end-user calls/tasks for help are often managed by the Go Live team in the Command Center during the first one to three weeks post-live. Daily meetings/huddles are held with the Go Live team and IT Help Desk staff to review both type and frequency of problems encountered by the end users. The most frequent type of call during the Go Live period is from users unable to log into the system. The proactive recommendation is to include a task in the Cutover Plan to assign user logins and have all users log into the new system two to seven days before Go Live. The second most frequently received calls stem from the user’s lack of knowledge on how to complete a specific task. Reinforcement of training through oneto-one interventions as well as via mass communications (e-mails/flyers/Web site updates) is done. Early resolution of problems and communication back to staff are imperative and fosters confidence that the Project Team and the organization are addressing their needs during this stressful period.

Ongoing Maintenance The requirement for support resources in the hospital/ healthcare environment is a challenge for organizations. Many organizations utilize technical, analyst, and nursing informatics resources to provide the 24/7 support coverage. Strong communication and issue/task resolution procedures assist in responding to user needs. The technical manager reviews requirements for networks, servers, hardware, and certain software concerns. Commercial software companies continue to provide upgrades and updates to their systems/applications. Ongoing review of new features and functions, federal and state requirements, and insurance and billing requirements occur. Nursing Informatics personnel must bridge the support of the basic system requirements with the fast paced release of new technologies used in patient care. The cost of purchase and implementation is high; maintaining and improving the system’s ability to support all aspects of patient care delivery are mandatory. To upgrade a system, the same phases and activities described for the SDLC must occur; however, when upgrading, dovetailing the changes into the current system will require close evaluation and planning.

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216    P art 3 • S ystem L ife C ycle

SUMMARY This chapter describes the process of designing, implementing, and/or upgrading a clinical information s­ ystem/ EHR in a patient healthcare facility. It outlines and describes the four phases of the SDLC -planning; system analysis; system design, development; implementation, evaluation, support and maintenance.” [as in Figure 12.8.] The upgrading process reviews all of the components described to assure a technically sound, regulatory complete implementation supporting safe patient care and streamlined workflow. A clinical system/EHR implementation checklist utilizing the SDLC process has been developed (Fig. 12.8). The planning phase determines the problem scope and outlines the entire project to determine if the system is feasible and worth developing and/or implementing. The analysis phase assesses the problem being studied through extensive data gathering and

1. PLANNING PHASE Project Governance Structure Project Purpose Feasibility Study Project Scope Document Resource Planning 2. ANALYSIS PHASE Data Collection Determine Information Needs Gap Analysis Workflow Document Functional Design Document Data Analysis Data Review Benefits Identification Technical Analysis Hardware Software Networks System Proposal Document 3. DESIGN, DEVELOP, AND CUSTOMIZE PHASE System Design Functional Design Document Technical Specifications Hardware and Peripheral Device Plan Networks Application Software Interface Legacy System Data Conversion

analysis. The design phase produces detailed specifications of the proposed system. Development involves the actual preparation of the system, support of workflow, review of policies and procedures impacted by the new system, and detailed implementation planning. Testing is generally conducted on three levels for both the design and implementation of a commercially available system. Training focuses on the use of the system to improve its everyday workflow. Implementation outlines the detailed plans for moving the new system into the production or live environment. Evaluating the system determines the positive and negative results of the implementation effort and suggests ways to improve the system. Upgrading the system involves expansion or elaboration of initial functions by expanding capability or function or by adding entirely new applications. Upgrading projects requires all implementation phases be reviewed to assure success.

3. DESIGN, DEVELOP, AND CUSTOMIZE PHASE (cont) Develop Detailed Workplan Focused Plans Communications Hardware and Peripheral Devices Interface Conversion Testing End-User Training Customize System Dictionary Data and Profiles Policies and Procedures Conduct Testing Functional Integrated Conduct End-User Training System Documentation 4. IMPLEMENT, EVALUATE, MAINTAIN, AND SUPPORT PHASE Implement Determine Go Live Approach Cutover Plan Go Live Plan Conduct Live Operations Evaluate Post-Live Daily Support Operations Ongoing Maintenance

•  FIGURE 12.8.  Clinical Systems/EHR Implementation Checklist.

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REFERENCES American Nurses Credentialing Center’s Nursing Informatics Test Content Outline. (2018). Retrieved from https://www.nursingworld.org/~490a5b/globalassets/ certification/certification-specialty-pages/resources/ test-content-outlines/27-tco-rds-2016-effective-datemarch-23-2018_100317.pdf. Accessed on May 5, 2020. Centers for Medicare & Medicaid Services (CMS). (n.d.). CMS roadmaps overview. Retrieved from https:// www.cms.gov/Medicare/Quality-Initiatives-PatientAssessment-Instruments/QualityInitiativesGenInfo/ Downloads/RoadmapOverview_OEA_1-16.pdf. Accessed on May 5, 2020. Dennis, A., Wixon, B. H., & Roth, R. M. (2015). Systems analysis and design (6th ed.). Hoboken, NJ: Wiley. Douglas, M., & Wright, B. (2003). Zoom-Zoom, turbo charing clinical implementation. Presentation at Toward an Electronic Health Record —Europe, London. Gugerty, B., Miranda, M., & Rook, D. (2006). The Clinical information system implementation evaluation scale. Student Health Technology Informatics, 122, 621–625. Health IT.gov. How do I conduct a post-implementation evaluation? Retrieved from https://www.healthit.gov/ faq/how-do-i-conduct-post-implementation-evaluation. Accessed on May 5, 2020. Healthcare Information and Management Systems Society [HIMSS]. (n.d.). HIMSS. Retrieved from https://www. himss.org/library/clinical-decision-support. Accessed on May 5, 2020. HHS.gov. (n.d.). HITECH Act Enforcement Interim Final Rule. Retrieved from https://www.hhs.gov/hipaa/forprofessionals/special-topics/hitech-act-enforcementinterim-final-rule/index.html. Accessed on May 5, 2020. Kendall, K. E., & Kendall, J. E. (2014). Systems analysis and design (9th ed.). Upper Saddle River, NJ: Pearson Education. Kesselheim, A., Cresswell, K., Phansalkar, S., Bates, D., & Sheikh, A. (2011). Clinical decision support systems

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Chapter 12 • System Design Life Cycle: A Framework    217 could be modified to reduce ‘alert fatigue’ while still minimizing the risk of litigation. Health Affairs (Millwood), 30(12), 2310–2317. Lorenzi, N., Novak, L., Weiss, J., Gadd, C., & Unerti, K. (2008). Crossing the implementation chasm. Journal of the American Medical Informatics Association, 15(3), 290–296. Melnyk, B. M., & Fineout-Overholt, E. (2015). Evidencebased practice in nursing and healthcare: A guide to best practice (3rd ed.). Philadelphia, PA: Lippincott Williams & Wilkins. Project Management Institute. (2019). What is project management? Retrieved from http://www.pmi.org/About-UsWhat-is-Project-Management.aspx. https://www.pmi. org/about/learn-about-pmi/what-is-project-management Schwalbe, K. (2016). Information technology project management (8th ed.). Boston, MA: Course Technology, Cengage Learning.

SUGGESTED READINGS Amatayakul, M. K. (2017). Health IT and EHRs: Principles and practice (6th ed.). Chicago, IL: American Health Information Management Association. American Nurses Association. (2015). Nursing informatics: Scope and standards of practice (2nd ed.). Silver Spring, MD: American Nurse Publishing. Centers for Medicare & Medicaid Services. (2019). Promoting interoperability programs. Retrieved from https://www.cms.gov/Regulations-andGuidance/Legislation/EHRIncentivePrograms/index. html?redirect=/ehrincentiveprograms. Accessed on May 5, 2020. McBride, S., & Newbold, S. K. (2019). Systems development life cycle for achieving meaningful use. In: S. McBride, & M. Tietze (Eds.), Nursing informatics for the advanced practice nurse: patient safety, quality, outcomes, and interprofessionalism (2nd ed.). New York, NY: Springer.

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13 System and Functional Testing Theresa (Tess) Settergren / Denise D. Tyler

• OBJECTIVES . Differentiate testing and quality assurance. 1 2. Differentiate testing types related to the systems life cycle. 3. Describe testing levels, methodologies, and tools. 4. Examine barriers and success factors related to testing. 5. Discuss roles and skillsets of the informatics nurse in system and functional testing.

• KEY WORDS Commercial (COTS) system Implementation strategy Integrated testing Interoperability testing Unit testing Usability testing

INTRODUCTION System and functional testing are critical components of the system life cycle, whether the software or system is under new development or is commercial software that will be configured to a customer’s specific needs. Testing definitions and goals have evolved over the past 60 years from the most simplistic “bug detection” process, conducted toward the end of the design and coding phases. The more contemporary definition goes far beyond bug detection to include dimensions of “correctness” (Lewis, 2009) and alignment of the technology to the business goals, with the intent that the software does what it is supposed to do, errors are caught and resolved very early in the development process, and testing includes the business and end-user impacts. Testing and quality assurance are not synonymous (Beizer, 1984). Testing is comprised of activities

performed at various intervals during the development process with the overall goal of finding and fixing errors. The system life cycle phase usually drives how testing activities are organized, but the testing plan will normally include coordination of test efforts (scheduling resources, preparing scripts, and other materials), test execution (running prepared test scripts with or without automated tools), defect management (tracking and reporting of errors and issues), and a formulation of a test summary. Quality assurance is a proactive, planned effort to ensure that a defect-free product fulfills the user-defined functional requirements. Testing is an indispensable tool, but it represents the most reactive aspect of the process. Testing is all about finding defects. Quality assurance is all about preventing defects. In theory, an exceptional QA process would all but eliminate the need for bug ­fixing. Although QA is utilized widely in the broader information technology industry to help guarantee 219

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220    P art 3 • S ystem L ife C ycle

Testing

Create/Enforce Standards Process Improvement Automated Tools Quality Measures

Coordinate Test Efforts Test Execution Defect Management Test Summary

Quality Assurance

Standards

Test Planning

Requirements Analysis Ambiguity Reviews Test Script Design Test Data Problem Analysis Coverage Analysis

•  FIGURE 13.1.  Testing and Quality Assurance. (Reproduced, with permission, from Shari Ellis, 2012.). that a product being marketed is “fit for use,” many— perhaps even most—healthcare organizations rely on testing alone. Quality assurance can be best envisioned as an integrated approach, comprised of test planning, testing, and standards (Fig. 13.1; Ellis, 2012). Test planning activities include the following:

• • • • • •

Requirements analysis—user needs compared to the documented requirements. Ambiguity reviews—identify flaws, omissions, and inconsistencies in requirements and specifications. Non-redundant test script design—all key functions are tested only once in the scripts. Creation of test data—the right kinds of test patients and data to test all functions. Problem analysis—defect management includes uncovering underlying issues. Coverage analysis—the scripts will test all key functions, and all key nonfunctional components and features.

Standards, the third element of quality assurance, involves the creation and enforcement of testing standards, process improvement activities related to QA, evaluation and appropriate use of automated testing tools, and quality measures (application of effectiveness metrics to the QA function itself ). Software quality assurance is a planned effort to ensure that a software product fulfills verification and validation testing criteria and additional

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attributes specific to the project, for example: portability, efficiency, reusability, and flexibility. This chapter will focus primarily on testing types and levels that are most commonly employed in the implementation and maintenance of commercial clinical systems; it will also include concepts more relevant to software development.

TESTING MODELS AND METHODOLOGIES Testing models have evolved in tandem with the everincreasing complexity of healthcare software and systems. Early software development models were derived from Deming’s Plan-Do-Check-Act cycle (Graham, Veenendaal, Evans, & Black, 2008; Lewis, 2009). The Waterfall model, for example, was characterized by relatively linear phases of design, development, and testing. The testing is sequential, each phase of testing starts after the previous phase is completed (Singh & Kaur, 2017). Detailed definition of end-user requirements, including any desired process redesign, flowed to logical design (data flow, process decomposition, and entity relationship diagrams), which flowed to physical design (design specifications, database design), then to unit design, and ultimately to coding (writing in a programming language). Testing occurred toward the end of the Waterfall development model—a bit late for any substantive code modifications. This iterative software development model employed cyclical repeating phases, with incremental enhancements to the software

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Chapter 13 • System and Functional Testing 

  TABLE 13.1   V-Model Development Phase User Requirements

Interactions

Testing Type

Development Phase Informs Appropriate Testing

User Acceptance

Testing Types Verify Appropriate Development

Integration Testing

Specifications Design

Implementation

System Testing

Unit Testing

during each define-develop-build-test-implement cycle. The main advantage of an iterative approach was earlier validation of prototypes, but the costs of multiple cycles were often prohibitive. As software development models evolved, testing levels were correlated with the Waterfall technical development phases (Table 13.1) to demonstrate how development phases should inform the testing plan and how testing should validate the development phases. Agile software development, in contrast to some of the earlier models, is characterized by nearly simultaneous design, build, and testing (Watkins, 2009). Agile methodology can work well in healthcare projects since it works well in a variable environment (Hakim, 2019). Extreme Programming (XP) is a well-known Agile development life cycle model that emphasizes end-user engagement. Characteristics include the following:

• • • • •

Generation of business stories to define the functionality. On-site customer or end-user presence for continual feedback and acceptance testing. Programmer-tester pairs to align coding with testing—and in fact, component test scripts are expected to be written and automated before code is written. Integration and testing of code are expected to occur several times a day. Simplest solution is implemented to solve today’s problems.

Scrum is a project management method that accelerates communication by all team members, including customers or end-users, throughout the project. A key principle of Scrum is the recognition that customers are likely to change their minds about their needs (requirements churn). Scrum approaches requirements churn as

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  221

an opportunity to respond quickly to emerging requirements and to better meet the business needs of the customer. The rapid improvement cycle associated with Scrum works well in organizations with a strong data governance structure (Chopra, & Bonello, 2019). The  Agile software development–Scrum project management model encompasses some key quality assurance principles, is adaptable to healthcare information technology commercial system implementation projects, and the intense end-user participation improves the end product’s “fitness for use.” The newer DevOps model combines traditionally siloed technical development and operations teams, and sometimes security and quality assurance teams, in the testing and ­quality assurance process (CapGemini, Sogeti, & MicroFocus, 2019). Speed of innovation for and delivery to customers and improved system reliability and security are expected benefits of DevOps collaborations. Information technology industry adoption of DevOps and Agile for testing and QA is rising, as is application of automation and analytics, including artificial intelligence, to testing processes.

TESTING STRATEGY AND PROCESS Broad goals for health information technology system and functional testing include building and maintaining a superior product, reducing costs by preventing defects, meeting the requirements of and maintaining credibility with end-users, and making the product “fit for use.” Failure costs in system development may be related to fixing the product, to operating a faulty product, and/or to damages caused by using faulty product. Resources required to resolve defects may start with programmer or the analyst time at the component level, but in the context of a clinical information system thought to be “ready for use,” defect resolution includes multiple human resources: testing analysts, interface analysts, analysts for upstream and downstream applications, analysts for integrated modules, database administrators, change management analysts, technical infrastructure engineers, end-users of the new system and of upstream and downstream interfaced systems, and others. In addition to the human resource expenses, unplanned fix-validate cycles require significant additional computer resources and may impact project milestones with deleterious cascading effects on critical path activities and the project budget. Operating a faulty software product incurs unnecessary costs in computer resources and operational efficiencies; potential damages include patient confidentiality violations, medical errors, data loss, misrepresentation of or erroneous

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222    P art 3 • S ystem L ife C ycle patient data, inaccurate data analytics, and lost revenue. There may be also costs associated with loss of credibility. An inadequately tested system that fails “fit for use” criteria will negatively, perhaps irreparably, influence end-user adoption. A testing plan is as indispensable to a clinical system project as an architect’s drawing is to a building project. You wouldn’t try to build a house without a plan—how would you know if it will turn out “right”? Test planning should begin early in the system life cycle (Douglas & Celli, 2011), and should be aligned with business and clinical goals. Project definition and scope, feasibility assessments, functional requirements, technical specifications, required interfaces, data flows, workflows and planned process redesign, and other outputs of the planning and analysis phases become the inputs to the testing plan. Technological constraints of the software and hardware, and the ultimate design configuration to support the workflows and use cases represent additional inputs to the testing plan. Testing predictably takes longer than expected, and the testing timeline is often the first project milestone to be compressed, so it is advisable to plan for contingency testing cycles. Depending on the complexity and magnitude of a clinical system implementation, three or more months should be reserved for testing in the project timeline. Figure 13.2 depicts a testing timeline for multiple concurrent projects, and reflects varying scope and complexity of the projects.

SYSTEM ELEMENTS TO BE TESTED Commonly tested clinical system elements include software functions or components, software features, interfaces, links, devices, reports, screens, and user security and access (Douglas & Celli, 2011). Components and features include clinical documentation templates and tools, order and results management functions, clinician-to-clinician messaging, care plans, and alerts and reminders based on best care practices. Testing of the documentation features should, at minimum, include how clinical data are captured and displayed. For example, are the data captured during a clinical documentation episode displayed accurately, completely, and in the expected sequence in the resulting clinical note? Are discrete data elements captured appropriately for secondary use, such as for building medication, allergy, immunization, and problem lists, driving clinical alerts and reminders, and populating operational and clinical reports? Can clinicians and other users, such as ancillary department or billing staff or auditors, easily find the data? Can the user add data and modify data in the way expected in that field—for example, structured

ch13.indd 222

coded values versus free text. Can data be deleted or modified with a versioning trail visible to an end-user? Do the entered values show up in the expected displays? System outputs to test include printing, faxing, and clinical messaging. Printing is often complex—it can be automated batches or on-demand local print jobs, and can be controlled at workstation (e.g., Windows printing), application (electronic health record [EHR] system), patient location, or network service (e.g., Citrix) levels (Carlson, 2015). Printing failures can completely disrupt clinical workflows, and must be thoroughly tested. Do test requisitions, patient education and clinical summaries, and letters print where they are supposed to print? Are prescriptions for scheduled medications printing on watermarked paper, if required? Do the documents print in the right format, and without extra pages? Faxing is important to thoroughly test, especially when it occurs automatically from the system, to prevent violations of patient confidentiality. The physical transmission is tested to ensure that it gets to the correct destination and includes all required data, formatted correctly, and inclusion of contact information in case a fax gets to the wrong recipient. Testing should also include the procedures that ensure fax destinations are regularly verified. Order entry and transmittal testing occurs at several levels. The simplest level of testing is unit testing: does each orderable procedure have the appropriate billing code associated? Are all orderables available to ordering providers in lists or sets, via searches, and are the orderables named in a clinically identifiable and searchable way, including any abbreviations or mnemonics? Does each order go where it is supposed to go and generate messages, requisitions, labels, or other materials needed to complete the order? For example, an order entered into the EHR for a laboratory test on a blood specimen collected by the ICU nurse generates an order message that is transmitted into the laboratory information system, and may generate a local paper requisition or an electronic message telling the nurse what kind and how many tubes of blood to collect, as well as generating specimen labels. These outputs for a nurse-collected lab specimen order are tested, as well as the outputs for the same ordered lab specimen collected by a phlebotomist. At the integrated level of testing, the order message and content received in the laboratory information system (LIS) are reviewed for completeness and accuracy of the display—does the test ordered exactly match the test received? Does the result value received from the LIS display correctly, with all expected details, such as the reference ranges and units, collection location, ordering provider, and other requirements? Interface testing may include inter- and even intrasystem modules, external systems, medical devices, and

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ch13.indd 223 27-Mar 28-Mar 29-Mar 30-Mar 31-Mar 1-Apr 2-Apr 3-Apr 4-Apr 5-Apr 6-Apr 7-Apr 8-Apr 9-Apr 10-Apr 11-Apr 12-Apr 13-Apr 14-Apr 15-Apr 16-Apr 17-Apr 18-Apr 19-Apr 20-Apr 21-Apr 22-Apr 23-Apr 24-Apr 25-Apr 26-Apr 27-Apr 28-Apr 29-Apr 30-Apr 1-May 2-May 3-May 4-May 5-May 6-May 7-May 8-May 9-May 10-May 11-May 12-May 13-May 14-May 15-May 16-May 17-May 18-May 19-May 20-May 21-May 22-May 23-May 24-May 25-May 26-May 27-May 28-May 29-May 30-May 31-May

Days to Go-live # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # A=AHSP P=Physician Billing R=Radiant LC-Licensed Clinic S=Shared OT-OpTime W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F S S M T W T F Finalize Build & Fix S Finalize Build/FIX S Integrated Testing - Round 2 Integrated Testing Cycle 2 Fix Cycle 2 S Fix Cycle - Round 2 R Radiant Mapped Record Testing in SUP Mapped Record Testing MR Validate/Fix/Retest Utility Update Testing R Radiant Utility Update Testing P User Acceptance Testing User Acceptance S GLRA GLRA GLRA Integrated Testing Cycle 3 S Integrated Testing - Round 3 Fix Cycle Round 3 S Fix Cycle - Round 3 P Begin Charge Testing Charge Testing P Begin Claims Testing Claims Testing Refund/Extract Testing P Refund/Extract Testing Statement Testing P Statement Testing P GL Testing GL Testing P Scanning Testing 4/8 Delivery Reports Validation (30 Clarity) P Reports Validation P Data Warehouse Validation 4/25 New Enterprise Data Warehouse Validation Available (All IDX Data + Testsing 1 & 2 Data) S Cutover Planning Cutover Planning A AHSP PGL & DR Go-Live (5/20) PGL Dress AHSP Cut-Over AHSP GO-LIVE S Practice Go-Live's 1, 2 & 3 PGL 1 PGL 2 S CCB Deadline for Refresh/Migration CC CCB S TSTA > TST Refresh TSTA Ref S TST Environment Validation TST Template Build P Template Build Workshop/Conversion 4/19 Training Valid Environment Appt Conversion P Prof Billing Appointment Conversion Frozen 12 noon P Tapestry Initial Enrollment Load Tapestry P Pre-Registration Go-Live Enrolement P Dress Rehearsals - Application R Radiant Accession Num Conversion Professional Billing Hardware Deployment P Hardware Deployment Prof Billing R Hardware Deployment Radiant Radiant Zebra Printer Deployment Radiant WOW Deployment S Command Center Set-Up S Command Center Shut-Down 100% Curricula Development Prof Billing 100% Review P Training Prg Development PB P Training Prg Development Access 100% Curricula Development Access 100% Review P Training Environment Build PB Training Patient Build Professional Billing P Training Environment Build Access Training Patient Build Prof Billing

Chapter 13 • System and Functional Testing 

•  FIGURE 13.2.  Testing Timeline Example.

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224    P art 3 • S ystem L ife C ycle file transfers. A data conversion from one system to another system is a set of one-way interfaces in addition to possible batch uploads. Interfaces are tested for messaging and content, data transformation, and processing time. Interface messages typically utilize Health Level 7 (HL7) standards (Carlson, 2015). Interface data flows may be unidirectional or bidirectional. System module interfaces include admission/discharge/transfer (ADT), clinical documentation, order entry and results management, document management, patient and resource scheduling, charge entry and editing, coding, claims generation and edit checking, and other major functions. In a fully integrated clinical system, the master files for specific data elements may be shared, so a change to the metadata in a master file needs to be tested for unintended impacts across modules. Clinical systems could alternatively have redundant master files within multiple modules, and testing plans should ensure that the master file data elements are mapped accurately among modules and various interfaced systems. Data conversion testing must ensure that the data being converted from one system will populate the new system accurately. This often requires multiple test cycles in nonproduction environments to test each component of each data element being converted, prior to the actual conversion into the live production environment. The conversion of historic data into the production system can take days, requiring spot check testing after each new conversion. Clinical systems testing include links to third-party content for patient education, clinical references and peerreviewed evidence, coding support systems, and others. Links to Web content resources for clinicians, including software user support, can be embedded into the software in multiple locations. These links should be tested in every location to ensure that they work properly and bring the user to the correct content display. Medical device interfaces include invasive and noninvasive vital signs monitoring, oximetry, wired and wireless cardiac monitoring, hemodynamic monitoring, ventilators, infusion pump integration, urimetry monitoring, and other devices. Middleware can be used to acquire the data and provide the nurse with an interim step of validating data before accepting it into the clinical system. Newer medical devices include wireless continuous vital signs monitoring worn in the hospital or at home, nurse call systems, and bed or chair fall alarm systems. Testing outcomes for medical devices should consider data validation and alert or alarm risk mitigation steps. Data validation refers to the accurate capture of physiological and device data, such as blood pressure

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and concurrent pump infusion rate, such as the dose of a vasopressor, and requires the nurse to review and accept or reject the data. Alerts and alarms for data captured into the clinical system must be thoroughly tested for relevant settings, such as range and sensitivity. It is critical to minimize false alarms, which may result in alert fatigue, yet still alert nurses and other clinicians to genuine patient status changes. Report testing should be performed at multiple levels. Reports may be static or dynamic. Examples of static reports include pre-formatted displays of documentation events, specialized reports that pull in data from multiple modules within the clinical system, requisitions generated from orders, and reports generated from the data warehouse and accessed within the clinical system. Data warehouse reports typically contain day-old data, intended to support individual clinical decision-making, as well as provide snapshots of population-level information used for trending, quality improvement, operational assessment, and various external reporting requirements. Dynamic reports (using “real-time” data) provide clinical and operations staff with current information on individual or aggregated patients, and can be updated real-time. Examples of dynamic reports include patient lists or registries, appointment no-shows, and queries relevant to a provider (“all of my patients that have a statin prescription”) or nurse (“all of my heart failure patients”). Reports testing checks accuracy of formatting and data display, but also compare the source data to the data in the reports. For example, an EHR must produce a data transmission that meets health information exchange standards, and testing ensures that data are transmitted with precision. Reports may also be sent to other systems, including a health information exchange (HIE). The content, structure, and timing of the transmission to the HIE will need to be tested. User security and access can make or break go-live success. User access is typically designed to be role-based, so that each user role has specific functions and data views tailored to specific use cases. For example, a physician requires the ability to order tests and procedures including referrals and consults, e-prescribe, and document with tools tailored to specialty. Physicians and other providers also manage electronic medication administration and reconciliation, update problem lists and allergy lists, view customized displays of data, query data real-time, calculate and submit professional charges, generate health information exchanges, among other functions. The registered nurse performs slightly different activities in the same system, some of which may be required, and will usually incorporate

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a subset of physician functions. Nursing activities generally depend on the location of care. Hospital nurses generally may not have full ordering privileges, but can enter orders under certain circumstances, and in some states may enter orders under protocol. Some of those orders require physician co-signature. Hospital nurses may or may not be able to update the patient’s problem list, per the organization’s policy, but typically can update allergy lists and priorto-admission medication lists. The security needed for ordering can be different in an ambulatory clinical setting, where physicians may delegate some ordering tasks. For an enterprise-wide EHR, practice scope differences must be thoroughly tested to ensure that each clinician has the right access in the right care location. Testing should also include what the person in that role should not be able to perform, such as a hospital registrar ordering medications. This is sometimes termed “negative testing.” Security testing should also include testing the ability to do audits. Audit logs are a regulatory requirement for the Joint Commission, the Health Insurance Portability and Accountability Act (HIPAA), meaningful use, and the Health Information Technology for Economic and Clinical Health (HITECH) Act (Greene, 2015). Audit logs or audit reports may be run automatically or manually by a patient or user. Audits are typically run on celebrities, VIPs, employees, and their families.

TESTING TYPES Testing types used vary based on where in the system life cycle testing is planned, and the degree of development or programming involved. Table 13.2 (Beizer, 1984; Graham et al., 2008; Lewis, 2009; Watkins, 2009) describes common testing types suggested, based on magnitude, for development of the testing plan. Table 13.3 describes testing done during implementations and upgrades in more detail. The first step in test planning is to define what is to be accomplished (Graham et al., 2008). The goals should include the scope, expectations, critical success factors, and known constraints, which will be used to shape the rest of the plan. Testing approach defines the “how”—the techniques or types of testing, the entrance and exit criteria, defect management and tracking, feedback loop with development, status and progress reporting, and perhaps most importantly, exceptionally well-defined requirements as the foundational component. The environment for testing defines the physical conditions: end-user hardware to be included in testing (desktops, laptops, wireless workstations, tablets, smartphones, thick client, thin client, scanners, printers, e-signature devices, and

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Chapter 13 • System and Functional Testing 

  225

other), the interfaces to be included, the test environment for the application and other systems to be tested, automated tools, type of help-desk support needed, and any special software or system build required to support testing. Multiple test user roles are needed for testing to assure appropriate role-based tools, functions, and views. Test patient records are created so that age- and genderappropriate documentation tools and decision support, including alerts and reminders, are tested. Laboratory values and other diagnostic results for test patients can be manually built or copied over from a real patient with the identifiers stripped, to allow testing of expected resultsdriven decision support, flags, displays, and temporal views such as graphs and charts. Next, testing specifications are developed that define format standards, identify features to be tested, cross-­ reference features to the functional requirements, and break down the work into manageable pieces. Ideally, a dedicated testing team documents how each feature will be tested, existing interdependencies, and the test workflows, which will be devised to include all of the required test specifications, but in efficient and non-redundant flows that normally will not match end-user workflows. In contrast, testing scripts that emulate end-user workflows are usually utilized for user validation and acceptance testing. Scheduling is a vital test planning component, and involves coordination of multiple human and technical resources, including the technical participants discussed earlier for integrated testing, plus others from the software, infrastructure, vendor, and operational clinical and business areas. Ideally, end-users have been involved in all system life cycle phases, including testing, but test planning includes scheduled formal user acceptance testing (UAT). Testers ideally will work together in the same location, which may require new set-up of workstations, printers, scanners, electronic signature devices, medical devices, and other peripheral equipment needed for endto-end testing scenarios. Magnitude and complexity of the system as well as testing goals and deadlines are inputs to determining the number of testing cycles, resources to be scheduled, progress checkpoints, and contingency planning. The final step in test planning is sign-off by stakeholders, a vital checkpoint for review of the goals, requirements, and resources, as well as contingency planning and criteria for “go-no go” decisions. A typical testing plan for a clinical system requires that all functional modules, features, and interfaces (upstream and downstream) are unit-tested prior to the first integrated test cycle. Three or more integrated test cycles are not uncommon for new implementations or extensive

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226    P art 3 • S ystem L ife C ycle

  TABLE 13.2    Testing Types Grid New Software Development

COTS System Configuration

Maintenance/ Optimization

End-user validation testing or final testing based on end-user requirements

X

X

X

Ad hoc

Off-script testing intended to discover issues; “try to break it”

X

X

X (major changes)

Alpha

First customer use of an application nearing ­development completion

X

Beta

Customer use when development is complete, to uncover any additional issues before general release

X

Black Box

Cases developed for functional testing

X

Comparison

Assessing against competitor products

X

X

Compatibility

Software performance in different environments (with various software, hardware, network, ­operating systems)

X

X

X

Coverage

Each branch has true and false outcomes; each ­condition considers all possible outcomes

X

Database

Integrity of DB field values

X

X

X

End-to-end

System testing that mimics real-world environment

X

X

X

Exception

Error messages and exceptions—identification and handling processes

X

Exploratory

Informal testing, often untrained users, to identify unimagined issues

X

X

Functional

Focused on meeting user-defined requirements; includes unit/component, integration, regression, and acceptance testing

X

X

X

Integration

Testing application parts to ensure they function together correctly; can mean “pieces of code” within a module, or between modules

X

X

X

Interoperability

Disparate systems are able to exchange a defined data set, as in Health Information Exchange

X

X

X (major changes)

Load

High volume testing, often automated, to determine point at which system response time degrades

X

X

Nonfunctional

Testing outside of the user requirements that define the functions; typically includes interoperability, load/ volume, performance/reliability, efficiency, security

X

X

Parallel

Literally “side by side” testing: Same work done in old system and new system to compare results

X

X (less common)

Performance

Often used interchangeably with stress—may include volume, reliability

X

X

Recovery

Testing recovery time after a software crash or ­hardware failure, or other failure

X

Regression

Testing planned changes for unintended ripple effects

X

X

X

Security

How well does system protect against unauthorized use, damage, data loss, etc.

X

X

X

Test Name

General Description

Acceptance

X (Major Changes)

(continued)

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Chapter 13 • System and Functional Testing 

  227

  TABLE 13.2    Testing Types Grid (continued) Test Name

General Description

Stress

Similar to load testing: Functional testing for very high loads of users, queries, repeated inputs, etc. Includes functional and nonfunctional testing, using both black box and white box techniques Lowest level that focuses on a selected function or piece of code to see if it works as designed—­usually performed by the programmer or builder Focus on ease of use as defined by end users Testing the logic paths based on the defined specifications

System Unit

Usability White Box

New Software Development

COTS System Configuration

Maintenance/ Optimization

X

X

X

X

X

X

X

X

X X

X

  TABLE 13.3    Testing for Implementations and Upgrades System Conversion Validation

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Unit Testing

Integrated Testing

End-User Validation

Compares each data field/data element that is converted

System configuration testing: • Data displays • Alerts and reminders • Care plan options/suggestions based on charting Initial interface test

Final multi-professional testing of all systems and interfaces—test each interface to each system

Requires ­knowledge of the new and old systems

A system-to-system interface check—for example, Lab to the clinical system and back

Data ­validators must be detail-oriented

May test single directional and bidirectional interfaces

The scripts should reflect the patient population, including challenging types of cases Include all interfaces, ­systems, and reports

User validation, or acceptance testing, is the sign off of the system design and usability. It may be done throughout the design, but a final sign-off is recommended Multi-professional

Multi-phased, small quantities converted and reviewed, then larger quantities

May be phased—for example: • Check the ADT to the Lab Information System (LIS) • Check orders from the Clinical Information System (CIS) to the LIS • Check the orders from the LIS to the CIS • Check the order status updates from the LIS to the CIS • Check the results from the LIS to the CIS • Check the charges from the LIS to the financial system

May include clinical, demographic, and financial data

Check each screen—verify that each ­element interface to the ancillary ­system correctly. Verify that each charted field display correctly on ­displays and reports. Initially done by the informatics team, then by end users

May be phased, utilizing analysts and informatics initially to work out bugs and issues and to validate tests scripts and issue reporting. The next phase may involve end users—this allows them to validate the usability of the s­ ystem, improves their comfort using the system, and reporting issues Should go from the initial registration through discharge, including scheduling, reviewing reports, charges, and bills

Uses scripts, or scenarios, based on using real-life scenarios Document and prioritize issues that require system changes versus change and optimization requests. Include the ease of printing and reading reports. Validate the security build for each user role

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228    P art 3 • S ystem L ife C ycle upgrades, though a subset of tests would be conducted for smaller system changes. Two weeks of integrated testing plus two weeks for fixes and unit testing are desirable for each cycle, always ending on a test cycle_never ending on a fix cycle. Daily status updates are essential

to monitor testing outcomes and the potential need for plan modifications (Fig. 13.3). During the two weeks of integrated testing, new software changes should not be introduced unless absolutely required. A “code freeze” is ideally instituted during the last scheduled integrated

From: Tester, Happy Sent: Monday, April 29, 2013 4:11 PM To: Group EIS QAT Integrated Test Status Subject: Status Report PRC - Radiant - AHSP (TST) Cycle 2 Day 3 Script Execution

On Track

Defects

Many normal defects

Resources

Short 3 resources for AMB

Overall Summary • Day 3 of Cycle 2 • 12 defects were found today. • 4 scripts are behind due to resource issues. Defect Summary Total NON-Script Error Defects

Open NON-Script Error Defects

ADT

2

1

High

3

AMB

4

4

Normal

8

Low

1

Module

Ancillary ASAP

1

0

1

0

6

2

1

1

HB

1

0

HID

2

1

Orders

5

2

Radiant

3

1

24

12

Priority (Open)

Total

0

Bridges Cadence Canto CDR ClinDoc Device Integration Epic Rx Haiku

Interfaces (SI)

PB Security Total

* Total #’s may differ due to defects having multiple teams assigned

•  FIGURE 13.3.  Testing Status Update Example.

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Chapter 13 • System and Functional Testing 

  229

Script Summary Complete

6

In Progress

8

Behind

0

Fail

0

Not Started

30

Total Scripts

44

Execution Schedule AHSP - PRC Radiant Cycle 2 Script

PRC01 Administration of Immunization PRC02 - Error Encounter PRC03 Insurance Referral PRC04 Mental Health PRC05 Mosaic PRC06 Research PRC07 - Vision Office Visit PRC08 - Reg Employee PRC09 - Reg Dual Insurance Commercial PRC10 - Reg Deal Insurance Medical PRC11 - Reg Minor PRC12 - Self Pay PRC13 Schuyler

Mon

Tue

Wed

Thu

Fri

4/29 Day 1

4/30 Day 2 1

5/1 Day 3 2

5/2 Day 4 3

5/3 Day 5

Status

Comments

In Progress Not Started

1 1

Complete 1

Behind Not Started

1 1

No resource available

Complete 1

Complete

1

1

Fail

Security files not migrated

1 Behind 1 Fail

Security files not migrated

Not Run

1

2

Not Run Not Started

•  FIGURE 13.3.  Testing Status Update Example. (continued)

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230    P art 3 • S ystem L ife C ycle testing cycle, so that only the highest priority changes are permitted, in order to reduce unintended ripple effects. The testing team works through all scripts, and documents expected and unexpected outcomes in the designated tool. Analysts and trainers may also participate, but programmers/builders typically do not test their own work during integrated testing. Builders inherently want their code to work, and they know how to make it work—and they did that during unit testing. Testers want to test for failures. In their attempts to “break” the system, defects are uncovered that might otherwise have been missed. Defects may include functions that do not work as designed, workflow issues that were not identified during requirements definition and workflow analysis, or unintended effects. Defects are usually given a priority. The project team should establish definitions for priorities. Priority examples include the following (Douglas & Celli, 2011):

• • • •

Critical: Significant impact to patient safety, regulatory compliance, clinical workflow or other aspect, for which no workaround is possible. High: Significant impact to workflow or training or other aspect, for which the resolution effort or the workaround is itself a major effort and may be high risk. Medium: Impact on workflow or training is noteworthy but a reasonable workaround exists. Low: Impact on workflow or training is minimal; workaround is not required.

Defects are communicated back to the programmers/ builders, and are followed through to resolution using a defect tracking tool. These cycles repeat until all critical and high defects, at minimum, are resolved. Contingency planning should allow for additional testing cycles. The testing plan usually culminates in regression and user acceptance testing. Regression testing ensures that fixes implemented during the integration testing cycles didn’t break something else. Acceptance testing allows users to validate that the system meets their requirements, and is usually conducted using use-case scripts that reflect processes designed during the workflow analysis phase. User acceptance testing ideally occurs after successful integrated and regression testing cycles are completed, and prior to the start of training to avoid the risk of changes that necessitate retraining—since users should have been involved throughout the project, UAT should represent a final validation of “fit for use’” for end-users. Alpha and/or beta testing may be scheduled when a software or system is newly developed, and both mimic

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real-world use. Healthcare organizations may avoid alpha and beta software because the cost of resources to test immature systems typically outweighs the benefit, but some healthcare systems design new software. Alpha testing is usually conducted in a simulated fashion. Beta testing is more apt to be conducted in a live setting. Both testing strategies can include parallel testing, wherein the users are performing dual data entry—entering data in the new system and in the old system (electronic or paper) to determine whether all required functions exist under normal conditions of use, and the system outputs are as expected. Parallel testing may be more useful for ensuring appropriate charges are generated, or other billing-related functions, than for clinician workflow testing, such as very resource-intensive “shadow charting.” Usability and safety are valuable barometers for evaluating testing success. The International Organization for Standardization defines usability in terms of a user performing a set of tasks effectively, efficiently, and with satisfaction, and includes learnability, error frequency, and memorability aspects of the user experience (Rose et al., 2005), with special focus on an attractive user interface that is easy to understand and navigate. Usability’s broadest definition, “quality in use,” includes the user and task dimensions along with the tool and the environment (Yen & Bakken, 2012). Usability testing ideally begins early in the design phase and is conducted throughout the system life cycle, through optimization and maintenance as well as during implementation. Iterative usability evaluations by actual users could be expected to enable the clinical system to better meet user efficacy and patient safety goals (Staggers & Troseth, 2011). Technology-induced errors are on the rise, and usability factors, such as small or very dense displays and lack of data visibility (Borycki, 2013), may be related to higher error rates. Information overload related to the sheer quantity of digital data is compounded by organization and display of data that fails to support pattern recognition and other cognitive tasks (Ahmed, Chandra, Herasevich, Gajic, & Pickering, 2011). User interface design factors contribute to technology-induced errors, and data displays related to a specific user task should be an integral component of testing plans. Data integrity failures in EHRs and other HIT systems were among the top 10 technology hazards for 2014 (ECRI, 2013), citing the following contributing factors:

• • •

Patient/data association errors—patient data associated with another patient’s record Missing data or delayed data delivery Clock synchronization errors

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Chapter 13 • System and Functional Testing 

• • •

Inappropriate use of default values

•

Basic data entry errors, which now can be propagated much further than could have occurred in paper records

Use of dual workflows—paper and electronic Copying and pasting of older information into a new report

While these issues are no longer among the top 10 health technology hazards, they still merit consideration during clinical system testing and quality assurance processes. Current patient safety concerns in which health information technology plays a role (ECRI, 2018) include diagnostic stewardship and test result management using EHRs, patient safety concerns using mobile health, recognition of infections in peripherally inserted IV lines, early recognition of sepsis, and provider burnout and its impact on patient safety. The top 10 health technology hazards include hacker remote access to healthcare systems, disrupting healthcare operations, and improperly customized physiological monitor alarm settings (ECRI, 2018). Safety testing incorporates the people, process, technology, environmental, and organization-related issues identified in the literature, with a focus on provider order entry, clinical decision support, and closed-loop (­ bar-coded) medication administration (Harrington, Kennerly, & Johnson, 2011). Based on the new ECRI (2018, 2019) reports, informatics nurses must be cognizant of the multiple patient safety hazards related to information technologies, and include appropriate testing to mitigate the risks.

CHALLENGES AND BARRIERS Resources, timeline pressures, and materials comprise the most common barriers to sufficient testing. Liberating end-users from their regular work for testing can prove difficult, especially in understaffed nursing environments. Direct care clinicians with varying computer skill levels, not just trainers or nursing “early adopters,” are essential to include in testing as they represent a realistic crosssection of users. This approach produces significant budgetary impacts due to the costs of back-filling staff to be “out-of-count,” including overtime and/or temporary staffing costs. A potential staffing crisis may be exacerbated if training is allowed to overlap testing cycles, which commonly occurs in an effort to meet the go-live date. Timeline pressures may be a significant barrier to sufficient testing. Pressure to attain project milestones may tempt even the most seasoned project managers to shorten test cycles or use testing shortcuts. Staffing and

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  231

other testing costs may be balanced by the benefits of going live with a fully tested and well-vetted clinical system that enables nurses and the care team to be efficient and effective, to provide safe care, and to feel satisfied with the system. A third major barrier to testing success is inadequate script development. Testing scripts for integrated testing cycles are typically non-redundant, and do not closely emulate a particular clinical workflow—this is efficient and very appropriate, and an ideal test script could be successfully run by a random person off the street. However, user acceptance testing requires detailed scenario-driven scripts that simulate real workflows. These more detailed scripts will be continually updated throughout the system life cycle, as clinicians provide feedback on overlooked nuances of use cases. These nuances are valuable findings to close safety gaps and avert potentially inefficient or even unsafe workarounds—nurses can be very creative. Nursing informatics experts play key roles in the testing process. Informatics nurses can help to make the case for sufficient testing by tying testing outcomes back to the original project goals for adoption and care improvements, including quantification of costs of clinical adoption failure, patient safety issues, and inability to meet efficiency and effectiveness targets. Informatics nurses can help to develop and execute testing plans and scripts, ensuring that appropriate use cases are iteratively documented for testing that will continue into the system optimization and maintenance phase. Informatics nurses play a critical role in ensuring data validation across disparate systems. Evaluation of testing effectiveness, usability, and user acceptance are key skills that informatics nurses bring to testing. Informatics nurses also bring value through research efforts. The Nursing Informatics International Research Network conducted a survey to identify nursing informatics research priorities. Development of systems that provide real-time safety-related feedback to nurses, systems impact on nursing care, nursing decision support systems, and systems workflow impacts were the topranked international research priorities (Dowding et al., 2013). These aspects are very tactical and close to nursing practice and have implications for various types of system and functional testing. In summary, system and functional testing are critically important to successful implementation and maintenance of clinical information systems, and informatics nurses have a responsibility to inform, champion, and evaluate testing processes and outcomes.

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Test Questions 1. Describe quality assurance.

A. Quality assurance (QA) is a proactive, planned effort to ensure that a defect-free product fulfills the user-defined functional requirements. B. Quality assurance (QA) is a method used to assess patient satisfaction.

C. Quality assurance (QA) is a way of problemsolving staffing issues.

D. Quality assurance methods must be certified. 2. Which best describes the importance of system and functional testing? A. Finding system bugs

B. Verifying the alignment of the technology with business and clinical goals C. Testing of the interfaces for new software

D. Documenting the activities required in system design 3. Which of the following are the correct descriptions of testing and quality assurance?

A. The focus of testing is to fix defects, the focus of quality assurance is to identify defects.

B. The focus of testing is to find defects, whereas the focus for quality assurance is to prevent defects.

C. The focus of quality assurance is to identify issues, and the purpose of testing is to verify issues. 4. Which of the following is not one of the three testing models/methodologies?

A. Waterfall is sequential, each phase of testing starts after the previous phase is completed where testing is toward the end of the build. This method works well for some software development projects. B. Agile is characterized by simultaneous designing, building, and testing. Agile works well when coupled with end-user involvement.

C. Scrum is a project management method that relies on communication between builders and end users. The rapid improvement cycle associated with Scrum works well in organizations with a strong data governance structure. D. Bug detection to find issues in system design.

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5. Which of the following is true of function (or unit) testing? A. Do the imputed data display correctly on reports and displays? B. Do reports, requisitions, and the results print as expected? C. Do the imputed data display correctly and are discrete data elements displayed correctly for used other pathways, including medication, allergy, immunization, and problem lists, clinical alerts and reminders, and reports? D. Are the imputed data displayed defined.

6. Which of the following describes major test planning activities? A. Bug fixing, problem, and coverage analysis

B. Requirements analysis, ambiguity reviews, problem analysis, and coverage analysis

C. Problem analysis, clinical analysis, testing, and bug detection D. Testing plan, training plan, system plan

7. Which of the following best describes interface testing?

A. Testing unidirectional and bidirectional interfaces, including interfaces for patient information, orders and results, and devices B. User acceptance of the user interface

C. Verifying that medications from pharmacy display on the medication record D. Verifying that the screens work as expected

E. Lab orders, including order status updates (OSUs), order cancellations, and results 8. Identify the option that best describes the three things to include when testing reports. A. Accuracy, timeliness, and font size

B. Size, graphic designs, and accuracy C. Timeliness, accuracy, and speed

D. Accuracy, timeliness, and readability (display and printing)

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Chapter 13 • System and Functional Testing 

9. Which of the following best describes user security testing? A. Verifying that users can access the Internet and lunch menu

B. Verifying that users can sign into the system and that they have the appropriate role-based access to perform the functions and access information necessary to do their jobs C. Verifying that users can sign into the system

D. Verifying that users can change their passwords 10. What are the most common barriers in system and functional testing? A. Resources, understaffing, and poorly developed scripts B. Resources, pressure to meet deadlines, and inadequately developed scripts

C. Pressure to meet deadlines, pressure to pass scripts, poor lighting D. Resources, lack of interest, pressure to meet deadlines

Test Answers 1. Answer: A 2. Answer: B 3. Answer: B

4. Answer: D 5. Answer: C 6. Answer: B

7. Answer: A

8. Answer: D 9. Answer: B 10. Answer: B

REFERENCES Ahmed, A., Chandra, S., Herasevich, V., Gajic, O., & Pickering, B. W. (2011). The effect of two different ­electronic health records user interfaces on intensive care provider task load, errors of cognition, and performance. Critical Care Medicine, 39(7), 1626–1634.

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Beizer, B. (1984). Software system testing and quality ­assurance. New York, NY: Van Nostrand Reinholt. Borycki, E. M. (2013). Technology-induced errors: Where do they come from and what can we do about them? Retrieved from http://cshi2013.org/files/Elizabeth%20 Borycki.pdf. Accessed on May 5, 2020. CapGemini, Sogeti, & MicroFocus. (2019). World quality report, 2018–19 (10th ed). Retrieved from https://www.microfocus.com/media/analyst-paper/ world_quality_report_analyst_report.pdf. Accessed on March 21, 2019. Carlson, S. (2015). Testing in the healthcare informatics environment. In P. P. Sengstack & C. M. Boicey (Eds.), Mastering informatics: A healthcare handbook for ­success (pp. 61–86). Indianapolis, IN: Sigma Theta Tau International. Chopra, S. J., & Bonello, J. (2019). How to achieve a return on an EHR. HFM (Healthcare Financial Management), 1–7. Douglas, M., & Celli, M. (2011). System life cycle: Implementation and evaluation. In V. A. Saba, & K. A. McCormick (Eds.), Essentials of nursing informatics (5th ed., pp. 93–106). New York, NY: McGraw-Hill Education. Dowding, D. W., Currie, L. M., Boryicki, E., Clamp, S., Favela, J., Fitzpatrick, G., Gardner, P., … Dykes, P. C. (2013). International priorities for research in nursing informatics for patient care. Medinfo 2013. Retrieved from http://ebooks.iospress.nl/volumearticle/34021. Accessed on May 5, 2020. ECRI Institute. (2013). Top 10 health technology hazards for 2014. Plymouth Meeting, PA: ECRI Institute. ECRI Institute. (2018). Top 10 patient safety concerns: Executive brief. Retrieved from https://www.ecri.org/top10-patient-safety-concerns. Accessed on March 26, 2020. ECRI Institute. (2019). Top 10 health technology hazards: Executive brief. Retrieved March 26 from https://www. ecri.org/top-ten-tech-hazards. Accessed on March 26, 2020. Ellis, S. (2012). Unpublished figure depicting a testing-­quality assurance model (Personal communication). Graham, D., Veenendaal, E. V., Evans, I., & Black, R. (2008). Foundations of software testing: ISTQB certification (2nd ed.). London: Cengage Learning EMEA. Greene, S. (2015). Audit logs. Journal of Legal Nurse Consulting, 26(2), 21–24. Hakim, A. (2019). Hybrid Project management has Role in health care today. Physician Leadership Journal, 6(2), 38–39. Harrington, L., Kennerly, D., & Johnson, C. (2011). Safety issues related to the electronic medical record (EMR): Synthesis of the literature from the last decade, 20002009. Journal of Healthcare Management, 56(1), 31–43.

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234    P art 3 • S ystem L ife C ycle Lewis, W. E. (2009). Software testing and continuous quality improvement (3rd ed.). Boca Raton, FL: Auerback. Rose, A. F., Schnipper, J. L., Park, E. R., Poon, E. G., Li, Q., & Middleton, B. (2005). Using qualitative studies to improve the usability of an EMR. Journal of Biomedical Informatics, 35, 51–60. Singh, A., & Kaur, P. J. (2017). A simulation model for incremental software development life cycle model. International Journal of Advanced Research in Computer Science, 8(7), 126.

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Staggers, N., & Troseth, M. R. (2011). Usability and ­clinical application design. In M. Ball, et al. (Eds.), Nursing Informatics: Where technology and caring meet (4th ed., pp. 219–242). London: Springer-Verlag. Watkins, J. (2009). Agile testing: How to succeed in an extreme testing environment. New York, NY: Cambridge University Press. Yen, P-Y., & Bakken, S. (2012). Review of health information technology usability study methodologies. Journal of the American Medical Informatics Association, 19, 413–422.

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14 System Life Cycle Tools Denise D. Tyler

• OBJECTIVES 1. Identify two tools to assist with each phase of the system life cycle. 2. Describe two pre- and post-implementation metrics. 3. Describe diagrams that can be utilized for clinical workflow and data workflow. 4. Discuss ways to assess the reporting needs of an organization in relationship to all levels of staff.

• KEY WORDS Business intelligence Change management Clinical workflow Data workflow Failure Modes and Effects (FMEA) Lean methodology Plan-Do-Study-Act (PDSA) Process diagram System Life Cycle System optimization

INTRODUCTION The American Nurses Credentialing Center defines the System Life Cycle (SLC) as having four phases: Planning and Analysis, Designing and Building, Implementing and Testing, and Monitoring, Maintaining, Supporting,  and Evaluating. (American Nurses Credentialing Center, 2018). There are variations of these phases, some experts include the functional testing in the analysis phase, and some include, for example, the development and ­customization in the system design phase. No matter how they are broken down, each of these phases has specific

elements and tools that can aide in the goal of a successfully installed and maintained system that meets the needs of all stakeholders. Like the nursing process, the SCL is a continuous series of system changes, or evolutions. Even after the system is installed, there is continuous analysis, design and implementation of new modules, upgrades, and improvements. SLC and project management share tasks and functions; the outstanding difference is that project management has a clear start and stop, whereas the system life cycle is a continuous process. Table 14.1 provides a visual comparison of the nursing process, the SLC, and project management methodology.

235

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236    P art 3 • S ystem L ife C ycle

  TABLE 14.1    Stages in the Nursing Process, System Life Cycle, and Project Management Nursing Process

System Life Cycle

Assessment Diagnosis

Planning and Analysis

• System planning • Strategic planning (long- and short-term) •  Informatics needs assessment •  Education •  Workflow •  Develop project plan •  Budget •  Identify team •  Timeline

Initiating

• Project initiation/integration •  Collect information/data gathering •  Identify project team •  Project charter •  Request for proposal •  Project kickoff •  Develop communication and marketing plan •  Integration management •  Quality management •  Human resource management •  Procurement ­management (vendor) •  Identify key performance indicators (KPIs)

Planning

Designing and Building

• Clinical content build •  Reporting •  System design and workflow •  Identify metrics

Planning

• Develop project plan •  Define scope •  Timeline •  Budget •  Risk assessment •  Identify resources •  Metrics/quality indicators •  Communication • Gap analysis • Decisions (documenting them) • Activation plan (including testing, education, readiness assessment, and go-live support)

Implementation/ Intervention

Testing and Implementing

• Systems implementation •  Conversion, backloading, and upgrades • Testing (including scripts) •  Regression •  Unit •  Integrated •  Regression

Launch or Executing

• Monitor status, timeline, and budget •  Integration management •  Quality management •  Human resource management •  Monitor KPIs

Evaluation

Evaluating, Optimizing, Maintaining, and Supporting

• System maintenance •  Enhancements •  Break fix •  Upgrades •  Monitoring system performance •  User satisfaction •  Usability •  Human factors •  Human–computer interface •  Supporting end-users •  Education •  Optimization •  Evaluating adoption and metrics

Monitoring and Controlling

• Testing • Education • Implementation • Readiness assessment • Go-live

Closing

• Measure post-implementation metrics • Project debriefing • Lessons learned • Project Close

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Project Management Heuristics

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Chapter 14 • System Life Cycle Tools    237



ANALYSIS AND DOCUMENTATION OF THE CURRENT PROCESS AND WORKFLOW A workflow analysis is a way to document each step or task in a process and is often done as part of a system implementation and optimization. Karsh and Alper (2005) describe system analysis and workflow analysis as a way to understand how a system works, and how the different elements in the system interact. The American Nurses Association (ANA) describes expertise in workflow analysis as an essential competency to enhance safety and reduce inefficiencies (ANA, 2015). Process mapping and flow charting are two methods to document the steps in a process in order to analyze it (California HealthCare Foundation, 2011). Workflow analysis can be used during implementation to plan for workflow, and after implementation as part of optimization and improvement initiatives. Workflow analysis can also be used to determine if staff are adopting changes, and help determine why if staff are not adopting change. Documenting a workflow may be done by walking through the steps via an intensive interview with those intimately familiar with the process, by observation, or by a combination of the two. A combination of observation and interviews is the most effective way to capture all of the nuances of a process. According to Kulhanek (2011, p. 5), “Analysis provides the data with which you will base decisions that must be made in the design, development, implementation, and evaluation stages of the training project. Using a workflow diagram to document the current state is useful when planning for a system implementation, and to identify problems or opportunities for improvement with an existing system or workflow. A workflow diagram documents the processes of the users; the data workflow diagram documents the interaction and flow of the information system(s) (The National Learning Consortium, 2012). There are several types of diagrams that can be utilized to document a process, including swim lanes, data flow diagrams (DFDs), fishbone diagrams, and process flow charts. A swim lane diagram represents a process and is usually grouped in lanes (either columns or rows) to help visualize the users or departments involved. Workflow can be documented using a simple list of the steps in the process, a swim lane, or a workflow diagram. No matter what method is used to display the process, it needs to be clear and complete for an accurate analysis. A simplified process for a diet order might be documented and displayed in the following manner:

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1. Nursing performs the assessment and documents no known allergies or issues related to swallowing or diet intake. 2. The provider reviews assessment data and enters the order. a.  Nursing reviews the order. b. The order prints in the dietary department.

3. The diet aide files the order.

4. The diet prints for the tray line preparation. 5. The diet is delivered to the unit.

6. The tray is delivered to the patient. Figure 14.1 displays an example of the same simplified process for a diet order in a swim lane format. The swim lanes can be displayed in either a vertical or horizontal format. Note that in Figure 14.1, each row is a “swim lane.” This and other workflow diagrams can be developed using sticky notes rather than a computer program effectively, but transcribing into an electronic display increases the ability to read and share the information. Workflow and process flows can be carried out by individual or group interviews, or by observation. The combination of both observation and interviews is the most effective way to capture each step of a process. Understanding how technology can impact and improve workflow and patient outcomes is an important informatics skill. Simpson (2013) includes the ability to document and evaluate workflow as important informatics skills.

System Selection and Implementation An information system should support patient care by allowing clinical staff to easily navigate the system to enter information about the system, monitor, and be alerted to changes. Selecting a system that meets the needs of all levels of stakeholders, from bedside staff to the executive team, is a complicated process with multiple factors involved. The TIGER Usability and Clinical Application design team came up with the following attributes of successful implementations (The TIGER Initiative, n.d., p. 20).

• •

User and key stakeholder involvement began early in the project with system requirements development and system selection. Clinicians worked with developers to create definitions, wording, and graphics that represented workflow process.

Systems must be easy to use in relation to entering and obtaining data and information. Today’s clinical systems

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238    P art 3 • S ystem L ife C ycle

Dietary

Provider

Nursing

Simplified Diet Order Process

Nursing assessment: No allergies or risks identified

The diet is delivered to the unit and reviewed

Nursing reviews and signs off on the order

The diet tray is delivered to the patient

Documentation reviewed and “regular” diet order entered

The order prints in dietary

The diet aide files the order

The diets print for preparation (tray line)

•  FIGURE 14.1.  A Swim Lane Presentation of a Simplified Diet Order Process. included embedded analytics, clinical decision support (CDS) to prompt clinicians with warnings and evidencebased suggestions, as well as business intelligence to capture financial and operational data. Clinical and Business Intelligence provides historical and predictive perspective of the operations and clinical areas to improve business and clinical decisions (Carr, n.d.). Understanding how the different parts of the system work together as well as how the system will impact the clinical workflow are key elements to consider during the system selection process. While having a system that is easy to learn and use is important, if the data cannot be reported or shared its value decreases significantly. Figure 14.2 is an example of a system diagram, or a data diagram, which shows how the Clinical Information System (CIS) relates to other systems required for patient care. For example, the CIS sends reports to the Document Imaging (DI) system, that also stores scanned documents such as a Durable Power of Attorney, which are in turn available in the CIS. The Pharmacy system works with the Bar Code Medication Administration (BCMA) system and Pyxis (Smart pumps thought not displayed here would be another component of medication safety). The financial components for revenue cycle management start with registration, admission, transfer, and discharge (ADT) through coding, billing, and

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accounts receivable (AR). As the patients move through the system. As the patient progresses through the system, ideally the clinical system and the financial system will update each other so that the systems are current and in sync. The Lab Information System (LIS) may work with an application similar to the barcoding used for drug administration to ensure that the correct tests are drawn; some may also be used for blood administration. Orders may be sent from the CIS to the LIS and Radiology Information System (RIS) that in turn send Order Status Updates (OSU) along with results, reports, and images back into the CIS.

System Implementation System implementation requires system design and building, and system testing. System design should involve key stakeholders, especially end users. Testing involves making sure that each part of the system works, and that the system works correctly with other modules and systems. Table 14.2 is an example of the unit testing required for a Lab interface, whether the LIS, the CIS, or both are being replaced or implemented. Each order needs to be tested from start to finish, so ordered, the Order Status Update (OSU) received in the CIS when Lab puts the order in progress, and when the preliminary and final results are

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Chapter 14 • System Life Cycle Tools    239

Clinical System Critical Care

ED System

Surgical Services

Maternal Child

AE/ADT

CIS Results Assessments Orders/CPOE Care Plans

CDS

DI

Pharmacy

LIG

RIG

Med Reconcilia tion

PACA

Lab scan

DCMA Pyxis connect

•  FIGURE 14.2.  An Example of a Data Workflow demonstrating How Systems Might Work Together with More Difficult Work and Maintenance.

  TABLE 14.2    An Example of Unit Testing: Lab Unit Testing: Lab

Date

Test

Order Received in Lab

Label Printed in Lab

OSU Received in CIS In Progress

Prelim Result

Result Displayed Correctly in CIS

Charge Dropped

Final Result

4/01

CBC

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4/05

4/01

Chem Panel

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4/05

4/01

Urinalysis

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4/05

4/01

Type and Cross

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4/05

posted. Each result associated with the order should display correctly (many Lab orders, such as a CBC have multiple results associated with them). The charge also needs to be verified for each order. Table 14.3 is an example of a tool that can be used when testing Radiology orders. Like Lab, each order needs to be tested from the time it is ordered through the time it is performed and the final

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Result Received in CIS

report is posted. Being able to display the report and the associated image from the picture archiving and communication systems (PACS) also needs to be verified for each exam. Verification that the appropriate charges drop is also important. Integrated testing is done after unit testing has been accomplished and is the last phase of testing, ensuring

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  TABLE 14.3    An Example of Unit Testing: Radiology Unit Testing: Radiology

Date

Test

Order Received in Lab

Transport Method

OSU Received in CIS In Progress

Prelim Result

Final Result

Result/ Report Received in CIS

Result Displayed Correctly in CIS with Link to PACS Image

Charge Dropped

4/01

Chest X-ray

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4/05

4/01

CT Head

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4/05

4/01

MRI Knee

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4/05

4/01

Ultrasound Abdomen

Yes

Yes

Yes

Yes

Yes

Yes

Yes

4/05

that all systems that share data are working correctly in real-life scenarios (National Learning Consortium, 2012). Similar tools can be used for integrated testing involving interfaced system or, conversion verification to ensure that converted information is correct. Table 14.4 is an example of a tool to verify that the fields related to patient information are correct when being converted from one system to another, or when being interfaced from one system to another. Attention to each field on both displays and printed documents is required. Doing an analysis of each type of admission to make sure a good sampling is done for integrated testing will help ensure that testing is comprehensive. Testing needs to include all aspects of the patients’ experience, from registration to any testing, documentation, and verification that the bill has dropped correctly. Testing the interfaces for fields that are used by multiple applications such as the height (ht), weight (wt), and allergies in Table 14.5 illustrates testing for interfaces between multiple applications. If both standard and metric values are allowed, both need to be tested. Even though the interfaces were already tested for ancillary departments during unit testing, they need to be tested again during integrated testing. This testing should include any printed notices that are associated with the orders, along with populating work lists and reports as shown in Table 14.6. Each field for each assessment needs to be tested and verified for all displays during unit testing and a large, realistic sampling also needs to be included in the integrated testing. Table 14.7 includes examples of a checklist for assessment testing that includes integration of plan of care and alerts based on the assessments. Any printed

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documents such as discharge instructions should also be included. Converting from one system to a new system is common in healthcare, which is converting not from paper to an electronic record, but from one electronic system to another system. Reasons for conversions include lack of support for an aging system, converting to a more integrated system, or converting to a different system due to an organizational merger. Converting from an existing or legacy system to a different system brings its own set of challenges. User requirements and user expectation may be higher. One challenge in converting to a new electronic health record (EHR) from a legacy system or from paper is taking the opportunity to make changes and improvements in workflow and screen design. Backloading, or manually entering information into the new system, is often done between 2 and 5 days before a new system is live so that important patient information is available. Table 14.8 is an example of a report from the legacy system with the information on it. The report may be very accurate, but the information may be outdated before it is printed and distributed, requiring a second or third report with updated information. The other option is a modification of the report available online; staff doing the backload can either work in pairs or work off of dual monitors with the legacy system on one monitor and the new system on the other monitor. Stakeholder involvement in a project is improved with regular status reporting (Pitagorsky, 2012). Stakeholders, according to Hanson, Stephens, Pangaro, and Gimbel (2012), include clinicians, nurse/ancillary, patients, and administrators. During the implementation, the timeline,

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Chapter 14 • System Life Cycle Tools    241



  TABLE 14.4   An Example of Integrated Testing Day 1: Demographics Integrated Testing Day 1: Verify Correct Patient Demographics Online and on Printed Documents Date

Patient 1 Verify:

Correct

Notes

  TABLE 14.4   An Example of Integrated Testing Day 1: Demographics (Continued) Integrated Testing Day 1: Verify Correct Patient Demographics Online and on Printed Documents Date

Patient 1 Verify:

Correct

Notes

Guarantor Address

Name DOB

Yes

Yes

Insurance 1 Name

SSN

Yes

Yes

Insurance 1 Address

Marital Status

Yes

Yes

Insurance 1 Phone

Mother Maiden Name

Yes

Yes

Insurance 1 Group # Insurance 1 Group #

Race Ethnicity Organ Donor Adv Directive

progress, teams, and schedule for training can be maintained and updated on the intranet site. Managers and executives might require more detailed report. A general status update can be helpful in visualizing the overall project status. Table 14.9 is an example of a columnar report, and Fig. 14.3 is an example of a chart that depicts the anticipated progress and budget compares it to the actual progress and budget.

Address Phone 1 Phone 2 Email Contact 1 Name Contact 1 Phone Contact 1 Relationship Contact 2 Name

System Optimization and Metrics

Contact 2 Phone

While project management has a pre-determined start and stop date, the SLC, like the nursing process, is a fluid process of assessing, diagnosis, planning, implementation, and evaluation. After the system is implemented, the process of system changes does not end. System optimization and support along with changes due to upgrades, regulatory and payer changes, as well as clinical changes due to new research all lead to constant changes to the system. As stakeholders become more familiar with the system, they will have ideas on improvements and enhancements to make the system more user-friendly, as well as improving the ability of the system to prompt staff to improve patient care. System and user optimizations after the system is implemented are important skills for the informatics nurse (ANA, 2015).

Contact 2 Relationship Employer: Status Employer: Name Employer: Job Title Employer: Address Employer: Phone MRN # Visit/Account # Hospital Nurse Station Room Isolation Visit Type Patient Type Admit date

MEASURING SUCCESS, CONTINUING TO IMPROVE

Chief Complaint Guarantor Name Guarantor Relationship Guarantor Phone 1 Guarantor Phone 2 (Continued)

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Along with the process improvement strategies listed below, getting pre- and post-implementation metrics will assist. Surveys can be done post-implementation to evaluate staff satisfaction with the system, the education, and/ or the support. Examples of pre- and post-implementation

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  TABLE 14.5    An Example of Integrated Testing: Interfaces Integrated Testing Day 1: Enter the following information and verify interfaces/integration of patient information

Date

Interfaced Field Entered in CIS

Received in:

Received in:

Received in:

Received in:

Pharmacy

Radiology

Cardiology

Dietary

CIS

Radiology

Cardiology

Dietary

CIS

Radiology

Cardiology

Dietary

Allergies (specify what to enter) Ht/Wt (specify what to enter) Pregnancy/Lactation (specify what to enter) Transportation (specify what to enter) Date

Interfaced Field Entered in Pharmacy Allergies (specify what to enter) Ht/Wt (specify what to enter) Pregnancy/Lactation (specify what to enter) Transportation (specify what to enter)

Date

Interfaced Field Entered in Radiology Allergies (specify what to enter) Ht/Wt (specify what to enter) Pregnancy/Lactation (specify what to enter) Transportation (specify what to enter)

metrics include accuracy and completeness of charting, accuracy and completeness of charging, and time studies. The evaluation of charges would include the timeliness, accuracy, and whether the charges were supported by the orders and the documentation. Also included would be the pre- and post-metrics for charting Core measures such as vaccinations and education on smoking. The time to document specific types of charting can be measured and recorded by using a tool similar to Table 14.10. Continuous Quality Improvement (CQI) is based on the principle that there is an opportunity for improvement in every process (Wallin, Bostrom, Wikblad, & Ewald, 2003). Informatics nurses can help improve the quality of patient care by assisting with the collection and analysis of data to participate in quality improvement teams (American Nurses Association, 2015). CQI begins with a culture of change and improvement, and encourages all levels of staff to look for ways to improve the current process, and for variations of the process, which can create errors. There are many philosophies that can be utilized to implement a CQI program; some can be used in combination. Some of these include the following:

•

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Six Sigma is a “data-driven quality improvement methodology that uses statistical analysis to reduce process variation. Six Sigma has a strong focus on reducing variation, in fact the term, Six Sigma refers

•

•

to a defect rate of 3.4 defects per million opportunities and represents a statistically high standard of quality” (Anderson-Dean, 2012). Six Sigma utilizes five steps to evaluate metrics, known as DMAIC: Define, Measure, Analyze, Improve, and Control. Six Sigma focuses on the reduction of variation to improve processes (Lee, 2016; Nave, 2002); the goal is to permanently resolve errors by focusing on the underlying processes (DMAIC, n.d.). Lean and Six Sigma has a strong focus on eliminating waste by removing non-value activities including defects, unnecessary or redundant steps, using value stream mapping (VSM). It focuses on the process, and works best if all employees are involved (Clancy, 2011). Lean and Six Sigma creates a “culture and practices that continually improve all functions by all people at all levels in the organization, and also utilizes the DMAIC model (Lee, 2016; Sitterding & Everett, 2013). Plan-Do-Study-Act (PDSA) is a process for enacting and evaluating change (Donnelly & Kirk, 2015; Nakayama et al., 2010) that has been recommended by the Joint Commission and the Institute of Medicine as an effective tool for complex processes. It is a four-step scientific method to enact and evaluate change: Planning, Do it—or implement the

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  TABLE 14.6    An Example of Testing Orders to Multiple Departments—Integrated Testing Day 1: Orders Integrated Testing Day 1: Enter orders for:

Date

Lab Order

Order Received in Lab

Label Printed in Lab

OSU Received in CIS In Progress

Prelim Result

Result Received in CIS

Result Displayed Correctly in CIS

Result / Report Received in CIS

Result Displayed Correctly in CIS with link to PACS Image

Result / Report Received in CIS

Result Displayed Correctly in CIS with link to Image

Final Result

CBC Chem Panel Urinalysis Type and Cross

Date

Radiology Order

Order with reason for study received in Radiology Information System (RIS)

Transport Method

Y

OSU received in CIS In Progress

Prelim Result

Final Result

Chest X-ray Daily

Date

Cardiology Order

Order with reason for study received in Cardiology System

Transport Method

OSU received in CIS In Progress

Prelim Result

Final Result

Doppler Study EKG Date

Dietary Order

Order received in Dietary System

Renal Diet, vegetarian, 2000 cc fluid restriction Date

Telemetry

Wave form Visible in CIS

Order Printed in Central Monitoring Area

Order on worklist for Department providing service

Order Printed in department

Telemetry Date

Order Discharge Planning Oxygen Wound Nurse Consult

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  TABLE 14.7   Assessment and Workflow: An Example of a Way to Verify That Order Entry and Charting Pathways Work as Designed Charted (specify what to document)

Pathway Flows (specify)

Yes/No

Kicked off Care Plan? (specify)

Yes/No

Kicked off Orders (specify)

Kicked off Alert (specify)

Yes/No

Yes/No

Charge Dropped

Risk for skin Risk for fall Risk for suicide Criteria for vaccinations

  TABLE 14.8   An Example of a Report That Can Be Utilized for Backloading Clinically Significant Information into the New System During a System Conversion Pt. Loc: 4E

Account #: 12345678

MR/MPI #: 54121

Pt Name: John Doe

Pt DOB: 05/25/1979

Pt Sex: M

Visit Status: IP

HEIGHT and WEIGHT Ht

Obtained

On

6/1/

Stated

4/04 1950

At

Wt

Obtained On

At

210

Standing 4/05

0600

ALLERGIES Alg name

Category

Reaction

Peanut

Food

Rash

Peanut

Drug

Rash

Penicillin

Drug

Rash

Severity

RISK DOCUMENTATION Fall Risk for Injury from Fall Nutrition Skin VACCINATION Influenza Pneumonia DPaT MEDICAL HISTORY CAD Hypertension 2001Arthtritis Obstructive Sleep Apnea Surgical History Education History of MRSA

No Yes No No

CHF Dialysis GERD Diabetes Type 1

Yes    Stroke Yes    Cancer Type Yes    Ulcer Yes    Diabetes Type 2

NO

History of VRE

No

Yes Prostate No No

(Continued)

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  TABLE 14.8   An Example of a Report That Can Be Utilized for Backloading Clinically Significant Information into the New System During a System Conversion  (Continued) Pt. Loc: 4E

Account #: 12345678

MR/MPI #: 54121

Pt Name: John Doe

Pt DOB: 05/25/1979

Pt Sex: M

Visit Status: IP

HOME MEDICATIONS Medication        Dose        Route        Frequency        Indication Humera          40mg        Subcutaneously    Weekly          Arthritis NON-MEDICATION ORDERS Lab CBC daily x 5 days, lab to collect start on 4/04 Radiology Chest X-ray in the morning, pneumonia transport: Wheelchair Diets Regular diet with snacks start 04/04 Nursing Change dressing to left foot daily Up in chair for meals Up to bathroom ad lib Code Status Full code LEGAL Durable Power of Attorney      No Legal Guardian           No

  TABLE 14.9    An Example of a Project Status Report Unit Testing: Radiology Application

Anticipated % Done

Actual % Done

Reasons

Nursing Assessments

50%

40%

New regulations

Rehab Therapies Assessments

50%

40%

Change in resources

Respiratory Assessments

50%

60%

Decrease in charging components

Care Planning

50%

55%

Orders

50%

45%

Change in resources

Reports

50%

40%

Dependent on orders and assessments

Budget

35%

45%

Loss of resources

•

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change, Study the results, Act—which may include modifying the change based on the results of the initial test (AHRQ, 2013). Failure Mode and Effects Analysis (FMEA) can be used throughout the SLC to evaluate processes for real or potential failure points. The processes are mapped out and each step of the process is documented. Flow charts, Swim Lane Process maps,

and fishbone diagrams can be used to help visualize the process maps. The process maps or process flows are then used to identify where in the process there are risks for failure (or errors), and the associated risks including the severity and likelihood. The Failure Modes and Effects (FEMA) team, which includes representatives of all affected areas, then identifies solutions and implements them.

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246    P art 3 • S ystem L ife C ycle 70% 60% 50% 40% 30% 20% 10%

Series1

t dg e Bu

Or de rs Re po rts

sm en ts As se ss m en Ca ts re Pl an ni ng

ira sp Re

ap er Th

Re

ha

b

to

ie

ry

sA

As g in rs Nu

Series2

ss

se

es

ss

m en ts

0%

•  FIGURE 14.3.  Table and Graphic Examples of Status Reporting   TABLE 14.10   Time Study Time Spent Before Date and time

Admission

7/18

19

Shift

7/18 7/18

23

Category

Unit/Area

Time Spent After

other

RN

3S

Admission

6

LVN

3S

22

8

LVN

3N

10

3S

Shift

other 4 10

20

9

7/18

6

RN

3N

5

7/20

2

RT

3W

2

7/20

3

RT

3W

3

7/20

3

RT

3W

3

7/20

2

3W

2

8/10

6

2

RT

3N

9

2

8/10

15

4

FS

3W

12

3

8/11

6

4

RN

3N

6

3

8/11

5

4

RN

3S

7

3

8/11

5

5.5

RN

3S

5

6

The implementation is followed by an evaluation, and reevaluation process until no failures can be ­identified (IHI, 2018; Mohiuddin, 2011). How does the CQI process affect the Clinical Information System (CIS)? System design issues can create

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errors or minimize the risk of errors. By studying and documenting the workflow and processes problems with the current system (paper or electronic) the clinical system can be designed to minimize the risk of errors, and to decrease the risk of errors by both good design and embedding EBP and CDS, using alerts as warnings and suggestions.

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  TABLE 14.11    Strategic Reporting: An Executive Dashboard with Both Clinical and Nonclinical Measurements Strategic Reporting Vision—ex. Quality, Vision, Integrity, Care, Respect, Stewardship Measurement

Goal

% of Goal

Stroke (Venous Thromboembolism (VTE) Prophylaxis, Discharged on Antithrombotic Therapy, Anticoagulation Therapy for Atrial Fibrillation/Flutter, Thrombolytic Therapy, Antithrombotic Therapy by End of Hospital Day 2, Discharged on Statin Medication, Stroke Education, Assessed for Rehabilitation)

100%

99.70%

Immunizations (Pneumococcal Immunization , Pneumococcal Immunization – Age 65 and Older, Pneumococcal Immunization – High Risk Populations (Age 6 through 64 years), Influenza Immunization)

100%

95%

Tobacco Treatment (Screening, Treatment, Treatment Provided or Offered at Discharge, Treatment at Discharge, Assessing Status After Discharge)

100%

100%

Value Based Purchasing (Consists of weighted components, with targets based on current performance and the goal of reaching the 90th percentile)

90%

99%

Readmissions (Composite score of AMI, HF and PN readmissions back to the same facility in 30 days)

10%

99%

Patient Satisfaction (Top priority that hospital staff took patient preference into account, Understood the purpose of taking meds, staff do everything to help with pain)

80%

85%

One way to visualize the progress of quality improvement initiatives as well as organizational goals is with reports and dashboards. Table 14.11 is an example of an executive dashboard that enables leadership to “visualize strategic metrics to guide decision making grounded in actionable information” (Aydin, Bolton, Donaldson, Brown, Mukerji 2008). A more detailed report that is printed, e-mailed, or viewed online each shift can give timely and meaningful feedback to the staff entering the information into the CIS so that any issues can be fixed in a timely manner, improving learning and compliance.

Change Management Informatics nurses manage change through communication, collaboration, change management, and education (ANA, 2015). The informatics nurse should lead the multi-professional team to embrace change through clear communication, active listening, and need to promote trust, teamwork, and mutual respect (Bender, Connelly, & Brown, 2013). Informatics nurses can encourage the adoption of change by involving staff in the decisionmaking process using multi-professional, collaborative teams. There are many change management theories; Kotter’s change management theory and Rogers’ diffusion of innovation theory work well with informatics projects.

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On Target

Kotter’s change management theory includes planning for implementing and sustaining change and emphasizes the importance of communication and of empowering staff (Mbmalu & Whiteman, 2014). Rogers’ diffusion of innovation theory also includes involving end users throughout the change process. According to Mascia, Richter, Convery, and Haydar (2009), education can improve compliance and the adoption of change. Informatics nurses can improve the adoption of change by involving staff in the planning and design of the change when possible. Controlling and managing change are important throughout the SLC. During the implementation, change needs to be controlled to avoid the addition of requirements, or scope creep. Involving end users in the design can help assure a design that includes required elements that can prevent the need for scope creep. Having a process to prioritize changes is necessary to determine which changes need to be adopted for a successful implementation and which changes can wait for the optimization stage. After the system is “live,” changes need to be prioritized based on the effect on patient safety, regulatory requirements, and end-user satisfaction. The risk of problems with changes, the amount of time (including testing) to implement the change, and the requesting and approving persons or groups should

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Test Questions

  TABLE 14.12   An Example of a Change Management Tool Description

Response

Comments

Date of Request

100%

99.70%

Requested By

100%

95%

Description of Request

100%

100%

Problem or Enhancement?

90%

99%

Regulatory (Yes/No)

10%

99%

Pt Safety (Yes/No)

80%

85%

Approved By Risk of Disruption (high, medium, low) Backout Plan Education Required? Education Provided By Change Approved? (Yes/No) Priority (high, medium, low) Started On Completed On Total Time Synopsis

be documented. Table 14.12 is an example of a simple change management tool. Ideally a system can track the initial request or problem ticket, the documentation of the changes, and the approval.

SUMMARY This chapter reviews the stages of the SLC and some of the tools available to assist with each phase in almost any scenario. Tools like workflow analysis and documentation can be utilized in multiple phases, and may be displayed in many ways. Like the nursing process of Assessment, Diagnosis, Outcomes/Planning, Implementation, and Evaluation,(American Nurses Association, n.d.), the SLC is a continuous cycle that involves complex teams, and requires involving those affected—or the end users and other stakeholders. Design is a continuous cycle based on user requirements and recommendations as well as regulatory changes and new research on best practices which need to be imbedded in the CIS. Because of these changes as well as upgrades, testing and evaluation will also be done throughout the SLC.

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1. Which of the following are skills required for the planning and analysis? A. Planning education, clinical workflow analysis, coordinating project plans B. Planning support, planning education, system design C. Planning education, planning marketing and communication, testing D. Clinical workflow analysis, planning marketing and communication, testing 2. Which component of the nursing process does not relate to the systems life cycle? A. Assessment and diagnosis—System planning and analysis B. Diagnosis, outcomes/planning—Designing and building C. Implementation/intervention—Testing and implementing D. Evaluation—Evaluating, optimizing, maintaining, and supporting 3. Which of the following is true of the system life cycle and project management? A. Systems life cycle and project management share tasks and functions, but the outstanding difference is that project management has a clear start and stop, whereas the system lifecycle is a continuous process. B. Systems life cycle and project management both have a clear start and stop. C. Systems life cycle and project management both are a continuous process. 4. Project management is similar to the nursing process because: A. Like the nursing process, project management requires skills in observation and analysis, and planning. B. Like the nursing process, project management requires skills in observation and analysis, communication between multi-professional teams, and in planning, initiating, and monitoring.

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Chapter 14 • System Life Cycle Tools    249

C. Like the nursing process, project management requires skills in analysis, initiating and monitoring.

D. Like the nursing process, project management requires skills in planning, initiating, and monitoring.

5. The informatics nurse uses workflow analysis to do which of the following (select all that apply): A. Document the current state and is useful when planning for a system implementation B. Identify problems or opportunities for improvement with an existing system or workflow C. Document problems with testing

D. Evaluate the impact of change associated with a new system or workflow 6. Which is not a method of workflow analysis?

A. Doing a literature review to determine best practicess

B. Walking through the steps via an intensive interview with those intimately familiar with the process C. Observing and the workflow

D. Combination of documenting via interview and then validating via observation 7. Which of the following are four types of workflow diagrams? A. Swim lanes, project objectives, fishbone diagrams, and process flowcharts

B. Swim lanes, financial dashboards, fishbone diagrams, and process flowcharts

C. Swim lanes, data flow diagrams (DFDs), fishbone diagrams, and process flowcharts D. Car lanes, data flow diagrams (DFDs), fishbone diagrams, and process flowcharts

8. Which of the following is not part of the testing phase? A. Unit testing

B. Integrated testing

C. User acceptance testing D. End-user support

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9. Which of the following includes four metrics that can be utilized to measure the success of an implementation or change project?

A. Surveys to measure user satisfaction, charting: accuracy and completeness, time studies—how long it takes to complete specific assessments or processes before and after implementation, and charging: accuracy, timeliness, and completeness B. Surveys, testing results, time studies—how long it takes to complete specific assessments or processes before and after implementation, and charging: entered C. Surveys to measure user satisfaction, charting: accuracy and completeness, time studies, and equipment usage

D. Surveys to measure staff satisfaction with parking, charting completeness, time studies, and charging: accuracy, timeliness, and completeness 10. Identify the two ways the informatics nurse can help facilitate the adoption of change.

A. Informatics nurses manage change through communication, collaboration, change management, and education, and by encouraging early and consistent user/stakeholder involvement. B. Informatics nurses manage change by promising no downtime after the system is implemented and providing food at all meetings.

C. Informatics nurses manage change through communication and education. D. Informatics nurses manage change through education and by providing food at meetings.

Test Answers 1. Answer: A 2. Answer: C

3. Answer: A 4. Answer: B

5. Answer: A,B,D 6. Answer: B,C,D 7. Answer: C 8. Answer: E

9. Answer: A 10. Answer: A

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REFERENCES AHRQ. (2013). Quality tool: Plan Do Study Act (PDSA) cycle. Retrieved from http://www.innovations.ahrq.gov/content.aspx?id=2398. Accessed on May 6, 2020. American Nurses Association. (2015). Nursing informatics: Scope and standards of practice (2nd ed.). Silver Spring, MD: Nursesbooks.org. American Nurses Association. (n.d.). The nursing process. Retrieved from https://www.nursingworld.org/practicepolicy/workforce/what-is-nursing/the-nursing-process/. Accessed on May 6, 2020. American Nurses Credentialing Center. (2018). Test outline: Informatics Nursing Board Certification Examination. Retrieved from https://www.nursingworld.org/~490a5b/ globalassets/certification/certification-specialty-pages/ resources/test-content-outlines/27-tco-rds-2016effective-date-march-23-2018_100317.pdf. Accessed on May 6, 2020. Anderson-Dean, C. (2012). The benefits of lean and Six Sigma for nursing informatics. ANIA-CARING Newsletter, 27(4), 1–7. Aydin, C., Bolton, L. B., Donaldson, N., Brown, D. S., & Mukerji, A. (2008). Beyond nursing quality measurement: The nation’s first regional nursing virtual dashboard. Retrieved from https://www.ahrq.gov/downloads/pub/advances2/ vol1/Advances-Aydin_2.pdf. Accessed on May 6, 2020. Bender, M., Connelly, C. D., & Brown, C. (2013). Interdisciplinary collaboration: The role of the clinical nurse leader. Journal of Nursing Management, 21(1), 165–174. doi:10.1111/j.1365-2834.2012.01385 California HealthCare Foundation. (2011). Workflow analysis: EHR deployment techniques. Retrieved from https:// www.chcf.org/wp-content/uploads/2017/12/PDF-Work flowAnalysisEHRDeploymentTechniques.pdf. Accessed on May 6, 2020. Carr, D. M. (n.d.). Clinical and business intelligence. Retrieved from https://www.himss.org/library/clinicalbusiness-intelligence?navItemNumber=17599. Accessed on May 6, 2020. Clancy, T. (2011). The integration of complex systems theory into Six Sigma methods of performance improvement: A Case Study. In V. A. Saba, & K. A. McCormick (Eds.) McGraw-Hill, San Francisco., Essentials of nursing ­informatics (5th ed., pp. 373–389). DMAIC Tools. (n.d.). Retrieved from http://www.dmaictools.com/. Accessed on May 6, 2020. Donnelly, P., & Kirk, P. (2015). Use the PDSA model for effective change management. Education for Primary Care, 26(4), 279–281. Hanson, J. L., Stephens, M. B., Pangaro, L. N., & Gimbel, R. W. (2012). Quality of outpatient clinical notes: a stakeholder definition derived through qualitative research. BMC Health Services Research, 12(1), 407–418. doi:10.1186/1472-6963-12-407 Institute for Healthcare Improvement (IHI). (2018). Failure modes and effects analysis (FEMA) tool. Retrieved

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from http://www.ihi.org/resources/Pages/Tools/ FailureModesandEffectsAnalysisTool.aspx. Accessed on May 6, 2020. Karsh, B. T. & Alper, S. J. (2005). The key to understanding health care systems. Agency for Healthcare Research and Quality (USA); 2005 Feb. Retrieved from http://www.ncbi. nlm.nih.gov/books/NBK20518/. Accessed on May 6, 2020. Kulhanek, B. (2011). Why Reinvent the Wheel? ANIACARING Newsletter, 26(3), 4–8. Lee, T. (2016). Lean and Six Sigma. Contemporary OB/GYN, 61(6), 28–42. Mascia, A., Richter, K., Convery, P., & Haydar, Z. (2009). Linking Joint Commission inpatient core measures and National Patient Safety Goals with evidence. Baylor University Medical Center Proceedings, 22(2), 103–111. Mbamalu, G., & Whiteman, K. (2014). Vascular access team collaboration to decrease catheter rates in patients on hemodialysis: Utilization of Kotter’s change process. Nephrology Nursing Journal, 41(3), 283-287. Mohiuddin, N. (2011). FMEA: Uses in informatics projects. ANIA-CARING Newsletter, 26(4), 6–7. Nakayama, D., Bushey, T., Hubbard, I., Cole, D., Brown, A., Grant, T., & Shaker, I. (2010). Using a Plan-Do-Study-Act cycle to introduce a new OR service line. AORN Journal, 92(3), 335–343. doi:10.1016/j.aorn.2010.01.018 National Learning Consortium. (2012). Electronic health record (EHR) system testing plan. Retrieved from https:// www.healthit.gov/sites/default/files/resources/ehrsystem-test-plan.docx. Accessed on May 6, 2020. Nave, D. (2002). How to compare Six Sigma, lean and the theory of constraints. Quality Progress, 35(3), 73. Pitagorsky, G. (2012). Status reporting, clarity and accountability. Retrieved from http://www.projecttimes.com/ george-pitagorsky/status-reporting-clarity-and-accountability.html. Accessed on May 6, 2020. Simpson, R. L. (2013). Chief nurse executives need contemporary informatics competencies. Nursing Economic$, 31(6), 277–288. Sitterding, M., & Everett, L. Q. (2013, February). Reaching new heights: A hospital system approach maximizing nurse work efficiency. Symposium conducted at the meeting of the American Organization of Nurse Executives. The National Learning Consortium. (2012). Workflow process mapping for electronic health record implementation. Retrieved from https://www.healthit.gov/resource/ workflow-process-mapping-electronic-health-recordehr-implementation. Accessed on May 6, 2020. The TIGER Initiative. (n.d.). Designing usable clinical information systems: Recommendations from the TIGER Usability and Clinical Application Design Collaborative Team. Retrieved from http://www.tigersummit.com/ Usability_New.html. Accessed on May 6, 2020. Wallin, L., Bostrom, A. M., Wikblad, K., & Ewald, U. (2003). Sustainability in changing clinical practice promotes evidence-based nursing care. Journal Advanced Nursing, 41(5):509–518.

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part

4

Informatics Theory Standards Virginia K. Saba

Part 4 of this book, entitled Informatics Theory Standards, addresses topics that are critical to the field of nursing i­nformatics. The first chapter of this part, Chapter 15, entitled Healthcare Project Management, by Drs. Barbara Van de Castle and Patricia C. Dykes augments Chapter 12, entitled System Design Life Cycle, by Dr. Susan K. Newbold. It provides an excellent overview of project management in healthcare from the planning stage through the close of the project. They provide multiple examples of tools as well as helping the readers understand why the tools are valuable and how to use them to ensure a successful project—beyond being “on time and within budget.” They provide excellent insight into the importance of the role of the project manager in healthcare and why and how informatics nurses can transition into this role. The nursing informatics role includes the role of project management profession (PMP) and certification recommended by the Project Management Institute (PMI). After the publication of the previous edition of this book, the new PMI book was published; the content of that book is discussed in this chapter. The update includes a definition of Project Management as “a systematic process for implementing systems on time, within budget, and in line with customer expectations of quality.” The chapter also includes seven new references including one of Bongiovanni et al. that make available case studies to provide insight into potential challenges that can arise with a project and buy-8n from stakeholders. The chapter content also covers the input from the new book by Garcia-Dia in 2019 entitled Project Management in Nursing Informatics. The chapter predicts the future of voice input and mobile technologies in project management. The concepts of project management needed for the American Nurses Credentialing Center(ANCC) exam are also covered in this updated chapter. Chapter 16, entitled The Practice Specialty of Nursing Informatics has a new primary author, Dr. Carolyn Sipes from the American Nurses Association (ANA). She is joined by Dr. Carol J. Bickford from ANA who coauthored the chapter in the past. This seventh edition chapter includes a dozen new references since the Code of Ethics for Nurses with Interpretive Statements, The Nursing: Scope and Standards of Practice, Third Edition, and The Nursing Informatics: Scope and Standards of Practice, Second Edition have all been published. It is a privilege to have these authors from ANA who have participated in the development of those standards to update this chapter for nurses taking the informatics certification exam to understand the practice specialty of nursing informatics. A new section of this chapter describes the certification of nurses in informatics and the link to the important ANA standards. New in this chapter is a presentation of the theory

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models contributing to nursing informatics, adapted from a new book edited by Dr. Sipes on Applications of Informatics and Technology in Nursing Practice Competencies, Skills, and Decision-Making. An overview of the main theories and models is presented in a new table that contributes to nursing informatics, such as project management and change ­management. Seven theories/models are presented in this valuable update. Chapter 17, entitled Healthcare Policy: Impact on Nursing Informatics, is an updated chapter with three new authors Dr. Sarah Collins Rossetti, Susan C. Hull, and Dr. Suzanne Bakken. In this seventh edition, the 21st Century Cures Act that authorized $6.3 billion in December 2016 is a focus of this chapter. The Cures Act provided authorization for ­enhancing electronic health records (EHRs), interoperability, patient access to health information, and measures to ­prevent ­information blocking, among the other charges. Of significance to the NI profession is the use of upgraded health IT, transparent data sharing through open application programming interfaces (APIs), and improvement in healthcare IT end-user ­experience. The chapter describes the roles of the U.S. Department of Health and Human Services (HHS), and the Office of the National Coordinator for Health Information Technology (ONC), in improving the goals of the Cures Act. Also discussed in this policy chapter is the Trusted Exchange Framework and Common Agreement (TEFCA) to improve information networks nationally and to advance interoperability. The role of the Fast Healthcare Interoperability Resources (FHIR), an HL7 standard for APIs, is new to this edition. Conditions of Maintenance and Certification for health IT and the importance of the U.S. Core Data for Interoperability (USCDI) are discussed. This chapter also describes the significant legislation to the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA) that establishes value-based payment. As described in this chapter, the MACRA includes the Merit-Based Incentive Payment System (MIPS), Alternative Payment Models (APMs), and Advance Alternative Payment Models which all require the use of certified EHRs. The Alliance for Nursing Informatics (ANI) has provided comments to several reports from ONC. Special coverage in this new policy chapter includes the ANI response to documentation burden. Valuable historical perspectives are provided on interoperability initiatives, strategic initiatives from many groups. This broad ­landscape of areas that impact nursing informatics through landmark healthcare policy is described by nurses intimately involved in responding to the policy initiatives that ultimately impact quality, efficiency, and effectiveness of patient care in the United States.

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15 Healthcare Project Management Barbara Van de Castle / Patricia C. Dykes

• OBJECTIVES 1. Define project management and why it is important for healthcare informatics. 2. Describe the five process groups in project management methodology, and identify key inputs and outputs for each. 3. Illustrate the “triple constraint” relationship between scope, cost, and time and how it can impact project quality. 4. Explain how a health information technology project is initiated and the role of the project charter. 5. Identify communication tools and techniques that will positively impact the quality, efficiency, and effectiveness of a health information technology project.

• KEY WORDS Health information technology Process groups Project management Project methodology Triple constraint

INTRODUCTION It is difficult to read a newspaper, magazine, or Web page today without hearing about the impact of information technology. Information in all forms is traveling faster and being shared by more individuals than ever before. Think of how quickly you can buy almost anything online, make an airline reservation, or book a hotel room anywhere in the world. Consider how fast you can share photos or video clips with your family and friends. This ubiquitous use of technology is permeating the healthcare industry as well, and the future of many organizations may depend on their ability to harness the power of information technology. Today there is good evidence that technology can decrease medical errors and adverse events in healthcare settings, but

the complexity of these settings can make technology implementation challenging. Poor implementation can contribute to unintended consequences ranging from work-arounds that do not deliver promised value to increasing the rates of medical errors. Implementation of technology in a healthcare setting is a “project”; e.g., a “temporary endeavor undertaken to create a unique product, service or result” (PMI, 2017, p. 4). Unlike routine operations, projects have well-defined start dates, end dates, and associated resources. Good project management is needed to accomplish the work, to facilitate the change, and to deliver the desired improvements facilitated by health information technology (IT) implementation. Project management is a systematic process for implementing systems on time, within budget, and in line with customer expectations of quality. Project 253

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254    P art 4 • I nformatics T heory S tandards management is essential to delivering on the promise of health IT, and the Standards of Nursing Informatics Practice (American Nurses Association, 2015) states that Project Management Skills are essential for successful projects. Health IT innovations improve patient care, but these same innovations can drive up the cost of healthcare when what is needed is a better value. Consistent project management methodologies will maximize benefits while decreasing the costs a­ ssociated with inadequate or failed health IT projects (Sellke, 2018). Project management is not a new concept—it has been practiced for hundreds of years, as any large undertaking requires a set of objectives, a plan, coordination, the management of resources, and the ability to manage change. Today, however, project management has become more formal with a specified body of knowledge, and many healthcare organizations have adopted the projectoriented approach as a technique to define and execute on their strategic goals and objectives. Good project managers for health IT projects are in high demand. Academic programs have responded by establishing courses in project management and making them part of the health informatics’ curriculums for continuing education, certificate, and degree programs. This chapter provides a high-level look at the methodology behind project management to provide a framework for the project manager skills’ development, structure for the implementation work processes, and organization of the projects’ tasks.

Project Management This chapter augments the Systems Life Cycle chapters, as it outlines the project management phases, called Process Groups. Project management process groups organize and structure the Systems Life Cycle to ensure successful project completion. This introduces project management, project definitions, and project manager skills. Each subsequent section will review each of the five project management process groups. The last section describes some additional considerations for health information technology projects, such as governance and positioning of project management in the healthcare organization.

What Is a Project? There are many different definitions of what a project is, but they all have the same components—a project is temporary, has a defined beginning and end, and is managed for time, budget, and scope. Distinguishing features of a project are specific objectives, defined start and end dates, defined funding limitations, and how they consume

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resources (human, equipment, materials); they are often multifunctional or cross-organizational by design (PMI, 2017). Schwalbe (2014) differentiates a project from operations by defining operations as ongoing work done to sustain the business. Projects are different from operations in that they end when the project objectives are reached, or the project is terminated whereas operations are daily services to support the business of the organization (Garcia-Dia, 2019).

What Is Project Management? Project management is facilitation of the planning, scheduling, monitoring, and controlling of all work that must be done to meet the project objectives. The Project Management Institute (PMI) states that “project management is the application of knowledge, skills, tools and techniques to project activities to meet project requirements” (Project Management Institute [PMI], 2017, p. 10). Project managers must not only strive to meet specific scope, time, cost, and quality project goals, but also facilitate the entire process to meet the needs and expectations of the people involved in or affected by project activities.

Introduction to the Five Process Groups The project management process groups progress from initiation activities to planning activities, executing activities, monitoring and controlling activities, and closing activities. Each of these will be described in detail in ensuing sections of this chapter. However, it is important to note here that these groups are integrated and not linear in nature, so that decisions and actions taken in one group can affect another. Projects use inputs, defined by PMI as “Any item, whether internal or external to the project, which is required by a process before that process proceeds” and outputs, defined by PMI as “a product, result, or service generated by a process” (PMI, 2017, pp. 708,712). Figure 15.1 shows the five groups and how they relate to each other in terms of typical level of activity, time, and overlap. The level of activity and length of each process group vary for each project and guide project managers throughout the progression (Garcia-Dia, 2019).

Project Management Knowledge Areas The Project Management Knowledge Areas describe the key competencies that project managers must develop and use during each of the Process Groups. Each of these competencies has specific tools and techniques associated with it, some of which will be elaborated in following

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Level of Activity

Executing Process Group

Planning Process Group Initiating Process Group

Closing Process Group

Monitoring & Controlling Process Group

Time

•  FIGURE 15.1.  Level of Activity and Overlap of Process Groups Over Time. (Republished from Schwalbe K. (2006). Information technology project management (4th ed., p. 73). Cengage Learning Inc. Reproduced by permission. HYPERLINK “http://www.cengage.com/permissions” www.cengage.com/permissions.)   TABLE 15.1    Knowledge Areas used in each Process Group Project Management Process Group Knowledge Area

Initiating

Planning

Executing

Monitoring and Controlling

Closing

Project Integration Management

X

X

X

X

X

Project Scope Management

X

X

Project Time Management

X

X

Project Cost Management

X

Project Quality Management

X

X

X

Project Human Resource Management

X

X

X

Project Communications Management

X

X

X

X

X

Project Risk Management

X

Project Procurement Management

X

X

X X

Bold indicates the four core knowledge areas. Source: Adapted from Schwalbe, K. (2010). Information technology project management (6th ed., pp. 83–84). Independence, KY: Course Technology, Cengage Learning.

sections of this chapter. Table 15.1 shows the nine knowledge areas of project management. The four core areas of project management (bolded in the table) are project scope, time, cost, and quality management. These are considered core as they lead to specific project objectives. The four facilitating knowledge areas of project management are human resources, communication, risk, and

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procurement management. These are considered facilitating as they are the processes through which the project objectives are achieved. The ninth knowledge area, project integration management, is an overarching function that affects and is affected by all of the other knowledge areas. Project managers must have knowledge and skills in all of these nine areas.

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  TABLE 15.2    Tools to Support the Initiating Process Group SWOT Analysis

Stakeholder Analysis

Value Risk Assessment

Method for identifying potential Strengths and Weaknesses of the project team/ organization relative to a proposed ­project and the potential Opportunities and Threats inherent in conducting a project.

Documents important information about stakeholders that makes explicit their support, level of influence on a project, and strategies for managing relationships to assure project success.

Tool that supports objective rating of a project using pre-established criteria that are consistent with the mission, vision, and values of an organization.

PLANNING PHASE Initiating Process Group The Initiating Process Group (IPG) is defined by the PMI as follows: “those processes performed to define the scope of a new project or a new phase of an existing project by obtaining authorization to start the project of phase” (PMI, 2017, p. 23). The purpose of the IPG is to formally define a project including the business need, key stakeholders, and the project goals. A clear definition of the business case is critical for defining the scope of the project and for identifying the opportunity associated with completing the project. The business case includes the potential risks associated with completing or not completing the project at a given point in time. The work completed throughout the IPG builds a foundation for buy-in and commitment from the project sponsors and establishes understanding of associated challenges. During the IPG a shared understanding of success criteria emerges that includes both the benefits and the costs associated with a given project (PMI, 2017). Historical information is assembled during the IPG to identify related projects or earlier attempts at similar projects. Historical information and case studies can provide insight into potential challenges that can arise with the project and buy-in from stakeholders (Bongiovanni et al., 2015). The IPG may lead to formal project selection or it may culminate in a ­decision to forgo or to postpone a project. The set of work completed in the IPG is often done directly for a business sponsor and may be accomplished without a formal project team in place. During the IPG the goals of the proposed project are analyzed to determine the project scope and associated time, costs, and resource requirements. Key stakeholders are identified and may be engaged in defining the project scope, articulating the business case and developing a shared vision for the project deliverables. The inputs needed to support the work of the IPG include tools and information that support the knowledge area of project integration management. Project integration management includes the processes

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and activities needed to identify, define, combine, unify, and coordinate the various processes and project management activities within the project management process groups (PMI, 2017). Sound integration management contributes to a solid understanding of whether the project is a good match for the organization and if so, how the project fits into the organizational mission and vision. The involvement of stakeholders in the process of project integration management is fundamental to their engagement in the project and involvement in defining and working toward project success. Informational inputs such as the sponsor’s description of the project, the organizational strategic plan, the published organizational mission, and historical information on related projects support the integration work of the IPG. Examples of tools and techniques that facilitate completing the information gathering, research, and related analysis required during the IPG include the SWOT (e.g., Strengths, Weaknesses, Opportunities, Threats) analysis, stakeholder analysis, and the value risk assessment (see Table 15.2 and Fig. 15.2). Tangible outputs of the IPG include the completed project charter that formally defines the project, including the business case, key stakeholders, project constraints, and assumptions. The project charter also includes signatures of the project sponsors and team members, indicating a shared vision for the project and formal approval to move forward with planning the project. The outputs from the IPG are used to inform project planning and reused during project closure to facilitate evaluation of the project deliverables.

PLANNING PROCESS GROUP The Planning Process Group (PPG) is often the most difficult and unappreciated process in project management, yet it is one of the most important and should not be rushed. This is the phase where decisions are made on how to complete the project and accomplish the goals and objectives defined in the IPG. The project plan is created, whose main purpose is to guide the project execution

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  257

Step 1: List stakeholders and assign code. Classify Stakeholders according to influence level Stakeholder Analysis Coding Key Influence Level (H) (H) (H) (H) (H) (H) (H) (H) (M) (H) (L) (L) (H) (H)

Code/Name A. B. C. D. E. F. G. H. I. J. K. L. M. N.

Step 2: Identify sources and causes of resistance and strategies for overcoming SOURCE OF RESISTANCE TECHNICAL

CAUSES OF RESISTANCE • • • •

LACK OF CODING SKILLS LACK OF DOCUMENTATION SKILLS LACK OF UNDERSTANDING INADEQUAATE TOOLS TO SUPPORT SKILL LEVEL

• •

POLITICAL

• • • •

JOB RESPONSIBILITY SCOPE TERRITORIALITY US VS WE (DAVID VS. GOLLITH)

• • • •

CULTURAL

• INGRAINED DEPARTMENTAL • RESISTANCE TO CHANGE • “IF IT AIN’T BROKE...

•

STRATEGIES FOR OVERCOMING RESISTANCE EDUCATION & TRAINING USE OF DISSEMINATION TOOL (MARKETING W/ “ON CALL”, MOX, EMAIL) INVOLVE STAFF IN TOOL DEVELOPMENT TO SUPPORT NEW SKILLS CLARIFY RESPONSIBILITY DISSEMINATE “ELEVATOR SPEECH” INVOLVING STAKEHOLDERS PAST SUCCESSES

• EDUCATION & TRAINING • INVOLVE STAFF IN TOOL DEVELOPMENT TO SUPPORT NEW SKILLS

Step 3: Plot strategies for managing stakeholders STAKEHOLDER ANALYSIS: Outpatient Oncology Care: Improving the Reimbursement Process NAMES/INFLUENCE LEVEL

A. B. C. D. E. F. G. H. I. J. K. L. M. N. O. P. Q. R. S. T. U. V. W. X. Y. Z. AA.

STRONGLY AGAINS (–2)

(H) (H) (H) (H) (H) (H) (H) (H) (M) (H) (L) (L) (H) (H) (M) (M) (L) (H) (H) (H) (H) (H) (H) (H) (L) (L) (L)

MODERATELY AGAINST (–1)

NEUTRAL (0)

MODERATELY SUPPORTIVE (+1)

X X

X (C,T) X T)

X (C) X (P) X (P) √ √ X X X (P,C,V)

√

X X X X X X X X X X

STRATEGIES FOR OVERCOMING RESISTANCE

X √ √

(4,8) (1,2,7) (2,4,7) (2,3,4) (7)

X

X X X X

1. USE OF DISSEMINATION TOOL (MARKETING/”ON CALL”, MOX, EMAIL, ECT..) 2. CLARIFY RESPONSIBILITY 5. DISSEMINATE “ELEVATOR SPEECH”

4. INVOLVE STAFF IN TOOL DEVELOPMENT TO SUPPORT NEW SKILLS

STRONGLY SUPPORTIVE (+2)

√ X

(2,3,4)

3. EDUCATION & TRAINING 6. PAST SUCCESSES

7. INVOLVING STAKEHOLDERS

•  FIGURE 15.2.  Stakeholder Analysis.

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258    P art 4 • I nformatics T heory S tandards phase. To that end, the plan must be realistic and specific, so a fair amount of time and effort needs to be spent and people knowledgeable about the work need to help plan the work. The project plan also provides structure for the project monitoring and controlling process, as it creates the baseline to which the work is measured against as it is completed. During the initiating phase, a lot of information is collected to define the project, including the scope document and project charter, which provide validation and approval for the project. During the planning phase, the approach to accomplish the project is defined to an appropriate level of detail. This includes defining the necessary tasks and activities in order to estimate the resources, schedule, and budget. Failure to adequately plan greatly reduces the project’s chances of successfully accomplishing its goals (PMI, 2017). Project planning generally consists of the following steps:

• • • • • • •

Define project scope.

•

Sequence the activities and define the critical path activities.

• • • • • • • •

Scope Statement

Defines the boundaries of the project work; often developed directly from: • Voice of the customer • Project charter • SWOT analysis (strengths, weaknesses, opportunities, and threats) • Stakeholder analysis • Value Risk Assessment

Project Charter

Describes the high-level scope, time, and cost goals for the project objectives and success criteria, a general approach to accomplishing the project goals, and the roles and responsibilities of project stakeholders.

RACI Chart

Helps define the roles and ­responsibilities of project teams and team members—shows who is: • Responsible—completes the task • Accountable—signs off on the task • Consulted—has information necessary to complete the task • Informed—needs to be notified of task status or results.

Work Breakdown Structure (WBS)

Displays the project graphically subdivided into manageable work activities, including the relationship of each task to other tasks, the allocation of responsibility, the resources required, and the time allocated.

Risk Register

Prioritizes the list of project risks, often including a plan for risk avoidance and risk mitigation strategies.

Refine project objectives. Define all required deliverables. Create framework for project scheduled. Select the project team. Create the work breakdown structure. Identify the activities needed to complete the deliverables.

Estimate the resource requirements for the activities. Identify required skills and resources. Estimate work effort; time and cost for activities. Develop the schedule. Develop the budget. Complete risk analysis and avoidance.

by the sponsor(s) and shared with the project team in a project kick-off meeting (Garcia-Dia, 2019).

Create communication plan.

IMPLEMENTATION AND TESTING

Gain formal approval to begin work.

Executing Process Group

Some of the tools and techniques employed during the PPG are listed in Table 15.3. One of the most important is the Work Breakdown Structure (WBS). Projects are organized and understood by breaking them into a hierarchy, with progressively smaller pieces until they are a collection of defined “work packages” that include tasks. The WBS is used as the outline to provide a framework for organizing and managing the work. The deliverable of this phase is a comprehensive project plan, which is approved

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  TABLE 15.3   Planning Process Group Tools and Techniques

The Executing Process Group (EPG) is defined by the PMI as follows: “Those processes performed to complete the work defined in the project management plan to satisfy the project requirements” (PMI, 2017, p. 23). The EPG is characterized by carrying out the work of the project and associated activities defined by the project plan to meet project requirements. During the EPG the project team follows the project plan and each team member contributes to the ongoing progress of the plan. Project

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deliverables are managed during the EPG by careful tracking of scope, time, and resource use with ongoing updates made to the project plan and timeline to reflect progress made. The key responsibilities of the project manager during the EPG are integration of the project team and activities to keep the work of the project moving toward the established milestones set during the project planning phase (e.g., PPG). Clear communication and effective management of project resources are essential. The inputs needed to support the work of the EPG include tools and information that facilitate assimilation of the following knowledge areas into project efforts (PMI, 2017; Schwalbe, 2014):

• • • • •

Integration management: Coordination of project resources and activities to complete the project on time, within budget, and in accordance with the project scope defined by the customer. Quality management: Monitoring of project performance to ensure that the deliverables will satisfy the quality requirements specified by the customer. Human resource management: Enhancing and motivating performance of project team members to ensure effective use of human resources to advance project deliverables. Communication management: Distribution of information in a complete and timely fashion to ensure all stakeholders are informed and miscommunication channels are minimized. Procurement management: Obtaining goods and services from outside an organization, such as identifying and selecting vendors and managing contracts.

As noted above, the inputs needed to support the work of the EPG include tools and information that promote clear communication, control the work, and manage project resources. Examples of common tools employed during the EPG are described in Table 15.4. The tools and techniques used during the EPG and associated documentation facilitate completion of project work and provide a means to identify and track ongoing activities against the project plan. Variances may arise during project execution and may trigger an evaluation and a replanning of activities. Deliverables produced through use of the EPG input tools and techniques are then used as outputs to inform the work conducted over subsequent process group phases. For example, during the EPG, the Gantt chart provides a means to monitor whether the project is on schedule and for managing dependencies between tasks. This same tool is useful in the monitoring

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  TABLE 15.4   Executing Process Group Tools and Techniques Project meetings

Gathering of project team for the purpose of advancing the work of the project. All participants have a predefined role; action items and decisions are tracked and formally communicated.

Gantt chart

Tracks and communicates project tasks, resources, and milestones against time over the course of a project.

Request for proposal

Used to solicit proposals from prospective vendors.

Issue log

Provides a means to prioritize and track items that represent a degree of risk to meeting project deliverables.

Progress reports

Keeps project team informed of ­project status, milestones to date, and areas of concern.

and controlling process groups (MCPGs) where the Gantt chart is used to proactively identify when remedial action is needed and to ensure that project milestones are met in accordance with the project plan.

MONITORING, MAINTAINING, SUPPORTING, AND EVALUATION Monitoring and Controlling Process Group The purpose of the Monitoring and Controlling Process Group (MCPG) is to “track, review and regulate the progress and performance of the project; identify any areas in which changes to the plan are required; and initiate the corresponding changes” (PMI, 2017, p. 23). This allows issues and potential problems to be identified in a timely manner and corrective action to be taken when necessary to control execution of the project. It is the process of measuring progress toward project objectives, monitoring deviation from the plan, and taking corrective action to ensure progress matches the plan. The MCPG is performed throughout life of project across all phases, and provides feedback between project phases (Schwalbe, 2014). The project manager facilitates project control by measuring actual performance against the planned or estimated performance from the project plan in the areas of scope, resources, budget, and time. The key benefits

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260    P art 4 • I nformatics T heory S tandards of project control are that when project performance is observed and measured regularly, variance to the plan can be identified and mitigated quickly to minimize delays and avoid cost overruns. According to the Standish Group 2015 Chaos Report (n.d.), 19% of all projects failed, 52% were challenged, and 29% were successful. The reasons for these failures vary, but project control can help improve on-time, on-budget, and in-scope delivery of projects. During the control phase, the project manager needs to support the project team with frequent checks and recognition of the completion of incremental work efforts. This way, the project manager can adapt the work as needed. Project managers also need to work with the project sponsors to identify the risks of keeping on time and on budget versus modifying the schedule or scope to better meet the organizations’ need for the project (Garcia-Dia, 2019).

The Triple Constraint Every project is constrained in some way by scope, cost, and time. These limitations are known as the triple constraint. They are often competing constraints that need to be balanced by the project manager throughout the project life cycle (Schwalbe, 2014). Scope refers to the specific project requirements and work that needs to be done to accomplish the project goals. Key responsibilities of the project manager are ensuring that the project scope is explicitly defined and is consistently reinforced to manage stakeholder expectations. A well-defined scope provides the foundation for task prioritization and resource allocation that is aligned with project goals and deliverables. Cost refers to the resources (materials and people) required to complete the project. Accurate cost estimates are needed to secure adequate project funding. Cost control throughout the project life cycle is crucial to keeping the project on track and within budget. Time is the duration of the project. Historical data from similar projects can be used to inform an accurate estimate of the project duration which can then be used to create the project schedule. A work breakdown structure is used to break the project down into manageable tasks that are then prioritized and scheduled. A Gantt chart can be used to visualize the project schedule including milestones, associated tasks, and dependencies. The concept of the triple constraint is that any modification to the project will impact one or more of the three constraints and will require trade-offs that can negatively impact the success of the project. For example, if there is an increase in scope, either cost or time or both will need to be increased as well. Or in another example, if time is decreased when a deadline is moved

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up, either scope will need to decrease or cost (resources) will need to increase. It is a balancing act. The project manager is responsible for understanding what aspects can be adjusted over the course of the project without compromising the stakeholders’ expectations of quality. The tools and techniques employed during the MCPG are described in Table 15.5.

Closing Process Group The Closing Process Group (CPG) is defined by the PMI as follows: “Those processes performed to formally complete or close the project, phase, or contract” (PMI, 2017, p. 23). The goal of CPG is to finalize all project activities and to formally close the project. During the CPG the project goals and objectives set during the IPG are compared with deliverables and analyzed to determine the project success. Key stakeholders are engaged with evaluating the degree to which project deliverables were met. The inputs needed to support the work of the CPG include   TABLE 15.5   Monitoring and Controlling Process Group Tools and Techniques Project management methodology

Follows a methodology that describes not only what to do in managing a project, but also how to do it. Examples are Agile, Lean, Scrum, and Waterfall.

Project management information systems

Hundreds of project management software products are available on the market today, and many organizations are moving toward powerful enterprise project management systems that are accessible via the Internet. Look for systems that also offer a mobile view so that team members can update tasks on the go.

Time reporting tools

Ability to enter and track project effort by resource and against the project tasks. Some functions to use are Dashboards, Timesheets, and Interactive Timelines.

Progress reports

Answers the questions: • How are my projects doing overall? • Are my projects on schedule? • Are my estimates accurate? • Are my resources properly utilized?

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Chapter 15 • Healthcare Project Management 

the outputs from earlier process group phases and the tools and information that support the knowledge areas of project integration and procurement management (PMI, 2017).

•

•

Integration management: Coordination of project closure activities including formal documentation of project deliverables and formal transfer of ongoing activities from the project team to established operational resources. Procurement management: Coordination of formal contract closure procedures, including resolution of open issues and documentation of archival information to inform future projects.

Some common tools employed during the CPG are described in Table 15.6. The tools and techniques used during the CPG and associated documentation facilitate project closure and provide a means to identify lessons learned. During the CPG, standard tools available to the project team to support best practices are identified and made available to support application of best practices in future projects (Garcia-Dia, 2019). Including a formal step for adapting   TABLE 15.6   Closing Process Group Tools and Techniques Post-implementation survey

Provides an opportunity for project stakeholders to evaluate the project from multiple perspectives including product effectiveness, management of the triple constraint, communication management, and overall performance of the project team.

Post-mortem review document

Provides a means to document the formal project evaluation and summarizes the pluses and deltas associated with a given project. Facilitates discussion related to lessons learned that can be applied to future projects.

Project closeout checklist

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Used to ensure that agreed upon features of project closure are completed related to the following: post-implementation review, administrative closeout procedures, and formal acknowledgement of the project team.

  261

the project management toolbox during the CPG ensures that the toolbox remains pertinent and continues to support best practices relative to the types of projects typically conducted within an organization.

PROFESSIONAL PROJECT MANAGEMENT PRACTICE Project Governance There is no question that successful projects are owned and sponsored by the leaders and staff that will be making the practice change and will be benefiting by the change. In the ideal scenario, the Governance Committee(s) are led by top business and clinical leaders, with membership built on broad representation from key business and clinical departments. In addition to evaluating and ranking the technology project proposals brought to them, they take responsibility for generating an overall roadmap to use to compare each proposal against. The leaders set guiding principles for concepts like integration vs. best-of-breed systems. They consider where the organization needs to be going and what practice and care changes are required, then solicit proposals from the strategic business units that are responsible for making those changes. In other words, leaders don’t just see themselves as “governing” the project selection process, but rather as “driving” the implementation of projects that support strategic initiatives and deliver strategic value. It may be a nuance here, but there is a real argument that these are not Governance Committees that pick IT projects, but rather are Governance Committees that allocate IT resources to strategic projects (PMI, 2017).

Skills Needed for Project Managers There is general agreement that good project managers need to have both interpersonal skills and leadership skills. Here is a skills list that describes the many facets of the project manager role:

• • • • • •

Communication skills: Listens, persuades, respects, provides clarity. Organizational skills: Plans, sets goals, analyzes. Team-building skills: Shows empathy, motivates, promotes esprit de corps and cohesion. Leadership skills: Sets examples, provides vision (big picture), delegates, positive, energetic. Coping skills: Flexible, creative, patient, persistent. Technology skills: Experience, project knowledge.

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Project Management Office (PMO) There are different ways to position project managers in a healthcare organization. Project managers can be in the information technology department, clinical department, and informatics department, and still others have matrix roles. Many project managers have created a project management office to provide best practices and support managing all projects in an organization. The office can also provide education, coaching, and mentoring, as well as project management resources. With the federal incentives for health information technology, the industry-implemented electronic health record (EHR) ­ system ­ projects to demonstrate meaningful use. Now the focus is on promoting interoperability. There is an increased demand for good project managers and consistent project management methodology for completing projects. Including project management experiences in schools will help students develop this skill (Sipes, 2016).

Project Management Institute The Project Management Institute (PMI) was founded in 1969 by a group of project managers and is considered to be the leading professional organization for project managers worldwide. The PMI publishes the Guide to the Project Management Body of Knowledge (PMBOK® Guide), which is a collection of consensus-based standards and best practices. The PMBOK® Guide is currently in its 6th edition and has been adopted as an American National Standard Institute. (ANSI). (PMI, 2017)

Project Management Professionals The PMI offers multiple levels of credentialing to assist project professionals with advancing their careers in project, program, and portfolio management. PMI credentials are available related to many aspects of project management including program management, scheduling, and risk management. PMI membership is not required for credentialing (PMI, 2017). In addition to meeting a set of published prerequisite requirements, PMI credentialing requires that candidates successfully pass a credentialing exam. Complete information about PMI credentials and the credentialing process can be accessed from the PMI Web site.

Project Management in the Future Technology continues to transform rapidly, causing a change process in how we do our daily work. Project management methodologies will integrate into other areas to improve management and outcomes, such as

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clinical research (Bongiovanni et al., 2015). Research in the field of project management is needed to understand failures and successes. (Gill, 2017) Mobile technology will work its way into the healthcare field including voice input methods at the patient’s bedside, and project management will incorporate these new techniques. New technologies will help create ways to improve communication and evaluate outcomes of our projects and hopefully make them more efficient and effective.

SUMMARY Today, as so often in history, healthcare resources are limited. Yet there are many, varied, and complex healthcare technology projects demanding resources. So how does one ensure that healthcare dollars and clinician time are spent wisely on projects that guarantee patient-driven outcomes? In addition to selecting and funding the projects wisely, the authors believe, good project management also minimizes risk and enhances success. We further contend that clinicians, and particularly nurses, are excellent candidates, once trained in project management techniques, to be good project managers. Executive management should support, and maybe even demand, project management training for the organization’s clinical IT project team leaders. Nursing informatics has been evolving since its inception some 34 years ago. Project management skills are included in training and formal education programs and are an essential competency for nursing informatics’ specialists (Sipes, 2016). Informatics nurses are stepping up to the plate and taking on key roles in the planning, selection, implementation, and evaluation of the critical clinical systems needed in healthcare today. We hope that we have provided a glimpse of the skills that will enable this to happen. Good project management holds part of the key to consistent success with EHR system implementations in healthcare today.

Test Questions 1. One tool used in project management is the SWOT. This acronym stands for: A. Subjective, Warnings, Operational, Treatments B. Strengths, Warnings, Operational, Threats

C. Strengths, Weaknesses, Opportunities, Threats D. Subjective, Weaknesses, Operational, Threats

2. Implementing technology in healthcare requires understanding of the five phases of project management. In the planning phase the Initiating Process

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Group will focus on which of the following: (PLEASE select all that apply) A. Identify key stakeholders

B. Define the project charter

C. Compare the objectives with the outcomes measured data to complete the project D. Develop a detailed budget

3. The Work Breakdown Structure in the Planning Process Group is used to: (PLEASE select all that apply) A. Take apart the go-live room after implementation B. Outline a framework for organizing and managing the work C. Develop a risk analysis assessment

D. Provide a comprehensive project plan

4. Tools to help with the creation of the Scope Statement for the Planning Process Group are outlined below. Which tool does not belong in this list: A. Value Risk Assessment B. Stakeholder Analysis C. Gnatt Chart

D. Project Charter

5. Engaging key stakeholders is an important part of the project. To plot strategies for engagement, which one of the following is to be considered: A. Provide detailed job responsibility B. Provide education on coding

C. Involve key stakeholders in developing a shared vision of project D. Include them on all e-mails

6. There are a number of tools used in the Planning Process Group. Which of the following tools deconstructs the project into a hierarchy of smaller, earlier to handle pieces? A. RACI Chart

B. Work Breakdown Structure C. Risk Register

D. Project Charter

7. The triple constraint affects every phase of every project. Scope, cost, and time must be balanced throughout the life cycle of the project. To balance these constraints, the project manager should do which of the following: A. Have a well-defined scope for the project

B. Use historical data from similar projects to guide estimates

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Chapter 15 • Healthcare Project Management  C. Prepare a Gnatt chart with appropriate milestones to guide progress

D. Understand that tradeoffs in one area may reflect other areas E. All of the above

8. Which is an important understanding of the five process groups?

A. They are linear in nature; decisions made in one group do not affect other group processes. B. The activities of each process should be completed prior to moving to the next process group.

C. The knowledge area of integration management only occurs in the initiation and planning process groups. D. They are integrated; decisions made in one group can affect other group processes.

9. Resistance to change can be attributed to which source? A. Financial B. Cultural C. Political

D. Technical

10. The goal of the Closing Process Group is to finalize all project activities and formally close the project. Which of the following is the best tool for discussing lessons learned? A. Post-implementation survey B. Autopsy review

C. Postmortem review

D. Project closeout checklist

Test Answers 1. Answer: C

2. Answer: A and B

3. Answer: B, C, and D 4. Answer: C 5. Answer: C 6. Answer: B 7. Answer: E

8. Answer: D 9. Answer: B

10. Answer: C

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264    P art 4 • I nformatics T heory S tandards

REFERENCES American Nurses Association. (2015). Nursing informatics: Scope and standards of practice (2nd Ed.). Silver Spring, MD. Nursebooks.org. Bongiovanni, A., Colotti, G., Liguori, G. L., Di Carlo, M., Digilio, F. A., Lacerra, G., . . . Kisslinger, A. (2015). Applying quality and project management methodologies in biomedical research laboratories: A p ­ ublic research network case study. Accreditation and Quality Assurance, 20(3), 203–213. doi:10.1007/ s00769-015-1132-57. Garcia-Dia, M. (2019). Project management in nursing ­informatics. New York, NY: Springer. Gill R, Borycki EM. The Use of Case Studies in Systems Implementations Within Health Care Settings: A Scoping Review. Stud Health Technol Inform. 2017;234: 142–149.

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Project Management Institute. (2017). A guide to the project management body of knowledge (PMBOK guide) (6th ed.). Newton Square, PA: Project Management Institute. Schwalbe, K., (2014). Information technology project management (7th ed.). Independence, KY: Course Technology, Cengage Learning. Sellke, C. (2018, December 28). Healthcare project management techniques: a pragmatic approach to outcomes improvement. Retrieved from https://www.healthcatalyst.com/insights/healthcare-project-management-techniques-pragmatic-approach-outcomes-improvement. Accessed on March 10, 2019. Sipes, C. (2016). Project management: Essential skill of nurse informaticists. Studies in Health Technology and Informatics, 225, 252–256. Standish Group 2015 Chaos Report: Q&A with Jennifer Lynch. (n.d.). Retrieved from https://www.infoq.com/ articles/standish-chaos-2015. Accessed on March 4, 2019.

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16 The Practice Specialty of Nursing Informatics Carolyn Sipes / Carol J. Bickford

• OBJECTIVES 1. Explore the nursing informatics definition and its characteristics as a distinct specialty. 2. Discuss models and theories that support nursing informatics. 3. Identify available organizational resources. 4. Describe the relationship of the standards of practice in nursing informatics to the scope of practice statement.

• KEY WORDS Competencies Informatics Models Nursing process Scope of practice Standards of practice Standards of professional performance

ABSTRACT Nursing informatics is an established nursing specialty. All nurses employ information technologies and solutions in their practice. Informatics nurses are key persons in the design, development, implementation, and evaluation of these technologies and solutions and in the development and enhancement of the specialty’s body of knowledge. This chapter addresses pertinent concepts, definitions, and interrelationships of nursing, nursing informatics, and healthcare informatics. The evolution of definitions for nursing informatics is presented. The recognition of nursing informatics as a distinct nursing specialty is discussed. Select models and theories of nursing informatics and supporting sciences are described. The identification of various sets of nursing informatics competencies is

explained. A collection of international and national organizations of interest to informatics nurses is presented. This chapter also addresses components of the scope of practice and the standards of practice and professional performance for nursing informatics.

INTRODUCTION Decision-making is an integral part of daily life. Good decisions require accurate and accessible data as well as skill in processing information. At the heart of nursing informatics (NI) is the goal of providing nurses with the data, information, and support for information processing to make effective nursing practice decisions in clinical care, research, education, administration, and policy development. Generation of knowledge and the application of wisdom also occur. 265

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INFORMATICS NURSE/INFORMATICS NURSE SPECIALIST An informatics nurse (IN) is a registered nurse who has an interest or experience in nursing informatics. Informatics nurse specialists (INSs) are registered nurses prepared at the graduate level (master’s degree or higher) in nursing informatics, informatics, or an informaticsrelated field. An INS functions as a graduate-levelprepared specialty nurse.

Foundational Documents Guide Nursing Informatics Practice Nursing informatics practice and the development of this specialty have been guided by several foundational documents. These documents are listed in Table 16.1 and described in this section. In 2001, the American Nurses Association (ANA) published the Code of Ethics for Nurses with Interpretive Statements, a complete revision of previous ethics provisions and interpretive statements that guide all nurses in practice, be it in the domains of clinical care, education, administration, or research. Nurses working in the informatics specialty are professionally bound to follow these provisions. Terms such as decision-making, comprehension, information, knowledge, shared goals, outcomes, privacy, confidentiality, disclosure, policies, protocols, evaluation, judgment, standards, and factual documentation abound throughout the explanatory language of the interpretive statements (American Nurses Association, 2001a). Although cited in the 2015 Nursing Informatics: Scope and Standards of Practice, Second Edition, that resource has been replaced with the contemporary 2015 Code of Ethics for Nurses with Interpretive Statements document that is available at: https://www.nursingworld.org/practice-policy/nursingexcellence/ethics/code-of-ethics-for-nurses/.

ANA’s Nursing: Scope and Standards of Practice, Second Edition (2010), referenced in the Nursing Informatics: Scope and Standards of Practice, Second   TABLE 16.1    Foundational Documents Code of Ethics for Nurses with Interpretive Statements

2001

Code of Ethics for Nurses with Interpretive Statements

2015

Nursing: Scope and Standards of Practice, Second Edition

2001

Nursing: Scope and Standards of Practice, Third Edition

2015

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Edition (American Nurses Association, 2015c), reinforced the recognition of nursing as a cognitive profession and provided the definition of nursing: “Nursing is the protection, promotion, and optimization of health and abilities, prevention of illness and injury, alleviation of suffering through the diagnosis and treatment of human response, and advocacy in the care of individuals, families, communities, and populations” (p. 9). The exemplary competencies accompanying each of the 16 Standards of Professional Nursing Practice comprised of Standards of Practice and Standards of Professional Performance, reflected the specific knowledge, skills, abilities, and judgment capabilities expected of registered nurses at that time. The standards included data, information, and knowledge management activities as core work for all nurses. This cognitive work began with the critical-thinking and decision-making components of the nursing process that occur before nursing action can begin (American Nurses Association, 2010). Consult Nursing: Scope and Standards of Practice, Third Edition (American Nurses Association, 2015b) for the contemporary definition of nursing and revised standards of nursing practice and professional performance. The nursing process provides a delineated pathway and process for decision-making. Assessment, or data collection and information processing, begins the nursing process. Diagnosis or problem definition, the second step, reflects the interpretation of the data and information gathered during assessment. Outcomes identification is the third step, followed by planning as the fourth step. Implementation of a plan is the fifth step. The final component of the nursing process is evaluation. The nursing process is often presented as a simplistic linear process with evaluation listed as the last step. However, the nursing process really is very iterative, includes numerous feedback loops, and incorporates evaluation activities throughout the sequencing. For example, evaluation of a plan’s implementation may prompt further assessment, a new diagnosis or problem definition, and decision-making about new outcomes and related plans. The collection of data about the healthcare consumer, client, patient, management, education, or research situation is guided by a nurse’s knowledge base built on formal and informal educational preparation, evidence and research, and previous experiences. In healthcare, as in most areas of our lives, data, information, knowledge, and wisdom (DIKW) are growing at astronomical rates and demand increasing reliance on computer and information systems for collection, storage, organization and management, analysis, and dissemination. For example, in clinical nursing practice, consider the significant expansion in the amount and types of data that must be collected for

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Chapter 16 • The Practice Specialty of Nursing Informatics 

legal, regulatory, quality, and other reasons. DIKW might include understanding of the following:

• •

Genetic profiles, related to specific healthcare consumer health conditions Information and knowledge about the healthcare environment and services, including data related to: ◦◦ billing and reimbursement ◦◦ health plans ◦◦ available formulary options

•

Standardized nursing terminologies and their capacity to contribute to codification, quantification, and evaluation of delivery of nursing care services (see Chapter 8, “Standardized Nursing Terminologies” for more detailed discussion)

Collecting data in a systematic, thoughtful way, organizing data for efficient and accurate transformation into information, and documenting thinking, decisions, and actions are critical to successful nursing practice. Nursing informatics is the nursing specialty that endeavors to make the collection, management, and dissemination of data, information, and knowledge—to support decisionmaking—easier for the practitioner and healthcare ­consumer, regardless of the domain and setting.

INFORMATICS AND HEALTHCARE INFORMATICS Informatics is a science that combines a domain science, computer science, information science, and cognitive science. Thus, it is a multidisciplinary science drawing from varied theories and knowledge applications. Healthcare informatics may be defined as “the integration of healthcare sciences, computer science, information science, and cognitive science to assist in the management of healthcare information” (Saba & McCormick, 2015, p. 232). Healthcare informatics is a subset of informatics, as is nursing informatics. Imagine a large umbrella named informatics comprised of many panels. Each panel represents a different domain science, one of which is healthcare informatics. The healthcare informatics panel could be comprised of many stripes depicting the composite of nursing informatics, dental informatics, public health informatics, etc. Because healthcare informatics is a relatively young addition to the informatics umbrella, you may see other terms that seem to be synonyms for this same area, such as medical or health informatics. Medical informatics

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historically was used in Europe and the United States as the preferred term for healthcare informatics but now is evolving to be more clearly realized as a subset of healthcare informatics. Similarly, health informatics may reference informatics used in educating healthcare consumers and/or the general public. As healthcare informatics evolves, so will the clarity in definition of terms and associated scopes of practice. Healthcare informatics addresses the study and management of healthcare information. A model of overlapping discrete circles could depict the integrated content most often considered representative of the multiple and diverse aspects of healthcare informatics. Healthcare informatics would be the largest encompassing circle surrounding smaller intersecting circles. These aspects include specific content areas such as information retrieval, ethics, security, decision support, patient care, project management including electronic health record (EHR) implementations, system life cycle (SLC) as a subcomponent of project management, evaluation, human– computer interaction (HCI) or user experience, standards, connected health/telehealth, healthcare information systems, imaging, knowledge representation, education, and information retrieval.

Nursing Informatics Nursing informatics (NI), as a subset of healthcare informatics, shares common areas of science with other health professions and, therefore, easily supports interprofessional education, practice, and research focused on healthcare informatics. Nursing informatics also includes unique components, such as standardized nursing terminologies, that address the special information needs for the nursing profession and healthcare consumers. Nurses practice interprofessionally as well as independently when engaged in clinical and administrative nursing practice. Nursing informatics reflects this duality as well, moving through the continuum of integration and separation as situations and needs demand. In 1985, Kathryn Hannah proposed a definition that nursing informatics is the use of information technologies in relation to any nursing functions and actions of nurses (Hannah, 1985). In their classic article on the science of nursing informatics, Graves and Corcoran presented a more complex definition of nursing informatics. Nursing informatics is a combination of computer science, information science, and nursing science designed to assist in the management and processing of nursing data, information, and knowledge to support the practice of nursing and the delivery of nursing care (Graves & Corcoran, 1989).

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268    P art 4 • I nformatics T heory S tandards With the development of the first scope of practice statement for nursing informatics, ANA modified the Graves and Corcoran definition to identify nursing informatics as the specialty that integrates nursing science, computer science, and information science in identifying, collecting, processing, and managing data and information to support nursing practice, administration, education, research, and the expansion of nursing knowledge (American Nurses Association, 1994). The explication of the accompanying first standards of practice for NI followed in 1995 with ANA’s publication of the Standards of Practice for Nursing Informatics (American Nurses Association, 1995). In 2000, the ANA convened an expert panel to review and revise the scope and standards of nursing informatics practice. That group’s work included an extensive examination of the evolving healthcare and nursing environments and culminated in the publication of the Scope and Standards of Nursing Informatics Practice (American Nurses Association, 2001b). This professional document included an expanded definition of nursing informatics that was then slightly revised in the 2008 Nursing Informatics: Scope and Standards of Practice to include wisdom: Nursing informatics (NI) is a specialty that integrates nursing science, computer science, and information science to manage and communicate data, information, knowledge, and wisdom in nursing practice. NI supports consumers, patients, nurses, and other providers in their decision-making in all roles and settings. This support is accomplished through the use of information structures, information processes, and information technology. (American Nurses Association, 2008, p. 1)

Then, in 2015, ANA’s second edition of Nursing Informatics: Scope and Standards of Practice presented an updated definition: Nursing informatics (NI) is a specialty that integrates nursing science with multiple information management and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice. NI supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in their decision-making in all roles and settings to achieve desired outcomes. This support is accomplished through the use of information structures, information processes, and information technology. (American Nurses Association, 2015c, pp. 1–2)

ANA has convened an expert group to review and revise the 2015 Nursing Informatics: Scope and Standards

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of Practice, Second Edition, which may result in further refinement of the current definition. Nursing: Scope and Standards of Practice, Third Edition, (American Nurses Association, 2015b) and the Code of Ethics for Nurses with Interpretive Statements (American Nurses Association, 2015a) will inform that discussion and development effort. These multiple definitions illustrate the dynamic, developing nature of this evolving nursing specialty. Development of different definitions and a healthy debate on those definitions promotes validation of key elements and concepts. A willingness to continue exploring possible definitions can prevent premature conceptual closure, which may lead to errors in synthesis and knowledge development.

Nursing Informatics as a Specialty Characteristics of a nursing specialty include differentiated practice, a well-derived knowledge base, a defined research program, organizational representation, educational programs, and a credentialing mechanism. In early 1992, ANA recognized nursing informatics as a specialty in nursing with a distinct body of knowledge. Unique among the healthcare professions, this designation as a specialty provided official recognition that nursing informatics is indeed a part of nursing and that it has a distinct scope of practice. The core phenomena of nursing are the nurse, person, health, and environment. Nursing informatics focuses on the information of nursing needed to address these core phenomena. Within this focus are the metastructures or overarching concepts of nursing informatics: data, information, knowledge, and wisdom. It is this special focus on the information of nursing that differentiates nursing informatics from other nursing specialties. Nursing informatics is represented in international, national, regional, and local organizations. For example, there is a nursing informatics working group in the American Medical Informatics Association (AMIA) and in the International Medical Informatics Association (IMIA). Nursing informatics is part of the clinical section of the Healthcare Information and Management Systems Society (HIMSS). There are additional organizations such as the American Nursing Informatics Association (ANIA) and the American Academy of Nursing (AAN) Informatics and Technology Expert Panel (ITEP). Increasingly, nursing school curricula include content, and sometimes complete courses, on information technologies in healthcare and nursing. In 1989, the University of Maryland established the first graduate program in nursing informatics. The University of Utah followed in 1990.

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Chapter 16 • The Practice Specialty of Nursing Informatics 

Now there are several established in-person and online programs for graduate work as well as doctoral programs in nursing informatics.

Certifications That Support the NI Specialty Following the publication of the first nursing informatics scope of practice and standards documents, the American Nurses Credentialing Center (ANCC) established a certification process and examination in 1995 to recognize those nurses with basic nursing informatics specialty competencies. The ANCC has used scholarship in nursing-informatics competencies and its own role-delineation studies to develop and maintain the nursing-informatics certification examination. The ANCC-designated NI contentexpert panel has oversight responsibility for the content of this examination and considers the current informatics environment and research when defining the testcontent outline. Application details and the test-content outline are available at https://www.nursingworld.org/ our-certifications/informatics-nurse/. Information for the HIMSS Certified Professional in Health Information and Management Systems (CPHIMS) and Certified Associate in Health Information and Management Systems (CAHIMS) certifications are available at https://www.himss.org/health-it-certification. Other Certification Programs That Support the NI Role.  As noted by McGonigle and Mastrian (2015), NI roles include those of project manager, consultant, educator, researcher, product developer, decision support/outcomes manager, advocate/policy developer, clinical analyst/system specialist, and entrepreneur. For example, the certification for: Project manager is the Project Management Professional (PMP), supported by the Project Manage­ment Institute: https://www.pmi.org/ certifications/types/project-management-pmp Nurse Executive and Nurse Executive-Advanced (NE & NE-A): https://www.nursingworld.org/our-certifications/ nurse-executive-advanced Nurse Educator–Certified Nurse Educator (CNE): http://www.nln.org/Certification-for-NurseEducators/cne.20,000 clinics

Healthcare Services

Insured

Copayment

Providers

•  FIGURE 35.1.  Taiwan’s National Health Insurance Administration Structure.

MyHealthBank in 2013 and 2014 to provide an informative and transparent platform for healthcare service providers and patients (National Health Insurance Administration, 2016). Taiwan’s PharmaCloud system was specifically developed to help reduce over-medication and improve medication safety (Huang et al., 2015). In 2014, the Taiwan Ministry of Health and Welfare (MOHW) along with National Health Insurance Administration (NHIA) established the official Web site for the MyHealthBank system (http://www.nhi.gov.tw/), and there are currently 330,000 MyHealthBank accounts. The aim of this platform is to provide instant access to all users about their visit details to NHIA-affiliated medical institutions (National Health Insurance Administration, 2016; San-Kuei, 2014). In future, NHIA is planning to promote the development of applications and links which could facilitate to interpret data stored in MyHealthBank and provide knowledge to people. Other countries could benefit from Taiwan’s experiences taken from using national-scale HIT systems to support universal healthcare.

Data Interoperability The healthcare systems vary greatly among countries, therefore, it important to share Taiwan’s national-level HIT system journey. Over the years, additional measures have been taken by the MOHW to further develop HIT on a national scale. A common information security infrastructure was implemented, an electronic medical records (EMR) interoperability subsidy program was created, and the MOHW took additional measures to make healthcare

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administration more efficient with HIT. The EMR system’s adoption in Taiwanese hospitals has been observed higher, compared with other developed countries. The computerized physician order entry and EMR usage rates have become high, which has supported the adoption of computerized decision support systems. Such systems are proven to improve the safety and quality of the healthcare system (Bates & Gawande, 2003). These initiatives taken by Taiwan set an example for other countries which have improved collaboration between different providers, and helped unify public health and clinical medicine information systems, which provide health data for research purposes (Yen, Chia, Chu, & Hsu, 2016). Taiwan’s 20-year-old NHIA program has managed to amass an extensive amount of medical data. The NHIA, in the initial application of this data, successively established 120 data sets at the Government’s Open Data Platform so that the public could conduct any value-added applications or innovations free of charge. NHIA’s data sets on the Government’s Open Data Platform include the following categories such as “Medical Care Quality,” “Medical Information Disclosure,” “Medical Institution Category,” “Important Statistics,” and “Drug and Medical Devices” (National Health Insurance Administration, 2016). For detailed information, please see the Government’s Open Data Platform (http://data.gov.tw).

Big Data Analysis Advancements in HIT has expedited the gathering of observational health data in Taiwan and worldwide. This is

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Chapter 35 • Consumer Patient Engagement and Connectivity in Patients with Chronic Disease  

easily reflected in the universal coverage of the 23 million Taiwanese populace with the hundred percent e-claims and very accessible clinics and hospitals. The standard number of doctor consultations per person per year is about 15 in Taiwan (Li et al., 2015). Hospitals have made efforts in the past years to preserve a steady quantity of inpatients and compete with free-standing clinics with beds owned by private practitioners by establishing affiliated clinics for primary care as well as sizeable outpatient departments. The NHIA Taiwan reported to cover 99.9% of the country’s total population 20 years ago since its launch, and has become one of the largest administrative healthcare databases around the world. Per visit there are approximately 1 to 5 diagnoses (ICD9-CM), and 15 drugs into 15 visits per year, which are then multiplied by 23 million people and the number of years of data accumulation. This is all extensive, potential data amassed by healthcare organizations. There is a secondary use of this health data which is of less disputable concerns and it concerns studies related to health insurance data claims. By using the National Health Insurance Research Database (NHIRD), over 3000 studies were published in 656 scientific journals by the year 2015. These are all indexed in PubMed, a service of the National Library of Medicine in the United States (Yen et al., 2016).

Opportunities and Challenges of PGHD Many healthcare systems, clinical practices of varying sizes, and research institutions lack the technical infrastructure, functional workflows, workforce capacity, and training to support PGHD intake (ONC, 2018a,b,c). However, the Taiwanese Ministry of Health and Welfare and NHIA are adamant in their constant efforts to strategies development and better the existing services for the sake of the society and to that end make full use of online technology. Keeping in line with the recent healthcare trends of the world, this NHIA system is a revolutionary medical service model that provides the public with the power to take their healthcare into their own hands by setting the country on the path to reach WHO’s 2020 health goal of people-centered care model “my data, my decision.” It is for certain that the “MyHealthBank” system will continue to grow with time and technology, capable of producing increasingly rich data and ease of access. On the other hand, to demonstrate the complexities of the clinical thought process and the factors that drive quality improvement, Taiwan is promoting health-IoT innovation events like Marathons and Hackathons to bring

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together multidisciplinary group of developers, data scientists, makers, business analysts, university students, and clinicians to collaborate and explore the next generation of connected devices to identify and solve major healthrelated problems around the globe (Iqbal et al., 2018).

ISSUES Health Literacy and Health Information Technology (HIT) Literacy The first barrier to patient engagement often concerns health literacy and HIT literacy. If this remains an impediment, patient engagement never progresses smoothly, and can even yield poor results. Health literacy and HIT literacy refer not only to the abilities of individuals but also to health-related systems and information providers within those systems, and to local areas or workplaces. The issue also includes whether patients receive support from these, and whether patients and families can participate in activities to change systems and circumstances. These issues should be considered not only by individuals but also by the entire local society and/ or workplaces. Increasingly, health literacy is recognized as a determinant of health—one that is closely related to social determinants of health such as literacy, education, income, and culture. Health Literacy  Canadian activities in public healthcare have been noted for their inter-sectional approach that connects, for instance, medical institutes, local areas, and workplaces. This section introduces it through “An Inter-sectoral Approach for Improving Health Literacy for Canadians” (Public Health Association of British Columbia, 2012). The approach’s framework consists of the following (WHO, 2013):

• • •

To identify priorities and organize them into a comprehensive framework for improving health literacy in Canada To recommend a set of actions that could be taken at the national, provincial/territorial, and local levels for the purpose of increasing health literacy among Canadians To facilitate conversations among practitioners, researchers, and policy-makers about health literacy and encourage cross-sectoral work around health literacy initiatives

To be health literate is to be able to access and understand the information required to manage one’s health on

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576    P art 6 • N ursing P ractice A pplications a day-to-day basis. Ideally, a health-literate individual is able to seek and access the health information required to: 1. understand and carry out instructions for self-care, including administering complex daily medical regimens; 2. plan and achieve the lifestyle adjustments required for improved health; 3. make informed positive health-related decisions; 4. know how and when to access healthcare when necessary;

5. share health-promoting activities with others; and 6. address health issues in the community and society.

Points 1–4 above are personal activities, but social activities such as in points 5 and 6 are also included. Public Health Association of British Columbia (2012) also presents fundamental components to improve health literacy. A list of objectives has been developed for each component, and these have been used to establish a compendium of relevant and effective possible actions for each of the settings.

• •

•

Component 1. Develop Knowledge: Develop and facilitate an extensive knowledge base that provides access to research and practice-based evidence on effective ways to improve health literacy. Component 2. Raise Awareness and Build Capacity: Develop and provide learning opportunities that enhance the knowledge, understanding, and abilities of the public and private sector workforce, professionals, and community members in their efforts to support and promote integrated health literacy. Develop, implement, and foster communication strategies that attract the attention of key stakeholders and convey the importance of health literacy. Component 3. Build Infrastructure and Partnerships: Allocate sufficient fiscal, human, organizational, and physical resources to support and sustain a coordinated effort to build the partnerships and implement the activities outlined in the approach.

Thus, we can understand that this social activity includes not only all citizens but also all sectors as partners of citizens. We must establish a society with various sectors that accept and support people with low health literacy (i.e., those who have low health literacy because they

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lack health literacy education). The need for this approach is due to societies that foster the low health literacy. The WHO published a report that collected evidence on health literacy in 2013. The report introduced about 10 characteristics of organizations in which all members can increase health literacy. 1. Has leadership that makes health literacy integral to its mission, structure, and operations 2. Integrates health literacy into planning, evaluation measures, patient safety, and quality improvement 3. Prepares the workforce to be health literate and monitors progress

4. Includes populations served in designing, implementing and evaluating health information and services

5. Meets the needs of populations with a range of health literacy skills while avoiding stigmatization

6. Uses health literacy strategies in interpersonal communication and confirms understanding at all points of contact 7. Provides easy access to health information and services and navigation assistance 8. Designs and distributes print, audiovisual and social media content that is easy to understand and act on

9. Addresses health literacy in high-risk situations, including care transitions and communication about medicines 10. Communicates clearly what health plans cover and what individuals will have to pay for services

HIT Literacy  Aging society has become a prominent issue in some developed countries. For public insurers, medical institutions, local governments, industries, nonprofit organizations, and other stakeholders to actively use patient engagement, it is suggested that the relevant older people amid this condition have sufficient HIT literacy. Improving HIT literacy is therefore a priority. However, there are limitations on older people acquiring HIT literacy. It is therefore necessary to develop devices and applications that older people can easily use. Additionally, there is a need to consider means of easily obtaining the cooperation of many stakeholders involved with older people, such as family and care providers. Improving HIT literacy of family members and surrounding businesses is therefore also important. The HIT literacy older people have already acquired is sometimes gradually lost over time, and older people’s responsibilities

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Chapter 35 • Consumer Patient Engagement and Connectivity in Patients with Chronic Disease  

and work often shift to other parties around them. Despite this reality, the limitations must be overcome. All of society, including remote areas, isolated islands, and small-scale local governments, must consider what applications or services will help them in advancing HIT literacy. This may necessitate telemedical communication systems using the latest telecommunications technology, at low cost, and that are easy to operate.

Quality and Safety Issues In strengthening patient engagement, we should pay attention to the possibility of patients having medical accident by patients or their family self-judgement, or their becoming unduly anxious or confused after being exposed to unsubstantiated information. Alert systems are increasingly being looked at for securing medical safety. Such systems are also useful for improving the quality of medical care and promoting patient engagement. Alert Systems  A typical and practical way of establishing an alert system is to predetermine each patient’s thresholds and notify the patient or their family when these are exceeded. In Japan, such alert values for diabetes, hypertension, dyslipidemia, and chronic kidney disease are determined by six clinical societies (Japan Diabetes Society, Japan Association for Medical Informatics, Japanese Society of Hypertension, Japan Atherosclerosis Society, Japanese Society of Nephrology, and Japanese Society of Laboratory Medicine) (Nakashima, 2019). These societies have formulated and published the “Recommended Configuration for Personal Health Records,” which additionally establishes reminder timing to promote clinical testing in accordance with standardized clinical guidelines. Healthix in New York provides an alert notifying a person designated by the patient, such as a family member, by an e-mail if a clinical event occurs during hospitalization. The notification contains the patient’s name and a link to a portal site where the designated person can view the notification details. This immediate understanding of the situation by a family member or other relevant person can reduce the patient’s anxiety and confusion. It also enables deeper understanding of the patient’s situation, and possible intervention in the care. Additionally, the family doctor of a patient transported to an emergency room can receive an alert and immediately notify the attending emergency doctor of information needed for treating the patient. This enables more appropriate care to be provided.

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Patient-Reported Outcome  Alert systems are mainly for sending information from the care provider to those dealing with the patient, whereas patient-reported outcome (PRO) is subjective information created on the patient’s side. While PRO has advanced in the field of clinical trials, it notably plays a major role in acquiring patient experience, and not only is an important component of patient engagement, but also helps in averting medical accidents and inappropriate medical care. PRO includes aspects such as degree of satisfaction with treatment, degree of symptoms and functioning, healthrelated quality of life, and compliance with treatment. It is important to measure it by an appropriate method that can ensure reliability and validity. For this purpose, in addition to development of scientifically reliable indicators, it is essential to understand how to conduct training for patients, families, and medical personnel. Paper-based PRO has also been promoted, but digitalized PRO combined with PHR in smartphone should be effective for reporting PRO on daily basis and for data collection.

SUMMARY Patient engagement is the process of building the capacity of patients, families, care givers, as well as healthcare providers to facilitate and support the active involvement of patients in their own care in order to enhance safety, quality, and people-centeredness of healthcare service delivery according to WHO. Key drivers of patient engagement are the growing demand for improved and more efficient communication between healthcare providers and patients, government policy, and accountable care models. Benefits of patient engagement include better communication, better care and improved outcomes, increased satisfaction, and lower costs. Patient engagement can be applied in myriad healthcare practices and in healthcare education/training for medical personnel. Examples include collection of patient experiences and care outcomes, healthcare education and training, design and development of patient-centered processes and systems, patient engagement in policy development, patients’ access to their own EHR, educating and empowering people to recognize their health needs, and seeking healthcare in a timely manner. Technologies such as biometric wireless devices, apps for smartphones, SMS appointment reminders, social media as a patient education tool, and medication adherence reminder are used to improve patient engagement. These tools help to fill the information gap among patients, their family members, and healthcare providers. Patients can communicate with healthcare providers and

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578    P art 6 • N ursing P ractice A pplications upload their health data to give more reference to their healthcare provider. Patient-generated health data can help to facilitate both patient-centeredness and preventive care. Taiwan’s MyHealthBank is a very good example of realizing a holistic, people-centered care model and making “My Data, My Decision” a reality to reach WHO’s 2020 health goal. However, there are concerns and issues for use of patient engagement technologies to improve patient engagement. They include health and health information literacy of the patients, and quality and safety of the technologies.

Questions 1. What is NOT a key driver of patient engagement? A. Rise of accountable care models and payment reforms B. Government policy such as U.S. Meaningful Use Program

C. Demand for improved communication between providers and patients D. Advancement of health information technology 2. What is NOT a benefit of improved patient engagement? A. Better communication with providers B. Increased patient outcomes C. Higher costs

D. Increased patient satisfaction 3. What is NOT a technology or tool used to improve patient engagement? A. Wearable and mobile devices B. Online communities C. Social media

D. Smartphone apps

E. Clinical point of care technology 4. What are the aspects of the patient-reported outcome? A. Degree of satisfaction with treatment

B. Degree of symptoms and functioning C. Health-related quality of life D. Compliance with treatment E. All of the above

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5. Which is NOT a typical behavior of an online patient community user? A. Seeking and providing informational support

B. Seeking and providing emotional support C. Seeking and providing financial support

D. Seeking and providing companionship 6. What is the greatest concern for Internet of health things for patient engagement? A. Security

B. Efficiency

C. Cost of device

D. Stability of connection 7. What are the examples of the direct-to-consumer (DTC) model in healthcare fields? 1. Personal genetic tests

2. Pharmaceutical market for over-the-counter drugs 3. Remote monitoring

4. Patient-generated health data A. 1 and 2 B. 2 and 3 C. 3 and 4 D. 1 and 4 8. How is patient-generated health data different from data generated in clinical settings? 1. Patients are primarily responsible for capturing and recording the data. 2. Providers are primarily responsible for capturing and recording the data.

3. Hospitals decide how to share or distribute these data to healthcare providers and others.

4. Patients decide how to share or distribute these data to healthcare providers and others. A. 1 and 2 B. 1 and 3 C. 1 and 4 D. 3 and 4

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Chapter 35 • Consumer Patient Engagement and Connectivity in Patients with Chronic Disease  

9. Enhanced patient-engagement, patient–provider interactions and reduced information gaps, and electronic patient-generated health data may facilitate both ______________. A. Patient-centeredness and preventive care B. Public and private partnership

C. Stakeholders engagement and quality improvement

D. Technology adoption and preventive care 10. What are the barriers to patient engagement? A. Low health literacy

B. Low health information system literacy

C. Quality and safety issues of patient engagement tools D. All of the above

Test Answers 1. Answer: D  Health IT is used by the healthcare providers or patients to improve patient engagement. The primary driver of improved patient engagement is communication. The growing demand for improved and more efficient communication between healthcare providers and patients has created an impetus to use patient-facing technologies to promote patient engagement. 2. Answer: C  Patient engagement lowers the medical costs.

3. Answer: E  Clinical point-of-care technology such as barcode medication administration system is used by care providers and not by patients; therefore, it is not a technology or tool that is used to improve patient engagement. 4. Answer: E  Patient-reported outcome (PRO) is subjective information created on the patient’s side. PRO includes aspects such as degree of satisfaction with treatment, degree of symptoms and functioning, health-related quality of life, and compliance with treatment.

5. Answer: C  Online Patient Community (OPC) is a subtype of Online Health Community (OHC), for its main users are patients or their relatives, though medical staffs may play the role of counselor or organizer. Behavior of OPC users can be classified into

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five categories: (1) seeking informational support, (2) providing informational support, (3) seeking emotional support, (4) providing emotional support, and (5) companionship.

6. Answer: A  The greatest concern for Internet of health things comes from its security.

7. Answer: A  Personal genetic tests and pharmaceutical market for OTC drugs. Direct-to-consumer (DTC) strategy is becoming critical in the business world for several reasons: (1) the demand of consumers for a better purchase experience; (2) DTC model gives companies an opportunity to build their brand relationship with customers; and (3) DTC model allows companies to collect customer data. DTC healthcare consists of products and services that a consumer can access without having to go through an intermediary, normally medical professionals or healthcare companies. Personal genetic tests and pharmaceutical market are two major healthcare areas involved in DTC model.

8. Answer: C  According to ONC, patient-generated health data (PGHD) are health-related data created, recorded, or gathered by or from patients (or family members or other caregivers) to help address a health concern. PGHD include health history, treatment history, biometric data, symptoms, and lifestyle choices. PGHD are different from data generated in clinical settings in two important ways: (1) patients, not providers, are primarily responsible for capturing or recording these data; (2) patients decide how to share or distribute these data to healthcare providers and others. Examples include blood pressure readings using home health equipment or exercise tracking using a mobile app or wearable device. 9. Answer: A  Globally, enhanced patient-engagement, patient-provider interactions and reduced information gaps, and electronic patient-generated health data may facilitate both patient-centeredness and preventive care. 10. Answer: D  All of the above. The first barrier to patient engagement often concerns health literacy and health information technology (HIT) literacy. If this remains an impediment, patient engagement never progresses smoothly, and can even yield poor results. Additionally, there are concerns and issues for use of patient engagement regarding the quality and safety of the technologies.

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ACKNOWLEDGEMENT We would like to acknowledge help from Dr. Usman Iqbal, an assistant professor from the global health department of Taipei Medical University, for this chapter.

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582    P art 6 • N ursing P ractice A pplications The Office of the National Coordinator for Health Information Technology (ONC). (2018a). Conceptualizing a data infrastructure for the capture, use, and sharing of patient-generated health data in care delivery and research through 2024. Retrieved from https://www.healthit.gov/sites/default/files/onc_pghd_ final_white_paper.pdf. Accessed on May 26, 2020. The Office of the National Coordinator for Health Information Technology (ONC). (2018b). Health IT Playbook, Section 5: Patient engagement. Retrieved from https://www.healthit.gov/playbook/patient-engagement/. Accessed on May 26, 2020. The Office of the National Coordinator for Health Information Technology (ONC). (2018c). What are patient-generated health data? Retrieved from https:// www.healthit.gov/topic/otherhot-topics/what-arepatient-generated-health-data. Accessed on May 26, 2020. Volpp, K. G., & Mohta, S. (2017). Patient engagement survey: Technology tools gain Support—But cost is a hurdle. NEJM Catalyst Insights Council Survey on patient engagement. Retrieved from https://catalyst.nejm.org/ patient-engagement-technology-tools-gain-support/. Accessed on May 26, 2020. Wang, X., Zhao, K., & Street, N. (2017). Analyzing and predicting user participations in online health communities: a social support perspective. Journal of Medical Internet Research, 19(4), e130. doi:https://doi.org/10.2196/ jmir.6834 World Health Organization. (2003). Adherence to long-term therapies: Evidence for action. Retrieved from https:// www.who.int/chp/knowledge/publications/adherence_ report/en/. Accessed May 26, 2020.

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part

7

Advanced Applications for the Fourth Nursing IT Revolution Kathleen A. McCormick

Not only are we in a fourth industrial revolution in IT and healthcare, but nursing is also preparing for the future of nursing practice and informatics skills by 2030. The fourth industrial revolution impacting nursing is driven by genomics and large clinical practice electronic health records. The new technologies described in this part are as follows: the big data resulting from genomics and electronic databases that are artificial intelligence, machine learning, deep learning, telehealth, robotics, and clouds. This collection of chapters describes the disruptive forces in healthcare driving the need for advanced technology, and the concepts of big data using examples internationally on how nursing informatics has captured big data to improve patient outcomes, the quality of care, and the economics of care. These are unusual times and the COVID-19 pandemic has triggered developments and expanded needs for technological innovations. During the same time that the authors were mostly in academic and business lockdown or working in practice to accelerate the use of technologies, we provided the authors the opportunity to update the innovations related to their chapters. Most authors in this part updated their chapters relevant to a pandemic response. Part 7 opens with Chapter 36 by Susan C. Hull titled New Models of Healthcare Delivery and Retail Clinics Producing Big Data. This chapter describes the four major disruptive technology companies producing big data: Apple, Amazon, Google and Alphabet, and Microsoft. Of these four major disruptive technologies, Ms. Hull added the Apple response to the COVID-19 pandemic by developing mapping and contact tracing enhancements. Also important to the future are the new payer models such as OSCAR, Walmart Health Plan, and Ochsner Health Network. New payer-provider models described are Optimum Health (United Health) and Humana. A major update on CVS, Walgreens Boots Alliance and Verily, and Walmart retail clinics is included in this chapter. The challenges to nursing informatics by 2030 are described since these new forces put the patient at the center of data collection, provide data anytime and anywhere, and are causing disintermediation of traditional boundaries producing big data.

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Eileen Koski and Judy Murphy from IBM in their chapter Artificial Intelligence in Healthcare (Chapter 37) provide the vision for nurses in informatics. They describe the fundamental concepts in artificial intelligence (AI). Data to be AI ready must be deliberated, constructed to be useful, spanned across multiple data sets, and harmonized with other data sets. Three major classes of data useful to the nursing profession that are highlighted are information synthesis, augmenting human performance, and surveillance data. Healthcare applications highlight the new type of clinical decision support integrated across the continuum of care, causal inference, personalized health, image and speech analysis, Internet of Things, syndromic surveillance, pharmaceutical applications, and nursing applications in prioritizing nurse’s workload, nurse coaches, and AI integrated with robotics to assist in many practice applications. When provided the opportunity to update their chapter, the authors added the dimension of AI accelerating the pace of existing trends in disease tracking, medical diagnosis and treatment, and vaccine discovery. Chapter 38 by Teresa A. Rincon and Mark D. Sugrue, titled Telehealth: Healthcare Evolution in the Technology Age, describes the focus on telehealth in nursing informatics. The most important priorities for nursing informatics in the intensive care environments are described in 15 points including clinical thinking skills, expert clinicians available outside the ICU, skillful communication, mutual respect for bedside and tele-ICU colleagues, emergency patient care management, monitoring physiologic status, knowledge of ventilator management, correlation of atrial blood gases to mechanical ventilation, knowledge of hemodynamic monitoring, understanding laboratory values, knowledge of medication, monitoring trends in vital signs, using tele-ICU to enhance patient safety, ability to interact with multiple disciplines, and ability to mentor. Factors for effectiveness are presented in this chapter, including the need for expert protocols and standards, which push the boundaries of decision support in telehealth. The chapter summarizes seven areas in the use of robots in healthcare. The area of telehealth is another area of rising demand during the COVID-19 pandemic and demonstrated the need to accelerate the use of telehealth in acute care and disease management in the community. Chapter 39 by Kathleen A. McCormick and Kathleen A. Calzone, titled Nursing’s Role in Genomics and Information Technology for Precision Health, begins with a definition of Precision Health demonstrating that nurses in informatics care for lifestyle, environment, access, as well as biologic information of which genomic is a part. They have provided an update of nursing’s engagement in genomics throughout the life cycle of care. They recommend the integration of genomic data into the electronic health record through expanding components of the nursing process. Finally, they provide a model of the components of public health, biotechnology, electronic health records, and the security needs of integrating genomics into information technology. Since precision health and the COVID-19 pandemic begin with identifying the genome associated with a tumor or virus, the informatics infrastructures needed for both are similar. Chapter 40 by Roy L. Simpson, titled Big Data Analysis of Electronic Health Record (EHR) Data, introduces the volume of data being acquired by the uptake of electronic health records. He provides the basics of big data as volume, velocity, variety, veracity, and value. He confirms the nine steps in preparing data for big data analytics, and describes the necessary building blocks to provide syntactic and semantic comparable data or harmonized data for integration, to provide algorithms for expert decision support, and to allow data mining of patient data integrated with clinician workflow to provide patient-centered accountable care. He focuses on the need for tools, nursing IT education, and culture change to advance big data analytics in the EHR environment. Chapter 41 by Lynn M. Nagle, Margaret Ann Kennedy, and Peggy White represents an international perspective of Nursing Data Science and Quality Clinical Outcomes. This chapter highlights the many years of work in developing a national model in Canada to capture data across the provinces to measure quality outcomes of care. Again, this chapter broadens the concept of decision support to using practice data to inform decision for healthcare delivery, policy, and costs. The authors describe the use of C-HOBIC tools/measures in different health sectors. They provide an optimistic view of the relationship of mining standardized nursing data and the impact on quality clinical outcomes. Finally, they state that the capacity to consistently measure and track clinical outcomes that are in place becomes critical during a global pandemic such as COVID-19 to effectively manage data nationally, and participate in data sharing globally. Another international perspective is given in Chapter 42 by Kaija Saranto, Ulla-Mari Kinnunen, Virpi Jylhä, and Eija Kivekäs titled Nursing informatics Innovations to Improve Quality Patient Care on Many Continents. They begin with the kinds of evidence that provide nursing care data. The 17 components of the Nursing Diagnosis and Classification are the necessary ingredients of mining data in a standardized and computerized way. At a country level the authors describe the KANTA Finish nursing elements representing guidelines for practice. They demonstrate the need to

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harmonize nursing data elements with the Oulu Patient Care Classification, and the integrated healthcare services data to be able to mine data for patients in all environments of healthcare. While the authors focus on examples from Finland, they also describe other countries doing similar work in nursing informatics innovations to improve quality patient care. The last chapter in this part (Chapter 43) is by Hyeoun-Ae Park and Heimar F. Marin, titled Global eHealth and Informatics. This chapter is an updated version of the chapter by Coenen, Bartz, and Badger in the 6th edition. The authors acknowledge the contribution of Dr. Nick Hardiker in providing new updates from the WHO, IMIA, and the list of organizations influencing nursing and eHealth globally. They finalize the chapter with trends supported by eHealth internationally, including Telemedicine and mHealth, Care Coordination, Self-management, Health Literacy, Digital Literacy, Health Equity, Patient safety, Electronic Health Records, Clinical Decision Support, Computerized Order Entry, Clinical Alarms, and Electronic Incident Reporting Systems.

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36 New Models of Healthcare Delivery and Retailers Producing Big Data Susan C. Hull

• OBJECTIVES . Describe healthcare initiatives of the “big four” disruptive technology companies. 1 2. Identify new payer models. 3. Discuss new payer-provider models. 4. Describe new retail partnerships and acquisitions. 5. Identify new retail partnerships for digital health. 6. Understand new digital health start-ups becoming vendor providers. 7. Provide background to engage informatics nurses in the design, socialization, implementation and evaluation of these models.

• KEY WORDS Big data production Care delivery models Digital care delivery Disruptive technologies Nurse informatics Retailers and Clinics Value-based care models

INTRODUCTION Informatics nurses bring over half a century of legacy to the global impact the effective use of health i­nformation technology (Health IT) has had on advancing the safety, quality, and efficiency of healthcare services to improve the health of individuals, communities and nations. The confluence of regulatory, business, and social changes with the activity of formidable industry digital disrupters are reframing relationships, care, and payment models across the healthcare ecosystem. While some of these efforts include informatics nurses in leadership roles, many have not fully tapped into the profound contribution nurses

must make in designing, leading, staffing, and supporting these emerging models. Informatics nurses are needed to bridge the knowledge gap to nurses in practice, education, research and policy, across all settings. Amazon, Apple, Google parent company Alphabet, and Microsoft, considered the “big four” disruptive technology companies, have been building momentum in healthcare for well over a decade. The confluence of their consumercentric focus, ability to understand the needs of individuals, massive AI/big data science capabilities, flurry of recent health partnerships, and advances in digital health innovation set context for these evolving new healthcare models. These digital disrupters bring market value directly related 587

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  TABLE 36.1    Confluence of Capabilities: Digital Health Disrupters Consumer-centric focus Ability to understand individual needs Massive AI/big data science capabilities Flurry of recent health partnerships Advances in digital health innovation

to the value of how data elements can be linked, classified, combined, and reorganized to create more value, and how innovative models, algorithms, and methods of analysis support emerging healthcare models (Press, 2018). Big data production supports rapidly emerging new value-based care models. See Confluence of Capabilities: Digital Health Disrupters, in Table 36.1 Healthcare retailers like CVS Health, Amazon, and Walmart are leading the movement to open their digital front door to healthcare. CVS Pharmacy, RiteAid, Target, Walgreens, and Walmart are making bold moves with a variety of healthcare payer, provider, and technology partners to offer direct-to-consumer care through telehealth platforms and mobile health apps, and direct-to-consumer video visits for non-life-threatening health concerns. These services complement their in-retail clinic and online health offerings and encourage convenience and avoidance of the timeconsuming and expensive visits to the hospital emergency department or doctor’s office (Wicklund, 2018a). Healthcare payers, both traditional and new entrants, are also experimenting with disruptive value-based care delivery models, by partnering and/or merging with these retailers and provider health systems to offer virtual and concierge services to large provider networks. Digital Health vendors are blurring the lines and becoming virtual primary, specialty, chronic, urgent and house-call care providers by establishing relationships with payers, providers, retailers, large employers, and consumers themselves. A diverse set of new partnerships are rapidly emerging, each bringing novel solutions to a rapidly changing ecosystem. This chapter describes examples of these digital disrupters and emerging healthcare models with a focus on implications for engaging informatics nurses in the design, socialization, implementation, and evaluation of these models. In some cases, nursing roles and scope of practice are well defined and demonstrated outcomes are further driving the spread and scale of these models. In most cases, the early approach and outcomes are speculative, and nursing roles beyond advanced nurse practitioners, are not clearly defined. Given its scope, this chapter recognizes yet excludes the U. S. Centers for Medicare & Medicaid (CMS) Innovation

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Center–funded projects and initiatives for care delivery and payment model innovation from consideration. These efforts continue to design and test innovation models and seek engagement of nurses and informatics nurses. The growing portfolio of projects that aim to achieve better care for patients, better health for our communities, and lower costs through improvement for our healthcare system are profoundly disruptive and will mash-up in novel ways with the new models described in this chapter.

DRIVERS FOR NEW CARE DELIVERY MODELS The primary driver for emerging value-based care models is the broad recognition that our health system is fundamentally broken, costs are unaffordable and unsustainable, and the system itself needs a serious repair. The continued escalation of the cost of care and the push for price and value transparency are adding substantial pressure to the urgency for change. It is difficult for any one stakeholder— individuals and families, business and employers, health care system providers, public and private payers, pharmaceutical and life science companies to grasp the magnitude of the cost of care and their contribution to it. Most Americans for example do not understand they are bearing the primary cost of healthcare because they are not directly paying for it. Despite efforts to reduce the total cost of care over the last three to four decades by coalitions of communities, businesses, and employers, it costs companies and their workers close to $20,000 to insure a family, up 55% in the last decade—before even seeing a healthcare practitioner. U.S. healthcare spending increased 3.9% to reach $3.5 trillion, or $10,739 per person in 2017. The private funding for healthcare accounted for 34% spending, Medicare 20%, Medicaid coming out of state budgets at 11%, out of pocket from consumers 10%, other private spending 7%, and other government such as military at 12% (Centers for Medicare and Medicaid Services (CMS), 2017). Communities, while long recognizing their role in transforming the health of individuals and communities are recognizing the disruptive power they can mobilize through collaboration and big data production to support the understanding and reduction of the total cost of care in their community (Mitchell, 2017; Hull & Edmunds, 2019). The Network for Regional Health Improvement’s (NRHI’s) Getting to Affordability Initiative: Regional Total Cost of Care project (funded by the Robert Wood Johnson Foundation) is an exemplar effort empowering the community to measure and impact the total cost of care (TCoC) within and across regions and the differences across communities (NRHI, 2019). With five pilot

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regions and thirteen expansion regions, these communities are tackling cost head-on by producing standardized, high-quality data analysis of the local cost of care, sharing information and collaborating to produce local change. NHRI’s related Affordable Care Together Movement is focused on disruptive care models by addressing three major drivers: health, price, and waste. One of the most tangible drivers catalyzing care model disruption is the almost decade momentum for consumers to have access to their digital health data (Markle Foundation, 2008; Ricciardi, Mostashari, Murphy,  Daniel,  & Simerino, 2013; Hull, 2014; Daniels, Deering, & Murray, 2014). Recent proposed rulemaking issued by CMS focuses on moving the healthcare system in the direction of interoperability and improve patient access to health data. It further signals CMS’s continued commitment to the vision set out in the 21st Century Cures Act and Executive Order 13813: to improve access to, and the quality of, information that Americans need to make informed healthcare decisions, including data about healthcare prices and outcomes, while minimizing reporting burdens on affected plans, healthcare providers, or payers (Centers for Medicare & Medicaid Services (CMS), 2020). Nonpartisan cross-sector industry alliances and collaboratives, healthcare advocacy and standard development organizations, are also supporting consumer’s access to and aggregation of health information, promoting technical solutions to facilitate ease and transparency in its exchange. The Alliance for Nursing Informatics (ANI) is supporting informatics nurses to be engaged in advocacy for consumer health at the board policy level, including roles in The CARIN Alliance and Exertia (Tiase & Hull, 2018; Dunn Lopez &Tiase 2020). Through collaboration with risk-bearing providers, payers, consumers, pharmaceutical companies, consumer platform companies, health IT companies, and consumer-advocates, CARIN Alliance members are working collaboratively with other stakeholders in government to overcome barriers in advancing consumer-directed exchange across the United States. Efforts include advancing a Common Payer Consumer Data Set (CPCDS) and increasing transparency of health data access and aggregation across clinical and claims data for citizens (CARIN & CPCDS, 2019).

DISRUPTIVE CARE DELIVERY MODELS Diverse industry partnerships, pilots, and market-moving activities are shaping major disruptions in how care is delivered and paid for. Disruptive technologies including cloud-based open collaboration and development platforms, artificial intelligence and advanced analytics,

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genomics and precision health, virtual care modalities, and population health tools are fostering experimentation, innovation, and pilots moving to scale. The big four disruptive technology companies are bringing new care delivery solutions and new tools for research and development. Of interest, few of these disruptive care models have defined roles for nurses explicitly, beyond care management and advanced nurse practice provider roles. Coverage of nursing roles across the models and examples is uneven, with many opportunities to advance nurse-led care models. Nurse informatics have many opportunities to partner, evaluate, and innovate in leading and supporting roles. While the use cases described here may individually signal a specific new care model, the synergy across these examples for integrating these disruptive technologies to bridge care and collaboration for consumers and clinical providers is not to be understated. The disruptive technologies cross and blur traditional care and payment boundaries and geographies, with technology bringing together patients, consumers, and c­linicians together in novel ways for both consumer-directed and clinically directed health care and research activities. These models are changing rapidly, with a tremendous focus on direct-to-­consumer options. Selected examples are organized within these categories:

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Healthcare Initiatives of the “big four” disruptive technology companies New payer models New payer-provider models New retail partnerships or acquisitions Digital health startups becoming providers

Healthcare Initiatives of the “Big Four” Disruptive Technology Companies Apple  Apple has accelerated momentum in healthcare innovation with the ubiquitous spread of the iPhone (launched in 2007), iPad (launched in 2010). Related capabilities with the release HealthKit and Healthkit API in 2014; Apple Watch in 2015; ResearchKit in 2015 and ResearchKit API in 2018; CareKit in 2016, CareKit API in 2017; and Health Records API in 2018 have catalyzed a flurry of diverse use cases and research studies across hospitals, clinics, physicians, labs, retail, life sciences companies, and with consumers themselves (9to5Mac, 2018; Elmer-DeWitt, 2019). Apple promotes IOS products and apps to improve care and efficiency for clinicians, nurses, and patients for care in the hospital, chronic care in the community and at home, while also promoting these tools to life sciences and pharmaceutical industries.

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Throughout 2017–2019, the confluence of Apple announcements signals the intensity of their healthcare interests and capabilities across use cases, products, partnerships, and geographies (Comstock, 2017b; Mack, 2017a; Elmer-Dewitt, 2019). Dates are included for ­relative momentum of efforts. Examples include, but are not limited to Apple’s:

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Partnership with UK-based Physitrack and mobileenabled electronic health record (EHR) developer DrChrono (February 2017) to integrate a home exercise program into the EHRs Web, iPad, iPhone, and Apple Watch (Mack, 2017b). Acquisition of sleep tracking hardware and software Finland-based company Beddit (May 2017), which works with apps for IOS and Apple Watch, signaling a deeper interest beyond the inclusion of their sleep monitor products in Apple stores since late 2015. Apple released their first Beddit 3.5 Sleep Monitor (December 2018), a 2-mm thin sensor strip placed under a bedsheet to track body movements, measure sleep time, heart rate, breathing, snoring, and bedroom temperature and humidity. The sensor can connect to the Apple watch and to Beddit’s third-party app for sleep analysis and heart rate tracking (Lee, 2018).

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CareKit enhancements (2016), while originally designed for consumers, that make it easier to connect apps to hospital back-ends, and provides a new view for patients that combines the information in the Care Card and Symptom Tracker views, for easier care plan progress assessment. The CareKit Blog describes progressive enhancements over time (CareKit Blog, 2016). ResearchKit 2.0 enhancements that include audiovisual and user interface improvements for developers and study participants, making it easier to add instructional videos. Improvements in attention measuring capabilities such as the Stroop test for mental processing and Trail Making Test to assess for visual attention and task swapping offer support for the proliferation of neurocognitive assessment apps (2017). HealthKit enhancements that focus on Apple’s deeper interest in closed-loop diabetes interventions. These include tracking blood glucose, relative mealtime to the sample (preprandial and postprandial glucose as different fields), insulin delivery dose for basil and bonus, carbohydrates and diverse activity data. In addition, Apple announced that Dexcom would take advantage of the Apple Watch’s native Bluetooth to

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allow Dexcom’s Continuous Glucose Monitoring (CGM) users to access their blood glucose data directly from the Watch, even if they’ve left their phone at home (Comstock, 2017a). Launch of the Apple Heart Study, a virtual health study funded by Apple, focused on Atrial Fibrillation with Stanford, American Well (for telemedicine consultations), and bio-telemetry (EKG patches) (November 2017). By March 2019, preliminary results revealed that this study enrolled an unprecedented 400,000 patients, and that wearable technology can safely identify heart rate irregularities, which subsequent testing confirmed to be atrial fibrillation (Stanford Medicine News, 2019). Launch of Zimmer Biomet mymobility™ health app in October 2018, and a two-year clinical study designed to measure patient outcomes and overall cost, for hip and knee replacement surgery. The study will track patient-reported feedback with continuous health and activity data from the Apple Watch in up to 10,000 patients. Seven hospitals and academic medical centers and eleven group practices and ambulatory surgical centers are participating (Zimmer Biomet, 2018). Launch of Health Records in IOS 11.3 (January 2018) with FHIR-based standard API, allowing consumers to (1) access and aggregate their health records to their iPhone or iPad, and (2) leverage OAuth 2.0 that allow users to authenticate once and create an enduring connection with the consumer’s EHR API to pull in any new health records and notify the user when new records are available. The effort started with 12 partnering health systems and the Department of Veteran’s Affairs, the support of the Carin Alliance, three EHR vendors (Athena Health, Cerner, and Epic), Lab Corp and Lab Quest data, and has expanded to near 300 organizations/ practices (as of June 2019). A recent announcement expands the offering further by allowing U.S. clinics and healthcare organizations with compatible EHRs to self-register for the personal health record system (Muoio, 2019a). Expansion of the Apple Watch Series 4’s ECG functionality, now in its fourth generation, to Hong Kong and 19 European countries including France, Germany, Italy, Spain, and the United Kingdom (March 2019). With De Novo clearance received in the United States from the FDA, the ECG app and irregular rhythm notification are now CE marked and cleared in the European Economic Area (Apple Newsroom, 2019).

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Offer of OneDrop’s wireless glucose monitoring system combined with a year of unlimited access to certified diabetes coaches, available in select US Apple stores (June 2019). The mobile app supports Apple’s HealthKit, CareKit, Health Records, and Siri Shortcuts on the iPhone, and the glucose monitor. The One Drop mobile app also forecasts user’s eight-hour project of glucose levels built on its machine learning-based predictions powered by over 2.2 billion data points, collected from more than 1.2 million users. Alongside each forecast, users also receive advice on relevant behaviors for maintaining time-in-range (One Drop, 2019). As part of multipronged effort to mitigate the spread of COVID-19, Apple announced April 2020 their global effort to support local governments and public health authorities by providing mobility data trends generated from Apple Maps. The reports show the change in volume of people driving, walking or taking public transit in their communities, as a proxy for understanding compliance with local “shelter in place” health orders. Additionally, Apple and Google announced a partnership to enable the use of Bluetooth technology to help governments and health agencies reduce the spread of the virus through citizen engaged contact tracing. (Apple Newsroom, 2020).

Amazon  In January of 2018, when CEOs Warren Buffet (Berkshire Hathaway), Jamie Dimon (J.P. Morgan Chase and Co), and Jeff Bezos (Amazon) got together to formulate a plan to address the rising cost of healthcare, for their 1.2  million employees, a frenzy of market interest and speculation ensued. These conversations created a ripple effect, with all parts of the healthcare ecosystem scrutinizing and speculating about the pace and reality of new healthcare catalyzed by these digital disrupters, the first CEO who has subsequently moved on in May 2020. In June 2018, the new nonprofit company, “focused on changing the way people experience health care so that it is simpler, better, and lower cost” and attracted Atul Gawande MD as CEO. The company has been recently named Haven in March 2019 (LaVito, Farr, & Son, 2019). Haven’s new Web site reveals additional focus, including improving the process of navigating the complex healthcare system and accessing affordable treatments and prescription drugs. Senior executive team members being recruited from diverse sectors, including nationally prominent healthcare providers, prayers, and start-ups. Clinical staff appears to be in recruitment, but few details frame unique roles for the nurse or nurse informatics beyond the generic “clinical” role, with limited information, and instructions to

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“apply here.” Industry speculation predicts that Haven will develop its own clinically integrated network for the employer to directly contract with by first building a curated network of doctors by analyzing performance, cost, and other data and steer members to the lowest cost provider and level of care (American Hospital Association, 2019). Amazon acquired PillPack, a virtual pharmacy company (September 2018), for an estimated $1billion, and began the initiation of a direct-to-the consumer marketing campaign to Amazon Prime customers (April 2019). Licensed to ship prescriptions to all states except Hawaii, it can fill schedule III, IV, and V medications, delivering individualized packages of presorted medicines. It is unknown how many of the estimated 85–100 million Amazon Prime members is it targeting (Pfifer, 2019). PillPack competes with Walmart and with major pharmacies including CVS Health (owned by Aetna); health plans who own pharmacy benefit managers including Express Scripts (owned by Cigna); OptumRx and UnitedHealth Group; and Anthem’s IngenioRx. Express Scripts, CVS Health, and OptumRx control pharmacy benefits for about 75 to 80% of the U.S. population. Amazon announced (April 2019) that its voice-based Alexa platform now supports the development of software that communicates HIPAA-protected health information through an invite-only Alexa Skills Kit program. Six pilots are being conducted (Jiang, 2019): 1. Pharmacy benefits manager members can query Alexa to check the status of home delivery prescriptions including receiving voice notifications of shipment (Express Scripts). 2. Health plan employees can manage personal health improvement goals and wellness incentives (Cigna).

3. Parents and caregivers enrolled in an Enhanced Recovery After Surgery Program can provide postoperative care team updates and recovery progress through Alexa and be updated on postoperative appointments (Boston Children’s Hospital).

4. Patients across a 51-hospital health system across seven states can query Alexa to find an urgent care center near them and schedule a same day appointment (Providence St. Josephs).

5. Customers in North and South Carolina can find an urgent care location within the 40 hospitals and 900 care location (Atrium Health).

6. Members of a digital health company for chronic conditions can query Alexa for their last blood glucose reading, measurement trends, and receive insights and health nudges personalized to the individual (Livongo).

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Google and Alphabet  Google has been engaged in healthcare well over a decade, including the start and then the failure of the personal health record, Google Health (2008–2011); Google Flu Trends (2009); the acquisition of London-based artificial intelligence firm, Deep Mind, and the release of Google Fit (2014). Alphabet, a multinational conglomerate established in 2015, is Google’s parent company. Like the other big four, Google’s health initiatives have intensified over the last 4 years. Examples include, but are not limited to Google and Alphabet’s:

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Calico, bringing scientists from the fields of medicine, drug development, molecular biology, genetics, and computational biology. It is focused on research to understand how biology controls the lifespan, including antiaging research. Formed as a research and development company by Google (2013), Calico has announced partnerships with: ◦◦ The Broad Institute of MIT and Harvard focusing on the biology and genetics of aging and early-stage drug discovery (2015); ◦◦ UCSF Walter Laboratory to license technology focused on modulators of the Integrated Stress Response (ISR), a set of processes activated in cells under conditions of stress (2015); ◦◦ The Buck Institute, to research the biology of aging and to identify potential therapeutics for age-related diseases (2015);

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◦◦ Ancestry DNA, to investigate human heredity of lifespan (2015); ◦◦ C4Therapuetics, a five-year collaboration to discover, develop, and commercialize therapies for treating diseases of aging, including cancer, by focusing on small molecule protein degraders as therapeutic agents to remove specific diseasecausing proteins (2017). Acquisition of London-based artificial intelligence company DeepMind (2014), followed by a variety of efforts, including the announcement that the team behind Streams will be joining Google (November 2018). Streams is an AI-powered assistant and mobile app for nurses and physicians to support rapid response teams, early detection of sepsis, and acute kidney injury (Postelnicu, 2018). Verily Life Sciences (formerly Google Life Sciences). It was created in 2015 to focus on harnessing health data, through the use of artificial intelligence, for clues that might predict and prevent diseases through partnerships with healthcare companies and universities (Harris,

2019). Verily is most known for an early project to develop smart contact lenses that can measure glucose levels for people with diabetes. In November 2018, the project was stalled with the recognition that the effort was not able to obtain accurate glucose readings from tears. Other Verily initiatives include, but are not limited to: ◦◦ Partnership with Duke University and Stanford University to launch Project Baseline (2017) to bridge the gap between research and care and create a more comprehensive map of human health. The 4-year observational populational health study hopes to recruit 10,000 volunteers to share data to the company, as well as add other partnering sites. ◦◦ Joint venture with ResMed (July 2018) to study the health and financial impacts of undiagnosed and untreated sleep apnea, and develop software solutions that enable healthcare providers to more efficiently identify, diagnose, treat, and manage individuals with sleep apnea and other breathing-related sleep disorders (Lovett, 2018). ◦◦ Partnership with the American Heart Association to expand the Project Baseline Health Study, to include the Baseline Platform, an end-to-end evidence generation platform for patients and clinicians. ◦◦ Partnership with the American Heart Association to initiate Research Goes Red that is both a movement and a research study, to engage women in heart disease research (February 2019). ◦◦ Direct-to-the-consumer Project Baseline studies, including: (1) heart biomarker study to understand an emerging risk factor Lipoprotein(a) or Lp(a); (2) a series of 1–2 hour in-person sessions or 5-minute surveys to engage consumers in research codesign; (3) a one-year, Type 2 Diabetes study with the goal of engaging 200 citizens to collaborate with Verily to test a smartphone app and health coaching program and use both medical and nonmedical devices; and (4) a 12-week, smartphone-based Mood Study. ◦◦ Creation of a new nonprofit health ecosystem OneFifteen with two health networks, Kettering Health Network and Premier Health to combat opioid addiction. Efforts include building a high-tech rehab campus in Dayton, Ohio, and Verily’s integration support to operate as a learning health system. Verily will support the

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application of analytics to measure the effectiveness of various interventions, with a focus on stringent standards for patient privacy and data security. Clinical care will be serviced by an operating partner of OneFifteen, Samaritan Behavioral Health, Inc., a subsidiary of Premier Health. The new campus opened for patients in June 2019. A Registered Nurse is the President and Chief Executive Officer and will oversee OneFifteen Health and OneFifteen Recovery (Precision Newswire, 2019; P&T Community, 2019). ◦◦ Strategic alliances with Novartis, Otsuka, Pfizer, and Sanofi (May 2019) to develop new clinical research programs utilizing the Project Baseline platform across therapeutic areas ranging from cardiovascular disease to oncology to mental health (Truong, 2019). ◦◦ New Project Baseline Health Consortium (May 2019) formed to bridge the gap between research and care, with inaugural members: Verily, Duke University Health System, Vanderbilt University Medical Center, University of Mississippi Medical Center, Mayo Clinic, Regional Health in South Dakota, and University of Pittsburgh. ◦◦ Joint venture Onduo by Verily and Sanofi as a virtual care program (launched February 2018), featuring a Virtual Diabetes Clinic, diabetes tools, coaching, and clinical support. Staffed with a team of expert Certified Diabetes Educators, doctors, nurses, nutritionists, pharmacists, data scientists, programmers, and engineers, the program is targeted to payers, employers, and primary care physicians. It does not appear that a nurse is in a leadership or advisory board role. Sutter Health of Northern California and Allegheny Health Network of western Pennsylvania are among the first healthcare networks to collaborate to test the Onduo platform. Partnerships are expanding, including with a diabetic foot ulcer sensor company Orpyx Medical Technologies (June 2019), with a vision of a world where everyone has access to the medical care and resources they need, no matter where they live or what health conditions they face. ◦◦ Dr. David Feinberg MD, a child psychiatrist and health executive from UCLA and Geisinger, was appointed Head of Google Health in January 2019 (Google, 2019). To follow, October 2019,

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Dr. Karen DeSalvo M.D., M.P.H., M.Sc., is named Google’s first Chief Health Officer (CNBC, 2019). Cityblock Health is a new care model developed through a 9-month design process in partnership with Alphabet’s Sidewalk Labs, an organization that designs and builds urban innovation. The Brooklyn-based, New York City effort, born in 2017, engaged Medicaid-eligible New Yorkers to codesign a groundbreaking care model designed to meet the complex health and social needs of low-income individuals. The primary emphasis of the delivery model is to engage members when and where it’s convenient to receive care, not in doctor’s offices, but instead in the community, their homes or mostly impoverished neighborhoods. Cityblock Health opened its first “Neighborhood Hub” in Crown Heights, Brooklyn, partnering with not-for-profit health plan EmblemHealth to care for a group of residents and plans to scale block-by-block, one relationship at a time. With approximately a hundred employees and over $23 million in an initial funding round (January 2018) plus a new series B round of $65 million (April 2019), founder and CEO, Iyah Romm, recognizes the challenges of long and entrenched complex barriers to care. The team has recognized that rebuilding trust with members in their neighborhood is the foundation. The new digital technology stack is not a solution in and of itself, yet it has enabled teams to reorient care around people themselves. The Commons is the technology platform to support members of the care team to bring data, documentation, and communication and collaborate. Functions include real-time hospital admission alerts, treatment tracking tools, and built-in SMS and video-visits to enable easy communication with members. Central to the new mobile- and team-based care model is a new role, the Community Health Partner (CHP). The CHP, the center of both communication and culture, is from the neighborhood community and is hired and trained in empathy and relationship building (Finnegan, 2019; Lovett, 2019).

Microsoft  Microsoft, like the other four disrupters, has well over a decade-long investment in healthcare, perhaps most well-known for starting in the personal health record Health Vault in 2007, with many challenges experienced in longitudinal adoption, and its planned shutdown in November of 2019. With a primary focus to build and expand solutions with cloud computing and artificial

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intelligence (AI), Microsoft tools and technology are almost ubiquitous in healthcare organizations across the world, from a nurse-led clinic in Kenya to some of the largest healthcare systems in the United States, United Kingdom. The Azure cloud was launched in 2010, and Microsoft formed an alliance with GE, resulting in the launch of Caradigm in 2012. In 2016, Microsoft sold its 50% stake in the company to GE Healthcare. Imprivata acquired a division of Caradigm, a GE Healthcare company to add to its identity and access management solutions (Arndt, 2017). In June of 2018, cancer informatics and digital pathology workflow vendor, Inspirata, announced that  it  was  purchasing GE Healthcare’s remaining population health management (PHM) interest, Caradigm (Inspirata, 2018). With the spin-off of the healthcare group as a separate business in 2012 and the Microsoft Health Platform launch in 2014, Microsoft is taking a different approach than the other three disrupters. Rather than building disruptive care model solutions, Microsoft is supporting clients to leverage their disruptive technologies, including the cloud, AI, and other development tools to research, develop, and scale new solutions easily and securely. For example, the Azure Security and Compliance Blueprint leverage Microsoft’s industry regulation and standards engagement. It is HIPAA and HITRUST compliant and offers turn-key deployment of a platform as a service (PaaS) and Infrastructure as a Service (IaaS) solution to ingest, store, analyze, interact, and securely deploy solutions with personal and nonpersonal health data. Of note, most of these projects are described as “research-based,” and there is minimal information on adoption and scale, and only speculation about nurse and nurse informatics roles in the research and development activities. Examples include, but are not limited to:

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Formation of Healthcare NExT in 2017 with the goal to integrate greenfield research, with a ­particular emphasis on cloud and AI, in health ­technology product development (Weinberger, 2018). Launch of the Microsoft Intelligent Network for Eyecare, a consortium including eyecare providers in India, the United States, Australia, and Brazil to leverage the Azure Machine Learning, for earlier screening and detection of eye diseases to reduce avoidable blindness. Over a quarter-million patient trials in India have been conducted using Microsoft AI models and are now being adopted by the Government for Public Health Screening Programs, hospitals, and medical systems (Lee, 2018b). Collaboration with St. Jude Children’s Research Hospital and DNAnexus, to create a cloud-based

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data-sharing and collaboration environment based on Microsoft Azure that contains an extensive public repository of pediatric cancer genomics data. The St. Jude Cloud stores and shares thousands of cancer patient samples mapped against the human genome template, enabling researchers around the world to access and exchange data on a global basis. Researchers from more than 450 institutions across 16 countries now have immediate access to data that previously could take weeks to download, as well as access to complex computational analysis pipelines. By 2019, St. Jude expects to make 10,000 wholegenome sequences available on the St. Jude Cloud. Collaborators hope the availability of these data could lead to progress in eradicating childhood cancer (Lee, 2018b; Proffitt, 2018). Microsoft Genomics brings the power of the Microsoft Azure cloud to genomic computation. It provides the performance, security, and scalability of a world-class supercomputing center, for on-demand gene sequencing services. It offers an Azure-powered genome analysis pipeline and an orchestrated ecosystem of innovative partners including BC Platforms and DNAnexus. The platform was born directly out of joint research between Microsoft and St. Jude Children’s Research Hospital, and Microsoft announced the general availability of Microsoft Genomics in February 2018. Microsoft’s AI health chatbot technology is a research-based project that will enable partners to build AI-powered conversational healthcare tools. Partners with work in development include MDLIVE with the intention to use this chatbot technology to help patients self-triage inquiries before they interact with a doctor via video. Premera Blue Cross, the largest health plan in the Pacific Northwest, plans to use the health bot technology to transform how members can look up information about their health benefits. Project InnerEye is a research-based, AI-powered software tool for radiotherapy planning to support dosimetrists and radiation oncologists to achieve 3D contouring of patients’ planning scans in minutes rather than hours. The machine learning technology builds tools for the automatic, quantitative analysis (and measurement) of threedimensional radiological images. Partnership with Adaptive Biotechnologies to leverage AI and machine learning to build a

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practical technology for mapping and decoding the human immune system. Their goal is to create a universal blood test that reads a person’s immune system to detect a wide variety of diseases including infections, cancers, and autoimmune disorders in their earliest stage when they can be most effectively diagnosed and treated (Lee, 2018a). Collaboration with Seattle Children’s Hospital and leading Sudden Infant Death Syndrome (SIDS) researchers are helping to create a collaborative genomics database to better detect in advance the correlation of risk factors for SIDs including the relationship to maternal smoking during pregnancy. The effort anticipates the use of whole genomes sequenced as an additional data set along with the CDC data and other diverse data sets levering Microsoft Azure, to identify other factors and ultimately ways to prevent SIDS (Lee, 2019a). Strategic alliance and multiyear partnership with Providence St. Joseph Health (PSJH) to accelerate the future of digital care delivery. By leveraging Microsoft Azure and AI, industry interoperability standards like FHIR to integrate siloed data sources in a secure and compliant cloud environment, the partnership will tap PSJH’s clinical expertise and innovation to transform the care experience. The effort is expected to standardize productivity and collaboration tools for 119,000 caregivers on Microsoft 365 and improve and support patient engagement using technologies including Dynamics 365, which combines the technologies of traditional enterprise resource planning (ERP) and customer relationship management (CRM) solutions into a single system. PSJH physicians and nurses will use Microsoft Teams for secure communication and collaboration, bringing together chat, video meetings and conferencing, and line-of-business applications into a single hub (Lee, 2019b; Providence St. Josephs, 2019).

New Payer Models Oscar, founded in 2012, is one example of a “new kind” of a health insurance company intentionally designed to be consumer focused, digital first and build on simple digital technologies like telehealth and virtual care. With a total of $1.3 billion in funding over eight rounds, Oscar has spread from its initial New York city base, to nine states and 14 markets planned for 2019. From 2018 to 2019, Oscar achieved a 250% increase in membership over 2017. Oscar is expected to launch Medicare Advantage plans in

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2020, with a $375 million Alphabet investment, which follows a previous $165 million investment from Alphabet and Founders Fund in March of 2018 (Livingston, 2018). With a plan for business and individual and families, Oscar has integrated free, unlimited, 24/7 telemedicine (through the mobile app) to talk with a board-certified doctor within 15 minutes, and in most plans offers a personalized geo-focused concierge team for members. Oscar claims that they are the most engaged members in the industry, with the highest mobile engagement of any insurer. Forty-three percent of members’ first visits to the doctor are routed through technology and customer ­service teams. Sixty-three percent of their member interactions with the healthcare system are virtual, and 41% of members turn to the Web and mobile apps every month (Gooch, 2018; Haefner, 2018). Oscar’s concierge team made up of a Care Guide, and a Nurse is available to answer questions about claims, make appointments, or help the patient find the best provider for their needs. The Nurse helps members prepare for procedures and discharge planning. Oscar has integrated a step-tracking feature in its app that allows members to sync the feature with their mobile device. If the member hits their step goal for the day, they can receive up to $1 in Amazon awards, with the possibility of up to $240 for the year in step tracking rewards. Oscar prides itself in going where members are, with 63% of member interactions with the healthcare system measured as virtual. Oscar is working with 3500 nationally ranked doctors across 140  specialties, and partnerships with more than half of the top 20 health systems in the United States. Nursing roles at Oscar are well defined and feature the Complex Case Management Nurse, a Registered Nurse (RN) who is the primary point of contact for members and their families, and who leads the care planning liaison/ reconciliation across all settings of care (Oscar Careers, 2019). The RN integrates into the person, the facility, and telephonic visits, across the members home, acute and subacute facilities, to assure care plan adherence. The RN initiates onsite hospital visits/rounds as needed to assess patient progress and meet with appropriate members of the patient care team when the patient is in a healthcare facility. The RN evaluates expected outcomes and associated costs of the plan of care as well as any proposed alternative plan of care; makes referrals to social work, pharmacy, and durable medical equipment. The RN assures that the care plan follows the member, with updates as to status changes, and is responsible for the coordination of postdischarge clinic appointments, medication reconciliation, primary care practices (PCP), and specialist visits. Other roles for nurses include Utilization Review and Case Management of noncomplex patients.

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Walmart Health Plan’s employer sponsored health coverage is expanding its Centers of Excellence programs for their employees, doubling the number of partners in the last two years to 15 sites, offering specialized treatments for cancer, organ transplantation, spine, knee, hip, and heart surgeries. Under the Centers of Excellence Program, Walmart contracts directly with providers including Cleveland Clinic, Mayo Clinic, Emory Healthcare, Geisinger Health System, Johns Hopkins, and Memorial Hermann. The direct-toprovider agreement is designed to ensure patients are getting evaluated to make sure they need care. And if they do need treatment, the Centers of Excellence are accountable to make sure the treatment is right the first time, to improve quality and keep the employee’s health and optimize return to work (Jaspen, 2018b). In a separate effort, Walmart is developing accountable care plans, with employees in certain markets. One such effort, with Ochsner Health Network, offers a Walmartspecific model, with reduced or waived copays and access to patient engagement specialists via a 24-hour call center explicitly designed for plan participants (Jaspen, 2018a). Like other large employers, Walmart’s experience for its employees in an accountable care model brings additional benefits, including carryover interest in designing better health services for their customers. In the United States alone, Walmart is positioned as the nation’s largest retailer to reach approximately140 million customers coming into its U.S. stores every week and 100 million shopping online. Walmart is in preliminary talks to buy Humana for $54 billion. With 4700 U.S. locations along with a national health plan, this acquisition would create a significant national healthcare force. Humana has partnered with Walgreens to test senior-focused primary care clinics in Kansas City (Ladika, 2019).

New Payer-Provider Models With efforts to more directly influence care decision and outcomes of providers, a series of payers have acquired or entered into joint venture agreements with providers. These efforts follow a trend to put providers of medical care under the same umbrella as health insurance companies. Examples include:

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Optum Health (United Health) $4 billion purchase of DaVita Medical Group, adding nearly 300 medical clinics across six states initiated in 2019. The DaVita operations sold to Optum include urgent care centers, surgery centers, and medical clinics with primary care doctors and specialists. DaVita Medical Group’s practices in California, Colorado, Florida, New Mexico, and Washington are now part of OptumCare. The FTC recently approved of the deal

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on divestiture conditions (June 2019). DaVita Medical Group, a subsidiary of DaVita Inc., is a large provider of kidney care and dialysis services. UnitedHealth is not acquiring the kidney cares centers (Jaspen, 2019). Optum Health (United Health) and Summit Partners $2.2 billion purchase of hospital medicine staffing company Sound Physicians (Sanborn, 2018). Humana and private equity consortium’s $4.1 billion purchase of Kindred Healthcare (2018), which includes 75 LTAC hospitals, 19 inpatient rehabilitation hospitals, 13 subacute units, 98 inpatient rehabilitation units and contract rehabilitation service businesses which served 1626 nonaffiliated sites of service (Commons, 2018). Humana and private equity consortium’s $1.4 billion purchase of Curo Healthcare Services (home health and hospice) By far the largest new payer-provider models were Cigna’s $67 billion acquisition of Express Scripts and CVS Health’s $69 billion acquisition of Aetna (Ladika, 2019).

New Retail Partnerships or Acquisitions Healthcare retailers like CVS Health, Walgreens, and Walmart are leading the movement to open a digital front door to healthcare, amplified by financial and reimbursement incentives made more transparent through recent partnership announcements. With a variety of healthcare payer, provider, and technology partners (including the big four digital disrupters), the retailers are offering a unique mix of direct-to-consumer care both through instore clinics and through virtual care modalities. Retail and healthcare are merging in unique ways with technology partners, life science companies, payers, and providers. Examples include, but are not limited to: CVS Health’s $69 billion bid to acquire Aetna represents the first combination of a retailer and a payer, combining the drugstore giant with one of the largest health insurers in the United States. Recent developments signal momentum in what care and payment model synergies and development may occur. In August 2018, CVS announced next steps in their partnership with Teladoc, making virtual healthcare offering available to patients 24/7, via video visits through Minute Clinics, nationwide. Visits are initiated via the CVS Pharmacy App, and patients/members will be connected to one of Teladoc’s providers rather than a MinuteClinic provider. Visits will cost $59, less expensive than most services offered in the stores (Wicklund, 2019a). CVS has recently developed three prototypes for a newly

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designed Health Hub in their stores in the Houston Texas area. With Health Hub expansion cities announced, CVS expects to spread this model to 1500 locations by 2021. With the Aetna acquisition, CVS is focusing on promoting a personalized approach to health for Aetna medical members. The Attain by AetnaSM App, which is the first-of-its-kind health experience designed in collaboration with Apple, is now available for download in the Apple App Store.  Walgreens Boots Alliance and Verily are partnering to deploy devices and other approaches designed to improve medication adherence as part of a broader strategic alliance designed to combine Verily’s healthcare technology innovation with Walgreens’ corner store presence and pharmacy services. Together with Onduo, Verily’s joint venture with Sanofi, Walgreens will also launch a Virtual Diabetes Clinic solution to their employees and family members with Type 2 diabetes through the Walgreens employee health plan. To help people with diabetes to manage their condition anytime, anywhere, Onduo provides tools, coaching, and remote access to healthcare professionals and specialty doctors. Walgreens reports that they are a retail pharmacy development and commercialization partner to Verily, and the organizations have agreed to work on and explore ways to improve access to advanced healthcare technologies and solutions. These may include sensors and software to help prevent, manage, screen, and diagnose disease – with a shared goal of scaling deployment at Walgreens retail locations. Walgreens Boots Alliance and the companies in which it has equity investments together have a presence in more than 25 countries and employ more than 415,000 people. With more than 18,500 stores in 11 countries, it also has one of the largest global pharmaceutical wholesale and distribution networks (Raths, 2018). With the launch of the MDLive platform through the Walgreen’s mobile app in 2014, the service has spread to stores across most states in the United States, and now Walgreen’s has added numerous partners through its curated app, Find Care Program. These partners include MDLive Behavioral, Dermatologist OnCall, Dexcom, Propeller Health, Second Opinion Today with Houston Methodist, Second Opinion Today with New York Presbyterian, Weill Cornell, Columbia (Comstock, 2018; Wicklund, 2018b). Walgreens has turned over the management of some of their instore clinics to healthcare system partners, including New York Presbyterian Hospital, Providence St. Josephs Health, Advocate Health Care, Norton Healthcare, and McClaren Healthcare. Most of these are branded to the healthcare provider. Clinic roles are primarily staffed by Advanced Nurse Practitioners, and some settings have

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kiosks with direct access to their Health System Emergency Physicians for an on-demand visit. Nurse informatics have many opportunities to partner in continuing to improve care delivery design and the integration of care and data in across person and virtual care platforms, home, health system, community and retail locations. United Health’s MedExpress Subsidiary and Walgreens established Urgent Care Centers adjacent to Walgreen’s stores in 2017, with separate entrances, with currently 15 locations as of February 2019. The goal of the partnership is to ease the accessibility for a pharmacist referral and immediate access to prescriptions following an urgent care visit (Jaspen, 2018c). LabCorp and Walgreens announced partnership to open Lab-Corps specimen collection sites inside 17 stores (June 2017) with plans announced (October 2018) to open 600 additional locations over the next four years, with services to include routine blood work, to employment drug testing to employee wellness, and in vitro diagnostics.

Digital Health Start-ups Becoming Vendor-Providers Omada Health is a digital health start-up focused on obesity-related chronic illnesses and the prevention of the onset of Type 2 diabetes and heart disease (hypertension and high cholesterol). A 16-week virtual care program focused on weight loss is combined with analytics, a digital scale, and mobile solutions. The company also has a national provider identifier, Current Procedural Terminology (CPT) codes, the endorsement from the U.S. Centers for Disease Control and Prevention (CDC) and bills claims as if it were a hospital. It is one of a few companies to challenge the traditional contract, and instead, develop one for value-based outcomes. Omada has successfully worked with Cigna to demonstrate a 4–5% weight loss target with a medical cost savings of $500–$1000 per member and is pursuing similar efforts with other health plans. With their branding as a digital therapeutics company for chronic care, this start-up is leveraging value-based reimbursement models and is blurring the lines of being both a health information technology vendor and a provider of ongoing care delivery (Sweeney, 2018). Livongo Health, the pioneer in Applied Health Signals, empowers people with a chronic condition to live better and healthier lives. The company got its start in 2012, by offering glucose monitors and test strips for people with diabetes. Today, the focus is a chronic disease, including behavioral health and weight loss, supporting more than 600 self-insured employers and health plans as customers, who cover the cost of the service for their members. Data

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scientists aggregate and interpret substantial amounts of health data and information to create actionable, personalized, and timely insights and nudges. Livongo recently announced the launch of a bidirectional integration with the top smartwatches in the market, including models from Apple, Fitbit, and Samsung. This follows a recent announcement that Livongo will leverage Amazon Lex and Amazon Polly to power its voice-enabled cellular blood pressure monitoring system. Livongo also announced a collaboration with Amazon Alexa to offer its members the ability to ask any of their Alexa-enabled devices to provide their blood glucose readings and health tips via the new HIPAA-compliant Livongo skill. Livongo purchased the behavioral health app MyStrength for more than $10 million at the beginning of the year and just a few months later unveiled behavioral health tools designed specifically for new and expecting parents. Livongo has officially filed for an IPO on the Nasdaq Global Select Market filed in June 2019, to go public with an initial public offering (Muoio, 2019b). DispatchHealth is another example of a disruptive care delivery model providing a hospital-without-walls. With a mission to “create the most advance and complete inhome care model in the world”, Dispatch Health offers on-demand urgent healthcare for people of all ages in the comfort of their home, with the intention of reducing unnecessary emergency room visits and hospitalizations while improving outcomes and the overall cost of care. Designed by clinicians, including nurses, nurse practitioners, paramedics and physicians, service lines are expanding to advanced and extended care, and other efforts to reinvent the house call.

COMMON PATTERNS IN NEW CARE MODELS The pervasive reach of the “big four” disruptive technology companies impact citizens and consumers in the broadest aspects of daily living. More specifically, they offer new tools and capabilities for real-time sensemaking, learning, and collaboration to coproduce research and development for new models and solutions to fix healthcare. Informatics nurses, have been in leading roles in the design, socialization, implementation, and evaluation of some of these emerging models. Thus, nurse informaticians should be early adopters using these technologies in practice, research, and education. These disruptive technologies are producing enormous amounts of data to analyze trends and how the technologies are impacting the value of care. However, the consumer and nurses need continuing education on these new technologies.

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Putting People at the Center of Care, Rather than Institutions Perhaps most profound, and directly related to professional nurse advocacy roles, is that fact that these new technologies and models of care are putting the person at the center of care. Care is whole person care, accessible anytime, anywhere, and without walls. Social determinants of health create the context for care planning and evaluation of outcomes. Care coordination activities are at the point of need, rather than simply at the point of service. The importance of care at home, including collaboration with community based organizations supports putting people, and their neighborhood health network at the center.

Care Anytime, Anywhere Nurses in all practice settings recognize that the place or location of care is no longer a limitation in how we provide care. Today’s digital care delivery platforms are changing the nature and location of care and how health and services are coproduced, untethered by walls or geographical setting. Professional Nursing Associations, like the American Academy of Ambulatory Care Nursing, are developing position statements (2017) that emphasize the explosive development of health information technology (Health IT) that enables ambulatory care nurses to provide care beyond a physical setting. Real time digital health data generated by the patient and about the patient including interactions with the nurse, care partners, and the environment, offer feedback loops to shape the design and evaluation of care interventions and care models. Nurses are leveraging smart devices, sensors and wearables, and digital twins of clinical operations, workplace data, and patient care interactions for real time adaptive learning and collaboration. The use of AI, algorithms, machine learning, advanced computer vision, and environmental sensing technologies require a next generation of decision support for shared care activities with patients. Informatics nurses are in key roles to partner with nurses across diverse settings to effectively integrate these novel data and technologies with electronic and personal health records and medical devices.

Disintermediation of Traditional Boundaries of Providers, Payers, Retail, Tech and Life Sciences Traditional boundaries of provider, payers, retail, technology, and life science start-ups are blurring, as care is orchestrated across ecosystems and the consumer is

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empowered to direct the aggregation and exchange of their health data. Health data may be clinically defined and collected, or consumer-defined and generated, through patientreported outcomes (PROs) and patient-generated health data (PGHD), wearables, and other connected devices. Vendors using value-based reimbursement models are further blurring the lines between vendor’s technology (apps, devices, wearables, sensors) and care delivery models. They too are providing big data from these devices and there are not enough qualified nurse clinicians, researchers, and educators to analyze these data. The primary model for healthcare information technology (Health IT) today is institution-focused, deployed around narrow use cases, and most often focused on the resolution or management of acute or chronic illness, rather than the promotion and maintenance of health and wellbeing. This model has resulted in innumerable data silos across a fragmented digital care ecosystem, divorced from the way patients, their families, and their caregivers wish to maintain health and wellness, experience care delivery, participate in research, and contribute to and benefit from their communities (Hull, Warner, & Smith, 2018).

Digital Workforce of the Future Efforts around the world are following suit, focused on harnessing the benefits of digital health technologies and market disruption to improve the health and care of individuals and populations. For example, the recent 2017 convening of an independent expert review board chaired by Eric Topol MD and three advisory panels in U.K.’s National Health Services considered how digital healthcare technologies, encompassing genomics, digital medicine, artificial intelligence (AI), and robotics, will change the roles and functions of clinical staff in all professions over the next two decades to deliver more effective, affordable, and more personal care for patients. The NHS Secretary of State for Health and Social Care commissioned the follow-on report, The Topol review: Preparing the healthcare workforce to deliver the digital future, as part of the strategy to enable the NHS to become the world’s largest learning organization for citizens and staff (Health Education England, 2019).

SUMMARY AND CONCLUSIONS Consistent with our nursing roles to improve health for the individual, family, communities and nations, these new care models represent a fundamental shift from an institutional centric of care and technology to a person-centered

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care model. Emerging models are focused on individual and caregiver empowerment and coproduction of services, caring, and value-based outcomes. The disruptive models build effective health information technology infrastructure around the individual and their caregivers, to experience a more integrated, connected, and affordable ecosystem. Affordability, easy access to care and information, and the push for transparency are driving significant innovation in how patients, consumers, and members access, aggregate, share, and exchange their health information. Our ability to continuously learn from the rich and novel clinical, research, community, and consumerdirected health data together, and to understand the impact of nursing and other care team interventions on quadruple aim outcomes, will support nurses to disrupt and transform care delivery and payment models successfully.

Test Questions 1. Who are considered the four major drivers of disruptive care models in the United States? A. Walgreens, Walmart, Amazon, and Microsoft B. Apple, Amazon, Google and Alphabet, and Microsoft C. Apple, Walmart, Walgreen, and Google D. None of the above

2. Who are the major retailers who have clinics involved in digital front doors to healthcare? A. Apple, Amazon, CVS, and Target B. CVS, Walgreens, and Walmart C. Target, Amazon, and Google D. None of the above

3. Which one of the four disruptors is bringing cloud technology to harness the power of genomics? A. Microsoft B. Amazon C. Apple

D. Google and Alphabet 4. Name a payer model focused on a “new kind” of health insurance. A. Microsoft

B. CVS clinics C. Oscar

D. Rite aid

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5. Whose new payer model focuses on a nurse who answers questions about claims, and makes appointments to help the patient find the best provider? A. CVS

B. Walmart C. Oscar

D. Amazon 6. In which new payer model does the nurse integrate in person, in facility, and telephonic visits across the members’ home, acute and subacute facilities to assure care plan adherence? A. Walmart B. CVS

C. Oscar

D. Microsoft 7. In which new payer model are the employers covered through a Center of Excellence program with providers such as Cleveland Clinic, Mayo Clinic, Emory Healthcare Geisinger Health System, Johns Hopkins, and Memorial Hermann? A. Oscar B. CVS

C. Walmart Health Plan D. Microsoft

8. Name a new payer-provider model of healthcare. A. Optum Health (United Health) B. Humana

C. Cigna and Express Scripts D. All the above

9. Which retail partnership will launch a Virtual Diabetes Clinic solution to their employees and ­family members with Type 2 diabetes? A. Walmart B. Apple

C. Walgreens Boot Alliance and Verily with Onduo and Sanofi D. CVS clinics

10. What is the name of a digital health start-up becoming a vendor provider focused on obesity-related chronic illness such as the prevention of Type 2 diabetes and heart disease, hypertension, and hypercholesterolemia?

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A. Apple

B. Microsoft C. Oscar

D. Omada Health

Test Answers 1. Answer: B  The four major drivers of disruptive care models in the United States are Apple, Amazon, Google and Alphabet, and Microsoft.

2. Answer: B  The three major retailers who have clinics are CVS, Walgreens, and Walmart. The others are also involved in digital retail but do not have retail clinics. 3. Answer: A  Microsoft is bringing cloud technology to harness the power of genomics. 4. Answer: C  Oscar is a new payer model focused on a “new kind” of healthcare insurance. 5. Answer: C  Oscar is a new payer model where the nurse is available to answer questions about claims and makes appointments to help the patient find the best provider for their needs.

6. Answer: C  Oscar has a nurse integrate in person, in facility, and telephonic visits, across the members’ home, acute and sub-acute facilities to assure care plan adherence. 7. Answer: C  Walmart Health Plan is a new payer model where the employers covered through a Center of Excellence program with providers such as Cleveland Clinic, Mayo Clinic, Emory Healthcare Geisinger Health System, Johns Hopkins, and Memorial Hermann.

8. Answer: D  All the above, Optum Health, Humana, and Cigna acquisition of Express Scripts are examples of new payer-provider models. 9. Answer: C  Walgreens Boots Alliance and Verily with Onduo and Sanofi will launch a Virtual Diabetes Clinic for employees and family members with type 2 diabetes. The purpose is to provide tools, coaching, and remote access to specialty doctors to help people with diabetes to manage their condition anytime, anywhere. 10. Answer: D  Omada Health is a digital health startup becoming a vendor provider focused on obesityrelated chronic illness such as the prevention of Type 2 diabetes and heart disease, hypertension, and hypercholesterolemia.

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Ladika, S. (2019). Why vertical mergers will continue to dominate health care. Managed Care. Retrieved from https://www.managedcaremag.com/archives/2018/12/ why-vertical-mergers-will-continue-dominate-healthcare Retrieved June 16, 2020 LaVito, A., Farr, C., & Son, H. (2019). Amazon’s joint health-care venture finally has a name: Haven. CNBC Health Tech Matters. Retrieved from https://www.cnbc. com/2019/03/06/amazon-jp-morgan-berkshire-hatha way-health-care-venture-named-haven.html Retrieved June 16, 2020 Lee, D. (2018). The Verge. Retrieved from https://www.thev erge.com/2018/12/7/18131220/apple-beddit-3-5sleep-monitor Lee, P. (2018a, January 4). Microsoft and adaptive biotechnologies announce partnership using AI to decode immune system; diagnose, treat disease. Microsoft Blog. Retrieved from https://blogs.microsoft.com/ blog/2018/01/04/microsoft-adaptive-biotechnologiesannounce-partnership-using-ai-decode-immune-systemdiagnose-treat-disease/ Retrieved June 16, 2020 Lee, P. (2018b, February 28). Microsoft’s focus on transforming healthcare: Intelligent health through AI and the cloud. Microsoft Blog. Retrieved from https://blogs. microsoft.com/blog/2018/02/28/microsofts-focus-transforming-healthcare-intelligent-health-ai-cloud/ Retrieved June 16, 2020 Lee, P. (2019a, May 21). Harnessing big data in pediatric research to reimagine healthcare. Microsoft Blog. Retrieved from https://blogs.microsoft.com/blog/2019/05/21/ harnessing-big-data-in-pediatric-research-to-reimaginehealthcare/ Retrieved June 16, 2020 Lee, P. (2019b, February 7). Microsoft for healthcare: Technology and collaboration for better experiences, insights and care. Microsoft Blog. Retrieved from https:// blogs.microsoft.com/blog/2019/02/07/microsoft-forhealthcare-technology-and-collaboration-for-betterexperiences-insights-and-care/ Retrieved June 16, 2020 Livingston, S. (2018, August 14). Oscar Health to launch Medicare Advantage plans in 2020 with $375 million Alphabet investment. Crain’s Cleveland Business. Retrieved from https://www.crainscleveland.com/ article/20180814/news01/171756/oscar-health-launchmedicare-advantage-plans-2020-375-million Retrieved June 16, 2020 Lovett, L. (2018, July 11). Verily, ResMed team up for joint sleep apnea venture. MobiHealth News. Retrieved from https://www.mobihealthnews.com/content/verilyresmed-team-joint-sleep-apnea-venture Retrieved June 16, 2020 Lovett, L. (2019). To address health disparities, Cityblock closes $65M Series B round. MobiHealth News. Retrieved from https://www.mobihealthnews.com/content/ address-health-disparities-cityblock-closes-65m-seriesb-round Retrieved June 16, 2020 Mack, H. (2017a). ResearchKit steps up AV features, adds new testing abilities. MobiHealth News. Retrieved from

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https://www.mobihealthnews.com/content/researchkitsteps-av-features-adds-new-testing-abilities Retrieved June 16, 2020 Mack, H. (2017b). Physitrack, drchrono integrate digital physical therapy with mobile EHR in Apple-orchestrated partnership. Retrieved from https://www.mobihealthnews.com/content/physitrack-drchrono-integratedigital-physical-therapy-mobile-ehr-apple-orchestrated Retrieved June 16, 2020 Markle Foundation. (2008). Consumers as network participants. Retrievable here: https://www.markle.org/sites/ default/files/CF-Consumers-Full.pdf Retrieved June 16, 2020 Mitchell, E. (2017). The road to affordability: How collaborating at the community level can reduce costs, improve care, and spread best practices. Health Affairs Blog. Retrieved from https://www.healthaffairs.org/do/10.1377/ hblog20171108.983176/full/ Retrieved June 16, 2020 Muoio, D. (2019a, June 28). Apple Health Records now available to all US providers with compatible EHRs. MobiHealth News. Retrieved from https://www.mobihealthnews.com/news/north-america/apple-healthrecords-now-available-all-us-providers-compatible-ehrs Retrieved June 16, 2020 Muoio, D. (2019b, June 28). Livongo makes its IPO plans official. MobiHealth News. Retrieved from https://www. mobihealthnews.com/news/north-america/livongomakes-its-ipo-plans-official Retrieved June 16, 2020 Retrieved June 16, 2020 Network for Regional Healthcare Improvement. (2019). Retrieved from https://www.nrhi.org/ Retrieved June 16, 2020 One Drop. (2019, June 29). One Drop’s Digital Diabetes Management System now available in select US Apple stores. One Drop Today Blog. Retrieved from https:// onedrop.today/blogs/press-releases/one-drop-s-digitaldiabetes-management-system-now-available-in-selectus-apple-stores Retrieved June 16, 2020 Oscar careers. (2019). Complex case manager nurse. Retrieved from https://www.hioscar.com/ careers/1712311 Retrieved June 16, 2020 Pfifer, R. (2019). “Meet PillPack”: Amazon rolls out Rx delivery direct marketing. Healthcare Dive. Retrieved from https://www.healthcaredive.com/news/meet-pillpackamazon-rolls-out-rx-delivery-direct-marketing/553364/ Retrieved June 16, 2020 Postelnicu, L. (2018). DeepMind’s Streams team to join Google. MobiHealth News. Retrieved from https://www. mobihealthnews.com/content/deepmind%E2%80%99sstreams-team-join-google Retrieved June 16, 2020 Precision Newswire. (2019). OneFifteen to offer comprehensive model of care to people with opioid use disorder in Dayton, Ohio. Retrieved from https://www.prnewswire. com/news-releases/onefifteen-to-offer-comprehensivemodel-of-care-to-people-with-opioid-use-disorder-indayton-ohio-300790366.html Retrieved June 16, 2020

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P&T Community. (2019). OneFifteen opens the first of its state-of-the-art facilities for the treatment of opioid use disorder in Dayton, Ohio. Retrieved from https://www. ptcommunity.com/wire/onefifteen-opens-first-its-stateart-facilities-treatment-opioid-use-disorder-dayton-ohio Retrieved June 16, 2020 Press, G. (2018). How Apple, Amazon, Facebook, Google, and Microsoft made 2018 the year that IT mattered a lot. Forbes. Retrieved from https://www.forbes.com/ sites/gilpress/2018/12/30/how-apple-amazon-facebookgoogle-and-microsoft-made-2018-the-year-that-it-mattered-a-lot/#415c4dd41cee Retrieved June 16, 2020 Proffitt, A. (2018). Clouds and collaboration: How St. Jude built the team that built the St. Jude Cloud. BioIT-World. Retrieved from http://www.bio-itworld. com/2018/08/16/clouds-and-collaboration-howst-jude-built-the-team-that-built-the-st-jude-cloud.aspx Retrieved June 16, 2020 Providence St. Josephs. (2019). Microsoft and Providence St. Joseph Health announce strategic alliance to accelerate the future of care delivery. Providence St. Joseph Press Release. Retrieved from https://www.providence. org/about/news/2019/07/microsoft-and-providence-stjoseph-health-announce-strategic-alliance Retrieved June 16, 2020 Raths, D. (2018, December 20). Walgreens, Verily partner on medication adherence. Healthcare Innovation. Retrieved from https://www.hcinnovationgroup.com/ finance-revenue-cycle/health-it-market/news/13030982/ walgreens-verily-partner-on-medication Retrieved June 16, 2020 Ricciardi, L., Mostashari, F., Murphy, J., Daniel, J., & Simerino, E. (2013). A national action plan for consumer engagement via E-Health. Heath Affairs, 32(2), 376–384. Sanborn, B. (2018, June 7). OptumHealth and Summit partners to acquire staffing firm Sound Inpatient Physician Holdings for $2.2 billion. Healthcare Finance. Retrieved from https://www.healthcarefinancenews.com/news/ optumhealth-and-summit-partners-acquire-staffingfirm-sound-inpatient-physician-holdings-22 Retrieved June 16, 2020 Stanford Medicine News. (2019). Apple Heart Study demonstrates ability of wearable technology to detect atrial fibrillation. Retrieved from https://med.stanford.edu/ news/all-news/2019/03/apple-heart-study-demonstratesability-of-wearable-technology.html Retrieved June 16, 2020 Sweeney, E. (2018). Omada Health made its name with its technology. Now it’s luring insurers with operational innovation. Fierce Healthcare. Retrieved from https://www.fiercehealthcare.com/tech/omada-health-diabetes-digital-healthoperational-innovation-cigna-bcbs-minnesota-sean-duffy Retrieved June 16, 2020 Tiase, V. L., & Hull, S. C. (2018). ANI involvement with consumer-directed exchange and the CARIN alliance. CIN: Computers, Informatics, Nursing, 36(2), 68–69.

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Truong, K. (2019). Alphabet’s Verily boosts its project baseline with new health system, pharma partners. Medcity News. Retrieved from https://medcitynews.com/2019/05/ alphabets-verily-boosts-its-project-baseline-with-newhealth-system-pharma-partners/ Retrieved June 16, 2020 Weinberger, M. (2018). How Microsoft’s top scientists have built a big business in hacking healthcare—and helped a lot of people along the way. Business Insider. Retrieved from https://www.businessinsider.com/peter-leemicrosoft-research-healthcare-next-interview-2018-2 Retrieved June 16, 2020 Wicklund, E. (2018a). CVS, Teladoc partner on directto-consumer telehealth service. mHealth Intelligence.

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37 Artificial Intelligence in Healthcare Eileen Koski / Judy Murphy

• OBJECTIVES 1. Outline the background of artificial intelligence, including definitions and how AI is differentiated from other computational methodologies. 2. Identify the value propositions for the use of AI in healthcare. 3. Describe the foundational components of most AI applications today. 4. Summarize some of the ways AI is being used in healthcare. 5. Articulate the basis for understanding and considering the implications of AI for healthcare in the future.

• KEY WORDS Artificial intelligence (AI) Artificial neural networks Augmented intelligence Classifiers Cognitive computing Deep learning Image analysis Machine learning (ML) Natural language processing (NLP)Public health surveillance

INTRODUCTION The term artificial intelligence (AI) is used to describe a computer program or system that can learn and make decisions based on its own accumulated experience. This is the primary capability that distinguishes AI from an expert, decision support, or rules-based systems, which are based on expert human reasoning (Weiss & Kulikowski, 1991). 1. How has this definition changed over time? In some ways, the definition itself has never really changed, but as systems have become more powerful and sophisticated, expectations for the extent of capabilities that might be possible have continued to expand. The earliest formulations of the concept

were that a computing machine could process information in such a way that it went beyond merely improving on the speed and accuracy of computational task programmed by humans to a state where its processes so closely resembled human decision-making that it could fool a human observer (Turing, 1950). As the speed and accuracy with which computers could process rules programmed for them increased, the concept was expanded to the idea that computers could both learn and extrapolate, essentially applying the rules created for one application to a different situation. Eventually, the definition evolved to mean a system that could digest information and extract pertinent concepts and outcomes on its own to apply to this process. 605

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2. What is specifically meant by the concept of “AI in Healthcare?” When we speak of AI in healthcare, we are essentially referring to the application of AI methodologies to a variety of significant challenges in healthcare. Many systems referred to today as AI in healthcare most closely resemble expert systems— i.e., rules-based systems. Such systems have been programmed with extensively detailed instructions that can be processed more rapidly, efficiently, precisely, and consistently than a human could, but with essentially no interpretation beyond the scope of the original programming and limited by the knowledge of the experts from whom the rules were obtained. Newer systems can learn and offer novel insights based on digesting, parsing, combining, and evaluating data directly. In practice, there is no sharp dividing line between expert-driven and data-driven systems, as most modern systems combine multiple modalities.

A BRIEF HISTORY OF AI The concept of artificial intelligence (AI) has been around since well before the birth of what we now think of as computers. Early “computers” or “calculators” were people who were skilled at mathematical computations. The early computing machines were primarily intended to enhance these human abilities with respect to speed, precision, and accuracy, but were not viewed as having intelligence per se. Much of the earliest work that formed the basis of what we now think of as artificial intelligence comes from advances in many fields including engineering, biology (neural networks in single-cell organisms), experimental psychology, communication theory, game theory (notably John Von Neumann and Oskar Morgenstern), mathematics and statistics, logic and philosophy (e.g., Alan Turing, Alonzo Church, and Carl Hempel), and linguistics (Noam Chomsky’s work on grammar) (Buchanan, 2005). In 1950, in his seminal paper “Computing Machinery and Intelligence” (Turing 1950), Alan Turing posed the question of whether a machine could think. In this paper, he described what he called the “imitation game” in which a computer is programmed to behave like a human in the context of a game. In his broader, philosophical exploration of this concept, Turing raised the question of whether a computer can learn, which remains one of the hallmarks of any definition of AI. Many realized that much of what Turing envisioned relied on the development of faster computing machinery as well as improved coding techniques. Progress continued along these lines, and a small group organized

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the Dartmouth AI Workshop in 1956, which is considered by many to be the seminal event in the formation of AI as a discipline (McCarthy, Minsky, Rochester, & Shannon, 1955). Over time, as different elements of a learning computer were developed, it was still believed that a computer could only follow instructions and could not compete in arenas believed to require the type of mental capacities that rely on complex strategies and intuition, such as playing chess. That notion was dispelled in 1997 when IBM’s Deep Blue computer bested the world chess champion, Garry Kasparov (Weber, 1997). Even so, that system was essentially a rules-based system, albeit one that could evaluate virtually all known strategies previously used in a given situation and select the best one in a fraction of a second, but it did not create novel strategies on its own. One of the more intriguing recent developments in 2017 was the decision by a group of engineers at Google DeepMind to build a machine that could play the game Go, not by training it on all the famous strategies used by grandmasters of the game, but by teaching it the rules of the game, training it with relatively low level matches, and then allowing it to play against another computer and learn on its own. This machine, Alpha Go, was able to defeat human grandmasters using strategies that completely confounded established theories of the game, clearly demonstrating that they could not have been pre-programmed into the machine (Sheldon, 2017, Silver et al., 2017). The rules of chess and Go are well known, even if the strategies for playing both games are extraordinarily complex and varied. In thinking about applying similar AI strategies to healthcare, it is important to remember that we have not yet uncovered all of the rules that govern how our bodies and minds function—and malfunction. Even so, as we continue to learn, we can certainly build on these principles moving forward.

FOUNDATIONAL CONCEPTS IN AI The following are 10 of the foundational concepts and methodologies that form the basis of AI applications in healthcare: 1. Natural Language Processing (NLP): Automated language analysis intended to parse unstructured text to respond to queries or otherwise extracting data in analyzable form (Sager, Lyman, Buchnall, Nhan, & Tick, 1994). In healthcare, this typically refers to the process of extracting salient clinical concepts such as symptoms, diagnoses, and treatments from the narrative text, such as clinical notes.

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2. Medical Language Processing: A term used to describe the application of NLP to address issues specific to medical data (Friedman, 1997; Sager, Lyman, Nhan, & Tick, 1995). 3. Classifiers: Processes that map input data into categories or classes, also referred to as predictions. Classifiers are trained on a data set for which the proper classification is known, i.e., labeled, so that new and unlabeled data can be correctly categorized (Weiss & Kulikowski, 1991). For example, diagnosis or relevant clinical outcome would be viewed as a classification or prediction. A set of data on a group of patients with a range of symptoms and other relevant characteristics that also include a clinician’s or expert’s diagnosis would be considered “labelled” data that could then train a system to predict which diagnosis would apply to patients in another data set with those same characteristics, but without a specified diagnosis. 4. Artificial Neural Networks: Computer systems loosely modeled on biological nervous systems that are a type of classifier, but in which no assumption is made about the underlying distribution of the population to which they are applied (Weiss & Kulikowski, 1991, pp. 81–82), allowing them to be applied to more complex data distributions. 5. Machine Learning (ML): An automated system able to process large volumes of data and extract meaningful information from it (data mining) as well as to use this information to address practical problems (decision support) (Weiss & Kulikowski, 1991, pp. 113–138). Machine learning is generally divided into supervised learning, which uses expert knowledge to guide its decision-making processes and which requires labeled data, and unsupervised learning, which is more oriented toward discovery of previously undefined patterns derived directly from the data itself. 6. Deep Learning: The process of employing multi-layered deep neural networks (DNNs), allowing integration of multiple data types. Deep learning can utilize supervised or unsupervised learning of feature presentations in each layer (Yu & Deng, 2011)

8. Augmented Intelligence: Technology that is intended to assist humans in utilizing or extending their capabilities, i.e., assistive technologies (Information Week, 2018)

7. Cognitive Computing: A term applied to computing that can involve multiple. AI methodologies applied in such a way as to replicate the human cognitive performance. Such systems can be capable of examining both questions and possible solutions from new perspectives allowing potentially novel, data-driven, as opposed to an expert-driven solution (Marshall, Champagne-Langabeer, Castelli, & Hoelscher, 2017).

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9. Image Analysis: The process of extracting meaningful information specifically from images as opposed to numeric, categorical, or text data. In healthcare, it is typically associated with the analysis of highly complex digital diagnostic data, such as MRI images and ultrasound scan data. 10. Speech Analysis: Similar to image analysis, however, focused on extracting meaningful diagnostic and prognostic insights from patterns discernible in recorded speech (Corcoran et al., 2018).

HEALTHCARE VALUE PROPOSITIONS There are numerous challenges in healthcare today that can potentially benefit from AI and others that may render AI virtually indispensable. While the science and practice of medicine have continually advanced, the reality is that our healthcare systems do not function as well as is needed, populations are not served equitably, the cost of healthcare is spiraling out of control and yet, by many standards, the health of our population is not what it should be. AI cannot address all of the societal, political, and environmental issues at play. However, there are many ways that AI can contribute to increasing efficiency, raising standards of care, delivering on the promise of precision medicine, and supporting research. While they are all deeply interrelated and overlapping, conceptually there are three major classes of activities in particular which are driving the need for AI solutions: 1. Information synthesis

2. Augmenting human performance 3. Surveillance

Information Synthesis On a very basic level, due to the dramatic increase in the amount of medically relevant data that is generated each year, there is too much information to be handled without computational help. Much of this increase falls into three categories: 1. Patient Data: Data specifically generated on or by patients, as individuals and as populations, has dramatically increased. Data on patients and groups of individuals include data both intentionally and traditionally generated for medical purposes, such as test results, diagnoses, treatments, and medical

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histories, as well as lifestyle data on behaviors such as diet and exercise that can have medical utility or implications. Patient data may now include such data as continuous biological measurements, self-reported data, sensor data, images, audio recordings, etc. 2. Data Complexity: The increasing complexity of the individual data elements that are now being stored electronically has expanded exponentially. The introduction of such data-rich testing methodologies as gene sequencing and MRIs represents both a qualitative and quantitative increase in the complexity of what could theoretically be viewed as single data elements, i.e., test results. As lifespans have increased and populations have aged, the number of people with multiple comorbidities has increased, yielding complex treatment regimens with more significant risks of interaction effects. 3. Literature: The amount of medical literature published each year continues to rise. Well over 1 million citations are added to MEDLINE/PubMed each year based on statistics published by the U.S. National Library of Medicine (NLM)(National Library of Medicine, 2017). The annual number of citations increased over 70% in the 10-year period from 2006 (688,444) to 2016 (1,178,360). While no researcher or clinician would need to read all literature published in any given year, a 2004 study estimated that a physician trained in epidemiology would need to spend 627.5 h per month to keep up with new professional insights that should be incorporated into their clinical knowledge base (Alper et al., 2004). Given the 70% increase in annual citations since that study was done, this would undoubtedly require over 1000 h per month by now, far exceeding the total number of hours in a month.

Augmenting Human Performance Augmenting human performance is perhaps the oldest and most straightforward application of AI in healthcare. In clinical settings, given the amount and complexity of data generated in healthcare today, it is not possible for even the most skilled clinicians to successfully digest all available information. Even if they could, not all clinicians have the same level of experience to inform them in this process. In cases of rare diseases or unusual presentations of common diseases, in particular, a patient’s presentation may fall well outside of most clinicians’ experience, often leading to delays or errors in diagnosis and treatment. The problem can be exacerbated in the case of emergency departments or underserved areas where clinicians may

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need to treat a wide array of problems. Treatment decisions are further complicated by the increasing number of patients with multiple comorbidities, increasing the likelihood of interaction effects that must be considered. The impact of medical errors was well documented in the landmark 2000 Institute of Medicine publication To Err Is Human (Institute of Medicine, 2000), and a call to improve quality was issued in their follow-up publication, Crossing the Quality Chasm (Institute of Medicine, 2001). These publications focused on addressing deficiencies in the quality of care, but the 2015 publication of Improving Diagnosis in Healthcare (National Academy of Sciences, Engineering, and Medicine, 2015) emphasized the continuing problems that arise specifically from an initial missed or delayed diagnosis. Such a delay typically leads to delays in correct treatment, which can, in turn, lead to negative outcomes, psychological stress, and excess costs potentially due to both wasted, incorrect treatments as well as the costs of treating a condition at a later stage than might have been possible with an earlier diagnosis. In research settings, many of the current issues related to the challenge of accelerating progress toward the application of personalized and precision medicine against the backdrop of the costly and time-consuming process of running clinical trials.

Surveillance There are two distinctly different types of surveillance that can, and already do, benefit significantly from AI. The first is the area of public health surveillance, which faces significant challenges due to increasingly urban and mobile populations accelerating the spread of infectious diseases. The second is in the form of oversight, such as post-market surveillance of adverse drug reactions, fraud detection, and achieving a better understanding of how medicine is practiced and how medical knowledge is distributed and adopted. Both forms of surveillance have moved beyond the most traditional forms of surveillance and reporting methods to incorporate analysis of massive real-world data sets. At the same time, both applications require advanced techniques to adjust for noisy and incomplete data to identify meaningful patterns.

HEALTHCARE APPLICATIONS AI can be applied to a wide variety of problems in healthcare. Many applications are focused on data-dense areas of medicine, such as genomics and proteomics, while others are focused on new data sources, such as the use of passive sensors in the home. While hardly comprehensive, representative applications spanning multiple disciplines and application areas in healthcare are described below.

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Clinical Decision Support

precision medicine is to use an individual’s genetic makeup to determine the correct choice and dose of treatment (Collins, 2010). As genetic testing has become less expensive, this goal has begun to seem reachable. However, the discovery of these diseases and treatment determinants remains an on-going process. Personalized medicine and healthcare allow us to anticipate this by applying patient similarity algorithms to population health databases to predict trends in patients with similar characteristics (Ebadollahi et al., 2010) without such knowledge. Personalized healthcare can also encompass the entire continuum of care from wellness to symptoms, to diagnosis and treatment, to the impact of behavioral and environmental influences on the totality of a patients’ experience.

Clinical decision support (CDS) systems emerged in the 1970s to 1980s to reduce variation in medical practice. Such systems were intended to assure that clinicians had information available about available treatment and diagnostic options, and even to make specific suggestions, supported by the basis for the suggestions given (Young, 1982). Due in part to the limitations of the computing power then available, many of the early systems addressed focused problems within a narrow application area, such as the CARE system designed to support decision-making on critically ill patients (Siegal et al., 1976; Siegal et al., 1980). Early CDS systems were also primarily rules-based, derived from expert knowledge. With increased capacity and processing speed, contemporary CDS systems can now integrate data across the continuum of care and can take advantage of newer technologies, such as machine-learning and deep learning, but there is always a need to continually balance the expert and data-driven perspectives (Bezemer et al., 2019). Some systems also focus specifically on diagnostic challenges, not just treatment decisions.

Causal Inference Causal inference is the process of establishing a causal relationship between an exposure and an outcome via mathematical and statistical means in the absence of a direct experiment capable of demonstrating causality. Causal inference has been well established in such fields as epidemiology (Susser, 1977) where population-based data is analyzed in place of experimentation that is not feasible due to the required time frames, sample sizes, or ethical considerations. Historically, such studies were primarily based on data collected by public health agencies and registries, but in recent years this has been augmented with data from large repositories of observational data such as claims data and EHR data. These data repositories are usually referred to as “real-world data” since they reflect the actual practice of medicine across divergent populations and potentially reflect much longer time periods than are feasible for clinical trials. While randomized clinical trials are considered the gold standard in clinical research, the cost and time required are significant, and requisite limitations on sample size can limit the generalizability of conclusions drawn from them. Clinical trials can now be emulated using causal inference against such databases (Hernán & Robins, 2016).

Precision/Personalized Medicine One of the most exciting potential application of AI in healthcare is certainly in the areas of precision medicine, and precision healthcare more broadly. The goal of

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Image and Speech Analysis The application of AI capabilities to image analysis has been particularly important in reducing variability in the interpretation of image data and has recently been shown to be on a par with expert analysis in such areas as retinal imaging (Hwang et al., 2019) and mammography (Rodriquez-Ruiz et al., 2019). Thanks to AI, speech analysis has also become a valuable diagnostic tool for conditions beyond traditional realms of speech pathology. For example, a small amount of normal conversation has been shown to predict psychosis across protocols and risk cohorts using a completely automated analysis (Corcoran et al., 2018). Both image and speech analysis can lead to quality improvements and earlier diagnosis.

Internet of Things (IoT) The widespread adoption of smart devices, combined with advances in sensor technologies, has yielded new opportunities to apply AI not just in traditional medical settings, but wherever a patient may be. Supportive IoT technologies in homes and outpatient settings may enable the elderly and chronically ill patients to retain more independence and support aging in place (Darwish & Hassanien 2011).

Syndromic Surveillance Public health surveillance has a rich history of collecting epidemiological data such as incidence reports on infectious diseases, but most reporting was based on confirmed cases of known conditions. Reporting on confirmed cases of known conditions created limitations when recognizing the appearance of previously unknown conditions, as well as emerging and re-emerging conditions. The field of syndromic surveillance emerged to apply a variety of computational

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technologies to the task of correctly identifying patterns of concern to public health agencies (Mandl et al., 2004).

Pharmaceutical Applications AI platforms are currently used in the process of drug discovery to predict the potential toxicity of target drugs as well in drug repurposing to capitalize on approved drugs that might have a potentially new clinical application. AI can also help identify drugs that may have passed safety tests, but either failed to pass efficacy tests for their original intended uses or were never commercialized for other reasons (Scheiber et al., 2009).

COVID-19 Response The recent COVID-19 pandemic has highlighted many of the shortcomings in our healthcare system. The need for improved AI applications to detect, address, and cope with sudden, dramatic and rapidly evolving needs, as well as to help understand and address inequities of care in our system, could not be more apparent. Several of the applications described earlier in this chapter, notably syndromic surveillance, drug repurposing, and image analysis, are already in use for these purposes. Tools such as adaptive CDS can be used to support rapid updates to triage and mechanical ventilation protocols. Other efforts, such as the use of AI to accelerate drug and vaccine discovery, require formidable computing power that is often out of the reach of many organizations. In response to this need, the COVID-19 High Performance Computing Consortium brought together government, industry, and academic leaders to provide access to the world’s most powerful high-performance computing resources in support of COVID-19 research. At the consumer facing end of the AI spectrum, many organizations have launched smart chatbots and mobile phone apps to help prevent misinformation, help people better understand their risk level, and engender infectionlimiting behaviors (Miner, Laranjo, & Kocaballi, 2020). Some phone apps have offered a form of digital contact tracing, which has also raised privacy concerns (Soltani, Calo, & Bergstrom, 2020), demonstrating the complexity and challenge of appropriately designing and deploying such tools. At biological, epidemiological, and societal levels, AI applications are helping to support advanced analytics to better understand all aspects of the pandemic from the genetic characteristics of the virus itself, to the behavior of the virus in populations, to the behavior of the healthcare system as it finds ways to adapt, and finally to the behavior of the populations themselves with respect to containment

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and return to work activity. The lessons learned and the opportunities identified during this difficult period will help to inform us as we seek to make our healthcare system more responsive, more resilient, and more equitable.

Nursing Applications Nurses will benefit from some of the other applications already described in this section. For example, CDS and analytics with AI can help nurses improve quality and safety, and reduce costs as they deliver care. The use of IoT can assist in the assessment and monitoring of patients remotely, eliminating the need for some home care visits. But there are some unique applications to nursing as well. IoT connected to the EHR as well as speech recognition with NLP can ease the burden of nursing documentation by automatically adding data from medical devices and voice notes into the record. AI can assist in the organization and prioritization of the nurses’ workload as their shift begins and adjusting interventions throughout the shift as new orders and patient needs change. The use of robots with AI can extend the reach of nurses by helping in some of the following situations: hospitalized children needing to overcome their fears, children on the autism spectrum, or adults with Alzheimer’s. Robots can also assist with medication management or monitoring the elderly for signs of dementia. AI applications can serve as “nurse coaches” to help patients manage a health condition or make behavior changes through the use of pre-recorded video clips and training materials that are triggered by algorithms as each patient uniquely works through the virtual session. AI can support nursing care management applications. AI can also be used to assist with simulation training of nursing and other healthcare professionals. Nurses are uniquely positioned to gain value from the use of AI. However, for systems to be configured correctly and work properly, nurses need to be involved and engaged from the outset to ensure such systems are well-engineered and can be trusted. The future will be informed by data and intelligent technologies that can recommend action based on information, harnessing the power of AI so nurses can deliver care better, faster, and more safely. Nursing’s charge will be to continue to integrate the human aspects of care while automating some of the detection and reasoning processes (Sensmeier, 2017).

CHALLENGES There are many challenges to building and adopting AI systems in healthcare as well as potential societal concerns posed by both privacy considerations and access limitations.

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From a technical perspective, one of the primary challenges to AI adoption relates to the data used to train systems. Training an AI system requires labeled data, which are data sets containing both the data that formed the basis of a decision or classification, such as a diagnosis and the resulting decision as made by an expert. AI imaging systems, for example, would be trained on data such as MRI or retinal scan data that have been annotated by an expert so that records are “labeled” with a diagnosis. Unfortunately, labeling data is a time-consuming and expensive process, and also limits the system to the knowledge base of the experts who labeled the data. While applying the highest expertise available to this task is generally viewed as positive—i.e., allowing wider access to the most expert thinking in the field— there is still a chance for experiential bias in any such process. Even when expert bias can be addressed, the highest quality labeled data can still have limitations. For example, there may be limited generalizability of a model due to characteristics of the population from which it was originally derived that may not apply to other populations. From a clinical user acceptance standpoint, the primary issues are transparency, explainability, and validation. Many AI systems seem like “black boxes” to clinicians, who may be uncomfortable accepting a recommendation when they cannot see how it was derived or validated. Even systems that have done validation studies may need to explain their reasoning since end users may not know how closely the validation was tied to guidelines or practice patterns that might be unique to a specific location (Keikes et al., 2018). The systems that are most likely to succeed, particularly in the near-term, will be those that can explain the basis for their conclusions and recommendations based on literature or other referenced scientific premises. Over time, such systems may gain broader acceptance. However, it would be wise for the AI community to closely examine the user community’s experience with decision support and recommender systems, as well as broader concerns about health information technology (HIT) safety. From a usability standpoint, it will be necessary to ensure that AI systems don’t simply add yet another layer of potential alert-fatigue onto clinicians. AI systems that can improve on the current processes and offer intelligent alerts, prioritized by criticality, should achieve much better acceptance among users. From a patient perspective, issues of privacy loom large, but there is likely to be a generational shift among younger patients already accustomed to digital life. From a cultural perspective, there will be changes in medical and nursing education, which have long stressed

knowledge acquisition and retention but will need to consider how to best balance that with knowledge management, interpretation, and appropriate application of AI (Wartman & Combs, 2019). It will take time and careful thought to calibrate how much information individuals need to directly acquire and retain to properly use and evaluate the systems at their disposal, but such adaptations are a normal feature of progress in all aspects of healthcare. In terms of the cultural perspective, it is also important to consider the impact of the technology hype cycle with respect to AI development and adoption. With virtually all disruptive technology, initial enthusiasm can lead to disappointment and disillusionment when early implementations fail to live up to expectations. It can take time for new technologies to be optimally adapted to all of the realities of the situation to which they are applied, and healthcare is one of the most complex domains to be addressed by AI, particularly due to the complex interrelationship and interdependence of scientific knowledge and its limitations, regulatory constraints and requirements, traditional and emerging practice patterns, patient and provider attitudes and beliefs, and organizational structures and imperatives. Despite this extraordinary complexity—or even because of the compelling need it engenders—­expectations can be unrealistically inflated. It will take patience, wisdom, and care to find the best ­applications and models for implementation of AI in healthcare, and this is still very much a work in progress. While it may be premature to consider equity and fairness in availability and access, it is important to consider both the role such systems can play as well as how they will be perceived and received by both clinicians and patients. At its best, AI can reduce the cost of healthcare by improving the speed and accuracy of both diagnostic and treatment decisions, thus reducing morbidity and mortality from treatment delays, as well as wasted expenses from incorrect treatment decisions. Additionally, AI systems can help to deliver expert input to patients who may not have direct access to such specialists, for whatever reason. Again, at their best, such systems can help to reduce clinician burden, thus facilitating more patient interaction. On the downside, such systems could also exacerbate the communication gap between patients and clinicians and contribute to alienation on both sides.

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SUMMARY The potential value of AI applications to healthcare has been well documented, and such systems may become virtually indispensable as ever more precise

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and detailed data continues to be amassed about every aspect of health. AI can help to reduce variability, improve precision, accelerate discovery, and reduce disparities. AI can empower patients and potentially allow clinicians to focus more on their patients than their data. This may finally allow healthcare professionals to fully relate to their patients not just as caring healers, but as healers supported by the combined wisdom of the best of medical research and analytic technology combined. Many of the challenges ahead will relate to the process of understanding the optimal uses of such systems; addressing the technological, systemic, regulatory, and attitudinal roadblocks to successful implementation, and finally, appropriately integrating such systems into the fabric of healthcare and society. An interesting question to consider is the evolution of responses to AI. Medical experts raised and trained before AI was part of daily life and might have a wary approach that demands both validation and explication of the processes by which AI systems reach their conclusions. Future generations, raised on digital assistants and recommendations, might be less skeptical and more accepting, but might they be too accepting? As with all new technologies, an appropriate balance will evolve, but we will need both visionaries moving us forward, and skeptics asking tough questions, to assure that the greatest possible benefits of AI in healthcare are achieved.

Test Questions 1. How many new articles are indexed by MEDLINE/ PubMed every year? A. 25,000–100,000

B. 100,000–500,000

C. 500,000–1,000,000 D. >1 million

2. What is the difference between labeled and unlabeled data?

A. Labeled data has clear field names, unlabeled data does not. B. Labeled data is historical data, unlabeled data is new.

C. Labeled data includes an expert classification, unlabeled data does not. D. Labeled data comes from a clearly identified source, unlabeled data does not.

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3. Why might causal inference be applied?

A. Some hypotheses cannot be tested for ethical reasons. B. Some data sets are too big or complex to analyze. C. When a clinical trial would be too expensive or time-consuming to conduct D. A or C but not B

4. When would syndromic surveillance be used?

A. To estimate the incidence of specific disease syndromes B. To detect unknown conditions C. To link symptoms to identified conditions D. To detect incorrect diagnoses

5. What best describes the relationship of AI-based clinical decision support (CDS) systems to traditional rules-based CDS?

A. AI-based CDS are not completely data driven.

B. AI-based CDS incorporates expert knowledge. C. AI-based CDS may use machine-learning. D. Some of the above

6. What is the true hallmark of an AI system?

A. The system can process vast amounts of data very quickly. B. The system can analyze data more quickly than a human. C. The system can learn from its own experience. D. The system is more precise than humans.

7. What is Natural Language Processing?

A. A system designed to extract clinical concepts from narrative text

B. A system designed to make a diagnosis based on speech

C. A system designed to translate from one language to another D. A system used in speech pathology 8. What does a classifier do?

A. It uses diagnostic codes to identify a patient with a specific disease. B. It groups patients by classes of diseases.

C. It tries to predict an outcome based on trained associations. D. It ranks the severity of disease.

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9. Which of the following is NOT a challenge to ­implementing an AI system? A. Concerns about patient privacy B. Mature EHR systems C. Inadequate training data

Bezemer, T., de Groot, M. C. H., Blasse, E., ten Berg, M. T., Kappen, T. H., Bredenoord, A. L., … Haitjema, S. (2019). A human(e) factor in clinical decision support systems. Journal of Medical Internet Research, 21(3), 2–9. Buchanan, B. G. (2005). A (very) brief history of artificial intelligence. AI Magazine, 26(4), 53–60. Collins, F. S. (2010). The right drug at the right dose for the right person. In F. S. Collins (Ed.), The language of life (pp. 231–250). New York, NY: HarperCollins. Corcoran, C. M., Carrillo, F., Fernández-Slezak, D., Bedi, G., Klim, C., Javitt, D. C., … Cecchi, G. A. (2018). Prediction of psychosis across protocols and risk cohorts using automated language analysis. World Psychiatry, 17(1), 68–75. Retrieved from https://covid19-hpc-consortium.org. Accessed on June 4, 2020. Darwish, A., & Hassanien, A. E. (2011). Wearable and implantable wireless sensor network solutions for healthcare monitoring. Sensors, 11, 5561–5595. Ebadollahi, S., Sun, J., Gotz, D., Hu, J., Sow, D., & Neti, C. (2010). Predicting patient’s trajectory of physiological data using temporal trends in similar patients: A system for near-term prognostics. AMIA Annual Symposium Proceedings, 2010, 192–196. Friedman, C. (1997). Towards a comprehensive medical language processing system: methods and issues. Proceedings of the AMIA Annual Fall Symposium, 595–599. Hernán, M. A., & Robins, J. M. (2016). Using big data to emulate a target trial when a randomized trial is not available. American Journal of Epidemiology, 183(8), 758–764. Hwang, D. K., Hsu, C. C., Chang, K. J., Chao, D., Sun, C.H., Jheng,Y.C., … Chiou, S.H. (2019). Artificial intelligencebased decision-making for age-related macular degeneration. Theranostics, 9(1):232–245. Information Week. (April 5, 2018). It’s about augmented intelligence, not artificial intelligence. Retrieved from https://www.informationweek.com/big-data/ai-machinelearning/its-about-augmented-intelligence-not-artificialintelligence/a/d-id/1331460. Accessed on May 27, 2020. Institute of Medicine. (2000). To err is human: Building a safer health system (pp. 26–48). Washington, DC: The National Academies Press. Institute of Medicine. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academies Press. Keikes, L., Medlock, S., van de Berg, D. J., Zhang, S., Onno R. Guicherit, O.R., … van Oijen, M.G.H. (2018). The first steps in the evaluation of a “black-box” decision support tool: a protocol and feasibility study for the evaluation of Watson for Oncology. Journal of Clinical and Translational Research, 3(Suppl 3), 411–423. Mandl, K. D., Overhage, J. M., Wagner, M. M., Lober, W.B., Sebastiani, P., Mostashari, F., … Grannis, S. (2004). Implementing syndromic surveillance: a practical guide informed by the early experience. Journal of the American Medical Informatics Association, 11(2), 141–150.

D. Concerns about black box solutions

10. What can make it difficult for a clinician to make a correct diagnosis? A. A patient may have waited too long to see a doctor. B. A patient may present with unusual symptoms or a rare disease. C. The physician may never have seen the patient’s condition before. D. Either B or C

Test Answers 1. Answer: D  There are over 1 million new articles indexed by MEDLINE/PubMed every year.

2. Answer: C  Labeled data include expert classifications, unlabeled data does not. 3. Answer: D  Causal inference might be applied when some hypotheses cannot be tested for ethical reasons, and when a clinical trial would be too expensive or time-consuming to conduct.

4. Answer: B  Syndromic surveillance would be used to detect unknown conditions. 5. Answer: A  AI CDS Systems are not exclusively data driven.

6. Answer: C  A true hallmark of an AI system is that the system can learn from its own experience. 7. Answer: A  Natural Language Processing is a system designed to extract clinical concepts from narrative text.

8. Answer: C  A classifier tries to predict an outcome based on trained associations. 9. Answer: B  A mature EHR system is NOT a ­challenge to implementing an AI system.

10. Answer: D  It is difficult for a clinician to make a correct diagnosis when a patient presents with unusual symptoms or a rare disease, and when the physician may never have seen the patient’s condition before.

REFERENCES Alper, B. S., Hand, J. A., Elliott, S. G., Kinkade, S, Onion, D.K. & Sklar, B.M. (2004). How much effort is needed to keep up with the literature relevant for primary care? Journal of the Medical Library Association, 92(4), 429–437.

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Marshall, T., Champagne-Langabeer, T., Castelli, D., & Hoelscher, D. (2017). Cognitive computing and science in health and life science research: artificial intelligence and obesity intervention programs. Health Information Science and Systems, 5(1), 13. McCarthy, J., Minsky, M. L., Rochester, N., & Shannon, C. E. (1955). A proposal for the Dartmouth summer research project on artificial intelligence. Retrieved from http:// www-formal.stanford.edu/jmc/history/dartmouth/dartmouth.html. Accessed on May 27, 2020. Miner, A. S., Laranjo, L., & Kocaballi, A. B. (2020). Chatbots in the fight against the COVID-19 pandemic. npj Digital Medicine, 3, 65. https://doi.org/10.1038/ s41746-020-0280-0. National Academy of Sciences, Engineering, and Medicine. (2015). Improving diagnosis in health care (pp. 19–30). Washington, DC: National Academies Press. National Library of Medicine. (2017). Yearly citation totals from 2017 MEDLINE/PubMed Baseline. Retrieved from https://www.nlm.nih.gov/bsd/licensee/2017_stats/2017_ Totals.html. Accessed on May 27, 2020. Rodriquez-Ruiz, A., Lang, K., Gubern-Merida, A., Broeders, M., Gennaro, G., Clauser, P., … Sechopoulos, I. (2019). Stand-alone artificial intelligence for breast cancer detection in mammography: comparison with 101 radiologists. Journal of National Cancer Institute, pii: djy222. doi: 10.1093/jnci/djy222. Sager, N., Lyman, M., Buchnall, C., Nhan, N., & Tick, L. (1994). Natural language processing and the representation of clinical data. Journal of American Medical Informatics Association, 1(2), 142–160. Sager, N., Lyman, M., Nhan, N., & Tick, L. (1995). Medical language processing: applications to patient data representation and automatic encoding. Methods of Information in Medicine, 34, 140–146. Scheiber, J., Chen, B., Milik, M., Sukuru, S. C., Bender, A., Mikhailov, D., … Jenkins J. L. (2009). Gaining insight into off-target mediated effects of drug candidates with a comprehensive systems chemical biology analysis. Journal of Chemical Information and Modeling, 49(2), 308–317. Sensmeier, J. (2017). Harnessing the power of artificial intelligence. Nursing Management, 48(11), 14–19.

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Sheldon, N. (2017). This more powerful version of Alpha Go, learns on its own. Wired. Retrieved from https:// www.wired.com/story/this-more-powerful-version-ofalphago-learns-on-its-own/. Accessed on May 27, 2020. Siegal, J. H., Fichthorn, J., Monteferrante, J., Moody, E., Box, N., Nolan, C. & Ardrey, R. (1976). Computer-based consultation in care of the critically ill patient. Surgery, 80, 350–364. Siegel, J. H., Cerra, F. B., Moody, E. A., Shetye, M., Coleman, B., Garr, L., … Keane, J.S. (1980). The effects on survival of critically ill and injured patients of an ITU teaching service about a computer- based physiologic care system. Trauma, 20, 558–579. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., … Hassabis, D. (2017). Mastering the game of Go without human knowledge. Nature, 550(7676), 354–359. doi:10.1038/nature24270 Soltani, A., Calo, R., & Bergstrom, C. Contact tracing apps are not a solution to the COVID-19 crisis. Brookings, April 2020. Retrieved from https://www.brookings.edu/ techstream/inaccurate-and-insecure-why-contact-tracing-apps-could-be-a-disaster. Accessed on June 4, 2020. Susser, M. (1977). Judgement and causal inference: criteria in epidemiologic studies. American Journal of Epidemiology, 105(1), 1–15. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 49, 433–460. Wartman, S. A., & Combs, C. D. (2019). Reimagining medical education in the age of AI. AMA Journal of Ethics, 21(2), E146–152. Weber, B. (1997). Swift, and slashing, computer topples Kasparov. The New York Times. Retrieved from https:// www.nytimes.com/1997/05/12/nyregion/swift-and-slashing-computer-topples-kasparov.html?module=inline. Accessed on May 27, 2020. Weiss, S. M., & Kulikowski, C. (1991). Computer systems that learn (pp. 1–10). San Francisco, CA: Morgan Kaufmann Publishers. Young, D. W. (1982). A survey of clinical decision aids for clinicians. British Medical Journal (Clinical research ed). 285(6351), 1332–1336. Yu, D., & Deng, L. (2011). Deep learning and its applications to signal and information processing. IEEE Signal Processing Magazine, 28, 145–154.

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38 Telehealth: Healthcare Evolution in the Technology Age Teresa A. Rincon / Mark D. Sugrue

• OBJECTIVES . Describe various applications of telehealth: past, present, and emerging. 1 2. Identify benefits and challenges to delivering telehealth services. 3. Understand modifiable factors that influence the adoption and effectiveness of telehealth services. 4. Recognize innovations that can be used to deliver telehealth services that enhance access, timeliness, and effectiveness of care. 5. Examine the impact of telehealth during the Coronavirus Disease 2019 (COVID-19)

• KEY WORDS Robotics Surveillance Telecommunication Telehealth Tele-ICU Telemedicine Telenursing TeleStroke

INTRODUCTION According to the American Telemedicine Association (ATA), the terms telemedicine and telehealth may be used interchangeably, but telehealth encompasses a broader application of healthcare services provided over health information exchanged from one site to another via advanced communication technologies (American Telemedicine Association, 2018b). Telehealth services are delivered via a variety of applications and services using two-way video, email, unified communication ­systems, handheld devices, wireless tools, and other forms of telecommunications technology over networked p ­ rograms, point-to-point connections, monitoring c­ enter links, and Web-based e-health

patient service sites (American Telemedicine Association, 2018a). Multiple acute, subacute, and critical care telehealth services will be discussed in this chapter. Terms related to different models of care delivery are described below.

Centralized versus Decentralized Centralized refers to a physical location, distant from the patient, where team members co-locate as they execute critical care services (Davis et al., 2016). In a ­decentralized model, team members interact with each other over audio-video conferencing and other telecommunication modalities to communicate care needs and goals versus colocation in a single defined structure. 615

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Originating versus Distant Site In a Telehealth Services booklet, the Centers for Medicare and Medicaid Services (CMS) define the originating site as the location of the patient receiving the telehealth services whereas the distant site is the site where the practitioners or providers of the service are located (CMS Medicare Learning Network [MLN], 2018b). The booklet goes on to describe that authorized originating sites are physician or practitioner’s offices, hospitals, health clinics, federally qualified health centers, skilled nursing facilities (SNFs), and community mental health centers (CMHCs). Depending on the service, Healthcare Common Procedure Coding System (HCPCS) modifiers for telehealth ­services may be restricted to originating sites designated as medically underserved areas/populations (MUA/P) by the Health Resources and Services Administration (HRSA, 2019). According to CMS, qualified distant site providers are not limited to physicians but also include clinical psychologists, clinical social workers (CSWs), registered dietitians or nutrition professionals, and other advanced practice providers (APPs) such as NPs, physician assistants (PAs), nurse-midwives, clinical nurse specialists (CNSs), and certified registered nurse anesthetists (CMS Medicare Learning Network [MLN], 2018b).

Continuous, Scheduled, and Reactive Care Models According to the ATA, many telehealth centers are engaged in continuous monitoring or surveillance for a defined period for specific populations of patients (Davis et al., 2016). Surveillance, a key component of a continuous model, is defined as the constant integration, interpretation, synthesis, and analysis of data (individual or population) to support clinical decision-making (CDM) and care coordination (Rincon & Henneman, 2018). Scheduled care models include scheduled telehealth visits that occur with a periodic consultation on a predetermined schedule such as with appointments or during bedside patient rounds. In reactive (also known as responsive) care models, a clinician is prompted to conduct a virtual or on-demand visit by a telephone call, page, text, or other notification methods.

User Experience and Usability Usability.gov describes the user experience (UX) as h ­ aving a deep understanding of what users of a system want, need, and value as well as knowing their limitations and abilities to work within a given system (usability.gov, 2019b). The authors go on to outline the factors that influence the UX

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(useful, usable, valuable, desirable, findable, accessible, and credible). Usability is how effective and efficient the user interacts within a system and how satisfied they are with their experiences when interacting with it (usability.gov, 2019a).

Virtual Care Interaction Types ATA defines remote patient monitoring (RPM) as defined as where patients use medical devices to perform routine tests on devices such as glucose meters and vital sign and heart rhythm monitoring equipment and send those data to a healthcare professional (American Telemedicine Association, 2018c). Teleconsultation (also known as eConsult) is defined as consultation between a provider and a specialist using store and forward or real-time telecommunication technologies, whereas telementoring is defined as using telecommunication technologies to provide individual guidance or direction (American Telemedicine Association, 2018c). Store and forward is a type of virtual provider-to-provider encounter that uses digital images and pre-recorded videos over secure electronic communications to seek diagnosis and expert opinion. It is used commonly in radiology, dermatology, ophthalmology, and wound care (Center for Connected Health Policy, 2019a). Virtual care interactions occur between patients and care providers without being in the same room and can occur using a variety of modalities such as exchanging messages asynchronously, via text, emails, or other formats (e-visits) or synchronously via telephone (teleconsultation) or video conferencing (virtual visits) (McGrail, Ahuja, & Leaver, 2017).

TELEHEALTH: PAST TO PRESENT The idea that a convenient and accessible healthcare system should be created began as early as the 1920s when visionaries imagined that a doctor could see patients in their homes using audiovisual transmissions (Institute of Medicine Committee on Evaluating Clinical Applications of Telemedicine & Field, 1996). But telehealth’s history started long before that with pilot tests of sending heart sounds over the telephone using a microphone. As early as 1878, physicians began to examine the transmission of heart sounds using a microphone attached to a telephone (McKendrick, 1878; U.S. National Library of Medicine, 2019). The first transmission of an electrocardiogram was in 1905 with radio consultations coming from Norway, Italy, and France in 1920s, 1930s, and 1940s. Radiographic images, videos, and other complex health information were also transmitted in the United States in the 1950s (Bashshur & Shannon, 2009). In the 1970s the Lockheed

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Missile and Space Company, the Indian Health Services (IHS), and the Department of Health, Education, and Welfare (DHEW now HHS) demonstrated the feasibility that healthcare could be delivered to remote locations (like Alaska) using telecommunications technologies (Freiburger, Holcomb, & Piper, 2007). Medical communication using the telephone, a major healthcare communication modality today, was adopted by physicians more than 100 years ago (Zundel, 1996). Telephone medicine has been reported as encompassing triaging and prescribing medical management for acute and chronic illness, chronic disease case management, patient education, counseling, and communicating laboratory and imaging results. However, adverse events related to telephone communication are significant and costly patient safety and malpractice issues (Katz, Kaltsounis, Halloran, & Mondor, 2008). A search of articles by year in PubMed for the search terms “telephone,” “telemedicine or telehealth,” and “telemonitoring or remote patient monitoring” demonstrates that even today the term “telephone” continues to dominate the literature when compared to the search terms “telemedicine or telehealth” and “telemonitoring or remote patient monitoring” (Fig. 38.1). In 1915, a recommendation was made that the use of the telephone for medication orders should be forbidden after a fatal error in medication dosing occurred when executing a telephone order (Unknown Author, 1915). Limitations of telephone communications have led to the use of more advanced telecommunications technologies

in all business sectors, including healthcare. Advancements in remote patient monitoring, surveillance tools, and other telehealth technologies are changing how care is delivered. In the book Health care without walls: A roadmap for reinventing U.S. health care, the Network for Excellence in Health Innovation (NEHI) challenges health professional to imagine a healthcare system that met patients’ needs in their homes, their workplaces, and in their communities (Network for Excellence in Health Innovation [NEHI], 2018). The authors go on to describe that healthcare should be a convenient, accessible, and cost-effective health-inducing system of care that is focused on keeping patients as healthy as possible.

EXAMPLES OF HEALTHCARE SYSTEMS USING TELEHEALTH Some examples of healthcare systems that are working to create such systems of care include: Kaiser Permanente health system where roughly 50% of more than 120 ­million patient encounters occur over phone, email, or video; the Veterans’ Health Administration where telehealth and digital services have been available for more than a decade; hospital-at-home services offered by Mount Sinai Health System in New York and Atrius of Massachusetts; retail giants like CVS, Target, and Walgreens offering on-site healthcare services as well as in-home telehealth services; and others who have ventured into remote patient monitoring in the home assisted-living environments (Network for Excellence in Health Innovation (NEHI), 2018).

1885 1905 1919 1927 1934 1948 1955 1962 1969 1976 1983 1990 1997 2004 2011 2018 Telephone

Telehealth or Telemedicine

Telemonitoring or RPM*

•  FIGURE 38.1.  PubMed search results for the search terms “telephone,” “telemedicine or telehealth,” and “telemonitoring or remote patient monitoring” 1878–2018.

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TELEHEALTH PUBLICATIONS IN POPULATIONS WITH DIFFERENT DIAGNOSES

TELEHEALTH RESOLVING WORKFORCE ISSUES

A search of articles in PubMed for various search terms (intensive care, stroke, pediatrics, psychiatry, specialty consult, and chronic health) paired with telemedicine or telehealth is depicted in Fig. 38.2. The search revealed that in the late 1970s critical care providers began to experiment with using telemedicine to solve workforce problems related to critical care specialists (Grundy et al., 1977) while pediatric providers began tests of bidirectional interactive cable television with nurse practitioners (NPs) to expand their reach (Muller et al., 1977). In the 1990s psychiatrists in the United States began to discuss whether telemedicine might be a solution for mental health access to underserved areas (Preston, Brown, & Hartley, 1992). In Ireland and Spain, remote monitoring of physiological signals and other data via the public telephone network began in the 1990s for chronic disease management (Rodriguez et al., 1995). By the late 1990s, articles related to the application of telemedicine for stroke were beginning to be published in the literature (Levine & Gorman, 1999). Figure 38.2 ­provides a visual snapshot of articles using the search terms that were found in the literature and the years in which they were published. The figure demonstrates that telepsychiatry has the most publications in telehealth literature.

According to a report commissioned by the Association of American Medical Colleges, projected U.S. physician workforce shortages of 61,700 to 94,770 for all physicians and 37,500 to 60,300 for non-primary care physicians paint a gloomy picture for improving access to care (Dall, West, Chakrabarti, & Iacobucci, 2016). Although advanced practice registered nurses (APRNs) and physician assistants (PAs) have been assisting with bridging gaps in physician services, shortfalls in supply and demand for APRNs/ PAs are also projected within this report. Some might say that there is not enough evidence that telehealth can fill that void. Given that medical communication has been conducted over the telephone for more than 100 years, why wouldn’t the use of more advanced technologies be considered?

NURSE PRACTICE STANDARDS AND PUBLICATIONS IN TELEHEALTH Physicians, nurses, respiratory care providers (RCPs), pharmacists, social workers, and dieticians/nutritionists can work within their scope of practice to impact the lives and quality of life of their patients within a telehealth program. The American Academy of Ambulatory Care Nurses (AAACN) published their first telehealth nursing

1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018 Tele-ICU

Tele-Stroke

Tele-Pediatrics

Tele-Psychiatry

Tele-Chronic Health

•  FIGURE 38.2.  PubMed search results for the search terms related to intensive care, stroke, pediatrics, psychiatry, specialty consult, and chronic health paired with telemedicine or telehealth 1978–2018.

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1976 1979 1982 1985 1988 1991 1994 1997 2000 2003 2006 2009 2012 2015 2018 Nurse

Pharmacist

Nurse Practitioner

Physician

•  FIGURE 38.3.  PubMed search results for the search terms for nurse, pharmacist, nurse practitioner, and physician paired with telemedicine or telehealth 1974–2018. practice standards in 1997 and continue to provide various resources and toolkits to support the growth and development of nurses working in telehealth programs (American Academy of Ambulatory Care Nursing [AAACN], 2019). APPs, NPs, PAs, and CNSs can assist in filling care gaps due to a shortage in specialists (Kleinpell, Buchman, & Boyle, 2012; Nevidjon et al., 2010). Figure 38.3 is a visualization of publication activity related to the roles of nurse, pharmacist, nurse practitioner, and physician paired with the terms telemedicine or telehealth. This visualization shows that the physician is the most published healthcare professional in telemedicine and telehealth, but nurses and NPs have substantially increased publications in the past ten years.

PAYMENT FOR TELEHEALTH AND  TELECOMMUNICATIONS The CMS has been considering the utility of telehealth in population health and will pay for telehealth services that are delivered by practitioners through interactive telecommunication technologies instead of providing these services in-person (CMS Medicare Learning Network [MLN], 2018b). There are five statutory requirements for payment of telehealth services by CMS: (1) originating site is located in a qualifying rural Health Professional Shortage Area (HPSA) or a county outside of a Metropolitan Statistical Area (MSA); (2) originating site qualifies as one of the eight authorized originating sites; (3) an eligible distant site practitioners provide

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the services; (4) the recipient of services and distant site practitioner communicate in real-time via an interactive audio and video telecommunications system; and (5) the Current Procedural Terminology/Healthcare Common Procedure Coding System (CPT/HCPCs) code for the service has been named on the list of telehealth services covered by Medicare. In the past few years, CMS has begun to rescind some of the geographical restrictions for specific services. For example, the Bipartisan Budget Act of 2018 made important statutory changes under the Medicare program that specifically relate to telehealth services for the treatment of end-stage renal disease (ESRD), acute stroke and individuals with substance use disorders (SUDs) or ­ co-occurring mental health disorders (Center for Connected Health Policy, 2019b). Moreover, Medicare two-sided Accountable Care Organizations (ACOs) can be reimbursed for telehealth-delivered services to the home and without geographic restrictions in 2020. Other important changes impact payment for remote communication and Medicare Advantage plans to offer telehealth benefits. These changes by CMS are leading the way for other payers to cover more for telehealth services.

TELEHEALTH APPLICATION: CRITICAL CARE SETTING Developed by two intensivists from John Hopkins Hospital in the late 1990s, Tele-ICU is the application of critical care using a network of audiovisual communication and health

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  TABLE 38.1    Most Important Priority Areas of Care for Tele-ICU Nursing 1

Critical thinking skills

9

Knowledge of hemodynamic monitoring

2

Expert clinician with ICU experience

10

Understanding laboratory values

3

Skillful communication

11

Knowledge of medications

4

Mutual respect for bedside and tele-ICU colleagues

12

Monitor trends in vital signs

5

Emergency patient care management

13

Use the tele-ICU system to enhance patient safety

6

Monitor for unstable physiological status

14

Ability to interact with multiple disciplines

7

Knowledge of ventilator management

15

Ability to mentor

8

Correlation of arterial blood gases to mechanical ventilation

Reproduced, with permission, from Kleinpell, R., Barden, C., Rincon, T., McCarthy, M., & Zapatochny Rufo, R.J. (2016). Assessing the impact of telemedicine on nursing care in intensive care units. American Journal of Critical Care, 25(1), e14-20. Permission conveyed through Copyright Clearance Center, Inc

information systems (ATA TeleICU Practice Guidelines Work Group, 2014; Rosenfeld et al., 2000). In the early 2000s, the first wave of Tele-ICUs opened up across the country with more than 40 Tele-ICU centers providing services to over 400 ICUs across the United States today (Lilly & Thomas, 2010). Services to the critically ill can be provided from centralized or decentralized remote locations using scheduled consultations or continuous surveillance models (Davis et al., 2016). Tele-ICU teams provide services in four distinct ways: (1) surveillance for physiological deterioration, (2) dissemination of evidence-based practice guidelines, (3) expert advice and guidance, and (4) collection, analysis, and quality performance reporting (Kahn et al., 2018). Tele-ICU teams are comprised of critical care clinical experts such as intensivists and other physician specialists, APPs (NPs, CNSs, PAs, …), pharmacists, RCPs, and critical care nurses whose knowledge and expertise are leveraged across a diverse spectrum of critically ill patients in a variety of clinically and geographically dispersed settings (Welsh et al., 2019). The composition of these teams is dependent on the types of services being provided. The American Association of Critical Care Nurses (AACN), the largest specialty nursing organization in the world with over 100,000 members, has developed and published consensus statements, scope and standards of practice, and clinical practice guidelines for nurses working in the critical care and acute care work environments in both bedside and virtual care units (American Association of Critical Care Nurses, 2008, 2016, 2019; American Association of Critical Care Nurses Tele-ICU Task Force, 2018). Tele-ICU nursing practice continues to evolve with a heightened focus on surveillance activities that lead to early identification of deadly syndromes like sepsis, prevention of falls and unintended extubations, and improved

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compliance to quality indicators (Rincon & Henneman, 2018). Rincon and Henneman go on to explain that TeleICU nurses receive extensive training in and have adapted to using clinical decision support tools and other health information systems and sources to conduct surveillance of high volumes of high acuity patients. Tele-ICU team members are an extension of critical care, and as such their roles vary according to the scope of practice and licensure as well as professional practice standards, Historically, Tele-ICU has referred to adult delivery of critical care services via telecommunication technologies. Most Tele-ICU centers deploy continuous monitoring and surveillance models. Tele-ICU Nursing competency was explored in a two-phase national benchmark survey published in the American Journal of Critical Care (Kleinpell, Barden, Rincon, McCarthy, & Zapatochny Rufo, 2016). Table 38.1 represents the most important priority areas of care for tele-intensive care unit (ICU) nursing.

TELE-PICU AND TELE-NICU SERVICES To date, Tele-PICU (pediatric intensive care unit) and Tele-NICU (neonatal intensive care unit) teams have delivered critical care services to children and newborns using episodic, consultative models (Dayal et al., 2016; Fang et al., 2016; Marcin, 2013). Approximately 10% of newborns will require some breathing assistance, and less than 1% will require more advanced resuscitation after delivery (Wyckoff Myra et al., 2015). Unfortunately, healthcare professionals cannot always predict which pregnancies might result in a high-risk event requiring neonatal resuscitation. There is mounting evidence that Tele-PICU and Tele-NICU services can result in better and safer

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care, more efficient resource utilization, more equitable and cost-effective care, and higher patient, p ­ arent, and ­provider satisfaction (Albritton, Maddox, Dalto, Ridout, & Minton, 2018; Ellenby & Marcin, 2015).

OUTCOMES IN MEDICARE PATIENTS According to a national study using Medicare claims data from 2001 to 2010, Tele-ICU adoption resulted in a small relative overall mortality reduction with largevolume urban hospitals experiencing the most significant mortality reductions (Kahn et al., 2016). A recent systematic review and meta-analysis of 13 studies from 2766 abstracts, Tele-ICU implementation, was associated with an overall reduction in mortality (Fusaro, Becker, & Scurlock, 2019). An ethnographic evaluation of 10 ICU telemedicine programs with various changes in riskadjusted mortality after adoption (decreased mortality, no change in mortality, and increased mortality) found that modifiable factors within the domains of leadership, perceived value, and organizational structure enhanced the effectiveness of Tele-ICU programs (Kahn et al., 2018).

COST OUTCOMES A systematic review published in 2013 reported that costs associated with implementing Tele-ICU programs were substantial (Kumar et al., 2013). A financial outcomes

study published in 2017 demonstrated that after the adoption of a Tele-ICU program, an academic medical center increased its case volume, saw higher case revenue relative to direct costs, and shorter lengths of stay leading to a substantial year-to-year improvements to direct contribution margins (Lilly et al., 2017).

TELEHEALTH APPLICATION: TELESTROKE Acute ischemic stroke (AIS) is the fifth leading cause of death in the United States with more than 140,000 people dying each year (Centers for Disease Control and Prevention [CDC], 2017). Every 40 minutes someone in the United States is having a stroke, yet nearly 50% of Americans live more than 60 miles of a primary stroke center (Centers for Disease Control and Prevention [CDC], 2017). In 2013, only 1100 vascular neurologists (VNs) were practicing in the United States, despite an incidence of 800,000 strokes per year (Akbik et al., 2017). In 2015, only 52% of eligible VN providers were recertified, and in 2016, 34% of VN fellowship training programs had unfilled positions (Kenton et al., 2017). Through video consultation with an examination of patients, stroke networks using TeleStroke have been able to mitigate this mismatch between the distribution and incidence of stroke with the availability of VNs (Akbik et al., 2017). The limited availability of specialists, the wide geographic distribution of

  TABLE 38.2    Factors that Enhance the Effectiveness of Tele-ICU Leadership

Perceived value

Organizational structure

Effective Strategies

Consequences if Missing

Regular in-person meetings between telemedicine and ICU leadership

Ad hoc leadership status updates via conference call

Focus on quantitative and narrative quality reporting

Unclear expectations around quality

Telemedicine staff well versed in local ICU protocols, policies, and procedures.

Misaligned protocols, policies, and procedures

Standardized communication practices and training

Poor communication

ICU has telemedicine champion to maintain engagement

Lack of engagement and an “us” vs. “them” environment

Telemedicine staff have clinical expertise specific to target ICU

↓ Credibility of telemedicine with target ICU

Two-way cameras

Absence of human connection

Routine documentation and charting by telemedicine staff

↓ Staff satisfaction and engagement and poor integration

Telemedicine staff attending local ICU training and in-services

Lack of integration and weak understanding of operations

Adapted from Kahn et al., 2016.

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disease, clinical findings that are identifiable on video, a narrow therapeutic window, and the presence of an existing, IV therapy that can be administered almost anywhere are all reasons why AIS is uniquely suited to telemedicine (Akbik et al., 2017). In 1999, Levine and Gorman introduced the term TeleStroke in an editorial published in Stroke (Levine & Gorman, 1999). Wechsler et al. (2017) describe the American Stroke Association (ASA) as identifying TeleStroke as serving multiple dimensions within stroke care systems in 2005 and by 2009 the American Heart Association (AHA) and the ASA published companion recommendations for the implementation of TeleStroke in stroke care systems (Wechsler et al., 2017). By 2014, it was reported that TeleStroke had become “mainstream” clinical practice in academic and community health environments (Mark & Bart, 2014). The ATA TeleStroke Guidelines describe the audiovisual communication platforms, equipment and computer systems that can be used for delivery of TeleStroke clinical services as well as operations, management, administration, and economic recommendations (Demaerschalk et al., 2017). In 2019, CMS established a new HCPCS modifier that removed restrictions on geographical locations and opened the door for TeleStroke networks to capture revenue regardless of originating site designation (CMS Medicare Learning Network [MLN], 2018a).

TELEMEDICINE IN THE EMERGENCY DEPARTMENT Telemedicine for emergency services is being used to support the care of patients with stroke, myocardial infarction, traumatic injuries, and other time-sensitive and complex conditions (Mohr et al., 2017, 2018, 2019). Results from a National Emergency Department Inventory-USA survey of over 4500 EDs demonstrated that over 1900 EDs receive telemedicine services, with most services related to stroke/neurology, psychiatry, and pediatrics (Zachrison, Boggs, Espinola, & Camargo, 2018).

TELEHEALTH APPLICATION: ACUTE CARE SETTING Neurology Shortages of services due to high demand and lack of specialists are driving health systems, individual hospitals, and beneficiaries to seek services using virtual presence technologies. The American Hospital Association supports the expansion of telehealth services from the emergency room to specialty consultations to remote patient

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monitoring (American Hospital Association [AHA], 2019). The American Psychiatric Association (APA) describes telepsychiatry as an effective modality for psychiatric evaluations, therapy, patient education, and medication management in a variety of settings such as private practice, clinics, hospitals, correctional facilities, schools, nursing homes, and military treatment facilities (Shore, 2017). The American Academy of Neurology supports the use of telehealth in the evaluation and treatment of neurological disorders (American Academy of Neurology [AAN], 2019).

Pediatrics Telehealth services for pediatric patients have been reported in the literature as providing many diverse applications that can overcome barriers of distance and time for underserved populations (American Academy of Pediatrics Committee on Pediatric Workforce, 2015; Burke & Hall, 2015). It is not just infants, children, adolescents, and their families living in rural areas to experience significant disparities in access to specialty care, they are also seen in suburban and urban communities (American Academy of Pediatrics Committee on Pediatric Workforce, 2015). Olson et al. describe that despite technical challenges, lack of reimbursement, and provider engagement and time-constraints, pediatric telehealth has expanded significantly over the past decade with neurology, psychiatry, cardiology, neonatology, and critical care as the top five service lines (Olson, McSwain, Curfman, & Chuo, 2018).

Critically Ill and At-Risk Patients The use of telehealth technologies to support critical care evaluation and therapies on the acute care floors by rapid response teams has been reported as an effective method of leveraging intensivist and other critical care resources to improve response time and time to treatment in both the adult and pediatric environments (Berrens, Gosdin, Brady, & Tegtmeyer, 2019; Fiero et al., 2018; Pappas, Tirelli, Shaffer, & Gettings, 2016; Youn, 2006). Two large health systems, Banner Health and Mercy, have deployed continuous telehealth programs that provide active surveillance and care coordination throughout the care continuum to support early identification and treatment of physiologic deterioration (Banner Health, 2018; Mercy, 2018b). Alert fatigue and the unintended consequences of the EHR revolution have become a high-profile patient safety concern (Agency for Healthcare Research and Quality [AHRQ], 2019). Telehealth programs that employ expert nurses to conduct surveillance activities are one potential solution to this ever-growing concern.

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According to the Agency for Healthcare Research and Quality, 700,000 to 1 million patients suffer a fall in a U.S. hospital each year. Some 30–50% of these falls result in injury, according to a Sentinel Event Alert published by The Joint Commission on September 28, 2015. Also, suicide is the 10th leading cause of death in the United States, resulting in the deaths of more than 42,500 people in 2014 (Curtin, Warner, & Hedegaard, 2016). The Centers for Disease Control and Prevention (CDC) estimates that in 2013, 9.3 million adults had some form of suicidal ideation, 2.7 million formulated a plan, and 1.3 million attempted suicide (Centers for Disease Control and Prevention, 2015). Besides, the CDC reports that in 2013, 494,169 people were treated for self-harm in emergency departments. The American Psychiatric Association said in 2003 that approximately 1500 completed suicides take place in inpatient hospital units in the United States each year and, despite focused efforts, one-third of these occur while the patient is being observed with 15-minute checks (“Practice guideline for the assessment and treatment of patients with suicidal behaviors, 2003). TeleSitter, which is also known as a virtual sitter program, has been deployed through the United States to provide 24/7 continuous observation of at-risk patients using two-way audio/video solutions to prevent falls and other adverse events (McCurley & Pittman, 2014; Mercy, 2018a; Westle, Burkert, & Paulus, 2017).

TELEHEALTH IN CHRONIC HEALTH CONDITIONS Avoidable hospitalizations are common, costly, disruptive, and disorienting for people with chronic health conditions, disabilities, and the frail elderly (Steiner & Friedman, 2013; Walsh et al., 2012). A systematic review of the literature for telehealth interventions for heart failure (HF), stroke and chronic obstructive pulmonary disease (COPD) patient populations yielded 19, 21, and 17 studies, respectively, that met minimum criteria for inclusion with another 14 studies that investigated cost (Bashshur et al., 2014). Telehealth interventions varied by technology (telephone, audio/video, scopes, sensors, and other devices), manual versus automated data entry, synchronous versus asynchronous visit types, and provider mix (physicians, nurses, therapists, etc.). The authors concluded that there was a “preponderance of evidence” to support the use of telehealth strategies to reduce admissions/readmissions, decrease mortality and length of stay, and reduce emergency department visits. Other key findings with positive effects were (1) care processes (timely detection and treatment, prompt referrals and follow-up, and accurate

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measurement and diagnosis); (2) patient quality of life outcomes (better symptom management, reduced disability, increased satisfaction, and increased longevity), and (3) cost-effectiveness. In a systematic review of 54 articles, remote patient monitoring as a telehealth intervention demonstrated a small but significant improvement in glycohemoglobin or hemoglobin A1c levels when compared with usual care (Lee, Greenfield, & Pappas, 2018). The University of Pennsylvania Medical Center and Geisinger Health Plan have reported that the use of RPM tools for patients with chronic health conditions has led to better management of conditions like HF, advanced illness, tobacco cessation, inflammatory bowel disease, and more (Beaton, 2018). Both health systems have seen lower hospital admissions/ readmissions and dramatic reductions in the need for patients to stay in observation units. Effective management of chronic health conditions for beneficiaries at home, in SNFs, rehabilitation centers, and in long-term acute care hospitals, and even in correctional facilities can be accomplished using telemonitoring, RPM, and other telehealth modalities. RPM services are not considered a Medicare telehealth service and as such are billed under CPT codes: (1) 99453: set-up and patient education of a device, (2) 99454: remote monitoring of physiologic parameter(s); and (3) 99457: 20 minutes or more of clinical staff time for interactive communication between patient/care providers during the month (Drobac, 2019).

DIRECT-TO-CONSUMER MONITORING Direct-to-consumer telemedicine care models, where patients access care outside of traditional brick-and-mortar health delivery facilities, are showing promise in increasing access to and engagement in medical care (Elliott & Shih, 2019; Vyas, Murren-Boezem, & Solo-Josephson, 2018; Yu, Mink, Huckfeldt, Gildemeister, & Abraham, 2018). A retail pharmacy, a grocery store with a kiosk, and at home on a computer are emerging examples where telehealth technology can connect patients directly to providers. In 2018, within weeks of each other, two health systems (New York-Presbyterian and Florida-based BayCare) expanded virtual care delivery by hosting telehealth kiosks at retail (Walgreens and Publix) pharmacy locations (Pecci, 2018). These models present potential new revenue streams for provider organizations who are seeking to increase their margins while leveraging their brand. Health systems are rapidly adopting online platforms and investing in telehealth technology to expand their reach for services.

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Another example of a direct-to-consumer offering is the Cleveland Clinic Express Care® Online, which offers a free application that can be run from a smartphone, tablet, or computer and offers a 10-minute virtual visit from a healthcare provider for nonemergency concerns for patients ages 2 and older (Cleveland Clinic, 2019). With increasing demand by consumers for faster, more efficient access to healthcare services, it is likely that direct-to-consumer models will continue to evolve and will serve early adopters as a viable option to traditional access.

TELEHEALTH IN CORRECTIONAL FACILITIES Telehealth services have been noted as a potential solution for healthcare service access issues for inmates within correctional facilities. In a comprehensive search of seven databases (PubMed, CINAHL, Informit, Embase, Scopus, PsycINFO, and Cochran Central Register of Controlled Trials), researchers identified 36 articles that were published between 2010 and 2018 from the United States, France, and Australia that discussed telehealth interventions (Senanayake, Wickramasinghe, Eriksson, Smith,  & Edirippulige, 2018). Types of services included ­general medicine, HIV and Hepatitis C case management, ­infectious disease consultation, diabetic retinopathy management, psychiatric services, cardiology, and other subspecialty evaluations.

TELE-PHARMACY A 50-State survey conducted in 2016 found that laws differ between states in the United States regarding the provision of pharmaceutical services (drug review and monitoring, medication therapy management, dispensing of medications, and patient counseling) at a distance using telehealth technologies (Tzanetakos, Ullrich, & Meuller, 2017). The researchers described that the use of tele-pharmacy is: (1) permitted, in varying capacities, in 23 states; (2) pilot programs were in development in six states; (3) waivers to pharmacy practice requirements (administrative or legislative) that could allow for tele-pharmacy future initiatives were in place in five additional states; and (4) approximately one-third of the states (16) did not permit nor do they appear to be considering the use of tele-pharmacy. Redefining the “practice of pharmacy” under state laws to include the provision of tele-pharmacy services and addressing the interstate practice of telepharmacy (allow pharmacists to provide tele-pharmacy services to patients located in other states would assist in expanding tele-pharmacy services).

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FUTURE OF TELEHEALTH: ROBOTICS A consensus definition of robotics has been elusive, but experts agree on common characteristics that define robots. According to an article in Wired Magazine by author Matt Simon, experts generally agree that a robot is an intelligent, physically embodied machine. A robot can perform tasks autonomously. And a robot can sense and manipulate its environment (Simon, 2017). Combined with Artificial Intelligence and machine learning, the potential for the application of advanced robotics capabilities in healthcare is profound. Shah and colleagues report that robots have been used in medicine for decades (Shah, Vyas, & Vyas, 2014). From needle placement for a CT-guided brain biopsy in the late 1980s to the first robotic device approved by the FDA to perform surgical procedures in 2000, robots have been used in various fields of surgery. Robots in the healthcare setting range from simple laboratory robots, to robots that deliver supplies, medications, and specimens, to highly complex robots that can either aid a human surgeon or execute operations independently (Meskó, 2016). More advanced robotics capabilities include cognitive therapy robots and robotic limbs and exoskeleton. Table 38.3 summarizes the different types of robots in healthcare. A 2018 study by Creswell et al. identified four major barriers to adoption of robots in healthcare (Cresswell, Cunningham-Burley, & Sheikh, 2018). These include: 1. No clear pull from professionals and patients 2. The appearance of robots and associated expectations and concerns 3. Disruption of the way work is organized and distributed 4. New ethical and legal challenges requiring flexible liability and ethical frameworks All of these challenges will need to be considered as developers continue to search for use cases for robotics in healthcare settings. The use of robotics will be particularly important for those use cases that bring robots closer to patient care.

DISCUSSION ON STRATEGIES TO IMPLEMENTING A TELEHEALTH PROGRAM Applications of telehealth are common, vary greatly, and are proliferating across the United States and globally. According to the ATA, in the United States, there

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  TABLE 38.3    The Use of Robotics in Healthcare Type

Function

Use Case

Surgical precision

Autonomous or semi-autonomous. Used to assist in performing surgical procedures.

DaVinci

Service robots

Perform a generally repetitive service.

Cleaning, sterilization robots.

Telepresence

Screens on wheels. Can be used in conjunction with telehealth capabilities.

Supply chain robots

Robots that assist with inventory.

Exoskeletons

Complex robots that function as arms and legs.

Companion robots

Robots that serve as a social companion in order to alleviate loneliness or treat mental health issues.

Humanoids

Human-like robots that interact with patients.

are about 200 telehealth networks with more than 3000 service sites providing services to millions of Americans (American Telemedicine Association, 2018b). Planning for implementing a telehealth program or service-line is not without its challenges. From understanding Medicare reimbursement regulations to determining the clinical roles required to meet the specific need and investing in the right technology and modality to support the needed service, care providers struggle to implement effective telehealth strategies. In a recent systematic review of articles on eConsults, a team of researchers used the Quadruple Aim Framework to synthesize outcomes of 43 studies that met the inclusion for asynchronous-directed consultations between providers over a secure electronic medium (Liddy, Moroz, Mihan, Nawar, & Keely, 2019). The four dimensions within this framework can be used to set metrics of success for telehealth programs: (1) population health outcomes with a defined denominator, (2) care experiences, e.g., the patientreported outcome and experience measures, (3) the per capita costs including downstream healthcare utilization costs and impacts on delayed medical referral, and (4) the provider experience, e.g., provider-reported outcome and experience measures. A focus on the Institute of Medicine’s (IOM) six aims for improvement: Safe, Timely, Effective, Efficient, Equitable, and Patient-centered (STEEEP) were also considered within this review framework (Institute of Medicine, 2004). Figure 38.4 provides a developmental perspective of developing a plan around telehealth. It is important to design a telehealth strategy on STEEEP principles as a foundation. The type of ­virtual care interaction will determine the modality and technology (platform,

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Pharmacy robotics

Robots used to distract pediatric patients during administration of vaccines (4).

devices, peripherals, blue tooth connectivity, bandwidth, cellular and WIFI ­connection, and other requirements). The Quadruple Aim Framework pillars (care experience, population health, cost and utilization, and the provider experience) ensure that the structure is built around improving quality of care delivery from both the patient and provider perspectives as well as with a cost and population health emphasis. Patient and provider experiences should include a focus on usability and the user experience of service delivery. Telehealth modalities can extend the practice of any care provider role and should be dependent on the particular healthcare needs and scope of practice standards within a specific state. The needs of the patient population, the complexity of the care, and the degree of knowledge translation required to provide care that meets the IOM’s six aims (STEEEP) should drive decisions related to continuous, scheduled or on-demand, and synchronous versus asynchronous care models as well as when to rely on a care coordination strategy versus a surveillance strategy. Surveillance care models, with and without robots, can power artificial intelligence and machine learning clinical decision support systems that not only identify conditions sooner but have the ability to predict which patients might be at highest risk. The use of these tools and care models will likely continue to evolve and become more accepted by patients and clinicians over time. Being able to offer new ways of delivering healthcare services is essential to advancing access and quality. It is important that core informatics competencies are used throughout the development life cycle.

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Cardiology, Intensivists, Neurology, Trauma, ED, Nephrology Stroke GI/GU ets Suicide/ Substance Abuse Screening

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Technology Enabled

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HF, COPD, Asthma, Diabetes

Alzheimer’s Disease/ Dementia

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Substance Abuse/ Psychiatry

90°

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Per Capita Cost/Utilization

Care Experience

Foundation Safe, Timely, Efficient, Effective, Equitable, Patient-Centered Level Elevation

0 ft.

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•  FIGURE 38.4.  Planning for Telehealth. Advanced practice providers, APPs (nurse practitioners, physician assistants, clinical nurse specialists); respiratory care providers, RCPs, emergency department, ED; obstetrics, OB; electronic consultations, eConsults; electronic visits, eVisits; remote patient monitoring, RPM; heart failure, HF; chronic obstructive pulmonary disease, COPD. (Original design by David Smith, Associate Vice President of Virtual Medicine for UMassMemorial Health Care. Adapted and used with permission.)

THE IMPACT COVID-19 ON TELEHEALTH During a disaster, appropriate, efficient and effective resource management is crucial. Resource capacity forecasting, assessment of resource risks, appropriate skill mix, optimization of resources, management of realistic schedules/ deadlines, consistent assignment of resources, mitigation and throttling of unplanned requests for resources, and ability to shift resources to respond to problems, are all fundamental resource management concepts during a crisis (Fan, French, Stading, & Bethke, 2015). Decision-making related to strategy development, execution of tactical steps and management of the deployment of solutions are also important concepts in project management. During a disaster situation, following the Incident Command Center framework can aid organizations in managing resources. There are 2 key components that impact the ability to provide adequate medical care during a mass event (medical surge): 1) ability

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to respond to an increased number of patients e.g. surge capacity and 2) the ability to address unusual or specialized care needs e.g. surge capability (Barbera & Macintyre, 2012). Five functional areas within an Incident Command System organize and delineate the roles and responsibilities of the response assets (Figure 38.4). During the Coronavirus Disease 2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) began to recommend “social distancing” or avoidance of close contact as a method of protection against its spread. The impact and challenges during the COVID-19. pandemic crisis thrust clinicians and patients into using telehealth and telecommunication tools to provide and receive care. This fundamental shift did not come without its pain points. There were and are many influencers of adoption of these tools that can be examined, and lessons gleaned from. Allocation of clinical resources, planning for surge capacity and surge capability, and training clinicians on proper use of personal protective equipment (PPE) were major focuses

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Command: Defines the incident goals and objectives

Admin/Finance: manages administrative issues, tracks/processes expenses, manages compliance and regulatoru requirements

Planning: Supports & coordinates overall planning AND information activities

Operations: Establishes, coordinates and executes the strategy and tactical steps

Logistics: Manages supplies, equipment and required technical activities

•  FIGURE 38.5.  Incident Command System Framework

of most hospital organizations during the first few weeks of the pandemic. It quickly became apparent that telehealth and telecommunication tools needed to be implemented rapidly to facilitate specialty consultations, remote assessments and social presence during social distancing (caregiver  patient/family and patient  family). Across the country reports of rapid increases in the use of virtual visits emerged with top Medicare administrator Seema Verna describing a 40-fold increase in virtual visits across the country in a three month period (Ross, 2020). One large health system reported a 683% increase in virtual urgent care visits from March 2-April 14, 2020 (Mann, Chen, Chunara, Testa, & Nov, 2020). National organizations began to rapidly publish materials to educate care providers in how to implement telehealth technologies, describe what telehealth and telemedicine is, explain regulatory and policy changes, and link out to additional resources (Table 38.4.)

HOW CAN HEALTH CARE INFORMATIC SPECIALISTS FILL THE TELEHEALTH KNOWLEDGE GAP? Both IT/IS and informatics specialist need to become acquainted with the technical know-how to support appropriate utilization of telehealth and telecommunication

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tools. A webinar series (https://www.telemedicine. com/webinars) from April and May of 2020 with Dan Kurywchak (founder and CEO of Telemedicine.com) sponsored by the Centers for Care Innovations provides information related to telehealth technologies e.g. description of the COVID-19 technical issues that many experienced, considerations when choosing a platform or medical devices, and common technical issues with troubleshooting techniques (telemedicine.com, 2020). Kurywchak 2020 describes that a Local Area Network (LAN) is the network connecting computer related systems on your floor, within your building, and/or close proximity buildings while Wide Area Networks (WANs) are the connection between LANs that are separated by a mile or more. He also goes on to describe that when a user purchases high speed Internet it simply means that they have a fast onramp to the Internet highway. If the Internet highway is jam packed with traffic then, just like driving in rush-hour traffic can be slow going, video calls can drop, have audio or video issues due to pixilation and buffering. He provides tips for home and office set up of technology and equipment as well basic 2-way audio-video etiquette to optimize the provider-patient experience. Informatic specialists can ensure that providers and other care givers have places to document in the EHR

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  TABLE 38.4    Telehealth Toolkits with Information related to COVID-19 pandemic Organization

URL

What you’ll find related to telehealth

American Academy of Allery, Asthma & Immunology (AAAAI) Resources for A/I Clinicians during the COVID-19 Pandemic

https://education.aaaai.org/ resources-for-a-i-clinicians/covid-19

webinars, podcasts, references, link to Utilize Telemedicine resource webpage

American Academy of Family Physicians (AAFP) What’s the difference between telemedicine and telehealth?

https://www.aafp.org/media-center/ kits/telemedicine-and-telehealth. html

definitions, library of links to documents: background, policies, news coverage, research, advocacy

American Association of Nurse Practitioners (AANP) Coronavirus Disease 2019 (COVID-19) Telehealth Updates

https://www.aanp.org/practice/ practice-management/technology/ telehealth

Links to external tools and resources, videos, opportunities to join specialty practice group

American College of Obstetricians and Gynecologists COVID-19 FAQs for Obstetricians and Gynecologists , Telehealth

https://www.acog.org:443/en/Clinical Information/Physician FAQs/COVID 19 FAQs for Ob Gyns Telehealth

definitions, links to resources, FAQs,

American College of Physicians (ACP) Telemedicine: A Practical Guide for Incorporation into your Practice

https://www.acponline. org/clinical-information/ clinical-resources-products/ coronavirus-disease-2019-covid19-information-for-internists

guides, policy documents, toolkits, CME/ MOC offerings, webinars

American Medical Association (AMA) quick guide to telemedicine in practice

https://www.ama-assn.org/practicemanagement/digital/ama-quickguide-telemedicine-practice

overview, implementation playbook, practice tips, billing & reimbursement, policy, and other resources

Center for Connected Health Policy (CCHP)

https://www.cchpca.org/

videos, resources and links related to current state laws & policies and legislation & regulation

Centers for Disease Control and Prevention (CDC) Using Telehealth to Expand Access to Essential Health Services during the COVID19 Pandemic

https://www.cdc.gov/ coronavirus/2019-ncov/hcp/telehealth.html

background, description of modalities, benefits and potenital uses, reimbursement, safegaurds, potential limitations, references

Centers for Medicare & Medicaid Services (CMS) Coronavirus (COVID-19) Partner Toolkit

https://www.cms.gov/outreach-education/partner-resources/coronavirus-covid-19-partner-toolkit

links to videos, toolkits, and other resources

National Consortium of Telehealth Resource Centers (TRC) Telehealth Resources to Address COVID-19

https://www.telehealthresourcecenter.org/covid-19-resources/

definitions, library of links to documents: background, tools & resources, funding opportunities, state-specific resources

UpToDate Coronavirus disease 2019 (COVID-19): Outpatient management in adults

https://www.uptodate.com/contents/ coronavirus-disease-2019-covid19-outpatient-management-inadults

rationale for outpatient management and remote care, telephone triage, telehealth follow-up etc.

U.S. Department of Health & Human Services Telehealth: Delivering Care Safely During COVID-19

https://www.hhs.gov/coronavirus/ telehealth/index.html

videos, HIPAA flexibility information, CMS telehealth waivers & temporary expansion of services, cost sharing, billing & reimbursement, links to other resources

COVID-19, Coronavirus Disease 2019; FAQs, frequently asked questions; CME, continuing medical education; MOC, maintence of certification; HIPAA, Health Insurance Portability and Accountability Act; Search tearms: telemedicine or telehealth combined with toolkit, covid 19, policy, and/or resource

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when they use telehealth tools during the care and assessment of patients. They can examine EHR data to better understand utilization of and tools being used to conduct virtual visits. In order to better understand influencers of adoption, informatic teams can listen to and document the voice of the customer via optimization huddles, interactive webinars, review of service desk tickets and survey instruments and semi-structured interviews of users of telehealth and/or telecommunication technologies. Many including Ms.Verma agree that Congress will ultimately need to permanently expand telehealth nationally by changing existing laws that limit coverage to people living in rural areas and around state licensing (Ross, 2020). The telehealth wave has hit and now more than ever we need healthcare informaticists to expand their knowledge in telehealth technologies and services so that they can guide user adoption through system design, human factors science, and usability.

Test Questions 1. Telehealth refers to the healthcare services ­delivered exclusively via sophisticated, two-way, video technology. A. True

4. Select the appropriate response. The most important priority areas of care for tele-intensive care unit (ICU) nursing includes: a) Critical thinking skills b) c)

d)

Ability to mentor Emergency patient care management Skillful communication

A. a and c

B. a, b, and c C. b and d

D. a, b, c, and d 5. Tele-ICU nursing practice continues to evolve with a focus that leads to which of the following: A. Identification of deadly syndromes like sepsis B. Prevention of falls

C. Prevention of unintended extubations

D. Improved compliance to quality indicators

B. False

E. All of the above

2. Match the telehealth definition with the appropriate term or label. A

Continuous 1

Care model where clinician is prompted to conduct a virtual or on demand visit

B

Scheduled

2

Location of the patient receiving telehealth services.

C

Reactive

3

Telehealth visits occur with a periodic consultation on a pre-determined basis.

D

Originating

4

Location of the practitioners or providers of telehealth services.

E

Distant

5

Ongoing surveillance over a defined period of time for a specific population of patients.

3. Reimbursement for telehealth services are:

A. The same as in traditional, face-to-face office visits B. Higher than traditional, face-to-face office visits due to added costs of telehealth technology

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C. Limited by CMS (Center for Medicare and Medicaid Services) criteria including ­geographic location, real-time and interactive ­communication, and appropriate coding

6. There is evidence to support that tele-ICUs that use effective strategies of leadership, perceived value, and organizational structure can achieve decreased mortality. A. True B. False

7. A ____________________________ strategy should be grounded on the Institute of Medicine’s (IOM) six aims for improvement: Safe, Timely, Effective, , Efficient, Equitable, and Patient centered (STEEEP) care. 8. The use case for robots in healthcare is limited to pharmacy robotics and medication inventory management due to the complex nature of care delivery. A. True B. False

9. In 2019, CMS established a new HCPCS modifier that removed restrictions on geographical locations so that _______________ networks could capture revenue regardless of originating site designation. 10. Remote patient monitoring (RPM) is where patients use medical devices to perform routine tests on

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devices such as glucose meters and vital sign and heart rhythm monitoring equipment and send those data to a healthcare professional. A. True B. False

Test Answers 1. Answer: B  False. Telehealth services are delivered via a variety of applications and services using twoway video, email, unified communication systems, handheld devices, wireless tools, and other forms of telecommunications technology over networked programs, point-to-point connections, monitoring center links, and Web-based e-health patient service sites. 2. Answers: C1, D2, B3, E4, A5

3. Answer: C  Limited by CMS (Center for Medicare and Medicaid Services) criteria including geographic location, real-time and interactive communication, and appropriate coding 4. Answer: D  a, b, c, and d

5. Answer: E  All of the above 6. Answer: A True

7. Answer:  Telehealth

8. Answer: B  False. Robotics are also used in surgery, service, telepresence, exoskeletons, companions, and humanoids. 9. Answer:  TeleStroke 10. Answer: True

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Telemed J E Health, 24(8), 582-593. doi:10.1089/ tmj.2017.0262 Mohr, N. M., Young, T., Harland, K. K., Skow, B., Wittrock, A., Bell, A., & Ward, M. M. (2019). Telemedicine Is Associated with Faster Diagnostic Imaging in Stroke Patients: A Cohort Study. Telemed J E Health, 25(2), 93-100. doi:10.1089/tmj.2018.0013 Muller, C., Marshall, C. L., Krasner, M., Cunningham, N., Wallerstein, E., & Thomstad, B. (1977). Cost factors in urban telemedicine. Med Care, 15(3), 251-259. Network for Excellence in Health Innovation (NEHI). (2018). Health Care Without Walls: A Roadmap for Reinventing U.S. Health Care. Washington DC: NEHI. Nevidjon, B., Rieger, P., Miller Murphy, C., Rosenzweig, M. Q., McCorkle, M. R., & Baileys, K. (2010). Filling the Gap: Development of the Oncology Nurse Practitioner Workforce. Journal of Oncology Practice, 6(1), 2-6. doi:10.1200/JOP.091072 Olson, C. A., McSwain, S. D., Curfman, A. L., & Chuo, J. (2018). The Current Pediatric Telehealth Landscape. Pediatrics, 141(3), e20172334. doi:10.1542/ peds.2017-2334 Pappas, P. A., Tirelli, L., Shaffer, J., & Gettings, S. (2016). Projecting Critical Care Beyond the ICU: An Analysis of Tele-ICU Support for Rapid Response Teams. Telemedicine and e-Health, 22(6), 529-533. doi:10.1089/ tmj.2015.0098 Pecci, A. (2018). Two Healthcare Systems Use Telehealth Kiosks to Expand Reach. Health Leaders. Retrieved from https://www.healthleadersmedia.com/innovation/twohealthcare-systems-use-telehealth-kiosks-expand-reach Practice guideline for the assessment and treatment of patients with suicidal behaviors. (2003). Am J Psychiatry, 160(11 Suppl), 1-60. Preston, J., Brown, F. W., & Hartley, B. (1992). Using telemedicine to improve health care in distant areas. Hosp Community Psychiatry, 43(1), 25-32. Rincon, T. A., & Henneman, E. (2018). An introduction to nursing surveillance in the Tele-ICU. Nursing2018 Critical Care, 13(2), 42-46. doi:10.1097/01. CCN.0000527223.11558.8a Rodriguez, M. J., Arredondo, M. T., del Pozo, F., Gomez, E. J., Martinez, A., & Dopico, A. (1995). A home telecare management system. J Telemed Telecare, 1(2), 86-94. doi:10.1177/1357633x9500100204 Rosenfeld, B. A., Dorman, T., Breslow, M. J., Pronovost, P., Jenckes, M., Zhang, N., . . . Rubin, H. (2000). Intensive care unit telemedicine: alternate paradigm for providing continuous intensivist care. Crit Care Med, 28(12), 3925-3931. Ross, C. (2020). ‘I can’t imagine going back’: Medicare leader calls for expanded telehealth access after Covid-19. STAT Plus Conversations. Retrieved from https://www.statnews.com/2020/06/09/ seema-verma-telehealth-access-covid19/ Senanayake, B., Wickramasinghe, S. I., Eriksson, L., Smith, A. C., & Edirippulige, S. (2018). Telemedicine in the

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39 Nursing’s Role in Genomics and Information Technology for Precision Health Kathleen A. McCormick / Kathleen A. Calzone

• OBJECTIVES . Describe the role of genomics in precision health. 1 2. Understand nursing’s professional role in genomics throughout the continuum of care. 3. Identify the role of pharmacogenomics throughout the lifespan and new guidelines with sufficient evidence to be implemented. 4. Define four areas where nurses can integrate genomics into the nursing process to participate in precision health. 5. Describe new technologies to integrate genomics into the electronic health record (EHR). 6. Summarize the challenges ahead in disseminating, educating, and implementing genomics into nursing informatics.

• KEY WORDS Culture Educational competencies Ethics Genomics Genomics through the continuum of care Nursing informatics in precision health Pharmacogenomics Precision Health Rapid risk assessment Reimbursement Symptom management

INTRODUCTION In a recent article, the new team required in precision health is described as doctors, nurses, pharmacists, geneticists, and genetic counselors (McCormick, 2017). This chapter describes precision health and updates the

professional nursing role in genomics throughout the continuum of care. This chapter also describes new technologies to integrate genomics into the EHR and the nursing process in the EHR. Nursing has a broad role in precision health encompassing preconception and prenatal assessment and 635

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counseling, newborn screening, risk identification, disease screening, disease prognosis, therapeutics, symptom science, and even utilization of omic data after death that will be described in this chapter. Pharmacogenomics provides a nursing role in precision health since it is relevant across the continuum of care from birth to death. Advanced practice nurses prescribe medications, and registered nurses administer medications and document medication outcomes and adverse drug reactions. This chapter focuses on documenting pharmacogenomics since this has clinical relevance to all nurses regardless of academic training. This chapter describes four areas that the nursing professional can integrate precision health into nursing informatics documentation through the nursing process in the EHR to improve the quality of care and patient outcomes: (1) Documentation of a Rapid Risk Assessment; (2) Family History and Ethnicity; (3) Medication Administration and Documentation, and (4) Evaluation of Medication Adverse Reactions. These recommendations have recently been included in a new policy brief recommended by the American Academy of Nursing (AAN), entitled “Strengthen Federal and Local Policies to Advance precision health Implementation and Nurses’ Impact on Healthcare Quality and Safety” (Starkweather et al., 2018). This chapter concludes with challenges going forward and the educational competencies recommended by the profession for nurses. Since genomics and nursing informatics are dynamic sciences, additional resources to keep up-to-date with information are provided in the chapter. The relationship between omic science and nursing informatics is summarized later in this chapter in Fig. 39.4, depicting not only the computational biology for testing genomics, but also the need to integrate the information into clinical electronic health data and population health data. This figure has been recently developed by McCormick and published by Whende Carroll in 2020 in a book entitled Emerging Technologies for Nurses: Implications for Practice (McCormick, 2020).

PRECISION HEALTH In 2016 the 21st Century Cures Act was signed into law by Congress (NIH, 2017). The law supports the Department of Health and Human Services (HHS) to pursue precision medicine by advancing disease prevention, diagnosis, and treatment, as well as implementing greater data sharing of genomic information. A part of this mandate to the National Institutes of Health (NIH) is the All of Us Research that aims to collect clinical, personal, environmental, and genomic information from 1 million or more

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Americans from diverse ethnic backgrounds. The project will collect data on different lifestyles, environments, and biology to discover pathways toward the delivery of precision medicine. The project will accelerate precision health by using tracking wearables and other home devices and technologies to measure personal health and correlate them with health outcomes. Precision health aims not only to cure diseases but also to prevent disease before it becomes manifest and improve symptom management during diagnosis and treatment in acute and chronic illness (NINR, 2017). This chapter adapts the model of health that Schroeder in his 2007 Shattuck Lecture has presented. It describes the contributions to Health versus Medicine as involving lifestyle and behaviors (40%) such as smoking, obesity, stress, nutrition, blood pressure, alcohol and drug use; genomics (30%) related to human biology; environment (20%) including social circumstances, and environmental exposure; and Access to Healthcare (10%) (Schroeder, 2007). Figure 39.1 depicts the contribution that genomics plays at the core of Precision Health, and the other contributors to health that require nursing care coordination.

Nursing’s History in Genomics Standards and Documentation Standards in Preparing for Precision Health Nursing has not been a passive bystander in the history of genomics. Of relevance is the (1) Documentation of a Rapid Risk Assessment; (2) Family History and Ethnicity are the American Nurses Association’s (ANA) addition of the concept of genomics to the third edition of the Nursing Informatics: Scope and Standards of Practice (ANA, 2014). These standards inform nurses that they must be able to “incorporate genetic and genomic technologies and informatics into practice” and “demonstrate in practice the importance of tailoring genetic and genomic information and services to clients based on their culture, religion, knowledge level, literacy, and preferred language.” Of relevance to (3) Medication Administration and Documentation, and 4) Evaluation of Medication Adverse Reactions implementing pharmacogenomics into nursing practice are the professional practice license mandates on medications administration ordered by a physician or nurse practitioners (NPs). ANA’s Principles for Nursing Documentation: Guidance for Registered Nurses nursing documentation standards indicate nurses must assess if the medication is appropriate to the patient’s diagnosis, if the dose is appropriate, what the reaction to the medication is, and whether there are adverse reactions to the medication (ANA, 2010).

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Chapter 39 • Nursing’s Role in Genomics and Information Technology for Precision Health 

Lifestyle and Behaviors Smoking Obesity Stress Nutrition Blood Pressure Alcohol and Drug Use

Genomics

Human Biology

Enviornment Including Social Circumstances Exposure

Access to Health Care

•  FIGURE 39.1.  Contributors to Precision Health. (Adapted from Schroeder, S. A. (2007). Shattuck Lecture. We can do better—improving the health of the American people. New England Journal of Medicine, 357(12), 1221–1228.)

NURSES’ HISTORY OF ENGAGEMENT IN GENOMICS THROUGHOUT THE CONTINUUM OF CARE Figure 39.2 is an image of the continuum of care that nurses have a history of engaging in genomics and some of the health conditions and genomic variants most commonly detected (McCormick & Calzone, 2016). The more than 3.9 million nurses in the United States and most nurses worldwide are familiar most with the use of genomics in the preconception and prenatal healthcare period. Family history is vital when interviewing parents for health conditions that they and their families may carry which could be passed down to their children. In the preconception period, the use of genomics can include testing for carrier status before pregnancy, often for autosomal recessive disorders such as MUTYH-associated polyposis, beta-thalassemia, or sickle cell trait (Ioannides, 2017). Additionally, individuals found to harbor a highpenetrance pathogenic susceptibility genetic variant may consider preimplantation genetic screening/diagnosis to avoid passing that variant on to their children (SullivanPyke & Dokras, 2018). In the prenatal period, noninvasive prenatal screening now can include cell-free fetal DNA testing (Badeau et al., 2017). Another area in the continuum that nurses are familiar with is newborn screening. Figure 39.2 lists some of the recommended screenings from the Health Resources and Services Administration 2018 Recommended Uniform Screening Panel (RUSP) (HRSA, 2018). About 3% of babies have a serious birth defect detected from newborn

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screening according to the Centers for Disease Control and Prevention (CDC) (CDC, 2018). Genomics use in risk assessment identifies individuals with an inherited predisposition, screening using genomic technology such as stool DNA testing for colon cancer screening and diagnosis to confirm a suspected diagnosis have accelerated in labs throughout the United States and globally with some genomic tests approved by the Food and Drug Administration (FDA) in the United States. There are approximately 75,000 genetic tests available from laboratories that have sprung up in every state of the union (Phillips, Deverka, Hooker, & Douglas, 2018). It is estimated that there are at least 10 new tests available per week. The cost of sequencing the genome has decreased from $100 million in 2001 to less than $1000 in 2017 rivaling costs of other medical tests or procedures (NHGRI, 2019). The genomics in the healthcare continuum also provides us with an improved understanding of the disease which informs disease prognosis such as tumor gene expression to inform recurrence risk for breast cancer and therapeutic decisions. Understanding the disease is the area where the genomics of the disease such as cancer is used to match to treatments targeting that genomic defect, a rapidly advancing field of precision medicine. Genomics also identifies potentials to develop new therapeutic approaches, and mechanisms to evaluate treatment responses. Some of the common health risks are also listed in Fig. 39.2. Targeting treatments is another area where considerable growth and discoveries are occurring. There is currently 10,703 expert reviewed human genomic variations in a database called ClinVar (Clinical Genome Resource, 2019), and 2.4 million molecular assays reported in the Database of Genotypes and Phenotypes (dbGaP) (dbGAP, 2019). To date, the use of genomic testing for prognostic or therapeutic purposes is occurring in most healthcare environments and is no longer limited to large academic and specialty care hospitals (Williams, 2019). The first FDA-approved companion diagnostic testing with Medicare coverage covers all solid tumors including non-small-cell lung cancer (NSCLC), colorectal, breast, ovarian, and melanoma. FoundationOne CDx can detect genetic variants in 324 genes and two genomic signatures in any solid tumor type (FoundationOne CDx, 2020). The next area of the continuum focuses on prognosis and therapeutic decisions. The final area of the continuum is monitoring disease progression through the use of new technology such as circulating tumor DNA and symptom management such as pharmacogenomics to inform pain management. Understanding and improving abilities to monitor treatment response and early evidence of disease recurrence

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Preconception and Prenatal Time Period

Sickle Cell, Cystic Fibrosis, Tay Sachs, Downs Syndrome, Edwards Syndrome

Newborn Screening

Phenylketonuria, Congenital Heart Disease, Hearing Loss, and others in the Recommended Uniform Screening Panel

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Risk Assessment Screening and Diagnosis

Diagnosis Matched to Treatment

BRCA1 and BRCA2 Cologuard® for evidence of colon polyps/cancer

Non Small Cell Lung Cancer, Colorectal, Breast, Ovarian, and Melanoma

Prognosis Matched to Therapeutic Decisions

Disease Progression and Symptom Management

Acute Myeloid Leukemia

Pain, dyspnea, fatigue, gastrointestinal disorders, impaired cognition, mood symptoms. depression, traumatic brain injuries, sleep disorders

Pharmacogenomics •  FIGURE 39.2.  Nursing’s Engagement in Genomics throughout the Continuum of Care with Examples of Some Diseases, Symptoms, and Disorders. are progressing using genomic technologies such as circulating DNA in cancer (Oellerich et al., 2019). Utilization of genomics to detect disease progression is also progressing, such as epigenetic changes in progressive Parkinson disease (Henderson-Smith et al., 2019). These discoveries help to inform not just the state of a given disease but provide the platform for the development of additional therapeutic options. Today, the research is progressing in almost all common health conditions including cardiovascular disease, stroke, cancer, arthritis, amyotrophic lateral sclerosis (ALS), HIV, multiple sclerosis (MS), type 1 and 2 diabetes, Parkinson disease, and depressive disorders. Nursing has defined a special role in the precision medicine Initiative through nursing research in precision health. Nursing science develops and applies new knowledge in biology and behavior, including genomics and biomarker identification, to improve symptoms. The National Institute of Nursing Research (NINR) at the NIH focuses on nurses’ ability to better understand the symptoms of chronic illness, such as pain, dyspnea, fatigue, gastrointestinal disorders, impaired cognition and mood disorders, depression, traumatic brain injuries, and sleep disorders because of the advances in genomics (Cashion, Gill, Hawes, Henderson, & Saligan, 2016). The research agenda focuses on improved personalized strategies to treat with precise interventions and to prevent adverse symptoms of acute and chronic illness across the continuum of care for populations in diverse settings. The area of

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symptom science is promoting personalized health strategies through a strategic plan and national research agenda (Dorsey et al., 2019).

PHARMACOGENOMICS The biggest areas in the continuum that permeate all stages in the continuum of life and affect the largest numbers in the population are the areas of pharmacogenomics. Figure 39.2 depicts the importance of pharmacogenomics throughout the continuum beginning in infancy. In infancy, during attention deficit management, pain management in children, nursing mothers, and adults, clot management in cardiovascular and stroke disease, and chemotherapy, there are pharmacogenomic potentials for nursing assessment and observations of adverse drug reactions. Variations in the human genome, specifically DNA sequence variants, could affect a drug’s pharmacokinetics (PK), pharmacodynamics (PD), efficacy, and safety. The genetic differences likely to be the most pertinent in nursing assessment are those associated with genes in four broad categories: (1) genes relevant to the drug’s PK related to absorption, distribution, metabolism (including formation of active metabolites), and excretion (ADME); (2) genes that code for intended or unintended drug targets and other pathways related to the drug’s pharmacological effect; (3) genes not directly related to a drug’s

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pharmacology that can predispose to toxicities such as immune reactions; and (4) genes that influence disease susceptibility or progression. The fate of drugs in the body depends upon ADME. Pharmacogenomics combines the science of drugs and their metabolism, with the genetics of enzymes that metabolize drugs to develop effective medications, safe medications, and doses tailored to the person’s genetic profile. An excellent literature review summary of pharmacogenomics and its implications for nursing practice was published in 2015 (Cheek, Bashore, & Brazeau, 2015). Since then, more precision has gone into the study of pharmacogenomics, and there are now 46 guidelines with sufficient evidence from systematic reviews of the literature to integrate into EHR and healthcare professional decision-making. Today, genomic testing in pharmacogenomics determines if it is the right drug, for the right person, at the right dose regardless of age (Collins & Varmus, 2015).

Pharmacogenomics and Nursing Documentation Pharmacogenomics is an important factor in precision health translated to nursing documentation of medication

administration and observation of adverse reactions. Thanks to the Pharmacogenomics Knowledge Base (PharmGKB) supported by the NIH, collaborations of scientists, researchers, pharmacists, and clinicians are collating data and disseminating information on the evidence between human genomic variation and individualized drug pharmacogenomics. Table 39.1 is a summary of the rating criteria for the level of evidence used to rate the systematic review of the literature and to recommend changing a prescription. The guideline methodology ranks the level of evidence like the methodology for developing clinical practice guidelines from the U.S. Preventative Services Task Force. Only those pharmacogenomic guidelines that are ready for implementation with level A evidence are recommended for translation into clinical practice. Currently the A-level of evidence on several drug categories is published in the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. There are currently 24 CPIC guidelines that have 62 medications associated with them (Clinical Pharmacogenetics Implementation Consortium [CPIC] Guidelines, 2020). Table 39.2 is the current list of practice guidelines by categories of drug type, drugs, and genes regulating their

  TABLE 39.1    Levels of Evidence for CPIC Guidelines CPIC Level

Clinical Context

Level of Evidence

Strength of Recommendation

A

Genetic information should be used to change prescribing of the affected drug

The preponderance of evidence is high or moderate in favor of changing prescribing

At least one moderate or strong action (change in prescribing) recommended.

B

Genetic information could be used to change prescribing of the affected drug because alternative therapies/dosing are extremely likely to be as effective and as safe as non-genetically based dosing

The preponderance of evidence is weak with little conflicting data

At least one optional action (change in prescribing) is recommended.

C

There are published studies at varying levels of evidence, some with mechanistic rationale, but no prescribing actions are recommended because (a) dosing based on genetics makes no convincing difference or (b) alternatives are unclear, possibly less effective, more toxic, or otherwise impractical or (c) few published studies or mostly weak evidence and clinical actions are unclear. Most important for genes that are subject to other CPIC guidelines or genes that are commonly included in clinical or DTC tests

Evidence levels can vary

No prescribing actions are recommended.

D

There are few published studies, clinical actions are unclear, little mechanistic basis, mostly weak evidence, or substantial conflicting data. If the genes are not widely tested for clinically, evaluations are not needed.

Evidence levels can vary

No prescribing actions are recommended.

Source: CPIC. Level definitions for CPIC genes/drugs. Retrieved from https://cpicpgx.org/prioritization/#leveldef. Accessed on May 28, 2020.

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  TABLE 39.2    List of 24 CPIC Guidelines, 62 Drugs Associated with Them, and Genes Guidelines

Drugs

Genes

UGT1A1 and Atazanavir

Atazanavir

UGT1A1

TPMT, NUDT15, and Thiopurines

Azathioprine Mercaptopurine Thioguanine

NUDT15 TPMT

SLCO1B1 and Simvastatin

Simvastatin

SLCO1B1

RYR1, CACNA1S, and Volatile anesthetic agents and Succinylcholine

Desflurane Enflurane Halothane Methoxyflurane Isoflurane Sevoflurane Succinylcholine

RYR1 CACNA1S

IFNL3 and Peginterferon-alpha-based Regimens

Peginterferon Alfa-2a Peginterferon Alfa-2b Ribavirin

IFNL3

HLA-B and Allopurinol

Allopurinol

HLA-B

HLA-B and Abacavir

Abacavir

HLA-B

HLA-A, HLA-B and Carbamazepine and Oxcarbazepine

Carbamazepine Oxcarbazepine

HLA-A HLA-B

G6PD and Rasburicase

Rasburicase

G6PD

DPYD and Fluoropyrimidines

Capecitabine Fluorouracil Tegafur

DPYD

CYP3A5 and Tacrolimus

Tacrolimus

CYP3A5

CYP2D6, CYP2C19, and Tricyclic Antidepressants

Amitriptyline Clomipramine Desipramine Doxepin Imipramine Nortriptyline Trimipramine

CYP2C19 CYP2D6

CYP2D6, CYP2C19, and Selective Serotonin Reuptake Inhibitors

Citalopram Escitalopram Fluvoxamine Paroxetine Sertraline

CYP2C19 CYP2D6

CYP2D6 and Tamoxifen

Tamoxifen

CYP2D6

(continued)

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  TABLE 39.2    List of 24 CPIC Guidelines, 62 Drugs Associated with Them, and Genes (continued) Guidelines

Drugs

Genes

CYP2D6 and Ondansetron and Tropisetron

Ondansetron Tropisetron

CYP2D6

CYP2D6 and Codeine

Codeine

CYP2D6

CYP2D6 and Atomoxetine

Atomoxetine

CYP2D6

CYP2C9, VKORC1, CYP4F2, and Warfarin

Warfarin

CYP4F2 VKORC1 CYP2C9

CYP2C9, HLA-B, and Phenytoin

Phenytoin

HLA-B CYP2C9

CYP2C19 and Voriconazole

Voriconazole

CYP2C19

CYP2C19 and Clopidogrel

Clopidogrel

CYP2C19

CYP2B6 and Efavirenz

Efavirenz

CYP2B6

CFTR and Ivacaftor

Ivacaftor

CFTR

CYP2C9 and NSAIDS

Aspirin Diclofenac Celecoxib Flubiprofen Aceclofenac Ibuprofen Indomethacin Lornoxicam Lumiracoxib Meloxicam Metamizole Nabumetone Naproxen Piroxicam Tenoxicam

CYP2C8 CYP2C9

metabolism. The list includes many common drugs used in clinical practice. The guidelines are meant to help clinicians optimize drug therapy based on available genetic test results and observe for adverse reactions if drugs cannot be metabolized by individuals. On the PharmGKB Web site (https://www.pharmgkb.org/guidelineAnnotations) there are also 93 guidelines listed from the Royal Dutch Association for the Advancement of Pharmacy–Pharmacogenetics Working Group (DPWG), 8 guidelines from the Canadian

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Pharmacogenomics Network for Drug Safety (CPNDS), and 17 other guidelines (from professional organizations) (PharmGKB, 2020). Besides CPIC consortia, international consortia are summarizing the body of evidence for similar drugs listed in the CPIC guidelines, and additional drugs and genes found in their countries and populations. Also, the FDA has a list of Pharmacogenomic Biomarkers in Drug Labeling. There are currently about 404 drugs with genetic biomarkers that are included in existing drug labeling (FDA, 2020). One might expect

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that these will be studied with sufficient evidence to incorporate into the CPIC clinical guidelines in the future.

From CPIC Guidelines to Dissemination, Implementation, and Measuring the Quality of Care and Cost Impact Just as there are teams of scientists and clinicians working on the development of CPIC guidelines, there are also teams disseminating and implementing the CPIC guidelines. A model for implementation described by Hicks et al. at the Moffitt Cancer Center, North Shore University, Boston Children’s Hospital, and St. Jude includes the transfer of a guideline into a workflow algorithm, then incorporates the guideline into the EHR via Computer Decision Support (CDS) (Hicks, Dunnenberger, Gumpper, Haidar, & Hoffman, 2016; Hoffman, et al, 2016). A group has also published similar integration and evaluation of the CPIC guidelines into CDS and the EHR at Mayo Clinic (Caraballo, Bielinski, St Sauver, & Weinshilboum, 2017). The evaluations of the outcome of implementing the guidelines on patient quality, safety, and costs are ongoing but the reviews are being summarized in Webcasts and knowledge presentations throughout the country, and the variance on patient race and ethnicity are being reported in publications. Levels of evidence translated into clinical practice and impacting costs of healthcare are very promising. While these studies have not included nursing impact per se, their effects of the patients’ quality and healthcare have been documented. A recent review of studies, developed by scientists for Translational Software, summarizes the economics of pharmacogenomics in several categories: Clopidogrel and Percutaneous Coronary Intervention, Psychiatric Pharmacogenomics, Polypharmacy, DPYD and Fluoropyrimidines, and Abacavir. The implications from reviews of 24 international studies were that pharmacogenomic testing was not only cost-effective but often cost-saving when drugs on the list of CPIC guidelines were studied (Translational Software, 2019). The most significant benefit was demonstrated in Psychiatric Pharmacogenomics. The mean cost savings in depression when pharmacogenomic testing was used, instead of trial and error drug treatments, was $3,000 per patient per year. Multiplying that cost by the number of people who are diagnosed with depression per year, that could be a cost savings of several billion dollars per year. Another area of cost savings included reductions in adverse drug reactions. The authors acknowledge that further testing is required in many categories of pharmacogenomic tests and guidelines.

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NURSING’S INFORMATICS ROLE IN PRECISION HEALTH Four ways that nursing informatics can participate in Precision Health are (1) Documentation of a Rapid Risk Assessment; (2) Family History and Ethnicity; (3) Medication Administration and Documentation, and (4) Evaluation of Medication Adverse Reactions. These are depicted in Fig. 39.3 integrated with the current Nursing Process, proposed in a recent publication by McCormick and recommended in the AAN policy brief recommendations (McCormick, 2017; Starkweather et al., 2018). These areas can improve care quality and patient outcomes, and safety which are also the future goals of the Quadruple Aim.

Documentation of Rapid Risk Assessment Putting genomics and pharmacogenomics advances into nursing practice includes enhancing the assessment by putting a RAPID risk assessment into the nursing process (Maradiegue &Edwards, 2016). The RAPID risk assessment includes the following: (1) assess the family history (usually recommended for at least three generations). Assess if they or anyone in their family has had a problem metabolizing drugs. (2) Identify the patient’s ancestry and ethnicity. This is becoming more important because, for example, patients from Ethiopia have an increased risk of toxicities based upon how they metabolize codeine. (3) Establish the probability of genetic condition or predisposition to an adverse drug reaction. Consult with a geneticist, genetic counselor, genetic nurse, pharmacist, and physician to determine a possible susceptibility to an adverse drug reaction after consulting the CPIC guidelines.

Documentation of the Family History and Ethnicity The family history is also known as a family health portrait or pedigree map. Family history is a record of first-, second-, and third-degree family and their medical information about an individual including the age of onset of health conditions, race and ethnicity, and age and cause of death in their biological family. Human genetic data of family members is becoming more prevalent and more accessible to obtain because of direct-to-consumer (DTC) screening and ancestry testing (FDA, December 2019). These data are being used to identify the risks of developing common diseases and a genetic disease that runs in families. Specific to cancer, the assessment of risk is thoroughly discussed in the NCI Physician Data Query (PDQ) site for professionals (NCI, 2019). The United States Public

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• PHARMCOGENOMIC INFLUENCES/ ADVERSE DRUG REACTIONS

• DRUG ADMINISTRATION

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• RAPID RISK ASSESSMENT

EVALUATE

ASSESS and DIAGNOSE

IMPLEMENT

PLAN and IDENTIFY OUTCOMES

• NEED FOR GENETIC COUNSELING • NEED FOR GENETIC or PGx TESTING

•  FIGURE 39.3.  Integration of Nursing Process with Pharmacogenomics and Genomic RAPID Risk Assessment. (Adapted, with permission, from McCormick, K.A. (2017). Together into the future... Pharmacogenomics and documentation. Nursing Management, 48(5), 32–40.)

Health Service (USPHS) Surgeon General recommends that during Thanksgiving Dinner, each family determines the history of family illnesses to add to the family history map. The facility to create a free Family History is available in a tool on the CDC Web site (CDC, 2019). Family gatherings present a time when families sometimes disclose ancestry discrepancies, or paternity hidden secrets including if the children are adopted or there is misattributed paternity. Approximately 28% to 30% of the time it is not the biological father who is the perceived father in the family (McCormick & Hoffman, 2006). If and/ or when they share their findings with nurses, who are still considered the most trusted healthcare professionals, ethics counselors and lawyers may have to be consulted. But how are the nurses going to record family history and ethnicity if there is no place in the EHR to record it? In a recent study of Magnet® hospitals, nursing administrators played a critical role in including the ability to document family history which includes ethnicity in the EHR (Calzone, Jenkins, Culp, & Badzek, 2018). One example of nurses integrating Family History, ethnicity, and pharmacogenomics into an EHR is occurring at the NIH Clinical Center (CC). In a March 2018 presentation at the HIMSS 2018 annual conference, two nurses from the NIH CC presented the plans for integration of genomics and Family History into their EHR. The NIH CC uses Allscripts with precision medicine functionality from the 2bPrecise precision medicine Knowledge Hub, a technology platform that integrates genomics with phenotypic data and plans on integrating with clinical workflow

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(Wallen & Lardner, 2018). They recommended a twopronged approach: (1) assess the limitations of the EHR for genomics, and (2) evaluate the preparedness of the nurses for genomics. While they found it was harder than they thought initially to integrate the family history, they also stressed the importance of the nurse’s role in expanding the family history in nursing documentation to include a family history or pedigree map in the EHR. In preparing nurses they recommend the Method of Introducing a New Competency (MINC) Implementation Model (MINC, 2019) that includes assessment of knowledge by nurses in genomics, providing staff development, assessment of hospital policy, providing staff knowledge, conducting professional development, anticipating obstacles and challenges, planning for integration into the EHR, and educating nurses how to use the tools (Wallen & Lardner, 2018). The Need to Document Ancestry and Ethnicity  The need to document ancestry and ethnicity in the EHR is becoming more critical as we examine the genetic differences in metabolizing drugs (as well as risk, tumor identification, and treatment) in populations throughout the globe (Manolio et al., 2015). Centers around the world are identifying ethnically related diseases associated with ethnic groups, and deficiencies in enzymes that help metabolize certain drugs in the CPIC guidelines and those under investigation by the FDA (FDA, 2019). For nurses working in the United States, ethnicity is relevant because the population of patients we see in hospitals, outpatient services, and retail pharmacies is a mix of many ethnicities.

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Nursing Role in Medication Administration and Documentation As previously stated, the nursing profession has license requirements and professional standards for nursing medication administration and documentation. The previous standards charged nurses with the five rights: the right patient, right dose, right drug, right route, and right time. Today with the CPIC guideline implementation and the foundation of pharmacogenomics in Precision Health in diverse ethnic populations, it is the Right Drug, for the Right Person, at the Right Dose regardless of age (Collins & Varmus, 2015). At the HIMSS annual conference in March 2018, Dr. McKeeby, the CIO of the NIH CC, and Dr. Jhanana Patel, Pharmacy Information Officer, described the integration of Pharmacogenomics within the EHR (McKeeby & Patel, 2018). Most patients at the NIH CC are genotyped because they are on complex research protocols for diagnosis and treatment of disease. The study determined how individual genetic inheritance affected the individual patient’s response to medications. They developed Computer Decision Support (CDS) to integrate the Pharmacogenomic testing to provide personized drugs for greater efficiency and safety of outcomes. Key to implementation was the composition of their team that included doctors, pharmacists, laboratory medicine personnel, nursing, and IT representatives. The project is determining which drugs require a point of contact decision support tool and further recommendations on who should receive Pharmacogenomic (PGx) testing. A review paper summarizing the integration of pharmacogenomics and decision support tools has been developed by the Translational Pharmacogenetic Program (TPP), a subgroup of the Pharmacogenomics Research Network. This group includes Mayo Clinic, Ohio State University, St. Jude Children’s Research Hospital, University of Florida, University of Maryland, Vanderbilt University Medical Center, the University of Chicago and Brigham and Women’s Hospital. Their goal is to determine models for implementing pharmacogenomics in diverse healthcare system environments with diverse patient populations (Dunnenberger et al., 2014). TPP is among the first groups to identify and overcome real-world barriers to adoption of evidence-based pharmacogenetics and to propose solutions to broad-based dissemination to healthcare professionals. Clinicians published a recent paper representing the integration of preemptive genomics and pharmacogenomics into the EHR used for decision-making at the University of Chicago (Borden & O’Donnell, 2018). They

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developed a genomic prescribing system. Unlike the CC at NIH, they were trying to determine which patients should have genetic testing matched to pharmacogenomic guidelines. Since the prescribing clinicians were not familiar with genomics or pharmacogenomics, they used red light, yellow light, and green light to determine the risk of a patient receiving a drug that they could not metabolize. They analyzed 2200 outpatients, and 546 had genotyping. They found that one-third of the medications that the patients were taking were associated with pharmacogenomic information. Medication change rates occurred when the genomic prescribing system alerted clinicians to red lights; that is, there was an indication that the drug should not be given. Indications that a drug should not be given occurred in 26 patients. Not only did clinicians feel they were delivering quality care, but patients were pleased with the clinician determining what drugs they should not take. Like the NIH CC, the University of Chicago created their interpretation of the genetic tests to make them more understandable to clinicians.

Evaluation of Medication Adverse Reactions The drugs listed in the CPIC guidelines are linked to adverse reactions when the drug does not work the same way in all persons. Medications are broken down in the liver by enzymes that may be affected by genomics. For example, in some persons, the enzyme is defective, or the person does not make the enzyme at all. In pain management, this is known to happen in persons taking codeine, who do not have the liver enzyme that converts codeine to morphine. A gene called CYP2D6 produces the enzyme that can convert codeine to morphine. Some people have variations in CYP2D6, so they don’t produce the enzyme at all. The codeine then cannot effectively help manage the pain. Persons from Ethiopia have a higher likelihood of having CYP2D6 variations that result in enzyme deficiencies. Another drug that is commonly used as a blood thinner after myocardial infarction, valve repairs, recent stroke, thrombus, heart transplant, or other coronary events is clopidogrel. The enzyme CYP2C19 has ultrarapid, extensive, intermediate, and poor subtypes (CPIC Guidelines, 2020). A person who cannot metabolize it may return to the emergency room or doctor’s office with recurrent blood clots and may need to be managed with prasugrel which would not interfere with the enzyme deficiency. Related to ethnicity, variants in CYP2C19 are common in persons with Asian ancestry. Warfarin is another drug that is used to prevent clotting in persons with arrhythmias, deep vein thrombus, after coronary surgery, and extensive orthopedic surgery.

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The CYP2C9, VKORC1, and CYP4F2 are genes known to be associated with responses leading to excessive bleeding in patients (CPIC Guidelines, 2020). These may result in the nurse observing bloody nose, blood in urine, and excessive bleeding events in patients. Abacavir is an antiretroviral drug used alone or in combination with other drugs in the treatment of HIV-1 infection (CPIC Guidelines, 2020). The HLA genes are specific for abacavir and can result in drug hypersensitivity reactions that can range from skin reactions such as eczema, urticaria, and angioedema, to severe reactions like severe cutaneous adverse reactions (SCARs). The sensitivities may be life-threatening and include drug reactions with eosinophilia and systemic symptoms DRESS/DIHS and Stevens–Johnson syndrome/toxic epidermal necrolysis (SJS/TEN). Sometimes the hypersensitivity reactions result in fever or rash or can affect the gastrointestinal tract and include nausea, diarrhea, vomiting, and stomach pain. In patients with hypersensitivity to abacavir, they could also have respiratory symptoms of cough, shortness of breath, and sore throat within the 6 weeks of treatment. In each of the examples, nursing assessments and observations are critical in determining if adverse drug reactions are occurring. New studies need to be conducted on nursing process documentation to determine retrospectively if adverse reactions observations were noted, and prospectively if adverse reactions could be prevented if guidelines were linked to computerized decision support systems in the EHR. One public database that nursing informatics should link to is the FDA Adverse Event Reporting System (FAERS) from the public dashboard. The name of the drug is entered, and the information on adverse reactions is reported (FDA, 2020). Another database that nurses in informatics may want to consider using is a terminology database of adverse reactions called Common Terminology Criteria for Adverse Events (CTCAE) (CTEP, 2019). Although developed for cancer and chemotherapy drugs, the site has a wide range of drug adverse event terms. The earlier terms were linked to MedDRA® v21 which has been used to codify terminology for drug and adverse event coding and is used globally.

KEY TECHNOLOGIES AND STRATEGIES FOR IMPLEMENTATION OF PRECISION HEALTH GOING FORWARD INTO THE FUTURE Going forward Precision Health will be a challenge to integrate the necessary information for nursing assessment, documentation, and assessment of adverse drug reactions

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and outcomes into the EHR. The technologies involve enormous amounts of data integrated into networks of health information. These data require additional storage (clouds), levels of security, unique patient identifiers, computer decision support tools, artificial intelligence, machine learning, and analytics to evaluate the quality and outcomes of these big data analytics. The concepts of artificial intelligence, machine learning, and analytics are described in Chapter 37 of this book by Koski and Murphy. Further research is required on the cost impact and cost savings of using pharmacogenomic decisions and symptom management in nursing care. In an effort to visualize the components of all of the information technology that incorporates the biotechnology informatics of the omics (whether genomes are from a person, a cancer tumor, or a virus of a pandemics), McCormick developed a diagram to incorporate the systems required for integration. The diagram in Fig. 39.4 includes population health captured from surveillance data to personal health data integrated into the EHR and personal health records. The omics components require testing and computational biology (bioinformatics) to determine cell signaling and function. The elements of the diagram are more complexly described by McCormick (2020). The omics components are adapted from a publication by Regan, Engler, Coleman, Daack-Hirsch, and Calzone (2018). In the Definitive Healthcare 2019 Survey of precision medicine in Healthcare, providers identified their top challenges concerning implementing precision medicine initiatives (Definitive Healthcare, 2019). Those challenges, in order of significance, were identified as (1) cost; (2) reimbursement challenges; and (3) patient compliance. Of those surveyed, 33% cited a lack of expertise as a barrier in going forward with a precision medicine program (Definitive Healthcare, 2019).

CHALLENGES—REIMBURSEMENT, ETHICS, EDUCATION, AND CULTURE Reimbursement As previously mentioned, the first FDA-approved genomic tumor profiling with Medicare coverage includes all solid tumors, including non-small-cell lung cancer (NSCLC), colorectal, breast, ovarian, and melanoma. On March 16, 2018, the Centers for Medicare & Medicaid Services (CMS) announced CMS reimburses for 324 genes and two genomic signatures in any solid tumor so that therapies can be targeted (CMS, March 16, 2018). CMS took action to advance innovative personalized medicine for Medicare patients with cancer. CMS finalized the National Coverage

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Access to Healthcare, Environment, Behaviors and Life Style Security Integrated Electronic Health Record Surveillance

Laboratory, Microbiome Imaging, Pathology, History and Physical, Medication Orders, Treatments, Evidence-based algorithms, CDS, Standards, Family Health History, Ethnicity, Nursing Documentation

Personal Health Record/Mobile  Devices

Bioinformatics Computational Biology, Natural Language Processing, Databases, Clouds, Standards, Data Mining Metabolome

Population Health

Precision Health

Personal Health

EpiGenomics

Methylation

DNA

mRNA

Proteins

Metabolites

Genomics

Transcriptomics

Proteomics

Metabolomics

Cell Signaling

Cell Function

The Future (4th Industrial Revolution) — Big Data, Artificial Intelligence, Machine Learning, Robotics

•  FIGURE 39.4.  Relationship Between Omics Science and Informatics. (With permission from McCormick, K. A. (2020). Precision health and genomics. In W. Carroll, Ed., Emerging technologies for nurses: Implications for practice (pp. 155–184). New York, NY: Springer. Omics adapted from Regan, M., Engler, M. B., Coleman, B., Daack-Hirsch, S., & Calzone, K. A. (2018). Establishing the genomic knowledge matrix for nursing science. Journal of Nursing Scholarship, 51, 50–57.)

Determination that covers diagnostic laboratory tests using Next Generation Sequencing (NGS) for patients with advanced cancer (i.e., recurrent, metastatic, relapsed, refractory, or stage III or IV cancer). CMS attests that when these tests are used as a companion diagnostic to identify patients with specific genetic mutations that may benefit from FDA-approved treatments; these tests can assist patients and their oncologists in making more informed treatment decisions. Additionally, when a known cancer mutation cannot be matched to treatment then results from the diagnostic lab test using NGS can help determine a patient’s candidacy for cancer clinical trials. Coverage decisions were made following the parallel review with the FDA, which granted its approval of the FoundationOne CDx (F1CDx™) test on November 30, 2017 (FDA, November 30, 2017). At the same time, CMS issued a proposed National Coverage Determination for NGS cancer diagnostics. F1CDx™ is the first breakthrough-designated, NGS-based in vitro diagnostic test that is a companion diagnostic for 15 targeted therapies as well as can detect genetic mutations in 324 genes and two genomic signatures in any solid tumor. Relevant to the impact of pharmacogenomics on Precision Health, a map of the United States and the CMS Medicare Administrative Contractors (MAC) who administrate reimbursement is provided on the IGNITE map

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page. At this time all the regions are currently reimbursing for the following genes: CYP2C19 for patients undergoing Percutaneous Coronary Intervention for stent procedures following up with Clopidogrel therapy, CYP2D6 for therapy with Amitriptyline/Nortriptyline (for depression) or Tetrabenazine, CYP2C9 for Warfarin treatment in anticoagulation, and VKORC1 for anticoagulation therapy (IGNITE, 2019). As the science moves further into areas as rheumatology, cardiovascular disease, neurological diseases, and behaviors, the challenge of funding will have to be reevaluated.

Ethics The privacy and discrimination concerns regarding the use of genetic and genomic data in healthcare raise new ethical and legal concerns where the potentials for genomics are being used for not only treatment but also enhancements to embryos and humans. The Genetic Information Nondiscrimination Act of 2008 (referred to as GINA) is a federal law that was enacted to prevent discrimination in health insurers and employers based upon genomic information. After GINA was passed, it is recognized that the current law does not include military personnel, nor does it cover persons acquiring life insurance,

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disability insurance, and long-term care insurance. The AAN Policy Brief entitled “Strengthen Federal and Local Policies to Advance Precision Health Implementation and Nurses’ Impact on Healthcare Quality and Safety” recommends enhancements to Health Insurance Portability and Accountability Act (HIPAA) and GINA that are appropriate to Precision Health Implementation (Starkweather et al., 2018). HIPAA does not address the broader security needed in patient records that include genomic information. Some of these ethical issues are being discussed by NIH and HHS for research subjects. The nursing professionals need to be vigilant, monitoring the policies and laws governing genomic data that protect the healthcare consumers.

Educating Nurses to Achieve Genomic Competency in the Era of Precision Health The rapidity of discoveries and uptake of the genomics into healthcare and society is driving the need for nurses competent in genomics in academia, practice, research, and education. The AAN policy brief recommended the following: sufficient education and continuing education on genomics and implementing Precision Health; the integration of data sources into the information technology

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infrastructure to provide clinical support for healthcare providers to document a rapid risk assessment, ethnicity, and family history; including CPIC guidelines for clinicians and computerized decision supports; and the ability to document adverse drug reactions in addition to other recommendations outlined in the policy (Starkweather et al., 2018). Additional resources to gain competency in genomics for nursing are presented in Table 39.3.

Culture The changes in healthcare and information technology are driving a Fourth Industrial Revolution described by Dr. Francis Collins at the July 23, 2018, open public workshop at the NIH on Harnessing Artificial Intelligence (AI) and Machine Learning (ML) to Advance Biomedical Research (Collins, July 23, 2018). He includes Big Data, Robotics, Clouds, AI, and ML as the principal drivers of this change in computing information technology necessitated by the volume of information produced by genomics, EHRs, and the integration of the genomics with measurements of lifestyle and behaviors, the environment, and access to healthcare. These are the forces described in Fig. 39.1 which separate precision medicine

  TABLE 39.3    Genomic Education Resources for Nurses Title

Description

Web site

Essentials of Genetic and Genomic Nursing: Competencies, Curricula Guidelines, and Outcome Indicators, 2nd Edition

Genomic competencies expected of all nurses regardless of the level of academic training, role, or specialty.

https://www.genome.gov/pages/ careers/healthprofessionaleducation/ geneticscompetency.pdf

Essential Genetic and Genomic Competencies for Nurses with Graduate Degrees

Genomic competencies expected of nurses with graduate degrees

http://www.nursingworld. org/MainMenuCategories/ EthicsStandards/Resources/ Genetics-1/Essential-Genetic-andGenomic-Competencies-for-NursesWith-Graduate-Degrees.pdf

Global Genetics and Genomics Community (G3C)

Online, unfolding, interactive genomic case studies

http://genomicscases.net/en

Telling Stories, Understanding RealLife Genetics

Stories based on video clip teaching and learning resource, including clinical genetics, ethical, legal and social implications, family history, genetic conditions, genetic counseling, genetic/genomic testing, risk assessment

http://www.tellingstories.nhs.uk/

Genomic Nursing Competencies

Genomic Cases and Stories

(continued)

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  TABLE 39.3    Genomic Education Resources for Nurses (continued) Title

Description

Web site

Talking Glossary of Genetic Terms

Written and verbal terms descriptions with corresponding graphics

https://www.genome.gov/ genetics-glossary

Your Genome

Basic biology, genomics, and implications for health and society

http://www.yourgenome.org/

Global Genomic Nursing Alliance (G2NA)

A global nursing alliance to accelerate genomic practice and education integration

https://g2na.org/

International Society of Nurses in Genetics

Provides genomic, scientific, and professional growth opportunities

http://www.isong.org/

Collaborative OMIC research network providing information, resources, as well as networking and data/sample sharing opportunities

https://omicsnursingnetwork.net

GenEquip— Genetics Education for Primary Care

Genomic learning modules, educational Webinars, and practice resources

https://www.primarycaregenetics. org/?page_id=109&lang=en

Method for Introducing a New Competency (MINC)

Toolkit for integrating genomics into a practice environment

http://genomicsintegration.net/

CPIC practice guidelines

https://cpicpgx.org/

Genomic Education Resources

Genomic Nursing Organizations

Genomic Nursing Research Resources Omics Nursing Science and Education Network (ONSEN)

Genomic Practice–Specific Resources

Pharmacogenomic Resources Clinical Pharmacogenetic Implementation Consortium (CPIC) Guidelines PharmGKB

Pharmacogenomic resources and guidelines

https://www.pharmgkb.org/

CPIC Guideline UTube videos

14 CPIC Guidelines

https://www.youtube.com/watch?v=VH IRIeQ2b08&list=PLbP5DJELA1WM1m gVf0OHfhxRoQtyb-QJh

Genetics and Genomics Competency Center (G2C2)

Genomic resource repository

http://genomicseducation.net/

Health Education England Genomics Education Programme

Genomic education resources

https://www.genomicseducation.hee. nhs.uk/

Resource Repositories

from Precision Health. These forces are compelling a cultural shift in the way we integrate data from many sources to look at the patient as an individual in addition to looking at health from a population of patients. These forces are expanding our thinking from diagnosis and treatment only, also to include prevention and symptom management. New nursing observations in patient care including the Rapid Risk Assessment, Family History, Ethnicity, Medication Administration and

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Documentation, and Evaluation of Medication Adverse Reactions become a part of the new culture of Precision Health and provide enormous opportunities for nurses to improve the quality and outcomes of care and improve patient safety. Nurses have always been early adopters of technology and changes in healthcare. The patient is moving quickly into accepting genomics as evidenced by the number of DTC tests being performed. To remain the most trusted profession and competent to practice,

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nurses need to embrace the culture changes required by Precision Health.

Test Questions 1. Health is made up of what components? A. Access to healthcare only

B. Access to Healthcare, Lifestyle and Behaviors, Genomics, and Environment C. Where you live only

D. How much you smoke and eat only 2. What are the six areas of nursing engagement of genomics throughout the continuum of care?

A. Preconception and prenatal health, Newborn screening, Risks assessment and screening matched to diagnosis, Diagnosis matched to treatment, Prognosis matched to a therapeutic decision, Spread of disease and symptom management B. In the fetus, in the infant, in the person with chronic disease, in oncology, and population health, and in nursing home care

C. In the hospital, in the home, in the outpatient, in the retail clinic, on the person, in skilled nursing care D. Nursing is not engaged in genomics throughout the continuum of care

3. What is an area of genomics that extends from birth to death? A. Genetic profiling B. Proteomics

C. Pharmacogenomics

D. Direct to consumer testing 4. How many CPIC guidelines are ready with class A levels of evidence in 2019? A. There are no CPIC guidelines with class A evidence.

B. There are 100 CPIC guidelines with class A evidence.

C. There are 234 CPIC guidelines with adverse reactions class A levels of evidence.

D. There are currently 23 CPIC guidelines with 46 individual drugs associated with them with class A levels of evidence.

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5. What are the four roles for nursing in Precision Health? A. Conduct consultations with geneticists, develop pharmacy partnerships, evaluate family pedigrees, interpret diagnostic tests

B. Diagnose a patients’ chronic diseases from consumer genetics, correct doctors’ orders, conduct family teaching, recommend genetic tests C. Document a rapid risk assessment, document a family history and ethnicity, medication administration and documentation, and evaluation of medication adverse reactions D. Evaluate medication adverse reactions, change doctors’ orders, conduct patient education, change hospital policies.

6. What are at least four toxic adverse events to drugs that could be observed by nurses? A. Swollen lymph nodes, blood in urine, temperature, skin reactions including eczema

B. Pain in eyes, redness in nose, temperature, earache C. Sleepiness, excessive blood clotting, pain in eyes, redness in nose

D. Pain and swollen lymph nodes, blood in the nose, pain in eyes, hyperactivity 7. What ANA documents describe a potential role for nurses in genomic informatics? A. ANA Code of Ethics, and Nursing Documentation Policies

B. ANA Principles for Nursing Documentation and Scope of Standards Practice C. ANA Code of Ethics and Standards of Nursing Informatics Practice

D. ANA Conflict of Interest Policies, and Standards of Conduct 8. What are four challenges facing the translation of genomics into nursing?

A. There are no challenges because the rewards are so great.

B. There are challenges in scientific discovery, technical supports, ethics, and legal challenges. C. Reimbursement, ethics, education, and culture are challenges. D. Reimbursement, scientific discovery, technical support, and legal challenges.

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9. Can nurses gain competencies in the era of Precision Health? A. Yes, there are many areas where nurses can gain competencies in Precision Health.

B. No, there are many areas where nurses can gain competencies in Precision Health, but they are at the advanced practice level. C. Yes, only online courses for industry employees are available to gain competencies in Precision Health.

D. Yes, online courses only for faculty are available to gain competencies in Precision Health. 10. The new AAN 2018 policy brief recommended what to advance implementation of Precision Health and nursing’s impact on quality and safety of care? A. Reimbursement for pharmacogenomics integrated into nursing practice

B. Sufficient education and continuing education on genomics to advance implementing Precision Health C. No recommendations for nursing to integrate Precision Health into practice or education

D. Changes in the nursing code of standards developed by ANA

Test Answers 1. Answer: B  According to the WHO and adapted by Schroeder and McCormick and Calzone, health is made up of Access to healthcare (10%), Lifestyle and behaviors (40%), Genomics (30%), and Environment (20%).

2. Answer: A  Preconception and prenatal health, Newborn screening, Risks assessment and Screening matched to Diagnosis, Diagnosis matched to treatment, Prognosis matched to a therapeutic decision, Spread of disease and symptom management 3. Answer: C  Pharmacogenomics extends from birth to death.

4. Answer: D  There are currently 23 CPIC guidelines with 46 drugs associated with them with class A levels of evidence, and they were listed in Table 39.2.

5. Answer: C  Document a rapid risk assessment, Document a family history and ethnicity, Medication administration and documentation, and Evaluation of medication adverse reactions

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6. Answer: A  Swollen lymph nodes, blood in urine, temperature, skin reactions including eczema 7. Answer: B  ANA Principles for Nursing Documentation and Scope of Standards Practice

8. Answer: C  Reimbursement, ethics, education, and culture are challenges

9. Answer: A  Yes, there are many areas where nurses can gain competencies in Precision Health, and they are listed in Table 39.3. 10. Answer: B  Sufficient education and continuing education on genomics to advance implementing Precision Health.

REFERENCES America Nurses Association. (2010). ANA principles for nursing documentation: Guidance for registered nurses. Silver Spring, MD: American Nurses Association. American Nurses Association. (2014). Nursing informatics: Scope and standards of practice (2nd ed.). Silver Spring, MD: American Nurses Association. Badeau, M.,., Lindsay, C., Blais, J., Nshimyumukiza, L., Takwoingi, Y., Langlois, S., … Rousseau, F. (2017). Genomics-based non-invasive prenatal testing for detection of fetal chromosomal aneuploidy in pregnant women. Cochrane Database of Systematic Reviews, 11, Cd011767. Borden, B. A., & O’Donnell, P. H. (2018). Implementing preemptive pharmacogenomics in clinical practice. Clinical Laboratory News. Retrieved from https://www. aacc.org/publications/cln/articles/2018/april/implementingpreemptive-pharmacogenomics-in-clinical-practice Accessed on April 3, 2019. Calzone, K. A., Jenkins, J., Culp, S., & Badzek, L. (2018). Hospital nursing leadership-led interventions increased genomic awareness and educational intent in Magnet® settings. Nursing Outlook, 66(3), 244–253. Caraballo, P. J., Bielinski, S. J., St Sauver, J. L., & Weinshilboum, R. M. (2017). Electronic medical record-integrated pharmacogenomics and related clinical decision support concepts. Clinical Pharmacology & Therapeutics, 102(2), 254–264. Cashion, A. K., Gill, J., Hawes, R., Henderson, W. A., & Saligan, L. (2016). National Institutes of Health Symptom Science Model sheds light on patient symptoms. Nursing Outlook, 64(5), 499–506. doi:10.1016/j. outlook. 2016.05.008 CDC. (2018). Prevention data and statistics on birth defects. Retrieved from https://www.cdc.gov/ncbddd/ birthdefects/data.html. Accessed on March 28, 2019. CDC. (2019). My family health portrait. Retrieved from https://phgkb.cdc.gov/FHH/html/index.html. Accessed on April 24, 2019.

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Chapter 39 • Nursing’s Role in Genomics and Information Technology for Precision Health 

Cheek, D. J., Bashore, L., & Brazeau, D. A. (2015). Pharmacogenomics and implications for nursing practice. Journal of Nursing Scholarship, 47(6), 496–504. Clinical Genome Resource. (2019). ClinGen. Retrieved from https://clinicalgenome.org/. Accessed on March 26, 2019. Clinical Pharmacogenetics Implementation Consortium (CPIC) Guidelines. (2020). PharmGKB. CPIC Guidelines. Retrieved from https://www.pharmgkb.org/guideline Annotations. Accessed on May 31, 2020. CMS. (March 16, 2018). CMS finalizes coverage of Next Generation Sequencing tests, ensuring enhanced access for cancer patients. Retrieved from https://www.cms.gov/Newsroom/ MediaReleaseDatabase/Press-releases/2018-Press-releasesitems/2018-03-16.html. Accessed on March 17, 2019. Collins, F. S. (Opening Statements). (July 23, 2018). Harnessing artificial intelligence (AI) and machine learning (ML) to advance biomedical research. Retrieved from https:// videocast.nih.gov/summary.asp?live=28053&bhcp=1. Accessed on February 22, 2019. Collins, F. S., & Varmus, H. (2015). A new initiative on precision medicine. The New England Journal of Medicine, 372(9), 793–795. doi:10.1056/NEJMp1500523 CTEP. (2019). Common Terminology Criteria for Adverse Events (CTCAE) v5. Retrieved from https://ctep.cancer. gov/protocoldevelopment/electronic_applications/ctc. htm. Accessed on April 24, 2019. dbGaP. (2019). Summary Statistics of dbGaP data. Retrieved from https://www.ncbi.nlm.nih.gov/projects/gap/ summaries/cgi-bin/molecularDataPieSummary.cgi. Accessed on March 28, 2019. Definitive Healthcare. (2019). Precision medicine study. Retrieved from https://blog.definitivehc.com/2019precision-medicine-study. Accessed on June 1, 2020. Dorsey, S. G., et al. (2019). Working Together to advance symptom science in the precision era. Nursing Research, 68(2), 86–90. doi:10.1097/NNR.0000000000000339 PMCID: PMC6399038 Dunnenberger, H. M., Crews, K. R., Hoffman, J. M., Caudle, K. E., Broeckel, U., Howard, S. C., & Relling, M. V. (2014). Preemptive clinical pharmacogenetics implementation: Current programs in five US medical centers. Annual Review of Pharmacology and Toxicology, 55, 89–106. doi:10.1146/annurev-pharmtox-010814124835 FDA. (November 30, 2017). CMS proposes coverage of first breakthrough-designated test to detect an extensive number of cancer biomarkers. Retrieved from https://www.fda.gov/newsevents/newsroom/ pressannouncements/ucm587273.htm. Accessed on February 22, 2019. FDA. (December 2019). Direct-to-consumer tests. Retrieved fromhttps://www.fda.gov/medical-devices/vitro-diagnostics/direct-consumer-tests. Accessed on June 4, 2020. FDA. (2018). Adverse drug reactions. Retrieved from https:// www.fda.gov/drugs/guidancecomplianceregulatory information/surveillance/adversedrugeffects/ucm070093. htm. Accessed on January 27, 2019.

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FDA. (2020). List of biomarkers. Retrieved from https://www. fda.gov/Drugs/ScienceResearch/ucm572698.htm. February 5, 2020. Accessed on May 20, 2020. FoundationOne CDx. (2020). Retrieved from https://www. fda.gov/medical-devices/recently-approved-devices/ foundationone-cdx-p170019. Accessed on May 31, 2020. Health Resources and Services Administration (HRSA). (2018). Recommended uniform screening panel. Retrieved from https://www.hrsa.gov/advisory-committees/ heritable-disorders/rusp/index.html. Accessed on March 29, 2019. Henderson-Smith, A., et al. (2019). DNA methylation changes associated with Parkinson’s disease progression: outcomes from the first longitudinal genome-wide methylation analysis in blood. Epigenetics, 14(4), 365–382. Hicks, J. K., Dunnenberger, H. M., Gumpper, K. F., Haidar, C. E., & Hoffman, J. M. (2016). Integrating pharmacogenomics into electronic health records with clinical decision support. American Journal of Health-System Pharmacy: Official Journal of the American Society of Health-System Pharmacists, 73(23), 1967–1976. doi:10.2146/ajhp160030 Hoffman, J. M., Dunnenberger, H. M., Kevin Hicks, J., Caudle, K. E., Whirl Carrillo, M., Freimuth, R. R., … Peterson, J. F. (2016). Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC). Journal of the American Medical Informatics Association, 23(4), 796–801. doi:10.1093/jamia/ocw027 IGNITE. (2019). Map of pharmacogenetic test reimbursement according to Medicare Administrative Contractor (MAC). Retrieved fom https://dcricollab.dcri.duke.edu/ sites/NIHKR/IGNITE%20Documents%20and%20Links% 20to%20Content/Clinicians/Clinical%20Implementa tion%20of%20Genomic%20Medicine%20and%20Pharma cogenomics/Map%20of%20the%20Pharmacogenetic%20 Test%20Reimbursement%20According%20to%20MAC. pdf. Accessed May 22, 2020. Ioannides, A. S. (2017). Preconception and prenatal genetic counseling. Best Practice and Research Clinical Obstetrics & Gynaecology, 42, 2–10. Manolio, T. A., Abramowicz, M., Al-Mulla, F., Anderson, W., Balling, R., Berger, A. C., … Ginsburg, G. S. (2015). Global implementation of genomic medicine: We are not alone. Science Translational Medicine, 7(290), 290ps13. doi:10.1126/scitranslmed.aab0194 Maradiegue, A. H., & Edwards, Q. T. (2016). A primer: Risk assessment, data collection, and interpretation for genomic clinical assessment. In D. C. Siebert, Q. T. Edwards, A. H. Maradiegue, & S. T. Tinley (Eds.), Genomic essentials for graduate level nurses (pp. 31–66). Lancaster, PA: DEStech Publications. McCormick, K. A. (2017). Together into the future … Pharmacogenomics and documentation. Nursing Management, 48(5), 32–40.

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McCormick, K.A. (2020). Precision health and genomics. In W. Carroll, Ed. Emerging technologies for nurses: Implications for practice (pp. 155–184). New York, NY: Springer. McCormick, K. A., & Calzone, K. A. (2016). The impact of genomics on health outcomes, quality, and safety. Nursing Management, 47(4), 23–26. McCormick, K. A., & Hoffman, M. (2006). Genomics and bioinformatics relationship to current day electronic health record. In C. Weaver, C. Delaney, P. Weber, & R. Carr (Eds), Nursing and informatics for the 21st century: An International look at cases, practice, and the future (1st ed.). Chicago, IL: Healthcare Information and Management Systems Society (HIMSS). McKeeby, J. W., & Patel J. T. (2018). Pharmacogenomics within the EHR. Retrieved from https://www.himssconference.org/session/pharmacogenomics-within-ehr. Accessed on January 27, 2019. Method of Introducing a New Competency Implementation Model (MINC). (2019). Retrieved from https://genomicsintegration.net. Accessed on April 26, 2019. National Cancer Institute. (2019). PDQ. Retrieved from https://www.cancer.gov/publications/fact-sheets#Risk+Fa ctors+and+Possible+Causes. Accessed on April 24, 2019. National Human Genome Research Institute (NHGRI). (2019). Cost of sequencing a human genome. Retrieved from https://www.genome.gov/27565109/the-cost-ofsequencing-a-human-genome/. Accessed on March 28, 2019. National Institute of Health (NIH). (2017). National Institutes of Health All of Us Research Program. Retrieved from https://allofus.nih.gov. Accessed on February 19, 2019. National Institute of Nursing Research (NINR). (2017). Symptom science research: a path to precision health. Retrieved from https://www.ninr.nih.gov/newsandinformation/events/symptom-science-event. Accessed on February 22, 2019. Oellerich, M., et al. (2019). Circulating cell-free dna-diagnostic and prognostic applications in personalized cancer therapy. Therapeutic Drug Monitoring, 41(2), 115–120.

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PharmGKB. (2020). PharmGKB.org/guidelines/annotations. Retrieved from https://www.pharmgkb.org/guidelineAnnotations. Accessed on May31, 2020. Phillips, K. A., Deverka, P.A., Hooker, G.W., & Douglas, M.P. (2018). Genetic Test availability and spending: Where are we now? Where are we going? Health Affairs (Millwood), 37(5), 710–716. Regan, M., Engler, M. B., Coleman, B., Daack-Hirsch, S., & Calzone, K. A. (2018). Establishing the genomic knowledge matrix for nursing science. Journal of Nursing Scholarship, 51, 50–57. Schroeder, S. A. (2007). Shattuck Lecture. We can do better—improving the health of the American people. New England Journal of Medicine, 357(12), 1221–1228. Starkweather, A. R., et al. (2018). Strengthen federal and local policies to advance precision health implementation and nurses’ impact on healthcare quality and safety. Nursing Outlook, 66(4), 401–406. Sullivan-Pyke, C., & Dokras, A. (2018). Preimplantation genetic screening and preimplantation genetic diagnosis. Obstetrics and Gynecology Clinics of North America, 45(1), 113–125. Transnational Software. (2019). An economic evaluation of pharmacogenomics testing. Retrieved from https://www. translationalsoftware.com/whitepaper-an-economicevaluation-of-pharmacogenomic-testing. Accessed on May 31, 2020. Wallen, G., & Lardner, M. (March 5–9, 2018). Genomics nursing and the EHR. HIMSS2018 conference. Las Vegas, Nevada. Retrieved from http://365.himss.org/sites/ himss365/files/365/handouts/550240981/handout-130. pdf. Accessed on Jan 27, 2019. Williams, M. S. (2019). Early lessons from the implementation of Genomic Medicine Programs. Annual Review of Genomics and Human Genetics. doi:10.1146/ annurev-genom-083118-014924

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40 Big Data Analysis of Electronic Health Record (EHR) Data Roy L. Simpson

• OBJECTIVES . Define the evolving definition of big data through the decades. 1 2. Understand the uses of big data surrounding commerce and the consumer to date. 3. Identify potential uses of the electronic health record (EHR) as a big data source of nursing information. 4. Describe the complex challenges for preparing big data for analytics.

• KEY WORDS Analytics Big data Electronic health records Nursing informatics Principles

INTRODUCTION This chapter examines data science’s most significant breakthrough—big data—from a nursing perspective. This chapter includes describing the value of big data to nursing practice and research, the role of maintaining a perspective on compassion in today’s society, and how nurses can participate in big data preparation through nursing documentation. Just hearing the term, “big data,” triggers confusion for many people, especially those in healthcare. Many think electronic health records (EHRs), which contain thousands of data points collected from across the continuum of care, are big data—that is only a small part of given health data. In this chapter, a strategy for using EHRs will be provided, and a foundation for big data will be described.

Additional confusion stems from the fact that big data sits at the intersection of social science and statistics, and information and computer science—disciplines that, for the most part, are outside of nursing. Finally, the nursing profession will require more specifically trained informaticians and researchers to efficiently and effectively mine the massive amounts of data nurses collect on every shift in many places of patient care.

DEFINING BIG DATA FROM A HISTORICAL PERSPECTIVE In the early 1980s, the nurses at the National Institutes of Health were grappling with the retrieval of data produced from automating what nurses documented. They calculated from the first electronic medical records at 653

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the NIH Clinical Center that a nurse collects more than 106,000 data points in a single shift. These systems did not meet four of the v’s as later described. The systems were based on hierarchical data structures and storage without artificial intelligence programmed in Mumps. While large data amounts, they did not align with today’s terms of a relational database for cleansing and aligning to a data dictionary. This research analysis was pioneering to our build-out of today’s big data and our future in big data. A seminal paper entitled “Application-controlled demand paging for out-of-core visualization” (Cox & Ellsworth, 1997) is thought to be the first mention of the big data concept. However, it was not until 11 years later that big data mainstreamed its way into healthcare in “Bigdata computing: Creating revolutionary breakthroughs in commerce, science, and society” (Bryant, Katz, & Lazowska, 2008). Both of these important papers warrant a read to understand the underpinnings of today’s push for big data in healthcare. Over the past decade, there have been numerous papers written on defining the elements of big data. In 2013 Paul Henchey described big data for healthcare providers having components of Volume, Velocity, and Variety. At that time, the healthcare providers were described as producing big data from laboratory results, and Medicare claims data and consumer searches of medical literature. Velocity was predicted to be for predictive analytics for clinical decision support, gaps in care alerts, and prepayments fraud alerts. The variety was predicted to result from the multitude of formats from ambulatory care EHR data, XML, and unstructured text documents, and genomic maps and medical device streams. In 2015, the HIMSS CNO-CNIO Roundtable prepared a white paper on big data and relied upon the definition by Gaffney and Huckabee in 2014 that also included Veracity (Gaffney & Huckabee, 2014). The nursing group defined the need for Veracity to assure the integrity, accuracy, and trustworthiness of data. They further delineated the volume of data would grow to a massive amount due to the research on genomics. Future models for patients include other ‘omics, i.e., genomics, epigenomics, lipidomic, proteomics, glycomics, foodomics, transcriptomics, metabolomics, pharmacogenomics, and toxicogenomics adding culture and more to the future of EHRS. In addition to the ‘omics, the symptom management science is adding to the volume of data for nursing to analyze and mine. Together with symptom management science and pharmacogenomics are contributing to precision health at the National Institutes of Health (NINR Symptom Science, 2019). It was reported in 2014 by Savage that the combined genome of normal and cancer in a single patient is 1 terabyte (1012 bytes), and 100 genomes and ‘omics in

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multiple patients would result in 1 exabyte (1018 bytes) of data (Savage, 2014). The estimated cost of storing and analyzing these data was estimated at $100 million per year. The HIMSS white paper further described four principles that would be required to use big data for nursing. They included privacy and security of health information, data standards including common formats, and interoperability to provide the ability to exchange data in comparable and meaningful ways. The focus of big data in nursing was defined as clinical, pharmaceutical, activity and cost data, and patient behavior and sentiment data. They described that the use of big data analytics would impact nursing’s role in precision health because of the volume of data resulting from genomics across the continuum of care. This group predicted that with standardized data captured, nurses could use big data to improve quality, outcomes, and reduce the cost of care. The concept of Value was added by nurses who focused on the conversion of data to information to knowledge to wisdom, which would result in Value to big data analytics of quality data, outcomes, and reduced costs (Westra et al., 2017). This excellent review of exemplars of big data analytics describes 17 studies by nurse researchers using EHR data in multiple environments. Data computing leader IBM’s Big Data and Analytics Hub also sets out four key characteristics to further describe big data from a computing standpoint (IBM Big Data & Analytics Hub, n.d.): 1. Volume—the scale of data. A quintillion of data is created every day. A quintillion equals a 1 followed by 18 zeroes.

2. Velocity—the analysis of streaming data. A modern car has more than 100 sensors, each of which collects, analyzes, and compares readings on a nearconstant basis.

3. Variety—different forms of data. More than 420 million wearable, wireless health monitors are in use today, and each collects different types of data in different formats. 4. Veracity—the uncertainty of data. Of every three business leaders, one does not trust the information he or she uses to make decisions.

While each of these characteristics is important, Veracity has an incredibly high impact on patient care, which needs to be based on evidence. What may have been a standard best practice a few years ago has likely been advanced through evidence-based research since most nurses received their education. Nurses must stay up-to-date on the latest research and incorporate these findings into the way they care for patients. Nurses must

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take professional obligations seriously to practice evidence-based nursing. More recently, Simpson defined big data at ANIA in 2019 keynote speech as: “The slight twists and turns of new and old data create a smorgasbord of new information deriving from a kaleidoscope of actions within mathematics and statistics, delivering new knowledge for applications into precision care for patients” (Delaney, Weaver, Warren, Clancy, & Simpson, 2017; Simpson, 2019). This definition includes a subtle but critical nuance. Looking at data from a different perspective will produce a different result—every time. These differences are not about context as much as they are about the small changes in queries that align the data to a different conclusion. For example, asking how many nurses were working on a service floor, the answer could reflect a total of 20 nurses. If you refined the query to ask how many nurses were providing direct patient care, the answer would likely be less, perhaps only 10.

BIG DATA USES As big data entered the clinical vernacular, one thing became clear: Big data is traditional data collection on steroids. Think about every electronic device you own or use. These devices continuously collect data about you—even when you are sleeping:

• • • • •

Your phone knows whom you call most frequently and how recently you spoke to each of them. Your tablet knows what you made for dinner and which ingredients in your pantry need to be replenished. Your GPS knows where you went yesterday, how long it took to get there if there was a better route available and your average rate of speed. Financial apps calculate your net worth with up-tothe-minute accuracy. Even your bed knows how well you slept during the night and what could be done to improve your sleep.

These expanding collections of data have already overtaken humans’ ability to comprehend or use it all. In all, 90% of the data we now know was created in the past two years. Consider these mind-boggling data stats (Marr, 2018):

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• •

3.7 billion humans use the Internet every day.

•

Every day, Google processes 3.5 billion searches— that’s 40,000 queries a minute.

More than half of all searches happen on a mobile phone.

• •

Every minute of every day, 456,000 tweets go out.

• •

16 million text messages are sent each minute.

1.5 billion people spend time on Facebook every day. Every minute, 103,447,520 spam emails are sent.

USING BIG DATA TO CHANGE CONSUMER BEHAVIOR Until recently, most Americans had their prescriptions filled at the drug store closest to their home or office. Now, a service company called Good RX has launched a national experiment to use transparent pricing and coupons to change this “closest to me” consumer behavior for pricing. On www.GoodRx.com, you can compare the prices of 70,000 FDA-approved drugs by a drugstore, right down to zip code. Zipcodes historically define pricing in drugs. You can download coupons to save when you have your prescription filled or refilled (GoodRx.com, 2019). In this scenario, the consumer leverages big data, engaging in transparency pricing models for the pharmaceutical industry that is using financial incentives to change consumer behavior (Marsh, 2019).

BIG DATA CONFIRMS INCOME AS A HEALTH INDICATOR Income has always been a key indicator of health. Individuals in low-income areas are more likely to suffer from environmental, infectious diseases, and nutritional deficiencies. An analysis of more than 50 million U.S. prescriptions filled in 39 of the largest Metropolitan Statistical Areas (MSAs) supports this long-held belief (GoodRx.com, 2019). Analysis has shown that lower-income Americans experienced depression, obesity, and diabetes more often than those with higher incomes. Also, the lower-income individuals self-reported an overall lower level of health than their counterparts from areas with a higher income. Lower-income individuals filled less than 105 prescriptions per 1000 people in 2018 (Marsh, 2019). At the other end of the economic spectrum, individuals with higher incomes were more likely to fill prescriptions for “lifestyle conditions,” such as eyelash growth, erectile dysfunction, hair loss, rosacea, facial wrinkles, and skin discoloration. In 2018, higher-income people filled approximately 200 prescriptions per 1000 people. In addition to the difference between the fill rate for lower-income people, which was about half of the rate for

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the higher-income group, another discrepancy stood out. While mental health conditions such as attention deficiet hyperactivity disorder (ADHD), alcohol addiction, anxiety, bipolar disorder, depression, eating disorders, fatigue, panic disorder, and obsession-compulsion disorder were more prevalent in lower-income populations, the prescription fill rate did not support this assumption. The research team pointed to more limited access to treatment and fewer resources to use in conjunction with prescriptive fills as two reasons for this disconnect.

BENEFITS OF BIG DATA ANALYSIS FOR NURSING What big data has quickly learned is what nursing as known all along—it’s a 24/7/365 world. Even when we humans take a break, the world continues to rotate, and data continues to be produced in all of its formats on many different computer platforms. Despite the confusion and lack of resources, nursing cannot afford to wait for another profession inside or outside healthcare to solve its big data conundrum. Too much is at stake, especially when you take into account what nursing can gain from big data: Only big data can sift through nursing’s massive data sets to reveal the hidden insights needed to advance best practices. For the first time, the professional will be able to quantify its Value—a prerequisite to gaining traction in America’s capitalistic society. Its best chance for compressing the time it takes for research to make its way to the bedside—currently an unbelievable 17 years. Seventeen years is a long time to wait! Many of the articles making their way through the editorial process associated with respected peer-reviewed nursing journals are based on data that is sometimes 7-years old. Publishers and nursing need to cooperate to compress this timeframe and slash wait time. Most nurses do not understand the difference between a predatory journal, one that accepts payment to publish your work, and a proprietary journal with peer review, where value to the profession drives acceptance. Not all publications deliver equal value, and nurses should know which ones do and which ones do not before they put in all the work needed to pursue professional publication. One national organization has taken a giant step forward to curtail this 17-year lag. The National Library of Medicine (NLM) now curates tweets to help fast-track

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research and the distribution of new information. We need more innovative approaches such as what NLM is doing to come online if we want to get out from underneath the 17-year review and publication process.

BIG DATA NURSING APPLICATIONS WITH A POTENTIAL TO IMPACTING QUALITY OUTCOMES Why is it that humans do not respond to the common sense associated with data’s Veracity? For example, artificial intelligence (AI) uses scientific evidence to predict, with 90% accuracy, an individual’s end of life. When you consider that approximately 80% of a patient’s lifetime spent on healthcare occurs in the last 10 days of life—and has no impact on the medical outcome—why do patients and their families continue to insist that everything is done for their loved one even though death is inevitable? Simply because the mind does not want to accept the information. This example offers nurses an opportunity to lead delicate palliative care discussions with patients and their families long before they face hospice options. Rather than boiling sensitive end-of-life decisions down to a dollar-and-cent discussion, nurses’ compassion can help patients and their families see that the expenses of endof-life care’s extreme measures do not extend life or its quality. Consider the fact that everyone knows that eating healthy foods in the proper amounts and exercising extends both lifespan and the quality of life. However, Americans continue to struggle to maintain a healthy weight. America’s adult obesity rate, the second-highest in the world, is 39.8%, and obesity-related conditions, such as heart disease, stroke, type 2 diabetes, and some types of cancer, continue to be a leading cause of preventable death in the United States (CDC, 2016). The pattern continues because humans disregard common sense, and the value of data when it indicates they should do something they do not want to do.

THE BUILDING BLOCKS TO USING BIG DATA FROM ELECTRONIC HEALTH RECORDS In a paper encouraged by the Big Data group at the University of Minnesota, a group of researchers summarized the building blocks from current exemplars of nursing use of big data from EHRs (McCormick et al., 2015).

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Patient Centered Accountable Care

Integrated Intelligent Workflow With Patient Centric Smart GUI

Data Mining

Decision Support

With Integrated Evidence Based Medicine

Analytics Ontology Intelligent Central Document /Image Repository

Logic NLP

Semantic Interoperability: Data Harmonization Facility/Patient/Provider Identification Syntactical Interoperability: Interfaces / Data Aggregation

•  FIGURE 40.1.  Data: Building Blocks to Insights (Rasu Shrestha, HIMSS 2019 Used with permission.)

In that paper, three leading national organizations, Partners HealthCare, Intermountain Healthcare, and Kaiser/VA collected and analyzed nursing data from documentation in an acute care setting. The data were not standardized, and the data were on multiple platforms. These nurse researchers reached consensus and recommended nine steps for analyzing big data in the nursing profession in the future of big data analytics. These nine steps are: (1) form a project team, (2) define the project scope, (3) identify the evidence, (4) identify and harmonize the data elements, (5) develop a conceptual model, (6) develop use cases, (7) describe optimal data sets, (8) map to reference terminologies such as SNOMED-CT and LOINC, and (9) formalize the data model in the Unified Modeling Language. These nine steps were known to be time-consuming and laborious when the data were not structured and across many platforms. To move forward more efficiently, the group recommended common terminologies as input would reduce the burden of harmonizing data elements. At the HIMSS meeting in February 2019, Rasu Shrestha described the data building blocks for analyses of

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big data. Figure 40.1 represents a summary of his recommendations. Inherent in the nursing recommendations in 2015, and his recommendations is the use of a foundation in syntactic interoperability: Interfaces/Data Aggregation. The next level up included facility/patient and provider identifier. Then assuming the data are not entered in a common standardized format, the semantic interoperability: data harmonization is recommended similar to the nursing paper in 2015. Once the large data repository is created, then the tools recommended include Natural Language Processing, statistical analytics, data mining, and decision support. The top layers are integrated intelligent workflow and accountable patient care.

OBSTACLE: LACK OF ANALYTICAL TOOLS If big data is such a powerful resource for healthcare and nursing, why is every healthcare facility not mining its data for insights? The biggest obstacle to harnessing the power of all these data is the lack of robust, easy-to-use

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data analysis toolsets capable of quickly and efficiently manipulating these massive data sets. Nurse informaticist often “hit the wall” from a technology perspective when they attempt to load all the data from EHRs into data warehouses for analysis. Computers only understand zeros and ones, which renders differing data formats, such as a patient’s birth date being listed on January 4, 1986, 01-04-1986, and 04-01-1986 in the same EHR, as unreadable. Reconciling these differing data formats and aligning incompatible computing platforms, which proliferate as data siloes across a patient care facility, required 3 years of data cleansing by nurse informaticians and data scientists at the Nell Hodgson Woodruff School of Nursing, Emory University. That is 3 years of work before the first analysis could begin. One of the key challenges of big data— it is simply too large and too complex to be managed manually or with today’s simple data manipulation tools. Mathematical-based toolsets, which are in short supply today, are needed to reveal the value inherent in big data. Traditional analytical tools, data’s original GPS, were built to navigate and manipulate small data sets inside a relatively narrow informational area. Big data’s value, however, comes from the wealth of information and insights contained in its massive data sets. The difference between traditional data mining and big data analysis is the difference in capacity and velocity of two similar water-delivery tools: a standard garden hose and a commercial fire hose. To tap into the fire hose that is big data, we need industrial-strength toolsets. They do not exist without doctoral preparation with a strong computer science background. We have to build them in real time as we push the boundaries of today’s data analysis capabilities. To analyze big data for nursing, Chief Nurse Informatician Officers (CNIOs) need a grounding in computer science, statistics, and scientific research methods. These advanced skill sets require doctorate-level preparation. Statistical competency forms the bedrock of big data analysis, and even doctorate-level preparation may not be enough when you consider the pace at which big data is advancing.

LABORIOUS DATA CLEANSING At the Nell Hodgson Woodruff School of Nursing at Emory University, 5 years of over a million patients’ records were pulled into a data warehouse. All of the data needed to be smoothed, aggregated, normalized, and discretized using computer coding and statistical programming before we could begin to use the data (Higgins, Simpson, & Johnson, 2018). The EMORY NELL Project required 3 years of data cleansing and normalization to get to a place where we were ready for the analysis phase. When there is a need

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to review patient data from multiple encounters, including clinical and financial data, the amount of data is enormous. Patient’s may also have had hundreds of encounters in their 5-year healthcare experience. This complex undertaking used demographics to create phenotypes of like patients’ post-hospitalization. For example, we looked at the records of 300 breast cancer patients with BRCA. Then, we filtered the data based on shared characteristics. We looked for the next patient that matched the narrow characteristics included in that phenotype. All of this was done to isolate the precise combination of drugs, therapies, and care that worked with this phenotype. Nursing needs to lead patient care to this level of precision health—care that is tailored to commonly held patient characteristics. When conducting this complex analysis, it is imperative that the CNIO select analytical tools that are relatively easy to use. Each toolset has a learning curve, so education has to be accounted for in the project plan. If you switch tools that become outdated, the staff will have to update their learning to be competent in the new toolset. By selecting a quality toolset that you do not have to switch tools and start over, the time for education will be kept to a minimum. The selection of quality tools cannot be overemphasized in planning time to learn and relearn new tools.

TECHNOLOGY LIFECYCLES MATTER Another consideration in tool selection is knowing where the preferred toolset stands in its lifecycle. Technology’s planned obsolescence has a substantial impact on big data. Technology products and toolsets are designed to be continually evolving, with new products being phased in and out of the market on a fairly predictable schedule. It is this planned obsolescence that jeopardizes the effectiveness of longitudinal studies and analysis critical to big data. Nurse informaticians, statisticians, and computer scientists would be wise to keep the technology lifecycle in mind when selecting analytical tools and other components of big data analysis. Knowing how to synch, as tightly as possible, your research timeline and the products’ lifecycles are critical. If you choose a tool or platform that is nearer to the end of its lifecycle, your longitudinal efforts could be severely marginalized.

NEED FOR AN EDUCATED NURSING COMMUNITY Both education and culture have been highlighted in this chapter. There is a need to develop a strategy for educating not only nursing students, but all nurse executives and

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policymakers on informatics, statistical, and analytic tools to examine big data. It is also essential to educate them on the value of standardized nursing data and the inclusion of nursing data into EHRs. Of equal importance to the terminology selection, the models defined by groups for different health conditions need to be published. The nursing profession needs a place to publish models of data in different groups of patients and consumers. The funding for nursing research into analytics of big data from many sources: acute care, ambulatory care, home care, and long-term care; population health, and bioinformatics is needed. All of this research contributes to patient quality outcomes and reducing costs. Cultural changes are also needed to enlist the innovative and entrepreneurial nurses to value the use of big data analytics to advance patient-centered care, and their role in precision health. While this paper attests that the consumer is driving this cultural change, nurses need to remain the most trusted professional by moving to where the consumer and patient are going.

quantify its contribution to healthcare, someone else with a different perspective will. As the big data kaleidoscope tells us, when a different perspective formulates the query, a different result follows.

CONCLUSIONS

3. Henchy’s work describes _____ would be a part of the EHR.

Consider the real-world plight of migrant workers in their twenties who were developing chronic kidney failure, a condition that usually occurs with patients in their seventies (Butler et al., 2018). Most of the workers were coming into the country to pick lettuce and other in-season produce and then returning to their home countries. When care providers tracked them, it became known that the pressure to maximize their paychecks, which were calculated on a per-head basis, prevented the workers from hydrating and taking bathroom breaks during their 12-hour shifts. Taking a more compassionate approach to managing workers in the field and removing the capitalistic penalty for breaks worked wonders. When everyone was required to stop for 15 minutes to drink water and use the bathroom, the incidence of chronic kidney failure returned to normal levels. This example shows big data at is best—capturing information accurately and consistently, and using that data to manage and prevent disease, improving patients’ quality of life. Big data is here to stay—it’s simply too valuable and the computing power too available to have it any other way. Nursing stands at a crossroads of harnessing the power and reach of big data to advance the profession. That’s why nurses must take on an evidence-based, computer scientist orientation to pull from big data the real value nursing makes to America’s capitalistic society. In 2019, it has never been more accurate to say that he or she who has the gold rules. If nursing doesn’t leverage big data to

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TEST QUESTIONS 1. Paul Henchey describes big data in 2013 as ______. A. Volume, Velocity, and Variety

B. Volume, Variety, and Veracity C. Veracity, Volume, Variety D. none of the above

2. The purpose of velocity per Henchy was to predict analytics for _____. A. Clinical decision-making B. Gaps in care alerts C. Payment fraud

D. All of the above

A. Genomics

B. Glycomics

C. Foodomics

D. Pharmacology

4. Simpson parallels the amount of data in big data to what? A. Smorgasbord B. Appetizer C. Salad bar D. Entrée

5. To help define big data, how many zeros are used to describe quintillion bytes? A. 8 zeros

B. 10 zeros C. 18 zeros

D. None of the above

6. Big data sits at the intersection of disciplines outside nursing and medicine; the three most commonly noted are ________. A. Computer science, information science, and social science

B. Statistics, computer science, and information science

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C. Social Science, information science, and statistics D. All of the above

7. Nursing’s biggest future in big data comes from ______. A. EHRs

B. Pharmacogenomics and symptom management C. Genomics and other omics D. All of the above

8. What two changes are needed to advance big data in nursing? A. Education and culture change B. More data with more variety

C. More platforms to hold big data D. None of the above

9. If a common terminology for nursing content is not used, what time-consuming process will have to be used? A. Computerized decision support

B. Artificial intelligence and algorithm development C. Harmonization and mapping of data elements D. None of the above

10. What are the five V’s attributable to nursing big data? A. Volume, Veracity, Variety, Velocity, and Value B. Vitality, Voluminous, Variable, Valuable, and Volatile C. Volume, Value, Vision, Vitality, and Variable D. None of the above

Test Answers 1. Answer: A  Paul Henchey described big data in 2013 as volume, velocity, and variety.

2. Answer: D  All of the above. The purpose of velocity per Henchey was to predict analytics for clinical decision-making, gaps in care alerts, and payment fraud. 3. Answer: A  Paul Henchey predicted genomics would be a part of the EHR.

4. Answer: A  Simpson parallels the amount of big data to a smorgasbord. 5. Answer: C  A quintillion bytes is 18 zeros.

6. Answer: D  Big data sits at the intersection of social science and statistics, and information and computer science.

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7. Answer: D  All of the Above. Nursing’s biggest future in big data comes from EHRs, Pharmacogenomics and Symptom Management, Genomics, and other omics.

8. Answer: A  Two changes that are needed to advance big data in nursing are education and culture changes. 9. Answer: C  If a common terminology for nursing content is not used, harmonization and mapping of data will have to be done, and it is very time-consuming.

10. Answer: A  The five V’s attributable to nursing big data described in this chapter are Volume, Veracity, Variety, Velocity, and Value.

REFERENCES Butler-Dawson, J., Krisher, L., Asensio, C., Cruz, A., Tenney, L., Weitzenkamp, D., … Newman, L. S. (2018). Risk factors for declines in kidney function in sugarcane workers in Guatemala. Journal of Occupational and Environmental Medicine, 1. doi:10.1097/ JOM.0000000000001284 Bryant, R., Katz, R. H., & Lazowska, E. D. (2008). Big-data computing: Creating revolutionary breakthroughs in commerce, science, and history. Computing Community Consortium. Retrieved from https://www.immagic.com/ eLibrary/ARCHIVES/GENERAL/CRA_US/C081222B. pdf. Accessed on June 16, 2020. Centers for Disease Control and Prevention. (2016). Adult obesity facts. Retrieved from https://www.cdc.gov/obesity/data/adult.html. Accessed on June 16, 2020. Cox, M., & Ellsworth, D. (1997). Application-controlled demand paging for out-of-core visualization. NASA Ames Research Center. Retrieved from https://www.nas. nasa.gov/assets/pdf/techreports/1997/nas-97-010.pdf. Accessed on June 16, 2020. Delaney, C. W., Weaver, C. A., Warren, J. J., Clancy, Th. R., & Simpson, R. L. (2017). Big data-enabled nursing: Education, research and practice. Springer. Retrieved from https://www.springer.com/us/book/9783319532998. Accessed on June 16, 2020. Gaffney, B., & Huckabee, M. (2014, July 8). Part 1: What is big data? HIMSS Data and Analytics Task Force. Retrieved from http://www.himss.org/ResourceLibrary/ genResourceFAQ.aspx?ItemNumber=30730. GoodRx. (2019). GoodRx.com. Retrieved from https://www. goodrx.com/?gclid=EAIaIQobChMIhKm38YLR4wIVR9 bACh1BFgeKEAAYASAAEgLLOvD_BwE. Accessed on June 16, 2020. Henchey, P. (2013). HIMSS clinical & business intelligence primer: Big data for providers. Retrieved from file:///C:/ Users/scimi/Downloads/Big%20Data%20for%20 Providers_Clinical%20&%20Business%20Intelligence%20

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Primer_06-30-2013_Web_Final%20(3).pdf. Accessed on June 10, 2019. Higgins, M., Simpson, R. L., & Johnson, W. G. (2018). What about big data and Nursing? American Nurse Today. Retrieved from https://www.americannursetoday.com/ big-data-nursing. Accessed on June 16, 2020. HIMSS Nursing Working Group on Big Data. (2015). CNOCNIO vendor roundtable guiding principles for big data in nursing using big data to improve the quality of care and outcomes. Retrieved from https://www.himss.org/sites/ himssorg/files/FileDownloads/HIMSS_Nursing_Big_ Data_Group_Principles.pdf. Accessed on June 10, 2019. IBM Big Data & Analytics Hub. (n.d.). The four V’s of big data. Retrieved from https://www.ibmbigdatahub.com/ infographic/four-vs-big-data. Accessed on June 16, 2020. Marr, B. (2018, May 21). How much data do we create every day? The mind-blowing stats everyone should read. Forbes. Retrieved from https://www.forbes.com/sites/ bernardmarr/2018/05/21/how-much-data-do-we-createevery-day-the-mind-blowing-stats-everyone-shouldread/#5669314b60ba. Accessed on June 16, 2020. Marsh, T. (2019). The effect of income on U.S. prescription fill patterns. Retrieved from https://www.goodrx.com/blog/ income-effects-on-prescription-drug-fills-in-the-unitedstates/. Accessed on June 16, 2020. McCormick, K. A., Sensmeier, J., Dykes, P. C., Grace, E. N., Matney, S. A., Schwarz, K. M., & Weston, M. J. (June

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2015). Exemplars for advancing standardized terminology in nursing to achieve sharable, comparable quality data based upon evidence. On-Line Journal of Nursing Informatics: OJNI, 19(2). Retrieved from https://www. himss.org/exemplars-advancing-standardized-terminology-nursing-achieve-sharable-comparable-quality-databased. Accessed on June 11, 2019. NINR Symptom Science. (2019, June 27). Video of presentations and slides. Retrieved from https://www.ninr.nih. gov/newsandinformation/events/sscevent. Accessed on July 25, 2019. Savage, N (2014). Bioinformatics: Big data versus the big C. Nature, 509, S66–S67. Shrestha, R (2019). Big data healthcare: A leaders story. Retrieved from https://365.himss.org/sites/himss365/ files/365/handouts/552564087/handout-BG4.pdf. Accessed on June 11, 2019. Simpson, R (2019, April 13). Kaleidoscope: Twists and turns in big data—General keynote address. ANIA 2019 Annual Conference. Retrieved from https://library.ania. org/ania/search/0/query?q=simpson. Accessed on June 16, 2020. Westra, B. L., Sylvia, M., Weinfurter, E. F., Pruinelli, L., Park, J. I., Dodd, D.,… Delaney, C. W. (2017). Big data science: A literature review of nursing research exemplars. Nursing Outlook, 65, 549–561.

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41 Nursing Data Science and Quality Clinical Outcomes Lynn M. Nagle / Margaret A. Kennedy / Peggy A. White

• OBJECTIVES 1. Describe the evolution and current state of nursing clinical data standards to support measuring quality clinical outcomes. 2. Using the Canadian healthcare system as an exemplar, describe the benefits of clinical data standards for nurses in particular. 3. Discuss the promise of “big data” and emerging “data science methods” for nursing.

• KEY WORDS Big data Clinical outcomes evidence-based practice Data science Data standards Practice-based evidence

INTRODUCTION Over the past two decades, governments and healthcare organizations in the developed countries of the world have invested heavily in the acquisition and deployment of health information technologies, particularly electronic health records (EHRs). In North America, nurses are typically the largest group of health professionals; hence they are also the predominant users of these systems and contributors of clinical data. To optimally leverage the investments both to date and going forward, nurses and others need to begin to utilize technology, informatics, and data science methods to mine evolving data repositories and extract practice-based evidence. In conjunction with classic approaches to research, practice-based evidence, generated and accessible to nurses in real time, has the

potential to be a major game changer for the delivery of nursing care. The generation of evidence from data derived from practice will expand nursing’s knowledge base; demonstrate the impact of nursing care on outcomes, clinical and financial; inform the delivery of appropriate, safe, quality patient care; inform health policy directions; and support the appropriate allocation and mix of skilled nursing resources. However, the realization of these goals is largely dependent upon the adoption of clinical nursing data standards in all clinical settings (e.g., acute care, primary care, long-term care, home care). With few exceptions, most countries, including Canada, continue to strategize and strive for this reality. In this chapter, we discuss the opportunity to optimize quality outcomes with the adoption of clinical data standards that reflect nursing practice, particularly with the emergence of “big 663

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data” and “data science” methods. Further, examples from the Canadian context are presented as illustrations of the promise and possibilities to be realized from mining the data that richly depicts realms of nursing practice.

BACKGROUND In 1992, nurses in Canada reached consensus on the data elements required to understand the impact of nursing practice including client status, nursing interventions, and client outcomes. In addition to these clinical data, nurses in Canada identified the need for unique nurse identifiers and nursing resource intensity information to represent nursing practice in the healthcare system (Canadian Nurses Association, 1993). While there has been progress in identifying, defining, and standardizing nursing data, these data are still not consistently collected or widely integrated into EHRs. In addition, these data are not captured within administrative systems nor abstracted into key data repositories. However, national endorsement of data and documentation standards such as the interRAI assessment tools, Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT), Logical Observation Identifiers Names and Codes (LOINC), and the International Classification of Nursing Practice (ICNP) have set the stage for the adoption of standards more broadly. In the Canadian context, nursing-specific initiatives such as the Canadian Health Outcomes for Better Information and Care (C-HOBIC) (Hannah, White, Nagle, & Pringle, 2009) and the National Nursing Quality Report for Canada (NNQR-C) (VanDeVelde, Doran, & Jeffs, 2015) have demonstrated the value of standardizing the collection of nursing data within specific jurisdictions and healthcare organizations. But currently, regardless of system vendor, the opportunity to adopt standardized models, tools, and measures is being lost with every healthcare organization that adopts its own approach. Ironically, the potential to design standardized data repositories and reporting tools is one of the greatest advantages of using EHRs, yet this has not been widely addressed by nursing or other health professions. With an aging demographic, increased incidence of chronic disease and demands for value- and outcomesdriven care (Australian Institute of Health and Welfare, 2018; Veillard, Fekri, Dhalla, & Klazinga, 2015), optimally leveraging technology and clinical data collection is an imperative. Collecting standardized information that supports continuity, care coordination, and the evaluation of outcomes (clinical and financial) as people transition across healthcare sectors has the potential to inform and transform health policy. In light of the recent global

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COVID-19 pandemic, there is no doubt that the capacity to consistently measure and track clinical outcomes within communities and institutions is critical to the effective management of such a crisis; if not globally, at the very least, nationally. Numerous efforts have been made to bring evidence to nurses in practice settings and support nurses to actually use the information they are gathering when making clinical decisions. The use of best practice guidelines/pathways, electronic order sets, smartphone apps (e.g., drug manuals, calculators), point-of-care documentation tools (e.g., barcode readers), plus access to Internet resources can facilitate and support evidence-informed practice. Nurses need to be held to account for taking appropriate clinical action based upon data gathered through the processes of care. Documentation standards should encompass the use of standardized nursing data and evidence-based tools to guide assessment, interventions, clinical decision-making, and outcomes evaluation. Healthcare delivery organizations need to consistently enable and support evidenceinformed practice and administration within and across the healthcare system. Moreover, with the adoption of standardized data and documentation methods, large volumes of comparable clinical data will become available for analysis and study, thereby facilitating the generation of new knowledge and evidence. Indeed, the future advancement of nursing practice will be underpinned by the emerging field of data science.

THE EMERGENCE OF NURSING DATA SCIENCE As digital health technologies have permeated all aspects of healthcare over the course of the past several decades, they are now considered to be essential tools for contemporary healthcare and evidence-informed nursing practice. The implementation and adoption of technologies have progressed and matured; hence, attention is shifting from the tactical implementation of digital systems to the strategic use of patient and healthcare data that are captured and stored as a product of system use. Data sources have also proliferated, including EHRs, patient monitoring devices, smart technologies and mobile health applications, social media, diagnostic testing, and clinical assessments—all of which cumulatively generate enormous amounts of data about patients and the healthcare system. This vast, untapped accumulation of information offers unparalleled opportunities to understand more about disease prevention and management, intervention (e.g., symptom management) evaluation, and health system use. However, it demands that we approach this

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opportunity with new perspectives, new methods and tools, and new ways of conceptualizing health information management. As early as 2001, the challenges of data management arising from the dramatic shifts in e-commerce and social drivers to greater digitization were identified as volume, velocity, and variety (Laney, 2001). Although the term “big data” didn’t emerge until later, the “3Vs” were adopted and embedded in the definition of big data (Gu, Li, Li, & Liang, 2017). Big data is generally accepted as a term used to describe massive data sets that exceed the capability of traditional database management approaches and methodologies to derive meaning from analysis (Brennan & Bakken, 2015; Gu et al., 2017). Although debate persists about the exact definition of “big data,” two additional distinct characteristics were introduced, ultimately contributing the additional characteristics veracity and value (Westra et al., 2017). There is broad acceptance of the 5 Vs of big data and current definitions of big data routinely cite the 5 Vs (Westra et al., 2017). Volume refers to the sheer scale of data generated by a variety of sources—and many consider this to be the key hallmark of big data (Gu et al., 2017; Westra et al., 2017). Industry research into data proliferation was consolidated by IBM, who projected that 40 zetabytes (ZB) of data will be generated by 2020, reflecting a 300-fold increase over 2005 data volume (IBM, n.d.). Further, IBM reported that 2.5 quintillion bytes (QB) are created on a daily basis. Recently, researchers projected that healthcare data will grow faster than other sectors, at a compound annual rate of 36% through to 2025 (Kent, 2018). Velocity reflects the unparalleled speed of proliferation (Westra et al., 2017). IBM projected that 18.9 billion network connections would exist by 2016 and reported that the New York Stock Exchange captures 1 terabyte (TB) in data during daily trading operations. Variety refers to the range of different data sources (Westra et al., 2017). IBM projected that by 2014, 420 million smart devices would be worn for health monitoring, and digital consultant David Sayce reported that by November 2018, approximately 6000 tweets per second were being sent, totalling 500 million tweets per day and 200 billion per year (Sayce, 2019). Veracity of the data reflects the degree of uncertainty of data elements and whether the data is fit for secondary analysis (Topaz & Pruinelli, 2017; Westra et al., 2017). IBM reported that poor data quality costs the United States in excess of 3 trillion dollars annually, while 30% of managers lack trust in the data used to make decisions and close to 30% survey participants are unable to confirm how much of their data is inaccurate (IBM, n.d.). Value reflects the perceived contribution the data are able to provide to support the organizational mission and objectives.

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Identification of the 5 Vs has led to a deeper appreciation of the vast, untapped potential of big data and a desire to examine the extent to which this data could be systematically analyzed and leveraged to improve outcomes and healthcare delivery. In their seminal review of nursing data science, Brennan and Bakken (2015) noted that a principled, scientific approach to big data emerged around 2015 to complement the popular big data narrative. “Data science” blends a multidisciplinary approach to data management, including math, computer science, statistics, modeling, predictive analytics, and others, offering greater philosophical and methodological rigor to all phases of the data management cycle (Fig. 41.1). However, diversity in perspectives about nursing data science exists. Broom (2016) defined big data science as a new field in which automated methods are applied to “collect, extract, and analyze” vast amounts of data to answer questions that were previously unanswered. Topaz and Pruinelli (2017) defined data science as the multidisciplinary scholarship approach to working with data and noted that researchers need to recognize how “messy” healthcare data is and be able to determine the optimal method for resolving this and applying appropriate analytic methods such as data mining, artificial intelligence, natural language processing, and visualization. Jeffrey (2019) suggests that data science lies at the convergence of “domain knowledge, computer science, statistics, and data visualization/presentation”

Data acquisition

Data interpretation

Data manipulation

Data curation

Data exploration

•  FIGURE 41.1.  Big Data Management Lifecycle. (Adapted from Brennan, P. F., & Bakken, S. (2015). Nursing needs big data and big data needs nursing. Journal of Nursing Scholarship, 47(5), 477–484. http://dx.doi. org/10.1111/jnu.12159.)

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Knowledge Discovery: Pattern Recognition, Hypotheses Generation, Hypotheses/ Theory Testing

Data Science Team: Data Analysts, Data Engineers, Informaticians, Biostatisticians, Nurse Scientists (Domain Experts)

Data Source Ex: EHR, PHR, Devices, Social Media, Internet, Sensors, Wearable Technologies

Analytic Methods: Multi-variate Analysis, Machine Learning, Data Mining

•  FIGURE 41.2.  (Reproduced, with permission, from Westra, B.L., Sylvia, M., Weinfurter, E.F., Pruinelli, L., Park, J.I., Dodd, D., ... Delaney, C. (2017). Big data science: A literature review of nursing research exemplars. Nursing Outlook, 65, 549-561. Copyright © Elsevier.)

and in his consultation of nurse leaders, a variety of perspectives were noted, including that data science is a tool and is naturally a subset of informatics, while others sought to discretely distinguish data science from the scope and knowledge of biomedical informaticists. Jeffrey concluded that data science is simply one of the numerous specialty areas open to informaticists. Brennan and Bakken (2015) noted that data science investigations typically involve four characteristics including (1) disparate data sources that remain under the governance of the data owner, (2) the application of attribution and security to data, (3) expanded networks of research collaborators sharing approaches and methodologies, and (4) accelerating research insights through the use of secondary data and emphasizing the integration of the data rather than on the actual data itself. Westra et al. (2017) proposed a nursing data science research model (Fig. 41.2) that articulates the core components of big data and data science–driven nursing research.

TRENDS IN BIG DATA AND NURSING DATA SCIENCE As the concept of big data gained momentum across all sectors of society, the number of publications started to increase significantly. In their study of the evolution of big data research in health informatics, Gu et al. (2017)

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applied a bibliometric analysis to track the volume and scope of publications focused on big data. They reviewed almost 2400 studies indexed on the Web of Science (WOS) and published between 2003 and 2016. The analysis documented a staggering increase over time. In 2003, 53 articles on big data were indexed in WOS, which increased gradually until 2013. Between 2013 and 2015, publications more than doubled, rising from 240 to 517. Similarly, they recorded a significant increase in the same timeline in the number of authors exploring big data and health informatics. Analysis of the key words indicated that big data, epidemiology, personality, breast cancer, and data mining formed the top five most common key words. In addition, diabetes was also one of the most popular key words in the international research studies. Further, this research was able to identify the countries and research institutions leading the number of contributions to the big data discourse, ranking the top three contributors as the United States (662 articles), China (235), and the United Kingdom (191). Canada was ranked seventh among 17 countries, with 84 publications indexed on the WOS. The only Canadian institution identified among the top 10 institutions with 20 or more published articles was the University of Toronto, with 21 published articles (Gu et al., 2017). A 2013 vision report on health system use of data in Canada by the Canadian Institute for Health Information (CIHI) (CIHI, 2013) addressed many of the characteristics

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Use of Health Information for Informed Decisions

Capacity and culture

Data is analyzed and used

2

Data is turned into actionable information and knowledge to support decision-making and continuous improvement.

Data is available

Data is collected

Data is accurate, reliable, timely, comparable and accessible.

Appropriate and standardized data is captured at the point of care.

Culture, capability and capacity aid people, processes and tools in the responsible collection, analysis and use of health information.

3

1

Enablers Governance, policies and technology are in place to enable safe, effective and efficient collection, analysis and use of health information.

•  FIGURE 41.3.  (Reproduced, with permission, from Canadian Institute for Health Information (CIHI). (2013). Better information for improved health: A vision for health system use of data in Canada. Ottawa: CIHI.)

identified by Brennan and Bakken (2015) and Topaz and Pruinelli (2017), and noted that as health information is generated from a diverse array of sources and is largely unstructured, there is a high need to apply both structured and standardized data to enable codification, integration, and comparability. CIHI’s report also addressed the concepts of governance, privacy and security, technology, data collection, availability and use, and capacity and culture as necessary to fostering the progression of data science and health system use of data. Figure 41.3 highlights the core components of the CIHI framework to support data use for improved health outcomes. With the broad emphasis on big data, organizations have established dedicated institutes to study this domain and stimulate both innovation and collaboration. For example, Dalhousie University established the Big Data Institute, which regularly hosts conference events that span all sectors. Other examples in Canada that

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are specifically health focused include the third IoT, Big Data Healthcare Summit Western Canada hosted by the Information Technology Association of Canada (ITAC), and the sixth Annual Big Data & Analytics Summit Canada hosted by Strategy Institute. Additionally, Canada Health Infoway (Infoway, 2019) is seeking funding to create an investment in digital health data platforms for Canada’s research hospitals and academic health sciences centers. HealthCareCAN (2019) will consult with Infoway given its expertise in the development, adoption, and effective use of digital health solutions across the country. Broom (2016) disputes the view that big data science or nursing data science will replace traditional research methodologies and suggests that the focus should be on ensuring that the profession is adequately and appropriately preparing future nurse researchers and leaders. This concern is shared by numerous authors and nursing leaders who address the competency needs for future

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nursing data scientists and leaders (Brennan & Bakken, 2015; Jeffrey, 2019; Topaz & Pruinelli, 2017; Westra et al., 2017). Competency in advanced statistics, data modeling, visualization, data mining, and other advanced data management techniques will be required to position nursing to continue to advance knowledge using emerging vast data sets. An example of this type of strategic competency development is occurring within the University of Victoria in Canada, where nursing students can complete a dual master’s degree in nursing and computer science, building skills to manage data and systems in addition to advanced nursing scholarship. Other programs include the Masters of Health Informatics at Dalhousie University and the University of Toronto, Western University’s Master of Data Analytics, and McGill’s Data Science.

NURSING DATA SCIENCE IN CANADA Nurses are investing considerable time documenting and capturing care data with the use of a variety of technologies (e.g., EHRs, smart devices, remote monitoring). Nonetheless they rarely receive any real-time evaluative feedback, reports, or outcome analysis outputs to further inform, revise, or refine their practice (Jeffrey, 2019; Westra et al., 2015). Westra et al. (2015) refer to this phenomenon as being “data rich and information poor” (DRIP). Increasingly, studies are using data science to explore practice outcomes to optimize clinical pathways and inform practice-based evidence. However, until technologies such as natural language processing become pervasive and refined for understanding complex practices like nursing, the promise of big data and the application of data science methods will be limited. Realizing the possibilities to be garnered from the application of data science largely rests with the ability to capture and share data that are comparable and shareable; that is, data and measures that are consistently used throughout the healthcare system—clinical data standards.

ADOPTING CLINICAL DATA STANDARDS—THE BENEFITS TO BE REALIZED While significant investments have been made within every jurisdiction in Canada for technology to create efficiency and improve health for Canadians, similar to other countries, healthcare organizations across Canada are in varying states of maturity related to EHRs and the integration of clinical data standards. In Canada, two national organizations are providing leadership in these areas. CIHI is a

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national organization with a mandate to deliver comparable and actionable information to accelerate improvements in healthcare, health system performance, and population health across the continuum of care (for more information see: https://www.cihi.ca/en/about-cihi/vision-and-mandate). CIHI collects clinical and administrative data from healthcare organizations and makes this information available to organizations, researchers, and decision-makers to examine and compare the delivery of health services and inform public health policy. A partnership exists between CIHI and interRAI, a research network committed to developing clinical standards across a variety of health and social services settings (For additional information see: http://www.interrai. org/organization/). CIHI serves as the custodian of the interRAI standards and as a repository of interRAI data submitted by healthcare organizations. Although much of the focus in Canada has been on organizations submitting interRAI data for home care, continuing care, and inpatient and community mental health, recently there has been recognition of the need for standardized clinical data from acute care. As part of the Discharge Abstract Database (DAD), CIHI currently collects information on all separations from acute care institutions, including demographics, diagnosis, comorbidities, discharges, deaths, and so on. The collection of clinical data from acute care to link with data from other sectors would facilitate local to national comparisons about clinical outcomes. Furthermore, the collection of a standardized suite of essential clinical information across all sectors of the healthcare system would allow for examining a person’s healthcare across settings and sectors, supporting continuity of care and improved health outcomes. Canada Health Infoway is a national organization with the goal of helping to improve the health of Canadians by working with partner organizations to accelerate the development, adoption, and effective use of digital health solutions across Canada (for more information see: https:// infoway-inforoute.ca/en). Through national and provincial investments, Infoway plays a leadership role in helping to deliver better quality and access to care and more efficient delivery of health services for patients and clinicians. Infoway’s ACCESS 2022 is a new program focused on providing health information access to Canadians so that they are better informed to manage their health (for more information see: https://www.infoway-inforoute.ca/ en/solutions/access-health). Infoway is also a source of interoperability standards (including data standards) and through investments plays a leadership role in helping to deliver better quality and access to care and more efficient delivery of health services for patients and clinicians. Infoway provides an online community (InfoCentral) for nursing and other disciplines to discuss and share experiences and learnings

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in the use of clinical data standards across Canada (for more information see: https://infocentral.infoway-inforoute.ca/en/).

FROM EVIDENCE-BASED PRACTICE TO PRACTICE-BASED EVIDENCE Harrington proposes that the ultimate benefit of the time, energy, and investments in EHRs is clinical intelligence whereby there is “aggregation of accurate, relevant

Health Policy • Legislation • Research •

and timely clinical data into meaningful information and actionable knowledge for clinicians and decision makers” (Harrington, 2011). Matney et al. (2017) argued that the standardization of healthcare data is essential for shareable and comparable data. It is only through standardization of clinical data and the ability to collect data once and use it for many purposes that we will be able to truly realize the value of investments made in EHRs (see Fig. 41.4) (Nagle & White, 2015). O’Brien, Weaver, Settergren, Hook, and Ivory (2015) discuss the need to optimize nurses’ documentation

National Comparative disease incidence, prevalence, & trends, resources utilization

Data Collected, Abstracted, Aggregated, Analyzed

Health Policy Legislation • Health System Performance • Funding • Public Reporting • Research • •

Regional/Jurisdictional Disease incidence & prevalence, outcome, cost of care, resource utilization

Data Collected, Abstracted, Aggregated, Analyzed

Safety & Quality Resource Management • Funding • Accreditation • Public Reporting • Research • •

Organization/Sector Case volumes, outcomes, cost of care, resource utilization

Data Collected, Abstracted, Aggregated, Analyzed

Safety & Quality Accountability • Outcomes • Evidence • •

Individual/CMG Assessments, interventions, outcomes, provider, hours of care, adverse events, cost of care

•  FIGURE 41.4.  The Vision: Data Collected Once for Many Purposes. (From Nagle, L. M., & White, P. A. (2015). Towards a pan-Canadian strategy for nursing data standards. https://infocentral.infoway-inforoute.ca/en/resources/docs/2188nursing-informatics-vision/view-document. Used with permission)

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efficiency while contributing to knowledge generation. Much of current nursing practice is evidence-based— nurses using the best available evidence to inform their practice; however, there can be gaps in existing evidence. Furthermore, nurses may have challenges in accessing current research studies. Electronic access to large amounts of data in a standardized format presents a great opportunity for creating new knowledge for the nursing profession. The use of clinical data standards in EHRs can facilitate practice-based evidence, whereby data about patient age, diagnoses, interventions, and outcomes are captured in the EHR and can be analyzed to support current individualized clinical practice (Miles & Loughlin, 2011). This will allow clinicians to learn from past patients with similar diagnoses about what interventions produced better clinical outcomes. Standardizing clinical data will benefit patients as they move through the healthcare system, making transitions and information sharing of health information seamless, and enhancing continuity of care and information. As an example, Canadians with chronic obstructive pulmonary disease are high users of healthcare (https://www.cihi.ca/en/copd-a-focus-on-high-users). Standardizing clinical data such as functional status, dyspnea, and fatigue and sharing this information across providers and settings will support better management of health care (White, 2016). The collection of standardized clinical data allows clinicians to visualize the flow sheet of essential data and identify trends, and compare current assessment in acute care to previous assessments such as previous acute care or home care admissions and trend assessment information over time and across settings to

support practice decisions. If trends show that dyspnea consistently deteriorates on discharge then clinicians need to ask what can be changed? Is there an intervention that could be added in the home care sector to maintain or improve the patient’s dyspnea? Subsequently, the capacity to aggregate the data of hundreds of similar patients would provide insights to the effectiveness of care interventions, which profession is best suited to deliver the care (e.g., nurse or respiratory therapist), and in which setting. But access to shareable, comparable data is not a possibility without the adoption of clinical data standards—at the very least, an agreed-upon essential clinical data set identifiable in any and every setting.

THE EVIDENCE UNTIL NOW Canadian Health Outcomes for Better Information and Care (C-HOBIC) is a Canadian initiative to advance the uptake and use of a suite of standardized nursing-sensitive patient outcomes in acute care settings (Hannah et al., 2009) (for more information see: https://c-hobic.cna-aiic.ca/ about/default_e.aspx). This suite of evidence-based clinical measures includes functional status, continence, symptoms (pain, fatigue, nausea, dyspnea), falls, pressure ulcers, and therapeutic self-care (TSC) (Doran, 2012). These concepts are assessed using the C-HOBIC and interRAI measures and they are harmonized across sectors to support sharing and comparing of clinical information across sectors of the healthcare system (see Table 41.1). Since 2006, standardized tools to gather this essential clinical data set have been embedded into admission and

  TABLE 41.1   Use of C-HOBIC Tools/Measures in Different Care Sectors. Concept

Acute Care

Long-Term Care and Complex Continuing Care

Home Care

Functional status Continence Pain—Frequency Pain—Intensity

interRAI AC interRAI AC interRAI AC 0–10 numeric

MDS 2.0 MDS 2.0 MDS 2.0 MDS 2.0

interRAI HC interRAI HC interRAI HC interRAI HC

Fatigue

interRAI AC

MDS 2.0

interRAI HC

Dyspnea

interRAI AC

MDS 2.0

interRAI HC

Nausea

interRAI AC

MDS 2.0

interRAI HC

Falls

interRAI AC

MDS 2.0

interRAI HC

Pressure ulcers

interRAI AC

MDS 2.0

interRAI HC

Therapeutic self-care

Doran and Sidani tool (Doran et al., 2002)

N/A

Doran and Sidani tool

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discharge assessments largely within acute care settings with the goal of providing real-time data to nurses that will support examining the impact of their practice on clinical outcomes. Increasingly, opportunities to align these data across care settings are being addressed. Research from demonstration projects in Ontario has supported the value of collecting the C-HOBIC suite of measures. Linking the acute care data set to the Discharge Abstract Data Base at the CIHI, Wodchis (2012) found that TSC scores showed a consistent and significant protective effect for readmission to acute care at 7, 30, and 90 days. The TSC presents questions to patients on admission and discharge from acute care to home care, assessing knowledge of their medications, ability to take medication as prescribed, as well as recognize and manage symptoms. A one-point improvement in TSC scores was associated with approximately a 10% reduction in the likelihood of readmission. Nausea was more strongly related to early readmissions (3, 7, and 30 days), while dyspnea was more strongly related to readmission at later stages (30 and 90 days) (Wodchis, 2012). Research examining the predictive ability of C-HOBIC scores on admission as a predictor of alternate level of care and length of stay found that higher fatigue and dyspnea scores on admission were significantly related to a longer length of stay. Furthermore, patients with high scores for fatigue and falls and, to a lesser extent, a high functional score on admission were more likely to be discharged to either complex continuing care, long-term care homes, or rehabilitation facilities than discharged home (Jeffs et al., 2013). A study in home care highlights the importance of TSC ability in the home care setting in preventing hospital readmissions and other adverse events. The authors found that clients with high TSC ability experienced fewer adverse outcomes than those individuals with low TSC ability. The study indicates that there is a need to focus on improving client self-care functioning, a domain frequently overlooked by health professionals but noted by the authors of this chapter (Sun & Doran, 2014).

existing assessments. Attention needs to be given to developing dashboards to provide clinical outcomes information back to clinicians in real time or near real time at the point of care to support the evaluation of practice on clinical outcomes. Education about collecting and using clinical data is also important. Ongoing engagement of clinicians to identify how clinical information can support practice needs to be incorporated into the culture of the organization. Significant expertise, time, and resources are required to support this work. Nursing leadership at all levels within an organization is key. Given the impact of nursing care on health outcomes, leaders need competencies specific to the use of data to evaluate the impact of nursing practice and implement quality improvement initiatives (Englebright & Caspers, 2016). To this end, and further to work completed in the United States (Collins, Yen, Phillips, & Kennedy, 2017), efforts have been underway to establish a core set of informatics competencies for Canadian nurse leaders (Strudwick, Nagle, Kassam, Pahwa, & Sequeira, 2019). Nurse leaders are responsible for creating environments that support clinicians in using clinical data to support safe quality care and as such need to be informatics savvy. Informatics knowledge and skills will become increasingly important as more data from EHRs and other technologies are available at the point of care to dynamically inform care decisions.

ADVANCING NATIONAL NURSING DATA STANDARDS (NNDS) IN CANADA In the Canadian context, there is a belief that the national adoption of standardized measures such as C-HOBIC and interRAI will produce standardized data to:

• •

LESSONS LEARNED The integration of data standards into nursing clinical documentation is important. An evaluation of the C-HOBIC initiative supports the importance of system design so that data collection supports the workflow of clinicians (Canadian Nurses Association, 2015). Organizations need to take the time to review existing assessment tools when implementing clinical data standards to determine whether any redundant tools are already in use and examine opportunities to embed standardized questions within

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•

allow for consistent monitoring of outcomes across the continuum of care, thereby facilitating safe, quality care and the continuity of care; enable national, peer-group comparability, providing both macro and micro insights to guide decision-making and inform funding requirements and health human-resource planning; and improve population health by enabling individuals to use consistently named, defined, and measured clinical outcomes data to understand and manage illness and improve the health of patients.

Over the last 4 years, more than 150 nurse leaders, vendors, government representatives, and professional stakeholder organizations (e.g., CIHI, Infoway, CNA) from across Canada have convened to discuss the need for and

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the benefits to be derived from the adoption of national nursing data standards (NNDS). More specifically these symposia have focused on the following:

• •

Development of short-term objectives and action plans to promote the adoption of NNDS Identification of stakeholders, accountabilities, and sponsorship for actions to advance this work in Canada

There is currently significant momentum to advance the use of data standards specific to nursing practice and clinical care. Subsequent to the symposia, numerous requests for consultation have arisen from organizations and jurisdictions as to how to build standards into clinical assessment tools as they implement new and legacy replacement EHRs. Working groups constituted by symposia attendees have been focused in the areas of clinical practice, administration, research, education, and policy. While many engagement activities and advances have occurred through the efforts of these groups, some of the key deliverables have included the following:

• • • • •

Clinical—the development of a standardized admission and discharge assessment for use in all clinical settings Education—the development of documentation to support the integration of clinical data standards into undergraduate curricula Administration—conduct of a national Delphi study to achieve consensus on informatics competencies for nurse leaders (Strudwick et al., 2019) Research—conduct of a national Delphi study to identify research priorities related to nursing data standards Policy—the advancement and approval of a resolution to the Canadian Nurses Association Board of Directors in support of adopting national nursing data standards

More details of the discussions and outputs can be found in published proceedings from each of the symposia which are available for download at: https://www. cna-aiic.ca/en/nursing-practice/the-practice-of-nursing/ nursing-informatics.

LOOKING TO THE FUTURE Given that nurses are the largest constituency of healthcare providers, it is vital that information about the impact of nursing care on patient outcomes is readily accessible. There is a need for nurses and other clinicians to adopt the use of clinical data standards to support measuring outcomes to

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better understand whether the healthcare system is deriving value for money and actually improving health outcomes (Veillard et al., 2015). With an increased focus on engaging patients in their care and sharing clinical information with patients/families, all clinicians need to use standardized measures for key clinical concepts to facilitate sharing and comparing information. As patient access and contribution to the health records increase, it will be important to demonstrate that all clinicians are assessing key clinical concepts using consistent measures. This approach will support patients’ involvement in using their health information and managing their self-care. Access to better data through the use of clinical data standards offers nurses and nursing leaders a unique opportunity to demonstrate the impact of their contribution to clinical, organizational, and system outcomes. Considering the current rate of growth of all Internet of Things (IoT) in healthcare, the generation of new and more diversified types of data will only continue to accelerate. The collective challenge for healthcare organizations and health professionals will be to ascertain means by which the value of these massive data sets can best be harnessed and utilized. Further, if nursing is to realize maximal benefit from the analyses of data relative to nursing practice, the adoption of clinical/nursing data standards is fundamental. Failure to do so will limit the capacity of nursing to demonstrate value and the essential nature of its practice to the delivery of safe, quality care in every setting and all but ensure an otherwise certain demise of the profession in the years ahead. The adoption of clinical data standards will secure a future that is informed and shaped by the outputs derived from big data and the imminent application of innovative data science techniques—the opportunities in doing so are tremendous, equaled only by the risks of not doing so!

Test Questions 1. The benefits of adopting clinical data standards include which of the following: A. Informed scope of practice

B. Increased variation in clinical documentation

C. Consistent approaches to outcome evaluation D. Unique approaches to clinical documentation

2. Practice-based evidence can be derived from realtime clinical data analytics with the use of: A. Standardized narrative notes B. Clinical pathways

C. Evidence-based practice D. Clinical data standards

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3. The collection of standardized clinical data can be used to: A. Engage patients in self-care

B. Identify nurse staffing requirements C. Evaluate clinical practice D. All of the above

4. Safe, quality care will be BEST supported with the use of:

D. All of the above 10. Practice-based evidence is derived from: A. Research findings B. Practice analyses

C. Clinical protocols D. Nursing literature

A. Clinical protocols

Test Answers

C. Standardized technologies

1. Answer: C  The benefits of adopting clinical data standards include consistent approaches to outcome evaluation.

B. Clinical data standards

D. Interprofessional practice guidelines 5. What does big data refer to?

A. Data derived from lengthy hospital admissions B. Longitudinal, personal health records

C. Large data sets derived from multiple sources D. Electronic health record data

6. Data science methods include the use of what informatics tools? A. Artificial intelligence B. Computer modeling

C. Pattern identification D. All of the above

7. Adoption of clinical data standards offers an opportunity to: A. Increase the visibility of nursing

B. Decrease interprofessional practice differences C. Strengthen nursing leadership

D. Increase nurse recruitment and retention 8. Nurse leaders can use clinical data standards to:

A. Create dashboards to support examining areas for quality improvement B. Identify areas of practice excellence within their organization C. Create reports on the clinical status of patients being discharged D. All of the above

9. Big data is characterized by: A. Velocity

B. Veracity

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C. Volume

2. Answer: D  Practice-based evidence can be derived from real-time clinical data analytics with the use of clinical data standards.

3. Answer: D  The collection of standardized clinical data can be used to engage patients in self-care, identify nurse staffing requirements, and evaluate clinical practice. 4. Answer: B  Safe, quality care will be best supported with the use of clinical data standards.

5. Answer: C  Big data refers to large data sets derived from multiple sources. 6. Answer: D  Data science methods include the use of artificial intelligence, computer modeling, and pattern identification.

7. Answer: A  The adoption of clinical data standards offers an opportunity to increase the visibility of nursing. 8. Answer: D  Nurse leaders can use clinical data standards to create dashboards to support examining areas for quality improvement, identify areas of practice excellence within their organization, and create reports on the clinical status of patients being discharged. 9. Answer: D  Big data is characterized by velocity, veracity, and volume.

10. Answer: B  Practice-based evidence is derived from practice analyses.

REFERENCES Australian Institute of Health and Welfare. (2018). Patient reported experience and outcomes (Chapter 7). In Australia’s Health 2018. Australia’s Health Series, no. 16, AUS 221. Canberra: AIHW.

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Brennan, P. F., & Bakken, S. (2015). Nursing needs big data and big data needs nursing. Journal of Nursing Scholarship, 47(5), 477–484. http://dx.doi.org/10.1111/ jnu.12159. Broom, M. E. (2016). Big data, data science, and big contributions. Nursing Outlook, 64, 113–114. Canadian Institute for Health Information (CIHI). (2013). Better information for improved health: A vision for health system use of data in Canada. Ottawa: CIHI. Canada Health Infoway. (2019). Digital health and data platforms: An opportunity for Canadian excellence in evidence-based health research. Retrieved from http:// www.healthcarecan.ca/2019/01/25/digital-healthand-data-platforms-an-opportunity-for-canadian-excellence-in-evidence-based-health-research/” http://www. healthcarecan.ca/2019/01/25/digital-health-and-dataplatforms-an-opportunity-for-canadian-excellence-inevidence-based-health-research/. Accessed on May 28, 2020. Canadian Nurses Association. (1993). Papers from the Nursing Minimum Data Set Conference. Ottawa: CNA. Canadian Nurses Association. (2015). C-HOBIC Phase 2 Final Report. Retrieved from https://www.cna-aiic.ca/-/ media/cna/page-content/pdf-en/2015jan_chobic-phase2final-report.pdf?la=en&hash=F857EFEFDB59BDE7113 0CAE5BA713DEAE45DC724. Accessed on May 28, 2020. Collins, S., Yen, P-Y., Phillips, A., & Kennedy M. (2017). Nursing informatics competency assessment for the nurse leader: The Delphi study. Journal of Nursing Administration, 47, 212–218. Doran, D. (Ed.). (2012). Nursing outcomes (2nd ed.). Sudbury, MA: Jones & Bartlett Learning. Englebright, J., & Caspers, B, (2016). The role of the Chief Nurse Executive in the big data revolution. Nurse Leader, 14(4), 280–284. Gu, D., Li, J., Li, X., & Liang, C. (2017). Visualizing the knowledge structure and evolution of big data research in healthcare informatics. International Journal of Medical Informatics, 9, 22–32. Hannah, K. J., White, P. A., Nagle, L. M., & Pringle, D. M. (2009). Standardizing nursing information in Canada for inclusion in electronic health records: C-HOBIC. Journal of the American Medical Informatics Association, 16, 524–530. doi:10.1197/jamia.M2974. Harrington, L. (2011). Clinical intelligence. Journal of Nursing Administration, 41(12), 507–509. HealthCareCAN. (2019). Digital health and data platforms: An opportunity for Canadian excellence in evidencebased health research. Retrieved from http://www. healthcarecan.ca/2019/01/25/digital-health-and-dataplatforms-an-opportunity-for-canadian-excellence-inevidence-based-health-research/. Accessed on May 28, 2020. IBM. (n.d.). The 4 Vs of big data. Retrieved from https:// www.ibmbigdatahub.com/infographic/four-vs-big-data. Accessed on May 28, 2020.

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Irvine Doran, D., Sidani, S., Keatings, M., & Doidge, D. (2002). An empirical test of the Nursing Role Effectiveness Model. Journal of Advanced Nursing, 38(1), 29–39. Jeffrey, A. (2019). ANI emerging leader project: Identifying challenges and opportunities in nursing data science. Computers in Nursing, 37(11), 1–3. Jeffs, L., Jiang, D., Wilson, G., Ferris, E., Cardiff, B., Lanceta, M., …. Pringle, D. (2013). Linking HOBIC measures with length of stay and alternate levels of care: Implications for nurse leaders in their efforts to improve patient flow and quality of care. Canadian Journal of Nursing Leadership, 25(4), 48–62. Kent, J. (December 03, 2018). Big data to see explosive growth, challenging healthcare organizations. Retrieved from https://healthitanalytics.com/news/big-data-to-seeexplosive-growth-challenging-healthcare-organizations. Accessed on May 28, 2020. Laney, D. (February 6, 2001). 3D data management: Controlling data volume, velocity, and variety. File 949. Application delivery strategies: META Group. Retrieved from https://blogs.gartner.com/doug-laney/files/2012/01/ ad949-3D-Data-Management-Controlling-Data-VolumeVelocity-and-Variety.pdf ” https://blogs.gartner.com/ doug-laney/files/2012/01/ad949-3D-Data-ManagementControlling-Data-Volume-Velocity-and-Variety.pdf. Accessed on May 28, 2020. Matney, S. A., Settergren, T., Carrington, J. A., Richesson, R. L., Sheide, A., & Westra, B. L. (2017). Standardizing physiologic assessment data
to enable big data analytics. Western Journal of Nursing Research, 39(1), 63–77. Miles, A., & Loughlin, M. (2011). Models in the balance: Evidence based medicine versus evidence informed individualized care. Journal of Evaluation of Clinical Practice, 17(4), 531–536. doi:10.1111/j.1365-2753.2011.01713.x Nagle, L. M., & White, P. A. (2015). Towards a pan-Canadian strategy for nursing data standards. Retrieved from https://infocentral.infoway-inforoute.ca/en/resources/ docs/2188-nursing-informatics-vision/view-document. Accessed on May 28, 2020. O’Brien, A., Weaver, C., Settergren, T., Hook, M. L., & Ivory, C. (2015). EHR documentation: The hype and the hope for improving nursing satisfaction and quality outcomes. Nursing Administration Quarterly, 39(4), 333–339.
 Sayce, D. (2019). Number of tweets per day? Retrieved from https://www.dsayce.com/social-media/tweets-day/. Accessed on May 28, 2020. Strudwick, G., Nagle, L. M., Kassam, I., Pahwa, M., & Sequeira, L. (2019). Informatics competencies for nurse leaders: A scoping review. Journal of Nursing Administration, 49(6), 323–330. Sun, W., & Doran, D. (2014). Understanding the relationship between therapeutic self-care and adverse events for the geriatric home care clients in Canada. Journal of the American Geriatrics Society, 62(S1), 1–7. Topaz, M., & Pruinelli, L. (2017). Big data and nursing: Implications for the future. In J. Murphy, et al. (Eds.),

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Forecasting informatics competencies for nurses in the future of connected health. Switzerland: IMIA and IOS Press. doi:10.3233/978-1-61499-738-2-165 VanDeVelde, S., Doran, D., & Jeffs, L. (2015). Update on the NNQR(C) pilot project. Canadian Nurse, 111, 10–11. Veillard, J., Fekri, O., Dhalla, I., & Klazinga, N. (2015). Measuring health outcomes more effectively holds great potential to improve the quality and effectiveness of healthcare in Canada, and ensure the system is delivering value for money. Commentary No. 438, November, Healthcare Policy. Retrieved from https://www.cdhowe. org/sites/default/files/attachments/research_papers/ mixed/Commentary_438.pdf. Accessed on May 28, 2020. Westra, B., Clancy, T., Sensmeier, J., Warren, J., Weaver, C., & Delaney, C. (2015). Big data science: Implications for

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nurse leaders. Nursing Administration Quarterly, 39(4), 304–310. Westra, B. L., Sylvia, M., Weinfurter, E. F., Pruinelli, L., Park, J. I., Dodd, D., … Delaney, C. (2017). Big data science: A literature review of nursing research exemplars. Nursing Outlook, 65, 549–561. White, P. (2016). The case for standardized data in nursing. Canadian Journal of Nursing Leadership, 28(4), 29–35. doi:10.12927/cjnl.2016.24558 Wodchis, W. P. (2012). Demonstrating value with HOBIC data. Workshop presentation, February 12, 2012, Toronto, Ontario. Retrieved from https://www.ices.on.ca/ Publications/Atlases-and-Reports/2014/HOBIC-2013. Accessed on May 28, 2020.

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42 Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents Kaija Saranto / Ulla-Mari Kinnunen / Virpi Jylhä / Pia Liljamo / Eija Kivekäs

• OBJECTIVES 1. Describe examples of milestones in nursing practice toward the achievement of high-quality care. 2. Discuss the importance of evidence-based nursing informatics innovations. 3. Highlight the importance of high-quality health data. 4. Describe examples of the development, use, and reuse of structured nursing data. 5. Describe patients’ new roles as data producers and users of digital tools for social and healthcare.

• KEY WORDS Digitalization eHealth Electronic health records Evidence-based practice Patient portal Quality indicators

INTRODUCTION There has been much advancement in nursing informatics education, practice, and research since Scholes and Barber (1980) proposed the groundbreaking definition of nursing informatics as “the application of computer technology to all fields of nursing: nursing service, nurse education, and nursing research” (p. 73). Although many different definitions have since been proposed, they all emphasize the role of computers and novel software and devices as supports for nursing. As in real life, the definitions include advancements in technology, science, knowledge, information structures and processes, and connection with patients and other care providers (Staggers & Thompson, 2002). Although it is not directly mentioned in the definitions, informatics is

connected to innovations, which are ideas, practices, or means of developing a new focus or target within healthcare. Innovations are adopted by a range of people, from innovators to laggards, which all can benefit from the implementation of informatics (European Commission, 2012a). When speaking about nursing informatics, researchers often mention technological innovations that take the form of products (e.g., devices or tools) or processes (e.g., telehealth). Less often, innovations are described as social innovations involving the development, adoption, and integration of new practices intended to change methods in healthcare. However, both types of innovations are needed in nursing practice. The World Health Organization highlighted the importance of using technology to support continuity and care coordination and the need for research and innovation to successfully 677

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implement predictive risk tools, decision support tools, algorithms, and guidelines intended to coordinate care and achieve the greatest effect on nursing practice (WHO, 2018a).

REQUIREMENTS FOR IMPROVING THE QUALITY OF PATIENT CARE The use of information technology, including applications and tools for processing, sharing, and storing information as well as connecting data at the personal or administrative level, in healthcare settings is usually referred to as health information technology (HIT) or eHealth (electronic health) (European Commission, 2012b). Many terms have been used to describe this phenomenon. Digitalization, which is most often used in strategical, steering, or policy texts, is the most recently developed term but the most incoherently applied. The complexity of the term is evident when one considers the history of documenting patient care. In the previous decades, it was crucial that computers were used for nursing notes, and so these notes were transferred from analog to digital form with the use of electronic health records (EHRs). The transfer of notes from analog to digital form improved the quality of documentation by increasing the readability of the notes and providing a structure that made it easier to find information. However, this process was not truly focused on digitalization; digitalization aims to create value from the use of new, advanced technologies by exploiting digital networks’ dynamics and the giant digital flow of information, but nursing information systems could not process notes. The Committee on Data Standards for Patient Safety classifies EHR systems based on their core functionalities (e.g., delivery of healthcare services, care management, and support processes), which can be classified as subfunctionalities based on users’ needs (D’Agostino et al., 2018). Core functionalities are also classified based on the administrative processes (e.g., billing and reimbursement) they support, which again may be classified into subfunctionalities based on users’ needs (Tang, 2003). The lack of software or functionality for information systems remains the greatest obstacle to information flow in nursing; when data cannot be accessed in a timely manner or the same data is recorded many times in various records, severe safety concerns, difficulties in decision-making, deficiencies in information exchange, and frustration in work processes may arise. Digitalization is connected to the interoperability of information systems or how various devices share, use, and produce information. In the healthcare context, interoperability refers to the ability of two or more healthcare providers to exchange and utilize the information with precoordination and context such that the information can be used to improve patient care. Thus, it is the key to seamless care, service, and data flow. In terms of digitalization,

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interoperability is often attached to technical interoperability, but in healthcare (e.g., standards, terminologies), organizational (e.g., structures, roles, responsibilities, policies, agreements) and legal (e.g., institutional arrangements, acts, degree, orders) contexts, interoperability must create real value using new, advanced technologies (European Union, 2017). Interoperability guidelines for nursing practice have effectively improved not only cooperation between care providers but also care coordination and patient outcomes by ensuring data sharing and fluid information flow, which facilitate patient safety and high-quality care.

DEFINING AND ANALYZING QUALITY Already, Florence Nightingale inspired nurses to stress guidelines for practice and ensure high-quality care. In her day, notes on nursing care and statistical methods of generating reports to correlate patient outcomes to environmental conditions were utilized. The process of developing quality measures from research involves several phases, beginning with the translation of evidence to clinical practice guidelines, which are the key components in the provision of high-quality nursing care. Further, these guidelines can be used to develop quality measures and define the parameters for measuring quality. Parameters are needed to create the best indicators for both quality and outcome measures. In many cases, the model proposed by Donabedian (1992), which focuses on the structure, process, and outcome measures, has increased the rigor of measures of nursing quality. Also, several indicators of nursing quality have been defined. One such indicator is the relation of patient processes and outcomes and the structure of nurse staffing. Today, innovative means, such as electronic databases and registers, are used to quantify this relation more efficiently and effectively. Also, several data sources are used to improve the quality of nursing. For example, administrative data from hospital registries (indicators of structure and process) and patient records (indicators of outcomes) as well as qualitative data from surveys or interviews with patients involving various tools and technologies (indicators of structure, process, and outcomes) may be used. Such means of measuring care quality are evolving (Heslop & Lu, 2014). In particular, face-toface encounters are transformed into virtual spaces through digital care pathways. Also, patients are gaining an active role as producers of their health-related data and users of information from repositories accessed with personal devices such as smartphones or tablets (Alsahafi & Gay, 2018). The use of data analytics has transformed the requirements of data structures. Structured nursing data based on standardized nursing terminology has enabled data to be reused for several purposes. Narrative data can be analyzed

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Chapter 42 • Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents 

with several methods, such as data or text mining and natural language processing (Kivekäs et al., 2016). These ­methods depend not only on data structures but also legislation, such as European General Data Protection Regulation (European Union, 2016). Data reuse is always connected to confidentiality, patient privacy, and data security; and in statistical processes, anonymity must be guaranteed utilizing authentication.

EVIDENCE-BASED HEALTH INFORMATICS INNOVATIONS TO ENSURE HIGH-QUALITY CARE The rapid development of technology makes it possible for innovations to transform healthcare practices. Innovations can take the form of replicable products, processes, or structures that satisfy a specific need (Varkey, Horne, & Bennet, 2008). Products typically consist of technology or digital services, such as a software application or medical device. A process, such as a digital care path, changes how care is delivered using technology. Structural innovations usually affect the internal and external infrastructure of healthcare organizations, and they require significant system-wide changes and adoption of new digital solutions. However, not all developments are innovative; multiple features determine the degree of innovativeness, including newness, availability, the degree of advancement of clinical practice with proven outcomes, use and usability, the supporting environment, other context factors, and stakeholder perspectives (Hübner, 2015). Nursing informatics innovations are expected to improve the effectiveness, safety, timeliness, patient/family-centeredness, and efficiency of care as well as patients’ access to services, improving the quality of care (Agency for Healthcare Research and Quality, 2014). However, these innovations might unintentionally have unexpected or

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negative impacts on patients, professionals, and organizations (Rigby & Ammenwerth, 2016). Therefore, the effects of new nursing informatics innovations need to be carefully evaluated with actual evidence, such as research or health data obtained from EHRs or other real-world sources. The adoption of nursing informatics innovations must be based on evidence and careful consideration of expected and possibly unintended outcomes. Traditionally, the effectiveness of interventions has been the main focus of evidence-based practice. Nowadays, however, it relies on much more. Evidence-based healthcare refers to decisionmaking, including that regarding the implementation of health informatics innovations in practice that considers the feasibility, appropriateness, meaningfulness, and effectiveness of healthcare practices (Jordan, Lockwood, Munn, & Aromataris, 2019). Feasibility is defined as the extent to which innovation is physically, culturally, or financially practical or possible within a given context. Appropriateness refers to the fit of an innovation in the context in which care is provided. Meaningfulness relates to the personal experiences, opinions, values, thoughts, beliefs, and interpretations of those using an innovation, such as patients. Evidence about the meaningfulness of innovation may improve the understanding of whether patients are likely to perceive innovations as positive or negative and whether the changes will be accepted (Fig. 42.1). The overarching principle driving evaluation and regulation of any nursing informatics intervention is to generate and synthesize evidence demonstrating that the use of a product, process, or structure is not only safe but also has benefits regarding the health or healthcare of the targeted individuals, patients, professionals, or society. Research evidence is obtained from original research studies and systematic reviews. These studies and reviews must apply a variety of methodologies to achieve high-quality care. However, systematic reviews that meet rigorous

Evidence

Real world data

Research studies

Systematic reviews

Feasible, appropriate, meaningful, and effective nursing informatics innovations

•  FIGURE 42.1.  Sources of evidence for nursing informatics innovations.

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methodological standards (e.g., those proposed by the Cochrane Collaboration and Joanna Briggs Institute) and synthesize research in the given context are said to provide the best evidence, regardless of methodology. The goal of systematic reviews is to make recommendations regarding decision-making (in this case, in the context of healthcare). However, the methodologies applied in systematic reviews have changed over time. Increasingly, these reviews are used to answer a broad range of questions regarding the health of societies and consider randomized controlled trials as well as other forms of research, such as qualitative studies of the meaningfulness of innovation. The Joanna Briggs Institute regards the results of high-quality research studies based on any methodological tradition as more credible than anecdotes or personal opinions (Jordan et al., 2019). As implementing health informatics innovations in practice is complex, evidence from multiple perspectives is needed to understand the factors that affect such innovations. Implementation should be encouraged only for health informatics innovations that have been proven to be feasible, appropriate, effective, and meaningful in the healthcare context. It is essential to secure sufficient research evidence as well as real-world data (RWD) so that nursing informatics innovation will provide benefits for patients, health professionals, and organizations and any negative effects of these innovations will be minimized. RWD is a term used to describe data related to patients’ health status and the delivery of healthcare. It is collected from a variety of sources other than randomized clinical trials, such as retrospective and prospective studies, registries, claims databases, electronic patient records, biobanks, social media, chat rooms, and patient communities (Miani et al., 2014). High-quality RWD can be used to support decisions regarding the implementation of nursing informatics innovations. Real-world evidence (RWE) is obtained in clinical settings, and it concerns the usage and potential benefits of risks of nursing informatics innovations according to the analysis of RWD. It is especially necessary for contexts characterized by new technologies and a lack of available research data. Technology enables various types of data to be transformed into knowledge through automated processes. It also provides access to accurate information and knowledge, which are required for the implementation of nursing informatics innovations for high-quality patient care (Moen & Mæland Knudsen, 2013). In the future, it will be possible to combine and process data from different registries and sources utilizing data analytics to produce RWE in order to monitor the outcomes of care. Together with research evidence, RWE will serve as the basis for nursing informatics innovations.

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USE OF NURSING DATA AS EVIDENCE FOR HIGH-QUALITY PATIENT CARE Importance of and Requirements for Data Quality The WHO (2017) proposed that health data must be complete, timely, consistent, reliable, and accurate to ensure its quality. Data quality is an important issue in patient data management. The importance of the quality of patient data, the possibilities of use and reuse of that data, and measuring its effects and preferences have been highlighted in the eHealth strategies of Nordic countries (Vehko, Ruotsalainen, & Hyppönen, 2019). The quality of data is a key factor in the evaluation of the use of electronic patient record (EPR) data for patient care as well as for secondary use of data, such as for research, statistics, treatment method development, and administrative purposes (Weiskopf & Weng, 2013; Meystre et al., 2017). The quality of notes in the EPR system affects the quality of patient care and patient safety (Jylhä, 2017; Palojoki, 2017). Specifically, there is a risk that inaccurate nursing diagnoses can lead to implementation of inappropriate interventions or misinterpretation of related outcomes. Also, missing and inadequate documentation can distort research results and prevent further development of patient care and data reuse (Sanson, Vellone, Kangasniemi, Alvaro, & D’Agostino, 2017; Sollie, Sijmons, Helsper, & Numans, 2017). Even though the value of high-quality health data and reuse of that data has been recognized for decades and in several contexts, there still exist many problems regarding this worldwide for both nursing and medical documentation. Requiring uniform data structures enables betterquality data for patient care management and secondary use, but concrete developments in the implementation of these structures are quite rare (Saranto et al., 2014; McCormick et al., 2015; O’Brien, Weaver, Settergren, Hook, & Ivory, 2015, Meystre et al., 2017, Vuokko, MäkeläBengs, Hyppönen, Lindqvist, & Doupi, 2017). Worldwide, there are over 20 million practicing nurses and midwives who document daily patient care (WHO, 2018b). Thus, it is important to discuss and harmonize how patient data are documented. The patient care process is the core of healthcare; other administrative processes, such as information management, financial management, human resource management, and education, are intended to support it. Each day, a huge amount of data is generated by healthcare professionals and imported into databases during different phases of the patient care p ­ rocess, including the care planning, intervention implementation, and outcome evaluation phases (Westra, Pruinelli, & Delaney, 2015; Westra et al., 2017).

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Chapter 42 • Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents 

The nursing process model is the standard of nursing documentation and nursing care plan. It enables data recording and sharing in different electronic medical record systems (Müller-Staub, de Graaf-Waar, & Paans, 2016). The need to develop standardized nursing terminology to ensure the comparability and ability to disseminate nursing data is well recognized (Westra, Bauman, Delaney, Lundberg, & Petersen, 2008; McCormick et al., 2015). Specifically, standardized models and terminologies for nursing documentation are required to generate valid data that can be reused (Liyanage et al., 2015; Whittenburg & Meetim, 2016). Also, data with standardized terminology supports evidence-based decision-making and facilitates the assessment of nursing care and outcomes (Saranto et al., 2014; Müller-Staub et al., 2016). Concepts related to unified nursing documentation are referred to as nursing content standards, and they may take the form of a data set, code set, terminology, dictionary, language, nomenclature, classification, vocabulary, or taxonomy (Cimino, 1998; Saba & Taylor, 2007; Westra et al., 2008). In particular, researchers across the world have developed terminologies to structure nursing documentation. Crossmapping and coordination across nursing classifications make it possible to evaluate the comparability of utilized content and concepts and promote shared use of various nursing classifications while avoiding redundancy in information (Lu, Park, Ucharattana, Konicek, & Delaney, 2006; Park, Lu, Konicek, & Delaney, 2007; Kim, Hardiker,  & Coenen, 2014).

Development and Validation of National Nursing Terminology In Finland, over the past decade, a standardized model of nursing documentation was developed as part of a national health information technology project intended to define the core components of the national EHR. This model is based on the nursing process for decision-making, core data regarding nursing (i.e., the Finnish Nursing Minimum Data Set, FNMDS), and the standardized nursing terminology to be used in care plans, summaries, and notes according to the Finnish Care Classification (FinCC). The FNMDS includes nursing diagnoses, interventions, outcomes, intensity data, and discharge summaries (Kinnunen, Ensio, & Liljamo, 2011; Kinnunen et al., 2014; Liljamo, Kinnunen, & Saranto, 2020). According to a recent survey of Finnish nurses, they are very competent regarding electronic documentation that complies with the national core structures (Kinnunen et al., 2019a). FinCC is based on the Clinical Care Classification (CCC), formerly called the Home Health Care Classification (HHCC), which was developed by Dr. Virginia Saba. Following the HHCC, the CCC has a three-level

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hierarchical format (Saba, 2007, 2012; Saba & Taylor, 2007; Ensio, Kinnunen, & Mykkänen, 2012). For international users, the CCC has been translated into various languages, including Chinese, Dutch, Finnish, German, Korean, Norwegian, Portuguese, Slovene, Spanish, Taiwanese, and Turkish (Saba, 2012). The FinCC consists of the Finnish Classification of Nursing Diagnoses (FiCND), the Finnish Classification of Nursing Interventions (FiCNI), and the Finnish Classification of Nursing Outcomes (FiCNO). Development and cultural validation of the FinCC started at the beginning of 2000 when nursing records were grouped, analyzed, and mapped with the HHCC (Saba, 2007, 2012; Saba & Taylor, 2007; Ensio et al., 2012). Concurrent with the development of the CCC, work to develop the FinCC continued in Finland as part of several national projects. The first versions of the FiCND and FiCNI were accepted in 2007 into the Finnish National Code Server, which is organized by the National Institute of Health and Welfare (Ikonen, Tanttu, Hoffren, & Mäkilä, 2007). To date, FinCC is the only nursing terminology in Finland to be accepted into the Code Server and thus remains freely available to all vendors (Kalliokuusi & Eerola, 2014). Finland’s national documentation model, which included the FinCC, was further developed from 2005 to 2009 through national projects. In 2008, the University of Eastern Finland (UEF) became responsible for the maintenance and development of the FinCC (Ensio, Saranto, Ikonen, & Iivari, 2006; Ikonen et al., 2007; Tanttu & Rusi, 2007). During a special documentation project conducted from 2008 to 2012 in cooperation with nursing education and nursing practice representatives, the competencies needed to use the model were defined by educational institutions and various healthcare facilities (Rajalahti, Heinonen, & Saranto, 2014). In 2010, the model was translated into Swedish for Swedish-speaking areas in Finland. Today, FinCC is widely used in specialized and primary healthcare in Finland. Both the FiCND and the FiCNI have 17 components (Fig. 42.2). Each component has a different number of main categories and subcategories. The content of the FinCC was revised based on user feedback in 2004 (Ensio et al., 2006), 2007 and 2010 (Kinnunen et al., 2011), and 2018 (Kinnunen et al., 2019b). The experts involved in the FinCC represent different healthcare organizations, including the THL, the Association of Finnish Local and Regional Authorities, and the UEF. The expert group supervises the development of terminology, networking with users and researchers, and continuous evaluation and validation of the FinCC. The most recent update to FinCC was implemented from 2018 to 2019. The first phase of the process included searching for evidence from, for example, clinical guidelines, other nations’ guidelines, laws, regulations, and scientific

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Health behaviors

1. Medication 2. Safety 3. Health behavior 4. Follow-up treatment and care coordination

Psychological

5. Coping 6. Mental capacity

for the

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Functional

7. Fluid balance 8. Nutrition 9. Activities of daily living and independence

Physiological

10. Breath/ Respiration 11. Metabolic 12. Skin integrity 13. Eliminations 14. Life cycle 15. Circulation 16. Sensory and neurologic functions 17. Pain management

•  FIGURE 42.2.  Components of Finnish Care Classification (FinCC), version 4.0. papers. This update aimed to increase the utilization of different scales (e.g., pain scales, wound scales, malnutrition risk scales) and evidence-based research for the development of terminology. First, a draft of version 4.0 of FinCC was developed (Fig. 42.2). Second, an e-questionnaire including 34 pages of statements concerning the 17 components of FiCND and FiCNI and all main categories and subcategories was sent to healthcare organizations (n=34) and universities of applied sciences (n=14) in order to assess how well the new version of the FinCC complied with actual nursing practices as well as its practicality and understandability. A Likert-type scale ranging from 1 to 5 (i.e., totally disagree to totally agree) was used to assess the understandability and practicality of the main and subcategories. Also, participants could freely comment after each statement. The respondents included nurses, nursing lecturers, senior nurses, senior nursing officers, and nursing students. The mean practicality and understandability of the components of the FiCND and FiCNI were 4.1–4.9. Also, the comments raised several questions, which led to consultation with different experts. Third, the update process included expert validation of terminology and then acceptance of the terminology into the Finnish National Code Server. The new version of the FinCC has been launched in the Autumn of 2019, after which it has been freely available to all vendors. The user guide book has also been published in Finnish, and in Swedish, and will be published in English in the Autumn 2020 (Kinnunen et al., 2019b).

Utilization of Structured Nursing Data for Better Patient Outcomes When a nurse regularly uses the Finnish national documentation model to record daily patient data, a standardized, holistic care process involving data collection, nursing diagnosis, planning and implementation of

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ePrescription service

Patient data repository

Radiology

EPR Encounters

Laboratory

Patient data management service Patient summary management Care Plans

Diagnoses

Other summary data

Consent and will management Consent and their restrictions

Living wills and other wills

My Data

•  FIGURE 42.3.  Kanta services. patient care, and evaluation of outcomes is followed. Also, final assessment using clinical reasoning and documentation of the whole care process is important for planning appropriate interventions for patients (ISO, 2014). According to the Finnish national guidelines, which are based on legislation, nursing summaries have been handled by Finland’s Kanta services since 2011. In principle, this means that nursing summaries are stored in the national eArchive and can be viewed from different patient record systems and any healthcare facility, regardless of the patient’s or doctor’s/nurse’s location (Fig. 42.3). The structure of nursing summaries is based

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Chapter 42 • Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents 

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  TABLE 42.1    Summary of Outcomes Achieved from Mining Structured Nursing Data for Date Reuse Purpose of Data Mining and Reuse

Outcome

Reference

Research and development of patient care processes Development of documentation and terminology Decision-making

•• Visibility and uniformity of nursing and medical documentation •• Triggers were used to indicate changes in the epilepsy patient’s health and well-being •• Development of reminders to the

Kivekäs et al., 2016

documentation

Research and development of patient care processes Development of documentation and terminology Decision-making Nursing management

•• Visibility of nursing documentation •• Triggers were used to indicate changes in the epilepsy patient’s health and well-being •• Analysis of patient profiles and populations

Kinnunen et al., 2016

•• Auditing nursing documentation is very

Mykkänen et al., 2012

•• Allocation of nursing resources, the data

Mykkänen et al., 2016

important for achieving unified, high-quality documentation, which is, in turn, connected to high-quality care

Nursing management

profiles of documented nursing data provide information and increased visibility of the whole patient care process, how the planned care was implemented, and patient outcomes for care needs

Nursing management Nursing management

•• Measure nursing intensity •• Resource planning, provide real-time esti-

Liljamo, Kinnunen, & Saranto, 2016 Liljamo et al. 2020

mates of NI without adding to nurses’ workload by requiring additional data collection or documentation

on the above-mentioned national core data set (i.e., they include nursing diagnoses, nursing interventions, nursing outcomes, and patient care intensity; Kinnunen et al., 2014; Liljamo et al., 2020). Also, these summaries utilize the standardized terminology adopted by all healthcare organizations (Kuusisto, 2018). Structured data provides many possibilities for data reuse (Saranto & Kinnunen, 2009; Kinnunen et al., 2014; Meystre et al., 2017). Structured data using a nursing terminology was shown to be valuable in data mining, and it made appropriate visible areas for documentation and development (Table 42.1). Data mining facilitates knowledge discovery from databases (Kivekäs et  al., 2016; Kinnunen, Kivekäs, Paananen, Kälviäinen, & Saranto, 2016). For managerial purposes, such as allocation of nursing resources, the data profiles of documented nursing data provide information and increased visibility of the whole patient care process, how the planned care was implemented, and patient outcomes for care needs (Mykkänen, Miettinen, & Saranto, 2016). Concerning

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the reuse of structured nursing data, auditing nursing documentation is very important for achieving unified, high-quality documentation, which is, in turn, connected to high-quality care (Mykkänen, Saranto, & Miettinen, 2012). The FinCC was cross-mapped with the Oulu Patient Classification (OPCq), which is designed to measure nursing intensity (NI) (Liljamo et al., 2016; Liljamo et al., 2020). In Finland, many hospitals have used these two nursing classifications as well as a stand-alone system. Traditional patient care classifications have been criticized for their subjectivity and for increasing nurses’ workload by requiring a manual assessment of patients’ NI scores once every day. Cross-mapping the two classifications, which were initially developed for different purposes, was intended to contribute to the reuse of coded data that were already available in the EHR for assessment of NI (Fig. 42.4). Also, it was intended to provide real-time estimates of NI without adding to nurses’ workload by requiring additional data collection or documentation. After the

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Oulu Patient Classification, OPCq sub-areas(n = 6) 1. Planning and Co-ordination of Nursing Care

Finnish Care Classification, FinCC 3.0, components (n = 17) FiCND, main and sub categories (n = 238) FiCNI, main and sub categories (n = 307)

Allmain and subcategories of FinCC 3.0 were cross-mapped With OPCq sub-areas

2. Breathing, Blood Circulation, And Symptoms of Illness 3. Nutrition and Medication

4. Hygiene and Secretion

5. Activity, Functioning, Sleep and Rest 6. Teaching and Guiding of Care / Continued Care and Emotional Support

•  FIGURE 42.4.  Cross-Mapping of the Finnish Care Classification (FinCC) and Oulu Patient Classification (OPCq). cross-mapping results were obtained for one EHR, it was possible to combine coded nursing data and NI data and analyze the relationship (Liljamo et al. 2020). A clear statistical relationship exists between the number of nursing diagnoses and interventions in the FinCC and the categories of NI measured by the OPCq; the more nursing diagnoses and interventions were documented, the higher the NI level. According to this initial study by Liljamo et al. 2020, it is possible to reuse coded nursing data for administrative and resource planning purposes. The results provide a good basis for continued elaboration on EHR data reuse and IT developments.

IMPROVING CARE QUALITY THROUGH PATIENT PARTICIPATION Person-centered care recognizes patients’ full autonomy as citizens in society who happen to need health-related services, and thus, it avoids the hierarchical patient–provider relationship. Recently, Rigby et al., (2015) defined a person-centered health system as one that “supports people to make informed decisions and to successfully manage their health and care and to invite others to act on their behalf. Person-centered care sees patients as equal partners in planning, developing, and assessing care.” Due

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to its ability to store and control sharing of electronic information, information and communication technology (ICT) has a tremendous potential to facilitate care coordination in healthcare contexts (Rigby et al., 2015; Saranto, Kivekäs, Kuosmanen, & Kinnunen, 2018). The widespread implementation of EHRs has led to new ways of providing access to healthcare information, such as allowing patients to view their medical notes, test results, and medicines. The term eHealth refers to health services and easily updatable health-related information provided or enhanced by the Internet (Niemi, Hupli, & Koivunen, 2016). In the context of healthcare interventions, such new technologies have been recognized for their tremendous potential to foster patient engagement. Specifically, they enable the development of integrated, sustainable, and patient-centered services and promote effective exchanges among the actors involved in the care process (Eysenbach, 2000). Also, self-management technologies such as patient-controlled EHRs may help people manage and cope with disease(s) (Schneider, Hill, & Blandford, 2016). Thus, providing patients with access to EHRs has emerged as a promising way to improve the quality and safety of care (Neves et al., 2018). Patient portals are an important technological means for supporting patient-centered care. Typically, these are Web-based applications developed by a healthcare

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Chapter 42 • Nursing Informatics Innovations to Improve Quality Patient Care on Many Continents 

institution (Saranto et al., 2018), and they allow access to all or part of the institution’s EHR data. More developed patient portals may also offer advanced communication functions and services intended to enhance medical treatment (Rigby et al., 2015). Tools such as patient portals and mobile health applications have been developed to engage patients in their care, or the care of dependents. Already, a significant number of patients use health information technology. Therefore, it is essential that patient-facing health IT be tailored to their needs. While portal functions vary, most allow patients to view laboratory and X-ray results and immunization, medication, and allergy information as well as to send secure messages to their physician or nurse (Kanta, 2019). However, patient portals can be difficult to navigate, and patients may struggle to understand their medical information (Powell, 2017; Fraccaro et al., 2018). Studies have revealed that patients want their providers to encourage them and explain how to use the portal as well as provide multiple opportunities for training (Sarkar & Bates, 2017; Vicente & Madden, 2017). Although new technologies can register and monitor behavioral, physiological, and emotional variables in patients’ daily life and provide immediate feedback, self-report measures asking the patient to report on different emotions could achieve the same results (Barello et al., 2016). Health information is increasingly obtained from the Internet. Previous studies have suggested that individuals who check their symptoms online tend to be risk-averse and seek medical care when self-care would be appropriate (Semigran, Linder, Gidengil, & Mehrotra, 2015; Powley, McIlroy, Simons, & Raza1, 2016). Technology, such as Web-based self-management tools, can be used to educate these patients and provide them with information and choice. These tools are intended to give patients a feeling of control that will help them to better cope with and manage their illness. Schneider et al., (2016) recognized that patients’ informational needs depended not only on their condition but also on the care context. Also, they found that not all patients and families are willing to take more control and responsibility for their health management or are motivated to use technology. An important source of differences in patient families’ needs and wants was found to be their coping style (Kruse, Argueta, Lopez, & Nair, 2015). Low health literacy has been associated with decreased use of preventive services, increased risk of developing a chronic disease, poorer treatment adherence, and poorer health outcomes (Champlin, Mackert, Glowacki, & Donovan, 2017). Health literacy also influences patient– provider communication. Individuals with low health literacy are less likely to engage in shared decision-making

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with their healthcare provider and are less likely to ask questions. Patients’ willingness and ability to access their health information through Web portals are influenced by both individual factors (i.e., age, education level, and health status) and healthcare delivery factors (i.e., provider endorsement and portal usability; Niemi et al., 2016; Neves et al., 2018). Clinicians often cite inadequate visit time as a barrier to developing a relationship and communication with patients. Also, creating EHRs during patient interviews has diminished clinicians’ ability to connect with patients and has led to clinicians’ dissatisfaction with clinical practice (Anderson et al., 2017). When patients were able to input agendas into EHR notes before visiting a healthcare provider, both patients and clinicians felt that communication during the visit was improved and that time was optimized. They expressed interest in patient-written agendas in the future. Self-treatment and digital value services (ODA, 2019) and Virtual Hospital 2.0 (2019) projects have led to the development of new eHealth services for citizens and healthcare professionals in Finland. These services have enabled service and treatment chains to merge in new ways in different specialized fields in both primary and specialized medical care service networks. In addition, eHealth services allow better cooperation between those working in social welfare and healthcare service organizations. The illustration of digital health services in the care process in Fig. 42.5 provides insight into the extent of the change when transforming traditional services to digital services. New services have increased customer satisfaction (Saranto et al., 2018) and the impact of services offered to patients or customers while continually gathering data for service development. The projects achieved their goals. Digital services integrated with care paths enhance the efficiency of treatments and preventive healthcare, allow customers to access care at the right time, reduce the number of outpatient visits, and increase the efficiency of working time (Digital Health Village, 2020). The use and impact of digital services should be monitored using a modern big data analytics methodology, and service paths and structures should be continuously developed based on customers’ behavior and the impact of services (Sebaa, Chikh, Nouicer, & Tari, 2018). Providing patients access to their health records has been linked to theorized benefits in four major domains of healthcare quality: patient-centeredness, effectiveness, safety, and efficiency (Neves et al., 2018). Also, secure access to medical and nursing records improves patient satisfaction and enhances patient–provider communication.

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Single appointment e.g. with the e-Doctor

for the

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Traditional service

Comparable to a traditional appointment

Digital My Path

Digital service

Long-term care relationship e.g. Treatment of rheumatism Digital

Appointment

Digital

Digital

Digital

My Path + digital care path

Long-term care relationship whose duration is difficult to predict

Combined care (traditional+digital) e.g. bariatric surgery Digital

Treatment period /procedure My Path + digital care path

Digital

A planned combination of treatment involving both a traditional service and a digital treatment path

Digital care programme only e.g. Online therapy

A planned treatment programme delivered entirely digitally

My Path + digital care path

•  FIGURE 42.5.  Integration of digital services (Digital Health Village, 2020. (Reproduced, with permission, from Arvonen, S., & Lehto-Trapnowski, P. (Eds.). (2019). We are getting there — Virtual Hospital 2.0 project summary. Hospital District of Helsinki and Uusimaa Helsinki. Retrieved from http://www.virtuaalisairaala2.fi/en/home. Accessed on May 15, 2019.)

SUMMARY Florence Nightingale’s achievements in nursing have been attached not only to practice but also to knowledge creation and in many occasions, she is regarded as the first nurse informatician. We can also find her achievements innovative as she was the first nurse to use data to validate her decisions. Still, the implementation of nursing informatics innovations must be based on sufficient evidence regarding the benefits and possible harms for patients, professionals, or organization/society. The feasibility, appropriateness, meaningfulness, and effectiveness of nursing informatics innovations should be carefully evaluated using research evidence as well as continuously generated real-world data (RWD) from nursing practices. High-quality health data enables reliable information regarding the impacts of nursing informatics innovations in practice, but also make possible to reuse health data for secondary purposes. Uniform data structures enable better-quality data for patient care management and secondary use of data, such as research, statistics, treatment method development, and administrative purposes. Thus, the use of structured terminologies for patient care documentation is crucial.

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In the future it is expected that even more technological devices will be used in healthcare not only by nurses and other health professionals but also by patients. This will only be beneficial if the functionalities and interoperability of information systems are improved support information flow in nursing practice. The new way of providing digital services will change not only the role of the patient but also the role of care providers. This innovation is both social through coherent interaction, and technical by means of providing access to information and RWD for decision-making.

Test Questions 1. In the healthcare context, interoperability refers to what?

A. The ability of two or more healthcare providers to exchange and utilize the information with each other B. Provide seamless care, service, and data flow

C. Allow cooperation between healthcare professionals in the same organization D. A and B

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2. What does real-world data include?

A. Data from electronic patient records

B. Data from randomized controlled trials C. Data from systematic reviews D. None of the above

3. What does the meaningfulness of nursing informatics innovation mean? A. The extent to which an innovation is physically, culturally, or financially practical or possible within a given context

B. The extent how an innovation fits with or is it apt in a situation C. The personal experience, opinions, values, thoughts, beliefs, and interpretations of users of the innovation D. None of the above

4. Which of the following does not apply to high-quality patient data? A. Narrative or unstructured nursing data gives a clear understanding of nursing practice. B. It does not matter how you document patient data.

C. Nursing documentation based on structured terminology that gives exact information about the patient care process by standard terms and nursing language.

D. A and B.

5. Which of these are examples of utilizing structured nursing data for patient outcomes? A. Auditing nursing documentation gives evidence of the quality of documentation content.

B. Structured nursing data provides valuable information about patient outcomes concerning the needs of care. C. Structured nursing data gives unclear information for a nursing summary. D. A and B.

6. How does structured data provide possibilities for data reuse? A. Very seldom in healthcare B. Only for physicians C. Using data mining D. A and B

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7. Who should provide input when updating nursing terminology? A. It is essential to hear from nurses, the end-users.

B. The opinion of chief medical information officers is the most important. C. Researchers can update the terminology using only their own opinion. D. Physicians only.

8. What are the core data in the Finnish Nursing Minimum Data Set (FNMDS)? A. Nursing intensity, nursing history, and nursing interventions

B. Nursing diagnoses, nursing interventions, nursing outcomes, nursing intensity, and the discharge summary C. Nursing diagnoses, nursing outcomes, and history D. None of the above are included in the FNMDS.

9. What are some of the effects of the use of the patient portal? A. Low health literacy has been associated with decreased use of preventive services. B. Electronic tools are intended to give patients a feeling of control that will help them to better cope with and manage their illness.

C. Low health providing patients with access to EHRs has emerged as a promising way to improve the quality and safety of care. D. All of the above.

10. What encourages patients to participate and manage their health and care? A. Allowing patients to be equal partners in ­planning, developing, and assessing care.

B. Providing patients with access to electronic health records

C. Offering advanced communication functions and services that are intended to enhance medical services D. All of the above

Test Answers 1. Answer: D  A and B. In the healthcare context, interoperability refers to the ability of two or more healthcare providers to exchange and utilize the information with each other and provide seamless care, services, and data flow.

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2. Answer: C  Real-world data includes data from systemic reviews. 3. Answer: C  The meaningfulness of nursing informatics innovation means the personal experience, opinions, values, thoughts, beliefs, and interpretation of users of innovation.

4. Answer: D  A and B. Narrative or unstructured nursing data and how you document patient data does not apply to high-quality patient care. What does apply is nursing documentation based on structure terminology that gives exact information of the patient care process by common terns and nursing language.

5. Answer: D  A and B. Auditing nursing documentation gives evidence of the quality of documentation content and utilizing structured nursing data gives valuable information about patient outcomes concerning the needs of care to measure patient outcomes. 6. Answer: C  Structured data provides possibilities for data reuse using data mining.

7. Answer: A  When updating nursing terminology, it is essential to hear from nurses, the end-users. 8. Answer: B  The Finnish Nursing Minimum Data Set (FNMDS) includes nursing diagnoses, nursing interventions, nursing outcomes, nursing intensity, and the discharge summary.

9. Answer: D  All of the above. Low literacy has been associated with decreased use of preventive services; electronic tools are intended to give patients a feeling of control that will help them to better cope with and manage their illness, and providing the patient with access to EHRs has emerged as a promising way to improve the quality and safety of care. All of these affect the use of patient portals. 10. Answer: D  All of the above. Allowing patients to be equal partners in planning, developing, and assessing their care; providing patients with access to electronic health records; and offering advanced communication functions and services that are intended to enhance medical treatment. All of these encourage patients to participate and manage their health and care.

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43 Global eHealth and Informatics Hyeoun-Ae Park / Heimar F. Marin

• OBJECTIVES 1. Describe international organizations and their roles in advancing global eHealth and informatics. 2. Discuss nursing’s contributions to international health informatics initiatives. 3. Discuss applications of eHealth in the global health environment. 4. Explore current issues and trends for their effects on global eHealth and nursing.

• KEY WORDS eHealth Global health mHealth Nursing informatics Telehealth

INTRODUCTION Nursing and healthcare informatics are often described as sciences in the literature (Nelson & Staggers, 2014). eHealth is usually defined more broadly as a set of activities, processes, or means for the delivery of health services using information and communication technologies at both the macro and the micro levels. For example, the World Health Organization (WHO) describes eHealth as the use of information and communication technologies (ICT) for health. Thus, eHealth activities include the use of ICT for treating patients, conducting research, educating the health workforce, tracking diseases, and monitoring public health (World Health Organization, n.d.-a). eHealth has been further described by the WHO as the transfer of health resources and healthcare by electronic

means, encompassing the following three main areas (World Health Organization, n.d.-b): 1. The delivery of health information, for health professionals and health consumers, through the Internet and telecommunications

2. Using the power of IT and e-commerce to improve public health services, e.g., through the education and training of health workers.

3. Using e-commerce and e-business practices in health systems management As a profession, nursing has championed and contributed to several international health informatics and eHealth initiatives (Abbott & Coenen, 2008; Coenen, Marin, Park, Bakken, 2001; Saba, Hovenga, Coenen, 693

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McCormick, & Bakken 2003). This chapter represents a revision of a chapter, authored by Coenen, Bartz, and Badger, which appeared in the sixth edition of this book. The purpose of this revised chapter is to inform nurses about these initiatives; to describe the influence of nurses on these initiatives; and to describe the ongoing influence of these initiatives in care delivery, education, administration, and research. This chapter includes a description of the roles of international health organizations in global eHealth and informatics, a discussion of eHealth applications in the global health environment with particular emphasis on nursing, and an exploration of healthcare issues and trends concerning global eHealth and nurses worldwide.

for Nursing Practice (ICNP). ICNP is a terminology for nursing that provides an international standard for facilitating the description and comparison of nursing practice locally, regionally, nationally, and internationally. Other important ICN eHealth activities have included the ICN Telenursing Network, which aimed to involve and support nurses in the development and the application of telehealth technologies, and Connecting Nurses, which provided an online forum for nurses worldwide to share ideas, advice, and innovations. ICN seeks to transform nursing and improve health through the visionary application of ICT. ICN aims to support eHealth practice, to be recognized as an authority on eHealth, and to promote nurses as experts in the eHealth international community.

INTERNATIONAL ORGANIZATIONS INFLUENCING eHEALTH AND NURSING

World Health Organization (WHO)

With advances in healthcare technology, international health-related organizations have focused their efforts on exploiting the potential of eHealth for improvements in healthcare delivery and infrastructure. New programs and organizations are being established to respond to the development and growth of eHealth policy and applications internationally.

International Council of Nurses (ICN) Founded in 1899, the International Council of Nurses (ICN) is the world’s first and most extensively reaching international organization for health professionals. ICN is a federation of more than 130 national nurses associations and represents more than 20 million nurses worldwide. The mission of ICN is to represent nursing worldwide, advance the nursing profession, promote the well-being of nurses, and advocate for health in all policies. ICN is involved in initiatives related to professional nursing practice, nursing regulation, and socio-economic welfare for nurses. ICN promotes quality nursing care for all, with and through a competent and satisfied nursing workforce. ICN supports the advancement of experiential- and research-based nursing knowledge, which are hallmarks of a respected nursing profession (ICN, 2019). ICN represents the profession of nursing in many international venues, including the United Nations, WHO, the World Health Professions Alliance, and other organizations discussed below. As the international voice for nursing, ICN has a program that takes eHealth as its focus. ICN’s long-standing engagement in eHealth is manifested primarily through the International Classification

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WHO is the authority for directing and coordinating health within the United Nations system. Each year, WHO holds the World Health Assembly (WHA) in Geneva, Switzerland, which is the decision-making body of WHO. ICN is a “non-State actor in official relations with WHO.” WHO provides leadership on global health matters such as shaping the health research agenda, setting norms and standards, articulating evidence-based policy options, providing technical support to countries, and monitoring and assessing health trends (WHO, n.d.-b). World Health Organization Family of International Classifications (WHO FIC) Network  WHO has long maintained and used the International Classification of Diseases (ICD) for national and international reporting of morbidity and mortality statistics. In addition to ICD, WHO has developed the International Classification of Functioning, Disability and Health (ICF) for documentation and reporting of functional abilities and health. A newer endeavor under the leadership of the WHO FIC Network is the development of the International Classification of Health Interventions (ICHI) which is a classification of interventions for use across all health professions (WHO, 2019). ICNP is recognized as a Related Classification within WHO-FIC.

International Medical Informatics Association (IMIA) Established in 1979, the International Medical Informatics Association (IMIA) is an independent, nongovernmental organization made up of national medical informatics associations, institutional (academic and corporate) and affiliate members, and honorary fellows. IMIA plays a global role in the application of information science and

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technology in the fields of health and biomedical informatics (IMIA, 2020). International Medical Informatics Association—Nursing Informatics Special Interest Group (IMIA-NI SIG)­ IMIA has a Special Interest Group (SIG) that focuses on nursing informatics. Membership includes over 25 society representatives and observer members. The SIG meets regularly at related informatics conferences. The focus of IMIA-NI SIG is to foster collaboration among nurses and others who are interested in Nursing Informatics to facilitate development in the field. IMIA-NI SIG aims to share knowledge, experience, and ideas with nurses and healthcare providers worldwide about the practice of Nursing Informatics and the benefits of enhanced information management (IMIA-NI SIG, 2019).

International Organization for Standardization (ISO) The International Organization for Standardization (ISO) is the world’s largest developer of voluntary international standards. ISO standards provide state-of-the-art specifications for products, services, and best practices to make the industry more efficient and effective. ISO was founded in 1947, and since then has published more than 22,656 reports that address almost all aspects of technology and business, including health (ISO, n.d.-a). ISO Technical Committee 215: Healthcare Informatics  ISO Technical Committee (TC) 215 for Healthcare Informatics has as its scope the standardization of health information resources, including health information and communications technology to facilitate the capture, interchange, and use of health-related data, information, and knowledge to support and enable all aspects of the health system. The Committee aims to promote interoperability between independent systems, to enable compatibility and consistency of health information and data, and to reduce duplication of effort and redundancy (ISO, n.d.b). ISO TC 215 is responsible for new technical standards that involve nursing informatics. ISO TCs are composed of a set of different countries’ Technical Advisory Groups (TAGs). The TAGs for TC 215 include individuals from standards development organizations, professionals, governmental or commercial organizations, as well as individuals representing themselves. One of the most important products of ISO for nursing is the international standard ISO 18104:2014 (Health informatics—Categorical structures for representation of nursing diagnoses and nursing actions in terminological systems) (ISO, n.d.-c). The purpose of ISO 18104 is to establish

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a nursing reference terminology model consistent with the goals and objectives of other specific health terminology models in order to provide a more unified reference health model. This ISO standard has been used to evaluate and support the ongoing development of nursing terminologies, especially in supporting the definition of high-level schemata in developing logic-based compositional terminologies such as ICNP (Hardiker & Coenen, 2007; Marin, Peres, & Dal Sasso, 2013).

SNOMED International SNOMED International is a not-for-profit organization based in the United Kingdom. Its primary purpose is to develop and maintain SNOMED Clinical Terms (SNOMED CT). The organization focuses on supporting the implementation of interoperable, semantically accurate health record documentation (SNOMED, n.d.-a). As the voice for nursing internationally and as the developer of the ICNP, ICN is aware of the need to collaborate with SNOMED International to assure nursing domain content is available to nurses in SNOMED International member countries. In 2010, ICN announced a collaborative agreement with SNOMED International (previously the International Health Terminology Standards Development Organization [IHTSDO]) to advance terminology harmonization and foster interoperability in health information systems, and to provide a vehicle for transforming ICNP-encoded data into SNOMED CT (ICN, 2018). A significant result of these harmonization efforts has included tables of equivalencies that can support data transformations between ICNP and SNOMED CT concepts.

International Society for Telemedicine and eHealth (ISfTeH) The International Society for Telemedicine and eHealth (ISfTeH) is a nonprofit organization with close ties to WHO and ICN, as well as other international organizations. Its mission is to facilitate the international dissemination of knowledge and experience, as well as to support developing countries in the fields of telemedicine and eHealth. ISfTeH is primarily an umbrella organization for national telemedicine and eHealth organizations and for individuals and academic centers working to integrate telehealth strategies and applications in healthcare. It promotes and supports telemedicine and eHealth activities worldwide, supports developing countries in the field of telemedicine and eHealth, and assists the start-up of new national organizations.

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ISfTeH Telenursing Working Group  The ISfTeH Telenursing Working Group is open to all interested nurses and other healthcare providers. Its mission is to provide a forum for the exchange of knowledge and experiences of nurses and others who are working with or supporting nurses’ use of eHealth applications (ISfTeH, n.d.). The objective of the ISfTeH Telenursing Working Group is to support technology, business, and professional actions for telehealth nursing. These actions include:

• • • • •

advocating for increased use and evaluation of telehealth services by nurses; stimulating innovative ideas and promoting initiatives for further development of eHealth; supporting interdisciplinary telehealth collaboration for improved healthcare delivery and outcomes; advancing nurses’ knowledge and skills in telehealth through the dissemination of research findings, education programs, and practice guidelines; and advocating for the ethical use of telehealth services.

Nurses from around the world are active participants in the annual ISfTeH conference, Med-e-Tel, describing their work and research in eHealth, telehealth, and nursing.

Healthcare Information and Management Systems Society (HIMSS) HIMSS is a global, not-for-profit organization focused on better health through ICT. HIMSS leads collaboratives and conferences to promote the positive use of ICT in healthcare. The HIMSS conferences in the United States, Europe, and Asia Pacific showcase exhibitors that represent commercial and noncommercial interests in the health ICT industry (Healthcare Information and Management Systems Society, n.d.-a). HIMSS Nursing Informatics Community  In 2003, the HIMSS Nursing Informatics Community was founded in response to the increased recognition of the role of the nurses in health informatics. This HIMSS community seeks to reach out to all nurses and promote the involvement of those working in nursing informatics. The HIMSS Nursing Informatics Community provides domain expertise, leadership, and guidance to the organization’s activities, initiatives, and collaborations with the global nursing informatics and eHealth communities (Healthcare Information and Management Systems Society, n.d.-b).

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Technology Informatics Guiding Education Reform (TIGER) TIGER began in 2006 with support from over 70 organizations (Healthcare Information and Management Systems Society, n.d.-c). The focus of TIGER is on the development of a clinical workforce that can use technology to improve the delivery of care. HIMSS currently hosts TIGER. One of its key products is a Virtual Learning Environment that acts as a portal to online resources that support educational reform.

Health Level 7 (HL7) International Health Level 7 (HL7) International is a not-for-profit, standards development organization dedicated to providing a comprehensive framework and related standards for the exchange, integration, sharing, and retrieval of electronic health information that supports clinical practice and the management, delivery, and evaluation of health services. “Level 7” refers to the seventh level of the ISO seven-layer communications model for Open Systems Interconnection (OSI) at the application level. The vision of HL7 International is to create the best and most widely used standards in healthcare in order to improve care delivery, optimize workflow, reduce ambiguity, and enhance knowledge transfer among all stakeholders, including healthcare providers, government agencies, the vendor community, fellow standards development organizations, and patients (HL7 International, n.d.). HL7 International Nurses Group  The HL7 International Nurses Group was started in 2009 during an HL7 International Working Group meeting. The goals of the HL7 International Nurses Group are to explore how nurses can become more involved in the HL7 International community, to exchange information, and to ensure that nursing practice and nurses are included in use cases and criteria for the HL7 International standards (HL7 International, 2019).

APPLICATIONS IN GLOBAL eHEALTH AND INFORMATICS With new technologies, the collection, storage, and transmission of health data and information are changing how health care is delivered. Nurses are leading advances in eHealth worldwide. In this section, selected applications for eHealth are discussed to provide an understanding of current nursing research, education, and practice in this area. Specifically, examples of innovations in the

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  TABLE 43.1    List of International Organizations Influencing Nursing and eHealth Acronym

Organization Name

Web Address

HIMSS

Healthcare Information and Management Systems Society

www.himss.org

HL7

Health Level 7 International

www.hl7.org

ICN

International Council of Nurses

www.icn.ch

IHE

Integrating the Healthcare Enterprise

https://www.ihe.net/

SNOMED

SNOMED International

www.snomed.org

IMIA

International Medical Informatics Association

imia-medinfo.org

ISfTeH

International Society for Telemedicine and eHealth

www.isfteh.org

ISO

International Organization for Standardization

www.iso.org

WHO

World Health Organization

www.who.int

advancement of the electronic health record (EHR) and telehealth are described.

•

Electronic Health Record (EHR) The EHR is a longitudinal electronic record of patient health information generated by one or more encounters across the care delivery setting. Optimally, the EHR supports and enhances the quality of care with content appropriate to the healthcare setting and processes that enable decision support, outcomes reporting, and ease of use. As one of the major innovations in healthcare, the EHR has brought both challenges and hope for improved health care delivery systems and better health outcomes for people worldwide. There has been steady growth in the adoption of national EHR systems with a 46% increase from 2010 to 2015 according to WHO’s global survey on eHealth (WHO, 2016). Out of 121 countries participated in the survey, 57 countries reported having introduced a national EHR systems. Countries answered no national EHR systems still have some form of EHR system used in local or regional facilities. These countries with national EHR systems have strongly supported and promoted standardization, interoperability, and information sharing among healthcare providers. The ability to document healthcare using standardized, interoperable system applications is recognized as essential to unleashing the tremendous capacity offered by information sharing through the EHR. Electronic capture, storage, and retrieval of comparable health data across providers, settings, and specialties are the basis for measuring value of the EHR. Recent initiatives by international groups of nursing informatics experts include the following efforts to promote resources and tools for interoperable systems:

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•

•

As a unifying international framework for nursing terminology, ICNP has great potential for describing nursing practice, facilitating care transitions, and using consistent and accurate data for decision-making and policy development. It is only through its use, internationally, in the EHR that the full potential of ICNP will be realized. In addition to commercial software vendors and healthcare organizations being early adopters, several countries have endorsed ICNP as a national standard for nursing. For countries implementing SNOMED CT as an interdisciplinary terminology for healthcare, nurses need to be involved and engaged in assuring the representation of the nursing domain content. As noted earlier in this chapter, ICN has partnered with SNOMED International to facilitate the harmonization of ICNP and SNOMED CT concepts (SNOMED, n.d.-b). With its worldwide influence, WHO has an opportunity to advance the reporting of health and illness globally through partnering with other professional organizations such as ICN in the achievement of standardization and interoperability. WHO–ICN collaborations have focused on harmonizing ICNP and ICF (Kim & Coenen, 2011) and in the development of the International Classification of Health Interventions.

In addition to the potential of interoperable data and information, the EHR has the potential to change the relationship between provider and consumer. Imagine, as a nurse, that the clients you interact with within healthcare episodes, be they individuals, families, or communities,

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can access their health data and information in real time electronically. As the EHR evolves, nurses will use this new technology to support the shift from a providercentered health system to a consumer-centric model of healthcare. Various EHR applications will facilitate nurses’ access to their patients and the patients’ access to their health providers.

Telehealth and mHealth in the eHealth Environment Nurses are already using telehealth applications in their work. Telehealth nursing extends the capability and reach of nurses and aims to improve access and quality while managing healthcare costs. While the use of technology changes the care delivery medium and may necessitate new competencies, the nursing process and scope of practice are not significantly different in telenursing, whether in direct care nursing, education, or management (Schlachta-Fairchild, 2007). Research in this area is growing, and there are many examples, including those conducted by nurses themselves (Farquharson et al., 2012; Kowitlawakul, 2011) and by nursing students (Chaung, Cheng, Yang, Fang, & Chen, 2010; Glinkowski, Pawlowska, & Kozlowska, 2013); in ICU (Rincon & Bourke, 2011), surgery (Inman, Maxson, Johnson, Myers, & Holland, 2011), orthopedics (Jones, Duffy, & Flanagan, 2011), and neurology (Young-Hughes & Simbartl, 2011); and in the field of communicable diseases (Côté et al., 2011), noncommunicable diseases (Baldonado et al., 2013; Wakefield et al., 2012), and mental health (Badger, Segrin, Pasvogel, & Lopez, 2013; Sands, Elsom, Marangu, Keppich-Arnold, & Henderson, 2013). Nurses are essential in building the body of knowledge about telehealth applications and technologies in furthering healthcare access, quality, and cost management.

GLOBAL TRENDS SUPPORTED BY AND DRIVING EHEALTH eHealth has influenced many changes in the health arena internationally. Important trends are discussed in this section: care coordination, self-management, health equity, and patient safety.

Care Coordination Based on evidence (ANA, 2012) policy-makers at the local, national, and international levels are recognizing the importance of continuity of care to decrease costs and increase quality. With the increase in the numbers of

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health providers involved in one person’s care and population mobility of both patients and health providers, countries are looking for solutions to reduce the fragmentation of healthcare. In addition to increasing communication by using the EHR, another proposed solution is care coordination programs. The American Academy of Nursing (Cipriano et al., 2013) offered a set of recommendations to guide the development of the EHR to support care coordination across the continuum of health service delivery. Along with the infrastructure needs, nurses’ participation in identifying data and information needs for care coordination across settings and services will be essential. One committee in Integrating the Healthcare Enterprise (IHE), a nonprofit initiative to engage healthcare professionals and industry to improve interoperability, is focused on patient care coordination (IHE, 2013). IHE has published a patient care coordination technical framework for testing that includes a patient plan of care profile based on data elements of the Nursing Process. Hübner, Kinnunen, Sensmeier, and Bartz (2013) have promoted the work of IHE in coordination with their research in eNursing Summary profiles to advance the exchange of nursing data and promote care coordination. Researchers in Germany (Hübner et al., 2012) and Finland (Häyrinen, Lammintakanen, & Saranto, 2010) have developed candidate models for an eNursing Summary. Both models include components of the Nursing Process. Understanding that context and environment impact local and national implementation of standards, the question arises on whether there could be a universal concept or shared model of an international eNursing Summary. To engage more nurses around the globe, the researchers have proposed ongoing collaboration with IHE, ICN, and IMIA-NI to continue work toward an international eNursing Summary framework. Care coordination is centered on the patient. A major focus of care coordination is to empower the patient as a partner in care. Promoting self-management has become an important component of nursing practice, especially in the care of our aging population, persons with chronic illness, and those with known risks for health problems.

Self-management Concepts such as person-centered care (Bernabeo & Holmboe, 2013; Daley, 2012), person-centered medicine (Miles & Mezzich, 2011a, 2011b), patient-centered care (Bartz, 2013), and personalized medicine (Swan, 2012) are topics in today’s healthcare literature in part due to the recognition that the digital revolution has encouraged people to become more involved with their health and

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wellness. An outcome of this involvement is the movement toward self-management, often supplemented with ICT in the person’s home or other environments. Literacy, described below in terms of health literacy and digital literacy, is one of many identified components included in the Individual and Family Self-Management Theory (Ryan & Sawin, 2009).

Health Literacy The Health Promotion Glossary of WHO states, “Health literacy represents the cognitive and social skills which determine the motivation and ability of individuals to gain access to, understand, and use information in ways to promote and maintain good health” (WHO, 1998, p. 10). The word “use” distinguishes health literacy from health knowledge, wherein a health consumer may know a lot about health promotion and disease prevention but is either unwilling or unable to translate this knowledge into action. Baur (2011) purported that, of all the clinical disciplines, “nursing has a unique relationship to health literacy because nurses are responsible for the majority of patient, caregiver, and community health education and communication” (p. 63). Speros (2005) added that nurses are invested in increasing healthcare consumers’ health literacy whose positive consequences include improved selfreported health status, lower health care costs, increased health knowledge, shorter hospitalizations, and decreased use of health services. Negative consequences of low health literacy include increased healthcare costs, poor adherence, medical and medication treatment errors, and “lack of skills needed to successfully negotiate the healthcare system” (Mancuso, 2008, p. 250). Regarding interventions to improve patients’ health literacy, one study of a nurse-delivered, though not nurse-developed, health literacy intervention showed an increase in adherence to antiretroviral medications among patients with a limited reading ability (Kalichman, Cherry, & Cain, 2005). A second study of a nurse-tailored health literacy intervention regarding HIV medication adherence among African Americans resulted in no statistically significant difference between the control and intervention groups (Holzemer et al., 2006). Despite a relative lack of published intervention studies, the relationship between patients’ and families’ access to health information and their engagement in their care has been supported (Schnipper et al., 2012). The importance of health literacy as a factor in promoting self-management will become more important with the implementation of the EHR and telehealth. Nurses can take the lead in examining interventions in promoting health literacy across their multiple specialty areas.

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Schaefer (2008) concluded that health literacy interventions could be categorized into various types by mode of delivery, which includes personal contact, computer, and written materials, as well as any combination of these types. Visscher et al. (2018) identified three types of interventions; aimed at improving health literacy, tailored to different health literacy levels, and aimed at improving health outcomes in general, which described the specific effects for patients with different health literacy or numeracy levels. Further, Speros (2011) suggested the following strategies to promote health literacy: creating a shamefree environment, using clear and purposeful communication, communicating in a patient-centered manner, reinforcing the spoken word, and verifying understanding.

Digital Literacy The Internet has become one of the most ubiquitous sources of healthcare information. Digital literacy, therefore, has become an important component of health literacy for patients and healthcare providers alike. Gilster (1997) defines digital literacy as “the ability to understand and use information in multiple formats from a wide variety of sources when it is presented via computers” (p.19). The Institute of Medicine (IOM, 2011) (now called the National Academy of Medicine [NAM]), in its report “Future of Nursing: Leading Change, Advancing Health” stressed the importance of digital literacy for nurses and other healthcare providers. Specifically, NAM recommended healthcare organizations engage nurses and other frontline healthcare workers in the design, development, purchase, implementation, and evaluation of medical devices and EHR software. Early involvement allows nurses to ensure that new technology enhances, rather than hinders, their workflow and that nursing-based content is included in documentation software. Digital literacy has the potential to enhance many aspects of human existence. According to the Bill and Melinda Gates Foundation (2019), access to information and knowledge is a great equalizer. It enriches lives, informs choices, and prepares people for meaningful employment and contribution to their communities. As part of the Gates-funded initiative, many countries have benefited. In Chile, a national digital literacy campaign trained hundreds of thousands of people in basic technology skills, largely via a network of more than 300 public libraries. In Mexico, public libraries provide the only Internet access for nearly two-thirds of rural communities. In rural Botswana, public libraries serve as small business owners’ offices, helping people make their businesses more sophisticated and competitive. In Ukraine, one community has used library Internet access to collect information about farming techniques, fundamentally changing

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the way they grow tomatoes and substantially increasing their crop quality and yield.

Health Equity In a 2014 position statement, ICN (2014) recognized that wealth influences readiness for ICT, with a clear demarcation between high-income countries and low-income countries. Nurses can demonstrate leadership in promoting policies at the global, regional, national, and local levels to provide the infrastructure and skills needed to make ICT a reality across all members of society, including both those receiving and those proving healthcare. Nurses work with individuals, families, and communities to promote health, to prevent illness, to restore health, and to alleviate suffering (ICN, 2019). eHealth has the potential to transform both nursing and healthcare. eHealth is not just about the ICT; it is about using technology to collaborate, to communicate, and to advocate. At the same time, unless there is equity in access to technology sources of health information and knowledge, eHealth could serve to disenfranchise a large proportion of the world’s population and a significant number of nurses and health providers in poor, disadvantaged areas of the world.

Patient Safety Patient safety can be defined as “freedom for a patient from unnecessary harm or potential harm associated with health care” (Council of the European Union, 2009). Since the IOM published To Err Is Human in 2001, there has been an accelerated development and adoption of health information technology (HIT) to improve patient safety (IOM, 2001). HIT provides important tools for patients’ safety by reducing medication errors, reducing adverse drug reactions, and improving compliance to practice guidelines (Ohno-Machado, 2017). These tools include the EHR, clinical decision support (CDS), computerized physician order entry (CPOE), clinical alarm systems, and incident reporting systems (Alotaibi & Federico, 2017). It is important for healthcare providers to understand which technology might be effective in improving patient safety outcomes before they introduce HIT to clinical settings. However, the impact of these technologies is not always clear. This section is intended to introduce different health information technologies used to improve patient safety, along with their impact. EHR  As discussed previously in this chapter, EHRs are increasingly used in healthcare settings and it is believed

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that they have improved patient safety by improving communications, making knowledge accessible, providing decision support, requiring key pieces of information for correct treatment, assisting with calculations, performing real-time checks, and assisting with monitoring (Bates & Gawande, 2003). Previous studies have explored the impact of EHRs on patient safety. A study by Hydari, Telang, and Marella (2015) found that hospitals that adopted EHRs experienced a 27% decline in overall patient safety events (PSEs), with a 30% decline in medication PSEs. Parente and McCullough (2009) found a small effect of EHRs on patient safety indicators such as infections, postoperative hemorrhage, and postoperative pulmonary embolism. Tubaishat (2019) explored the effect of EHRs on patient safety using semi-structured interviews with staff nurses working in hospitals that employed the same EHR system in Jordan. Two major themes emerged, one regarding the enhancements that EHRs make to patient safety and the other surrounding concerns raised by the use of these systems. According to the study, EHRs directly or indirectly improve patient safety by minimizing medication errors, improving documentation of data, enhancing the completeness of data, and improving the sustainability of data. However, EHRs also jeopardize patient safety with data entry errors, technical problems, minimal clinical alerts, and poor use of system communication channels. However, these concerns do not obviate the potential value of EHRs in reducing PSEs. Clinical Decision Support (CDS)  CDS is a HIT component that provides clinicians or patients with the knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and healthcare. CDS encompasses a variety of tools to enhance decision-making in the clinical workflow. These tools include computerized alerts and reminders to care providers and patients; clinical guidelines; condition-specific order sets; focused patient data reports and summaries; documentation templates; diagnostic support; and contextually relevant reference information, among other tools (ONC, 2019). Nearly half of healthcare providers responding to a HIMSS survey conducted by LogicNets indicated that CDS was an important tool for improving patient safety. Forty-three percent believed that CDS technology reduces the occurrence of errors by providing prescription help, standardizing medication orders, and supporting evidence-based medicine and more informed diagnostic decision-making (Bresnick, 2015). According to a systematic review examining evidence that relates HIT functionalities prescribed in meaningful use to key aspects

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of healthcare, 57% of the 236 studies evaluated CDS and CPOE, whereas other meaningful use functionalities were rarely evaluated (Jones, Rudin, Perry & Shekelle, 2014). Fifty-six percent of studies reported uniformly positive results, and an additional 21% reported mixed positive effects. In some cases, CDS did not have the desired effect on medication safety. Alert fatigue and incongruent workflows were barriers to successful use of these systems. Computerized Physician Order Entry  Computerized physician order entry (CPOE) refers to a system that allows physicians to electronically order prescriptions, laboratory tests, and X-rays and make referrals. CPOE systems were originally developed to reduce errors related to the illegible handwriting. CPOE systems are one of the most rigorously evaluated HITs, with a high level of scientific evidence regarding the reduction of medical errors (Charles, Willis, & Coustasse, 2014). For example, electronic prescribing implemented in a medical group reduced medical errors by 70% (Devine et al., 2010). CPOE implementation at an academic medical center decreased the length of stay by 0.9 days and motility by 1 to 3 deaths per 1000 admissions in medical and surgical units (Lyons et al., 2017). Other benefits include a decrease in patient paper charts, improvements in accessing patient information, additional coordination of care, and reduction in prescription ordering by the physician. CPOE systems are often coupled with CDS, which acts as an error prevention tool by guiding the user, for example, on preferred drug doses, route, and frequency of administration or prompting them toward interventions that should be prescribed based on clinical guideline recommendation. There are benefits to CPOE. However, it is possible for CPOE to have zero impact on patient safety and patient outcomes, or even a negative impact when it is inadequately designed, for example, on nursing workflow (Al-Dorzi et al., 2011; Househ, Ahmad, Alshaikh, & Alsuweed, 2013). Clinical Alarms  Clinical alarm systems which warn caregivers of immediate or potential adverse patient conditions are increasingly recognized as valuable tools for improving patient safety. For a clinical alarm to be effective, it must be triggered by a problem that adversely affects the patient, and caregivers must identify the source and meaning of the alarm and correct the problem before an adverse safety event. However, the number of alarm signals per patient per day can reach several hundred depending on the setting. It is estimated that between 85% and 99% of alarm signals do not require clinical intervention (The Joint Commission, 2013). As a result, caregivers

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suffer from “alarm fatigue,” a state in which caregivers become overwhelmed and desensitized to the alarms (Johnson, Hagadorn & Sink, 2017). In response to near constant alarm signals, caregivers may turn off the alarm, or adjust the alarm settings outside the limits that are appropriate for the patient—all of which can be a serious threat to patient safety (Korniewicz, Clark, & David, 2008; Phillips & Barnsteiner, 2005). The ECRI Institute has reported on the dangers related to alarm systems since 2007. In its annually published “Top 10 Health Technology Hazards” list, clinical alarm conditions consistently appear as the number one or two device-related hazard, reflecting serious consequences of alarm-related problems (ECRI Institute, 2013). The U.S. Joint Commission received 98 reports of alarmrelated events between January 2009 and June 2012. Of the 98 reported events, 80 resulted in death, 13 in permanent loss of function, and 5 in unexpected additional care or extended stay. Common injuries or deaths related to alarms included those from falls, delays in treatment, ventilator use, and medication errors. The major contributing factors were absent or inadequate alarm system (30); improper alarm settings (21); alarm signals not audible in all areas (25); and alarm signals inappropriately turned off (36). There have been various attempts to mitigate alarm fatigue. For example, Lee, Mejia, Senior, & Jose (2010) introduced an automated system to filter overridden alerts by EMR users so that the users can concentrate on relevant alerts to prevent harmful adverse drug events. Electronic Incident Reporting Systems  Electronic incident reporting systems are Web-based systems that allow healthcare providers who are involved in safety events to report such incidents voluntarily. Electronic incident reporting systems can be integrated with the EHR to enable abstraction of data and automated detection of adverse events through trigger tools. With electronic incident reporting systems, it is possible to standardize reporting structure, standardize incident action workflow, and rapidly identify serious incidents and trigger events, while automating data entry and analysis. Clinical processes may be improved with the incident reporting systems. However, there is little evidence that electronic reporting systems ultimately reduce medical errors (Stavropoulou, Doherty, & Tosey, 2015).

SUMMARY Nurses play a key role in the advancement of health informatics and eHealth worldwide. A number of international organizations influence both eHealth and nursing,

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including ICN with its commitment to supporting the eHealth agenda, for example, via ICNP; WHO and its WHO-FIC Network; IMIA and IMIA-NI (and associated regional and national associations); ISO and its Health Informatics Technical Committee; SNOMED International; ISfTeH and its Telenursing Working Group; HIMSS and its Nursing Informatics Community; TIGER; and HL7 International and its Nurses Group. Nurses are central to success for both application areas such as the EHR and new modalities of care delivery such as Telenursing and mHealth. Finally, it is natural that nurses are engaged in several trends such as care coordination, self-management, health equity, and patient safety as these have naturally long been a focus of attention for the profession.

ACKNOWLEDGMENTS This chapter represents a revision of a chapter, authored by Coenen, Bartz, and Badger, which appeared in a previous edition of this book. The authors would like to acknowledge the contribution of Dr. Nicholas R. Hardiker, PhD, RN, FACMI at the University of Huddersfield, UK, who assisted in the revision of the chapter.

B. ISO Technical Committee (TC) 215 for Healthcare Informatics, which has as its scope the standardization of health information resources, including nursing informatics and telehealth nursing C. Both A and B

D. None of the above 3. The name of the international not-for-profit organization that develops and maintains SNOMED CT is: A. WHO B. ICN

B. IMIA

C. SNOMED International 4. ISfTeH Telenursing Working Group supports technology, business, and professional actions for telehealth nursing. True or false? A. True B. False

Test Answers Test Questions* 1. What is the name of the international organization with the widest reaching to nursing health professionals? A. The American Nurses Association

B. The International Council of Nurses (ICN) C. The International Medical Informatics Association D. None of the above

2. Nursing-specific diagnoses and actions in terminological systems are included in which international standard?

A. The International Organization for Standardization (ISO) standard ISO 18104:2014 (Health informatics—Categorical structures for representation of nursing diagnoses and nursing actions in terminological systems)

Questions and Answers—taken from Dr. Brixey et al. Study Guide for sixth edition.

*

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1. Answer: B  Founded in 1899, the International Council of Nurses (ICN) is the world’s first and widest reaching international organization for health professionals. 2. Answer: A  ISO 18104:2014 (Health informatics— Categorial structures for representation of nursing diagnoses and nursing actions in terminological systems) includes nursing-specific diagnoses and actions. 3. Answer: D  SNOMED International is a not-forprofit organization based in the United Kingdom. Its main purpose is to develop and maintain the Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT).

4. Answer: A  True. The ISfTeH Telenursing Working Group supports technology, business, and professional actions for telehealth nursing. Nurses participate in advancing nurses’ knowledge and skills in telehealth through the dissemination of research findings, education programs, and practice guidelines; and advocating for the ethical use of telehealth services.

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part

8

Educational Applications Diane J. Skiba

Part 8, Educational Applications, by Diane Skiba provides a synopsis of various educational technologies and trends that relate to the informatics preparation of the healthcare workforce. This part highlights the use of various distance education opportunities for nurses to earn their degrees or access professional development content. There is also an emphasis on current educational technologies in online education and a look at emerging technologies such as virtual simulations, virtual reality, robotics, and drones. The use of social media in our ever-evolving healthcare system is also addressed. In addition, there is content related to the necessary curriculum requirements and informatics competencies needed in nursing education as well as the evolution of the TIGER Initiative and its global impact on nursing education. Building on the current changes in the healthcare delivery system, Drs. Eun-Shim Nahm, Mary Etta Mills, and Marisa L. Wilson in Chapter 44, entitled Nursing Curriculum Reform and Healthcare Information Technology, describe the necessary informatics competencies needed by all nurses. They provide a historical overview of nursing informatics education and how trends in healthcare have influenced the development of initial competencies. They speak on the various initiatives, such as Quality and Safety Education for Nurses (QSEN) and the TIGER (Technology Informatics Guiding Educational Reform) and their impact on the development of recommended nursing informatics competencies by nursing organizations. Certification in the field of nursing informatics or health IT is also covered. The chapter ends with an examination of future trends and potential gaps in informatics competencies. Toria Shaw-Morawski and Dr. Joyce Sensmeier in Chapter 45, entitled The Evolution of the TIGER Initiative, describe a grassroots effort—TIGER (Technology Informatics Guiding Educational Reform)—that was initiated in 2004 as a result of the National Health Information Technology government agenda. The chapter highlights the evolution of this initiative and its impact both nationally in the United States and internationally. The impact includes the development of a virtual learning environment to learn about healthcare informatics as well as the development of TIGER competencies and accompanying knowledge resources. Drs. Patricia E. Allen, Khadija Bakrim, and Darlene Lacy explore the world of online learning in Chapter 46, entitled Initiation and Management of Accessible Effective Online Learning. They begin with the history of distance e­ ducation and end with the future trends in online education. There is an examination of current technologies being used to

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implement online learning. Throughout the chapter, they emphasize the necessary requirements to provide highquality, cost-effective, and learner-centered education. The chapter also describes the role of an online faculty and the preparation needed to not only teach but also develop course materials for online delivery. The topic of student support and communication is highlighted, as well as the legal, ethical, and copyright issues. The end of the chapter summarizes the effectiveness of online education, quality standards, and accreditation. Dr. Diane J. Skiba, Sarah Mattice, and Chanmi Lee in Chapter 47, entitled Social Media Tools in the Connected Age, explore the evolution of Connected Age and how social media is becoming integrated throughout education and the delivery of healthcare. A historical perspective begins the chapter as it describes the movement from computer access to the Internet to the Internet of Everything where mobile devices, apps, and wearable technologies are all interconnected. It is within the context of the connected care ecosystem; the authors describe the use of social media as one digital tool being used to transform healthcare delivery systems and consumer engagement. The chapter examines the benefits and challenges that are associated with the use of social media in healthcare. To complement online learning, Drs. LaVerne Manos and Nellie Modaress provide an excellent Chapter 48, entitled A Paradigm Shift in Simulation: Experiential Learning in Virtual Worlds and Future Use of Virtual Reality, Robotics, and Drones on the paradigm shift in simulations to the world of experiential learning in virtual worlds. The chapter starts with an overview of their use of Second Life as a simulated learning experience to teach nursing informatics concepts. The authors emphasize the pedagogy of teaching in a virtual world and provide numerous examples of their use in graduate education for healthcare informatics, PhD nursing, nurse anesthesia, and dietetics and nutrition programs. The chapter ends with a glimpse into future technologies that are being used in education, such as virtual and augmented reality as well as the use of robotics and drones.

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44 Nursing Curriculum Reform and Healthcare Information Technology Eun-Shim Nahm / Mary Etta Mills / Marisa L. Wilson

• OBJECTIVES 1. Describe the background of and need for curriculum change in nursing informatics education in the 21st century. 2. Discuss prior academic and other professional organizational efforts to transform nursing education with an emphasis on healthcare information technology. 3. List information technology competencies required by nurses with different levels of education. 4. Identify current national trends in nursing education associated with informatics. 5. Explain content and process of the American Nurse Credentialing Center (ANCC) Nursing Informatics Certification examination and other informatics certifications. 6. Identify current informatics trends and gaps in current informatics education.

• KEY WORDS American Association of Colleges of Nursing (AACN) essentials for nursing Curriculum in nursing education Electronic health records (EHR) Healthcare information technology (HEALTH IT) Informatics competency Knowledge, skills, and attitudes (KSA) Nursing informatics (NI) Patient safety Quality and Safety Education for Nurses (QSEN) Technology Informatics Guiding Educational Reform (TIGER) Nursing informatics education focuses on the use of health information technologies (IT) and data to promote the health of individuals and populations. To provide efficient, effective, and safe care in this rapidly changing and technology-laden healthcare environment, nurses must be prepared to optimally use the technologies in

their practice and either participate in or lead groups that develop, implement, maintain, evaluate, and optimize technologies. It is vital to understand that health IT encompasses a wide variety of technologies including electronic health records (EHR), personal health records, mobile applications, devices, communication 709

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710    P art 8 • E ducational A pplications technologies, and telehealth/telenursing. To prepare clinicians who can effectively use the technologies and data, educational programs constantly update informatics content to support current nursing and clinical practice at all levels of nursing education.

CURRENT CHANGES IN HEALTHCARE AND TRENDS IN HEALTH INFORMATICS In the past few years, health IT has revolutionized various aspects of healthcare delivery, including the way health data is generated, the use of that data to drive care, and patients’ use of their health data for self-care. Currently, healthcare providers gather a significant amount of digitally stored data that is used to create information about their patients even before they visit their providers. Patients receive previsit assessment forms through their patient portals prior to outpatient visits. When patients go to a clinic or hospital, they are admitted to a registration system and complete check in assessments even before they see their providers. After that, the amount of personal data within the electronic care system incrementally increases as patients receive care. Eventually, the patient’s health data collected by a provider at the point of care is then stored within the healthcare system. Many data are forwarded to finance systems, and sent to necessary insurance companies and other regulatory organizations. Currently, some selected data are exchanged with appropriate public health agencies and other healthcare providers through health information exchange services to deliver better care. Another notable change is the increasing use of various health IT devices. With the advancement of the Internet of Things (IOT) era, various electronics, software, and sensors are embedded into networks, which enable these devices to collect and exchange data (Zanellam, Bui, Castellani, Vangelista, & Zorzi, 2014). With these changes, health information exchange and interoperability have become national priorities (Bloomfield, PoloWood, Mandel, & Mandl, 2017; Massoudi, Marcial, Tant, Adler-Milstein, & West, 2016). While more patient data are gathered online and health data is being exchanged, it is becoming increasingly difficult to maintain the safety and security of health data. The HIPAA Omnibus Rule was introduced in 2013 to protect patient privacy and safeguard patients’ health information in an ever-expanding digital age (U.S. Department of Health and Human Services, 2013). The 2013 HIPAA rules are then revisited and strengthened in the 2019 DHHS proposed new rules which promote immediate access to health information

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for patients and providers within a safe and secure system that is not subjected to information blocking, all of which sets up unsafe care environments for patients and populations (Department of Health and Human Services, 2019). At the systems’ level, healthcare providers must ensure the accuracy and completeness of data, as well as appropriate interoperability between systems, while safeguarding patients’ data. Implementation and maintenance of health IT are complex and dynamic processes, and an increasing number of health IT experts and clinicians are becoming involved. An enormous challenge for healthcare organizations and educational institutions is the preparation of competent healthcare informaticians and clinicians competent in the use of health information technologies.

HISTORICAL OVERVIEW OF NURSING INFORMATICS EDUCATION Introduction In the past three decades, there have been a few major efforts to transform healthcare. The seminal healthcare report To err is human, published by the Institute of Medicine (IOM) in 2000, revealed a serious healthcare problem related to medical errors in the United States and emphasized the importance of health information technology (IT) to prevent medical errors and improve healthcare quality (Institute of Medicine, 2001; Newhouse, Dearholt, Poe, Pugh, & White, 2007). Since then, the use of health IT and electronic healthcare records (EHR) has become a national priority. Title XIII of the ARRA Health Information Technology for Economic and Clinical Health Act (HITECH) accelerated the spread of EHRs through incentive payments by Medicare and Medicaid to clinicians and hospitals when providers used EHRs privately and securely to achieve specified improvements in care delivery (Blumenthal, 2009; Blumenthal & Tavenner, 2010). The main goals of the original “Meaningful Use” (MU) regulations were to : (1) improve quality, safety, efficiency, and reduce health disparities; (2) engage patients and families; (3) improve care coordination, and population and public health; and (4) maintain privacy and security of patient health information. Since then, the name of “MU” evolved to “EHR Incentive Payment Program,” and then to “Promoting Interoperability (PI) Program,” in 2018 (Centers for Medicare & Medicaid Services, 2019). In particular, the recent change, the PI program, reflects current changes in the healthcare model. In 2016, the MU requirements were folded into the Medicare Access and CHIP Reauthorization Act of 2015 (MACRA). MACRA supports the movement from the fee-for-service

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Chapter 44 • Nursing Curriculum Reform and Healthcare Information Technology 

reimbursement model to value-based care by incentivizing quality, clinical practice improvement activities, and cost containment (Office of the National Coordinator for Health Information Technology [ONC], 2019). To achieve the successful adoption of health IT in the dynamically changing healthcare landscape, it is critical to ensure clinician competency to use healthcare IT. Nurses’ competency in using health IT and the data is particularly important because they are the largest group of direct healthcare providers in the United States, accounting for 19% of all healthcare workers in 2017 (approximately 2.9 million employed) (U.S. Bureau of Labor Statistics, 2018). Recognizing the importance of informatics and nurses’ competency in the use of health IT and informatics processes, the Essentials for Baccalaureate, Masters and Doctor of Nursing Practice Education developed by the American Association of Colleges of Nursing (AACN) have included mandates for competency in Informatics and Healthcare Technology for over a decade (American Association of Colleges of Nursing [AACN], 2019). Moreover, the Quality and Safety Education for Nurses (QSEN) project has also described knowledge, skills, and attitudes related to informatics for all nurses (QSEN, 2019a). These and other professional organizations drive the content of nursing curricula and graduate outcome expectations. In addition, nursing as a healthcare discipline has been at the forefront of educating healthcare professionals who are specialized in healthcare IT and informatics theory. For instance, nursing informatics (NI) was created as an area of graduate specialization at the University of Maryland School of Nursing (UMSON) in 1988, and NI was officially recognized as a specialty practice area by the American Nurses Association (ANA) in 1992 (Gassert, 2000). Since then, informatics has become a core course in many programs, and many nursing schools offered graduate degree programs focusing on nursing and healthcare informatics. Despite the promotion of informatics and health IT competency, many nursing schools struggle with the inclusion of informatics and health IT content in their programs of study at all levels since many faculty members are unfamiliar with informatics content. This significant gap has been recognized by several organizations who are working to fill the gap in educator knowledge as it relates to informatics and heath IT use in care. The Nursing Knowledge Big Data Science Initiative’s Education Workgroup is tackling this problem by developing resources and tools for educators (University of Minnesota School of Nursing, 2019). In addition, the Technology Informatics Guiding Education Reform (TIGER) project, under the Health Information and Management Systems Society (HIMSS), is also aiming to address the faculty competency

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gap through the collaborative efforts of the Scholars Workgroup (HIMSS, 2019a). TIGER is described in more detail later in the chapter. These collaborative and open efforts should provide solid foundations for faculty who support the competency development of nurses. The advancement of available information communication technologies and simulation environments has also changed nursing education drastically. Many nursing schools have high-fidelity simulation labs allowing students more opportunities to learn about critical components of practical cases from school. Stakeholders expect nursing students to be competent in using health IT when they arrive in practice settings. Nursing as a profession has recognized the major reform of nursing education, and significant efforts have been made in many areas of the nursing domain, including revision of essentials for all levels of nursing education (American Association of Colleges of Nursing, 2008, 2011, 2014). Since then, the healthcare environment has changed drastically, and the AACN is in the process of further revising the essentials (American Association of Colleges of Nursing, 2019b).

Efforts in Nursing Informatics Curriculum Revisions An increased awareness of patient safety and the increasing use of health IT in healthcare called for changes in the nursing curriculum. The IOM report Health professions education: A bridge to quality (Institute of Medicine, 2003) is a result of a 2002 summit followed by the IOM’s report, Crossing the quality chasm (Institute of Medicine, 2001). This interdisciplinary summit was held to discuss reforming education for health professionals to enhance quality and patient safety (Institute of Medicine Committee on Health Education Profession Summit, 2002). The report proposed five core competencies for healthcare professionals; one of these core competencies is the use of informatics (Institute of Medicine, 2001; Institute of Medicine Committee on Health Education Profession Summit, 2002). Since then, many efforts have been made by nursing professional organizations and the AACN to revise the nursing curriculum to be aligned with the IOM competencies. Quality and Safety Education for Nurses (QSEN). The overarching goal of the three phases of the QSEN project, which was supported by the Robert Wood Johnson Foundation (RWJF), is to address the competencies necessary to continuously improve the quality and safety of healthcare systems in which nurses work (Cronenwett et al., 2007; QSEN, 2019a, 2019b). Phase I of the project

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712    P art 8 • E ducational A pplications   TABLE 44.1    QSEN Competencies Patient-centered care

Recognize the patient or designee as the source of control and full partner in providing compassionate and coordinated care based on respect for a patient’s preferences, values, and needs.

Teamwork and collaboration

Definition: Function effectively within nursing and interprofessional teams, fostering open communication, mutual respect, and shared decision-making to achieve quality patient care.

Evidence-based practice (EBP)

Integrate best current evidence with clinical expertise and patient/family preferences and values for the delivery of optimal healthcare.

Quality improvement (QI)

Definition: Use data to monitor the outcomes of care processes and use improvement methods to design and test changes to continuously improve the quality and safety of healthcare systems.

Safety

Minimize the risk of harm to patients and providers through both system effectiveness and individual performance.

Informatics

Use information and technology to communicate, manage knowledge, mitigate error, and support decision-making.

identified six competencies that needed to be developed during pre-licensure nursing education (Table 44.1). The group also proposed clarified competencies in the areas of knowledge, skills, and attitudes (KSAs). Phase II work of QSEN was focused on competencies for graduate and advanced practice nurses (APNs). The QSEN faculty members collaborated with APNs who practiced in direct patient care and worked on the development of standards of practice, accreditation of educational programs, and certification (Cronenwett, Sherwood, Pohl, et al., 2009). The workgroups that participated in Phase II generated KSAs for graduate-level education. In Phase III, the AACN worked on developing the capacity of faculty engaged in pre-licensure nursing education to mentor their colleges’ faculty integration of the evidencebased content on the six QSEN competencies (QSEN, 2012). Phase IV supports the Institute of Medicine’s recommendation of increasing the number of nurses with an advanced degree. These efforts are being led by the Tri-Council for Nursing, consisting of the American Association of Colleges of Nursing, National League for Nursing, the American Nurses Association, and the American Organization of Nurse Executives (AONE). The IOM/QSEN competencies and the pre-licensure KSAs are embedded in the AACN Essentials for nursing education (Cronenwett, Sherwood, & Gelmon, 2009; Cronenwett et al., 2009; Dycus & McKeon, 2009). The American Association of Colleges of Nursing Essentials for Nursing.   In response to the calls to transform healthcare delivery and to better prepare today’s nurses for

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professional practice, the AACN convened a task force on essential patient safety competencies in 2006 (American Association of Colleges of Nursing, 2006). The taskforce recommended specific competencies that should be achieved by professional nurses to ensure high-quality and safe patient care. Those competencies were identified under the following areas: (1) critical thinking; (2) healthcare systems and policy; (3) communication; (4) Illness and Disease Management; (5) ethics; and (6) information and healthcare technologies. Since then, the AACN revised the Essentials of Baccalaureate Education for Professional Nursing Practice in 2008 (American Association of Colleges of Nursing, 2008). In regards to the essentials for graduate programs, the AACN made a decision to migrate advanced practice nursing programs (APN programs) from the master’s level to the doctorate level (doctor of nursing practice [DNP] program) by the year 2015 (American Association of Colleges of Nursing, 2014). Currently, most master’s programs that prepare advanced practice registered nurses (APRNs) have transitioned to DNP programs. The Essentials of Doctoral Education for Advanced Nursing Practice were developed in 2006 (American Association of Colleges of Nursing, 2006), and the informatics competency is one of the essentials for this education program. This has a major impact on education at the graduate level. Some non-APRN master’s specialty programs (e.g., informatics, healthcare leadership and administration, and community-health nursing) still maintain master’s programs. The essentials for master’s education were revised in 2011 and a new revision

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Chapter 44 • Nursing Curriculum Reform and Healthcare Information Technology 

process initiated in 2019 to establish educational benchmarks to advance nursing practice across the healthcare system and continuum of care (American Association of Colleges of Nursing [AACN], 2019b). Among various changes regarding essentials in nursing education, major emphasis had been on patient safety and healthcare IT. This chapter focuses on nursing curriculum from the context of health IT, and Table 44.2 describes AACN essentials in the area of information management and technology. HIMSS Technology Informatics Guiding Educational Reform (TIGER) Initiatives.  The recent Technology Informatics Guiding Educational Reform (TIGER) Initiatives epitomize nurses’ efforts to translate highlevel initiatives on nursing education reform to a practice level (TIGER, 2014). TIGER is an education reform initiative that strives to foster interprofessional community

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development and global workforce development by maximizing the integration of technology and informatics into seamless practice, education, and research resource development.(Healthcare Information and Management Systems Society (HIMSS), 2019c). In Phase I of the TIGER summit, stakeholders from various fields, including nursing practice, education, vendors, and government agencies, developed a 10-year vision and 3-year action plan for transforming nursing practice and education (HIMSS, 2019c). In Phase II, TIGER formalized cross-organizational activities/action steps into nine collaborative TIGER teams (TIGER, 2009) addressing specific topic areas. In 2014, TIGER transitioned to HIMSS and is now under professional development with an interprofessional, interdisciplinary, and international focus. Some of the important activities include offering learning opportunities and support for clinicians, educators, and informaticians through: (1) the Virtual Learning Environment Center (VLE);

  TABLE 44.2    Information Management and Technology-related Essentials for Nursing (Continued) Baccalaureate Education Retrieved from https:// www.aacnnursing. org/Portals/42/ Publications/ BaccEssentials08.pdf (American Association of Colleges of Nursing, 2008)

Essential IV: Information Management and Application of Patient Care Technology: - Knowledge and skills in information management and patient care technology are critical in the delivery of quality patient care.

The baccalaureate program prepares the graduate to:   1. Demonstrate skills in using patient care technologies, information systems, and communication devices that support safe nursing practice.   2. Use telecommunication technologies to assist in effective communication in a variety of healthcare settings.   3. Apply safeguards and decision-making support tools embedded in patient care technologies and information systems to support a safe practice environment for both patients and healthcare workers.   4. Understand the use of CIS systems to document interventions related to achieving nurse-sensitive outcomes.   5. Use standardized terminology in a care environment that reflects nursing’s unique contribution to patient outcomes.   6. Evaluate data from all relevant sources, including technology, to inform the delivery of care.   7. Recognize the role of information technology in improving patient care outcomes and creating a safe care environment.   8. Uphold ethical standards related to data security, regulatory requirements, confidentiality, and clients’ right to privacy.   9. Apply patient care technologies as appropriate to address the needs of a diverse patient population. 10. Advocate for the use of new patient care technologies for safe, quality care. 11. Recognize that redesign of workflow and care processes should precede implementation of care technology to facilitate nursing practice. 12. Participate in the evaluation of information systems in practice settings through policy and procedure development. (Continued )

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  TABLE 44.2    Information Management and Technology-related Essentials for Nursing (Continued) Master’s Education Retrieved from https:// www.aacnnursing. org/Portals/42/ Publications/ MastersEssentials11. pdf (American Association of Colleges of Nursing, 2011)

Essential V: Informatics and Healthcare Technologies

The master’s degree program prepares the graduate to: 1. Analyze current and emerging technologies to support safe practice environments, and to optimize patient safety, costeffectiveness, and health outcomes. 2. Evaluate outcome data using current communication technologies, information systems, and statistical principles to develop strategies to reduce risks and improve health outcomes. 3. Promote policies that incorporate ethical principles and standards for the use of health and information technologies. 4. Provide oversight and guidance in the integration of technologies to document patient care and improve patient outcomes. 5. Use information and communication technologies, resources, and principles of learning to teach patients and others. 6. Use current and emerging technologies in the care environment to support lifelong learning for self and others.

Doctoral Education for Advanced Nursing Practice Retrieved from https:// www.aacnnursing. org/Portals/42/ Publications/ DNPEssentials.pdf (American Association of Colleges of Nursing, 2006)

IV: Information Systems/ Technology and Patient Care Technology for the Improvement and Transformation of Healthcare

The DNP program prepares the graduate to: 1. Design, select, use, and evaluate programs that evaluate and monitor outcomes of care, care systems, and quality improvement including consumer use of healthcare information systems. 2. Analyze and communicate critical elements necessary to the selection, use, and evaluation of healthcare information systems and patient care technology. 3. Demonstrate the conceptual ability and technical skills to develop and execute an evaluation plan involving data extraction from practice information systems and databases. 4. Provide leadership in the evaluation and resolution of ethical and legal issues within healthcare systems relating to the use of information, information technology, communication networks, and patient care technology. 5. Evaluate consumer health information sources for accuracy, timeliness, and appropriateness.

(2) the Health Information Technology Competencies tool (HITComp.org); and (3) the Informatics Education Resource Navigator (IERN). The VLE, HITComp, and the IREN are interactive Web-based learning environments where the learners can develop knowledge and skills in the area of health information technology.

INFORMATICS COMPETENCIES FOR PRACTICING CLINICIANS The IOM, AACN, QSEN, and TIGER addressed essential competencies that need to be addressed in educational programs. A great deal of effort also has been made in

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developing more executable competency lists that can be used in practice settings.

American Nurses Association Nursing informatics: The scope and standard of practice addressed an NI-specific domain. Nursing informatics is an essential component for any nurse. The second edition of the NI Scope and Standards matrix, containing a new set of recommended competencies, was published in 2015 (American Nurses Association, 2015). This comprehensive edition outlines the nursing informatics competency levels expected of informatics nurses and informatics nurse specialists (master’s prepared

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Chapter 44 • Nursing Curriculum Reform and Healthcare Information Technology 

INS). It also details the nursing informatics competencies needed by any clinical nurse, spanning all nursing careers and roles. The scope of practice addresses the context of the who, what, where, when, why, and how of NI practice. That detailed scope of practice covers the following topics:

• • • • • •

Metastructures, concepts, and tools of nursing informatics Functional areas of nursing informatics Preparation for nursing informatics specialty practice Ethics in nursing informatics The future of nursing informatics Trends in regulatory changes and quality standards

The 16 nursing informatics standards, which provide a framework for evaluating practice outcomes and goals, are accompanied by a set of specific competencies for each standard. With the recent changes, the ANA recently solicited a working group to revise the scope and standards to reflect the recent advancement of technology, changing healthcare landscape, and the requirements of policy and regulation.

TIGER Informatics Competencies Collaborative Recommendations In the second phase of the TIGER initiative, the TIGER Informatics Competency Collaborative (TICC) recommends specific informatics competencies for all practicing nurses and graduating nursing students (TIGER, 2014). The TIGER NI competencies model consists of the following three areas: (1) basic computer competencies; (2) information literacy; and (3) information management. For the basic computer competencies, the TICC adopted the European Computer Driving License (ECDL) competencies and made its recommendations. The European Computer Driving License (ECDL)/International Computer Driving License (ICDL) is an internationally recognized information and communication technology and digital literacy certification (European Computer Driving License [ECDL] Foundation, 2019). The specific recommendations made by the TICC were based on the old ECDL/ICDL Syllabus 5.0. As a further refinement of the TICC, the TIGER International Committee has conducted further far-reaching work to synthesize informatics competencies that have been produced for interprofessional

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and international consumption. In 2015, the TIGER International Task Force began comprehensive activities to compile all recommended international informatics competencies. These efforts resulted in a set of recommendations that fall under ten core competency areas within five role types, including Clinical Nursing (Direct Patient Care), Quality Management, Coordination of Interprofessional Care, Nursing Management, IT Management in Nursing (Hübner et al., 2018).

Nursing Informatics as a Specialty Program at the Graduate Level The ANA defines Nursing informatics (NI) as: “a specialty that integrates nursing science with multiple information and analytical sciences to identify, define, manage, and communicate data, information, knowledge, and wisdom in nursing practice. NI supports nurses, consumers, patients, the interprofessional healthcare team, and other stakeholders in their decision-making in all roles and settings to achieve desired outcomes. This support is accomplished through the use of information structures, information processes, and information technology” (American Nurses Association, 2015). The NI Scope and Standards of Practice clearly differentiate between informatics nurses (INs) and informatics nurse specialists (INSs). The INSs are those formally prepared at the graduate level in informatics and who also are certified, while INs are generalists who have gained on-the-job training in the field but do not have educational preparation at the graduate level in an informaticsrelated area. With the national emphasis on HIT education, various types of informatics-related educational programs have become available at the graduate level, such as nursing informatics, healthcare informatics, biomedical informatics, etc. Most informatics educational programs are moving toward online programs and/or hybrid (mainly online with some face-to-face classes) programs. The curriculum and credits vary a great deal depending on the program. The nursing informatics field also has unique characteristics. For instance, the roles the INSs assume vary. In 2017, as part of the Nursing Informatics Workforce survey (N = 1,2) conducted by the Healthcare Information and Management Systems Society (HIMSS), participants were asked to indicate the title of the position they assumed (HIMSS, 2017). Findings showed a wide range of positions, including nursing informatics specialist (20%), director of clinical informatics (7%), clinical analyst (5%), consultant (4%), educator/instructor (3%), and clinical application specialist (2%). The areas of practice also varied, including

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716    P art 8 • E ducational A pplications hospital, health system, academic, vendor, government, ambulatory, and other settings. More than half (57%) had a postgraduate degree in one of a variety of fields, and the majority were highly satisfied with their job and career in clinical informatics. In addition to the findings from the HIMSS 2017 survey, recent scientific discoveries in biomedical informatics and genomics, as well as rapid growth in big data and mHealth/eHealth research, have made a significant impact on healthcare informatics and the roles of nursing informatics specialists. Considering these varying roles and areas of practice, it is logical that each program may have a different emphasis or strength. Assurance of quality standards of each program, however, is particularly concerning, considering that there is no regulatory body or specialty organization responsible for setting standards for educational programs in nursing informatics.

Certification in Nursing Informatics and Related HIT Currently, the American Nurses Credentialing Center (ANCC), an accredited agency, offers the generalist nursing informatics certification (RN-BC) (American Nurses Credentialing Center, 2019). The minimum academic degree required to take the examination for this certification is a bachelor’s or higher degree in nursing or a bachelor’s degree in a relevant field. The test content outline for the nursing informatics certification examination can be found on ANCC’s Web site (https://www.nursingworld. org/our-certifications/informatics-nurse/; American Nurses Credentialing Center, 2014). The main content as of October 2019 includes: 1. Foundations of practice (77 items, 51%)

2. System design life cycle (SDLC) (42 items, 28%) 3. Data management and healthcare technology (31 items, 21%)

Nursing informaticians’ primary responsibilities vary a great deal depending on their job and work environments, and each job or setting may require a different certificate, such as project manager or information administrator. In the 2017 HIMSS Nursing Informatics Workforce Survey, 47% of respondents had some type of certificate, and 27% held a certificate in nursing informatics offered through the ANCC (HIMSS, 2011, 2017). Other certificates include a Project Management Certificate and the Certified Professional in Healthcare Information and Management Systems (CPHIMS) certificate. The American Medical Informatics Association (AMIA) is also in the process

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of developing an advanced health informatics certification (AHIC) for nonphysician informaticians. The AMIA AHIC will be similar to that developed by AMIA for physicians holding clinical informatics subspecialty training that is currently active. Eligible professionals for AHIC will hold graduate-level degrees from an accredited institution reviewed by the Commission on Accreditation for Health Informatics and Information Management (CAHIIM) (Gadd, Williamson, Steen, & Fridsma, 2016). Table 44.3 summarizes information for selected certifications relevant to nursing informatics.

Informatics Competencies for Faculty Members In the past few years, there have been significant changes in nursing education (American Association of Colleges of Nursing, 2019a, 2019b; American Nurses Association, 2015). Informatics competency has been addressed as a vital component in those changes. In addition, the current emphasis on meaningful use of EHRs to achieve the Triple Aim of healthcare demands that nurses be competent in managing health data and information (Institute for Healthcare Improvement, 2019). These changes also require that nursing faculty members be competent in healthcare informatics. In addition, with exponential growth in information communication technologies, more instruction is being delivered using an online format or with the aid of instructional technologies. The current popularity of videos and social networking programs has added additional complexity to the current online learning environment about engaging students in learning. Simulation-Based Learning.  Use of high-fidelity simulation has become the gold standard in current nursing education (Boling & Hardin-Pierce, 2016; Cant & Cooper, 2017; Keating & DeBoor, 2018). The purpose of simulation in clinical settings is to replicate the important aspects of a clinical situation where students or clinicians can work to gain knowledge and experience. Most nursing schools have multiple high-tech simulation labs including highfidelity simulators. Those labs provide students with various simulated clinical settings. For instance, the University of Maryland School of Nursing has an entire simulated hospital in its school consisting of 20 labs including an operating suite, a community/home healthcare lab, and a diagnostic laboratory, as well as a SIMMan® family and a robotic baby. To augment the simulation environment, many nursing schools use an academic version of an EHR system. Implementing an EHR in simulation labs allows students to have an opportunity to develop competencies in using health IT before they go into the clinical setting.

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Chapter 44 • Nursing Curriculum Reform and Healthcare Information Technology 

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  TABLE 44.3    Selected Certifications Relevant to Nursing Informatics Certifications Informatics Nursing certificate

Organization ANCC

Qualification Hold a bachelor’s or higher degree in nursing or a bachelor’s degree in a relevant field.

Requirements 1. Hold a current, active RN license within a state or territory of the United States or hold the professional, legally recognized equivalent in another country. There may be additional requirements for candidates outside the United States. 2.  Have practiced the equivalent of 2 years full-time as a registered nurse. 3.  Have completed 30 hours of continuing education in informatics nursing within the last 3 years. 4.  Meet one of the following practice hour requirements: Have practiced a minimum of 2000 hours in informatics nursing within the last 3 years; or Have practiced a minimum of 1000 hours in informatics nursing in the last 3 years and completed a minimum of 12 semester hours of academic credit in informatics courses that are part of a graduate-level informatics nursing program; or Have completed a graduate program in informatics nursing containing a minimum of 200 hours of faculty-supervised practicum in informatics nursing.

URL https://www.nursingworld.org/ our-certifications/ informatics-nurse/

Baccalaureate degree

• 5 years of associated information and

Graduate degree

• 

https://www.himss. org/health-itcertification/ eligibility https://www.himss. org/health-itcertification/ cphims

•  • 

• 

Certified Professional in Health Information & Management Systems (CPHIMS)

Registered Health Information Administrator (RHIA®)

HIMSS

AHIMA

Baccalaureate degree

management systems experience, 3 of those years in healthcare. 3 years of associated information and management systems experience, two (2) of those years in healthcare.

• S uccessfully complete the aca-

demic requirements, at the baccalaureate level, of an HIM program accredited by the Commission on Accreditation for Health Informatics and Information Management Education (CAHIIM); or Successfully complete the academic requirements, at the master’s level, of an HIM program accredited by the CAHIIM; or

http://www.himss. org/health-it-certi fication?navItemN umber=17564 http://www.ahima. org/certification/ RHIA

• 

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  TABLE 44.3    Selected Certifications Relevant to Nursing Informatics (Continued) Certifications

Organization

Qualification

Requirements

URL

• 

Successfully complete the academic requirements of an HIM Certificate of the Degree (PostBaccalaureate) program approved by the CAHIIM; or Graduate from an HIM program approved by a foreign association with which AHIMA has a reciprocity agreement; or An RHIT who meets the Proviso conditions approved by the 2017 Commission on Certification for Health Informatics and Information Management (CCHIIM). 1. At least five years/60 months of unique nonoverlapping project management experience 2. 7500 hours leading and directing projects 3. 35 hours of project management education Or 1. At least three years/36 months of unique nonoverlapping professional project management experience 2. 4500 hours leading and directing projects 3. 35 hours of project management education General requirements: 1. Medical License—An unrestricted and currently valid license(s) to practice medicine in a State, the D.C., a Territory, Commonwealth, or possession of the United States or in a Province of Canada is required. 2. Medical Degree—Graduation from a medical school in the United States which at the time of the applicant’s graduation was accredited by the Liaison Committee on Medical Education, a school of osteopathic medicine approved by the American Osteopathic Association, an accredited medical school in Canada, or from a medical school located outside the United States and Canada that is deemed satisfactory to the Board is required. 3. ABMS Member Board Certification— Primary board certification is a core requirement. Some ABMS boards accept subspecialty certification as meeting the primary certification requirements. In those cases, the individual would be listed as certified in the primary specialty. The status of primary certification is determined by each individual board.

•  • 

Project Management Certifications

PMI

A secondary degree (high school diploma, associate’s degree, or the global equivalent) Or A 4-year degree (bachelor’s degree or the global equivalent)

American Board of Preventive Medicine

MD

Project Management Professional (specific example)

Physician board— Clinical Informatics (as a comparison to nursing discipline)

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https://www.pmi. org/-/media/ pmi/documents/ public/pdf/ certifications/ project-management-professional-handbook. pdf?la=en

https://www. theabpm.org/ become-certified/ subspecialties/ clinical-informatics/

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In addition, most EHRs have decision support systems that could significantly augment students’ learning (Chung & Cho, 2017; Herbert & Connors, 2016). When schools implement these academic EHR systems, they must have a thorough plan and a multitude of resources. For instance, the school must have network infrastructures that can support the program and resource personnel who can support faculty members (Herbert & Connors, 2016). In addition, the school will require a designated project manager who is familiar with system deployment. There will be a great deal of work in developing use cases and building tables in the system, which also requires the clinical faculty members’ participation. It will be necessary to educate faculty members about the system since they must be competent to teach classes using the EHR. Policies and procedures for using the system within the lab must be developed and clearly communicated to the students before the system is deployed. The project manager must also consider various human factors and ergonomic issues, as well as system characteristics (Nielsen, 1993). Interprofessional Collaboration. Interprofessional collaboration (IPC) is vital to achieving efficient quality outcomes for patients (Asmirajanti, Syuhaimie Hamid, & Hariyati, 2018). Effective communication, clearly defined roles, and a culture of teamwork are fundamental tenets of well-functioning interprofessional teams. Nursing has been a leading force in ICP collaboration, and ICP has been an important component in nursing education and practice (Gormley et al., 2019; Labrague, McEnroe-Petitte, Fronda, & Obeidat, 2018). Various health information communication tools and EHRs facilitate and accelerate IPC, which ultimately improves patient outcomes. Thus, it is vital that nursing education provide students the opportunity to develop competency in those tools. In addition, informatics nurses play major roles while healthcare providers implement such programs, and must be prepared to lead those projects. Upon the completion of system deployment, they also need to maintain and optimize those systems in collaboration with various healthcare professionals. Learning about interdisciplinary collaboration is critical in nursing education and is becoming more important as technology becomes more advanced and as healthcare becomes more complex. Informatics Competencies for Faculty Members.   Innovative technologies in teaching and learning can produce optimal outcomes only when the instructors are competent in using those technologies. Previously we discussed the essential educational components needed to ensure

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Chapter 44 • Nursing Curriculum Reform and Healthcare Information Technology 

nursing students’ and practicing nurses’ competencies in using healthcare information technologies and managing information. Current students who grow up with technologies often outpace their faculty members in using technologies (Foster & Sethares, 2017). When faculty members need to teach online classes, they have to not only learn about using technology but also reorient themselves to a completely new way of teaching the content. For instance, the way that online students respond may be different from the students who take face-to-face classes. Faculty members must be properly supported to fully adopt the newest technologies. Some continuing education modalities for faculty members include halfday workshops, short refresher courses before the beginning of each semester, or online self-learning modules. If the school offers many online classes, a sufficient number of instructional design specialists should be a part of the staff. The need to address faculty competencies in instructional design and online learning, as well as institutional information technology support, became glaringly obvious during the 2019 coronavirus pandemic. Unanticipated and immediate requirements to convert from face-to-face instruction to online learning left many instructors with the dilemma of working from home to adapting courses to a new medium without the necessary preparation or technology support to offer high-quality instruction. This experience has served to emphasize the importance of ongoing faculty development in the application of technology to support student learning even if the availability of other modalities of teaching are available or considered preferable.

CURRENT TRENDS IN HEALTH INFORMATION TECHNOLOGIES, GAPS IN NURSING INFORMATICS Healthcare informatics is a dynamic field with rapid advancement, which requires frequent updates in the educational content and competency requirements. It is vital that nursing faculty members who teach this content stay up-to-date. Currently, most hospitals are in maintenance and optimization phases for EHRs. They are constantly implementing new technologies and trying to meet healthcare regulations requiring data from systems. There are some notable trends in current health IT, including more active use of mobile devices, eHealth and mHealth technologies, patient portals, setting up big data and data analytics (predictive modeling) structures within organizations, and protecting health data from cybersecurity

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720    P art 8 • E ducational A pplications issues. In addition, new healthcare models, such as patientcentered medical homes, population health, and precision medicine, add to the complexity of health IT. It is unclear how many nursing schools have embraced this important health IT content and incorporated it in their education. As healthcare informatics is an essential component of nursing education, it is vital that nursing education institutions have faculty members who have expertise in this field. With the revised essentials for the baccalaureate and the DNP curriculums, increasing numbers of informatics classes are being required as core courses in nursing programs. Migration of ANP programs to the DNP level has further accelerated the need for adding complexity to create pedagogical content that is suitable for DNP students. However, there is a significant shortage of faculty members who have an expertise in the dynamic healthcare informatics field and who can teach students (American Association of Colleges of Nursing, 2017). More doctorally prepared informatics faculty members with proper education/training are needed. One strategy that could alleviate this could be an education funding mechanism. Unfortunately, faculty education support grants usually focus on clinical areas and do not include healthcare informatics. This area needs reform.

CONCLUSION Information technology has revolutionized current healthcare. Consumers now can access enormous amounts of health information online even before they come to the hospital. Healthcare providers and students can access evidence-based health information right at the bedside. However, adoption of EHRs in healthcare has been slow, resulting in missed opportunities to provide safer and better quality care. Recently, the government has made significant efforts to implement EHRs nationwide to deliver safer care and improve the efficiency of healthcare delivery. These changes in healthcare delivery present multiple exciting opportunities for nursing education. In addition, with the advancement of information communication technology, face-to-face classes are being replaced by online classes, and high-tech and high-fidelity simulation-based nursing education has become a standard. New generations of nursing students are expected to be informatics competent. This chapter reviewed major HIT-related changes in our current healthcare system and efforts made by nursing organizations to reform the nursing curriculum. In the past decade, the nursing profession has made great advancements in transforming nursing practice, education, and research. Recent emphasis on

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interdisciplinary collaboration will further accelerate its progress.

Test Questions 1. Which is the main health care issue that initiated nursing education reform, which added informatics competency as an essential component to nursing education at all levels? A. Increasing complexity of health care

B. Increasing medical errors in health care C. Shortage in health care professionals

D. Initiation of Magnet Recognition from the American Nurses Credentialing Center (ANCC) 2. Please select one area that is not included in the Quality and Safety Education for Nurses (QSEN) competencies. A. Knowledge B. Skills

C. Communication D. Attitudes

3. Which is the nursing organization that defined the essentials for nursing education? A. American Nursing Association (ANA)

B. The American Association of Colleges of Nursing (AACN)

C. The American Nurses Credentialing Center (ANCC) D. The Institute of Medicine

4. Currently, informatics is included as an essential component for all nursing programs, except: A. Baccalaureate education programs B. Master’s education programs

C. Doctor in Nursing Practice education programs D. PhD education programs

5. HIMSS Technology Informatics Guiding Educational Reform (TIGER) competencies are designed for ———. A. BSN prepared nurses B. Informatics nurses

C. Informatics nurses with graduate level informatics degree D. Practicing nurses

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Chapter 44 • Nursing Curriculum Reform and Healthcare Information Technology 

6. Which is the nursing organization that defined Nursing Informatics as a specialty in nursing? A. American Nursing Association

B. The American Association of Colleges of Nursing (AACN)

C. The American Nurses Credentialing Center (ANCC) D. The Institute of Medicine

7. What is the difference between Informatics Nurses (INs) and Informatics Nurse Specialists (INSs)? A. There is no difference.

B. INSs are nursing informaticians who are certified on nursing informatics by the American Nurses Credentialing Center. C. INSs are formally prepared at the graduate level in informatics. D. INs has master’s degree in other health care fields and INSs has master’s degree in nursing informatics.

8. What is the minimum education requirement needed to take the Informatics Nursing certification examination offered by the American Nurses Credentialing Center (ANCC)? A. Associate degree in nursing

B. Baccalaureate education in nursing C. Master’s education in nursing

D. Doctor in Nursing Practice education 9. Informatics education will include information on ———. A. Health information technology, ethical use, information literacy B. Information literacy, statistics, assessment

C. Health information technology, ethical use, computer repair D. Communication, information literacy, computer repair 10. The ANCC Certification exam contains content on ———. A. Systems design life cycle B. Nursing assessment C. Budgeting specifics

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Test Answers 1. Answer: B

2. Answer: C 3. Answer: B

4. Answer: D 5. Answer: D 6. Answer: A 7. Answer: C 8. Answer: B

9. Answer: A 10. Answer: B

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722    P art 8 • E ducational A pplications American Nurses Credentialing Center (ANCC). (2014). Nurse credentialing: Informatics nursing. Retrieved from http://www.nursecredentialing.org/InformaticsNursing American Nurses Credentialing Center (ANCC). (2019). Informatics Nursing Certification (RN-BC). Retrieved from https://www.nursingworld.org/our-certifications/ informatics-nurse/ Asmirajanti, M., Syuhaimie Hamid, A. Y., & Hariyati, T. S. (2018). Clinical care pathway strengthens interprofessional collaboration and quality of health service: A literature review. Enfermeria Clinica, 28(Suppl 1), 240-244. doi: 10.1016/s1130-8621(18)30076-7 Bloomfield, R. A., Jr., Polo-Wood, F., Mandel, J. C., & Mandl, K. D. (2017). Opening the Duke electronic health record to apps: Implementing SMART on FHIR. International Journal of Medical Informatics, 99, 1–10. doi:10.1016/j. ijmedinf.2016.12.005 Blumenthal, D. (2009). Stimulating the Adoption of health information technology. The New England Journal of Medicine, 360, 1477–1479. doi:10.1056/NEJMp0901592 Blumenthal, D., & Tavenner, M. (2010). The “Meaningful Use” regulation for electronic health records. The New England Journal of Medicine, 363, 501–504. doi:10.1056/ NEJMp1006114 Boling, B., & Hardin-Pierce, M. (2016). The effect of highfidelity simulation on knowledge and confidence in critical care training: An integrative review. Nurse Education in Practice, 16(1), 287–293. doi:10.1016/j. nepr.2015.10.004 Cant, R. P., & Cooper, S. J. (2017). The value of simulationbased learning in pre-licensure nurse education: A stateof-the-art review and meta-analysis. Nurse Education in Practice, 27, 45–62. doi: 10.1016/j.nepr.2017.08.012 Centers for Medicare & Medicaid Services. (2019). Medicare and Medicaid Promoting Interoperability Program Basics. Retrieved from https://www.cms.gov/regulationsand-guidance/legislation/ehrincentiveprograms/basics. html. Accessed on April 24, 2019. Chung, J., & Cho, I. (2017). The need for academic electronic health record systems in nurse education. Nurse Education Today, 54, 83–88. doi: 10.1016/j. nedt.2017.04.018 Cronenwett, L., Sherwood, G., Barnsteiner, J., Disch, J., Johnson, J., Mitchell, P., ... Warren, J. (2007). Quality and safety education for nurses. Nursing Outlook, 55, 122– 131. doi:10.1016/j.outlook.2007.02.006 Cronenwett, L., Sherwood, G., & Gelmon, S. B. (2009). Improving quality and safety education: The QSEN learning collaborative. Nursing Outlook, 57, 304–312. doi:10.1016/j.outlook.2009.09.004 Cronenwett, L., Sherwood, G., Pohl, J., Barnsteiner, J., Moore, S., Sullivan, D. T., ... Warren, J. (2009). Quality and safety education for advanced nursing practice. Nursing Outlook, 57, 338–348. doi: 10.1016/j. outlook.2009.07.009 Dycus, P., & McKeon, L. (2009). Using QSEN to measure quality and safety knowledge, skills, and attitudes of

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experienced pediatric oncology nurses: An international study. Quality Management in Health Care, 18, 202–208. doi:10.1097/QMH.0b013e3181aea256 European Computer Driving License (ECDL) Foundation. (2019). European Computer Driving License (ECDL) programs. Retrieved from http://www.ecdl.org/about-ecdl Foster, M., & Sethares, K. (2017). Current strategies to implement informatics into the nursing curriculum: An integrative review. Online Journal of Nursing Informatics, 21(3). Retrieved from https://www.himss.org/library/ current-strategies-implement-informatics-nursing-curriculum-integrative-review Gadd, C. S., Williamson, J. J., Steen, E. B., & Fridsma, D. B. (2016). Creating advanced health informatics certification. Journal of the American Medical Informatics Association: JAMIA, 23, 848–850. doi: 10.1093/jamia/ ocw089 Gassert, C. (2000). Academic preparation in nursing informatics. In M. J. Ball, K. J. Hannah, S. K. Newbold, & J. V. Douglas (Eds.), Nursing informatics: Where caring and technology meet (pp. 15–32). New York, NY: Springer. Gormley, D. K., Costanzo, A. J., Goetz, J., Israel, J., HillClark, J., Pritchard, T., & Staubach, K. (2019). Impact of nurse-led interprofessional rounding on patient experience. The Nursing Clinics of North America, 54, 115–126. doi: 10.1016/j.cnur.2018.10.007 Healthcare Information and Management Systems Society (HIMSS). (2011). 2011 HIMSS Nursing Informatics Workforce survey. Retrieved from https://s3.amazonaws. com/rdcms-himss/files/production/public/HIMSSorg/ Content/files/2011HIMSSNursingInformaticsWorkforce Survey.pdf Healthcare Information and Management Systems Society (HIMSS). (2017). HIMSS 2017 Nursing Informatics Workforce survey. Retrieved from http://www.himss.org/ sites/himssorg/files/2017-nursing-informatics-workforce-full-report.pdf Healthcare Information and Management Systems Society (HIMSS). (2019a). TIGER scholars workgroup. Retrieved from https://www.himss.org/professionaldevelopment/ tiger-scholars-workgroup Healthcare Information and Management Systems Society (HIMSS). (2019b). TIGER virtual learning environment. Retrieved from https://www.himss. org/professional-development/tiger-initiative/ virtual-learning-environment Healthcare Information and Management Systems Society (HIMSS). (2019c). The TIGER initiative. Retrieved from https://www.himss.org/professionaldevelopment/ tiger-initiative Herbert, V. M., & Connors, H. (2016). Integrating an academic electronic health record: Challenges and success strategies. Computers, Informatics, Nursing: CIN, 34, 345–354. doi: 10.1097/cin.0000000000000264 Hübner, U., Shaw, T., Thye, J., Egbert, N., Marin, H. F., Chang, P., … Ball, M. J. (2018) Technology informatics guiding education reform: TIGER. Methods of

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Information in Medicine, 57(S 01), e30–e42. doi:10.3414/ ME17-01-0155 Institute for Healthcare Improvement (IHI). (2019). The IHI triple aim. Retrieved from http://www.ihi.org/engage/ initiatives/tripleaim/Pages/default.aspx Institute of Medicine (IOM). (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: The National Academy Press. Institute of Medicine (IOM). (2003). Health professions education: A bridge to quality. Washington, DC: The National Academy Press. Institute of Medicine (IOM). (2010). The future of nursing: Leading change, advancing health. Washington, DC: The National Academies Press. Institute of Medicine Committee on Health Education Profession Summit. (2002). Health professions education: A bridge to quality. Washington, DC: National Academy Press. Keating, S. B., & DeBoor, S. S. (Eds.). (2018). Curriculum development and evaluation in nursing education (4th ed.). New York, NY: Springer. Labrague, L. J., McEnroe-Petitte, D. M., Fronda, D. C., & Obeidat, A. A. (2018). Interprofessional simulation in undergraduate nursing program: An integrative review. Nurse Education Today, 67, 46–55. doi: 10.1016/j. nedt.2018.05.001 Massoudi, B. L., Marcial, L. H., Tant, E., Adler-Milstein, J., & West, S. L. (2016). Using health information exchanges to calculate clinical quality measures: A study of barriers and facilitators. Healthcare, 4, 104–108. doi: 10.1016/j. hjdsi.2016.04.003 Newhouse, R. P., Dearholt, S. L., Poe, S. S., Pugh, L. C., & White, K. M. (2007). Johns Hopkins nursing: Evidencebased practice model and guidelines. Indianapolis, IN: Sigma Theta Tau International. Nielsen, J. (1993). Usability engineering. San Francisco, CA: Morgan Kaufmann. Office of the National Coordinator for Health Information Technology (ONC). (2019). Meaningful Use and MACRA. Retrieved from https://www.healthit.gov/topic/ meaningful-use-and-macra/meaningful-use-and-macra

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Quality and Safety Education for Nurses (QSEN). (2019a). Quality and safety education for nurses. Retrieved from http://www.qsen.org/ Quality and Safety Education for Nurses (QSEN). (2019b). QSEN competencies. Retrieved from http://qsen.org/ competencies/pre-licensure-ksas/ Technology Informatics Guiding Education Reform (TIGER). (2009). Collaborating to integrate evidence and informatics into nursing practice and education: An executive summary. Retrieved from https://www.himss. org/collaborating-integrate-evidence-and-informaticsnursing-practice-and-education-executive-summary Technology Informatics Guiding Education Reform (TIGER). (2014). Informatics competencies for every practicing nurse: Recommendations from the TIGER collaborative. Retrieved from http://s3.amazonaws.com/ rdcms-himss/files/production/public/FileDownloads/ tiger-report-informatics-competencies.pdf University of Minnesota School of Nursing. (2019). 2018 Workgroups. Retrieved from https://www.nursing. umn.edu/centers/center-nursing-informatics/newsevents/2019-nursing-knowledge-big-data-science-conference/2018-workgroups U.S. Bureau of Labor Statistics. (2018). Employment projections: Labor force data. Retrieved from https://www.bls. gov/emp/data/labor-force.htm U.S. Department of Health and Human Services (HHS). (2013). Omnibus HIPAA rulemaking. Retrieved from https://www.hhs.gov/hipaa/for-professionals/privacy/ laws-regulations/combined-regulation-text/omnibushipaa-rulemaking/index.html U.S. Department of Health and Human Services (HHS). (2019). HHS proposes new rules to improve the interoperability of electronic health information. Retrieved from https://www.hhs.gov/about/news/2019/02/11/hhsproposes-new-rules-improve-interoperability-electronichealth-information.html Zanellam, A., Bui, N., Castellani, A., Vangelista, L., & Zorzi, M. (2014). Internet of things for smart cities. IEEE Internet Things Journal, 1(1), 22–32.

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45 The Evolution of the TIGER Initiative Toria Shaw Morawski / Joyce Sensmeier

• OBJECTIVES . Discuss the TIGER Initiative agenda. 1 2. Describe the evolution of the TIGER Initiative. 3. Describe examples of how the TIGER Initiative has impacted education reform, community development, and global workforce development.

• KEY WORDS Collaborative workgroups Decade of healthcare technology eHealth Global workforce development Grassroots effort Health informatics competencies HIMSS TIGER Initiative Interdisciplinary Interprofessional Invitational summit Technology Informatics Guiding Education Reform The TIGER Initiative Foundation Virtual Learning Environment

INTRODUCTION This chapter describes a wonderful story of what can occur when individuals committed to a common cause come together and take action. The best part is that this story has no ending—the roots of the TIGER (Technology Informatics Guiding Education Reform) Initiative are already having a ripple effect across the healthcare industry and the work that is being accomplished through TIGER is now being cited, as it will continue to be in the future, as being a significant catalyst for change in addressing the call for healthcare transformation by the Institute of Medicine (IOM, 2001, 2003). These two landmark

reports addressed major changes needed for both practicing clinicians and educators. For practicing clinicians, the first report notes, “The use of tools to organize and deliver care has lagged far behind biomedical and clinical knowledge. Carefully designed, evidence-based care processes, supported by automated clinical information and decision support systems, offer the greatest promise of achieving the best outcomes from care for chronic conditions. Systems must facilitate the application of scientific knowledge to practice and provide clinicians with the tools and supports necessary to deliver evidence-based care consistently and safely” (IOM, 2001, p. 12). For educators, the second report called for new ways for health professions 725

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726    P art 8 • E ducational A pplications to be educated and it identified five core competencies for all healthcare professionals: provide patient-centered care, work in interdisciplinary teams, employ evidence-based practice, apply quality improvement, and utilize informatics (IOM, 2003, p. 3). Both of these reports, along with the federal efforts described below to address the need for widespread health information technology adoption, were the catalysts for the beginning of the TIGER Initiative, and its continued evolution.

initial health IT summit and moved to start a movement that would assure nursing was at the table and where key stakeholders/advocates of health information technologies would be integrated into the nation’s healthcare delivery systems and academic programs.

THE DECADE OF HEALTHCARE TECHNOLOGY (2014)

Challenges and Opportunities Facing Nursing

National Health IT Agenda In early 2004, the then U.S. President George W. Bush declared the Decade for Health Information Technology and created the Office of the National Coordinator of Health Information Technology (ONC). In May 2004, the then Secretary of Health and Human Services, Tommy Thompson, appointed Dr. David Brailer as the first National Health Information Technology Coordinator. This was an exciting time for health professions committed to the transformational role health information technology (IT) could play in substantial improvements in safety, efficiency, and other health reform efforts. In July 2004, Brailer convened the first national health IT summit in Washington, DC and launched the strategy to give U.S. citizens the benefits of an electronic health record within a 10-year timeframe.

Where Is Nursing? A very important observation was made at this first ONC event. The nation’s 3 million nurses who comprised up to 55% of the workforce were not represented and/or clearly identified as an important integral part of achieving the ONC vision and strategy. It left many begging the question, “Where is nursing?” There was also a keen awareness that without nursing engagement not only was the National Health IT Agenda at risk, but nursing would be at risk by not acting on a wonderful opportunity to significantly advance the agenda to transform practice and education with evidence and informatics. In his books, Leading Change (Kotter, 1996) and A Sense of Urgency (Kotter, 2008), Kotter describes the impact that having a true sense of urgency can have on large-scale effective change. When the sense of urgency is as high as possible, and among as many people as possible, the greater the successes of leading transformational change efforts will be. Leaders in nursing realized the sense of urgency to begin a grassroots effort following this

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THE BIRTH OF THE TIGER INITIATIVE (2005) The grassroots leadership efforts began to take action and network with others to determine first steps and gather key individuals to consider nursing’s future role related to health IT. The first official TIGER gathering was held on January 14, 2005 hosted by Johns Hopkins University School of Nursing. A diverse group of nursing leaders across the country engaged in conversation about the skills and knowledge needed by the healthcare provider/ nurse of the 21st century. Trends and patterns on topics such as basic skills, critical thinking, change management, evidence-based practice, knowledge workers, curriculum integration, professional practice, interdisciplinary collaborative practice, leadership, global military systems, national standards, clinical documentation, public policy, and more emerged as current challenges and opportunities facing nurses during this informatics revolution. It was noted that the opportunity was more than just considering “informatics”—the focus needed to also be on quality and evidence-based care. There was a unique window of opportunity for TIGER to build on the successes of informatics and to connect more key stakeholders in an effort to move more rapidly forward in guiding true transformation. Lastly, TIGER needed to tap into the power of the 3 million nurses in the workforce and get them engaged in advancing the TIGER agenda. It was decided, at that time, to hold an invitational summit in an effort to bring together a diverse group of stakeholders (professional organizations, governmental organizations, technology vendors, informatics specialists, etc.) to accelerate the sense of urgency and take further action to assure that nurses were able to leverage information technology to provide safe, efficient, and patient-centered care to all. At that time, questions were raised concerning whether or not the summit should include all disciplines to help meet the IOM aims and competencies. While this was recognized as being very important, there was consensus that it was critical to focus on the nursing workforce and then to expand the effort as recommendations were made from the summit.

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TIGER VISION • Allow informatics tools, principles, theories, and practices to be used by nurses to make healthcare safer, effective, efficient, patient-centered, timely, and equitable. • Interweave enabling technologies transparently into nursing practice and education, making information technology the stethoscope for the 21st century.

TIGER EXPECTED OUTCOMES • Publish a Summit report, including Summit findings and exemplars of excellence. • Establish guidelines for organizations to follow as they integrate informatics knowledge, skills, and abilities into academic and practice settings. • Set an agenda whereby the nursing organizations specify what they plan to do to bridge the quality chasm via information technology strategies.

Setting the Vision for TIGER

THE TIGER SUMMIT (2006)

The following vision statement and expected outcomes were developed to guide the early stages of the TIGER Initiative.

The Invitational Summit To prepare for the invitational summit, a program committee was formed that planned for over a year for the

•  FIGURE 45.1.  Ten-Year Vision. (With permission from the HIMSS TIGER Initiative.)

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728    P art 8 • E ducational A pplications event. A fundraising committee was also formed to secure funds to support the TIGER Summit and expected outcomes. Over 25 diverse sponsors made contributions to the summit, including grants that were received from the Agency for Healthcare Research and Quality (AHRQ), Robert Wood Johnson Foundation (RWJF), and National Library of Medicine (NLM). The TIGER Summit was held at the end of October 2006, hosted by the Uniformed Services University of the Health Sciences in Bethesda, MD. Over 100 national leaders from nursing administration, practice, education, informatics, technology organizations, governmental agencies, and other key stakeholders participated in the interactive two-day event. External facilitators from Bonfire Communications created an open-space experience that included small and large group dialogues; unique graphic art to capture the vision, outcomes of the dialogues and action plans; and the use of an audience response system (ARS) to capture current realities as well as gain consensus. To stimulate imagination and thinking, a Gallery Walk experience was featured on Day One in which participants were able to “walk through” and review cutting-edge technology and clinical decision support systems utilized in the current healthcare environment. The TIGER Executive and Program Committee felt that it was important to build on national exemplars in practice and education today. A total of seven national exemplars were shared including interactive dialogue with participants.

10-Year Vision and 3-Year Action Steps The TIGER Summit was focused on creating momentum toward consensus on a 10-year vision and a 3-year action plan. The 10-year vision was more clearly articulated by performing collective work around seven pillars and then content streaming the patterns and most salient points. With the seven pillars and rich content as its framework, a 3-year action plan was identified to achieve the 10-year vision of evidence and informatics transforming practice and education. This effort required intense group work and collaboration among the participants. The last Call for Action before participants left the Summit was for each leader of a participating organization to identify definable action plan goals that they could take back to their organization. Each participant signed the “TIGER Commitment Wall” to show their commitment to the Vision and Action Plans as well as to continue to promote and engage others in the TIGER Initiative. Following the TIGER Summit a Web site was established to record the several events and actions as well as to post new information. In addition, the Summit report

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Evidence and Informatics Transforming Nursing: 3-Year Action Steps toward a 10-Year Vision (2007) was published and made available via the Web site. The report provided a summary of the Summit as well as recommendations for specific stakeholder groups: Professional Nursing Organizations, Academic Institutions, Information Technology, Government and PolicyMakers, Healthcare Delivery Organizations, and Health Information Management Professionals/Health Science Libraries. Leaders from five major nursing organizations including the American Colleges of Nursing, American Nurses Association, American Organization of Nurse Executives, National League for Nursing, and Sigma Theta Tau International affirmed their commitment and need for the profession to support the TIGER Initiative.

THE TIGER COLLABORATIVE WORKGROUPS (2007–2008) Several months after the Summit, and after several followup meetings with the TIGER Executive Steering Committee, it was decided to move into phase II of TIGER. Building off the Summit pillar and action plans, nine key “collaboratives” were identified to dig deeper and tap a broader engagement from the nursing community to address the recommendations. Each collaborative was assigned coleaders to facilitate the workgroup and write a report and share the workgroup findings and final recommendations. A summary of the purpose, outcomes, and access to these published reports can be found in Table 45.1.

THE TIGER INITIATIVE (2009–2019) The TIGER Initiative Foundation (2009–2014) The years of 2009–2010 kept critical TIGER leader volunteers busy with sharing the collaborative reports as well as seeking new opportunities for further TIGER engagements with key stakeholders, nursing, and other interdisciplinary professional organizations. During this time the foundation was being laid for building out a Virtual Learning Environment (VLE) with collaborating partners such as the National Library of Medicine and the Uniform Services University of the Health Sciences. In July 2011 the TIGER Initiative Foundation was formed as a 501(c)(3) organization operating for charitable, educational, and scientific purposes. This was a significant milestone for TIGER as it provided a structure to grow strategically. A TIGER Web site was established to serve as a central hub for connecting TIGER members and sharing the many

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  TABLE 45.1    TIGER Phase II Collaborative Workgroups (Continued) TIGER Collaborative

Purpose

Outcome Summary

Standards & Interoperability

To accelerate the following action steps identified at the Summit: • Integrate industry standards for health IT Interoperability with clinical standards for practice and education • Educate practice and education communities on health IT standards • Establish use of standards and set hard deadlines for adoption

Provides definitions and rationale for standardization and interoperability. Developed “Nursing Health IT Standards Catalog” and provided Web-based tutorials on benefits of interoperable systems and standard harmonization. For more detailed information: http://tigerstandards.pbworks.com/w/page/22250630/FrontPage

National Health IT Agenda

To identify the most relevant health IT agenda and policies that are important to the TIGER and nursing profession’s mission and to assist in closing any representation gaps on policy issues.

Identified major national health IT organizations that need nursing engagement and participation. Developed tutorials to educate and encourage nurses to participate in HIT-related policy development, healthcare reform, and accelerate widespread HIT adoption. For more detailed information: https://slideplayer.com/slide/16680193/ Request a digital copy from TIGER: [email protected]

Informatics Competencies

To establish the minimum set of informatics competencies for all practicing nurses and graduating nursing students.

Collected over 1000 informatics competencies than narrowed focus to describe the minimum set of competencies around: 1. Basic Computer Competencies 2. Information Literacy 3. Information Management (including use of an electronic health record) Several educational resources for each type were identified and provided. For more detailed information: http://s3.amazonaws.com/rdcms-himss/files/production/public/ FileDownloads/tiger-report-informatics-competencies.pdf

Education & Faculty Development

To engage key stakeholders to integrate informatics into curriculums and create resources and programs to implement and sustain changes. Collaborate with industry and service partners to support faculty creativity in the adoption of informatics tools within the curriculum.

Formed seven working groups to work with key stakeholders. Effective in influencing the accrediting agencies to ensure informatics education be incorporated into nursing curriculum. NLN position statement, titled Preparing the Next Generation of Nurses to Practice in a Technology-Rich Environment: An Informatics Agenda. AACN took the lead incorporating informatics as an essential element of Baccalaureate and the Doctor of Nursing Practice Education. Participation in surveys for ADN and State Boards related to informatics were done. HRSA collaboration that resulted in the Integrated Technology into Nursing Education and Practice Initiative (ITNEP). Webinars for educators on how schools can integrate informatics into curriculum and partnership examples to teach about the electronic health record and clinical documentation. For more detailed information: http://s3.amazonaws.com/rdcms-himss/files/production/public/ FileDownloads/tiger-report-education-faculty-development.pdf

(Continued )

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730    P art 8 • E ducational A pplications   TABLE 45.1    TIGER Phase II Collaborative Workgroups (Continued) TIGER Collaborative

Purpose

Outcome Summary

Leadership Development

To engage nursing leadership to develop revolutionary leadership that drives, empowers, and executes the transformation of healthcare.

Usability & Clinical Application Design

To further define key concepts, patterns, trends, and recommendations to HIT vendors and practitioners to assure usable clinical systems at the point of care.

Virtual Demonstration Center

To explore the creation of a virtual “Gallery Walk” to all nurses, nursing faculty, and nursing students via a Web access to technology applications.

Consumer Empowerment & Personal Health Records (PHRs)

To make information available to nurses about PHRs and to engage inclusion of this content into nursing curricula and practice.

Evaluated current nursing leadership development programs for the inclusion of informatics competencies. Built upon the American Organization of Nurse Executives (AONE) and developed a survey to identify most urgent program development needs. The survey provided insight into leadership competencies required. Aligned with the Magnet® Recognition Program to highlight how nurse leaders use major HIT implementations as an integral part of their Magnet journey and meeting the 14 forces of magnetism. Identified criteria for leadership development related to informatics. For more detailed information: https://cdn.ymaws.com/www.texasnurses.org/resource/resmgr/ Docs/2014_TIGER_TheLeadershipImpe.pdf Synthesized a comprehensive literature review from nursing and other disciplines and analyzed in the areas of Determining clinical information requirements, safe and usable clinical design, usability evaluations, and human factor foundations. Collected case studies that illustrated usability/clinical application design that are good examples to follow and bad examples to avoid. Reviewed the AAN Technology Drill Down (TD2) Project findings. Developed recommendations for HIT vendors and practitioners to adopt sound principles of usability and clinical design for healthcare technology. For more detailed information: http://s3.amazonaws.com/rdcms-himss/files/production/public/ FileDownloads/tiger-report-usability-clinical-application-design.pdf Provided visibility to the vision of IT-enabled practice and education. Demonstrated future IT resources. Demonstrated collaboration between industry, healthcare organizations, academic institutions, and professional organizations to create educational modules for nurses that are based upon informatics competencies. Used practice examples from different practice environments to demonstrate best practices, results of research, case studies, and lessons learned by partnering with professional nursing organizations. For more detailed information: http://s3.amazonaws.com/rdcms-himss/files/production/public/ FileDownloads/tiger-report-virtual-learning-center.pdf Identified several ways that nurses can impact the adoption and use of the consumer empowerment strategies such as the PHR. For more detailed information: http://tigerphr.pbworks.com/w/page/22248999/Overview Request a digital copy from TIGER: [email protected]

(With permission from the TIGER Initiative Foundation. Copyright © Healthcare Information and Management Systems Society, Inc. [HIMSS].)

activities that occur with the more than 1500 volunteers that have been engaged with the TIGER Initiative.

The HIMSS TIGER Initiative (2014–Present) The TIGER Foundation was not able to maintain sufficient funding to support a sustainable organization. Thus, on September 22, 2014, TIGER transitioned into the Healthcare Information and Management Systems

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Society (HIMSS) enterprise, and today is supported by the Professional Development department. HIMSS is a global advisor and thought leader supporting the transformation of health through the application of information and technology. Headquartered in Chicago, IL, HIMSS serves the global health information and technology communities with focused operations across North America, Europe, the United Kingdom, Middle East, and Asia Pacific

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(HIMSS, 2019a). The HIMSS global scope of work and expertise provided the perfect new home for the initiative. Today, TIGER is a stable, grassroots initiative focused on education reform, fostering community development and global workforce development using an interprofessional approach (Shaw, Sensmeier & Anderson, 2017). TIGER landing pages on the HIMSS Web site (located at www.himss. org/tiger) offer a wealth of information inclusive of history documents, landmark reports, VLE overview and demo, and volunteer community pages (TIGER International Task Force, Scholars Workgroup, and VLE Workgroup) that highlight projects underway, a marketing toolkit, as well as ways to get involved with this important work.

Refreshed TIGER Focus After transition to HIMSS, TIGER refined its core focus to embrace an interprofessional approach as the Initiative recognized that education in health informatics should expand to other clinical fields and beyond (O’Connor, Hübner, Shaw, Blake, & Ball, 2017). In essence, today, TIGER advances the integration of health informatics to transform education and practice by carrying out the following:

Chapter 45 • The Evolution of the TIGER Initiative    731

• • •

Enabling the interprofessional clinical workforce to use informatics and technology to improve patient care Interweaving evidence and technology into seamless practice, education, and research Fostering a learning health system

TIGER offers tools and resources for learners to advance their skills and for educators to develop technology and health informatics curricula. The spirit of TIGER continues to support a learning health system that maximizes the integration of technology and informatics into seamless practice, education, research, and resource development (Shaw et al., 2017).

VIRTUAL LEARNING ENVIRONMENT In February 2012, the TIGER Initiative Foundation launched the VLE to provide an interactive Web-based learning opportunity that included information about health IT and related topics for healthcare professionals and consumers (Schlak, 2013). The Web-based format provided dynamic

•  FIGURE 45.2.  TIGER VLE Lobby. (With permission from the HIMSS TIGER Initiative.)

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732    P art 8 • E ducational A pplications and real-time information on topics such as electronic health records, usability, clinical decision support, health information exchange, care coordination, meaningful use, standards and interoperability, consumer health information, mobile health, privacy and security, health IT, and nursing practice among many other related topics. Following transition to HIMSS, the VLE was officially relaunched in February 2015 and again in April 2019 (Fig. 45.2). Powered by HIMSS, the TIGER VLE is an interactive, online learning platform for academic professionals, students, adult learners, and clinical educators. This personalized learning experience—containing courses and Webinars—expands knowledge and skillset in a self-paced format. In essence, the VLE is a personalized learning experience designed to expand skillset and knowledge on important health IT subjects while highlighting the work of open-source collaborators such as the ONC, Quality and Safety Education for Nurses (QSEN), and courses developed by HIMSS among others. A few highlights of the education portal include the following: Certificates of completion aligned with two health IT–focused courses

•

•

Health Information Technology Foundations: Foundational courses for interprofessionals new to the health IT field which are intended to create familiarity with applications of health IT in care delivery. This course is hosted in collaboration with Carnegie Mellon University’s (CMU) Open Learning Initiative (OLI). The curriculum is aligned to the Certified Associate in Healthcare Information and Management Systems (CAHIMS) certification administered by HIMSS. CAHIMS certification is designed for those who have previous experience in IT or healthcare and is designed to serve as a pathway into health IT careers (HIMSS, 2019b). For more information on CAHIMS certification, visit https://www.himss. org/health-it-certification/cahims. Information Technology in Healthcare: An intermediate course by HIMSS for experienced interprofessionals interacting with health IT and integrating it into workflow.

Certificates of completion are awarded once all course modules have been accomplished and an assessment completed with a passing score of 80%. Webinar series and archive

•

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Offer live events focused on hot topic and emerging trends that showcase the work of global leaders

in health IT/informatics. All events are recorded and archived for on demand viewing within the VLE’s Events Theatre. Currently, there are over 400 individuals across multiple countries that have active TIGER VLE memberships and two universities are using the VLE to augment classroom curriculum. There is great opportunity for interprofessional teams in practice and academia to leverage the TIGER VLE to learn (in some instances, in lieu of a text book) and develop knowledge and skills regarding technology and informatics to better integrate into their daily work. Through the work of the TIGER VLE Workgroup formalized in late 2018, an updated and refined version of the education platform is being planned to launch in spring 2019. More information can be found at https://www. himss.org/professional-development/tiger-initiative/virtual-learning-environment. Here, you will discover how to become a member and gain access to the plethora of great learning materials while tapping into this dynamic global community.

International and Interprofessional Expansion Prior to the HIMSS transition in 2014, the TIGER Initiative Foundation focused on two strategic priorities to increase the TIGER vision/mission across international borders and to more actively engage interprofessional colleagues in education and practice. The effort toward international expansion began at the NI2012 Conference in Montreal Canada, where the International Committee was officially launched including five countries Brazil, the United Kingdom, Taiwan, Germany, and Canada. The TIGER International Committee led a TIGER session at the 14th World Congress on Medical and Health Informatics (MedInfo) 2013 in Copenhagen which generated much interest from other countries to become engaged. In 2019, the TIGER International Task Force (previously the International Committee) is charged with providing domain expertise, leadership, and guidance to all activities, initiatives, and collaborations within the inter-professional community (HIMSS, 2019c). Activities include the following:

• •

Reforming clinician education curriculum through the integration of IT, information literacy, informatics, and the infusion of technologies for learning in a robust Virtual Learning Environment Developing and implementing learning innovations; fostering faculty development and ensuring universities and providers have the necessary health IT education infrastructure

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•

•

Identifying, modeling, and implementing collaborative partnerships among public and private academic enterprises to launch a global Informatics Scholar Program directed at students seeking advanced level degrees Increasing faculty and student acceptance and understanding health IT through education and training, incentives, and necessary resource support

Today, the TIGER International Task Force (TITF) is comprised of 60 members representing 29 countries worldwide: Australia, Austria, Brazil, Canada, Chile, China/Taiwan, England, Denmark, Finland, Germany, Iran, Ireland, Israel, Japan, Mexico, New Zealand, Nigeria, Panama, Peru, the Philippines, Portugal, Qatar, Saudi Arabia, Singapore, Scotland, South Korea, Switzerland, and all regions of the United States (HIMSS, 2019c). TITF members serve as subject matter experts to inform all projects and research efforts while sharing information about local and regional learning priorities and needs as a way to come together and tackle issues collaboratively. There continues to be a growing momentum in healthcare today focused on creating true interprofessional education and practice environments (Christopherson & Troseth, 2013) and it is critical for leaders in informatics to be aware of this momentum. For more information about the TITF, please visit: https://www.himss.org/professionaldevelopment/ tiger-international-task-force. The TIGER Community also includes a Scholars Workgroup which was launched in 2017 with the goal of providing an informatics educator-focused network to inform and enhance current educational practices by sharing knowledge and learning about state-of-theart approaches and best practices within the academic informatics community (HIMSS, 2019d). At the time of writing this chapter, the Workgroup is investigating the launch of a Scholars Program directed at graduate and PhD level students. The program’s mission will seek to advance the spirit of TIGER as an interprofessional initiative by contributing to the future informatics workforce. Finally, the VLE Workgroup was launched in fall of 2018 with the goal of refining and relaunching the education portal in 2019. It has been over 4 years since the VLE was re envisioned and many exciting updates are being planned to upgrade the look, feel, navigation, and resource offerings tied to a learner action framework. For more information about TIGER’s Workgroups, please visit: www.himss.org/tiger.

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INTERNATIONAL COMPETENCY SYNTHESIS PROJECT In 2015, the TIGER Initiative began comprehensive activities to launch the International Competency Synthesis Project (ICSP) by compiling core competencies utilizing a global perspective. This project was executed in three phases. 1. Compilation of national case studies submitted from Australia, Brazil, China/Taiwan, Finland, Germany (inclusive of Austria and Switzerland), the Philippines, Portugal, UK-Scotland, and the United States.

2. Deployment of a survey composed of 24 areas of competencies in clinical informatics within five domains: (1) clinical nursing, (2) nursing management, (3) quality management, (4) IT management in nursing, and (5) coordination of interprofessional care which yielded responses from 43 experts in 21 countries to truly capture a global perspective. The survey was designed based on a compilation of competencies from international literature in medical and health informatics ((Hübner et al., 2018). 3. A Recommendation Framework was derived from case studies, survey results, and stakeholder input.

The framework for competencies in health informatics is directed at nurses to provide a knowledge grid for teachers and learners alike instantiated with knowledge about informatics competencies, professional roles, priorities, and practical, local experience. It also encompasses a framework development methodology for other professions/disciplines. Finally, this framework also lays the foundation for cross-country learning in health informatics education for nurses and other health professionals (Hübner et al., 2018). TIGER’s ICSP project served as one of the foundational building blocks for many outputs of the EU*US eHealth Work Project. Refer to the next section, EU*US eHealth Work Project, for details on the interdisciplinary focused Recommendation Framework v2.0 set to be released by summer 2019.

EU*US EHEALTH WORK PROJECT Building on its grassroots foundation and community support, the TIGER mission continues to advance its global outreach and stakeholder adoption. In September 2016, TIGER was co-awarded funding to address the need, development, and deployment of workforce IT

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734    P art 8 • E ducational A pplications

•  FIGURE 45.3.  EU*US eHealth Work Project Logo. (With permission from the European Union’s 2020 EU*US eHealth Work Project (This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 727552 EUUSEHEALTHWORK.) skills, competencies, and training programs for trained eHealth workers from the European Commission’s Horizon 2020 research and innovation grant program (HIMSS, 2017a). The 21-month EU*US eHealth Work Project, spanning from September 2016 to May 2018, (Fig. 45.3) worked to measure, inform, educate, and advance development of a skilled eHealth workforce throughout the European Union, the United States, and globally. The white paper EU*US eHealth Works to Improve Global Workforce Development details how the project came into existence and provides a comprehensive overview of what the project strived to accomplish. The overall project goal was to create a legacy of digitally empowered healthcare professionals at present and in the future (2017). Consortium member organizations include Omni Micro Systems and Omni Med Solutions (OMS-UG) of Germany that served as project coordinator; European Health Telematics Association (EHTEL) of Belgium; Steinbeis Innovation GmbH (SIG) of Germany; University of Applied Sciences Osnabrück of Germany (FH OS); Tampere University of Technology (TUT) of Finland; and the HIMSS Foundation (project execution by TIGER). The EU*US eHealth Work Project Consortium’s Mission was to map skills and competencies; provide access to knowledge, tools, and platforms, and strengthen,

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disseminate, and exploit success outcomes for a skilled Transatlantic eHealth Workforce. The Consortium and its global stakeholders were uniquely poised to answer this call as together; they form a network of partners from academic, healthcare providers, and industry, providing access to a wealth of experience and knowledge in health informatics (or eHealth) education and training with the common goal of positively impacting the health IT workforce by heightening skills and knowledge (HIMSS, 2017a). eHealth is a term that parallels the phrase health IT, and is commonly used throughout the European Union (EU). It is defined as “an emerging field in the intersection of medical informatics, public health and business, referring to health services and information delivered or enhanced through the Internet and related technologies” (Eysenbach, 2001). The EU*US eHealth Work Project’s scope of work was organized into five work packages (WPs) with corresponding deliverables tied to major project milestones: (Fig. 45.4).

• • • •

WP 1: Management (led by OMS)

•

WP5: Strengthen (led by EHTEL)

WP2: Map (led by FH OS) WP3: Access (led by TUT) WP4: Assess (led by the HIMSS Foundation/ TIGER)

The EU*US eHealth Work Project was an incredible opportunity for HIMSS and TIGER to collaborate with European partners and implementers on an international level. As a Consortium member, TIGER worked to transform health through information and technology in workforce development. By mapping, quantifying, and projecting the need, supply, and demand for competencies and for developing IT skills, we are moving closer to realizing a trained and skilled transatlantic eHealth workforce (EU*US eHealth Work Project, 2019). Although the project has come to a close, our work has only just begun. TIGER, in collaboration with the FH OS, compiled 22 global case studies (under WP5: Assess) that brought findings from the survey and gap analysis to life in practical ways that are meant to be learned from and built upon. TIGER and FH OS continue to collect global studies to aid the project findings. A second Recommendation Framework (2.0), populated based on interdisciplinary findings from the project’s survey and case studies, is set to be released in summer 2019. To access the EU*US eHealth Work Project’s Global Case Study’s Executive Summary inclusive of survey findings, gap analysis overview, individual studies, discussion, and more, visit:

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Chapter 45 • The Evolution of the TIGER Initiative    735



WP3: Access

WP2: Map

Map, compile and develop curriculum Develop an interactive webbased skills and knowledge platform Establish a sustainable system to maintain curricula Continue mapping and aligning HITCOMP Produce an education demonstrator module

Map the current eHealth knowledge structure Identify main trends and gaps catalysts and barriers Document where IT has impacted healthcare through case studies Analyze and map gaps between current state, needs and future states

WP4: Assess WP1: Manage Provide management Lead coordination and support efforts Serve as a focal point for communications and documentation Meet financial and operational requirements Provide updates, summaries and reports

Perform an impact assessment, including auditing Generate additional case studies Develop a dissemination strategy action plan Develop a skills and knowledge assessment and development framework Host a mid-term stakeholder workshop to validate findings and share results

WP5: Strengthen Strengthen the impact of the project, its reach, and its outcomes, by: Dissemination Exploitation

Major Milestones of Project: Case Studies Foundational Curricula in eHealth Interactive Web site Platform Educational Demonstrator Module Stakeholder Engagement Events Health IT Skills and Knowledge Assessment and Development Framework For use by: current and future healthcare workers, educators, Governments and Federal Ministries, Health Care systems, Regulation Authorities, Politicians, Industry, SMEs, Researchers, ICT, eHealth Networks

•  FIGURE 45.4.  EU*US eHealth Work Project Major Milestones. (With permission from the European Union’s 2020 EU*US eHealth Work Project (This project has received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 727552 EUUSEHEALTHWORK.) https://www.himss.org/professional-development/ tiger-case-studies-executive-summary. For more information about the EU*US eHealth Work Project and to access project tools, resources, and archived stakeholder event recordings, visit: http:// ehealthwork.eu/.

THE EVOLUTION OF TIGER COMPETENCIES AND INFORMATICS RESOURCES In 2010, the TIGER Informatics Competencies Collaborative (TICC) was formed to develop informatics recommendations for all practicing nurses and nursing

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students. In 2011, the group published a landmark report titled Informatics Competencies for Every Practicing Nurse: Recommendations from the TIGER Collaborative. It has been nearly 10 years since the TICC was formed and there have been many exciting changes. TIGER now collaborates with and calls upon the work of the European Computer Driving License (EDCL), the Health IT Competencies Tool and Repository (HITComp) 2.0, TIGER’s VLE, TIGER’s International Competency Synthesis Project (ICSP), and the EU*US eHealth Work Project to continue the conversation around competencies to add global, interdisciplinary, and interprofessional perspectives (HIMSS, 2017b). This new report, aptly named The Evolution of TIGER Competencies and Informatics Resources, is an update to the original TICC landmark report, where each of TIGER’s most recent

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736    P art 8 • E ducational A pplications projects, resources, and tools are discussed in detail. With a renewed effort to incorporate informatics into the education and training of nurses and other health professionals globally, it is time for educators, researchers, practitioners, and importantly, policy-makers to take heed of the work started by TIGER and continued through the EU*US eHealth Work Project collaboration (O’Connor et al., 2017). For more information regarding TIGER competencies, refer to the International Competency Synthesis Project section. For nurses also seeking to be recognized nationally as a provider of competent care, we encourage you to connect with the American Nurses Credentialing Center (ANCC) at https://www.nursingworld.org/certification/ for more information on how to become certified.

impact. This expansion also includes a social media presence on Twitter. You can find us at @AboutTIGER and also by the hashtags #tigervle and #tigersightings. The evolution of the TIGER Initiative continues to grow and expand within the stable structure of HIMSS. This important transition has resulted in many exciting global projects and a relaunched TIGER VLE that continues to assist with the expansion of health IT, eHealth, and informatics knowledge. Expanding TIGER beyond international boundaries and interprofessional silos has also helped to spread the vision and propel the actions necessary to integrate evidence and technology informatics into our daily work to make healthcare safer, effective, efficient, patientcentered, timely, and equitable. TIGER’s future has never looked so bright!

TIGER Informatics Definitions

Test Questions

In 2015, TIGER began compiling interprofessional informatics definitions to collaboratively define and document health informatics terminology with the goal of providing context to our global community when the terms were referred to within official documents and the TIGER VLE. The intention is for this resource to serve as a helpful tool for those seeking to learn more about informatics and informatics competencies. As the field continues to grow and change, so will these very definitions (HIMSS, 2018). With this, TIGER has committed to updating the document terms on an annual basis. In 2018, TIGER published the third version of the document that can be downloaded at: https:// www.himss.org/library/tiger-informatics-definitions. TIGER encourages new or revised terminology to be shared via e-mail at: [email protected].

SUMMARY: ONCE A TIGER ALWAYS A TIGER The sign of significant changes to come was palpable in 2004 as our nation began to address healthcare reform by announcing this would be the decade for healthcare information technology. A great sense of urgency to set a vision and course of action for nurses to lead and, in turn, engage all nurses was the beginning of Technology Informatics Guiding Education Reform. The acronym TIGER was perfect as hundreds of nurses launched into action. The grass-roots effort took hold and emerged into an innovative social disruption that continues to grow. Many TIGERS have shared that the sense of collaboration and teamwork has been an amazing experience. The number of volunteer hours has been simply astounding! Today, TIGER continues to expand its global reach and

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1. President George W. Bush launched the strategy to give U.S. citizens the benefits of an electronic health record within which X-year timeframe? A. 5

B. 10 C. 15

D. No timeframe was given 2. In what year was Dr. David Brailer named as the first National Health Information Technology Coordinator? A. 2003 B. 2004 C. 2005 D. 2006 3. At the first ONC event, nurses comprised what percent of the workforce? A. 55% B. 65% C. 75% D. 85% 4. The first official TIGER gathering was held on January 14, 2005 at which university campus? A. Columbia University B. Harvard University

C. Johns Hopkins University D. Northwestern University

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Chapter 45 • The Evolution of the TIGER Initiative    737

5. In what year was the TIGER Summit hosted? A. 2005 B. 2006 C. 2007

D. 2008 6. In which year did TIGER transition from a standalone foundation into the Healthcare Information and Management Systems Society (HIMSS) enterprise? A. 2012 B. 2013 C. 2014 D. 2015 7. Which statement is false? TIGER works to advance the integration of health informatics to transform education and practice by:

A. Enabling the interprofessional clinical workforce to use informatics and technology to improve patient care

B. Interweaving evidence and technology into seamless practice, education, and research C. Fostering a population health system D. Fostering a learning health system

8. Powered by HIMSS, the TIGER Virtual Learning Environment (VLE) is an interactive, online learning platform for academic professionals, clinical educators, students, and adult learners. True or false? A. False B. True

9. Today, the TIGER International Task Force (TITF) is represented by how many countries worldwide? A. 14 B. 29 C. 42 D. 44 10. The TIGER International Competency Synthesis Project (ICSP) derived a Recommendation Framework from which of the following? A. Blog posts, survey results, and white papers

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B. Case studies, survey results, and stakeholder input

C. Case studies, telephone interviews, and survey results D. Podcasts, survey results, and white papers

Test Answers 1. Answer: B 2. Answer: B

3. Answer: A 4. Answer: C 5. Answer: B

6. Answer: C 7. Answer: C 8. Answer: B 9. Answer: B 10. Answer: B

REFERENCES Christopherson, T., & Troseth, M. (2013). Interprofessional education and practice: A 40-year-old new trend experiencing rapid growth. Computers, Informatics, Nursing. doi:10.1097/CIN.0000000000000022. E U*US eHealth Work Project. (2019). The EU*US eHealth Work Project: Project overview. Retrieved from http:// ehealthwork.eu/FC/Presentations/EU-US_eHealth_ Work_Project_Overview-Final.pdf. Accessed on May 30, 2020. Eysenbach, G. (2001). What is e-health? Journal of Medical Internet Research, 3(2), E20. HIMSS. (2017a). EU*US eHealth works to improve global workforce development. Retrieved from https://www. himss.org/library/euus-ehealth-works-improve-globalworkforce-development. Accessed on May 30, 2020. HIMSS. (2017b). The evolution of TIGER competencies and informatics resources. Retrieved from https://www.himss. org/sites/hde/files/media/file/2020/03/10/the-evolutionof-tiger-competencies-and-informatics-resourcesfinal-10.2017.pdf . Accessed on May 30, 2020. HIMSS. (2018). TIGER informatics definitions 3.0. Retrieved from https://www.himss.org/library/tiger-informaticsdefinitions. Accessed on May 30, 2020. HIMSS. (2019a). About HIMSS. Retrieved from https:// www.himss.org/about-himss. Accessed on May 30, 2020. HIMSS. (2019b). CAHIMS certification. Retrieved from https://www.himss.org/health-it-certification/cahims.

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738    P art 8 • E ducational A pplications HIMSS. (2019c). TIGER International Task Force. Retrieved from https://www.himss.org/professionaldevelopment/ tiger-international-task-force. Accessed on May 30, 2020. HIMSS. (2019d). TIGER Scholars Workgroup. Retrieved from https://www.himss.org/professionaldevelopment/ tiger-scholars-workgroup. Accessed on May 30, 2020. Hübner, U., Shaw, T., Thye, J., Egbert, N., de Fatima Marin, H., Chang, P., O’Connor, S., … Ball, M. J. (2018). Technology Informatics Guiding Education Reform (TIGER)—An international recommendations framework of core competencies in health informatics for nurses. Methods of Information in Medicine. doi.org/10.3414/ ME17-01-0155. Institute of Medicine (IOM). (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: The National Academy Press.

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Institute of Medicine (IOM). (2003). Health profession education: A bridge to quality. Washington, DC: National Academy Press. Kotter, J. (1996). Leading change. Boston, MA: Harvard Business Press. Kotter, J. (2008). A sense of urgency. Boston, MA: Harvard Business Press. O’Connor, S., Hübner, U., Shaw, T., Blake, R., & Ball, M. (2017). Time for TIGER to ROAR! Technology informatics guiding education reform. Nurse Education Today, 58, 78–81. Schlak, S., Anderson, C., & Sensmeier, J. (2013). The TIGER has jumped into the virtual learning environment! Computers, Informatics, Nursing, 31(2), 57–58. Shaw, T., Sensmeier, J., & Anderson, C. (2017). The evolution of the TIGER Initiative. Computers, Informatics, Nursing. doi:10.1097/CIN.0000000000000369.

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46 Initiation and Management of Accessible, Effective Online Learning Patricia E. Allen / Khadija Bakrim / Darlene Lacy

• OBJECTIVES . Explore the past and present perspectives of distance education. 1 2. Compare and contrast important interactive electronic tools that support online learning. 3. Examine essential strategies and types of support required for the online learner and faculty. 4. Recognize future trends in online education.

• KEY WORDS Distance education Faculty development Faculty workload Online learning

DEFINITIONS The literature still tends to use a variety of terms such as distance education, Web-based or online learning, and online education to reflect this type of nontraditional education, which in educational reality is becoming a mainstream approach to learning. Some definitions include “institution-based, formal education where the learning group is separated, and where interactive telecommunications systems are used to connect learners, resources, and instructors” (Simonson, Smaldino, Albright, & Zvacek, 2014, p. 6). The concept is now associated with learner accessibility, since online learning is experienced locally or globally, at home, a dormitory, or in the work place, regardless of a rural or urban setting, across state lines, and even internationally. The American Association of Colleges of Nursing (AACN, 2008) continues to use Reinert and Fryback’s (1997) and Russell’s (1998) definitions to further clarify this type of learning as “a set of teaching/learning

strategies to meet the learning needs of students that are separate from the traditional classroom setting and the traditional role of faculty.” Today with the use of the Internet, the terms online education andonline ­learning (which will be used interchangeably in this chapter) are being used to reflect the broader view of these educational experiences.

GOALS FOR THIS CHAPTER Following a brief historic review of distance education, this chapter focuses on today’s high-quality, cost-effective, learner-centered approach to online education, examining content from both the student and faculty perspectives. This includes the importance of applicable educational principles needed to promote interactivity; legal, ethical, and copyright issues; active learning; and effective learner and student support, as well as some of the major a­ cademic and pedagogic issues impacting faculty developing creative courses. 739

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740    P art 8 • E ducational A pplications

THE HISTORICAL EVOLUTION This type of education has always experienced bumps and surges of acceptance. Even the term distance education denotes remoteness or isolation to call attention to the differences from the traditional classroom education. While distance education has been available in the United States since before the turn of the nineteenth century, schools and educators have often required a reason to develop and conduct education for students beyond the traditional classroom setting. Initial development centered primarily on vocational training. Historically, educational regulatory agencies have not been very supportive; approval for off-campus or extension sites was needed when the sites were separated from the originating school or when geographical barriers existed, even when the same faculty were teaching both types of courses. Some states even defined the number of miles for approval. Another approach to distance education, depending on the school’s technological resources, could also mean the faculty drove “the distance” to the offcampus sites, then provided face-to-face (F2F) instruction. Colleges and students have come to realize the advantages and convenience of online education. The enrollment in online courses has consistently increased for the fourteenth straight year. According to a survey by Babson Survey Research Group, 7.1 million plus students have enrolled in at least one college course online (Babson Survey Research Group, 2017).

Use of Technology The advent of print, audio, television, and the computer has assisted distance education strategies, and eventually led to online learning. In the United States, the distance education movement began with the Boston-based Society to Encourage Studies at Home in 1873, followed in 1885 by the University of Wisconsin developing “short courses” and Farmer’s Institutes. By 1920, a Pennsylvania commercial school for correspondence studies had enrollments of more than 2,000,000. Unfortunately, dropout rates averaged around 65%. In 1919, radio was the first technology used for distance education, later followed by telephone service. Wisconsin again became a pioneer by using audio conferencing equipment with telephone handsets, speaker phones, and an audio bridge to connect multiple phone lines for the first two-way interactive distance education for physicians and nurses (Armstrong, 2003; Schlosser & Anderson, 1994). Next came television, so that complex and abstract concepts could be illustrated through motion and visual simulation. Satellite technology for distance education in the United States was implemented in the early 1980s. As these methodologies

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grew in sophistication and complexity, distance education students began to experience greater transparency of the technology, which enhanced the educational experience. Computer technology came slowly to the forefront of distance education with computer-based education (CBE), computer-assisted instruction (CAI), and computermanaged instruction, and then its use exploded. Yet, it has been the combination of the various interactive Webbased technologies that have really provided the force for creative educational strategies, as well as innovative ideas from faculty that have provided the momentum and impact of online education.

EXAMINING TECHNOLOGIES USED IN ONLINE LEARNING A number of technologies are employed in the delivery of online learning, yet not all online programs use all of the technologies described.

Learning Management Systems A learning management system (LMS) is a software product that was first designed for corporate and government training divisions as a tool to assess workers’ skills for job positions, and then provide specific training, either individually or in groups. Learning management systems are also commonly used for K-12 education and the higher education level to track student achievement in outcomesbased educational programs (Waterhouse, 2005). Another term for LMS that is used more frequently in academic settings is course management system (e.g., Blackboard and Canvas). They provide the same functionality as an LMS. The general functions for LMS software includes distribution of course content, communication among the users, interaction with course resources, testing, grading, and tracking records. LMS becomes significantly more powerful by incorporating third-party applications such as Turnitin, Respondus, Lockdown Browser, all providing an array of sophisticated features. The major functionalities and tools of LMS are summarized in Table 46.1.

Content Management Systems A content management system (CMS) is a database of learning objects, which may include many items developed for instructional use. A CMS allows course developers to develop learning objects such as videos, modules, assessments, or any other materials used for online learning. It provides version tracking so that changes to these learning objects can be implemented without losing previous versions of the items.

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  TABLE 46.1    LMS Features LMS Features

Tools

Synchronous Communication

Chat Whiteboard Video conference

Asynchronous Communication

Discussion Forum E-mail Journal Wiki Blog

Collaborative Projects Development

Testing and Grading

Social Media Integration

Reporting and Tracking Content Organization

Online tests Self-assessments Survey Polls Twitter Facebook YouTube RSS feeds Analytics Statistics Modules Pages Folders

Another benefit to such systems is the ability of developers to share learning objects. They can be used as previously developed or modified to fit the need of the current course. Finally, CMSs are designed to integrate with course management systems. This allows the development of materials to take place outside the course itself. Then, building the course becomes as simple as selecting learning objects and placing them into the course. Course management systems such as Canvas offer a content management system that is designed to fully integrate with their system.

Emergence of Massive Open Online Courses A massive open online course (MOOC) is a model for delivering free learning content. Many MOOCs do not require pre-requisites other than Internet access and interest. Recently, MOOCs have begun to offer academic credit. The concept of MOOCs originated in 2008 among the open educational resources (OER) movement, and cover a wide variety of topics. Designing for a MOOC course is very different from designing for a CMS-based course. The assessment is a challenging area of instruction within the MOOC, often resorting to objective tests or peer-reviewed comments. Since 2008 MOOCs have become individual online courses allowing thousands to participate with varying

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Functionality

• Virtual office hours • Online tutoring and training sessions • Real-time communication • Student-led meeting • Visual presentation of concepts • Guest speakers • Read and reply to others • Peer interaction • Self- or group reflections • Group collaboration • Sharing information • Self-reflections • Formative and summative assessment • Informal test questions • Vote on issues • Social connections • Collaboration • Building community • Activity and log Tracking • Statistics for assessment performance • Store and organize the course materials results (Perez, n.d.). Considerations for use are cautioned by many faculty whose universities have embraced this model in a concern for completion rates and basic skill needs of the learner. MOOCs provide participants with course materials that are normally used in a conventional education ­setting— such as examples, lectures, videos, study materials, and problem sets. MOOCs are typically provided by higher education institutions, often in partnership with “organizers” such as Coursera, edX, and Udacity, though some MOOCs are being offered directly by a college or university, such as John Hopkins University, Harvard, or MIT. A new option added to the world of MOOCs is the Quasi-MOOC. A Quasi-MOOC may not be designed by a university educator, rather a team of content specialists such as the online content delivery of Khan Academy. A Quasi-MOOC does not assess your progress, rather releases content to explain topics of complexity or of interest to the participant (www.khanacademy.org). Finally, MOOCs have been a great value to the corporate world in the last few years. Here connection, engagement, and scaling up are the key components for large corporations such as Microsoft who used Intrepid software to train thousands in a very large global sales

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742    P art 8 • E ducational A pplications team (Vital Source, 2019). MOOCs today do provide connection to those who may not be able to seek traditional education and allow engagement among learners as well as allowing providers to scale up to capture thousands of learners at one time; something not imagined in MOOC creation in 2008.

Virtual Reality Virtual reality (VR) is a form of computer-generated simulation that brings a unique experience to education and training. Second Life and Active Worlds have brought these technologies to the World Wide Web and made them accessible to much broader populations, including educators. VR is used as a learning environment where learners can interact with others while carrying out tasks or gain new skills. VR appears to provide exciting real-life applications of course content, for example in problemsolving situations, and specialized experiences in other places and times that would otherwise be inaccessible.

MOBILE COMPUTING The rapid changes in new technologies and access to content anywhere and anytime allow learners to experience learning in a variety of settings and not just in schools (Prensky, 2012). Mobile computing devices are playing an increasingly important role in our personal, professional, and educational life. There are many different mobile devices including smart phones, tablet PCs, and laptop computers. The recent advances in mobile devices make online learning possible through the powerful computing capability built into their conveniently small sizes, Internet connectivity, and the availability of many types of mobile software applications (apps) (Johnson, Levine, Smith, & Stone, 2010). Because of the mobility and strong Internet connectivity, learning becomes ubiquitous and seamless (Liu, Tan, & Chu, 2009). Learners who are taking online courses can use mobile devices anywhere to access the course content, complete learning activities, communicate with classmates, and work on group projects. Many students access the CMS by available apps via phone. Mobile devices become usable and functional enough to produce an impact on the education software industry, including LMS software. The number of applications for mobile devices has increased dramatically. For example, Google Docs for mobile allows accessing, editing, and sharing documents. Books have also gone digital and for many people e-books are now more desirable than books. Readers have the freedom to read e-books using e-raiders like Kindle or Nook, tablets, and smart phones.

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Although the integration of functional mobile computing devices is no longer the real challenge, the focus becomes mainly on how this technology should be used to fulfill the core mission of learning (Cain, Bird, & Jones, 2008). Adeboye (2016) found the effective use of the mobile device increases the quality and quantity of student work.

FACULTY SUPPORT With the number of online courses increasing, the American Association of State Colleges and Universities emphasizes the critical need for faculty well experienced in teaching online (Orr, Williams, & Pennington, 2009). In order to assist in successful online education, faculty must receive appropriate support, technical expertise, and online infrastructure. The role of the online instructor has developed into that of a facilitator rather than a knowledge distributor. This is achieved by engaging and guiding students through the use of active learning strategies to learn critical concepts, principles, and to develop skills, rather than just lecture material.

Roles of Online Faculty With the development of new and emerging technologies the instructor role has advanced in favor of more engaging activities for students. The role of the instructor shifts from authoritative, knowledgeable presenter to facilitator, planner, coach, and communicator. The classic work by Oblinger (1999) suggested the shift away from teachercentered to student-centered could be outlined as:

• • •

From lecture to coaching

•

From credit hour “seat time” to performance standards

• • • •

From taking attendance to logging on From distribution of requirements to connected learning

From competing to collaborating From library building collections to networked connections From passive to active learning From textbooks to customized materials, such as electronic materials accompanying textbooks

Faculty Development Faculty development is a critical component to the success of any online education, especially as colleges and

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Chapter 46 • Initiation and Management of Accessible, Effective Online Learning 

universities are using numerous Adjunct Faculty to assist with the increased student enrollments and teaching responsibilities (Allen, Arnold, & Armstrong, 2006). Academic institutions are taking a proactive approach to faculty support. Numerous workshops and one-to-one support in course development and technical issues are the most common types of training faculty receive. The faculty development activities are designed to assist and improve faculty teaching at all levels of the educational programs. Workshops, seminars, Webinars, and peer coaching are among services available for faculty development. The focus of these services should not be limited to technical skills development, but must include pedagogical issues. For example, strategies to create active learning activities, engage online learners, or motivate online students are topics that, if explored in depth, would help faculty be more effective online teachers. Today, many universities pair faculty with expert instructional designers to build courses with the latest resources.

Disaggregated Faculty According to Allen, Keough, and Armstrong (2013) a new disaggregated model for faculty content delivery has emerged and is designed for consistency of content delivery as programs respond to large numbers of students. This model segregates design, teaching, and assessment of student learning into a team approach to course delivery (Rosenbloom, 2011). Robison (2013) indicates the disaggregated model helps build a network of student support while the student is learning. This model also provides access to a variety of perspectives due to the availability of different faculty in areas of knowledge providing learning enhancement (Robison, 2013). In addition, the disaggregated model allows universities to scale up more rapidly, allowing course enrollments to soar while maintaining educational quality.

Support for Course Development As noted previously, developing and delivering an effective online course requires pedagogical and technological expertise. Instructors new to online teaching are not likely to have such skills. An example used at our university is the Jumpstart Program. Hixon (2007) defined Jumpstart as a series of workshops that may take more than a week. These workshops include a team of support professionals, including instructional designers, librarians, and media production specialists, who help faculty increase their knowledge, productivity, and teaching experience with technology. Evaluation findings document that this

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Jumpstart program significantly influences the faculty members in their online course development process. Allen, Bakrim, Lacy, Boyd, and Armstrong (2015) note online course development requires a team involving instructional designers, technical support staff, and content experts. The content expert offers an outline of topics that should be covered. The instructional designer provides help in course structure organization and functionality, and the technical support provides assistance with integration of technology tools. This model of team course development is common in most nonacademic settings and has been adopted by a number of academic organizations. However, course development carried out by the instructors who will be teaching the course is still a common practice in many colleges and universities offering online learning. Technological tools for online learning are constantly being developed and improved, with the aim to make online learning more interesting and more effective. Regardless of faculty teaching experience, technology support is critical in online teaching.

Faculty Workload Faculty workload refers to the number of courses taught by an instructor. The allocation of faculty time in higher education usually includes teaching, scholarship activities, and community service. Because teaching online is thought to require more time and effort compared with traditional face-to-face teaching, workload adjustment is usually used by institutions to promote faculty involvement in nonteaching activities. Actual research into the assumption of increased development time has been limited. Research findings by Freeman and Urbaczewski suggest that the time spent with online course development seems to be proportional to classroom teaching. As with traditional classroom development, usually the extra time devoted to making the course effective and applicable then produces a significant reduction of faculty time after first-time delivery. Their research findings further suggest a model for online learning satisfaction that includes course conduct, admissions, curriculum, and prior experience with online courses at that same location to be significant predictors of program satisfaction (Freeman & Urbaczewski, 2019, p. 44). Although academic leaders recognize the critical aspect of long-term planning for online education, there continues to be significant barriers to overcome. As indicated by Allen and Seaman (2015), 78% of academic leaders and only 28% of chief academic officers report that faculty members accept the value of online education. In 2013, there were a number of reasons identified for online education to be under-valued, time commitment and compensation,

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744    P art 8 • E ducational A pplications difficulty keeping up with changing technology, and lack of support with course development are major obstacles (Khalil, 2013). Today, “distance education courses and programs provide students with flexible learning opportunities. In fall 2016, nearly one-third of undergraduate students (5.2 million) participated in distance education, with 2.2 million students, or 13 ­percent of total undergraduate enrollment, exclusively taking distance education courses” (National Center for Educational Statistics, 2018, para. 6). Colleges and universities with appropriate technology capabilities as well as progression away from infrastructure deficiencies in distance education are now employing

creative, engaging learning management systems and harnessing the skills of knowledgeable faculty, supportive administration, and the addition of instructional designers for quality online education.

COURSE DEVELOPMENT The use of the Web for courses can be divided into three categories: hybrid courses, Web-enhanced face-to-face courses, and fully online courses. The selection of approach depends on the needs of the organization, the nature of the content, and the faculty as summarized in Table 46.2.

  TABLE 46.2   Advantages and Disadvantages of Each Type of Course Delivery Mode Course Delivery Mode Hybrid

Web-Enhanced

Fully Online

Description A portion of the course is delivered online and a portion is delivered on site

Face-to-face courses use Web-based technology to facilitate self-studying All of content and communication are conducted entirely online and no face-to-face component

Advantages

Disadvantages

• Moderate level of real-time social interaction • Building learning community prior to moving to the online environment • Broadening communication using technology and face-to-face meeting • Accommodate a variety of learning styles • Flexibility with reduced meeting times • Extensive level of real-time social interaction • Supplement in-class materials with

• Keep up with face-to-face and online components • Having scheduled sessions on campus may be less flexible • Require basic computer skills • Possibility of incompatible technology • • •

and Internet connection with that of the institution Requires much upfront time and effort for the instructor Lecture-oriented class Technical difficulty if technologies are not used correctly

various multimedia tools as well as be the main source of information needed for in-class discussions

• 24/7 access, time to digest and reflect • Lack of rich, contextual cues from on content face-to-face interaction, potential feeling of separation from class, instruc• Cost—flexibility, convenience, savings of travel time tor, classmates, difficulty in using technologies • Students perform assignments and take tests online • Lack of rich, contextual cues from face-to-face interaction, potential feel• Activities are student centered rather than instructor centered ing of separation from classmates and instructor • Convenience, accessibility, ability to spend time on course content and • Writing is often intensive class discussions before responding • Required mastered basic computer skills • Student attendance and content con- • Academic Dishonesty—No proctored sumption can be tracked online exams tend to entail cheating • Discussion Grading—Discussion par- • Speed and mediation of discussion— ticipation can be tracked and graded The flow of the discussion is slower • Possibility of limited local networking opportunities • Computer and troubleshooting skills

(Aydogdu & Tanrikulu, 2013; Dell, Low, & Wilker, 2010; Herman & Banister, 2007; Brinthaupt, Clayton, Draude, & Calahan, 2014; Iwasiw, Andrusyszyn, & Goldenberg, 2020)

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Learner Assessment in Online Courses Assessment is an important aspect in the learning process. Assessment is defined as a means to gather, summarize, and interpret data to decide on an outcome and differs from the previous widely held definition of assessment as a test to evaluate student performance (Bastable, Grannet, Sopezyk, Jacobs, & Brunngart, 2020; Waterhouse, 2005). Using Bastable’s definition, learning may be assessed through a variety of ways such as:

• • • •

•

• • • • •

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Online discussion: Students respond to questions, reply to peers messages, and discuss course materials. Papers: Students submit research papers, or essays. Posting papers to the online discussion forum can spark discussion. Rubrics provide guidelines and a method for self-evaluation. Individual or collaborative projects: Students develop a project individually or as members of a group by using clear directions and guidelines. Presentations/Performances: Synchronous communication systems can be used to make presentations or even have debates. A student can use a whiteboard or show a Web site they would like everyone to view while holding a live discussion. ePortfolio: It is an online application for collecting the student’s work that demonstrates meaningful documentation of individual abilities. Electronic portfolios can serve as a means to assess student’s ability over time, and if the student has met each objective or learning outcome as determined by the instructor or the academic program (McDonald, 2018). Reflection: Reflect on the lesson, projects, actions, and reactions and write down what they have learned. Then, ask them to consider how they would apply this concept or skill in a practical setting. Peer review: Students review each other’s work and provide evaluative feedback. Problem-based activities: Learners are presented with a case or scenario and are expected to analyze the situation and provide resolution or recommendations. Task-based simulation: This involves incorporating a variety of multimedia elements and equipment to test practical and experimental knowledge. Concept mapping: Structure, connect, or manipulate the concepts and contexts in relation to other (Billings & Halstead, 2019).

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STUDENT SUPPORT One of the most critical factors in a student’s success with online learning is student support. Today schools have a wide range of student support services that should help students be successful. These services include precourse orientation, free tutoring services, online writing centers, access to needed learner accommodations, advising, online library source, a standardized CMS, and online technical support. These academic services allow students to be familiar with the technology and improve studentto-instructor and student-to-student communication. The main goal is to increase students’ ease with the cyber environment and encourage constant connection with their peers. In addition to academic support, services that focus on students’ affairs are also important to success and retention.

Orientation to the Online Environment Orientation programs designed to introduce new students to the online environment are crucial to assure a smooth transition, especially for students without prior experience in online learning. The goals of orientations and tutorials are to ensure that students are familiar with the online environment and are aware of expectations. Free tutorials are also helpful, especially with difficult or challenging tasks such as navigating the Web course space, using new software packages and/or equipment, or performing technical procedures (e.g., uploading a file to a Web site).

Communication and Flexibility There are two basic types of Web-based communication:

•

•

Asynchronous communication tools such as e-mail, discussion boards, and blogs. Course participants use these tools when they are online; however, the person to whom they communicate may not be online. They serve as a messaging interface between communicators. Synchronous communication tools require participants to be online to communicate at the same time. These tools include chat, whiteboard, desktop conferencing, and video conferencing such as ZOOM.

To ensure effective communication, instructors must select the most appropriate tool for the course. This will depend on accessibility to the technology and the levels of students’ skills. Communication is strongly affected by course flexibility (due dates and/or assignment

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746    P art 8 • E ducational A pplications s­ubmission). Building flexibility in the course structure allows the faculty to compensate for unexpected technological problems, as well as provides opportunities to respond to student feedback. One successful strategy for communication is to always email within the course using the course inbox or email tool, rather than, allowing the student and faculty to communicate through university email services such as Outlook. This recommendation ensures all communication is captured and remains within the course and no communication or messaging is missed.

LEGAL, ETHICAL, AND COPYRIGHT ISSUES Accessibility in Online Learning To avoid creating barriers in online learning, federal and state laws, and local guidelines and policies for online learning such as Americans with Disabilities Act (ADA) and Rehabilitation Act (American Disabilities and Rehabilitation Act, Office of Special Education and Rehabilitative Services, U.S. Department of Education, n.d.) require that the online learning should be accessible to the broadest range of possible learners. Accessibility of content becomes a legal requirement in many situations. It is important to present instructional content in a format that accommodates the diverse needs and learning styles. Some elements for accessibility include alternative text for images, appropriate color and contrast, accessible and consistent navigation, closed captioning for audio/video materials. Accessibility also applies to online testing. Students with disabilities can have many different types of limitations that affect their abilities to take tests. These individuals who are protected by disability legislation can ask for alternative format and extra time to take tests. Students must apply for an “accommodation” through the university’s student services for accommodations to be made by the school. In 2010 the U.S. Department of Education ­established rules that required state authorization of any distance or online education. This regulation requires any institution enrolling students from outside the state where the institution has physical presence, to obtain authorization from the correct regulatory agency in the state where the student resides (Weeden, 2015). To help navigate this federal regulation, a collaboration of the four regional higher education compacts came together to create the State Authorization Reciprocity Agreement (SARA) (Weeden, 2015). The four regional higher education compacts are New England Board

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for Higher Education (NEBHE), Southern Regional Education Board (SERB), Midwestern Higher Education Compact (MHEC), and Western Interstate Commission for Higher Education (WICHE). According to Weeden (2015), “SARA is a voluntary agreement entered into by states to establish minimum authorization criteria and processes to ensure students’ interests are protected” (para. 5). Each state has established its own process to become a member of SARA and most states require legislative process through an appropriate entity such as the higher education coordinating agency (Weeden, 2015). There is an application process to become a member of SARA and each state must now pay a user fee for membership. The faculty is accountable for educational content they teach. However, accountability is even more at the forefront of education at this time. Eaton (2011) defines accountability as the “how and the extent to which higher education and accreditation accept responsibility for the quality and results of their work and are openly responsive to constituents and the public” (p. 8). Most recently the National Council of State Boards of Nursing (NCSBN) has included this information on their Web site with access to determine your individual states rules and regulations regarding teaching across state lines. NCSBN states, Sometimes nursing programs offer courses outside of where the program has its legal domicile. When that happens, the host state/territory (defined as that state/territory, outside the home state/territory, where students have clinical and/or didactic nursing education) might have its own rules or regulations, in addition to those from the home state/territory, which the nursing program must follow to be in compliance. Additionally, if the home state is not part of the Nurse Licensure Compact (NLC), nurse faculty may need a license in all host states where students take either didactic or clinical courses. Likewise, even if faculty are located in a home state that is part of the NLC, they may need a license in all non-NLC host states where students are taking didactic or clinical courses. (National Council of State Boards of Nursing, 2019, para. 1 & 2)

The Higher Education Act, reauthorized in 2008, made additional demands on accreditors to be more accountable and subsequent creation of rules during 2009 and 2010 expanding accountability expectations even more (Eaton, 2011).

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Legal concerns relate to established laws associated with telecommunication technologies, whereas ethical concerns relate to the rights and wrongs stemming from the values and beliefs of the various users of the distance education system. Three major areas that are of concern regarding legal issues include copyright protection, interstate commerce, and intellectual property. Privacy, confidentiality, censorship, freedom of speech, and concern for control of personal information continue to be as relevant today as in 1998 when Bachman and Panzarine (1998) identified these cyber ethical issues.

Copyright Protection Copyright is a category of intellectual property and refers to creations of the mind (World Intellectual Property Organization, n.d.). According to the World Intellectual Property Organization (WIPO) Web site (www.wipo.int/ policy/ed/sccr/), the Standing Committee on Copyright and Related Rights (SCCR) is currently engaged in the discussion of:

• •

Limitations and exceptions Broadcasting organizations

This protection for Copyright is based on the Copyright Act of 1976, and was last amended November, 1995 (World Intellectual Property Organization, n.d.). Copyright law protects “works of authorship,” giving developers and publishers the right to control unauthorized exploitation of their work (Radcliff & Brinson, 1999). Although there have been no new federal laws since 1976 to address educational multimedia concerns, the Consortium of College and University Media Centers has published the Fair Use Guidelines for Educational Multimedia (Dalziel, 1996). When combining content such as text, music, graphics, illustrations, photographs, and software, it is important to avoid copyright infringement (Radcliff & Brinson, 1999). In addition, the Digital Millennium Copyright Act was passed in October 1998. The U.S. Copyright Office Summary can be located at www.copyright.gov/legislation/dmca.pdf. As noted by the dates of citations here, regulations and legislative guidance seem to lag from the technological changes incorporated within the online educational arena.

faculty may own the materials they have developed for use in their online courses, it is always good to have a memo of understanding documenting the specific use of the materials as well as the accrued benefits (Billings & Halstead, 2019). The issue of “work made for hire” is the point of controversy. According to the 2003 U.S. Copyright Office document, as indicated by Kranch (2008), a “work made for hire” is defined in the following ways: 1. A work prepared by an employee within the scope of his or her employment

2. A work specially ordered or commissioned for use as a contribution to a collective work The bottom line of this section is that faculty should know their employer’s policy pertaining to intellectual property rights. Over the last several years, universities, government, and private organizations have noted the need to clearly delineate their policies in this area. For example, our school has an established university-wide committee providing advisory opinions to the Provost on matters related to patentable discoveries and inventions, and/or copyrightable material, which had been developed by university employees. MIT Open Courseware (2010) is a free and open digital publication of educational material (http://ocw.mit.edu/ index.htm). However, there are specific guidelines and requirements for the use of Open Courseware. Although Open Courseware is available to anyone, material used in education from any Open Courseware participant is consistent with materials from any university and/or faculty. Additional information on Open Courseware can be found at http://ocw.mit.edu/help/. Extensive resources on intellectual property law and rights can be found at the following sites:

• •

Intellectual Property A common question by faculty is, “Who owns the course?” According to Kranch (2008) there is a great deal of controversy over who owns academic coursework materials. U.S. copyright law is intended to provide ownership and control of what an individual has produced. However, its relationship to faculty-produced work is not as clear. Although

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Indiana University Information Policy Office (informationpolicy.iu.edu). Office of Technology Transfer and Intellectual Property at Texas Tech University Copyright (https://www.ttuhsc.edu/administration/documents/ ops/op57/op5702.pdf) Intellectual Property (https:// www.ttuhsc.edu/compliance/documents/op5206. pdf). Legislative initiatives regulating intellectual property and copyright are found in the Technology, Education and Copyright (TEACH) Act (www.arl. org/pp/ppcopyright/index.shtml) The Creative Commons Web site https:// creativecommons.org/about/program-areas/ education-oer/ is a nonprofit organization that works to increase the amount of creativity in the body of work available to the public for free and legal sharing, use, repurposing, and remixing.

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748    P art 8 • E ducational A pplications OERs have shown an increase in use and is cost effective. According to the 2017–2018 survey conducted by the Babson Survey Research Group, a steady increase in faculty awareness of OERs is reported. According to the survey, about 50% of faculty report awareness of OER. Therefore, even though faculty reportedly have concerns about the cost of textbooks, slightly more than half remain unaware of OER alternative (Babson Survey Research Group, 2017). Ethical behavior in the nursing profession has been established by groups such as the American Nurses Association (ANA) in the Code of Ethics (ANA, 2001) and the American Association of Colleges of Nursing’s (AACN, 2008) competencies for baccalaureate nursing education. These nursing values and ethics are fundamental in practice decisions and are just as applicable in nursing education, whether education be face to face or online. Mpofu (n.d.) regards ethical considerations in online teaching as performing your work within the context of professional practice and the confines of institutional regulations. However, over and above professional and institutional ethics, nurse educators must contend with legal and ethical issues that take on a new dimension when applied to online education. While issues such as copyright, privacy, licensing, fair and acceptable use, and plagiarism are certainly not unique to online education, they assume new dimensions and different proportions.

Academic Integrity The Web has provided global access to unlimited information and resources. Advances in technology has created new modes and avenues of academic misconduct in online learning (Etter, Cramer, & Finn, 2006). While maintaining academic integrity is of utmost importance in any educational settings, it often proves to be an even greater challenge within the online format. Violations of academic integrity such as plagiarism, cheating, and other dishonest behaviors become easier and more prevalent because of the lack of direct contact with students (Watson & Sottile, 2010). Plagiarism is an ongoing challenge in academia today, and an excellent resource for faculty and students established by Story-Jackson, Rogers, and Palmquist (2018) can be found at: https://wac.colostate.edu/resources/teaching/guides/plagiarism/. However, a number of other vendors have seen this situation as an opportunity to market plagiarism detection services, such as Turnitin.com, and online proctoring for assessments such as Proctorio and Respondus Monitor. Creating an environment of academic integrity is crucial in all course work whether face to face or online.

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Institutional policies and clear expectations regarding academic honesty and dishonesty should be available in program and course orientation as well as throughout the curriculum. In order to create and encourage a culture of honesty, clear explanations should be provided of what constitutes academic honesty and dishonesty and what plagiarism is and is not and this information should be part of the course syllabus. Students need to be guided to understand that cheating is unacceptable in course work and in the profession of nursing. “Discussion about how academic honesty relates to professional values and ethics can add importance to ideas of academic integrity for students” (Iwasiw, Andrusyszyn, & Goldenberg, 2020, p. 463).

EFFECTIVENESS OF ONLINE EDUCATION Online learning for nursing courses is exploding. Advertisements about “new online education for working professionals” certainly have appeal, capturing the attention of many people seeking to fit further education into their busy schedules. Yet, there are still some traditional students who do not pay attention to online education, there are still some faculty who avoid the concept by raising questions of quality rather than exploring the educational principles used in online learning, and there are still some who believe the only “gold standard” of education continues to be the traditional classroom setting (Allen et al., 2015). In addition, questions emerge concerning the validity of the courses: Is it really possible to earn a degree while at home or in the work setting without driving long distances and sitting in tedious lecture classes? Is the interaction with the faculty equal to the same interaction that occurs in the classroom? Is this really applicable to clinical nursing? Overall, market-driven demands of educational reform and creative, visionary faculty have moved online learning, transforming both academic and continuing nursing education, by capturing new types of educational experiences and innovative kinds of pedagogy (Allen & Seaman, 2015; Allen et al., 2015). The outcomes have been an empowerment of the nursing student and working professional to have numerous important educational choices. Now, in addition to quality, the educational decisions are often based on accessibility and the amount of time needed to complete the course or program. Online learning offers greater alternatives to accommodate individual circumstances and educational needs. Now, it is becoming a commonly accepted instructional method in higher education institutions, and the numbers

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of online courses are constantly increasing to accommodate the large number of students enrolling. For the past six years, online enrollment has grown at a greater rate than the total higher education enrollment (Allen & Seaman, 2018). According to the Sloan Consortium Report (2017), overall online enrollment increased to 6 million in 2016, with the majority of doctoral-granting universities (80%) offering online courses or programs. In order to purport quality, educational outcomes must be similar for both the on-campus and online learning students; countless studies over at least three decades have documented this (Dede, 2013; Mahan & Armstrong, 2003; Schlosser & Anderson, 1994; Campbell, Taylor, & Douglas, 2017; Desteghe et al., 2018). Findings reflect that regardless of the delivery method, online learning students receive the same grades or do better than those students receiving traditional instruction. Overall, student evaluations are good to very good following online education activities. In essence, good online education theory and good education theory are actually the same; the education just transcends the barriers of time and space. One relatively new education theory has emerged through the work of Brown, Roediger, and McDaniel (2014). The research has redefined the learning process into three distinct stages in order for learning to be long term and not just short-term recall. The stages for this theory of “Making It Stick” are encode, consolidate, and retrieve. All stages require very specific learner interactions, but for online educators who build learning opportunities with the option to revisit content, practice for new knowledge and skills, and layer new concepts with previously learned concepts “deeper thinking” emerges for the learner.

administrators should be completed and analyzed annually. In addition, course surveys should be completed by students at the end of each course. Regional accreditation agencies assist in guiding programs for maintaining standards in program delivery, and regional credentials are sought after by major colleges and universities. Regional accreditation is a continuous improvement process involving the entire university or college. Many of the regional accrediting agencies, such as the Southern Association of Colleges and Schools (SACS), engage the college or university to pursue a continuous improvement process of self-evaluation, reflection, and improvement for not only face-to-face learning but distance learning as well (Southern Association of Colleges and Schools, 2010). Other regional accrediting agencies providing excellent resources for online program assessment and evaluation include Western Interstate Commission on Higher Education (WICHE) and WICHE Cooperative for Educational Technologies (WCET). WCET, a division of WICHE, provides information on excellent practices and policies to ensure the effective adoption and appropriate use of technologies in teaching and learning online (wcet.wiche.edu/advance). Accreditation agencies require that each facet of the online program be critically and logically appraised to reflect the quality of the programmatic goals and outcomes designated within the program. There is no one type of accreditation applied to online education. In fact, there are several types of accreditations for different institutional statuses, and they are categorized into regional, national, and professional accreditations (see Table 46.3).

PROGRAM EVALUATION AND ACCREDITATION

STANDARDS FOR QUALITY IN ONLINE EDUCATION

Program evaluation is an ongoing process in online education and requires a framework for evaluation to be adopted by the faculty, standards, and outcomes to be defined, as well as a timeline for measurement of outcomes. Program evaluation focuses on review and improvement. The need for curriculum revision, resources, and faculty and staff may become apparent during this ongoing review process. Program evaluation allows educators to facilitate meaningful change, while providing feedback. All program evaluation gathers evidence for measurement against predetermined outcomes. The framework will provide the steps to outcome attainment. With systematic program evaluation, revision decisions are based on the evidence from findings rather than assumptions. To obtain this evaluative data, program surveys by faculty, students, and

To ensure the quality of online education, various organizations (Table 46.4) have recently developed standards for this type of education. The main purpose of these standards is to guide the development and evaluation of online learning programs offered through colleges and universities.

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FUTURE TRENDS The future trends in online learning will be defined by student empowerment and technological advancements. The population and student enrollments have grown extensively during the last ten years (Allen & Seaman, 2018) and it is anticipated that the field of online education will witness a tremendous growth both in terms of quantity

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750    P art 8 • E ducational A pplications

  TABLE 46.3    Regional, Professional, and National Accreditation Regional Accreditation Organizations

Professional Accreditation

There are six regional accreditation agencies. These ­agencies accredit both public and private schools, and 2-year or 4-year institutions by reviewing their program and ­delivery methods based on established standards and requirements. The online programs are expected to meet the general institution standards as well as other criteria specific to online setting such as faculty support, faculty qualification, student support, and the necessary ­infrastructure to develop and deliver effective online ­education. For more information, see www.neasc.org.

Professional accreditation varies by discipline and is a key to defining a quality unit or discipline within a community college, college, and/or university. For nursing, two widely sought professional accrediting agencies are the Accreditation Commission for Nursing Education (ACEN) and Commission on Collegiate Nursing Education (CCNE). Both ACEN and CCNE are recognized by the U.S. Department of Education. The CCNE agency accredits only baccalaureate and higher degree programs in nursing. ACEN offers accreditation to vocational and practical nursing programs, associate’s, diploma, bachelor’s, master’s, and doctoral programs within nursing schools and/or departments. Both require online education offerings to be equivalent to site-based offerings and may have specific standards to be addressed by the school in the accreditation process.

  TABLE 46.4    Organizations With Standards Supporting Quality Online Education Quality Matters

The Online Learning Consortium

Quality Matters (QM) is another evaluative process that employs a set of standards based on best practices in instructional design developed to verify the quality of online or hybrid courses. It is based on faculty-oriented peer review and provides tools for the review process while focusing on course design more than content or delivery method. For more information, visit www. qualitymatters.org.

The Online Learning Consortium (2013) is dedicated to making online learning a mainstream higher education delivery methodology, and this international consortium provides tools, best practices, and directions for educators. The Sloan Best practice model can be viewed at http://olc.onlinelearningconsortium.org/conference/2014/ ALN/welcome.

as well as quality for higher education. For example, for education at all levels and in corporate learning the areas predicted to grow are blended learning for merging types of learning such as the flipped classroom with unfolding cases, project-based learning, adaptive learning (adjusts to learners needs), redesigned classroom that are tech savvy and interact immediately with the instructor and peers, mobile learning, and learning analytics and visualization software (allowing the faculty and the student to view where they are the curriculum and what areas of content may need to be revisited by faculty or student) (Lynch, 2018). Gaming will continue to grow as previously highlighted by Skiba (2013).

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iNACOL National Standards for Quality Courses iNACOL provides the benchmark for establishing quality online courses in the areas of content, instructional design, student assessment, technology, and course evaluation and support. iNACOL creates standards for online teaching and online programs to help faculty improve the quality and accessibility of online courses. The standards are used as guidelines for districts and organizations implementing blended or online learning. Standards can be viewed at https://www.inacol. org/resource/inacol-national-standards-for-quality-online-teaching-v2/

By focusing on processes of actual cognitive development Stanford University is researching new forms of learning such as immersive learning where “Hands-on, interactive learning is an effective method because it immerses the learner in the experience. However, due to limited resources, space or distance, not all learning experiences can be physically immersive. Our team has been experimenting with interactive video, VR and 360 to digitally emulate an immersive environment that engages the learner in the experience” (Adams, 2019, para. 4). In addition, immersive virtual learning environments will be tailored to the learner’s desired competency set. Here the student will enter an immersive virtual environment

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Chapter 46 • Initiation and Management of Accessible, Effective Online Learning 

which transcends real-world time and is paired with virtual teammates designed to enable the student to meet identified competencies (Dede, 2013). The next apparent trend changing online learning is the advancement of technologies. We now expect tools to have geo-everything and gesture-based computing through a tap or a swipe, but the future may bring technology that allows computing through subtle body gestures with wearable computing (Skiba, 2013). Along with the wearable technology, 3D printing will become commonplace (Hidalgo, 2013). A ubiquitous device is one where users have become so accustomed, they no longer notice the device itself when they are using it. Instead, users tend to focus on what they get from the device not the microprocessor engaged. “The goal of pervasive computing is to make devices ‘smart,’ thus creating a sensor network capable of collecting, processing, and sending data, and, ultimately, communicating as a means to adapt to the data’s context and activity; in essence, a network that can understand its surroundings and improve the human experience and quality of life (Iot, 2016, para. 3). Today we do have the ubiquitous device as a single device or service that takes care of all of our computing needs. These computing devices will continue to be part of an exciting new world for online learning opportunities as well as the human experience. The 2017 Horizon Report by the New Media Consortium described six technologies that universities will likely mainstream within the next 5 years. One example is virtual and augmented reality. It is expected to show the greatest increase in adoption in various disciplines. Health-related online degrees can teach students how to interact with patients that would be otherwise impossible to replicate in the real world. Virtual and simulation technology will increase and become the norm with all online training experiences taking place in virtual environments that mimic the workplace. Employees can establish their skills without having to worry about realworld risks. Like immersive learning, adaptive learning is another approach that is reshaping higher education and corporate training (Walkington, 2013). This is also known as “personalized learning.” Adaptive learning uses artificial intelligence (AI) to adjust content and assessment to each individual’s needs, styles, and progress. A good example in the K-12 education is the company Dreambox, which teaches math and actively tailor content to the learner based on level of comprehension, efficiency of strategy, amount of help needed, and learner response time. An increase in adaptive learning technologies that could affect the LMS market is expected. LMS functions deviates from one-size fits all approach to meet specific needs

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of students and teachers. An example of adaptive learning tool is ActiveTextbook (www.activetextbook.com). Microlearning is another growing trend in education and training. Microlearning is presenting the content and skills into bite-sized, small learning units with just the necessary amount of information to help learners achieve a goal. In the online learning and instructional design realm, it’s the latest buzzword. One of the more popular forms of microlearning today is podcasts and short videos. There will be more technologies that offer live interactive instruction. With all the growth in online education, the need for effective course management systems will be ever more crucial. Furthermore, technological advancements will also increase the need for developing effective teaching strategies that exploit the capabilities of technology. Massively open online courses (MOOCs) will continue to explode as noted in the latest NMC Horizon Report (Johnson et al., 2013; Adams Becker et al., 2017). And the movement on the horizon is for MOOCs to determine mechanisms for awarding credit (Kolowich, 2013). Skiba (2016) recently noted students of the future will use mobile devices more, but will also still value a mix of online and face-to-face learning environments and although technology will enhance achievement of their learning goals, students will continue to value privacy. There is a limit to connectivity and students will continue to keep academic and social lives in separate silos (Skiba, 2014). In 2016, Skiba predicted a growing use in predictive analytics obtained through data mining and harvesting data to examine student readiness, progression, and remediation needs. Predictive analytics in education is aiding educators to determine the best method to measure learning (Skiba, 2016). And Skiba points out how the use of this mined data has raised ethical questions concerning student consent. The pandemic of 2020 has vastly impacted distance education. Today, all students from kindergarten to doctoral studies are experiencing a new realm for learning with the widespread closure of face-to-face classes. Educators and students alike are learning to manipulate this virtual space with innovation and the use of video platforms such as ZOOM at the forefront of teaching and learning. There are many resources available for rapid innovation to move curriculum to an online format. One comprehensive resource from the Commonwealth of Learning (2020) may be retrieved from https://www.col.org/resources/keepingdoors-learning-open-covid-19. This resource addresses administrative and faculty concerns as well as providing an abundance of university resources.

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752    P art 8 • E ducational A pplications Finally, interprofessional educational opportunities will continue to emerge as platforms continue to be created to allow disciplines to work across learning management systems. One current platform in use at many universities is the “I-Wall,” which allows students to work on a case study together in real time from multiple sites while being able to visualize the actions of all using an interactive whiteboard.

Test Questions 1. A number of technologies are employed in the delivery of online learning, yet not all online programs use all of the technologies. An example of an online delivery system is: A. Mobile commuting

B. Learning management system C. Web-enhanced

D. Reflection technology 2. Web for courses can be divided into three categories: A. Hybrid courses, Web-enhanced face-to-face courses, and fully online courses

B. Face-to-face courses, fully online, and mobile commuting courses

C. Mobile commuting, hybrid, and fully online courses D. Hybrid courses, Web-enhanced courses, and face-to-face courses

3. “Employing creative, engaging learning management systems and harnessing the skills of knowledgeable faculty, supportive administration and the addition of instructional designers for quality” could be the definition for: A. Learning management systems B. Reflections technology

C. Online distance education D. Hybrid courses

4. Online discussion, eportfolios, presentations, and collaborative projects are all examples of: A. Learner assessment

B. Teacher assessment C. Course modules

D. Learner questioning

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5. Pre-course orientation, free tutoring services, online writing centers, access to needed learner accommodations, advising, online library source, a standardized CMS, and online technical support are all examples of: A. Tools for course building B. Services for faculty

C. Services for students

D. Tools for instructional technologists 6. Asynchronous communication tools include tools such as: A. E-mail, discussion boards, and blogs

B. Chat, whiteboard, desktop conferencing, and video conferencing such as ZOOM C. Email, whiteboard, conferencing, and blogs

D. Video conferencing, desktop conferencing, and blogs 7. The Americans with Disabilities Act (ADA) and Rehabilitation Act (www.ed.gov/about/offices/list/ osers/osep) require that: A. Online learning should not be accessible to the broadest range of possible learners.

B. Online learning may be accessible to the broadest range of possible learners. C. Online learning could be accessible to the broadest range of possible learners.

D. Online learning should be accessible to the broadest range of possible learners. 8. Although online education continues to grow, there are significant barriers to overcome. According to Khalil (2013), some of the barriers identified are: A. Time commitment, compensation, and keeping up with changing technology B. Internet accessibility, lack of interest in course development, and decreased student engagement C. Perceived ease of plagiarism, test integrity, and lack of peer support and student networking

D. Traditional education continues to be the method of choice for most students and faculty.

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Chapter 46 • Initiation and Management of Accessible, Effective Online Learning 

9. A common question by faculty is “who owns the course” developed for online courses. This question differs from face to face in what way?

A. Online courses are not covered under U.S. Copyright laws, providing faculty ownership and control of material created. B. Faculty development of online and face-to-face courses are both covered by U.S. Copyright laws but become property of the university or college faculty teach. C. Faculty should know their employer’s policy ­pertaining to intellectual property rights for both online and face-to-face course development. D. Due to the rapid increase of on line material, course content is copyright but formating of material does not fall under copyright laws.

10. Ethical behavior in the nursing profession including education has been established by: A. Strategies to Promote Academic Integrity B. ANA and AACN

C. Standards for Quality Online Education D. State Boards of Education

Test Answers 1. Answer: B

2. Answer: A 3. Answer: C

4. Answer: A 5. Answer: C

6. Answer: A

7. Answer: D 8. Answer: A 9. Answer: C 10. Answer: B

REFERENCES Adams Becker, S., Cummins, M., Davis, A., Freeman, A., Hall Giesinger, C., & Anathanarayanan, V. (2017). NMC horizon report: 2017 higher education edition. Austin, TX: New Media Consortium.

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Simonson, M., Smaldino, S., Albright, M., & Zvacek, S. (2014). Teaching and learning at a distance: Foundations of distance education (6th ed.). Boston, MA: Allyn & Bacon & Pearson. Skiba, D. J. (2013). On the horizon: The year of the MOOCs. Nursing Education Perspectives, 34(2), 136–138. Skiba, D. J. (2014). The connected age: Implications for 2014. Nursing Education Perspectives, 35(1), 63–65. Skiba, D. J. (2016). On the horizon: Trends, challenges, and educational technologies in higher education. Nursing Education Perspectives, 37(3), 183–185. doi:10.1097/01. NEP.0000000000000019 Southern Association of Colleges and Schools (SACS). (2010). Standards and accreditation process for schools. Retrieved from http://www.sacscoc.org/principles.asp Story-Jackson, L., Rogers, C. A., & Palmquist, M. (2018). Dealing with plagiarism: The WAC clearinghouse. Retrieved from https://wac.colostate.edu/resources/ teaching/guides/plagiarism/.Vital Source technologies. (2019). The corporate MOOC to solve high-stakes business challenges through engaging and applied learning at scale. Intrepid. Retrieved from https://news. intrepidlearning.com/microsoft-transforms-globalsalesforce-corporatemooc?utm_campaign=Intrepid%20 Website&utm_source=ppc&utm_medium=google-corpor atemooc&msclkid=e02e78ae41eb1d5b47e5240c194add5a Walkington, C. A. (2013). Using adaptive learning technologies to personalize instruction to student interests: The impact of relevant contexts on performance and learning outcomes. Journal of Educational Psychology, 105(4), 932–945. Waterhouse, S. (2005). The power of eLearning: The essential guide for teaching in the digital age. Boston, MA: Pearson Education, Inc. Watson, G., & Sottile, J. (2010). Cheating in the digital age: Do students cheat more in online courses? Online Journal of Distance Learning Administration, 13(1). Retrieved from https://eric.ed.gov/?id=EJ877536 Weeden, D. (2015). Authorizing higher education across state lines, 23 (29). Retrieved from https://www.ncsl. org/research/education/authorizing-higher-educationacross-state-lines.aspx World Intellectual Property Organization. (n.d.). What is intellectual property? Retrieved from http://www.wipo. int/about-ip/en

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47 Social Media Tools in the Connected Age Diane J. Skiba / Sarah Mattice / Chanmi Lee

• OBJECTIVES 1. Describe the evolution of the Internet of Things and the movement toward the Connected Age. 2. Describe the use and benefits of social media tools in the connected care ecosystem. 3. Identify the challenges and issues related to the use of social media tools in the Connected care ecosystem.

• KEY WORDS Connected Age Social media Social networking

INTRODUCTION The Internet has revolutionized the computer and communications world like nothing before. The Internet is at once a world-wide broadcasting capability, a mechanism for information dissemination, and a medium for collaboration and interaction between individuals and their computers without regard for geographic location (Leiner et al., 1997, p. 102). There is no doubt the Internet provided the necessary infrastructure to revolutionize the way scientists and researchers from the worlds of academia, business, and government could share data, interact, and collaborate with each other. But it was not until the introduction of the World Wide Web that “everyday people” without computer programming skills were enabled to reap the benefits of this revolution. The Web not only changed how governments and businesses operate, it has impacted every facet of society—how we work, learn, play, and now, even how we manage our health.

In this chapter, there is a brief history of the evolution of the Internet to the Web and now to the Connected Age. There is a specific focus on the use of social media as digital health tools. This is particularly true as we evolve from the Web 2.0 era to the Connected Age where it is not only access and interactions but about establishing relationships. As Sarasohn-Kahn (2008, p. 2) noted, “social media on the Internet are empowering, engaging and educating consumers and providers in healthcare.” In the Connected Age, everything and everyone is interconnected that ultimately will have an impact on how we learn as well as how we receive healthcare. The benefits and challenges related to the growing use of these tools are also discussed.

HISTORICAL PERSPECTIVE Internet As early as the 1960s, computer scientists began to write about the creation of a network of interconnected 757

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758    P art 8 • E ducational A pplications computers where scientists could share and analyze data by interacting across the network (Leiner et al., 1997). According to Cerf (1995), “the name ‘Internet’ refers to the global seamless interconnection of networks made possible by the protocols devised in the 1970s through DARPA-sponsored research.” The Internet is defined as “a computer network consisting of a worldwide network of computer networks that use the TCP/IP network protocols to facilitate data transmission and exchange” (http:// wordnetweb.princeton.edu/perl/webwn). Over the next decade, various government agencies and companies conducted considerable research to support the advancement of the Internet. It was not until 1985 that a broader community, in particular the academic community beyond the computer scientists, was given access to the Internet. NSF funding for the Internet continued for almost a decade before the Internet was redistributed to regional networks with the eventual move toward interconnecting networks across the globe. As the Internet came to expand, Tim Berners-Lee wrote his seminal paper “Information Management: A proposal” that circulated throughout the European Council for Nuclear Research (CERN) organization. The paper explicated his ideas that using a hypertext system that would allow for storage and retrieval of information in a “web of notes with links (like references) between them is far more useful than a fixed hierarchical system” (Berners-Lee, 1989). In 1990, Berners-Lee’s paper was recirculated and he began development of a global hypertext system that would eventually become the World Wide Web (WWW). As the WWW concept evolved, Marc Andreessen and Eric Bina at the University of Illinois developed a browser called Mosaic that provided a graphical interface for users. This browser is credited with popularizing the Web.

World Wide Web It is important to note that although many use the terms Internet and Web synonymously, there are differences between them. While the Internet is the network of interconnected computers across globe, the Web is an application that supports a system of interlinked, hypertexted documents. One uses the Internet to connect to the Web. A Web browser allows the user to view Web pages that contain text, images, and other multimedia. Web 1.0.   The Web in its first iteration (Web 1.0) allowed users to access information and knowledge housed on Web pages complete with text, images, and even some multimedia. It was considered a dissemination vehicle that democratized access to information and knowledge. Many in the field designate the time period between 1991 and

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2004 as Web 1.0. This was an important era and, as noted by Friedman (2005), the world suddenly became flat—his metaphor for the leveling of the global playing field. The convergence of the personal computer with the world of the Internet and all its services facilitated the flattening. The flattening was particularly powerful in the world of commerce but also exploded in higher education, making it easier for students to access knowledge beyond their own academic campus. For healthcare, it was a time when consumers could then have access to health information and knowledge that was not locked in an academic library or in a distant place. Web 2.0.   O’Reilly and Doughtery introduced the term Web 2.0 at a 2004 conference brainstorming session (http://oreilly.com/web2/archive/what-is-web-20.html) about the failures of the dot.com industry. It was apparent that despite the demise of the dot.com industry “the web was more important than ever, with exciting new applications and sites popping up with surprising regularity” (O’Reilly, 2005). There were several key concepts that formed the definition of Web 2.0. First, the Web is viewed as a platform rather than an application. Second, the power of the Web is achieved by harnessing the collective intelligence of the users. A third important principle was that the Web provided rich user experiences. The introduction of Web 2.0 embodies the long history of community spirit of the Internet conceived by its originators. As Leiner et al. (1997, p. 206) noted, “the Internet is as much a collection of communities as a collection of technologies, and its success is largely attributable to satisfying basic community needs as well as utilizing the community effectively to push the infrastructure forward.” The transition from an information dissemination platform to an engaging, customizable, social and media-rich environment epitomizes this next generation of the Web. As Downes (2005) stated, “the Web was shifting from being a medium, in which information was transmitted and consumed, into being a platform, in which content was created, shared, remixed, repurposed, and passed along.” Another important feature was the idea of users interacting and sharing information, ideas, and content. Owen, Grant, Sayers, and Facer (2006) aptly described the transition of the Web, “we have witnessed a renaissance of this idea in the emergence of tools, resources and practices that are seen by many as returning the web to its early potential to facilitate collaboration and social interaction.” Although some have predicted (Berners-Lee, Hendler, & Lassila, 2001) that there will be Web 3.0, known as the Semantic Web, this never materialized as projected. There have been more recent references to such terms as the Internet of Things (IoT) and the Connected Age. Both are

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fairly similar but there are some distinctions. Ashton (2009) first described the IoT as “a system where the Internet is connected to the physical world via ubiquitous sensors.” In the 2012 Horizon Report (Johnson, Adams, & Cummins, 2012, p. 30), IoT “is the latest evolution of network-aware smart objects that connect the physical world with information.” Skiba (2013, p. 63) noted, “Several attributes are associated with these smart objects; they are small, easy to attach and unobtrusive, contain a unique identifier and data or information, and can connect with an external device on demand (e.g., your smartphone or tablet).”

CONNECTED AGE Oblinger (2013) introduced the concept of the Connected Age in higher education. Abel, Brown, and Suess (2013) described the Connected Age as an environment that “offers new ways to connect things that were previously considered disparate and ‘un-connectable’: people, resources, experiences, diverse content, and communities, as well as experts and novices, formal and informal modes, mentors and advisors.” Oblinger (2013, p. 4) further noted, “Connecting is about reaching out and bringing in, about building synergies to create a whole that is greater than the sum of its parts. Connecting is a powerful metaphor. Everyone and everything—people, resources, data, ideas—are interconnected: linked and tagged, tweeted and texted, followed and friended. Anyone can participate.” As noted by Skiba (2014, p. 63), “In higher education, we can think of these as learning pathways, created by the individual or guided by other students or faculty. The bottom line is that learning pathways are about connecting the dots—in the classroom, online, or even with people and places outside the traditional academic environment.” In healthcare, Caulfield & Donnelly (2013) offered a model of Connected Health that “encompasses terms such as wireless, digital, electronic, mobile, and tele-health and refers to a conceptual model for health management where devices, services or interventions are designed around the patient’s needs, and health related data is shared, in such a way that the patient can receive care in the most proactive and efficient manner possible. In this model, patients, caretakers, and providers are ‘connected’ by means of timely sharing and presentation of accurate and pertinent information regarding patient status through smarter use of data, devices, communication platforms and people.” Iglehart (2014, p. 2) concurred that Connected health is “an umbrella term to lessen the confusion over definitions of telemedicine, telehealth and mHealth.” Iglehart (2014) also considered connected health as an emerging disruptive technology that has the potential to transform the healthcare delivery system.

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•  FIGURE 47.1.  The concept of connected care. (From Skiba, D.J. (2016) Nursing & Connected Care: Blending High Touch with High Tech. Faculty Presentation at Florida Atlantic University College of Nursing. Boca Raton, Florida.) Although both terms, IoT and Connected Age, speak to connections to everything and everyone, IoT focuses on those connections with physical objects whereas the Connected Age refers to more virtual connections especially with people, resources, and ideas. Cisco (https:// www.cisco.com/c/dam/global/en_my/assets/ciscoinnovate/pdfs/IoE.pdf ) considers this the Internet of Everything (IoE) as it represents an “intelligent connection of people, process, data and things.” Skiba et al. (2016) refer to this as Connected Care as the intersection of high tech and high touch (Fig. 47.1). It is within the context of the Connected Care that we examine the use of social media as one digital health tool being used to transform healthcare. The increasing use of digital health tools is part of a healthcare revolution. According to Bazzoli (2018), there are six trends that are catalyzing this revolution. The first and probably the most important is the rise of consumerism and patients wanting more control of their health. The growth of nontraditional healthcare delivery systems is another trend. As consumers pay more for their healthcare, there is considerable dissatisfaction with rising healthcare and drug costs. Providers are no longer the gatekeepers to healthcare is the fourth trend. As noted by Sarasohn-Kahn (2018), “The new front door for health/care will be the patient’s home front door.” The increased focus on health, wellness, and preventative care is the fifth trend. The last trend relates to data and technology. As noted by Skiba (2018), “the more data that can be collected and shared, the more likelihood patients can get the right care at the right time and place.

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760    P art 8 • E ducational A pplications The more digital health tools, the more likely the consumer will be able to manage their health.” Other researchers refer to the five disruptors that will revolutionize healthcare: personalized medicine, consumerism, digital revolution, regulatory change, and the Amazon effect (Murphy & Jain, 2018). The “Amazon effect” refers to a consumer-centric organization that provides convenience and availability of services online. Murphy and Jain (May 2018) stated that “among the most important players in the healthcare’s disruption story are patients. After years of contending with limited options regarding where, when, how, and from when they get care, healthcare consumers now have choices. They can pick from a variety of delivery models, including telemedicine, concierge care, and online self-help.” Many believe that it is technology that is the disruptor but according to Manis (2018), the disruption is from consumer-centric organizations that allow more consumer-centric tools. Manis stated, “healthcare customers expect much more than access, quality and affordability. They expect exceptional, retail-like experience: ease of use and immediacy of service, how, when and where it is most convenient for them and not us” (meaning healthcare systems). Manis acknowledged that it is true that many consumer-centric organizations use digital tools, their driving force is exceptional consumer services. This notion is echoed in an AMIA (2017) White Paper in Redefining Our Picture of Health: Towards a Person-Centered Integrated Care, Research, Wellness and Community Ecosystem. In this report, the AMIA notes that over the last few years, new data types and technologies have provided us a more complete picture of an individual’s health. This is spearheaded by the abundance of available patient-generated health data (PGHD). “These trends are converging to deliver a more refined and complete picture of health, where personalized care can deliver treatments tailored to the individual, where a single patient can inform and improve the health of populations, and where the ‘n-of-many’ can be leveraged to better understand the ‘n-of-1’ and vice versa (AMIA, 2017, p. 4).” In the Connected Age, digital tools are primarily associated within the broad context of social media and mobile applications. Each year, We Are Social, a creative social media agency, prepares a global digital yearbook (https://wearesocial.com/global-digital-report-2019). The purpose of their report is to highlight the growth of the Internet and digital tools across countries including developing nations. For 2019, there were 4.338 billion Internet users (57% of the total global population) and 5.112 billion unique mobile users (67% of the global population). They report that the average user spends 6.5 h per day online,

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of which the greatest portion of that time is using social media tools. Although there has been a steady increase in the number of social media users, there have been some declines in the use of specific social media platforms. The Pew Research Center’s report on Social Media Use in 2018 (Smith & Anderson, 2018) noted that the majority of Americans used Facebook and YouTube but the younger generations gravitate toward Snapchat and Instagram. There has also been an increase in the use of Twitter.

Social Media Digital Health Tools To better understand the tools being used in the Connected Age, it is important to define social media. In some cases, social media is used as the broad category that encompasses all of the Web 2.0 tools. Anthony Bradley (2010) in his blog (blogs.gartner.com/anthony_bradley/2010/01/07/anew-definition-of-social-media) offered a new definition, “social media is a set of technologies and channels targeted at forming and enabling a potentially massive community of participants to productively collaborate. … enable collaboration on a much grander scale and support tapping the power of the collective in ways previously unachievable.” According to Bradley (2010), there are six defining characteristics that distinguish social media from other collaboration and communication IT tools. These characteristics are Participation, Collective, Transparency, Independence, Persistence, and Emergence. Participation echoes the “wisdom of the crowds” concept, but note that there is no wisdom if the crowd does not participate. The term collective refers to the idea that people collect or congregate around content to contribute, rather than the way individuals create and distribute content in the Web 1.0 world. Transparency refers to the fact that everyone can see who is contributing and what contributions are made. Independence refers to the anytime, anyplace concept; people can participate regardless of geography or time. Persistence refers to the notion that information or content being exchanged is captured and not lost as in a synchronous chat room. Lastly, “the emergence principle embodies the recognition that you can’t predict, model, design and control all human collaborative interactions and optimize them as you would a fixed business process” (Bradley, 2010). Taken together these characteristics define the new world of social media. Social networking, a major social media platform, embraces many of the defining characteristics of the Connected Age and is a major component of connected care. First, participation and collaboration were two of the principal themes in Web 2.0 (Eysenbach, 2008) and are the driving forces behind the social media movement with

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continued relevance in the Connected Age. Eysenbach (2008) further noted, “Social networking … involves the explicit modeling of connections between people, forming a complex network of relations, which in turn enables and facilitates collaboration and collaborative filtering processes.” Another aspect of social networking is the ability to share user-generated content in the form of videos, stories, or photographs. In addition to adding and viewing content, consumers can also post comments to media someone else has contributed, thus adding another level of communication to these sites (Skiba, 2007). Of the available digital tools, social networking offers the most opportunity for peer support and consumer engagement. Users can make connections with people that they already know in person or may connect with others through associations that they create (Boyd & Ellison, 2007). Essentially, the social networking site serves as a powerful tool to engage and motivate consumers to share personal information, establish relationships, and communicate with others. With the driving force of consumerism and its use of digital tools, especially smartphones, it is important to examine how healthcare systems can use social media to engage patients in their health and becomes partners in their care. Healthcare institutions and consumers have already begun to capitalize on the limitless utility of social networking. Numerous hospitals and healthcare-related organizations have social networking sites where patients and visitors can explore details about the facility, learn more about available services, and find information about diseases and/or treatments (Sarasohn-Kahn, 2008). Of the available social networking sites, Facebook was considered one of the more popular sites and allowed for resource sharing, communication, and collaboration (Mazman & Usluel, 2010). One institution that was an early adopter of social media in healthcare was the Mayo Clinic. In 2010, the Mayo Clinic Center for Social Media was established and is still promoting the use of social media (https:// socialmedia.mayoclinic.org/#). From a historical perspective, one of the first social networks in healthcare was Matthew Zackery’s i2y social network (I am too young for this Cancer Foundation). At one of the first Health 2.0 conferences, Zackery presented his experiences in creating the social network targeted for young adults with cancer. To learn more, you can visit the following Web site: http://stupidcancer.org/. The Centers for Disease Control and Prevention (CDC) has embraced the use of social media and was initially used extensively in their H1N1 campaign. It is still very active in the use of social networking tools such as Facebook and Twitter to communicate health information and to engage

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the population in health promotion and health prevention activities. Its Web site (https://www.cdc.gov/socialmedia/ index.html) contains a variety of resources such as current Social Media Toolkit to help people create their own social media campaigns and a listing of all their available social media tools. Another pioneer in social networking is PatientsLikeMe (https://www.patientslikeme.com/). Through this social network, patients from all over the world convene and share their experiences while dealing with chronic conditions such as multiple sclerosis (Sarasohn-Kahn, 2008). The creators’ brother, who was living with amyotrophic lateral sclerosis (ALS), was the inspiration for the network. Two brothers and a friend, all Massachusetts Institute of Technology engineers, created this network with the following goals in mind: (1) share health data, (2) find patients with similar conditions, and (3) learn from each other. Patients are asked to share data in the hope of improving the lives of all diagnosed with that particular disease. The site does not have any fees and is kept free from advertising through revenues stemming from research awareness programs, market surveys, and the sale of processed anonymized data (Brownstein, Brownstein, Williams, Wick, & Heywood, 2009). Members use aliases rather than real names and can openly share details about their healthcare experiences, drug regimens, and treatment side effects (Hansen, Neal, Frost, & Massagli, 2008; Sarasohn-Kahn, 2008). The primary motives behind such sharing are to ask or offer advice and to build a relationship with others in similar situations (Hansen et al., 2008). “Rather than disseminating medical advice, PatientsLikeMe serves as a platform for peers to interact with one another in a datadriven context” (Brownstein et al., 2009, p. 889). Patients have actually taken information they have learned from PatientsLikeMe to their own healthcare providers to request to be put on specific treatments (Goetz, 2008). This networking project is thriving as more and more patients want to interact with other patients. The company is also introducing a new project DigitalMETM that will allow people to participate by having multiple sources of health data to be collated and shared to gain a better understanding of health conditions and patient outcomes. Moorhead et al. (2013) conducted a systematic review of social media in healthcare. They noted there were seven primary uses of social media: to provide information to consumers, provide answers to medical questions, facilitate dialogue between patients and healthcare providers, collect data on patient experiences and opinions, serve as a health intervention for health promotion, reduce the stigma of specific illnesses, and provide a mechanism for online consultations.

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762    P art 8 • E ducational A pplications With the advent of mobile devices and healthcare apps, social media is intricately tied together. As new tools are introduced in healthcare, it is not only changing the delivery of care but it is changing the dynamics of healthcare and the interactions between consumers and their providers. This is particularly true as one explores the growing of mobile devices and apps that allow for Connected Health interactions. Many healthcare apps also contain connections to user communities. For example, people can decide to share their FITBIT data with friends and receive encouragement in maintaining their steps per day. There are also social media sites that contain consumer ratings or hospitals or healthcare providers. World Health Organization (WHO) (2018) recently developed a classification system of digital interventions. The system categorizes digital technologies as being used to support health systems, clients (patients), healthcare providers, and data services. There is one category related to clients that define digital interventions to targeted and untargeted client communication, client-to-client communication, personal health tracking, citizen-based reporting, and on-demand health information (https:// www.who.int/reproductivehealth/publications/mhealth/ classification-digital-health-interventions/en/). Recently, WHO (2019) has published “Recommendations on Digital Interventions for Health Systems Strengthening” (https:// www.who.int/reproductivehealth/publications/digitalinterventions-health-system-strengthening/en/). In these guidelines, WHO states, “Digital health, or the use of digital technologies for health, has become a salient field of practice for employing routine and innovative forms of information and communications technology (ICT) to address health needs.” It goes one to define digital health as “a broad umbrella term encompassing eHealth (which includes mHealth), as well as emerging areas, such as the use of advanced computing sciences in ‘big data’, genomics and artificial intelligence.”

BENEFITS OF SOCIAL MEDIA To understand the benefits of social media, it is important to examine the growing number of studies over time. In the past, most studies were descriptive. In a review by Skiba, Guillory, and Dickson (2014), there are three general areas of research in social media. The first focused primarily on the content being shared on social media, in particular social networks and Twitter. The second area was the specific use of social media by patient populations such as diabetics or cancer patients. The final area was related to the use of social media for recruitment of patients for research studies and the collection of data

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from social media could be used as an additional form of research data. Some interesting findings were that Facebook, YouTube, and Twitter were the most common social media platform and PatientsLikeMe was the most studied network to date (Skiba et al., 2014). The steady growth in studies has generated several systematic reviews to provide evidence for the use of these tools in promoting and managing various patient populations. Here is a sampling of some systematics studies. Moorhead et al. (2013) completed a systematic review to examine the uses, benefits, and limitations of social media for health communications. Capurro et al. (2014) conducted a systematic review of social networking sites for public health practice and research. Chang, Chopra, Zhang, and Woolford (2013) analyzed studies in the role of social media in online weight management. Maher et al. (2014) conducted a systematic review of the effectiveness of behavior change interventions through social networks. Although most studies found promising results, but there was a need for additional studies. Smailhodzic, Hooijsma, Boonstra, and Langley (2016) conducted a systematic review of effects on patients as well as healthcare professional relationships with their patients. The study yielded a total of 22 articles. One of the key findings was that patients use social media not to bypass the healthcare system but to fulfill needs not provided by their particular health care provider—for example, getting emotional support, knowing the latest breakthrough treatments, and what it is like in everyday life to have a particular health condition. The study divided the use of social media into several categories: social support that included emotional support, esteem support, informational support, and network support. The other two areas were labeled emotional expression (freely express feelings and concerns without judgment) and social comparison (how bad their condition was in comparison to others). In addition, the effects of different social media were analyzed. “The most common effect of patients using social media for health related reasons is patient empowerment, which is represented through three categories: enhanced subjective well-being, enhanced psychological well-being, and improved self-management and control” (Smailhodzic et al., 2016, p. 450). They also noted that there were some other effects that were rarely mentioned in most literature reviews: “diminished subjective well-being, loss of privacy, addiction to social media, and being targeted for promotion” (Smailhodzic, et al., 2016, p. 442). Another component of this systematic review was to examine how social media affected their relationship with their individual healthcare providers. There were four major themes that emerged. First, patients felt they had a more equal communication with their providers.

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Second, some patients decided to change providers as they had negative views of patient’s use of social media. Third, there were examples of how social media promoted more harmonious relationships with providers and lastly, sometimes there were suboptimal interactions between the patient and provider. Giustini, Ali, Fraser, and Kamel Boulos (2018) conducted a systematic review of social media use in public health and medicine. Their extensive review yielded 42 studies that received a score of 9 out of 10 on the Critical Appraisal Skills Programme (CASP) tool for systematic reviews. The majority of the studies (N = 30) were related to patients, while others were related to healthcare professionals. The bottom line is that “social media have been used across a range of populations with both positive and negative effects. Some studies found a positive impact of social media on patient behaviors and health outcomes.” More recent reviews also found “social media triggered positive health changes in managing health problems.” There were also negative effects that included reliability of information, increase in risky behaviors, and diminished sense of well-being. In summary, the researchers concluded, “In this review, the evidence was revealed to be inconclusive with respect to benefits or harms. Not surprisingly, the quality of the primary research is weak. Further, the tools and platforms revealed only moderate positive and negative effects.” There has been substantial growth over time in the use of social media channels for healthcare research. According to Azer (2017), social media has allowed researchers to examine the impact of social networks for patients with chronic diseases and their perceived social support; recruit patients for clinical trials; spread information and misinformation about public health issues such as cancer awareness and opioid use; examine how users gather and share health information with others; and study the effect of social media exposures on certain behaviors. Sinnenberg et al. (2017) conducted a systematic review of Twitter as a tool for health research. There were a total of 137 articles that met their criteria for inclusion in the review. The collection of studies analyzed more than 5 billion tweets. One-third of the studies were conducted in 2015 and public health and infectious disease accounted for at least 43% of the studies. Their study yielded a new taxonomy of Twitter uses in healthcare research. The first category was Twitter as a data source that consisted of content analysis, surveillance, and network analysis. The second was utilization of Twitter as a platform for recruitment and intervention. The last category was the use of Twitter data in terms of how to mine Twitter data and the use of Twitter metadata.

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As more and more healthcare system focus on patient experience, here are some recommendations from a content strategist from Healthcare Weekly. Bulgaru (February 11, 2019) recommended the following ways to use social media to build trust: “Specifically, healthcare organizations need to:

• • • • •

Listen attentively to what their patients are saying online (and implement this knowledge) Tap online influencers’ ability to reach crowds and spread your message Create valuable, digestible content that educates your audience Use social networks as a deliberate space to build your brand Engage, support, and create a space for productive conversations between patients, doctors, industry leaders, and policymakers.”

The development and continuing research in the use of social media will expand and more studies will continue to provide additional evidence of their effectiveness. Despite their prospects, digital tools in the Connected Age do not come without certain limitations and risks. Like any element of our digital environment, they pose concerns for privacy, security, and legal issues.

CHALLENGES OF SOCIAL MEDIA According to the eHealth Initiative Report (2014), there are several key challenges affecting the widespread adoption of social media in healthcare. First, there are concerns about privacy and HIPAA compliance. There are also concerns about the balance of transparency and anonymity associated with the sharing of personal information online. The quality, validity, reliability, and authenticity of information are an issue especially when there is user-generated information. This is of utmost importance as social media sites are invaded by trolls posting misinformation. PBS recently reported about the misinformation spread on social media related to vaccines (https:// www.pbs.org/newshour/health/how-social-media-istrying-to-contain-the-spread-of-misinformation-overvaccines). In light of the COVID-19 pandemic, there are numerous examples of misinformation. According to Tedros Adhanom Ghebreyesus, Director-General of the World Health Organization (WHO) (www.un.org/ en/un-coronavirus-communications-team/un-tackling%E2%80%98infodemic%E2%80%99-misinformation-

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764    P art 8 • E ducational A pplications and-cybercrime-covid-19 ), “We’re not just fighting an epidemic; we’re fighting an infodemic.” The WHO has a team of people who consistently check on Web resources to identify misinformation. They moderate such social media sites such as Facebook, Google, Pinterest, Tencent, Twitter, TikTok, and YouTube. Xenia Shih Bion wrote in the California Health Care Foundation’s blog (https:// www.chcf.org/blog/finding-cure-pandemic-misinformation/) that “Whenever there is a vacuum of information, misinformation will find a way to fill it.” There is also the challenge of the digital divide specifically with differing populations such as the elderly, minorities, the disabled, those living in rural areas, and those in poor or underserved areas without access to broadband. The final challenge in this report also mentions the lack of theoretical and evaluation models for social media given the paucity of effectiveness data. Grajales, Sheps, Ho, Novak-Lauscher, and Eysenbach (2014) also echo many of the same challenges, “The potential violation of ethical standards, patient privacy, confidentiality, and professional codes of practice, along with the misrepresentation of information, are the most common contributors to individual and institutional fear against the use of social media in medicine and health care.” Unfortunately many of these challenges are still valid. The Connected Age places unique circumstances around the sharing of protected health information, as it is generally patient or consumer driven. That is, the consumer voluntarily divulges his or her information. In such cases the HIPAA regulations do not apply; however, healthcare institutions’ attempts to abide by the law may hinder their adoption of social media tools (Hawn, 2009). This challenge has recently been brought into focus as many companies who host social media are using personal data for targeted marketing and selling of social media users’ data. For example Facebook, one of the common social media sites, that hosts numerous patient support groups has been under fire for how it collects and shares personal data. Matloff (2018) wrote that many women, who had participated in a supposedly private Facebook support group, had their personal data shared with marketing firms (https://www.forbes.com/ sites/ellenmatloff/2018/07/29/facebook-violates-trust-ofprivate-patient-groups/#513958901524). There are also concerns about privacy and confidentiality. Their concerns are not unfounded since the rates of identity theft continue to persist and with the growth of social media on mobile devices, there is more potential for accessing personal data. Social media applications promote information sharing and the open display of personal information, such as age, gender, and location. Posting this and

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other content creates digital footprints or lingering information that can be connected back to the consumer who provided it (Madden, Fox, Smith, & Vitak, 2007); these bits of information can then be found and coalesced to form a more complete picture of the individual, thus negating the apparent transparency (Madden et al., 2007). Users of social media continue to be at risk for social threats (Nosko, Wood, & Molema, 2010). Characterized as stigmatizing and bullying, social threats can pose significant dangers to consumers and those with whom they are affiliated (Nosko et al., 2010). This was supported by the finding of Giustini et al.’s (2018) systematic review. This is equally important in the age of COVID-19 pandemic. There has been rapid growth of contact- tracking applications developed for smart phones. These apps are used to track people and their interactions with others. There is a concern that one’s privacy can be affected as these tools use GPS to track you and may have access to personal information on your smart phone. In addition, there may be legal issues related to risk management and liabilities. It has long been known that Internet content is not regulated and may be unreliable (Eysenbach & Diepgen, 1998; Powel, Darvell, & Gray, 2003). Healthcare and educational organizations in the Connected Age must also be cognizant of the legal implications. Not only will they have to monitor the content being shared on their site for appropriateness, reliability, and quality of their information, they will also need to be sure there are no copyright infringements (Lawry, 2001). Healthcare practice licenses are also an issue considering that in the Connected Age, there are no real geographic boundaries (Grajales et al., 2014) and therefore providing medical advice across state lines may be an issue. With the increased use of social media in healthcare research, there are important ethical challenges. Many researchers use social media sites to recruit potential subjects or to implement interventions or collect data. These uses necessitate careful review by institutional review boards to ensure the consent, privacy, confidentiality, and security of patient data are protected. Azer (2017) lists four key recommendations that all researchers consider when choosing to use social media tools in healthcare research. His recommendations include the following: “The use of social media in research should be justified; social networking sites should be considered private spaces, and consent to participate in research should be obtained. Researchers should outline a plan to ensure the confidentiality of data collected and an examination of potential sources of harm or risk.” A digital divide still exists across different populations that have access to social media. Despite the growth of mobile technologies, there are costs associated with

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Chapter 47 • Social Media Tools in the Connected Age  

streaming a YouTube health video on your cellphone or participating in social media networks. There is also a digital divide, or gap in usability, for some consumers who either lack physical access to the Internet or do not have knowledge or skills to navigate the myriad information on the Internet safely and effectively (Baur & Kanaan, 2006; Cashen, Dykes, & Gerber, 2004). Physical access limitations can be described as lack of resources to obtain the hardware or software to utilize these tools (Baur & Kanaan, 2006; Cashen et al., 2004). Even with cellphones, it would be hard for a non-English-speaking person or an elderly with sight problems to use these small devices to access and read health information. Lack of experience describes the knowledge and skill deficit that hinders a consumer’s ability to navigate tools effectively and safely. As noted in recent studies, there still exists digital divide across ethnic groups as well as gender. For example, more women tend to use social media in healthcare. As with most innovations, these challenges can be partially addressed through the development and implementation of social media policies by organizations, including user-generated networks. This is particularly important given that most healthcare agencies are risk-adverse regarding patient care. Professional organizations, such as the American Nurses Association, American Medical Association, and National Council of State Boards of Nursing, have provided guidance and social media policies (Skiba et al., 2014).

SUMMARY There is no doubt that digital tools have transformed the world around us. Through the use of digital tools such as social media, consumers can become empowered to take responsibility for their healthcare and their well-being. The addition of mobile devices facilitates the continuous use of social media tools in every aspect of our lives. For consumers, patients, and their families, the Connected Age provides opportunities for them to become more engaged in their healthcare decisions by expanding their connections from just family and friends to a cadre of other patients/consumers who are like them as well as a community of healthcare professionals who are experts in their particular health or disease conditions.

Test Questions 1. The Internet was initially created by what agency? A. National Security Administration B. Health and Human Services

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C. National Institute of Health

D. Defense Advanced Research Projects Agency 2. What was the name of the first Internet browser? A. Google

B. Chrome C. Mosaic

D. Mozilla 3. Connected Health encompasses: A. Telemedicine/Telehealth B. mHealth C. eHealth

D. all of the above 4. Connected care is the intersection of: A. Connections across hospitals B. Patients to pharmacies

C. Information and People Resources connected with technology D. Only for patient connections

5. Which is not an example of social media? A. Twitter

B. Formstack C. Facebook

D. PatientsLikeMe 6. Which characteristic is not associated with social media? A. Participation

B. Quantification C. Collaboration D. Transparency

7. Based on systematic reviews, what is not a benefit of social media? A. Emotional support

B. Patient empowerment

C. Informational support

D. Increase in health outcomes 8. What strategies can healthcare organizations use to foster trust in social media? A. Engage users to connect

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766    P art 8 • E ducational A pplications B. Create valuable, digestible content that educates users C. Create space to engage space for productive conversations D. All of the above

9. A Twitter message advocated the use of a dangerous chemical as a method to prevent a virus. This is an example of: A. Misinformation B. Security C. Privacy

D. None of the above 10. What are criteria to use when judging a social media information? A. Quality and Authentication B. Validity and Reliability C. Both A and B

D. None of the above

Test Answers 1. Answer: D 2. Answer: C

3. Answer: D 4. Answer: C 5. Answer: B 6. Answer: B

7. Answer: D 8. Answer: D 9. Answer: A 10. Answer: C

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Ashton, K. (2009, June). That ‘Internet of things’ thing. RFID Journal. Retrieved from http://www.rfidjournal.com/ article/view/4986. Accessed on June 2, 2020. Azer, S. (2017). Social media channels in health care research and rising ethical issues. American Medical Association Journal of Ethics, 19(11), 1061–1069. Baur, C., & Kanaan, S. (2006, June). Expanding the reach and impact of consumer e-health tools. Washington, DC: U.S. Department of Health and Human Services Office of Disease Prevention and Health Promotion Health Communication Activities. Retrieved from https://www. unapcict.org/resources/ictd-infobank/expanding-reachand-impact-consumer-e-health-tools. Accessed on June 2, 2020. Bazzoli, F. (2018, March 30). HIT think: Why six trends are pointing to a revolution in healthcare. Health Data Management. Retrieved from https://www.healthdatamanagement.com/opinion/why-six-trends-are-pointingto-a-revolution-in- healthcare. Accessed June 1, 2019. Berners-Lee, T. (1989). Information management: A proposal. CERN. Retrieved from http://www.w3.org/ History/1989/proposal.html. Accessed on June 2, 2020. Berners-Lee, T., Hendler, J., & Lassila, O. (2001). The semantic web. Scientific American, 290(4), 35–43. Boyd, D. M., & Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer Mediated Communication, 13(1), 210–230. Article 11. doi: https://doi.org/10.1111/j.1083-6101.2007.00393.x. Bradley, A. (2010, January 7). A new definition of social media. Gartner blog. Retrieved from http://blogs.gartner. com/anthony_bradley/2010/01/07/a-new-definition-ofsocial-media. Brownstein, C. A., Brownstein, J. S., Williams, D. S., Wick, P., & Heywood, J. A. (2009). The power of social networking in medicine. Nature Biotechnology, 27(10), 888–890. Bulgaru, I. (February 11 2019). Healthcare social media strategy: 5 Ways to build trust. Healthcare Weekly. Retrieved from https://healthcareweekly.com/social-media-inhealthcare/. Accessed on June 2, 2020. Capurro, D., Cole, K., Echavarría, M. I., Joe, J., Neogi, T., & Turner, A. (2014). The use of social networking sites for public health practice and research: A systematic review. Journal of Medical Internet Research, 16(3), e79. Retrieved from http://www.jmir.org/2014/3/e79/. Accessed on June 2, 2020. Cashen, M. S., Dykes, P., & Gerber, B. (2004). eHealth technology and internet resources: Barriers for vulnerable populations. Journal of Cardiovascular Nursing, 19(3), 209–214. Caulfield, B., & Donnelly, S. (2013). What is connected health and why will it change your practice? QJM: An International Journal of Medicine, 106(8), 703–707. Cerf, V. (1995). Computer networking: Global infrastructure for the 21st century. Computer Research Association. Retrieved from http://www.cs.washington.edu/homes/ lazowska/cra/networks.html. Accessed on June 2, 2020.

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Chang, T., Chopra, V., Zhang, C., & Woolford, S. J. (2013). The role of social media in online weight management: Systematic review. Journal of Medical Internet Research, 5(11), e262. Retrieved from http://www.jmir.org/2013/11/ e262/. Downes, S. (2005, October 17). E-learning 2.0. eLearn Magazine. Retrieved from https://elearnmag.acm.org/ featured.cfm?aid=1104968. Accessed on June 2, 2020. eHealth Initiative Report. (2014). A report on the use of social media to prevent behavioral risk factors associated with chronic disease. California Health Foundation. Retrieved from https://www.ehidc.org/ resources/report-use-social-media-prevent-behavioral-risk-factors-associated-chronic-disease. Accessed on June 2, 2020. Eysenbach, G. (2008). Medicine 2.0: Social networking, collaboration, participation, apomediation, and openness. Journal of Medical Internet Research, 10(3), e22. Eysenbach, G., & Diepgen, T. L. (1998). Towards quality management of medical information on the internet: Evaluation, labeling, and filtering of information. British Medical Journal, 317, 1496–1502. Friedman, T. (2005). The world is flat: A brief history of the 21st century. New York, NY: Farrar, Straus & Giroux Publishers. Goetz, T. (2008, March 23). Practicing patients. New York Times. Retrieved from https://www.nytimes. com/2008/03/23/magazine/23patients-t.html. Accessed on June 2, 2020. Grajales, F., Sheps, S., Ho, K., Novak-Lauscher, H., & Eysenbach, G. (2014). Social media: A review and tutorial of applications in medicine and health care. Journal of Medical Internet Research, 16(2), e13. Retrieved from http://www.jmir.org/2014/2/e13/. Accessed on June 2, 2020. Giustini, D., Ali, S. M., Fraser, M., & Kamel Boulos, M. N. (2018). Effective uses of social media in public health and medicine: a systematic review of systematic reviews. Online Journal Public Health Informatics, 10(2), e215. doi:10.5210/ojphi.v10i2.8270. Hansen, D., Neal, L., Frost, J. H., & Massagli, M. P. (2008). Social uses of personal health information within PatientsLikeMe, an online patient community: What can happen when patients have access to one another’s data. Journal of Medical Internet Research, 10(3), e15. Hawn, C. (2009). Take two aspirin and Tweet me in the morning: How Twitter, Facebook, and other social media are reshaping healthcare. Health Affairs (Millwood), 28(2), 361–368. Iglehart, J. (2014, February). Connected health: Emerging disruptive technologies. Health Affairs (Millwood), 33(2), 190. doi:10.1377/hlthaff.2014.0042. Johnson, L., Adams, S., & Cummins, M. (2012). The NMC horizon report: 2012 Higher education edition. Austin, TX: The New Media Consortium. Retrieved from https:// library.educause.edu/resources/2012/2/2012-horizonreport. Accessed on June 2, 2020.

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Lawry, T. C. (2001). Recognizing and managing website risks. Health Progress, 82(6), 12–13, 74. Leiner, B., Cerf, V., Clark, D., Kahn, R., Kleinrock, L., Lynch, D., ... Postel, J. (1997). The past and future history of the Internet. Communications of the Association of Computing Machinery, 40(2), 102–108. Madden, M., Fox, S., Smith, A., & Vitak, J. (2007). Digital footprints: Online identity management and search in the age of transparency. Washington, DC: Pew Internet and American Life Project. Maher, C. A., Lewis, L. K., Ferrar, K., Marshall, S., De Bourdeaudhuij, I., & Vandelanotte, C. (2014). Are health behavior change interventions that use online social networks effective? A systematic review. Journal of Medical Internet Research, 16(2), e40. Retrieved from http://www. jmir.org/2014/2/e40/. Accessed on June 2, 2020. Manis, J. L. (2018, April 9). Let’s stop talking about digital disruption. Becker’s Hospital Review. Retrieved from https://www.beckershospitalreview.com/hospital-management-administration/let-s-stop-talking-about-digitaldisruption.html. Accessed June 2, 2020. Matloff, E. (July 8 2018). Facebook violates trust of ‘private’ patient groups. Forbes Magazine. Retrieved from https://www.forbes.com/sites/ellenmatloff/2018/07/29/ facebook-violates-trust-of-private-patientgroups/#513958901524. Accessed June 2, 2020. Mazman, S. G., & Usluel, Y. K. (2010). Modeling educational usage of Facebook. Computers & Education, 55(2010), 444–453. Moorhead, S. A., Hazlett, D. E., Harrison, L., Carroll, J. K., Irwin, A., & Hoving, C. (2013). A new dimension of health care: Systematic review of the uses, benefits, and limitations of social media for health communication. Journal of Medical Internet Research, 15(4), e85. Retrieved from http://www.jmir.org/2013/4/e85/. Accessed June 5, 2020. Murphy, K., & Jain, N. (2018, May 1). Riding the disruption wave in healthcare. Forbes Magazine. Retrieved from https://www.forbes.com/sites/baininsights/ 2018/05/01/riding-the-disruption-wave-inhealthcare/#2d3e15872846. Accessed June 2, 2020. Nosko, A., Wood, E., & Molema, S. (2010). All about me: Disclosure in online social networking profiles: The case of FACEBOOK. Computers in Human Behavior, 26, 406–418. O’Reilly, T. (2005, September 30). What is Web 2.0? Retrieved from https://www.oreilly.com/pub/a//web2/ archive/what-is-web-20.html. Accessed June 2, 2020. Oblinger, D. G. (2013). The connected age for higher education is here. Are we ready for the future? EDUCAUSE Review, 48(5), 4–5. Retrieved from https://er.educause. edu/articles/2013/4/higher-education-in-the-connectedage. Accessed June 2, 2020. Owen, M., Grant, L., Sayers, S., & Facer, K. (2006). Social software and learning. Future Labs. Retrieved from http://www.futurelab.org.uk/research/opening_education.htm.

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768    P art 8 • E ducational A pplications Powel, J. A., Darvell, M., & Gray, J. A. (2003). The doctor, the patient and the World Wide Web: How the Internet is changing healthcare. Journal of the Royal Society of Medicine, 96, 74–76. Sarasohn-Kahn, J. (2008, April). The wisdom of patients: Healthcare meets online social media. Oakland, CA: California HealthCare Foundation. Retrieved from om:www.chcf.org/publication/the-wisdom-of-patientshealth-care-meets-online-social-media/. Accessed on June 2, 2020. Sarasohn-Kahn, J. (2018, January 10). Health care comes home at CES 2018. Huffington Post. Retrieved from www. huffingtonpost.com/entry/healthcare- comes-home-atces-2018_us_5a553760e4b0e3dd5c3f8cec. Accessed on June 2, 2020. Sinnenberg, L., Buttenheim, A. M., Padrez, K., Mancheno, C., Ungar, L., & Merchant, R. M. (2017). Twitter as a tool for health research: A systematic review. American Journal of Public Health, 107(1), e1–e8. Skiba, D. (2007). Nursing education 2.0: YouTube. Nursing Education Perspectives, 28(2), 100–102. Skiba, D. (2013). The Internet of things (IOT). Nursing Education Perspectives, 34(1), 63–64. Skiba, D. (2014). The Connected Age: Implications for 2014. Nursing Education Perspectives, 35(1), 63–65. doi:10.5480/1536-5026-35.1.63. Skiba, D. (2018). The invisible health care professional: Exploring the Intersection of data, devices and artificial intelligence. Nursing Education Perspectives, 39(4), 264–265. Skiba, D., Barton, A., Estes, K., Gilliam, E., Knapfel, S., Lee, C., Moore, G., & Trinkley, K. (2016). Preparing the next

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generation of advanced practice nurses for connected care. In P. Weber, W. Seremus, & P. Proctor (Eds.), The 13th International Nursing Informatics Congress. IOS Press. Skiba, D., Guillory, P., & Dickson, E. (2014). Social media in health care. In: N. Staggers & R. Nelson (Eds.), Health informatics: An interprofessional approach. St. Louis, MO: Elsevier. Smailhodzic, E., Hooijsma, W., Boonstra, A., & Langley, D. (2016). Social media use in healthcare: A systematic review of effects on patients and on their relationship with healthcare professionals. BMC Health Services Research, 16, 442. doi:10.1186/s12913-016-1691-0. Smith, A., & Anderson, M. (March 2018). Social media use in 2018. Pew Center Research. Retrieved from https:// www.pewresearch.org/internet/2018/03/01/socialmedia-use-in-2018/. Accessed on June 2, 2020. World Health Organization (WHO). (2018). Classification of digital health interventions v1.0: A shared language to describe the uses of digital technology for health. WHO Reference Number WHO/RHR/19.06. Retrieved from http://www.who.int/reproductivehealth/publications/ mhealth/classification-digital-health-interventions/en/. Accessed on June 2, 2020. World Health Organization. (2019). WHO guideline: Recommendations on digital interventions for health system strengthening. Retrieved from https://www.who.int/ reproductivehealth/publications/digital-interventionshealth-system-strengthening/en/. Accessed on June 2, 2020.

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48 A Paradigm Shift in Simulation: Experiential Learning in Virtual Worlds and Future Use of Virtual Reality, Robotics, and Drones E. LaVerne Manos / Nellie Modaress

• OBJECTIVES . Describe the use of Virtual Worlds such as Second Life as simulated learning. 1 2. Discuss the pedagogy that drives teaching and learning in the virtual world. 3. Create a supportive learning environment for educational innovation in virtual worlds. 4. Describe common applications to enhance learning in the virtual world. 5. Discuss future trends for learning utilizing virtual worlds, virtual reality, robotics, and drones.

• KEY WORDS Augmented reality (AR) Health professional education Informatics education Mixed reality (MR) Online education Second Life® (SL) Simulation User computer interface Virtual environment (VE) Virtual reality (VR) Virtual worlds (VW)

INTRODUCTION Hundreds of leading schools and universities across the globe employ multiple user virtual worlds (VW) such as Second Life® (SL) as an innovative part of their educational courses and programs. Online VWs have multiple uses for teaching and learning. This environment enhances student engagement with course content, develops a sense of community among and between students and faculty, and

creates a powerful platform for interactive experiences that brings new dimensions to support best practices for learning. In this virtual environment, students and faculty work together from anywhere in the world giving education a global perspective and an expanded reach. A major challenge for online education is student engagement and the evaluation of skill attainment. Virtual worlds provide an online, virtual laboratory that addresses this challenge. Faculty and student avatars can interact with 769

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770    P art 8 • E ducational A pplications each other, physically and verbally, in real time, thus facilitating simulations where students engage in demonstrating skill acquisition. Faculty can coach the skill development, as they now control the environment and can see what the student is doing. This type of evaluation in a real or simulated environment was previously unattainable in an online course. Furthermore, the VW environment provides a forum for student presentations and interactions with a live audience. Field trips to other VW environments create opportunities to hone skills in information searching and observation of activities and settings that can be viewed by the faculty and other students. The practice of virtual learning environments alongside advance uses of digital devices has grown to include virtual and augmented realities. In addition, virtual learning environments have led practitioners to contribute to research on experiential learning. This chapter is an in-depth discussion of the educational application of virtual worlds with emphasis on Second Life and a look toward the future use of several technologies in healthcare education including an introduction to newer innovative technologies. An exemplar describing current innovative implementation of technology and a use case to explore potential future use of innovative technologies in education are also presented. Exemplars and use case examples at the end of the chapter are related to virtual reality (VR), mixed reality (MR), augmented reality (AR), robotics, and drones.

VIRTUAL WORLD: SECOND LIFE Second Life (www.secondlife.com ), a 3-dimensional (3D) VW developed by Linden Lab and uniquely imagined and created by its residents, was launched in 2003. Virtual worlds and augmented reality are subsets of virtual reality, which is defined as “an artificial environment which is experienced though sensory stimuli (such as sights and sounds) provided by a computer and in which one’s actions partially determine what happens in the environment” (Merriam-Webster, n.d.-b). SL is considered the largest virtual world with tens of millions of square meters of virtual land and more than 36 million registered users. Currently, SL is the most mature and popular virtual world platform being used in education. There are several dozen VWs giving SL serious competition Most of these are still small, and even the largest does not come close to matching Second Life’s massive land and user base. Over the past decade, a large number of colleges and universities have established a presence in SL (Michels, 2008; Knapfel, Moore, & Skiba, 2014; Vrellis, Avouris, & Mikropoulos, 2016). These efforts are largely for teaching courses, but they also include recruitment activities for prospective students, fund raising, and research endeavors. Today, disenchanted with commercial VWs but still convinced of their educational value, some institutions have started to build their

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own environments in which they have more control over the learning space (DePaul, 2018; Young, 2010).

TEACHING AND LEARNING IN VIRTUAL WORLDS Teaching in a virtual environment differs from teaching a traditional online course due to the 3D setting and use of avatars to represent the participants and the sense of presence (Hellyar, Walsh, & Altman, 2018; Johnson, 2009; Calongne, 2008; Richardson & Swan, 2003). The lack of sense of presence has always been a major difficulty and critique of online education, and educators of distance learners face pressure to meet the needs of the students. Technology is a platform that provides opportunities to reduce the online learner’s sense of isolation and distance. Virtual worlds such as SL facilitate real-time interaction between faculty and students when they are geographically apart. Furthermore, the environment can be controlled or simulated to create learning experiences designed by the faculty member to achieve pedagogical goals. These planned learning environments previously had to be in one physical place (e.g., learning laboratories, clinical facilities). SL supports online education by moving the geography to a virtual space, thus creating a sense of presence for the faculty and students. The sense of presence is important for learner engagement, regardless of whether the experience is real or virtual. Presence is defined as “the subjective experience of being in one place or environment, even when one is physically situated in another” (Witmer & Singer, 1998). When in-world, the students feel as if they are actually in the virtual environment. A sense of immersion is also necessary for learning, immersion is the sense of being enveloped by and interacting with the environment. Involvement is “a psychological state experienced as a consequence of focusing one’s energies and attention on a coherent set of stimuli or meaningfully related activities and events” (Witmer & Singer, 1998). Second Life activities and simulations create presence and immersion for faculty and students, whether they are in traditional classes or online classes. Students involved in the SL learning experience report a “sense of presence and connectedness” (Tiffany & Hoglund, 2014). Faculty use virtual environments for learning activities that require students to use higher order thinking. Students have the opportunity to exercise higher order thinking skills through creativity, application of concepts, analysis, synthesis, and problem-solving using course content and previous knowledge. Teaching strategies include role-play, gaming, simulation of social and clinical skills, collaboration, social networking, and participation in live events such as lectures, conferences, and celebrations.

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Faculty are finding that they can stage clinical simulations, guide students through the inside of cell structures, or present other imaginative teaching exercises that cannot be done in “real life” due to cost, scheduling, location, or safety issues. The truth is we do not yet know all the possibilities of what we can and cannot do with this tool for education. There are active educational special interest groups, conferences, and listservs enabling faculty to share pedagogical strategies, ideas, and simulations. As Knapfel, Moore and Skiba (Knapfel et al., 2014) recommend, continued research is needed to explore best practices for use of virtual environments in education and practice.

learning environments for developing and facilitating learning activities that promote the use of these theories. In the authors’ opinions, no one model fits best as it will depend upon the goals of the course as well as the teaching and learning style of the faculty and students. Also, some components of a particular theory may not be satisfied in a virtual world like SL. Today, although growth and the use of this technology is explosive, only the tip of the iceberg is being seen by colleges, universities, and training programs using VWs. As the trend and use of this technology continues, educators and researchers will realize the expansion of current theories and develop new theories and patterns of learning.

THE PEDAGOGY OF TEACHING IN THE VIRTUAL WORLD

Designing the Learning Space

Although it is not the purpose of this chapter to discuss learning theory in detail, it is important to know there are several learning theories that support teaching and learning in virtual worlds. Technology for teaching and learning should always be selected to fit with the pedagogy. First consider the goals for the course, and then select the technology tools and strategies that will help to meet the proposed outcomes. To begin with, learners in SL are adults, and therefore Malcom Knowles’ (Knowles, 1984) theory of andragogy provides an overarching framework for designing learning activities for adult learners. Andragogy is based on the following assumptions about adult learners: (1) adults are self-directed, goal-oriented, and need to know why they are required to learn something; (2) they approach learning as problem-centered rather than content-centered; (3) they need to recognize the value of learning and how to incorporate that learning into their jobs or personal lives; and (4) they learn best through experiential learning that incorporates their diverse life experiences in the development of new knowledge. Since adult learners take a great deal of responsibility for their own learning, this alters the role of the faculty in learning environments in general but especially in virtual worlds such as SL. It also should be noted that environments like SL are well suited to applying the assumptions of adult learning theory; however, teachers and learners must adapt to this paradigm shift and to this new environment. Other learning theories utilizing the principles of andragogy that educators most frequently apply to SL are experiential learning theory, social learning theory, constructivism, connectivism, and collaborative learning theory (Kolb, Boyatzis, & Mainemelis, 2000; Bandura, 1977; Bruner, 1966; Bruner, 1996; Siemens, 2004; Smith & MacGergor, 1992). Many of these theories have overlapping principles that can be mixed and matched to enhance best practices in education (Chickering & Gamson, 1987). Technology advancement and social networking tools such as SL provide rich

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Working in virtual worlds is not always intuitive. Faculty need to rethink how the course material is structured and delivered. They need technical assistance to help with complex instructional design decisions that are congruent with the pedagogy, teaching strategies, and outcomes. Faculty should not be expected to be the technology experts; rather, they should team up with a technology professional for design and delivery support associated with technical training and technical issues. As a team they will work together to facilitate, guide, adopt, and integrate this technology as an innovative practice for teaching, learning, and research. The challenge that faces faculty is determining the various nuances of their audience, understanding the content, determining the best approach to deliver the content, and developing a comfort level with the technology (Hodges & Collins, 2010). The virtual world learning space is generally designed to replicate the traditional learning space. Areas are developed to support broadly defined educational activities. These virtual areas typically include a large lecture hall or auditorium for presentations, smaller classrooms for discussion, an exhibition hall for displaying student work, and faculty office space for meeting with students. However, this real-world approach to VW learning space brings with it similar constraints on the types of teaching and learning that can happen in those spaces (Gerald & Antonacci, 2009). For example, large lecture halls, whether in the real world or the virtual world, are based on an objectivist approach to course design, and such spaces do little to support more collaborative and constructivist learning approaches. Gerald and Antonacci (Gerald & Antonacci, 2009) suggest that in addition to designing spaces to meet traditional learning needs, the majority of the learning space can be designed to meet specifications for course projects. These spaces might include a home to practice assessing and remediating disability issues, a community living center as the context for database development, an operating room simulation for learning complex procedures, a health clinic for interacting with

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772    P art 8 • E ducational A pplications simulated patients and interprofessional team members, or perhaps a grocery store, restaurant, and exercise facility for teaching learners healthy living skills. Faculty should project goals and let individual creativity guide the design. Important factors to consider in designing the VW learning space are orientation and course design.

them that they are developing health informatics competencies. Specific orientation to participating in SL is also needed. Within directions for this exercise, a possible statement to achieve this purpose may be: Second Life is an immersive virtual environment. An avatar is a user’s self-representation in the form of a 3-dimensional model. In Second Life, creating your avatar is part of how you will interact with other ­residents. Some people design their avatar as a ­life-like representation of self. As the popularity of Second Life grows, many professional meetings will occur in this virtual world; please dress your avatar in casual or professional clothes. The user controls the avatar through the use of the mouse or keyboard to walk, fly, and sit. An avatar can interact with other avatars through instant messages or the audio function (using a headset).

Orientation Calongne (Calongne, 2008) points out that although it is tempting to begin a VW class with an orientation to the software and the virtual world itself, students need action and excitement to help them envision how the technology will enrich their learning experience. She recommends that faculty sell the benefits first, and then discuss how it works. Begin with exciting examples from other classes or research projects to make the experience real, personal, and engaging, then provide a brief introduction demonstrating how to use the tool effectively. Keep in mind that in higher education, not all students in the course are at the same level of technology literacy. Creating an avatar and figuring out how to move, look around, and interact with others may be a challenge for some students, but not all. Getting everyone to the in-world class site may require extra time initially, so plan for it. It may be necessary to provide alternative communication support for added assistance. Finally, if some students are hesitant, mitigating any fears or risks associated with using the technology can help create a safe learning environment. Orienting students to virtual worlds should follow the precepts of experiential learning. The SL Web site has very clear directions for downloading the portal to the environment and then leading the individual through creating an avatar. Encourage students to engage in this experience of downloading software and creating an avatar by assuring

Once the avatar is created, there are several video tutorials to teach students how to navigate their avatars in SL (see  Table 48.1 for suggestions). Upon completion of the tutorials, the faculty and student avatars should be ready to enter SL. Students need to feel competent enough with the technology to carry out required tasks and to meet the learning objectives. Basically, they should be able to move, look around, customize avatars, and interact with each other. During the first SL activity, a support person knowledgeable in SL technology should be available to trouble-shoot problems with software and microphone use. After the first SL experience, students are ready to engage in more activities and openly share their enthusiasm for this type of interaction. As faculty begin to envision more activities, they will need additional support to make these happen without having to take time to become experts in using and building in SL.

  TABLE 48.1    Resources for Orienting New Second Life Users Resources for Orienting New SL Users

URLs and SLURLs

Virtual Ability, http://www.virtualability.org/

If you have an SL account, teleport to http://maps.secondlife.com/secondlife/ Virtual%20Ability/127/127/23

YouTube videos created by Virtual Ability

Part One: www.youtube.com/watch?v=XAjG4Tv6LvU Part Two: www.youtube.com/watch?v=AVzyi0MOsJM&feature=related Part Three: www.youtube.com/watch?v=Cnyt6rASfo0&feature=related

Getting started in Second Life

http://wiki.secondlife.com/wiki/Help:Getting_started_with_LSL http://community.secondlife.com/t5/English-Knowledge-Base/ Second-Life-Quickstart/ta-p/1087919

Getting started in Second Life by Savin-Bade, Tombs, White, Poulton, Kavia, and Woodham

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www.jisc.ac.uk/publications/generalpublications/2009/gettingstartedsecondlife.aspx

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Course Design Virtual worlds use a mix of media-rich course materials selected to correspond to the learning activities and the students’ learning needs. Learning activities are experiential and can be designed to be synchronous or asynchronous, allowing students to interact with the subject matter to study, discuss, create, and express their views of the content under the supervision of the faculty member. The faculty role shifts from the authoritative expert to that of the dominant expert who stimulates and supervises exploration while providing structure, guidance, feedback, and assessment (Calongne, 2008). Virtual worlds provide immense opportunities for innovation and cultivate new ways to meet higher order learning. Rather than lecture, the class activity may involve teams of students taking a virtual field trip, gathering information, and later submitting their assignment through an SL group chat space or by collaboratively creating a presentation, a project management plan, or some other scholarly product to illustrate application, analysis, synthesis, or evaluation of the learning. The exemplars that follow describe one institution’s design principles and learning activities.

EXEMPLARS OF LEARNING IN SECOND LIFE The University of Kansas Background and Experience The University of Kansas Medical Center (KUMC) is organized into three major schools: Medicine, Nursing, and Health Professions. These schools are supported by the Department of Teaching and Learning Technology (TLT) located within the Division of Information Resources. In the early 1990s, KUMC’s Division of Information Resources, in collaboration with all three schools and the Division of Continuing Education, embarked on a strategic planning process to position the academic environment for the new wave of technology-based education. This planning process resulted in the formation of a re-envisioned academic support department—the Department of Teaching and Learning Technologies (TLT). The department is housed within the KUMC Information Resource Division. Central to its mission, the department has evolved over time to support. One of these technologies is Second Life, which KUMC faculty use for communication, presentations, immersive learning activities including simulation and role-playing, and research projects. The staff in the TLT department began exploring and researching the Second Life virtual world in 2004, just after it was released from beta. Struck by the

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educational potential of this new learning space, TLT staff began working with interested faculty to connect real-world course content with virtual world learning activities. Because of the interest expressed by faculty in the informatics, nursing anesthesia, and physical and occupational therapy programs, KUMC administrators in 2007 decided to purchase its own island or private space and named it KUMC Isle. An island or private region allows for restricted access and other levels of control not available on the virtual mainland. Faculty worked closely with TLT to establish goals and objectives for teaching in SL and to build the necessary learning space. Collaboration among the campus academic programs helps to set standards and creates academic environments that are efficient and effective and that model the real-world academic environment. Building on the success experienced in health informatics, physical therapy, and nurse anesthesia, other KUMC programs began to use SL to enhance learning and conduct research studies. TLT is also aware of promising new technologies and has both the technical and pedagogical support to explore and evaluate the educational possibilities of these tools. As technologies mature, those with the greatest potential to enhance teaching and learning are integrated into our core technologies, establishing a pattern of innovation and success. This infrastructure, which includes instructional designers and technology specialists, has served the campus well over the ensuing years as educational technologies advanced and became more affordable and acceptable. Key to KUMC’s success is the partnership between faculty and the TLT staff to design, develop, and implement courses using enhanced technologies and to work collaboratively across KUMC academic programs to develop a community of technology educators who share ideas and challenge each other.

Graduate Health Informatics Program Learning to be a health informatician requires developing skills identifying use cases for technology, and workflows in clinical environments. These are experiential skills and are difficult to master in an online environment. Simulations and clinical experiences are the traditional approaches for teaching these skills yet are not feasible in online courses where students reside in multiple states and time zones. Virtual reality environments, however, provide the online platform for simulations in which to experience and practice informatics skills, and SL was selected as the simulation environment for our online health informatics graduate program. These simulations facilitate the development of informatics competencies for future work environments. In the curriculum, we teach information system design and database development among other skills. An SL

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•  FIGURE 48.1.  The Jayhawk Community Living Center (JCLC). simulation was constructed to facilitate learning these skills. The faculty designed the Jayhawk Community Living Center (JCLC), an assisted living facility, for the simulation. The JCLC was designed to include rooms for six residents, a day room, dining room, clinic room, nurses’ station, healthcare records room, medication room, director’s office, and conference room. Landscaping, including a deck over the water surrounding KUMC Island, was built to enhance the reality of the simulation (Fig. 48.1). Cues and artifacts concerning information system requirements were placed in various locations within the JCLC so that students learned to observe the environment. Some of these cues were multiple telephones for residents, computer locations for staff, and floor plans for workflows. Faculty avatars simulated the roles of Director of Nursing and staff nurse. The purpose of one particular simulation is to design a fall-risk management information system for the JCLC. This would be the first electronic health record for the JCLC. Students are given a request for proposal (RFP) and information about falls: evidence-based protocols, workflows and policies for the management of fall risk, and resident data concerning fall risk. Their first task is to meet with the Director of Nursing in the JCLC conference room to clarify the requirements for the information system. This meeting is conducted through text messaging within SL so that a transcript of the meeting is available for analysis (Fig. 48.2).

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Next the students are taken on a tour of the JCLC, as they would be in real life, to observe and ask questions to clarify the requirements for the fall-risk information system. Students must design the entire system—architecture, software, Internet access, security and confidentiality constraints, and other relevant system functions. The deliverables for the design are storyboards, use cases, use case diagrams, workflow diagrams, and activity diagrams for both current and future states. In the database theory course, the students return to the JCLC to design and build an access database for the fall-risk management assessments. They must work with the staff again to determine database table structures (conceptual, logical, and physical data models), data entry forms, standard data queries, required reports, and training needs. This time the cues are very important, as the students must realize that each resident has two telephones that must be addressed as fields in the database as well as other physical cues regarding data collection and input. Incorrect field content is a common problem in database design. Information concerning each resident is posted on a “Touch Me” card outside the resident’s room (Fig. 48.3). The database produced by the students must contain all the information and address each design challenge embedded in the simulation (http://www.u.arizona.edu/~nhuber/Ambiguity article DRAFT.pdf).

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•  FIGURE 48.2.  JCLC Conference room.

•  FIGURE 48.3.  “Touch Me” cards with resident information to be placed in the database.

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776    P art 8 • E ducational A pplications Students enjoy the experience, request more class time in SL, and successfully develop informatics projects. SL is a great way to simulate a facility so students can learn to elicit user requirements for information systems. The challenges are scheduling meeting times, managing group interactions, practicing etiquette in group interactions, and learning to use the technology of SL. Students present posters in SL as a way to demonstrate learning. Many of the presentations cover usability and design issues, system security approaches, federal regulations impacting the discipline of informatics, and database management systems. The simulation helps students learn to prepare a poster and answer questions of attendees at the poster session. A poster pavilion module was created with six poster boards (Fig. 48.4). This module can be recreated to host as many presenters as required. As a by-product of the primary content covered through simulations in SL, learners gain hands-on experience with a new technology and a greater awareness of and tolerance for ambiguity. Learners often have difficulties with the technology during their activities as they guide their avatars and interact with others. Students may have trouble and need

to troubleshoot their microphone use or have difficulty with the avatar function, such as commands that allow the avatar to sit on an object. Various difficulties are discussed, along with an explicit ambiguity tolerance dialogue at the end of the virtual activity as part of postactivity debriefing.

Course Evaluations A serendipitous finding in using SL was creating the Beach on KUMC Isle as a place to celebrate the end of a course and for students to share with faculty what worked and what did not work. Early on, students suggested adjourning to the beach after the last class. Faculty facilitated the meeting and engaged the students in informal discussions about the use of SL. Students shared their enthusiasm for SL and then began to share perspectives on the course. The informal environment, outside the course, encouraged very productive discussions that led faculty to change several course strategies. Now, a Beach Party is conducted for debriefing. Students continue to be very professional in their desire to help the courses evolve into highly successful experiences. The Beach is shown in Fig. 48.5.

•  FIGURE 48.4.  Poster pavilion.

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•  FIGURE 48.5.  The Beach, complete with palm trees, fire pit, places to sit, and tiki torches.

Use of Second Life in Doctoral Nursing Courses During the first semester, all doctoral students enroll in a technology and informatics course. This course is designed to assist the student in developing skills to complete an online doctoral program, and SL is one of several Web 2.0 programs introduced to the students. Formal presentations of team projects as a simulation of a conference presentation are required. Students use instant messaging and SL to meet as a team to organize the work of their projects, thus enhancing their informatics skills. The presentations are conducted in the conference center using microphones and speakers. Students are able to see the audience, pace the presentation, and answer questions just as they would in the real world (Figs. 48.6 and 48.7). These students also enjoy the Beach Party at the end of the course and have helped to make this course very popular.

Use of Second Life in the Nurse Anesthesia Program TLT staff in collaboration with faculty in the Nurse Anesthesia Program developed a VW operating room simulation to assist first-year nurse anesthesia students with learning the basic induction procedure. Nurse anesthesia faculty members were already experienced with physical

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patient simulators, such as SimMan®; however, they were especially interested in virtual simulations because much of their program trains students in specialized processes and procedures. The objective for this project is to learn how the operating room is organized, to navigate the environment, to learn workflow and organizational skills, and to practice the basic induction procedure before stepping into the actual operating room. The SL operating room is designed to look exactly like the operating room at KU Hospital (Fig. 48.8). The clocks, tables, and other objects are in the same locations as they are in the real operating room. This allows the students to be exposed to the layout of the operating room before they experience the real environment. In addition, some of the equipment in the SL space is designed to be interactive so the students can manipulate it and gain confidence. To see how the student progresses through this learning activity and how the objects interact with each other, view the video tutorial of the Second Life Operating Room (http://www.youtube. com/watch?v=70CkcswfDe4).

Use of Second Life in the Physical and Occupational Therapy Programs The physical and occupational therapy f­aculty partnered with TLT staff to develop virtual home e­ nvironments in

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•  FIGURE 48.6.  The Conference Center used for formal presentations.

•  FIGURE 48.7.  Student giving the presentation and using slides.

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•  FIGURE 48.8.  Operating room. Second Life (Figs. 48.9 and 48.10). The home environment is a critical part of everyday life and ­participation in activities of daily living and instrumental activities of daily living. The homes were designed with a focus on objects within the home to assist students in identifying environmental barriers and support and to make critical decisions regarding environmental and task modification for clients. The student learning outcomes include interprofessional collaboration, patient-centered decision-making, and appreciation of the environmental and social context of functional mobility and occupational performance. Physical and occupational therapy students working in teams use SL to evaluate the home of a disabled client. There are a series of three homes with different hazards to be identified. Students are given a patient record with information concerning the patient’s abilities and disabilities. They then conduct a walk-through and make recommendations for creating a safer home environment. Faculty control the visual cues and hazards and so know when the students

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make accurate assessments. Once students make recommendations, the TLT staff modify the home according to the recommendations, and faculty and peers evaluate the modifications based on levels of support, unintended challenges, and client preferences. Seeing the outcome of their recommendations in SL is valuable because students are not always able to see their modifications carried out in the real world (Sabus, Sabata, & Antonacci, 2011).

Use of Second Life in a Dietetics and Nutrition Weight Management Program A faculty researcher in the Department of Dietetics and Nutrition saw a presentation by a faculty member in occupational therapy who was teaching a class in SL and immediately thought this would be a good environment to use for weight management (Sullivan et al., 2013). Sullivan’s research team studied 20 overweight and obese people in a program that involved either real-life or virtual

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•  FIGURE 48.9.  The row houses for home evaluations.

•  FIGURE 48.10.  Living room and kitchen of one of the row houses, showing safety hazards.

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reality meetings every week for 3 months. At the end of that period, all the subjects took part in a weight-maintenance program using SL. Sullivan found that while VR compares favorably with face-to-face interactions in controlling weight loss, its true benefits were more readily apparent in weight maintenance. In the study, participants created avatars that could interact with the other cyber-dwellers in the group. Training and education took place on KUMC Isle.

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Participants used headsets and microphones to communicate with others within the group. Since SL can automatically work with Web sites like YouTube® to pull in content to use within the simulation, group leaders could show videos or present other materials during meetings of the avatars in a virtual conference room. KUMC’s original island environment included a conference room, house, gymnasium (Figs. 48.11 and 48.12),

•  FIGURE 48.11.  Gymnasium for weight management program.

•  FIGURE 48.12.  Gymnasium for weight management program.

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•  FIGURE 48.13.  Grocery store with note cards. g­ rocery store (Fig. 48.13), restaurant, and buffet. Each space provided the avatars with a setting to interact with each other as well as to check on calorie counts in food items, calories burned during exercise, and other helpful information. By using SL, participants can simulate real-life situations without many of the consequences and repercussions that occur in real life. For example, the avatars can practice meal planning complete with calorie counts for items in the grocery store, dining out, or attending holiday parties, all in the anonymity of cyberspace. The goal of the simulation is to create a friendly environment where people can spend time researching healthier lifestyles without the fear of being judged. As a result of Sullivan’s preliminary research, she and her team received a grant from the National Institutes of Health to continue the research. Through this grant KUMC created a new island called KUMC Healthy U that expands opportunities for the participants. On KUMC Healthy U, avatars are able to take advantage of restaurants with cashiers that total the calories on customers’ trays as they check out. A kiosk, known as Fast Food Frenzy, links avatars to the Web sites of various restaurants, which allows them to calculate

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the calories in their meals. The new island also includes a more elaborate gymnasium, complete with a swimming pool where avatars can register the calories burned as they swim, tread water, or take part in activities in the water. Trainers in the gym are able to help the research subjects by answering basic fitness questions. Avatars can also access fitness videos while doing their simulated running on treadmills. One of the highlights of the new island is trails where avatars can walk, run, or bike while SL keeps tabs on the calories burned. All participants in this study will receive the same weight-loss program for 6 months and then be randomized to either virtual reality or a traditional method for 12 months of weight-loss maintenance. The overall aim is to compare the difference in weight-loss maintenance between the two groups. The follow-up 18-month randomized trial utilized the new SL isle and included 128 participants (Sullivan et al., 2016). Sullivan’s study included participants who achieved a 5% weight loss following a 6-month weight-loss intervention. The participants were randomized into two groups receiving weight-maintenance interventions: one group

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received the intervention by phone conference call, the other group received intervention in SL. Sullivan’s findings show that “Internet based virtual reality platforms, such as SL, may be an effective approach for weight maintenance, as there is evidence to suggest that skills and behaviors acquired in virtual environments transfer to the real world” (Sullivan et al., 2016, p. 82). The resources developed in SL for this research project are also used by other faculty to meet their course objectives. For example, informatics students have developed smartphone apps that can be used for weight loss and maintenance. Undergraduate students can be assigned to shop in the virtual grocery store or dine in the many restaurants to learn about healthy food choices. Virtual worlds are valuable new research tools to study human behavior. Researchers can inexpensively prototype models and explore data visualization in unique ways.

FUTURE TRENDS UTILIZING INNOVATIVE TECHNOLOGY In a very short time, the utilization of innovative technologies in education has skyrocketed. Several technologies in healthcare education fit into the “innovative” group including VR, AR, robotics, and drones. Educators benefit from development and iteration already accomplished in other industries. Integration into learning is cheaper and is therefore growing much faster. Another driver in innovation is the ubiquity of smartphone devices, which can now be used as VR and AR devices, and the apps that are used on these devices for VR and AR as well as in robotics and drones. According to Newzoo® analytics (Newzoo, 2018) the total number of smartphone users worldwide reached 3 billion in 2018. In the United States smartphone penetration is 71.5% of the population. This explosion of development demonstrates the current reality that we are in times of “bring your own device” (Skiba, 2016). All of these trends are driving costs down and making it easier for educators to utilize the technology. Because of the need for innovative development, wider usage in education, and a better user experience, institutions are building their own VR environments and working with industry to utilize innovative technology for educational purposes. Hewlett Packard’s collaboration with EDUCAUSE® for the Campus of the Future Project is one example (https:// er.educause.edu/blogs/2018/8/the-campus-of-the-future). This collaboration involves eleven institutions across the United States that have integrated AR, VR, and 3D printing and scanning within their curricula. HP supported and supplied the hardware, software, and technical support while EDUCAUSE assisted with the design and research aspect of it (DePaul, 2018).

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Virtual Reality and Augmented Reality There are certain differences in virtual and augmented environments. For example, augmented reality allows users to add digital pieces or layers to a “real-time” view mainly by using the camera feature on devices. An augmented experience might include the use of Snapchat lenses or filters or games such as Ingress, Pokemon Go, or Dragon Mania Legends. Virtual reality, on the other hand, is fully immersive through headsets allowing users to experience constructed virtual environments that can be both visual and auditory. The use of VR enhanced by the integration of AR, also referred to as mixed reality (MR), has expanded teaching and learning opportunities for campus-based and online classes. Smart glasses such as Google Glass® are an example of augmented reality. They resemble a standard pair of glasses integrated with a camera that can take 5.0 megapixel photos and 720p video. The glass has a touchpad, on the temple near the hinge, used to navigate its menu system and voice command activated by saying “OK Glass.” This command activates the glass so that the user can follow up with commands such as “Record a video,” “Take a Picture,” etc. Oculus Rift® is a virtual reality headset that works with gaming software and is integrated with 3D audio and hand-held controllers. Mixed reality smart glasses such as Microsoft’s HoloLens is a head-mounted display connected to an adjustable, cushioned inner headband. The inner headband allows HoloLens to be tilted up and down or forward and backward for better visualization of the display. The user fits the HoloLens on the head by using an adjustment wheel at the back of the headband to secure it around the crown. Creating dynamic simulations that go beyond the visual aspects of a VW by utilizing Google Glass, VR headsets, and HoloLens while considering how the student learner/ user interacts with the environment will improve learning outcomes. Integrating these instruments increases the degree of immersion and interactivity available in virtual environments, allowing for a greater sense of presence that is believed to contribute to meaningful learning, especially when the course is online.

Robotics and Drones Robots are virtual or automated or semiautomated entities that are utilized to perform complex activities often mimicking human activities. They also facilitate repetitive patterns of activity and are used in place of humans (Merriam-Webster, n.d.-a). Robots can also be used in activities deemed too dangerous for humans. Use of robots in laboratories and surgical suites began to be seen about 1985 (Hussain, Malik, Halim, & Ali, 2014). Robotics in healthcare include many uses: telehealth or telepresence, robotic-assisted surgery, robotic anesthesia administration, nanorobotics, aid for patients with disability,

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784    P art 8 • E ducational A pplications musculoskeletal-assistive devices, and transferring/lifting patients (All on robots: Medical robots of today and tomorrow, n.d.), (Deloitte, 2016). Use of robotics in the healthcare education is lagging. The expanded use of robotics and drones will change how students learn and enhance what they need to know to practice. Like other technologies in healthcare, the best way to teach is to have student users experience the use as it is happening in care. Furthermore, the environment can be controlled or simulated to create learning experiences designed by the faculty member to achieve pedagogical goals. Robotics can be used in healthcare education to bring faculty expertise to students in remote areas where expertise is lacking. This can be done using a faculty robot as a virtual faculty expert. The robot is a mobility base that moves forward, backward, and can turn 360 degrees and has a display monitor where the distance faculty appears on the screen. The mobility cart is equipped with camera, printer, and the display that rotates 360 degrees. The mobility base equipment is controlled by the distance faculty. The robot can be used with simulation and teach-back methodology. Unmanned aerial vehicles (UAV) more commonly known as drones are essentially flying robots. These vehicles are various types of aircraft without a human pilot. UAV may fly by means of a remote control with a human operator or autonomously through the use of onboard computer systems programmed with flight plans and utilizing global positioning system, or GPS, receivers (https://www.dronezon.com/learn-about-drones-quadcopters/what-is-dronetechnology-or-how-does-drone-technology-work/). With increased need to expand healthcare in rural areas and in a cost-effective manner, the use of drones is part of the technology toolbox of the future.

Administrative Considerations: Creating a Supportive Environment Educational innovation is a process of bringing new teaching strategies to satisfied learners and future knowledge workers. It is a conversion of new knowledge into value-added outcomes enhanced by novel teaching strategies. Innovation in education involves not only technological advances, but also pedagogical approaches. Most innovative educators are recognizing and experimenting with the educational possibilities of innovative technologies including VR, VW, robots, and drones. Student enthusiasm for these learning formats is also strong, creating uniquely powerful interactive and compelling educational possibilities. At the same time, for many faculty members, teaching with innovative technologies can be daunting. Learning the new technology, meeting the needs of the technologically diverse students, understanding

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innovative technology pedagogy, and managing workload and time are some of the challenges. It is up to academic administrators to provide the support and resources to encourage faculty to use new technologies such as Second Life and other emerging technologies. The goal is to minimize organizational barriers to student and faculty success. Challenges at the organizational level need to be anticipated, and policies, procedures, and guidelines should be in place to help mitigate their impact on faculty and students. Faculty who desire to use innovative technologies need to be heard, to feel supported, and to have an infrastructure in place that not only supports the present but allows for growth and rewards faculty efforts. Creating a supportive environment for successful adaptation of innovative teaching strategies requires resources, but, more importantly, it requires a cultural shift for many academic institutions.

A Culture of Innovation As technology advances, today’s learning environment needs to convey a culture of innovation and strategically plan to meet the challenge of change. Every academic organization has a culture; the issue is whether and how that organization supports innovation. A culture of innovation provides a competitive edge, because the organization is more nimble with an increased ability to respond to change. To be successful, a culture of innovation should reflect a balance between an openness to allow ideas to flow and the creation of controls and supports around those ideas. Academia is steeped in a tradition of hierarchical beliefs in which research and scholarship is rewarded and educational innovation is not. To fully integrate a culture of innovation within the organization, key concepts need to be reflected in the organization’s mission, vision, leadership, core values, hiring practices, metrics, rewards, and compensation. These concepts call for new interactions and partnerships involving a team approach to teaching, learning, and research. Success also requires clear communication from leadership that describes how the institution understands educational innovation and then builds that understanding into the organizational behavior modeled by the leader. Faculty and staff should feel comfortable and supported to take risks without fear of failure or retribution. As Melnyk and Davidson point out, “Success in an innovative culture is viewed as going from one failure to the next with enthusiasm” (Melnyk and Davidson, 2009, p. 2).

School of Nursing Administrative Support The School of Nursing (SON) administration fully supports teaching in SL and serves as a liaison between the faculty and the TLT administrator to assure academic innovation and maintain quality and integrity of academic programs. Clear

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Chapter 48 • A Paradigm Shift in Simulation 

communication about the pedagogical needs of faculty is essential to good outcomes and assures that faculty receive the support they need. Faculty who are champions in this new learning environment need to be encouraged to take risks and should be rewarded for their efforts. To demonstrate its commitment to innovation, the SON revised its appointment, promotion, and tenure criteria, using Boyer’s model of scholarship, to reflect the value of innovation in education, practice, and research. Boyer proposed an expanded definition of scholarship within the professoriate based on four functions: discovery, integration, application, and teaching (Boyer, 1997). He argues that all forms of scholarship should be recognized and rewarded, and that this will lead to more personalized and flexible criteria for gaining tenure. Boyer proposes using “creativity contracts” that emphasize quality and innovation in teaching, while fostering professional growth that supports individuals and their passions (Boyer, 1997). A balanced focus on all forms of scholarship is critical to meet the challenges in creating and sustaining innovative

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academic programs. Using this model, faculty are encouraged, supported, and rewarded for risk taking, pilot testing, and design thinking in their teaching practices. The technology service support in the SON is another example of administration’s support for the use of SL and other technology-supported practices. These services include an advanced technology environment for all faculty and staff, coordinated with all services at the KUMC campus level. The SON supports a dedicated professional who exclusively serves the technology needs of the school. The school’s support staff also provides services such as notebook computer support for faculty and staff, assistance in purchasing hardware and software to support research and educational innovation, and assistance with mobile computing devices. Teaching with innovative technologies often requires a computer with hefty specifications to run properly. Through these technology services and futuristic planning, the SON assures that faculty have what it takes to successfully teach using innovative technologies. (See Table 48.2 for resources.)

  TABLE 48.2    Faculty Resources for Learning More about Virtual Reality and Virtual Worlds Articles

Antonacci, D., et al. (2009). The power of virtual worlds: A second life primer and resource for exploring the potential of virtual worlds to impact teaching and learning. Angel Learning White Paper. Retrieved from http://www.angellearning.com/products/secondlife/downloads/The%20Power%20of%20Virtual%20Worlds%20in%20Education_0708.pdf Dangleish, K., & Laurenso, M. (2019). Practical learning in virtual worlds: Confronting literature with health educators’ perspectives. Journal for Virtual Worlds Research 12(1). Retrieved from https://jvwr-ojs-utexas.tdl.org/jvwr/index.php/ jvwr/article/view/7314 EDUCASE. 7 Things you should know about Virtual Worlds. Retrieved from https://www.educause.edu/ir/library/pdf/ELI7015.pdf Green, J., Wyllie, A., & Jackson, D. (2014). Virtual worlds: A new frontier for nurse education. Collegian 21(2). Retrieved from https://www.sciencedirect.com/science/article/pii/S1322769613001212 Oaks, S. (2011). Real learning in a virtual world. The International HETL Review 1(3). Retrieved from https://www.hetl.org/ feature-articles/real-learning-in-a-virtual-world/ Skiba, D. (2009). Nursing education 2.0: A second look at Second Life. Nursing Education Perspectives, 30(2), 129–131. Journal of Virtual Worlds Research. Retrieved from https://jvwr.net/category/home/ Wiecha, J. (2010). Learning in a virtual world: Experience with using second life for medical education. Journal of Medical Internet Research 12(1).

Books

Boellstorff, T. (2010). Coming of age in second life: An anthropologist explores the virtually human. Princeton, NJ: Princeton University Press. Krotoski, A., Cezanne, P., Rymaszewski, M., Rossignol, J., Wagner, & Au, J. (2008). Second Life: The official guide (2nd ed.). New York: John Wiley & Sons. Robbins, S., & Bell, M. (2011). Second life for dummies. New York: John Wiley & Sons. Bruns, A. (2008). Blogs, Wikipedia, Second Life, and beyond: From production to produsage. New York: Peter Lang Pub Inc. Weber, A., Rufer-Bach, K., Platel, R. (2007). Creating your world: The official guide to advanced content creation for Second Life. New York: John Wiley & Sons. Wankel, C., & Kingsley, J. (2009). Higher education in virtual worlds: Teaching and learning in second life. Emerald Group Publishing. Percival, S. (2008). Second Life: In-world travel guide. Indianapolis, IN: Que Publishing.

Web sites

Examples of education and non-profits in SL. Retrieved from http://secondlife.com/destinations/learning Second Life education Wiki. Retrieved from http://wiki.secondlife.com/wiki/Second_Life_Education

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786    P art 8 • E ducational A pplications Faculty are the most important factor in the overall success of using innovative technologies and innovative teaching strategies. If faculty feel well supported (technology, design, administration) and have a voice in determining policies and procedures for fostering innovative environments, they will be more willing to adapt and adopt the use of innovative technologies. Specifically, faculty need training, professional development, and release time for initial increased workload and design issues. Since faculty work collaboratively with instructional design and technology specialists, recognize that training needs shift from training faculty on software to training them on new teaching approaches, instructional design strategies, and workload management topics. Keep in mind that use of innovative technologies in teaching may not be for everyone; work with your champions and let them be the driver and set the standard. Celebrate your successes by having your champions showcase their work at faculty meetings and professional development sessions.

Future Trend: Bringing It All Together Utilizing Technology Educators, especially in healthcare education, are challenged with adding concepts and competencies as they teach. These  concepts include interprofessional competencies, informatics competencies, and many other policy-driven components outside and including the core components of the program of study. Using innovative technology is often underutilized as a fulcrum for bringing all the components together within one technology-enhanced activity. The truth is we do not know all the possibilities of what we can and cannot do with innovative tools new to the education arena. Use of technology like VR, AR, robotics, and drones support education by facilitating interactions of faculty to student and student to student who are geographically apart. These technologies, with the addition of a bit of imagination and knowledge about what the technologies are capable of, can be used to create simulated learning experiences designed by faculty to achieve pedagogical goals. Imagine the use of drones in an activity designed to require students to exercise critical thinking. The simulation is a tool to create an environment where students learn and apply the core concepts of the simulation and as a by-product problem-solve with technology. Then bring it all together as students and faculty join together in a brainstorming debrief related to future use of technology. With simulation and debrief guided toward a goal for students to understand the application in a real healthcare setting, the simulation creates an environment for synthesis and realization that what they are learning is applicable in many settings students will assume in roles as practitioners. The model described, using technology simulation with a healthcare subject, allows

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healthcare education to integrate the core competencies for the program of study and meet the informatics and interprofessional competencies that have been added to many programs of study.

CONCLUSIONS As computer power and networking speeds are evolving, innovative technologies used for learning will continue to advance. The question is, are most educators ready to enter this space? Many faculty and administrators are not imaginatively equipped for what the technology can do or how to use it to enhance learning. However, this may change as the “gamer” generation becomes more mature and able to recognize the value of immersive learning from their past experiences. The future of innovative technology, current and in development, is bright. It is estimated that there are over 450 VWs. Some educational institutions, disenchanted with SL’s pricing and policy changes have switched to other platforms such as OpenSim®, ReactionGrid®, OSgrid®, and Active Worlds® to name a few. These are evolving open-source VR online platforms. Each virtual world will have advantages and disadvantages. For example, SL has a large following of educators who share ideas, content, and teaching strategies and collaborate across learning spaces. That cohesiveness could be lost as institutions become more scattered in their choice of virtual spaces. As these other options for virtual space mature, it is important to advocate for interoperability to enable avatars as well as content to be transported from one virtual space to another. When selecting any innovative technology platform, it is important to consider institutional needs, affordability, availability, usability, maintenance, vendor stability, and the ability to create a realistic environment. Robust user groups who provide amazing support is also something to look for in any platform. Selecting and utilizing technology to give the closest real-world experience will help learners feel immersed in the activity because they perceive it as real. The outcomes are well worth the effort and resources required to produce highquality learning experiences. Virtual environments and technologies beyond have extended the reach of educators into worlds without boundaries. These worlds are free from selfimposed constraints and open to new ways of thinking, imagining, expressing, and building. Technology use in education including VR, VW, robotics, and drones can provide many unique learning opportunities; however, it is important to use technology to facilitate learning and not for the sake of using the tool. Further, as these technologies continue to evolve and expand in use, it will be essential to conduct research to identify best practices for VW education and collaboration.

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EXEMPLARS AND USE CASE Examples of innovative technology use in education are provided below. Exemplars and use cases are the format. Exemplars that follow describe one institution’s design principles and learning activities. Use cases include examples of current, innovative, and potential future technology use in education. A use case, for our purpose, outlines the content and how a technology might be utilized in certain situations. The use case example provided is intended to be used as a modular template, for use with different core content, by removing a component and “plugging-in” another depending on the need of the students and faculty.

Exemplar: Google Glass Simulation at the University of Kansas Medical Center (KUMC) Bachelor of science nursing completion students, in their fourth year, are exposed to Google Glass and Vibe. Both technologies allow students to interact with different forms of realities. The Google Glass is used to record role-play simulations where five groups of two, one being the patient and the other being the nurse, practice nursing skills. The nurses wear the glasses and record the interactions in which they obtain the patient’s health history and explain their conditions. The simulation is recorded from the nurses’ perspectives, and the glasses are then switched and worn by the patients instead. The same simulations are then played out, this time from the patients’ perspectives and recorded with the Google Glass. The recordings are made available to students and the clinical faculty afterward for debriefing. Skills are discussed, and students talk about the importance of nonverbal interactions and various approaches to the communication process, deciding which approaches would have fit best in the simulation. The Google Glasses were purchased via grants made possible by KUMC’s Schools of Medicine and KU’s Interprofessional Center for Health Informatics. The partnership with the Department of Teaching and Learning Technologies allowed students to receive brief technical training on how to use the Glass and record the group sessions. In clinical sessions after these events students could view the video and reflect on their skills which were made available via links provided by TLT’s eLearning Specialist for the SON after the sessions were downloaded from the Glass to a shared drive.

Drone Use Case Interprofessional Synchronous and Asynchronous Use Case:  This use case is a 50,000-foot view of how a robot

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drone might be used in an interprofessional (IPE) simulation across a geographic area including three different professions: medicine, nursing, and pharmacy. The simulation could be utilized as a cross-university simulation or at one university with several campuses. An academic electronic health record (AEHR) could be utilized for the orders and documentation. The simulation could be played out utilizing synchronous and asynchronous interaction. This use case is designed to be presented at a conceptual level so that it might remain malleable enough to be extended and used with other learning concepts or adjusted to meet the need of the institution. Perhaps there is not a medical school or pharmacy on campus, this activity could then become a cross-university simulation or could focus on advance practice nurse providers as the prescribers. The design is intended to help faculty begin to think about how to integrate the core learning with innovative technology as a tool. An example would be a case integrating competency skills from medicine, nursing, and pharmacy professions. Students would need to demonstrate: assessment of taking, reading, and reviewing patient history, writing orders and prescribing medication, nurse and pharmacy verification, consultation, and filling, followed by delivery of medications or supplies by a remotely operated drone to a rural area including a nurse-run clinic where nursing receives the medication and dispenses to patient(s).  The curriculum built within the simulation would facilitate student understanding of communication skills in which each profession gains understanding of how to articulate and convey information based on their respective roles and include instruction on how to operate the drone and learn its delivery process/ procedures. The simulation curriculum could include learning objectives and competency skills based on interprofessional education technology and informatics. Student tasks could include preinstruction and interactivity access to the AEHR as part of the simulation. Electronic Health Portals could be used to send secure email between patient, provider, and other health professions, thus helping to understand privacy and security concepts. Through this simulation, students will also learn how to operate a drone, monitor its mileage and charging processes, and verify/troubleshoot its delivery or any challenges it might have faced making its delivery or its return. Learning Concepts:  Learning objectives could be written for many different areas of learning, including core content, IPE, technology, and informatics. 1. Patient history review

2. Population health/rural health and needs of the specific underserved community

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788    P art 8 • E ducational A pplications 3. Understanding of the medication prescribing process 4. Learning from, with, and about other professions 5. Interprofessional communication

6. Patient teaching related to medication

7. Technology and informatics objectives Medicine specific tasks:

• • •

Patient history Order writing Medication prescribing

Pharmacy specific tasks:

• •

Patient history/medication review

• • • •

Order fill

Pharmacy order verification (verify patient, allergies, drug–drug interactions, age, weight, labs, appropriate drug, dose, and route) Send drone Visual verification of delivery Pharmacy/nurse verify medication delivery by phone communication

Nursing specific tasks:

• • •

Patient history review and assessment

•

Nurse dispense of medication

Receive drone delivered medications Nurse/pharmacy verify medication delivery by phone communication

All student tasks:

• • • • •

Understanding tasks of each profession and articulation of each Population health readings Underserved community readings Patient history review and assessment Understanding drone use ◦◦ Drone to be used ◦◦ Miles per charge

Debrief could follow many methodologies including the PEARLS framework (Eppich & Cheng, 2015). This framework includes learner expression of feelings about the simulation; description of what the simulation was about; faculty guidance related to the learning objectives; learners describe what went well and what did not; and if there was a gap or need in the simulated activity; faculty provide a few key points. For simulation with innovative technology, there should also be

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discussion related to future use of technology. Simulation and debrief should be faculty guided toward a goal for students to understand the application in a real healthcare setting.

Test Questions 1. How does teaching in a virtual environment differ from teaching in a traditional online course?

A. In a virtual environment, users are immersed in a 3D setting and use avatars to represent themselves and their sense of presence. B. There is no difference between the two environments.

C. Very similar to online learning where users will need a computer, microphone headset, and joystick to manipulate the virtual world. D. Both A and C.

2. Which learning theory mentioned in this chapter was used to provide an overarching framework in creating some of the learning activities in second life? A. Systems design theory

B. Human-centered design theory C. Cognitive theory

D. Theory of andragogy

3. What are some challenges in teaching an online course? (More than one answer may apply.)

A. Receiving technical assistance to help with complex instructional design decisions congruent with pedagogy, teaching strategies, and outcomes. B. Once you load course materials into a Learning Management System, there are no other tasks. C. Finding creative solutions to bridge what may seem like lack of instructor presence. D. New technologies are not always intuitive.

4. How can virtual reality be defined?

A. Technology that overlays digital information on the real world. B. Virtual objects from the real world can interact and respond as if they were real objects.

C. A fully immersive environment provided through VR gear (e.g., headsets) allowing users to experience constructed virtual environments (can be both visual and auditory). D. Reality is simulated to where users can’t distinguish whether it’s real or not.

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Chapter 48 • A Paradigm Shift in Simulation 

5. What is augmented reality?

A. Addition of digital pieces or layers to a “real-time” view mainly by using camera features on mobile devices B. Able to render basic text and data which happens to be readable hands-free

C. An artificial world consisting of images or sounds created digitally which can be affected by human actions D. Implies a complete immersion experience

6. What are a few other learning theories where principles of andragogy can be applied? A. Experiential learning theory, Social learning theory, Constructivism, Connectivism, and Collaborative learning theory B. Behaviorism learning theory

C. Human-centered design theory D. Connectivism learning theory 7. What is mixed reality?

A. A form of entertainment for gamers

B. The use of virtual reality enhanced by the integration of augmented reality (e.g., Microsoft HoloLens)

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10. When is it appropriate to integrate virtual worlds within online instruction?

A. When you would like to simulate processes and procedures where it would be harmful to implement in real life. B. When you expect high-order thinking/critical reasoning from students C. Both A and B

D. None of the above

Test Answers 1. Answer: A

2. Answer: D

3. Answer: A,C,D 4. Answer: C

5. Answer: A 6. Answer: A 7. Answer: B

8. Answer: C

9. Answer: D 10. Answer: C

C. Very similar to movie theatre experience

D. An immersive technology where it discourages any physical activity 8. What is another name for drones?

A. Datsun Roadster Owners of New England B. Defense and Rescue Oriented Navigation Expert C. Unmanned Aerial Vehicles (UAV)

D. Digitally Recording Our Nation’s Existence 9. What are the principles of the theory of andragogy? A. Adults are self-directed, goal oriented, and need to know why they are required to learn. B. Adults approach learning as problem centered rather than content centered.

C. Adults need to recognize the value of learning and how to incorporate that learning into their jobs or personal lives. D. All of the above.

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REFERENCES All on robots: Medical robots of today and tomorrow. (n.d.). Retrieved from http://www.allonrobots.com/medicalrobots.html Accessed date Feb, 2019. Bandura, A. (1977). Social learning theory. New York: General Learning Press. Boyer, E. L. (1997). Scholarship reconsidered: Priorities of the professoriate. San Francisco: Jossey-Bass. Bruner, J. (1966). Towards a theory of instruction. Cambridge, MA: Harvard University Press. Bruner, J. (1996). The culture of education. Cambridge, MA: Harvard University Press. Calongne, C. M. (2008). Educational frontiers: Learning in a virtual world. Educause Review, 43(5), 36–42. Chickering, A. E., & Gamson, Z. F. (1987). Seven principles for good practice in undergraduate education. AAHE Bulletin, 39(7), 3–6. Deloitte. (2016). Deloitte insights: Will patients and caregivers embrace technology-enabled healthcare? Findings Deloitte Survey of US Health Care Consumers. Retrieved from https://www2.deloitte.com/insights/us/en/focus/

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790    P art 8 • E ducational A pplications internet-of-things/digitized-care-use-of-technology-inhealth-care.html Accessed date Feb, 2019. DePaul, K. (2018). Blog-EDUCASE review. XR-based learning: How institutions engage through immersive experiences. Retrieved from https://er.educause.edu/blogs/2018/11/ xr-based-learning-how-institutions-engage-throughimmersive-experiences Accessed date Feb, 2019. Eppich, W., & Cheng, A. (2015). Promoting Excellence and Reflective Learning in Simulation (PEARLS): Development and rationale for a blended approach to health care simulation debriefing. Simulation in Healthcare, 10(2), 106–115. Gerald, S. P., & Antonacci, D. M. (2009). Virtual world learning spaces: Developing a Second Life operating room simulation. EDUCAUSE Quarterly, 32(1). Hellyar, D., Walsh, R., & Altman, M. (2018). Improving digital experience through modeling the human experience: The resurgence of virtual (and augmented and mixed) reality. In: V. R. Lee, & A. L. Phillips (Eds.), Reconceptualizing libraries (pp. 115–136). New York, NY. Routledge. Hodges, E. M., & Collins, S. (2010). Collaborative teaching and learning in virtual worlds. Educause Review, 45(3), 62–63. Hussain, A., Malik, A., Halim, M. U., & Ali, A. M. (2014). The use of robotics in surgery: A review. International Journal of Clinical Practice, 68, 1376–1382. Johnson, C. M. (2009). Virtual worlds in healthcare higher education. Journal of Virtual Worlds Research, 2(2), 3–12. Knapfel, S., Moore, G., & Skiba, D. J. (2014). Second Life and other virtual emerging simulations. In: P. R. Jeffries (Ed.), Clinical simulations in nursing education: Advanced concepts, trends, and opportunities (pp. 90–100). Philadelphia, PA: J.B. Lippincott, Williams, and Wilkins. Knowles, M. (1984). The adult learner: A neglected species (3rd ed.). Houston, TX: Gulf Publishing. Kolb, D., Boyatzis, R., & Mainemelis, C. (2000). Experiential learning theory: Previous research and new directions. In: R. J. Sternberg & L. F. Zhang (Eds.), Perspectives on cognitive, learning and thinking styles. Mahwah, NJ: Lawrence Erlbaum. Melnyk, B. M., & Davidson, S. (2009). Creating a culture of innovation in nursing education through shared vision, leadership, interdisciplinary partnerships, and positive deviance. Nursing Administration Quarterly, 33(4), 288–295. Merriam-Webster. (n.d.-a). Robotics. Retrieved from https:// www.merriam-webster.com/dictionary/robotics Merriam-Webster. (n.d.-b). Virtual reality. Retrieved from https://www.merriam-webster.com/dictionary/virtual%20reality. Accessed date Feb, 2019.

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Michels, P. (2008, February 26). Universities use Second Life to teach complex concepts. Government Technologies. Retrieved from http://www.govtech.com/ gt/252550?topic=118264. Accessed date Feb, 2019. Newzoo. (2018, September). Top 50 countries/markets by smartphone users and penetration. Retrieved from https://newzoo.com/insights/rankings/top-50-countriesby-smartphone-penetration-and-users/. Accessed date Feb, 2019. Richardson, J. C., & Swan, K. (2003). Examining social presence in online courses in relation to students’ perceived learning and satisfaction. Journal of Asynchronous Learning, 7(1), 68–88. Sabus, C., Sabata, D., & Antonacci, D. M. (2011). Use of a virtual environment to facilitate instruction of an interprofessional home assessment. Journal of Allied Health, 40(4), 199–205. Siemens, G. (2004, December 12). Connectivism: A learning theory for the digital age. Elearnspace. Retrieved from http://www.elearnspace.org/Articles/connectivism.htm. Accessed date Feb, 2019. Skiba, D. J. (2016). On the horizon: Trends, challenges, and educational technologies in higher education. Nursing Education Perspectives (National League for Nursing), 37(3), 183–185. Smith, B. L., & MacGergor, J. T. (1992). What is collaborative learning? Retrieved from https://umdrive.memphis.edu/ ggholson/public/collab.pdf. Accessed date Feb, 2019. Sullivan, D. K., Goetz, J. R., Gibson, C. A., Washburn, R. A., Smith, B. K., Lee, J., . . . Donnelly, J. E. (2013). Improving weight maintenance using virtual reality (Second Life). Journal of Nutrition Education and Behavior, 45(3), 264–268. Sullivan, D., Goetz, J., Gibson, C., Mayo, M., Washburn, R., Lee, Y., . . . Donnelly, J. (2016). A virtual reality intervention (Second Life) to improve weight maintenance: Rationale and design for an 18-month randomized trial. Contemporary Clinical Trials, 46, 77–84. Tiffany, J., & Hoglund, B. A. (2014). Teaching/Learning in second life: Perspectives of future nurse-educators. Clinical Simulation In Nursing, 10(1), e19–e24. Vrellis, I., Avouris, N., & Mikropoulos, T. A. (2016). Learning outcome, presence and satisfaction from a science activity in Second Life. Australasian Journal of Educational Technology, 32(1). Witmer, B. G., & Singer, M. J. (1998). Measuring presence in virtual environments: A presence questionnaire. Presence, 7(3), 229–240. Young, J. R. (2010, February 14). After frustration in second life colleges look to new virtual worlds. The Chronicle of Higher Education.

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part

9

Research Applications Veronica D. Feeg

Part 9, which is the last part of this book, entitled Research Applications, includes computer use and software a­ pplications in nursing research to drive evidence-based inquiry, professional collaboration and practice, and i­nformation literacy and computerized information resources to aide practicing nurses through identification and application of essential and supportive computerized resources. Chapter 49 by Drs. Veronica D. Feeg, Theresa A. Rienzo, Marcia T. Caton, and Olga S. Kagan discusses Computer Use in Nursing Research. Computers are indispensable resources for researchers to drive the individual or collaborative p ­ rojects. With emerging technologies and applications, researchers find efficiencies and innovation in conducting s­cience while consumers experience real-time benefits of research applied to delivering high-quality evidence-based care. The ­chapter summarizes the applications for scientists in both quantitative and qualitative methodological applications with links to helpful resources. It also focuses on the research published about health information technology (HIT) as well as ­consumer use of its many products. Chapter 50 by Diane S. Pravikoff and June Levy discusses Information Literacy and Computerized Information Resources. Being “information literate” is a basic competence for nurses. Easily available and accessible electronic resources can assist nurses in maintaining and enhancing their professional practices. How to search and how to use the resources available are part of this chapter. These resources aid in keeping current with the published literature, in developing a list of sources for practice, research, and/or education, and in collaborating with colleagues.

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49 Computer Use in Nursing Research Veronica D. Feeg / Theresa A. Rienzo / Marcia T. Caton / Olga S. Kagan

• OBJECTIVES 1. Describe general data and computer applications related to research in nursing, including proposal development and project implementation in both quantitative and qualitative research (computer use in research). 2. Discuss how cloud computing has changed the management of research, development of proposals and reports, storage of shared documents and data, and execution of procedures including data collection and data sharing. 3. Summarize a range of computer-based Web innovations, tablet, and innovative mobile applications (apps) that facilitate or support the steps of the research process, including data collection, data management and coding, data analysis, and results reporting. 4. Compare and contrast select computer software applications that can be used in quantitative and qualitative research data analysis related to the steps of the research process. 5. Describe specific research studies in the literature that exemplify quantitative and qualitative methodologies on computer applications in healthcare. 6. Describe general categories of research that focuses on computer use and digital technologies in healthcare, from clinical applications and informatics integration to health and wellness (nursing research on computer use). 7. Describe how the drivers of “big data” and growing interest in D2K (data to knowledge) have promoted analytics and population health with informatics research and computer applications today and will change healthcare in the future (research on nursing informatics, data mining, artificial intelligence [AI]).

• KEY WORDS Artificial intelligence (AI) Big data Computer applications D2K (data to knowledge) Data analysis Data collection Data management Data mining Internet research 793

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794    P art 9 • R esearch A pplications

Meta-analysis Mobile apps Nursing informatics research Qualitative Quantitative Research applications Research methodology Research process Secondary analysis

INTRODUCTION Nursing research involves a wide range of tools and resources that researchers employ throughout the research process. From the individual or collaborative project initiation, through refinement of the idea, selection of approaches, development of methods, capturing the data, analyzing the results, and disseminating the findings, computer applications are an indispensable resource for the researcher. The investigators must be well prepared in a variety of computerized techniques for research activities as they are employed in the domain of knowledge that will be investigated. Without the power of technology, contemporary research would not reach the levels of sophistication required to discover and understand health and illness today. With emerging technologies and applications, researchers will continue to find efficiencies and innovation in conducting science while consumers will feel real-time benefits of research conducted on computer technologies used in healthcare delivery. In addition to the traditional approaches of the scientific method, researchers today have new avenues to explore in the development of knowledge. New opportunities to mine existing “big data” for evaluation, discovery, and transforming data to knowledge (D2K) are forming a bridge between the process of conducting research and the products of discovery. New tools for automatic capture and analysis are changing methods. New online strategies and exponential growth of mobile and tablet apps are being implemented as the process of researching health and the product of researched interventions in health. Computer use in nursing has exploded concomitantly, including mobile applications that are downloadable to smart phones for researchers and patients, and tools for exploring large data sets that have been already captured. Today, hospital-wide information technology (IT) is the spine of all healthcare delivery, which is tied to reimbursement, and which inevitably forms the data engine that health systems put to work to research improvement and

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outcomes-driven questions. With the power of systemwide integration supported by the proliferation of cloud technology, nursing is becoming a discipline that must be data-centric and caring at the same time for nurses to function in their roles. It is therefore imperative to understand the underlying terminologies and sources of data, communication of those connected pieces of information, and the elements in the nursing environments through informatics research if one is to understand how computers and nursing research co-exist. Cloud computing has reshaped the IT industry with the potential to transform a large part of how organizations purchase and implement technology, and allow the power of managing, communicating, analyzing, and sharing large system databases. Software development can exponentially grow as a service and it redesigns how information is stored and reported. Cloud computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centers that provide those services (Armbrust, et al. 2010). The availability of cloud services for storage, communication, and access to statistics and other licensed research products will enhance the researchers’ activities across the cycle of inquiry—from idea to dissemination. Outside the electronic health record (EHR) and computerized health systems, in the rapidly changing world of Internet technologies and the growing era of machine learning and artificial intelligence (AI) applications, information management and computer-enhanced intervention research on the use of computers have produced a new body of science that will continue to grow. Blending the focus of computer use in research (tools and process) and research on computer use (informatics research, secondary analysis, data mining and AI) calls for an understanding of process and products. This chapter will provide an overview of the research process for two separate and fundamentally different research approaches—quantitative and qualitative—and discuss select computer applications and uses relative to these approaches. The discussion

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will be supplemented by examples of current science and the trajectory of research on the impact of informatics, electronic records, treatments, and integrated technologies using the computer as a tool. Research tools today have evolved in many aspects of the research process and have gone beyond its historic application once limited to number crunching and business transactions. Field-notes binders, ring tablets, index cards, and paper logs have all but disappeared in the researchers’ world. Personal computers, laptops, PC-tablets and iPads, and handheld devices have become part of the researcher’s necessary resources in mounting a research project or study. Wireless technologies are ubiquitous and connect people to people as well as researchers to devices. Cloud computing today connects diverse enterprises with stable sources of software and data that can be shared or used by anyone, at any time or any place. Numerous enhancements have been added to the well-known text processing products to store and manage data that reduce time and effort in every research office. In addition, a wide range of new hand-held, mobile, flexible, and interconnected “blue tooth” technologies for database management and sharing of subjects, contacts, or logistics have emerged in the research product marketplace. Nurse researchers use a range of hardware and software applications that are generic to research development operations in addition to the tools and devices that are specific to research data collection, analysis, results reporting, and dissemination. New apps appear continuously, customized to the data collection, management, or analysis process. In today’s electronic healthcare environment, numerous advances have been made with the sources of data collection relative to general clinical applications in nursing, health, and health services. System implementations for large clinical enterprises have also provided opportunities for nurses and health service researchers to identify and extract information from existing computer-based resources. The capturing of rich nursing data from these systems that can be managed and mined for advanced analyses should be recognized in the development of EHRs and other sources to promote organizations such as hospitals to become “learning organizations”—where sharing and learning from analysis are continuously integrated into the organization-planned change to enhance outcomes of care. In addition, the era of Web-based applications has produced a wide range of innovative means of entering data and, subsequently, automating data collection in ways that were not possible before. With the advancements in clinical systems, acceptable terminology and vocabularies to support nursing assessment, interventions, and evaluation, computers are increasingly being used for clinical

and patient care research. Although research is a complex cognitive process, certain aspects of conducting research can be aided by software applications. For example, examination of nursing care/patient outcomes and the effect of interventions would have been ­prohibitive in the past, but with the aid of computers and access to large data sets, many health outcomes can be analyzed quantitatively and qualitatively. Data analytics built into software and the visualization ­capabilities using large samples of existing data can help predict best outcomes. For example, one hospital examined procedures from a rapid analysis of system-wide data that were d ­ ifferent across systems to determine how to minimize postoperative complications of infection. Their preoperative procedure was standardized and changed in 6  months, a change that in the past would have taken years of r­ andomized trials (Englebright, 2013). With a wider view of computer use in nursing research, the objective of this chapter is fourfold to: (1) provide an overview of innovations in systems, software, and mobile applications related to the stages of the research process; (2) describe how new technologies and mobile and wireless tools facilitate the work of the researcher in both quantitative and qualitative aspects; (3) highlight how research on widespread technology in healthcare has influenced patient care and health systems; and (4) give attention to the explosion of research in categories delineating clinical and nursing informatics research. These will serve as a snapshot of the research on computer use and research using computers and related technologies for the future with contextual influences. The chapter begins with a focus on some of the considerations related to the logistics and preparation of the research proposal, project planning, and budgeting, followed by the implementation of the proposal with data capture, data management, data analysis, and information presentation. The general steps of proposal development, preparation, and implementation are applicable to both quantitative and qualitative approaches with the explosion of apps to aid available in these processes. However, no chapter about computer use in research could be complete without acknowledging the range of research now appearing in the literature that examines the trajectory of how innovative technologies, integration, and Web-based applications are used in patient care. With increasing emphasis on cost and quality of healthcare, the computersources-of-data and computer-as-intervention must be part of understanding computer use in nursing research today. The chapter closes with the projections of D2K plans across healthcare, learning organizations and the horizon of AI that will soon disrupt health delivery as we know it today.

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PROPOSAL DEVELOPMENT, PREPARATION, AND IMPLEMENTATION Research begins with a good idea. Good science is typically based on the nurse researcher’s identification of a problem that is amenable to study from a theoretical perspective and existing evidence choosing a paradigmatic approach. This sets the stage for selecting one’s methods to investigating the problem or developing the idea. Because the theoretical paradigm emerges from an iterative process, and because the theoretical perspective will subsequently drive the organization to the research study, it is important to distinguish between these two distinct approaches—quantitative or qualitative, or some combination of both in mixed methods (Polit & Beck, 2017). Each approach can be facilitated at different points along the proposal process with select computer applications. These will be described as they relate to the methodology.

Quantitative or Qualitative Methodology The important distinction to be made between the quantitative and qualitative approaches is that for a quantitative study to be successful, the researcher is obliged to fully develop each aspect of the research proposal before collecting any data, that is, a priori, whereas for a qualitative study to be successful, the researcher is obligated to allow the data collected to determine the subsequent steps as it unfolds in the process and/or the analysis. Quantitative research is derived from the philosophical orientations of empiricism and logical positivism with multiple steps bound together by precision in quantification (Polit & Beck, 2017). The requirements of a hypothesis-driven or numerically descriptive approach are logical consequences of, or correspond to, a specific theory and its related tenets. The hypothesis can be tested statistically to support or refute the prediction made in advance. Statistics packages are the mainstay of the quantitative methodologist, but are not the only connection to computers for the researcher. The qualitative approaches offer different research traditions (e.g., phenomenology, hermeneutics, ethnography, and grounded theory, to name a few) that share a common view of reality, which consists of the meanings ascribed to the data such as a person’s lived experiences (Creswell & Cresswell, 2018; Cresswell & Poth, 2018). With this view, theory is not tested, but rather, perspectives and meaning from the data narratives by participants are described and analyzed. For nursing qualitative studies, knowledge development is generated from the participant’s experiences and responses to health, illness, and treatments as

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voiced by the participants. The requirements of the qualitative approach are a function of the philosophical frames through which the data unfold and evolve into meaningful interpretations by the researcher (Polit & Beck, 2017). There have been many new interview transcription devices for data capture and analytics software applications to assist the qualitative methodologist to enter, organize, frame, code, reorder, and synthesize text, audio, video, and sometimes numeric data.

General Considerations in Proposal Preparation The cloud has revolutionized the connectivity that has become indispensable for all users of software and sharing resources. It has facilitated development of the research proposal, communication with team members, documentation, and planning for the activities that will take place when implementing the study. These include broad categories of cloud-based office programs including word processing, spreadsheet, and database management applications. Office 365 is the new release from Microsoft (microsoft365.com), programs with cloud capability that continues to offer improved clerical tools to manage the text from numerous sources and assemble them in a cogent and organized package. The cloud connectivity gives researchers access to all programs and data virtually from anywhere. All versions of user hardware from smartphones to tablets to PCs have broadened the connectivity for researchers with a range of handheld apps that make users in constant connection with the research progression, participant log-ins, and ongoing data analytics throughout the execution of the project. Cloud-based products from Google (google.com) to Microsoft Office 365 provide capabilities and a platform into which other off-the-shelf applications can be integrated. Tables, charts, and images can be inserted, edited, and moved as the proposal takes shape, with final products in publishable forms. Line art and scanned images using Adobe industry standards such as Illustrator CC (www. adobe.com) or Photoshop CC (www.photoshop.com), now with cloud capability, can be integrated into the document for clear visual effects. These offer the researcher and grant managers tools to generate proposals, reports, and manuscripts that can be submitted electronically directly or following conversion to portable document formats (PDFs) using Adobe Acrobat (www.adobe.com) or other available conversion products. There are a variety of Web-based reference management software products available as add-ons to word processing, with ranging prices and functionalities that leverage the power of connectivity and sharing with team members. For example, unique template add-ons give Microsoft Word in

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Microsoft Office 365 additional power to produce documents in formatted styles. Bibliographic management applications emerge frequently and librarians often help sort out best ways to keep reference materials in order. Common Web resources such as reference managers that are subscriptionbased maintain resources that are available to users from anywhere. For example, RefWorks from ProQuest (https:// www.proquest.com/products-services/refworks.html) provides options for reference management from a centrally hosted Web site. Searching online is one function of these applications, and then working between the reference database and the text of the proposal document is efficient and easy, calling out citations when needed with “cite as you write” capability into the finished document. Members of the research team can share files, materials, and the ongoing development of the proposal. Output style sheets can be selected to match publication or proposal guidelines. Research applications and calls for proposals are often downloadable from the Internet into an interactive form where individual fields are editable and the documents can be saved in a portable, fillable format such as Adobe Acrobat, printed, or submitted from the Web. The Web also allows the researcher to explore numerous opportunities for designing a proposal tailored to potential foundations for consideration of funding. Calls for proposals, contests, and competitive grants may provide links from Web sites that give the researcher a depth of understanding of what is expected in the proposal. There are more and more home-grown submission procedures today for grants, journal manuscripts, and conference “call for abstracts” with Web-based instructions. These often convert the documents automatically to PDF for submission with key data fields organized and sorted for easier review procedures. Instructions are customized for the user.

The ubiquitous Microsoft Office 365 suite and Google systems include programs that (1) manage data in a relational database (Microsoft Access), (2) number crunch in a flat database (Microsoft Excel), and (3) share through document storage with hyperlink Web capabilities. Proprietary database applications and new customized, more sophisticated, integrated, and proprietary database management applications from locally produced Web-based systems provide the researcher with ways to operationalize the personnel, subjects, forms, interviews, dates, times, and/ or tracking systems over the course of the project. Many of these proprietary systems can map out the research flow for enrollment of subjects, consenting, and data capture all together in one solution. Clinical trials management software (CTMS) is available from a variety of vendors. For example, one vendor, Trial By Fire Solutions, is the team behind SimpleTrials, an eClinical software application with a focus on clinical trial management to improve planning, execution, and tracking of clinical trials (www. simpletrials.com/why-simpletrials-overview). Most of these applications require specially designed screens that are unique to the project if the research warrants complicated connections such as reminders, but simple mailing lists and zip codes of subjects’ addresses and contact information in a generic form can also be extremely useful for the researcher. Some of these traditionally designed clinical tool applications are emerging as portable apps with devices such as smartphones and tablets (Table 49.1). Scheduling and project planning software is also available from cloud products such as Microsoft Project that allows the project director to organize the work efficiently and track schedules and deadlines using Gantt charts over the lifetime of the project. In more sophisticated research offices, customized tracking and data capture devices, programs, and systems have been launched, including the exemplar of data management tools from the recent U.S. Census, that have captured and made data available to researchers with data tools (https://www.census.gov/ quickfacts/fact/table/US/PST045218). One more important consideration related to the development of the plan for the seasoned researcher or novice, doctoral dissertation investigator is the essential step of submitting the proposal to the Institutional Review Board (IRB). Home institutions that have IRBs will have specific procedures and forms for the researcher who can benefit from the proposal development electronically. In some institutions, the IRB document management has been done through contracts with outside Internet organizations providing mechanisms for posting IRB materials, managing the online certifications required, and communicating with the principal investigators. One such example is IRBNet.org, hosting services for organizations to

Research Study Implementation A funded research study becomes a logistical challenge for most researchers in managing the steps of the process. Numerous demands for information management require the researcher to maintain the fidelity of the procedures, manage the subject information and paper flow, and keep the data confidential and secure. These processes require researchers to use a database management system (DBMS) that is reliable. Several DBMS software applications exist and have evolved to assist the researcher in the overall process of study implementation. These applications are operations oriented, used in non-research ­programs and projects as well, but can assist the researcher in management of time, personnel, money, products, and ultimately dissemination, with reporting capability for reviews and audits.

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App Name

Description

Web Link/Device

CalcKit

Creating personalized calculators with over 150 unique calculators and unit converters, with a highly customizable scientific calculator and capability of building your own calculators and converters.

Compatible with iPhone, iPad, and iPod touch https://play.google.com/store/apps/ details?id=com.ivanGavrilov. CalcKit&hl=en_US

eLABJournal

A Web-based Electronic Lab Notebook that helps laboratories to efficiently track research data in a GLP-compliant manner. In addition, the system offers tools to standardize lab protocols, keep track of the lab inventory, and facilitate collaboration and communication in the lab. It is an extension to the eLABJournal Web App and is compatible with the eLABJournal Cloud as well as eLABJournal Private Cloud and eLABJournal On-Premise installations.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/elabjournal/ id932410151?mt=8 and on Android from Google play

BrightLab™

This companion app makes inventory management effortless for both scientists and lab managers.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/brightlab/ id1441291610?mt=8 and on Android from Google play

MDCalc Medical Calculator

The app provides access to more than 450 easy-to-use clinical decision tools including risk scores, algorithms, equations, diagnostic criteria, formulas, classifications, dosing calculators, and more.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/ mdcalc-medical-calculator/ id1001640662?mt=8 and on Android from Google play

Statistics and Sample Size

A tool to calculate sample size for scientific studies as well as doing basic statistics.

Compatible with Android https://play.google. com/store/apps/details?id=thaithanhtruc. info.stat

PatienTrials

PatienTrials helps pharma companies and clinicians run global clinical trials virtually, more efficiently in a closed community setting over HIPAA & GDPR compliant collaboration platform. It aims to improve patient adherence in a community setting that promotes self-care. It captures high-quality patientgenerated data through solutions based on instant messaging, AI, and bots. Monitored by proprietary AI agent for adverse events, PatienTrials reduces study risk and lowers development costs.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/patientrials/ id1386748565?mt=8 and on Android from Google play

GoLifeLab

GoLifeLab brings clinical trials and remote patient monitoring to people’s home. It offers end-to-end decentralized services, allowing researchers and sponsors to streamline trial setup, increase the speed of recruitment, improve patient retention and offer rich datasets. Data can also be collected from wearable devices and a fully integrated liquid biomarker service. GoLifeLab aims to reduce the time and cost of care by using technology to improve the patient experience and simplify patient data collection.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/golifelab/ id1432587883?mt=8

Clinical Research Trials

Offers an easy-to-use interface on a mobile as well as iPad to search federal and privately supported clinical trials conducted in the United States and around the world. Obtain information including a trial’s purpose, who may participate, locations, and phone numbers for more details about clinical trials being conducted throughout the United States and in many countries throughout the world.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/ clinical-research-trials/id511192008?mt=8 and on Android from Google play

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  TABLE 49.1    Clinical Application Tools



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Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/trialx/ id333747465?mt=8

Research Hive

Helps save the site time and recruit more high-quality patients for clinical trial research, to vastly improve clinical trial enrollment with minimal effort. This app has data encryption and other built-in safeguards to ensure HIPAA compliance, protecting patient privacy and organizational security.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/ research-hive/id1040112330 and on Android from Google play

TrialKit

The mobility and versatility of TrialKit allows for seamless regulatory compliant (21 CFR Part 11) data capture from mobile devices anytime, anywhere. Once collected, data can be easily aggregated, analyzed, and shared, making collaboration between research teams more productive. Key features include build/create a paperless and compliant study using any iOS device, without programming expertise; manage a study from start to finish on an iOS device from anywhere with the ability to access, monitor, and review data or respond to queries on-the-go; maximize productivity, minimize cost. Feature and Functionality: Study Manager; User Manager; Form Manager.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/trialkit/ id907547854

Trial Explorer

Trial Explorer is a noncommercial application designed for educational and research purposes to make clinical trial data accessible to patients and researchers. Patients can search for trials that are currently recruiting. Researchers view high-level analysis for their field based on disease, intervention, location, and time frame. This app helps locate the top investigators and the top facilities in seconds; view timelines, charts, graphs, and maps based on the custom filters; and export data and figures for external use.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/ trial-explorer/id1355971655?mt=8 and on Android from Google play

Randomizer for Clinical Trial

This application is designed for those looking for a practical, intuitive and reliable randomization system. Ideal for phase I and II monocentric studies or multicentric studies balancing by center. This Randomizer for Clinical Trial version can generate multiple randomization; automatically balance per blocks the arms of your study; set the study name, arms names, the size of the randomization block, the number of patients expected; set optional information about the patient as name, date of birth, ICF signing, inclusion criteria matching; have access to the randomization follow-up table; have access to the randomization list; have access to the patient details table (randomization number, patient’s name, date and time of the randomization, arm allocation).

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/ randomizer-for-clinical-trial/ id578254014?mt=8

PatientOne

PatientOne dramatically improves informed consent by offering patients procedure-specific videos, quizzes, and direct communication with providers. It captures patient progress and understanding of provided tutorials in the cloud. Results may be accessed instantly by providers. PatientOne provides an unprecedented level of documented informed consent, improving outcomes for patients and providers.

App for Android https://play.google.com/ store/apps/details?id=com.patientone. patientone

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Enables patients and doctors to find relevant clinical trials near them and connect with the trial investigators, from a constantly updated database of more than 17,000 currently recruiting clinical trials from ClinicalTrials.gov, CenterWatch, and registered sites/investigators on TrialX.com. Some of the key features include ability to search for trials matching a user’s health condition, age, gender, and other parameters; automatic filtering of search results based on the user’s most current location using the iPhone’s in-built location services; display of search results in a List view or a Map view, with the results sorted by distance from the user’s zip code. The Map view also provides directions to a particular trial site; enabling patients to select a trial of interest and connect with the trial personnel by using the “Call Investigator” or “Email investigator” buttons; providing doctors the ability to select a trial for their patient and use the “Refer Patient” button to connect the patient with a trial investigator, right at the point of care.

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TrialX

800    P art 9 • R esearch A pplications manage IRB and other administrative documents associated with the research enterprise and reported use across 50 states and more than 1600 organizations (IRBNet, n.d.). In summary, the general considerations of developing and conducting a research study are based on philosophical approaches and will dictate which methodology the researcher will use to develop the study. This will subsequently influence the research and computer applications to be used in carrying out the project, followed by the steps of proposal preparation, depending on the choice of application most useful for the quantitative or qualitative study to be planned. After identifying the research problem, the researcher must proceed through the steps of the process, where computers play an important role that is unique to each of the methodologies.

THE QUANTITATIVE APPROACH Data Capture and Data Collection Data capture and data collection are processes that are viewed differently from the quantitative and qualitative perspectives. Data collection can take a number of forms depending on the type of research and variables of interest. Computers are used in data collection for paper-andpencil surveys and questionnaires as well as to capture physiological and clinical nursing information in quantitative or descriptive patient care research. There are also unique automated data capturing applications that have been developed recently that facilitate large group data capture in single contacts or allow paper versions of questionnaires to be scanned directly into a database ready for analysis or provided online with Web-based survey tools. Paper and Pencil Questionnaires.  Paper and booklet surveys do still exist today in data collection, but new enhancements aid the researcher in time-saving activities. Surveys and questionnaires can be scanned or programmed into a computer application. Researchers are also using computers for direct data entry into studies via automated data capture where subjects enter their own responses via a device with simultaneous coding of responses to questions. These online survey tools can provide a wide range of applications, including paper or portable versions, and range in price and functionality. Many proprietary tools have been automated to be executed by the researchers and distributed to the subjects enrolled in the individual studies, to capture data efficiently on the Web and provide a number of analytic and comparative norm-referenced scores (capterra.com). One example is the computerized neurocognitive testing produced and delivered to subjects online by CNS Vital signs

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(https://www.cnsvs.com/) with demonstrated reliability and validity (Gualtieri & Johnson, 2006). The use of Web-based responses to questions for clinicians as well as researchers has grown. Respondents or their surrogates can enter information directly into the computer or Web site through Internet access. There are several research study examples where patients with chronic conditions used a computer application or the Internet as the intervention as well as the data capture device; patients or caregivers responded to questions directly and the data were processed with the same system (Berry et al. 2010; Berry, Halpenny, Bosco, Bruyere, & Sanda, 2015). Automated Data Capture. Other examples of unique data capture in research include individual devices such as the “Smart Cap” used to measure patient compliance with medications. The Medication Event Monitoring System (MEMS® 6) (Fig.  49.1) automates digitized data that can be downloaded for analysis in research such as patient adherence studies (El Alili, Vrijens, Demonceau, Evers, & Hiligsmann, 2016; Figge, 2010). A variety of online survey tools also provide researchers the power to collect data from a distance, without postage, using the Internet. These applications can present questionnaire data in graphically desirable formats, depending on the price and functionality of the software, to subjects delivered via e-mail, Web sites, blogs, and even social networking sites such as Facebook or Twitter if desirable. Social media mechanisms such as blogs and tweets are often providing sources of data analyses, albeit questionably scientific, that have sometimes been harnessed to extract meaning for researchers. Web surveys, although previously criticized for yielding poorer response rates than traditional mail (Granello & Wheaton, 2004), are becoming increasingly popular and deemed appropriate for their cost and logistical benefits (Dillman, 2011). The data from the Internet can be downloaded for analysis and several applications provide instant summary statistics that can be monitored over the data collection period. Several of these programs are available for free with limited use; others yield advanced products that can be incorporated into the research, giving mobility (e.g., smartphones) and flexibility (e.g., scanning or online entry) to the data capture procedures. Several of these applications include (1) Survey Monkey (www.surveymonkey.com); (2) E-Surveys Pro (www.esurveyspro.com); and (3) Qualtrics (www.qualtrics. com). Many of these products continue to enhance functionality, team, and sharing capabilities with integration with statistical analysis, graphics, and qualitative narrative exportability (capterra.com).

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•  FIGURE 49.1.  MEMS (Medication Event Monitoring System) SmartCap Contains an LCD Screen; MEMS Reader Transfers Encrypted Data from the MEMS Monitor to the Web Portal. (Published with permission of MWV Aardex Group, www.aardexgroup.com.) Software packages also exist that can be integrated with the researcher’s scanner to optically scan a specially designed questionnaire and produce the subjects’ responses in a database ready for analysis. OmniPage 19 (Nuance Imaging, 2014) is a top-rated optical character recognition (OCR) program that converts a scanned page into plain text. Programs such as SNAP Survey software (www.snapsurveys.com) and Remark Office OMR 10 (www.remarksoftware.com) can facilitate scanning large numbers of questionnaires with speed and accuracy. These products, enhanced even more with Web-based products, increase the accuracy of data entry with very low risk of errors, thereby improving the efficiency of the data capture, collection, and entry processes. Physiological Data. The collection of patient physiological parameters has long been used in physiological research. Some of these parameters can be measured directly from patient devices such as cardiac monitoring of heart rhythm, rate, and fluid or electrolytes and be captured in the patient care records of the hospital systems. For example, hospitals have developed mechanisms to use information from intensive care unit (ICU) data to calculate benchmarks for mortality and resource use. Now that many measurements taken from various types of imaging (e.g., neurological, cardiovascular, and cellular) have

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become digitized, they can also be entered directly from the patient into computer programs for analysis. Each of these applications is unique to the measures, such as systems to capture cardiac functioning and/or pulmonary capacity, devices that can relay contractions, or monitors that pick up electronic signals remotely. Numerous measurements of intensity, amplitude, patterns, and shapes can be characterized by computer programs and used in research. For example, the APACHE IV system and its multiple development versions have been tested in benchmarking hospital mortality and outcomes from captured physiological data in several groups of patients in the ICU (Dahhan, Jamil, Al-Tarifi, Abouchala, & Kherallah, 2009; Paul, Bailey, Van Lint, & Pilcher, 2012; van Wagenberg, Witteveen, Wieske, & Horn, 2017). Each of these measurement systems has evolved with the unfolding of research specific to their questions, and within each community of scholars, issues about the functionality, accuracy, and reliability of electronic data extracted from these physiological devices are debated. Along with the proliferation of clinical diagnostic measurement systems, there has been a rapid expansion of unique computer applications that have emerged for the data analysis aspects of these clinical systems, and physiological and record sources. Millions of gigabytes of data are stored in machines that can be tapped for multiple

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802    P art 9 • R esearch A pplications studies on the existing data. Data mining is a powerful tool in the knowledge discovery process that can now be done with a number of commercial and open-source software packages (Khokhar et al. 2017). Data mining and the evolving “big data” initiatives to make patient care data available introduce new ways to manipulate existing information systems. With increased attention to comparative effectiveness research (CER), several government and private organizations are encouraging researchers to hone the techniques to extract valid and reliable information from these large data sets (Sox & Greenfield, 2009). For example, the Agency for Healthcare Research and Quality (AHRQ) developed its Effective Health Care (EHC) as a partnership with researchers to examine scientific evidence and compare effectiveness (https://effectivehealthcare.ahrq.gov/). The Effective Health Care Program was initiated in 2005 to provide valid evidence about the comparative effectiveness of different medical interventions. The object is to help consumers, healthcare providers, and others in making informed choices among treatment alternatives. Through its Comparative Effectiveness Reviews, the program supports systematic appraisals of existing scientific evidence regarding treatments for high-priority health conditions. It also promotes and generates new scientific evidence by identifying gaps in existing scientific evidence and supporting new research. (Full reports are available at http:// www.effectivehealthcare.ahrq.gov/reports/final.cfm.) Data mining is a mechanism of exploration and analysis of large quantities of data in order to discover meaningful patterns and rules, applied to large physiological data sets as well as clinical sources of data. The nature of the data and the research question determine the tool selection (i.e., data-mining algorithm or technique). Analytics tools and consultants exist to help researchers unfamiliar with these data mining algorithms use data mining for analysis, prediction, and reporting purposes (Lebied, 2018). Many of the first commercial applications of data mining were in customer profiling and marketing analyses. Today, many special technologies can be applied, for example, to predict physiological phenomena such as genetic patterns with the promise of therapeutics in the next generation through genomics research (Issa, Byers, & Dakshanamurthy, 2014). The National Institutes of Health (NIH) is undertaking several initiatives to address the challenges and opportunities associated with big data. As one component of the NIH-wide strategy, the Common Fund in cooperation with all NIH Institutes and Centers was supporting the Big Data to Knowledge (BD2K) initiative in 2012, which aimed to facilitate broad use of biomedical big data, develop and disseminate analysis methods and software,

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enhance training for disciplines relevant for large-scale data analysis, and establish centers of excellence for biomedical big data (NIH, 2012). Large volumes of digital data that can come from multiple sources such as EHRs, genomics, monitoring devices, population surveys, automatically captured coded health-related reports, and nursing care–related data elements all have the potential to provide data that can be used in secondary analyses to describe, explore, predict, compare, and evaluate healthrelated data to answer researchable questions (Westra & Peterson, 2016). Patient data captured to provide patient care has the potential to be subsequently used for additional purposes beyond patient care, yielding new insights extracted from big data (Brennan & Bakken, 2015). Unique Nursing Care Data in Research. Scientists and technologists from a variety of disciplines are working hard to identify the domain of data and information that is transferable across situations, sites, or circumstances that can be captured electronically for a wide array of analyses to learn how the health system impacts the patients it serves. The American Nurses Association (ANA) has supported the need to standardize nursing care terms for computer-based patient care systems. The clinical and economic importance of structured recording to represent nursing care was recognized by the acceptance of the nursing minimum data set (NMDS) (Werley, Lang, & Westlake, 1986). As the integration of EHRs has proliferated since the 1990s, ANA has incrementally accepted multiple terminologies for the description of nursing practice, including the North American Nursing Diagnosis Association (NANDA) taxonomy of nursing diagnosis, Clinical Care Classification (CCC) System; Nursing Interventions Classification (NIC); and Nursing Outcomes Classification (NOC), patient care data set, Omaha Home Healthcare, and the International Classification of Nursing Practice (ICNP). The Clinical Care Classification System (sabacare.com) nursing terminology has been accepted by the U.S. Department of Health and Human Services (HHS) (DHHS, 2007) as a named standard within the Healthcare Information Technology Standards Panel (HITSP) Interoperability Specification for Electronic Health Records, Biosurveillance and Consumer Empowerment as presented to a meeting of the American Health Information Community (AHIC), a federal advisory group on health IT (Saba, 2012, 2014). In 2014, the National Action Plan for Sharable and Comparable Nursing Data for Transforming Health and Healthcare was published to coordinate the long-standing efforts of many individuals. Foundational to integrating nursing data into CDRs for big data science and research is the implementation of standardized nursing terminologies,

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common data models, and information structures within EHRs. The plan built on existing federal health policies for standardized data that are relevant to meaningful use of eHRs and clinical quality eMeasures (Westra et al. 2015). Since its first meeting, the group continues to work to advance this National Action Plan through efforts of the original 12 subgroups which includes care coordination, context of care, mobile health, nursing value, and the social and behavioral determinants of health workgroups, among others up through 2019 (https://www.nursing.umn.edu/ centers/center-nursing-informatics/news-events/2019nursing-knowledge-big-data-science-conference). Over the next years, the conference has continued to grow and expand its reach. In addition to the articulated plan of the Nursing Knowledge: Big Data Science Committee, the buy-in of senior nursing leadership in national and international healthcare organizations, such as the Chief Nursing Officer (CNO) and the Chief Nursing Informatics Officer (CNIO), is critical in order to influence future data system adoption and the integration of a standardized nursing language in the EMR. Although no standardized product from the multiple working groups has emerged as a single standard, they provide guidance toward a structured coding system to record patient care problems that are amenable to nursing actions, the actual nursing actions implemented in the care of patients, and the evaluation of the effectiveness of these actions—researchers can analyze large nursing data (Bakken, 2013; Byrne & Lang, 2013; Englebright, Aldrich, & Taylor, 2014). With the federal government “interoperability” incentive to enhance of cross-platform compatibility and collaboration, “harmonized” data elements with nursing and SNOMED-CT (Systematized Nomenclature of Medicine—Clinical Terms (Coenan, 2012; Coenen & Jansen, 2013) are critical to the development of nursing research using nursing data. Research on outcomes of care is one of the centerpieces of this massive policy that has begun to show an impact on integrated information technologies in healthcare that can transform practice. Nursing research on nursing practice captured from standardized terminology will be essential to document outcomes of nursing care. Big data initiatives will promote data mining of nursing data that can fuel the ongoing development of health services research focusing on nursing (Glassman & Rosenfeld, 2015; Khokhar et al. 2017; Westra & Peterson, 2016).

the physiological data and many of the electronic surveys. The coding may be generated by a computer program from measurements directly obtained through imaging or physiological monitoring, or entered into a computer by a patient or researcher from a printout or a questionnaire or survey into a database program. Most statistical programs contain data editors that permit the entry of data by a researcher as part of the statistical application. In such a situation, fields are designated and numerical values can also be entered into the appropriate fields without the use of an extra program. For mechanisms that translate and transfer source data to prepare it for analysis, generic programs such as Microsoft Excel provide basic to complex statistical analysis and visualization options. Other analytical tools maximize visualization such as open access “R” (www.r-project.org) and proprietary Tableau (www.tableau.com) with robust graphic capabilities. Coding data is a precise operation that needs careful consideration and presents the researcher with challenges that warrant technical or cognitive applications. Coding data is a combination of cognitive decisions and mechanical clerical recording of responses in a numerical form with numerous places that errors can occur. There are several ways of reviewing and “cleaning” the data prior to analysis. Some computer programs allow for the same data to be entered twice called double-data entry or two-pass verification. This is done preferably by different people to check for errors, with the premise that if the double entry does not match, one entry is wrong. One also must check for missing data and take them into consideration in the coding and analyses. New versions of advanced statistical software help in these activities. Another type of data coding can be described in the example of the process of translating data from documentation of patient care using coding strategies. Current research on coding nursing data using standardized nursing terminology from standardized codes is evident in several research studies in the literature (Englebright et al. 2014). For example, using precisely coded data from a standardized terminology can produce data that can be aggregated and statistically analyzed into meaningful information. In studies such as Saba and Taylor (2007), Moss and Saba (2011), and Dykes and Collins (2013), researchers have discussed mechanisms of aggregating nursing action types, e.g., assess, perform, teach, or manage, into aggregated information on the amount of time or effort a nurse spends in a day and concomitant costs associated.

Data Coding In most quantitative studies, the data for the variables of interest are collected for numerical analysis. These numerical values are entered into designated fields in the process of coding. Coding may be inherent in software programs for

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Data Analysis There are many ways to consider data analysis. These considerations are focused around the broad types of research of interest in nursing and general research goals

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804    P art 9 • R esearch A pplications or questions. These goals may require different statistical examinations: (a) descriptive and/or exploratory analyses, (b) hypothesis testing; (c) estimation of confidence intervals; (d) model building through multivariate analysis; and (e) path analysis and structural equation model building. Various types of nursing research studies may contain a number of these goals. For example, to test an intervention using an experimental or quasi-experimental design, one may first perform descriptive or exploratory analyses followed by tests of the hypotheses. Quality improvement, patient outcome, and survival analysis studies may likewise contain a number of different types of analyses depending on the specific research questions. These analyses can all be calculated with traditional statistics packages that have evolved over multiple versions, each new version adding editing, layout, and exporting efficiencies. More recent enhancements have included modeling abilities with varying strengths in the visual graphic productions of the packages. Two of the most popular programs in use today are the IBM SPSS Statistics 24 (formerly Statistical Package for Social Sciences) (https://www.ibm.com/analytics/spss-statisticssoftware) and Statistical Analysis Services (SAS) ( https:// www.sas.com/en_us/software/stat.html); however, a variety of other packages and programs exist, such as STATA 15 (https://www.stata.com) or the open-access “R” software available for free download (www.r-project.org). R acts as a free alternative to traditional statistical packages such as SPSS, SAS, and Stata such that it is an extensible, open-source language and computing environment for Windows, Macintosh, UNIX, and Linux platforms. It performs a wide variety of basic to advanced statistical and graphical techniques at little to no cost to the user. These advantages over other statistical software encourage the growing use of R in cutting edge social science research (Muenchen, 2009).Which package one selects depends on the user’s personal preference, particular strengths, and limits of the applications including number of variables, options for analyses, and ease of use. These packages have given the user the power to manipulate large data sets with relative ease and test out statistical combinations that have exponentially improved the analyses possible in a fraction of time that it once took. The different types of analyses required by the goals of the research will be addressed further. This description will be followed by examples of types of nursing research that incorporate some of these types of analyses. Descriptive and Exploratory Analysis.  The researcher may first explore the data means, modes, distribution pattern, and standard deviations, and examine graphic representations such as scatter plots or bar graphs. Tests of

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association or significant differences may be explored through chi-squares, correlations, and various univariate, bivariate, and trivariate analyses, and an examination of quartiles. During this analysis process, the researcher may recode or transform data by mathematically multiplying or dividing scores by certain log or factor values. Combining several existing variables can also create new variables. These transformations or “re-expressions” or “dummy-coding” allow the researcher to analyze the data in appropriate and interpretable scales. The researcher can then easily identify patterns with respect to variables as well as groups of study subjects of interest. Both commercial statistical packages IBM SPSS Statistics 24 (https://www.ibm.com/analytics/spss-statistics-software) and SAS (https://www.sas.com/en_us/software/stat.html) provide the ability to calculate these tests and graphically display the results in a variety of ways. With SPSS, the researcher can generate decision-making information quickly using a variety of powerful statistics, understand and effectively present the results with high-quality tabular and graphical output, and share the results with others using various reporting methods, including secure Web publishing. SAS provides the researcher with tools that can help code data in a reliable framework, extract data for quality assurance, exploration, or analysis, perform descriptive and inferential data analyses, maintain databases to track, and report on administrative activities such as data collection, subject enrollment, or grant payments, and deliver content for reports in the appropriate format. SAS allows for creating unique programming within the variable manipulations and is often the format for large publicly available data sets for secondary analysis. SAS product lists have expanded with potential for application in AI, particularly as it relates to business enterprises. As part of exploratory analysis, simple, binary, and multiple regression analyses can be used to examine the relationships between selected variables and a dependent measure of interest. Modeling is a new area of these statistics application that manipulates variables into generalizable mathematical formulas. Printouts of correlation matrices, extensive internal tests of data assumptions on the sample, and regression analysis tables provide the researcher with condensed, readable statistical information about the relationships in question. Hypothesis Testing or Confirmatory Analyses.  Hypothesis testing and advanced analyses are based on an interest in relationships and describing what would occur if the null hypothesis is statistically rejected, leaving the alternative as true. These are conditional relationships based on the variables selected for study and the typical mathematical tables and software for determining P values are accurate

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only insofar as the assumptions of the test are met (Polit & Beck, 2017). Certain statistical concepts such as statistical power, type II error, selecting alpha values to balance type II errors, and sampling distribution are decisions that the researcher must make regardless of the type of computer software. For example, one Web-based application to calculate power is G*Power (http://www.psycho. uni-duesseldorf.de/abteilungen/aap/gpower3/) which is a free, downloadable calculator to compute power. These concepts are covered in greater detail in research methodology courses and are outside the scope of the present discussion.

proportion to their sample size and focuses on the size of the outcomes rather than on whether they are significant. Although the computations can be done with the aid of a reliable commercial statistical package such as MetaAnalysis (Borenstein, Hedges, Higgins, & Rothstein, 2009), the researcher needs to consider the following specific issues in performing the meta-analysis (Polit & Beck, 2017): (1) justify which studies are comparable and which are not, (2) rely on knowledge of the substantive area to identify relevant study characteristics, (3) evaluate and account for differences in study quality, and (4) assess the generalizability of the results from fields with little empirical data. Each of these issues must be addressed with a critical review prior to performing the meta-analysis. Meta-analysis offers a way to examine results of a number of quantitative research studies that meet meta-analysis researchers’ criteria. Meta-analysis overcomes problems encountered in studies using different sample sizes and instruments. The software application Comprehensive Meta-Analysis (https://www.meta-analysis.com/pages/ features.php?cart=BD482503003) provides the user with a variety of tools to examine these studies. It can create a database of studies, import the abstracts or the full text of the original papers, or enter the researcher’s own notes. The meta-analysis is displayed using a schematic that may be modified extensively, as the user can specify which variables to display and in what sequence. The studies can be sorted by any variable including effect size, the year of publication, the weight assigned to the study, the sample size, or any user-defined variables to facilitate the critical review done by the researcher (Fig. 49.2).

Model Building.  An application used for a confirmatory hypothesis testing approach to multivariate analysis is structural equation modeling (SEM) (Byrne, 1984). Byrne describes this procedure as consisting of two aspects: (1) the causal processes under study are represented by a series of structural (i.e., regression) equations and (2) these structural relations can be modeled pictorially to enable a clearer conceptualization of the theory under study. The model can be tested statistically in a simultaneous analysis of the entire system of variables to determine the extent to which it is consistent with the data. If goodness of fit is adequate, the model argues for the plausibility of postulated relationships among variables (Byrne, 1984). Most researchers may wish to consult a statistician to discuss the underlying assumptions of the data and plans for testing the model. IBM SPSS 22 offers Amos 22 (https://www.ibm.com/ us-en/marketplace/structural-equation-modeling-sem), a powerful SEM and path analysis add-on to create more realistic models than if using standard multivariate methods or regression alone. Amos is a program for visual SEM and path analysis. User-friendly features, such as drawing tools, configurable toolbars, and drag-and-drop capabilities, help the researcher build structural equation models. After fitting the model, the Amos path diagram shows the strength of the relationship between variables. Amos builds models that realistically reflect complex relationships because any variable, whether observed (such as survey data) or latent (such as satisfaction or loyalty), can be used to predict any other variable. Meta-Analysis.  Meta-analysis is a technique that allows researchers to combine data across studies to achieve more focused estimates of population parameters and examine effects of a phenomenon or intervention across multiple studies. It uses the effect size as a common metric of study effectiveness and deals with the statistical problems inherent in using individual significance tests in a number of different studies. It weights study outcomes in

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Graphical Data Display and Analysis.  There are occasions when data need to be displayed graphically as part of the analysis and interpretation of the information or for more fundamental communication of the results of computations and analyses. Visualization software is becoming even more useful as the science of visualization in combination with new considerations of large data from the “Fourth Paradigm” unfolds (Hey, Tansley, & Tolle, 2009). These ideas begin with the premise that meaningful interpretation of data-intensive discoveries needs visualizations that facilitate understanding and unfolding of new patterns. Nurses are currently discovering new ways to present information in meaningful ways through these visualization techniques (Delaney, Westra, Monsen, Gillis, & Docherty, 2013). Most statistical packages including SPSS, SAS, STATA, and R, and even spreadsheets such as Excel provide the user with tools for simple to complex graphical translations of numeric information, thus allowing the researcher to display, store, and communicate aggregated data in meaningful ways. Special tools for spatial representations

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•  FIGURE 49.2.  Comprehensive Meta-Analysis (CMA) User Interface. (Published with permission of Biostat, Inc., https:// www.meta-analysis.com/pages/features.php?cart=BD482503003.) exist, such as mapping and geographic displays, so that the researcher can visualize and interpret patterns inherent in the data. Geographic information system (GIS) technology is evolving beyond the traditional GIS community and becoming an integral part of the information

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infrastructure of visualization tools for researchers. For example, GIS can assist an epidemiologist with mapping data collected on disease outbreaks or help a health services researcher graphically communicate areas of nursing shortages. GIS technology illustrates relationships,

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Chapter 49 • Computer Use in Nursing Research 

  807

Avg. Total Time 911 to Hosp 32.43

75.43

•  FIGURE 49.3.  Used with permission. Tableau Map Screenshot—Geographic Map of Ambulance Time to Hospital using Zip Codes on 5 Boroughs of NYC Ambulance Time. (From V. Feeg, the author. Used with permission) connections, and patterns that are not necessarily obvious in any one data set, enabling the researcher to see overall relevant factors. ArcGIS Online system by ESRI (https:// www.arcgis.com/home/index.html) is one of several GIS Web-based systems, some of which are open access, for management, analysis, and display of geographic knowledge, which is represented using a series of information sets. Tableau (www.tableau.com) is an individual and cloud-based subscription that can be used by the subscriber on any computers or laptops. Tableau includes extensive analytics and visualization exports. It also includes maps and globes with three-dimensional capabilities to describe networks, topologies, terrains, surveys, and attributes (Fig. 49.3). In summary, the major emphasis of this section has provided a brief discussion about the range of traditions, statistical considerations, and computer applications that aid the researcher in quantitative data analysis. As computers have continued to integrate data management functions with

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traditional statistical computational power, the researchers have been able to develop more extensive and sophisticated projects with data collected. Gone are the days of the calculator or punch cards, as the computing power now sits on the researchers’ desktops or laptops, with speed and functionality at their fingertips. The future of machine learning to enhance AI capabilities of dynamic research is upon us.

THE QUALITATIVE APPROACH Data Capture and Data Collection The qualitative approach focuses on activities in the steps of the research process that differ greatly from the quantitative methods in fundamental sources of data, collection techniques, coding, analysis, and interpretation. Thus, the computer becomes a different kind of tool for the researcher in most aspects of the research beginning with the capture and recording of narrative or textual data.

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808    P art 9 • R esearch A pplications In terms of qualitative research requiring narrative content analysis, the computer can be used to record the observations, narrative statements of subjects, and memos of the researcher in initial word processing applications for future coding. Software applications that aid researchers in transcription tasks include text scanners, voice recorders, and speech recognition software (Table  49.2). New digital recorders are also on the market that use sophisticated and higher cost voice-recognition software. From these technologies, researchers or transcriptionists can easily manipulate the recording and type the data verbatim. Even iPhones and smartphones have high-quality recording applications that aid the qualitative research capture narrative statements. These narrative statements, like the quantitative surveys, can be either programmed for use in other applications or subjects’ responses can be entered directly into the computer. Qualitative Data Collection.  Audiotaping is often used for interviews in qualitative studies, whereby the content is transcribed into a word processing program for analysis. The narrative statements are stored for subsequent coding and sorting according to one’s theoretical framework. Through analysis, categories from the data emerge as interpreted by the researcher. It is important to point out that for both quantitative and qualitative data, the computer application program is only a mechanical, clerical tool to aid the researcher in manipulating the data. Using the Internet for indirect and direct data collection in qualitative studies can also provide a vehicle for data analysis that yields a quantitative component as well as the qualitative analysis. Computers are not only able to record the subject’s responses to the questions but can also record the number of minutes the subject was online and the number of times they logged in. Many new online technologies are providing functionality for qualitative studies: for example, Audacity (audacity.sourceforge.net), an open-source free audio-recording package, can edit captured voice and export audio data to be analyzed; conversely, simple, free online survey packages such as SurveyMonkey (surveymonkey.com) can now export participants’ free text data into qualitative software packages.

Data Coding and Analysis Historically, qualitative researchers have relied on narrative notes, often first recorded as audio and later transcribed by a typist. Coding qualitative text data was a time-consuming task, often involving thousands of pages of typewritten notes and the use of scissors and tape for the development of coding and categories. With the

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advent of computer packages, the mechanical aspects of the coding and sorting have been reduced. The researcher must decide on which text may be of interest and can use a word processing program to search for words, phrases, or other markers within a text file. However, this process is cumbersome and time consuming, with limited ways to aggregate text into meaningful combinations for identifying themes from the narrative. Some specific newer software packages developed for qualitative data analysis (QDA) interface directly with the most popular word processing software packages. These programs assist the researcher to establish an index of data codes and seek relationships among the coding categories. The ease with which researchers can code and recode large amounts of data with the aid of computerized programs encourages the researcher to experiment with different ways of thinking about data and recategorizing them. Retrieval of categories or elements of data is facilitated by computer storage. Newer technologies have evolved since Ethnograph and NUD-IST with improved user interfaces and enhanced quantitative and graphic features, including the latest versions of NVivo 12 from QSR (https:// www.qsrinternational.com/nvivo/nvivo-products/nvivo12-plus), MAXQDA (https://www.maxqda.com), and ATLAS.ti 8.4 (https://atlasti.com/). Qualitative research, like quantitative research, is not a single entity, but a set of related yet individual traditions, aims, and methods. Some individual traditions within qualitative research are ethnography, grounded theory, phenomenology, and hermeneutics. The distinguishing feature of qualitative research is that the goal is to understand the qualities or essence of phenomena and/ or focus on the meaning of these events to the participants or respondents in the study. The forms of data are usually the words of the respondents or informants rather than numbers. Computerization is especially helpful to the researcher in handling large amounts of data. However, it must be stressed that the computer applications aid the analysis as a management tool rather than an analytical one. Synthesis of the data is still the interpretive work of the researcher. Data Analysis for Qualitative Data.  Qualitative data analyses often occur on an ongoing basis with data collection in a reflexive and iterative fashion. There is no clear demarcation of when data collection should end and analysis should begin. The process of obtaining observations, interviews, and other data over a period of time results in a vast body of narrative that may include hundreds or thousands of pages of field notes and researcher memos. Although computer applications can aid considerably in

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  TABLE 49.2    Voice and Dictation Apps for Interviews Description

Web Link/Device

Dictation —Talk to Text

Allows to dictate text messages into the device instead of typing. It uses speech-to-text voice recognition technology. Using the clipboard technology, nearly every app that can send and receive text messages can be configured to operate with it. The speech-to-text recognition engine is the built-in one of iOS devices. It provides convenience, time saving, and no need to type. This app supports multiple languages.

Compatible with iPhone, iPad, and iPod touch. https://itunes.apple.com/us/app/dictation-talk-to-text/ id1124772331

Otter Voice Notes

Otter creates smart voice notes that combine audio, transcription, speaker identification, inline photos, and summary keywords in English. It helps people to be more focused, collaborative, and efficient in meetings, interviews, lectures, and other important conversations.

Compatible with iPhone, iPad, and iPod touch. https://itunes.apple.com/us/app/otter-voice-notes/ id1276437113?mt=8 and on Android from Google play

Cogi

A note taking and voice recording app that records only the important parts of conversations and lets you add images, hashtags, and text notes, keeping everything in one place. Improve productivity, share and collaborate with others, and keep everything in the Cogi Cloud.

Compatible with iPhone, iPad, and iPod touch. https://itunes.apple.com/us/app/cogi-beyond-notes/ id804942087?mt=8 and on Android from Google Play

Voicea A.I. Note Taker

Voicea provides EVA, the Enterprise Voice Assistant. EVA is an AI that listens, takes notes, and captures important moments from meetings. Add EVA to conference calls, direct calls, or in-person meetings and EVA will automatically e-mail notes from your meeting. Voicea is currently only available for English language use.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/voicea-a-i-note-taker/ id1174858245 and on Android from Google play

Dragon Anywhere

Get documents done anywhere with Dragon Anywhere, professionalgrade mobile dictation. Easily dictate documents of any length, edit, format, and share them directly from your iPhone or iPad—whether visiting clients, a job site, or a local coffee shop.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/dragon-anywhere/ id1024652126?mt=8 and on Android from Google play

Dictate2us

Dictate2us is a transcription service provider of accurately typed documents with fast turnaround times. The typists are experienced in specific fields and are familiar with different terminologies and formatting styles. Data is protected with the ICO and fully HIPAA compliant.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/dictate2us-transcription/ id341741314?mt=8 and on Android from Google play

SmartRecorder and transcriber

Smart Recorder is a full featured recorder and transcriber offering many features, such as record, e-mail/share, transcribe, trim/edit, or organize.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/smart-recorder-and-transcriber/ id700878921?mt=8

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Chapter 49 • Computer Use in Nursing Research 

App Name

App Name

Description

Web Link/Device

TranscribeMe

TranscribeMe uses speech recognition and human transcriptionists to convert any audio or video files to text. The TranscribeMe iOS app makes transcriptions on-the-go even easier. Record audio directly in the app or import audio and video files from other apps like Dropbox and Voice Memos. Transcriptions are delivered straight to e-mail and available in-app or via the Web browser. Transcriptions and accompanying media files can be shared in e-mail or text through the app.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/transcribeme/ id543407200?mt=8 and on Android from Google play

Voice Dictation— Speechy Lite

Speechy is the powerful, real-time dictation solution based on the artificial intelligence and using a powerful speech recognition engine, Speechy will easily transcribe words and thoughts. Text and audio files can be shared with Evernote, Dropbox, Google Drive, OneDrive, Facebook, Twitter, Snapchat, WhatsApp, and other iOS-supported sharing apps. Speechy is global-focused, and will recognize and translate dictated text into other languages (currently more than 88 languages supported).

Compatible with iPhone, iPad, and iPod touch. https://itunes.apple.com/us/app/voice-dictation-speechy-lite/ id1239150966?mt=8

Rev Voice Recorder

Rev Voice Recorder is used to record and share audio for free.* Trim a recording or add to an existing recording. Sync all recordings automatically with Dropbox. Send recordings to Evernote, Google Drive, iCloud Drive, and more. Order transcripts completed by professional typists and delivered or e-mailed in Word doc. or in-app in 12 hours or less. * for recordings under 30 minutes

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/rev-voice-recorder/ id598332111?mt=8 and on Android from Google play

AudioNote2 Voice recorder

AudioNote links the notes to the audio recorded, resulting in a linked index of recording that quickly provides invaluable audio context for the notes. Other features: organize files; sync between devices using iCloud or Dropbox; and share via Email, AirDrop, Wifi & more.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/audionote-2-voice-recorder/ id1118127184?mt=8

QuickVoice Recorder by Rev

QuickVoice is a full-featured iPhone/iPad/iPod voice recorder. Record ideas, voice memos, voice e-mail, dictation, lists, meetings, classes, or entire lectures. Can be used with other apps while still recording in the background and ringtone recording. Convert QuickVoice recordings for free to iPhone ringtones.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/quickvoice-recorder/ id284675296?mt=8

Temi Record and transcribe

Temi is a professional audio recorder that captures and transcribes important ideas and conversations. Automatically transcribe recordings with transcript synced to recording.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/temi-record-and-transcribe/ id1269856195?mt=8 and on Android from Google Play

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  TABLE 49.2    Voice and Dictation Apps for Interviews  (Continued )

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Trint uses smartphone to record and send files directly to Trint for transcription from anywhere. Audio can be directly uploaded from other iOS apps to Trint on the Web; view and play Recent Trints from within the mobile app (audio and video), with ability to manage multiple recordings, and drop a marker while recording to note important points.

For iPhone and iPad https://itunes.apple.com/us/app/trint/id1312813822?mt=8

M*Modal Mobile Microphone

Mobile application that allows clinicians to dictate using the M*Modal Fluency Direct Desktop Application without the need for a physical microphone attached to the PC. Once the mobile application is paired onetime with a M*Modal Fluency Direct User ID, the user of the application can dictate using M*Modal Fluency Direct running on any physical desktop or virtual device without a need for docking, Bluetooth or physical connections. The Application uses secure data transmission to stream audio from the mobile device directly to M*Modal Fluency Direct running elsewhere. It builds on the same cloud-based M*Modal Speech Understanding™ technology powering all M*Modal solutions, so existing clinician voice profiles can be used easily and instantly for optimal accuracy.

Compatible with iPhone, iPad, and iPod touch. https://itunes.apple.com/us/app/m-modal-mobile-microphone/ id975544301?mt=8 and on Android from Google Play

Informed Consent and Dictation

Performing informed consent and dictation of procedures are often skills that are learned on the job. Given the importance of both of these tasks, this application has been developed as a guide. The application has templates for informed consent and dictation of common procedures.

Compatible with iPhone, iPad, and iPod touch. https://itunes.apple.com/us/app/informed-consent-and-dictation/ id1376735775?mt=8

Rati-Fi

The Rati-Fi Informed Consent System is an mHealth-ready mobile app that improves patient education and comprehension for both pretreatment and postcare. Rati-Fi uses high-quality medical animations to explain treatment options, a quiz to test for comprehension, surveys to measure patient satisfaction, and a videobased informed consent process that records the conversation with the doctor and stores the video and a PDF of the signed file securely on a HIPAA-compliant cloud server.

Compatible with iPhone, iPad, and iPod touch. https://itunes.apple.com/us/app/rati-fi/ id1053252809#?platform=iphone

cubeCONSENT

cubeCONSENT supports electronic Informed Consent Form (ICF) solution. It provides an optimal learning environment for future subjects and assures that the signing of the subject’s consent is based on the patient’s accurate understanding on the test and participation in responsibility. It is interoperated with the cubeCDMS, capturing subjects’ informed consent information in real time.

Compatible with iPhone, iPad, and iPod touch https://itunes.apple.com/us/app/cubeconsent/ id1350698986?mt=8

Chapter 49 • Computer Use in Nursing Research 

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812    P art 9 • R esearch A pplications organizing and sorting this mass of data, the theoretical and analytical aspects of decision-making about concepts and themes must be made by the researcher. Researchers can only use the tools to help in creating composites described by methodologists in coding levels and categorical clusters (Polit & Beck, 2017). As an example, some of the tasks the computer can facilitate in data analysis using grounded theory (one approach to qualitative research) are as follows. Once a researcher has determined which parts of the interviews and observations can be tagged as categories, certain properties or dimensions can be determined and coded up through levels. The researcher may engage in “constant comparison,” comparing the meanings of all incidents that have been similarly categorized. This process should continue until the researcher determines that the categories are internally consistent, fit with the data, and are saturated. Saturation is achieved when the researcher can find no more properties for a category and new data are redundant with the old (Cresswell & Cresswell, 2018; Cresswell & Poth, 2018). Using software, these cognitive processes are applied by the researcher in data analysis of narrative interviews, field notes, and supplementary data. Computer Application Programs.  A number of generalpurpose or specific software packages can be used in qualitative analysis: one package is a free text retrieval program

such as that available in a word processing program; another is any number of standard database management or indexing programs; third is a program specifically developed for the purpose of qualitative analysis. Special Purpose Software.  Several QDA software products have evolved and improved for the specific purpose of analyzing qualitative data. From transcribed narratives and other collected elements in the data collection process including video, graphics, and Web site references, the researcher doing qualitative analysis can import or open these files into a variety of proprietary, subscriptionbased or licensed applications. Programs such as NVivo 12 and XSight from QSR (http://www.qsrinternational. com/nvivo/case-studies/software-to-xsight) provide a new generation of software tools with multiple advantages for researchers. Because qualitative research takes many forms, these two applications can be selected based on the user’s specific methodological goals, the nature and scale of the study, and the computer equipment. While NVivo 12 supports fluid, rich data, detailed text analysis, and theory building, it also can manage documents, audio and video files as categories, attributes, or nodes in visual displays that show the structure and properties of the document (Fig. 49.4). The latest version of NVivo 12 also allows researchers to import exported data from other applications such as the online survey tool, Survey Monkey, as

•  FIGURE 49.4.  Used with permission. Screenshot of NVivo11—SurveyMonkey® Data on Stress in the New Nurse Workplace—One Node with Comments. (From V. Feeg, the author. Used with permission).

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well as bibliographic management programs, Facebook and Twitter. New analysis tools provide the research ways to cluster, map, and visualize text and images in meaningful ways to aid the interpretive process in the qualitative analysis.

the relationships of an object in the system, the researcher examines the node in the graph and follows the arcs to and from it. Semantic network applications may be useful in model building and providing a pictorial overview. NVivo 12 has these added capabilities and Decision Explorer (Banxia, 2014) offers the user a powerful set of mapping tools to aid in the decision-making process for audience response activities. Ideas can be mapped and the resulting cognitive map can be further analyzed (Fig. 49.5). The software has many practical uses, such as gathering and structuring interview data and as an aid in the strategy formulation process. The software is primarily described as being a recording and facilitation tool for the elicitation of ideas, as well as a tool to structure and communicate

Conceptual Network Systems.  A system known as concept diagrams, semantic nets, or conceptual networks is one in which information is represented in a graphic manner. The objects in one’s conceptual system (e.g., age and experiences) are coded and represented by a box diagram (node). The objects are linked (by arcs) to other objects to show relationships. Like rule-based systems, semantic nets have been widely used in AI work. In order to view

•  FIGURE 49.5.  Example of Cognitive Mapping Analysis. (Published, with permission, from BANXIA® Software Ltd., www. banxia.com/dexplore/screenshots/.)

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814    P art 9 • R esearch A pplications qualitative data. It allows the user to gather and analyze qualitative data and thus make sense of many pieces of qualitative data in order to achieve a coherent picture of a given issue or problem (Banxia, 2014).

Dissemination of Results While dissemination of results continues to occur by traditional means such as presentations at professional meetings and publication in journals and monographs, online reporting is becoming increasingly common. Other increasingly used forms of research dissemination include a variety of podcasts, YouTube, Webcast videos, as well most notable “Ted Talks” style presentations. Some journals request audio-visual slides with the submission of the research. Some Web sites frequented by nurses are peer-reviewed journals such as Online Journal of Nursing Informatics (www. cisnet.com) and selected nursing articles on various Web sites such as that of the ANA (www.nursing-world.org). Nursing forums sponsored by various professional nursing organizations (e.g., American Journal of Nursing, Sigma Theta Tau, and National League for Nursing) often allow participants to chat online with presenters or authors of certain articles on designated dates during scheduled times. Nearly all organizations have their own Web sites. Some examples are the Alzheimer’s Disease Education and Referral Center (www.alzheimers.org), American Heart Association (www.americanheart.org), American Medical Informatics Association (www.amia.org), and RAND Corporation (www.rand.org). The Cochrane Collection has numerous centers all over the world through the Cochrane Collaborative (www.cochrane. org). As with all publications, online as well as hardcopy, the information accessed must be evaluated by the users regarding appropriateness for the purpose for which it was retrieved. Large publishers today are promoting sharing and open access to scientific discussion. Reports to most government and some nongovernment agencies require the researcher to submit a converted document online. Grant proposals submitted to the federal government currently require online submission with conversion to PDF. NIH applicants are directed to a page with downloadable programs to convert the documents before submitting them (www. grants.gov/ help/download_software.jsp). In fact, there is a trend for all manuscripts to be submitted online for print, online, or both. Online journals continue to grow. In addition, there has been a rise in the number of open access journals that give researchers more options for dissemination. Online journals have been discussed in the nursing and academic community with mixed reception; while it allows

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the researcher-consumer of articles the ability to search wider publicly available science, other stakeholders in the publishing and academic worlds have been concerned on the ramifications of this disruptive innovation (Broome, 2014). NIH grant recipients are mandated to make their scientific findings accessible, and the data use agreements with funded research, especially clinical trials, have changed some of the traditional publishing processes. This chapter has summarized the processes of quantitative and qualitative research and described select computerized tools that can assist the researcher in proposal preparation, data collection, data coding, data analysis, and dissemination for both types of research. The following section highlights examples for three categories of research on computer use and nursing informatics in (1) electronic data such as data mining of large electronic data sets and electronic nursing documentation; (2) W ­ eb-based interventions; and (3) specialized computer applications in clinical practice. The examples include both quantitative and qualitative studies in which the nurse researchers inevitably used a variety of software tools in the proposal development, data collection, measurement of variables, analysis, and dissemination activities.

EXAMPLES OF RESEARCH STUDIES While computers are inextricably linked to conducting research, there are also good examples of research on computer use in the nursing literature. Several of the following examples also describe computerized processes for conducting quantitative and qualitative research approaches. These examples provide focus on nursing research related to computer use and informatics as well as using computers in the process of doing the research.

Clinical Interventions with Computers The use of Internet applications that are executed and tested in a variety of clinical trials aim to improve conditions for patients. For example, a team of researchers developed the Personal Patient Profile-Prostate (P3P), a Web-decision support system for men newly diagnosed with prostate cancer that assesses patients’ preferences prior to clinic visit and gives providers’ and patients’ information to aid decision-making among choices of treatment. The studies showed that decision support was feasible with the technology support and decision regret was significantly influenced by personal characteristics and post-treatment symptoms, although the P3P was not itself significant on the outcomes measured in the study (Berry, Wang, Halpenny, & Hong, 2012). In another Web-based

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intervention, caregivers were randomly assigned to one of two types of online support groups and compared to nonactive participants on their depressive symptoms, caregiver burden, and quality of life. In this study, both types of online support groups reduced depressive symptoms and improved quality of life over nonactive participants (Klemm, Hayes, Diefenbeck, & Milcarek, 2014).

information. Decision support is an essential component of care to assist patients to manage symptoms because patients and caregivers are often uncertain about when to call clinicians regarding uncontrolled symptoms. The study findings suggest that eHealth features such as the ability to track symptoms, access disease-specific information, and alert clinicians about symptoms severity have demonstrated efficacy for increasing communication and decreasing symptom distress. In another example, the EHR was studied by Almasalha et al., (2012). The researchers examined 596 episodes of care that included pain as a problem on a patient’s care plan using statistical and data mining tools to identify hidden patterns in the information on end-of-life (EOL) hospitalized patients. Findings suggested new understanding about patient care; for example, EOL patients with hospital stays less than three days were less likely to meet the pain relief goals than those with longer hospital stays. Additionally, Westra et al. (2011) studied urinary and bowel incontinence for home health patients using EHR data to predict improvements. In these cases, the EHR served as the data sources.

Technology, Electronic Data, and Electronic Documentation Research There are several different studies that highlight using electronic data and EHRs in data mining or care documentation. Secondary Analysis of Large Data Sets.  Large public data sets are becoming more available to nurse researchers to explore health-related questions. The sites provide tutorials and assistance, making them more accessible for secondary analyses. For example, the Centers for Disease Control and Prevention (CDC) and AHRQ provide clinical researchers and health services investigators with the tools and data sources for a variety of health-related systems. One data source is the Medical Expenditure Panel Survey (MEPS) database, a multi-year set of large-scale surveys of families and individuals, their medical providers, and employers across the United States (meps.ahrq. gov). MEPS is the most complete source of data on the cost and use of healthcare and health insurance coverage (AHRQ.gov). Another collection used by a variety of nurse researchers is the HCUP data from AHRQ. HCUP databases bring together the data collection efforts of state data organizations, hospital associations, private data organizations, and the federal government to create a national information resource of encounter-level healthcare data. It includes the largest collection of longitudinal hospital care data that enable research on a broad range of health policy issues, including cost and quality of health services, medical practice patterns, access to healthcare programs, and outcomes of treatments at the national, state, and local market levels (hcup-us.ahrq.gov) (AHRQ, 2014). The EHRs today are frequently providing source data for studies. Cooley et al., (2017) conducted a qualitative study to explore patient and caregiver perspectives on the components of the decision support that would be desirable for enhancing communication with clinicians about the quality of life management through the use of eHealth system. Sixty-four patients participated in this study through focus groups and questionnaires. Cancer patients are faced with a profuse amount of information during their interactions with healthcare providers and often rely on caregivers to help them understand the

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Computerized Documentation of Nursing Care Plans.  Feeg, Weiner, Raposo, and Saba (2015) studied the usability of the clinical care classification (CCC) for presenting nursing actions empirically to represent the range of nursing interventions performed on hospitalized patients. Using a descriptive methodology on secondary analysis of captured data from three New England hospitals, investigators uncovered information on care components, nursing actions, expected outcomes, and actual outcomes. The study demonstrated the feasibility of documenting nursing interventions using clinical care classification. Using another system, Bose, Maganti, Bowles, Brueshoff, and Monsen (2019) studied the use of mRMR (Minimum Redundancy–Maximum Relevancy) to increase efficiency and decrease documentation burden in public health nursing and maternal-child home visit. Due to the decreasing resources and time spent with the patient, having a compressed representation of the data set that is much smaller in volume but produces the same analytical result would be beneficial. The researchers tested two different machines learning technique of feature selection, mRMR and glmnet, and applied them on a data set generated by Public Health Nurses using the Omaha System. The 206 features of the mRMR were reduced to 50 elements, and for the glmnet, 206 features were reduced to 63. This inquiry concluded that feature selection techniques show promise toward reducing public health documentation burden by identifying the most critical data elements needed to predict risk status.

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816    P art 9 • R esearch A pplications Moss and Saba (2011) studied the utility of costing out nursing care with the CCC terminology on five most commonly executed interventions. Using an observation study of nurses performing routine care on an acute-care unit, investigators collected data with a specialized data collection program entered directly into the PC database. A total of 251 interventions were observed, coded, and analyzed. From the analysis of time spent on each entered intervention, researchers could describe the four action types by average cost and percent of activity. The study demonstrated the feasibility of valuating the nursing care given to patients based on the standardized CCC terminology. In a study by Dykes (2013), nursing care delivered was analyzed using CCC coding through hospital observations of nursing interventions and in a subsequent study by Feeg, and Greenidge-Adams (2018), nurses estimated costs of selected CCC-coded interventions to begin to understand the potential cost estimates of select coded nursing interventions. Web-Based Tools and Interventions.  A significant body of research has been conducted on how the Internet can be used as a tool for doing research as well as studies on Web-designed interventions for clinical problems. For example, Cooley et al. (2018) tested the usability of a Webbased simulated model of an algorithm-based clinicaldecision support program for self-management of cancer symptoms. It is essential that cancer patients understand anticipated symptoms, how to self-manage the symptoms, and when to call the clinicians. The researchers tested the usability of a simulated symptom assessment and management intervention self-care clinical-decision program using focus groups, interviews, and surveys with cancer patients, caregivers, and clinicians. The simulated model of clinical-decision support identified patient barriers and clinical concerns using this prototype. This inquiry finding may be used to inform the development of clinical decision support resources for self-management of other chronic conditions. In a decision support application study, Berry et al. (2015) developed the Spanish Version Personal Patient Profile–Prostate (P3P), a Web-decision support system for Latino men newly diagnosed with prostate cancer that assesses patients’ preferences before clinic visit and gives providers’ and patients’ information to aid decision-making among choices of treatment. Although the study identified usability issues such as unfamiliarity with Internet use and navigation problems; however, a Web-based decision aid is feasible with technology support. In a different kind of Web based clinical intervention study, Geraghty et al. (2017) undertook a single-blinded randomized controlled trial among patients who were 50

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years and older with a mean age of 67, from 54 primary care practices, all who had been experiencing dizziness for the prior two years. The randomization process was automated and online. Additionally, 296 patients from a population in 54 practices were randomized into Internet-based vestibular rehabilitation or traditional care. The experimental group received an automated, six-session, Internet-based intervention over 6 weeks. The protocol for vestibular rehabilitation exercise was based on patients’ symptoms. The vestibular rehabilitation exercises were demonstrated through video demonstration with audio descriptions. This study demonstrated that elderly patients with access to Internet-based vestibular rehabilitation had reduced dizziness and dizziness-related disability without clinical support. Yen and Bakken (2009) tested the usability of a Web-based tool for managing open shifts on nursing units. Using nursing observational and interview approaches, they evaluated the Web communication tool (BidShift) designed to allow managers to announce open work shifts to solicit staff to request their own work shifts. They used specialized software to capture screens and vocal utterances as participants were asked to think aloud as they completed three subtasks associated with the open-shift management process. After task completion, the participants were asked about the process and their responses were recorded. Furthermore, their data were managed and coded using Morae, a specialized software developed for usability testing (https://www.techsmith.com/morae. html). This example of qualitative research reported participants’ patterns of use and themes related to their perceptions of usability of the communication tool. In a qualitative study by Lichenstein, McDonough, and Matura (2013), 98 participants who self-identified as caregivers for a person with pulmonary hypertension (PH) engaged in an online discussion board posted by the Pulmonary Hypertension Association over an 18-month period. Clinical variables collected were medications and oxygen use, and years since diagnosis. Thematic analysis yielded four themes: fear and frustration, questions and concerns, someone to listen to, and moving on with life. Results showed that caregivers of people with pulmonary hypertension may be ill-equipped to care for their loved one because of lack of knowledge or psychological distress. In a review of Web-based cognitive behavioral interventions for chronic pain, researchers conducted a systematic review and meta-analysis to quantify the intervention efficacy for treatment of patients with chronic pain. Using 11  studies from MEDLINE and other data sources, the investigators found that Web-based interventions for chronic pain resulted in small pain reductions in the intervention groups compared with waiting-list control groups (Macea, Gajos, Calil, & Fregni, 2010).

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Specialized Computer Applications in Clinical Care.  Computer algorithms have become a reliable means to determine differences in health patterns over some time. The changes in pattern in sensor data associated with illness generate alerts to clinicians of potential disease or functional decline for earlier intervention. TigerPlace uses this enhanced technology to proactively manage the health of older adults living in place housing. Rantz et al. (2015) compared the length of stay of 52 residents living with and 81 residents living without embedded sensor systems at TigerPlace. Furthermore, these scholars evaluated the impact of this enhanced technology on the cost for both populations aging in place housing. The findings indicate that elderly living with sensors were able to live at TigerPlace 1.7 years longer than those without detectors. Moreover, cost estimates comparing the cost of living at TigerPlace with sensor technology versus nursing home reveal a saving of $30, 000 per person and to Medicaid $87,000 per individual. Mobile technologies have also proliferated in healthrelated applications today. For example, Smartphone supports monitoring and managing patients’ health conditions. Naslund et al. (2016) study highlights the use of Fitbit wearable devices with companion Smartphone for supporting behavioral changes resulting in weight loss for patients with severe mental illness. Moreover, Wu et al. (2018) study explored the feasibility and acceptability of the use of smartphone medication reminder application to promote adherence to oral medications among adolescents and young adults with cancer. One hundred percent of the participants (n = 23) used the app at least once, and more than 50% took their medications immediately when they received the reminders. Some studies have incorporated the use of the Smartphone to foster positive interactions among patients and the healthcare systems. Serrano et al. (2016) explored patients’ willingness to exchange health information electronically with their healthcare providers. The researchers analyzed data for 3,165 patients captured in the 2013 Health Information National Trends Survey. The study uncovered that age, socioeconomic status, trust in the clinicians, and education correlate with the willingness to exchange certain types of information. The findings indicated that respondents, especially those 50 years and above, were less willing to exchange information that may be considered sensitive or complex via mobile devices. Similarly, Tofighi et al. (2017) looked at the feasibility of text message appointment reminders to improve patient adherence in an office-based Buprenorphine program. The study population (n = 93) completed a feasibility survey twice, the first one following the delivery of the initial text message reminders and the second time at 6 months. The study demonstrated acceptability and feasibility of text message appointments reminders in the office-based

Buprenorphine program. Moreover, older age and length of duration in the Buprenorphine program did not diminish interest in receiving text message reminders. Smartphone applications have the potential to remove the barriers to quality healthcare. Mobile health (mHealth) presents an opportunity to manage post-surgical site infections through the use of Smartphone high-quality cameras. Clinicians will be able to collect data about the site in real time and communicate with the patients. Sanger et al. (2014) conducted a mixed method design with semi-structured interviews and surveys to understand patients’ perspectives on mHealth management of the surgical site. Participants (n = 13) had post-discharge surgical wound complications. The significant themes emerged from the interviews on patient self-management of post-discharge wound complications: knowledge for self-care and self-monitoring, efficacy for self-care and wound monitoring at home, and communication with providers. Nonetheless, patients found mHealth wound monitoring application an acceptable solution to enable patients to engage in wound monitoring. Smartphone as a self-triage tool has been used for abdominal pain, influenza-like-illness, sexual health problems, and pediatric emergency care. The “Should I see a doctor” Smartphone application is a self-triage tool for acute primary care. Furthermore, the device has a built-in questionnaire to ascertain if the users plan to follow the app advice. The prospective, cross-sectional study among app users (n = 4456) in a routine primary care setting indicated that patients of all ages have used the app, and in 81% of participants the app’s advice corresponded to the triage call outcome (Verzantvoort, Teunis, Verheij, & Velden, 2018). Development of the “NOWIKNOW” mobile application to promote completion of the HPV vaccine series among young adult women. Teitelman, Kim, Wass, DeSanna, and Duncan (2018) identified salient beliefs about HPV vaccine completion among young adult women who live in urban communities and integrate these beliefs into the development of the mHealth application to promote vaccine completion. The study findings suggest that content tailored to the population has the potential to foster completion of HPV vaccine series. Electronic medication distribution devices such as the medication event monitoring systems (MEMS) (El Alili et al. 2016) and mobile technologies have also proliferated in health-related applications today. Other studies on systems that support medication management of patients with SMS texting and Web-based interface programs have emerged using simple cell phones and sophisticated smartphones. The MyMediHealth (MMH) is a medication management system that includes a medication scheduler, a medication administration reminder engine, and sends text messages to patient phones (Stenner, Johnson, & Denny, 2012).

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818    P art 9 • R esearch A pplications These and other innovations that connect computer technology with nursing practice have emerged with research to support their functionality, ease of adoption, and efficacy. The range of innovation has been astounding as the technologies have increasingly become less expensive, smaller, wireless, and now interconnected with cloud computing. Nursing research and computers today are inseparable in both areas of using computers for the research and studying the impact of computers on patient care.

SUMMARY This chapter has reviewed two research paradigms and philosophical orientations—qualitative and quantitative methods—that specify different underlying approaches to research and the use of computers in various stages of the research process. It also highlighted examples of research on computer use with these quantitative and qualitative approaches. A variety of computer applications are available via commercialized packages that serve the nurse researcher in conducting research. With the advancements in applications that are developed and delivered wirelessly, and the wide use of smaller devices such as smartphone technologies, the potential for innovation and research on health abounds. Prompted by large consortia that foster technology innovation such as Health Datapalooza (healthdatapalooza.org), an annual conference for researchers and entrepreneurs, one can expect the emergence of many evidence-based interventions aided by smartphones, tablets, and minis in the near future. As the federal government promotes the use of big data, D2K, precision health, and comparative effectiveness research (CER) that aid researchers to tackle existing data sets, coupled with the cloud to house and connect researchers and subjects from distances, one can expect a proliferation of research techniques and acceptable evidence using secondary data. In addition, research on computer applications and informatics is a growing body of science that will continue to appear in the literature with a renewed purpose of collecting and storing meaningful data for multiple uses. Computer technologies and data are at the center of nursing’s future. Nursing research—capturing, coding, storing, organizing, and analyzing health care and nursing data to answer important questions—will build a foundation for accurate measurement and clinically meaningful progress in nursing practice. Brennan and Bakken (2015) propose that “big data encompasses data that exceed human comprehension, that exist at a volume unmanageable by standard computer systems, that arrive at a velocity not under the control of the investigator and possess a level of imprecision not found in traditional inquiry” (p. 477). The surge in AI in all

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industries is a consequence of an exponential growth in data, specifically unstructured data, advances in algorithm development, and cheap computing power (IBM Research Blog, 2017). With emerging data science methods, nurse researchers can explore, code, organize, and develop new insights for nursing practice from big data. Using data elements that are optimized for efficiency of analytics is critical for research to improve nursing care and healthcare delivery systems. Understanding big data and the tools to navigate and synthesize practice level improvements, surveillance, population health, and decision support is a priority for nurse researchers. According to Sensmeier (2015), preparing for the big data future and the flood of data, innovation and access will enable us to advance the vision of a transformed health system. The evolution to a learning healthcare system (IOM, 2012) with computer technology, research tools, and benefits of big data science will help us “apply the best evidence for collaborative health care choices of patients and providers” (p. 117).

Test Questions 1. Cloud computing has reshaped the research process. Which of the following area of cloud computing is uniquely troublesome? A. Data collection B. Data breach

C. Data sharing D. Data storage

2. Web-based reference management software is used to organize references. The tool can do all of the ­following except: A. Insert references in alphabetical order B. Create different bibliographic styles

C. Revise inaccurate or incomplete information recorded in the database D. Pull citation automatically from the Internet search engine.

3. Mobile technologies have become ubiquitous because approximately 99% of the world’s population has access to the Internet. The nurse researcher can use mobile health technologies in quantitative and qualitative inquiries to capture: A. Behavioral changes

B. Adherence to medication C. Knowledge of self-care D. All of the above

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4. Statistical packages work in concert with computer software to provide nurse researchers with tools for simple to complex graphical translation of numeric information in quantitative research. Computer software programs can perform the following except to:

9. Which document articulates the standardization of nursing terminologies, common data model, and information structure within electronic health record?

A. Protect the data from virus attack B. Capture physiologic data and clinical nurse information C. Share the document through SharePoint online D. Manage data in a database 5. Regarding data entry, researchers can use computer software applications to: A. Capture data distribution in real time B. Code the responses to questions in real time C. Scan a large number of surveys with speed and accuracy D. All of above 6. The focus of data analysis is to: A. Transform data into a usable form B. Evaluate data efficiency C. Identify data sources for later inclusion D. Collect data from various sources 7. Several computer software are available for qualitative research data analysis. The software can assist the nurse researcher in performing the following except: A. Coding and sorting of data B. Establishing an index of codes

B. Code of Ethics for Nurses with Interpretive Statements C. 21st Century Cures Act

D. National Action Plan for Sharable and Comparable Nursing Data for Transforming Health and Healthcare 10. Principles of meta-analysis include focused estimates of population, effects of a phenomenon, or intervention across: A. Educational level B. Multiple studies C. Generations

D. Socio-economic class 11. Which software is associated with mapping disease outbreaks? A. SPSS B. SAS C. GIS

D. STATA 12. Although some nurse researchers continue to disseminate research findings at professional meetings, access to the Internet provides other modes for dissemination such as: A. Podcasts

C. Reducing bias and improving reliability

B. YouTube

D. Identifying relationships among the coding categories

D. All of the above

8. The American Nurses Association supports the need to standardize nursing care terms for computerbased patient care systems. Adherence to a standardized nursing language will lead to: A. A more extensive database of interventions B. Improved evaluation of nursing outcomes C. Increased nursing competencies

D. A barrier in national interoperability

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A. Health Insurance Portability and Accountability Act

C. Ted Talks

Test Answers 1. Answer: B

2. Answer: C

3. Answer: D 4. Answer: A

5. Answer: D 6. Answer: A

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820    P art 9 • R esearch A pplications 7. Answer: C 8. Answer: B

9. Answer: D 10. Answer: B

11. Answer: C

12. Answer: D

REFERENCES Agency for Healthcare Research and Quality (AHRQ). (2011). Medical Expenditure Panel Survey (MEPS). Rockville, MD: Agency for Healthcare Research and Quality. Retrieved from www.meps.ahrq.gov/mepsweb/. Accessed on April 18, 2019. Agency for Healthcare Research and Quality (AHRQ). (2014, January). HCUP Home. Healthcare cost and utilization project (HCUP). Rockville, MD: Agency for Healthcare Research and Quality. Retrieved from www.hcup-us.ahrq. gov/home.jsp. Accessed on April 18, 2019. Agency for Healthcare Research and Quality (AHRQ). (n.d.). Comparative effectiveness program (AHRQ. gov). Retrieved from www.effectivehealthcare.ahrq.gov/ reports/final.cfm. Accessed on April 18, 2019. Almasalha, F., Xu, D., Keenan, G., Khokhar, A., Yao, Y., Chen, Y., ... Wilkie, D. (2012). Data mining nursing care plans of end-of-life patients: A study to improve healthcare decision making. International Journal of Nursing Knowledge, 24(1), 15–24. Armbrust, M., Fox., A., Griffith, R., Joseph, A., Katz, R., Konwinski, A., … Zaharia, M. (2010). A view of cloud computing. Communications of the ACM, 53(4), 50–58. doi:10.1145/1721654.1721672. ATLAS.ti 7. (2014). Retrieved from http://www.atlasti.com. Accessed on June l 15, 2019. Bakken, S. (2013, December 3). Why a nursing terminology? Presentation at the CCC Workshop, Nashville, TN. Banxia. (2014). Decision explorer. Cumbria, UK: Banxia Software Ltd, Kendal. Retrieved from http://www.banxia. com/dexplore. Accessed on April 18, 2019. Berry, D. L., Halpenny, B., Bosco, J. L. F., Bruyere, J., & Sanda, M. G. (2015). Usability evaluation and adaptation of the e-health personal patient profile-prostate decision aid for Spanish-speaking Latino men. BMC Medical Informatics and Decision Making, 15, 56. Berry, D. L., Halpenny, B., Wolpin, S., Davison, J., Ellis, W., Lober, W. B., ... Wulff, J. (2010). Development and evaluation of the personal patient profile-prostate (P3P), a web-based decision support system for men with newly diagnosed with localized prostate cancer. Journal of Medicine Internet Research, 12(4), e67. Berry, D. L., Wang, Q., Halpenny, B., & Hong, F. (2012). Decision preparation, satisfaction and regret in a multicenter sample of men with newly diagnosed localized prostate cancer. Patient Education Counseling, 88(22), 262–267.

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Bose, E., Maganti, S., Bowles, K. H., Brueshoff, B. L., & Monsen, K. A. (2019, January/February). Machine learning methods for identifying critical elements data elements in nursing documentation. Nursing Research, 68(1), 65–72. Brennan, P., & Bakken, S. (2015). Nursing needs big data and big data needs nursing. Journal of Nursing Scholarship, 47(5), 477–484. Borenstein, M., Hedges, L., Higgins, J., & Rothstein, H. (2009). Introduction to meta-analysis. Chichester, UK: John Wiley & Sons. Broome, M. (2014). Open access publishing: A disruptive innovation. Nursing Outlook, 62(2), 125–127. Byrne, B. M. (1984). Structural equation modeling with EQS and EQS/Windows: Basic concepts, applications, and programming. Thousand Oaks, CA: Sage. Byrne, M., & Lang, N. (2013). Examination of nursing data elements from evidence-based recommendations for clinical decision support. Computers, Informatics, Nursing, 31(12), 605–614. Coenan, A. (2012). Harmonizing nursing terminologies: CCC System© and ICNP®. In V. Saba (Ed.), Clinical Care Classification (CCC) System, Version 2.5 User’s Guide. New York, NY: Springer. Coenen, A., & Jansen, K. (2013, December 3). Harmonising ICNP and the CCC—International Council of Nursing. Presentation at the CCC Workshop, Nashville, TN. Cooley, M. E., Abrahm, J. L., Berry, D. L., Rabin, M. S., Braun, L. M., Paladino, J., … Lobach, D. F. (2018, May). Algorithm-based decision support for symptom selfmanagement among adults with cancer: Results of usability testing. BMC Medical Informatics and Decision Making, 18(1), 31. Cooley, M. E., Nayak, M. N., Abrahm, J. L., Braun, L. M., Rabin, M. S., Brzozowski, J., … Berry, D. L. (2017, August). Patient and caregiver perspectives on decision support for symptom and quality of life management during cancer treatment: Implications for eHealth. Psychooncology, 26(8), 1105–1112. Creswell, J. W., & Cresswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Thousand Oaks, CA: Sage. Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). Thousand Oaks, CA: Sage. Dahhan, T., Jamil, M., Al-Tarifi, A., Abouchala, N., & Kherallah, M. (2009). Validation of the APACHE IV scoring system in patients with severe sepsis and comparison with the APACHE II system. 29th International Symposium on Intensive Care and Emergency Medicine. Critical Care, (Suppl 1). doi: doi.org/10.1186/cc7675. Delaney, C., Westra, B., Monsen, K., Gillis, C., & Docherty, S. (2013, November 6). Big data 4th paradigm nursing research: Informatics exemplars. Presentation at the CTSA Nurse Scientists SIG Meeting.

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Department of Health and Human Services (DHHS). (2007, January). Breaking news: Nationwide health information technology standard for nursing. Washington, DC: Department of Health and Human Services. Dillman, D. (2011). Mail and Internet surveys: The tailored design method—update (2nd ed.). Hoboken, NJ: John Wiley & Sons. Dykes, P. (2013, December 3). Coding Clinical Care Classification System Research Model. Presentation at the CCC Workshop, Nashville, TN. Dykes, P. C., & Collins, S. A. (2013). Building Linkages between Nursing Care and Improved Patient Outcomes: The Role of Health Information Technology. Online Journal of Issues in Nursing, 3(18). doi:10.3912/OJIN. Vol18No03Man04. El Alili, M., Vrijens, B., Demonceau, J., Evers, S. M., & Hiligsmann, M. ( 2016, May). A scoping review of studies comparing the medication event monitoring system (MEMS) with alternative methods for measuring medication adherence. British Journal of Clinical Pharmacology, 82, 268–279. doi:10.1111/bcp.12942. Englebright, J. (2013, December 3). Who is adopting the CCC? Nurse executives. Presentation at the CCC Workshop, Nashville, TN. Englebright, J., Aldrich, K., & Taylor, C. (2014). Defining and incorporating basic nursing care actions into the electronic health record. Journal of Nursing Scholarship, 46(1), 50–57. Feeg, V., & Greenidge-Adams, L. (2018). A Delphi study to explore clinical nurses’ report of frequency and estimated duration for selected nursing actions using the Clinical Care Classification (CCC) standardized terminology on four hospital medical surgical units. December 2018. Video presented at the HCA-CCC Conference, Nashville, TN. Feeg, V. D., Weiner, K., Raposo, D., & Saba, V. (2015, March/ April). Electronic nursing documentation: A descriptive analysis of coded nursing actions from three hospitals using the standardized terminology of the Clinical Care Classification (CCC) system. Nursing Research, 64(2), E7. Figge, H. (2010). Electronic tools to measure and enhance medication adherence. U.S. Pharmacist, 36(4), (Compliance and Adherence suppl.) 6–10. Geraghty, A. W. A., Essery, R., Kirby, S., Stuart, B., Turner, D., Little, P., … Yardley, L. (2017, May). Internet-based vestibular rehabilitation for older adults with chronic dizziness: A randomized controlled trial in primary care. Annals of Family Medicine, 15(3), 209–216. Glassman, K., & Rosenfeld, P. (2015). Data makes the difference: The smart nurse’s handbook for using data to improve care. Silver Spring, MD: American Nurses Association. Granello, D., & Wheaton, J. (2004). Online data collection: Strategies for research. Journal of Counseling and Development, 82, 387–393. Gravic, Inc. (2014). Remark Office(R), remark Classic(R), Remark Web Survey(R). Retrieved from from http:// www.remarksoftware.com.Accessed on June l 15, 2019.

Gualtieri, T., & Johnson, L. (2006). Reliability and validity of a computerized neurocognitive test battery, CNS Vital Signs. Archives of Clinical Neuropsychology, 21, 623–643. Hey, H., Tansley, S., & Tolle, K. (2009). The fourth paradigm: Data-intensive scientific discovery. Seattle, WA: Microsoft Corporation. IBM Research Blog (Staff Writer). (October 11, 2017). The era of AI—and the technologies that will deliver it. Retrieved from https://www.ibm.com/blogs/ research/2017/10/ai-era-technologies/. Accessed on April 18, 2019. Institute of Medicine (IOM). (2012). Best care at lower cost: The path to continuously learning health care in America. Washington, DC: The National Academies Press. Retrieved from http://nationalacademies.org/hmd/ Reports/2012/Best-Care-at-Lower-Cost-The-Path-toContinuously-Learning-Health-Care-in-America.aspx. Accessed on April 18, 2019. IRBNet (n.d.). Innovative solutions for compliance and research management. Retrieved from https://www.irbnet.org/release/index.html. Accessed on April 18, 2019. Issa, N. T., Byers, S. W., & Dakshanamurthy, S. (2014). Big data: The next frontier for innovation in therapeutics and healthcare. Expert Review of Clinical Pharmacology, 7(3), 293–298. Klemm, P., Hayes, E., Diefenbeck, C., & Milcarek, B. (2014). Online support for employed informal caregivers: psychosocial outcomes. Computers, Informatics, Nursing, 32(1), 10–20. Khokhar, A., Lodhi, M. K., Yao, Y., Ansari, R., Keenan, G., & Wilkie, D. (2017). Framework for mining and analysis of standardized nursing care plan data. Western Journal of Nursing Research, 39(1), 20–41. Lebied, M. (July 18, 2018). 12 Examples of big data analytics. Healthcare That Can Save People, Business Intelligence. Retrieved from https://www.datapine.com/blog/big-dataexamples-in-healthcare. Accessed on April 18, 2019.. Lichenstein, S., McDonough, A., & Matura, L. (2013). Cyber support: Describing concerns of caregivers of people with pulmonary hypertension. Computers, Informatics, Nursing, 31(12), 581–588. Macea, D., Gajos, K., Calil, Y., & Fregni, F. (2010). The efficacy of web-based cognitive behavioral interventions for chronic pain: A systematic review and meta-analysis. Journal of Pain, 11(10), 917–929. Moss, J., & Saba, V. (2011). Costing nursing care: Using the Clinical Care Classification System to value nursing intervention in an acute-care setting. Computers, Informatics, Nursing, 29(8), 455–460. Muenchen, R. A. (2009). R for SAS and SPSS Users. Springer Series in Statistics and Computing. New York, NY: Springer. Naslund, J. A., Aschbrenner, K. A., Scherer, E. A., McHugo, G. J., Marsch, L. A., & Bartels, S. J. (2016, October). Wearable devices and mobile technologies for supporting behavioral weight-loss among people with serious mental illness. Psychiatry Research, 244, 139–144.

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822    P art 9 • R esearch A pplications National Institutes of Health, Big Data to Knowledge (BD2K). (2012 Update 2018). Big Data to Knowledge Program Resources. Retrieved from https://commonfund.nih.gov/ bd2k/resources. Accessed on June l 15, 2019. Nuance Dragon Solutions. (2014) Dragon Naturally Speaking (R)10. Retrieved from https://www.nuance.com/dragon. html. Accessed on June l 15, 2019. Nuance Imaging. (2014). OmniPage Pro(R)17. Burlington, MA. Retrieved from http://www. nuance.com/imaging. Accessed on June l 15, 2019. Paul, E., Bailey, M., Van Lint, A., & Pilcher, D. (2012). Performance of APACHE III over time in Australia and New Zealand: A retrospective cohort study. Anaesthesia Intensive Care, 40, 980–994. Polit, D. F., & Beck, C. T. (2017). Nursing research: Generating and assessing evidence for nursing practice (10th ed.). Philadelphia, PA: Wolters Kluwer/Lippincott, Williams & Wilkins. Rantz, M., Lane, K., Phipps, L. J., Despins, L. A., Galambos, C., Alexander, G. L., … Miller, S. J. (2015). Enhanced registered nurse care coordination with sensor technology: Impact on length of stay and cost in aging in place housing. Nursing Outlook, 63, 650–655. Saba, V. K. (2012). Clinical Care Classification (CCC) System, Version 2.5 User’s Guide). New York, NY: Springer. Saba, V. K. (2014). Overview of Clinical Care Classification: A national nursing standard coded terminology. In V. K. Saba & K. A. McCormick (Eds.), Essentials of nursing informatics (6th ed.). New York, NY: McGraw-Hill. Saba, V., & Taylor, S. (2007). Moving past theory: Use of a standardized, coded nursing terminology to enhance nursing visibility. Computers, Informatics, Nursing, 25(6), 324–331. Sanger, P. C., Hartzler, A., Han, S. M., Armstrong, C. A. L., Stewart, M. R., Lordon, R. J., Lober, W. B., Evans, H. L. (2014, December). Patient perspectives on post-discharge surgical site infections: Towards a patient-centered mobile health solution. PLoS ONE, 9(12), 1–14. Sensmeier, J. (2015). Big Data and the future of nursing knowledge. Nursing Management, 46(4), 22–27. doi:10.1097/01.NUMA.0000462365.53035.7d. Serrano, K. J., Mandi, Y., Riley, W. T., Patel, V., Hughes, P., Marchesini, K., & Atienza, A. A. (2016, January/ February). Willingness to exchange health information via mobile devices: Findings from a population-based survey. Annals of Family Medicine, 14(1), 34–40. Sox, H. C., & Greenfield, S. (2009). Comparative effectiveness research: A report from the Institute of Medicine, Annals of Internal Medicine, 151, 203–205. Stenner, S., Johnson, K., & Denny, J. (2012). PASTE: Patientcentered SMS text tagging in a medication management system. Journal of the American Medical Informatics Association, 19, 368–374. Teitelman, A. M., Kim, S. K., Wass, R., DeSanna, A., & Duncan, R. (2018, November). Development of the NOWIKNOW mobile application to promote

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completion of HPV vaccine series among young adult women. Journal of Obstetrics Gynecology & Neonatal Nursing, 47(6), 844–852. Tofighi, B., Grazioli, F., Bereket, S., Grossman, E., Aphinyanaphongs, Y., & Lee, J. D. (2017, September). Text message reminders for improving patient appointment adherence in an office-based Buprenorphine program: A feasibility study. The American Journal on Addictions, 26(6), 581–586. The Thomson Corporation. (2009). Reference Manager (Version 8). Carlsbad, CA: Thomson ISI Research Soft. Retrieved from http://www.refman.com/pr-rm11.asp. Accessed on April 18, 2019. Van Wagenberg, L., Witteveen, E., Wieske, L., & Horn, J. (2017). Causes of Mortality in ICU-Acquired Weakness. Journal of Intensive Medicine, XX. doi:10.1177/0885066617745818. Verzantvoort, N. C., Teunis, T., Verheij, T. J. M., & Velden, A. W. V. (2018, June). Self-triage for acute primary care via a smartphone application: Practical, safe and efficient? PLOS ONE. Retrieved from https://doi.org/10.1371/journal.pone.0199284. Accessed on February 8, 2019. Werley, H. H., Lang, N. M., & Westlake, S. K. (1986). The nursing minimum data set conference: Executive summary. Journal of Professional Nursing, 2, 217–224. Westra, B., Latimer, G., Matney, S., Park, J. I., Sensmeier, J., Simpson, R., …, & Delaney, C. (2015). A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care. Journal of the American Medical Informatics Association, 22, 600–607. Westra, B., & Peterson, J. (2016). Big data and perioperative nursing. AORN Journal, 104, 286–292. Westra, B., Savik, K., Oancea, C., Chormanski, L., Holmes, J., & Bliss, D. (2011). Predicting improvements in urinary and bowel incontinence for home health patients using electronic health record data. Journal of Wound, Ostomy and Continence Nursing, 38(1), 77–87. Wu, Y. P., Linder, L. A., Patsaporn, K., Fowler, B., Parsons, B. G., MacPherson, C. F., & Johnson, R. H. (2018). Use of a smartphone application for prompting oral medication adherence among adolescents and young adults with cancer. Oncology Nursing Forum, 45(1), 69–76. Yen, P., Lober, W. B., & Bakken, S. (2009). Usability testing of a web-based tool for managing open shifts on nursing units. In K. Saranto, M. Tallberg, A. Ensio, P. Flatley, & H. Park (Eds.), Connecting health and humans. Fairfax, VA: IOS Press.

WEB RESOURCES Acrobat (www.adobe.com) AHRQ Medical Expenditures Panel (MEPS) (www.meps. ahrq.gov) ArcGIS Online system (ESRI) (https://www.arcgis.com/ home/index.html) Audacity (www.audacity.sourceforge.net)

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Clinical Care Classification System (www.sabacare.com) Clinical Trials (www.simpletrials.com/ why-simpletrials-overview) CNS Vital signs (https://www.cnsvs.com/ Endnotex9 (Clarivate Analytics) (https://endnote.com/) Electronic Data Capture Software Tools (EDC) Capterra. com (https://www.capterra.com/sem-compare/ electronic-data-capture-software) E-Surveys Pro (www.esurveyspro.com) G*Power (http://www.psycho.uni-duesseldorf.de/ abteilungen/aap/gpower3/) Google (google.com) HCUP from AHRQ (www.hcup-us.ahrq.gov) Health Datapalooza (www.healthdatapalooza.org) IBM SPSS Statistics 24 (formerly Statistical Package for Social Sciences) (https://www.ibm.com/analytics/ spss-statistics-software) Illustrator CC (www.adobe.com) IRBNet (www.IRBNet.org) Microsoft Office 365 (http://microsoftoffice365.com) Morae (https://www.techsmith.com/morae.html)

NVivo 12 from QSR (https://www.qsrinternational.com/ nvivo/nvivo-products/nvivo-12-plus) Photoshop CC (www.photoshop.com) Qualtrics (www.qualtrics.com) RefWorks from ProQuest (https://www.proquest.com/ products-services/refworks.html) “R” (www.r-project.org) Remark Office OMR 10 (www.remarksoftware.com) SNAP Survey software (www.snapsurveys.com) STATA 15 (https://www.stata.com) Statistical Analysis Services (SAS) (https://www.sas.com/ en_us/home.html) Survey Monkey (www.surveymonkey.com) Survey tools (Capterra.com) (https://www.capterra.com/ sem-compare/survey-software) Tableau (www.tableau.com) United States Census Bureau (https://www.census.gov/ quickfacts/fact/table/US/PST045218) XSight from QSR (http://www.qsrinternational.com/nvivo/ case-studies/software-to-xsight)

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50 Information Literacy and Computerized Information Resources Diane S. Pravikoff / June Levy

• OBJECTIVES . Define information literacy. 1 2. Identify steps in choosing appropriate databases. 3. Identify steps in planning a computer search for information. 4. Identify sources of information for practicing nurses. 5. Identify the difference between essential and supportive computerized resources.

• KEY WORDS Health reference databases Information literacy Information resources Information retrieval

INTRODUCTION This chapter presents information about electronic resources that are easily available and accessible and can assist nurses in maintaining and enhancing their professional practices. These resources aid in keeping current with the published literature, in developing a list of sources for practice, research, and/or education, and in collaborating with colleagues. As is evidenced in earlier chapters, nurses use computers for many purposes. Recently, most of the focus has been on computerized patient records, acuity systems, and physician ordering systems. One of the major purposes for which computers can be used, however, is searching for information. Many resources are available on computer, and the information retrieved can be used to accomplish different ends. Computers also are available in various sizes, improving portability and availability wherever a

nurse is practicing. Many of the resources described in the following sections will be available via mobile devices. To maintain professional credibility, nursing professionals must 1. Keep current with the published literature

2. Develop and maintain a list of bibliographic and other sources on specific topics of interest for practice, research, and/or education 3. Collaborate and network with colleagues regarding specifics of professional practice

Electronic resources are available to meet each of these needs. This chapter addresses each of these requirements for professional credibility and discusses both essential and supportive computerized resources available to meet them. Essential computerized resources are defined as those resources that are vital and necessary 825

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826    P art 9 • R esearch A pplications to the practitioner to accomplish the specific goal. In the case of maintaining currency, for example, these resources include bibliographic retrieval systems such as MEDLINE or the CINAHL database, current awareness services, review services, or point-of-care tools and may be accessible on the World Wide Web. Supportive computerized resources are those that are helpful and interesting and supply good information but are not necessarily essential for professional practice. In meeting the requirement of maintaining currency, supportive computerized resources include document delivery services, electronic publishers, and various sites on the World Wide Web. There are many resources available to meet each of the above requirements for professional credibility. For the purposes of this chapter, selective resources are identified and discussed as examples of the types of information available. Web site URLs of the various resources are included as well. It is important that the nursing professional determine her or his exact requirements before beginning the search. Planning the search will be stressed throughout this chapter.

INFORMATION-SEEKING BEHAVIOR OF REGISTERED NURSES Multiple practice standards organizations (Institute of Medicine [IOM], Agency for Healthcare Research and Quality [AHRQ], American Nurse Credentialing Center [ANCC]’s Magnet Recognition Program, American Association of Colleges of Nursing [AACN], National League for Nursing [NLN], The Joint Commission [TJC]) insist that nursing care be based on information derived from best practice evidence. To identify best evidence and apply it in the care of the patient, the nurse must apply the information literacy process as defined by the American Library Association (American Library Association, 2018): 1. Recognize the need for evidence.

2. Know how to search and find relevant information.

3. Access, utilize, and evaluate such information within the practice environment. These components are identified as competencies for the basic nurse (American Nurses Association, 2015). In 1989, the American Library Association (ALA) described the “information literate” person as one who can “recognize when information is needed and have the ability to locate, evaluate, and use effectively the needed information” (American Library Association, 1989). The ALA continues to identify it as a basic competency for higher education (American Library Association, 2018).

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While the importance of maintaining currency with published literature is stressed, research has indicated that nurses often do not access the tools needed to do so. Neither do they have the ability to utilize these tools, if available, within their work setting. A national study of 3000 nurses licensed to practice in the United States was conducted in 2004 to better understand the readiness of nurses to utilize evidence-based nursing practice, based on their information literacy knowledge and competency (Pravikoff, Pierce, & Tanner, 2005). This landmark study demonstrated that many nurses were not aware that they needed information; once they recognized a need, online resources available for them to use were inadequate and respondents had not been taught how to use online databases to search for the information they needed. In addition, they did not value research as a basis on which to formulate and implement patient care. Since these findings were published, many subsequent studies have been conducted in various specialty areas and countries with similar results (Ross, 2010; Majid, Foo, & Luyt, 2011; O’Leary & Mhaolrunaigh, 2011; Yadav & Fealy, 2012). Melnyk, Fineout-Overholt, Gallagher-Ford, and Kaplan surveyed randomly selected members of the American Nurses Association and found that over 70% of respondents either needed or strongly needed: 1. Tools to implement evidence-based practice

2. Online education and skills-building modules in evidence-based practice 3. An “online resource center where best EBPs for patients are housed and experts are available for c­ onsultation” (Melnyk, Fineout-Overholt, & Gallagher-Ford, 2012, p. 412)

They—and others (Miglus & Froman, 2016)—determined that nurses valued and were ready to practice ­nursing based on evidence but need several things to be able to do so: more time, knowledge, skills, access, and a supportive organizational culture. These barriers continue to inhibit the development of evidence-based practice (EBP) competencies (Melnyk, Gallagher-Ford, & Thomas, 2016; Sadoughi, Azadi, & Azadi, 2017). The rate of expansion in health information technology (e.g., electronic health records) is phenomenal; in ­addition, clinical knowledge is multiplying exponentially and dissemination methods are changing to include scholarly databases and social networking. Despite the demands for EBP, nurses continue to have difficulty finding ­ information they need for practice and prefer colleagues as their ­primary source (Marshall, West, & Aitken, 2011). Alving, Christensen, and Thrysøe identified Google and ­colleagues or peers as the preferred sources of

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Chapter 50 • Information Literacy and Computerized Information Resources 

information in a review of studies conducted from 2010 to 2015 (Alving, Christensen, & Thrysøe, 2018). Poor search skills and time pressures were a possible explanation. Researchers in a Canadian study found nursing students used “mobile information mainly to support patient care-related tasks but did not access research-based journal articles to support evidence-based practice” (Doyle, Furlong, & Secco, 2016, p. 300). Nurses—students, clinicians, educators, and managers— must develop efficient and effective search strategies that embrace information literacy as a framework to search the myriad of information resources available for evidence. According to recent efforts, education is embracing the change by embedding well-designed courses that offer opportunities to develop these skills throughout program curricula (Moreton, 2013; Powell & Ginier, 2013; Stombaugh, Sperstad, & Van Wormer, 2013). Results indicate that such courses are indeed effective in improving the nurse’s skills and confidence in searching for evidence (Boden, Neilson, & Seaton, 2013; Clapp, Johnson, & Schwieder, 2013; Friesen, Brady, & Milligan, 2017; Sleutel, Bullion, & Sullivan, 2018). Change in practice culture is needed to infuse information literacy throughout the workplace. The resources and search strategy introduced in this chapter provide the reader with tools that will become the basis of life-long learning for the nurse—tools for EBP.

MAINTAINING CURRENCY WITH THE PUBLISHED LITERATURE It is obvious that one of the most important obligations a nurse must meet is to maintain currency in her or his field of practice. With the extreme demands in the clinical environment—both in time and amount of work—nurses need easily accessible resources to answer practice-related questions and ensure that they are practicing with the latest and most evidence-based information. Information is needed about current treatments, trends, medications, safety issues, business practices, and new health issues, among other topics. The purpose of the information retrieved from the sources listed below is to enable nurses to keep abreast of the latest and most evidence-based information in their selected field. Both quantity and quality must be considered. When using a resource, check that: 1. The resource covers the required specialty/field.

2. The primary journals and peripheral material in the field are included. 3. The resource is updated regularly and is current. 4. The resource covers the appropriate period.

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5. The resource covers material published in different countries and languages.

6. There is some form of peer review, reference checking, or other means of evaluation.

Essential Computerized Resources Essential computerized resources for maintaining currency include bibliographic retrieval systems for the journal literature, current awareness services, review services of the journal literature, point-of-care tools, and currently published books. All of these assist the nurse in gathering the most current and reliable information.

Bibliographic Retrieval Systems One of the most useful resources for accessing information about current practice is the journal literature. Although there may be a delay between the writing and publishing of an article, this time period is seldom more than a few months. The best way to peruse this literature is through a bibliographic retrieval system, since there is far too much literature published to read it all. Bibliographic retrieval systems also allow filtering and sorting of this vast amount of published material. A bibliographic retrieval system database allows the nurse to retrieve a list of citations containing bibliographic details of the material indexed, subject headings, and author abstracts. The nurse can search these systems using specific subject headings or key words. Most bibliographic retrieval systems have a controlled vocabulary, also known as a thesaurus or subject heading list, to make electronic subject searching much easier. For this reason, the vocabulary is geared toward the specific content of the database. These controlled vocabularies are made available online as part of the database. Key word searching is necessary when there are no subject headings to cover the concepts being searched. The nurse can also search by specific fields including author, author affiliation, journal title, journal serial number (ISSN), grant name or number, or publication type. In bibliographic retrieval systems, most fields in the records are word-indexed and can be searched individually to retrieve specific information. Previously available as print indexes, these systems are now available electronically through online services, or via the World Wide Web. To access them, a computer with a modem and/or Internet access is required. Since each of these bibliographic retrieval systems has its own specific content, a nurse may have to search several systems to retrieve a comprehensive list of citations on a particular topic. Directories of descriptions of bibliographic retrieval systems can be found at many sites

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828    P art 9 • R esearch A pplications on the World Wide Web, e.g., universities (University of California, San Francisco [www.library.ucsf.edu]), medical centers (University of Kansas Medical Center A. R. Dykes Library [https://guides.library.kumc.edu/az.php]), and government agencies (National Library of Medicine [https://eresources.nlm.nih.gov/nlm_eresources/]). The main bibliographic retrieval systems that should first be considered are MEDLINE/PubMed and the CINAHL database, but there are several others to consider as well. These are discussed below. MEDLINE/PubMed.  The NLM provides free access to many online resources (social media tools in the connected age). (1) One of these, MEDLINE, covers 5200 journals in 40 languages (60 languages for older journals) with over 25 million references from 1946 (includes OLDMEDLINE data) to the present in the fields of medicine, nursing, preclinical sciences, healthcare systems, veterinary medicine, and dentistry. The nursing subset in MEDLINE covers 112 nursing journals. The database is updated weekly on the World Wide Web (https://www.nlm.nih.gov/bsd/medline. html—Fact Sheet MEDLINE®: Description of the Database 12/27/18 U.S. National Library of Medicine, 2013). The NLM’s databases use a controlled vocabulary (thesaurus) called MeSH (Medical Subject Headings) (MeSH: https:// www.ncbi.nlm.nih.gov/mesh). These index terms facilitate subject searching within the databases. MEDLINE and the nursing subset are available free over the World Wide Web through the NLM’s home page at https://www.nlm.nih.gov. The database is also available through the commercial vendors mentioned below (e.g., ProQuest, Ovid, EBSCO). These options allow the nurse to search by subject, key word, author, title, or a combination of these. An example of different searches with a display using the EBSCOhost interface is shown in Figs. 50.1 and 50.2. Loansome Doc allows the nurse to place an order for a copy of an article from a medical library through PubMed (https://docline.gov/loansome/login.cfm). The full text of articles for some journals is available via a link to the publisher’s Web site from the PubMed abstract or record display. Some of the full text is available free of charge. The links indicate free full-text display on the Loansome Doc order page prior to order placement and on the Loansome Doc Order Sent page immediately after the order is finalized. NLM has a fact sheet for Loansome doc users covering the registration process, how to place an order, order confirmation, check order status, and updating account information (https:// www.nlm.nih.gov/loansomedoc/loansome_home.html). CINAHL.  The CINAHL database, produced by Cinahl Information Systems, a division of EBSCO Information Services (EBSCO), provides comprehensive coverage

ch50.indd 828

of the literature in nursing and allied health from 1937 to the present. CINAHL has expanded to offer five ­databases including three full-text versions. The database covers nursing and 17 allied health disciplines, as well as chiropractic, podiatry, health promotion and education, health services administration, biomedicine, optometry, women’s health, consumer health, and alternative therapy. The most comprehensive version, CINAHL Complete, provides indexing to nearly 5500 journals from all over the world. It has full text dating back to 1937 and more than 6 million records. The nurse can earn ceus (contact hours) with CINAHL Complete and the CINAHL Plus versions. Medline Plus.  MedlinePlus is the National Institutes of Health’s Web site for patients and their families providing information about diseases, conditions, and wellness issues in language you can understand. It provides information about the latest treatments, drugs or supplements, meanings of words, and access to medical videos or illustrations. You can also get links to the latest medical research on your topic or find out about clinical trials on a disease or condition. MedlinePlus is updated daily and can be bookmarked at the URL: https://medlineplus.gov/. There is no advertising on this site, nor does MedlinePlus endorse any company or product. MedlinePlus Connect helps healthcare providers and patients access consumer health information at the point of need in a health IT system. Patient portals, patient health record (PHR) systems, and electronic health record (EHR) systems can use MedlinePlus Connect to provide health information for patients, families, and healthcare providers using standard clinical vocabularies for diagnoses (problem codes), medications, and laboratory tests. MedlinePlus Connect is a free service of the National Library of Medicine (NLM), National Institutes of Health (NIH), and the Department of Health and Human Services (HHS). OVID EmCare.  Ovid EmCare, a partnership with Elsevier, is a nursing and allied health database of over 5 ­million records across 3500 journals. It uses the ENTRÉE ­thesaurus, expanded with nursing and allied health terms (www.ovid.com/site/catalog/databases/14007:jsp). ProQuest Nursing and Allied Health Database.   ProQuest provides access to nursing and allied health literature, ­videos, reference materials, and evidence-based resources, including dissertations and systematic reviews. ProQuest British Nursing Index.  ProQuest’s Nursing Index is a full-text database supporting nurses and midwives in the United Kingdom.

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  TABLE 50.1    Selected Online Databases Database General databases

URL

Subject

Type

AIDSInfo ClinicalTrials.gov Health Services Research Projects (HSRPro) Health Services and Sciences Research Resources (HSRR) Health Services/Technology Assessment Texts (HSTAT) LocatorPlus

https://aidsinfo.nih.gov https://clinicaltrials.gov https://wwwcf.nlm.nih.gov/ hsr_project/home_proj.cfm https://wwwcf.nlm.nih.gov/

HIV/AIDS clinical trials; prevention, medical practice guidelines Patient studies for drugs and treatment Ongoing grants and contracts in health services research

Factual and referral Factual and referral Research project descriptions Factual

MEDLINE/PubMed MedlinePlus

https://medlineplus.gov/ https://medlineplus.gov/organizations/all_organizations.html https://medlineplus.gov

Research datasets and instruments used in health services research (links to PubMed) Full-text document providing health information and supporting health care decision making Catalogs of books, audiovisuals, and journal articles held at National Library of Medicine Biomedicine. Abstracts to articles in thousands of biomedical journals Directory of organizations providing specialized information services Health information – Includes illustrations and videos

Factual, may include links to full text and content from PubMed Central and publisher websites

Full text Bibliographic citations Bibliographic citations Factual and referral

MeSH Vocabulary File

https://www.nlm.nih.gov/mesh/ meshhome.html

Thesaurus of biomedicine-related terms

Factual

Women’s Health Resources

https://www.nichd.nih.gov/health/ topics/womenshealth/resources

Health topics and research initiatives for women’s health

Factual

TOXNET databases (Toxicology Data Network) https://toxnet.nlm.nih.gov/newtoxnet/ccris.htm https://chem.nlm.nih.gov/ chemidplus/chemidlite.jsp https://toxnet.nlm.nih.gov/newtoxnet/dart.htm https://toxnet.nlm.nih.gov/newtoxnet/genetox.htm https://hazmap.nlm.nih.gov/

Chemical carcinogens, mutagens, tumor promoters, and tumor inhibitors

Factual

https://chem.nlm.nih.gov/chemidplus/chemidlite.jsp

Factual

Teratology, developmental, and reproductive toxicology

Bibliographic citations

Genetic toxicology test results on chemicals

Factual

Effects of exposure to chemicals. Links jobs and hazardous tasks with occupational diseases

Factual

Integrated Risk Information System (IRIS) International Toxicity Estimates for Risk (ITER)

https://toxnet.nlm.nih.gov/newtoxnet/iris.htm https://toxnet.nlm.nih.gov/newtoxnet/iter.htm

Hazard identification and dose-response on chemicals

Factual

Data of human health risk assessment

Factual

Toxicology Literature Online (TOXLINE)

https://toxnet.nlm.nih.gov/newtoxnet/toxline.htm

Over 3 million references from toxicology literature

Bibliographic citations

Toxics Release Inventory (TRI)

https://toxnet.nlm.nih.gov/newtoxnet/tri.htm

Annual releases of over 600 toxic chemicals to the environment, amounts transferred to waste sites, and source reduction and recycling data

Numeric

Source: U.S. National Library of Medicine. (2018). NLM Products and Services: Databases, resources & APIs. Retrieved from https://eresources.nlm.nih.gov/. Accessed on January 30, 2019

  829

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Chemical Carcinogenesis Research Information System (CCRIS) Chemical Identification Plus Database (ChemIDplus) Developmental and Reproductive Toxicology Database (DART) GENE-TOX (Genetic Toxicology DataBank) Haz-Map

Chapter 50 • Information Literacy and Computerized Information Resources 

MedlinePlus

https://www.ncbi.nlm.nih.gov/ books/NBK16710/ https://locatorplus.gov

830    P art 9 • R esearch A pplications

•  FIGURE 50.1.  MEDLINE Search. (Reproduced with permission from EBSCO Information Services.)

•  FIGURE 50.2.  MEDLINE Search Result. (Reproduced with permission from EBSCO Information Services.)

ProQuest Health & Medical Collection. This collection is a comprehensive medical resource providing full-text journal content, eBooks, and evidence-based i­ nformation,

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including dissertations and systematic reviews. It includes MEDLINE®, which contains journal citations and abstracts for biomedical literature from around the world.

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Chapter 50 • Information Literacy and Computerized Information Resources 

Elsevier’s ScienceDirect.  ScienceDirect contains over 3800 journals & serials, and 37,000 books. The digital archives go back as far as 1823. There are over 600 peer-reviewed open-access journals. ERIC.  The ERIC (Educational Resources Information Center) database is sponsored by the Institute of Education Sciences (IES) of the U.S. Department of Education and contains more than 1,600,000 citations covering education-related literature (Educational Resources Information Center, 2018). It covers virtually all types of print materials, published and unpublished, from 1966 to the present day. Currently more than 1000 journal titles are indexed in ERIC. It is updated monthly. This database gives the nurse a more comprehensive coverage of education than any other bibliographic retrieval system. The Thesaurus of Eric Descriptors, a controlled vocabulary, assists with computer searches of this database on the Internet through the World Wide Web (Educational Resources Information Center, 2018). As with the other two bibliographic databases mentioned, nurses are able to access all of the data in each record on ERIC by searching, using subject headings or key words or by searching for a word(s) in a specific field. PsycINFO.  The PsycINFO database, produced by the American Psychological Association, provides access to psychologically relevant literature from more than 2500 journals, dissertations, reports, scholarly documents, books, and book chapters with more than 3 million references from the 1880s to the present. Updated weekly, most of the records have abstracts or content summaries from material published in over 50 countries. Using the Thesaurus of Psychological Index Terms of more than 8400 controlled terms and cross references, the nurse can search for specific concepts effectively. Key word and specific field searching are also available (American Psychological Association: PsychInfo, 2018). Social Sciences Citation Index.  Social Sciences Citation Index (SSCI) can be accessed via Web of Science™ Core Collection for a fee. Web of Science, part of Clarivate Analytics, offers citations from over 20,000 journals carefully selected and evaluated and indexed to deliver influential scientific information Clarivate Analytics: Social Sciences Citation Index, 2018). SSCI contains over 6 million records in various social science fields and covers nearly 3000 journals in the social, behavioral, and related sciences. The Century of Social Sciences™ is a comprehensive backfile covering 1900 to 1955. Nurses can search back and forth in time to track research trends and findings.

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  831

SocINDEX.  SocINDEX uses a sociological thesaurus to index over 2.5 million records from over 800 full-text sociology journals, with abstracts from over 1500 additional journals. It also provides some conference papers in full text. It covers all subdisciplines of sociology and social work including social psychology, gender or racial issues, criminal justice, and religion. (SocINDEX: EBSCO, 2018). Google Scholar.  Google Scholar (GS) offers a version of the Google search engine with which most nurses are familiar. In fact, approximately 67% of the more than 170 billion Internet searches each month are conducted using Google (Sullivan, 2013). Like PubMed, GS is free to access and use links to some full-text articles. It offers broad search capability across many disciplines and types of literature including articles from scholarly journals, theses, books, Web sites, and court opinions. This capability may be useful in an initial search on a topic to get an overview, but may not be precise enough for a specific search. Limited advance search capability is available but a searcher cannot limit to a specific resource type (e.g., research, clinical trial) or discipline. Good analytical and evaluative skills are essential (Badke, 2013). GS is not a replacement for the above-described academic databases but one to be used in conjunction with them. In fact, repeatability of search results cannot be relied upon (Bramer, 2016) because of the nature of the search engine. It may be particularly useful for nurses who do not have easy access to a hospital or academic library where such bibliographic databases are typically housed. ProQuest’s Ex Libris Primo Discovery Service (Primo)  Labeled as the next-generation approach to research, discovery solutions have become a critical component within most academic library systems, playing a vital role in the effort to showcase the value of a library’s collection, providing a unified index and changing the way resources are searched. As there has been a shift of focus from print resources to e-journals, e-books, subject indexes, and full-text databases, the perception and habits of the search experience has evolved as well. Primo provides a gateway to a wealth of scholarly content, including print, electronic, and digital collections. Primo’s search and relevancy ranking algorithm ensures the most relevant results, based on the context of the search and the user’s profile. EBSCO Discovery Service (EDS)  EDS provides a fast, streamlined search through a single search box, but within the context of a greater experience that pulls together intuitive features and functionality, indexing, and instant access

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832    P art 9 • R esearch A pplications to full text leveraged from the EBSCOhost research platform and databases, as well as from key information providers. 

Current Awareness Services Most bibliographic retrieval systems are updated weekly or monthly. In addition to the delay between the writing and publishing of the material that is indexed in the database, there is also a delay between the receipt of material, the indexing, and finally the inclusion of the citations for the indexed material in the database. To obtain access to more current material than that available in a bibliographic database, the nurse should use a current awareness service. Current awareness services are helpful when used in addition to bibliographic retrieval systems. These services provide access to tables of contents of journals and allow individuals to request articles of interest. They may include not only journal articles but also proceedings from conferences, workshops, symposia, and other meetings. Often, hospital or university librarians may provide these services as well. Unlike the bibliographic databases, where subject searching using controlled vocabulary is available, only key word searching for the subject, author, title, or journal is available in current awareness services or databases. Some current awareness services or databases are Web of Science Current Contents Connect, the in-process database for MEDLINE (formerly PREMEDLINE), and the PreCINAHL records on EBSCOHost. Current Contents Connect from Clarivate Analytics provides a Web-friendly current awareness service to table of contents, abstracts, and bibliographic information from over 10,000 scholarly journals (Web of Science platform: Current Contents Connect, 2018). The PreCINAHL records offered by Cinahl Information Systems, a division of EBSCO, publishers of the CINAHL database, provide a method to access basic information, abstracts, and sometimes full text before the citations are formally indexed. The records are retrieved as part of a topic search. They can also be excluded from a topic search. The second type of current awareness provided by Cinahl Information Systems is within the bibliographic database itself, where the searcher is able to choose from a group of 36 specific or special interest categories, which actually function as “virtual” databases. Possibilities include such areas as advanced nursing practice, case management, home healthcare, or military/uniformed services. By selecting one of these categories, documents are retrieved that are either in specific journals in the field or have been selected by indexers as being of interest to those in that field. The results can be limited by any of the available limits on the database, e.g., publication type such as research,

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journal subset such as blind peer reviewed, and presence of full text. A nurse with limited time can peruse the latest literature in one of these fields in this way (Fig. 50.3).

Review Services Although the bibliographic retrieval systems and the current awareness services and databases act as filters to the ever-exploding volume of literature, sometimes the information retrieved needs to be evaluated to determine whether or not it is appropriate. For example, a monthly literature search might be done on a bibliographic and current awareness database and then a review service checked for commentaries on the sources retrieved. Supportive computerized resources that synthesize the literature include the Joanna Briggs Institute (JBI; joannabriggs.org), Best Practice (BMJ Publishing at https://bestpractice.bmj. com/), or the Cochrane Library Database of Systematic Reviews (https://www.cochranelibrary/cdsr/about-cdsr) . Review services such as Doody’s Review Service (https:// www.doody.com/dej/) or reviews noted in bibliographic databases or review journals, such as Evidence-Based Nursing, Evidence-Based Practice, Best Practice, and ACP Journal Club can also be used to evaluate sources. Review services provide information to searchers about recently published books, journal articles, audiovisuals, and software. These reviews may also include ratings, opinions, or commentaries about the material. Doody’s Review Service is a service in which members develop a profile and a weekly bulletin is emailed describing books and software that meet the parameters of the profile. According to the Web site, the service currently contains over 32,000 reviews updated weekly The searcher can use author names, title, specialty, publisher, star rating, and key words to find books of interest. The information presented allows serious consideration of the book along with information to assist in making choices. It is well known that books are generally long in the development stage and are not as current as journal articles or documents on the World Wide Web; however, the depth of material presented in books must be considered. An in-depth discussion of all aspects of cardiac rehabilitation, for example, may be valuable in planning care and would probably not be included in a journal article where space is a consideration. Yet it would still be necessary for maintaining currency in the field.

Point-of-Care Resources Point-of-care resources are resources that support patient care and clinical decision making at the bedside.

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Chapter 50 • Information Literacy and Computerized Information Resources 

  833

•  FIGURE 50.3.  Search for Special Interest Categories. (Reproduced with permission from EBSCO Information Services.) Dynamic Health.  Dynamic Health, published by EBSCO Information Services, is an evidence-based resource that helps nurses and allied health professionals master critical skills at the bedside. Over 2200 skills on core nursing competencies, transcultural care, patient teaching, physical and occupational therapy, speech therapy, and dietetics are provided.

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Elsevier Clinical Skills.  Elsevier combines over 1600 evidence-based skills and procedures with competency management functionality. Content is updated annually or more frequently if required. The continuing education skills follow the American Nurses Credentialing Center (ANCC) educational design. Clinical competence is documented and education can be assigned to address knowledge gaps.

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834    P art 9 • R esearch A pplications Lippincott® Procedures. Lippincott provides evidencebased information needed at the point of care. There are over 1700 procedures and skills from a wide variety of nursing specialties (lippincottsolutions.lww.com/solutions/procedures.html). The product is updated to reflect changes in current guidelines and standards. All procedures are reviewed for updates at least annually on a rolling basis. Nursing Reference Center Plus.  Nursing Reference Center Plus (NRCP), published by EBSCO, is a point-of-care tool designed to provide relevant clinical resources to nurses and other healthcare professionals. It offers the best available and most recent clinical evidence from thousands of fulltext documents. NRCP contains over 4700 Quick Lessons and Evidence-Based Care Sheets covering conditions and diseases, cultural competencies, patient education resources, drug information, continuing education, lab and diagnosis detail, legal cases, research instruments, and best practice guidelines. It also contains over 1600 nursing skills and procedures detailing the necessary steps to achieve proficiency in a specific nursing task. Over 2800 continuing education modules are available which are accredited through ANCC and the International Association for Continuing Education and Training (IACET). Over 50 CE modules have been accredited by the Commission for Case Manager Certification. There are 8700 evidence-based customizable patient handouts (English and Spanish) together with thousands of detailed medical illustrations. Content is updated annually or more frequently, if required.

Supportive Computerized Resources Supportive computerized resources that assist the nurse in maintaining currency provide additional information and enhance the value of the essential computerized resources described previously. Obtaining a bibliographic list of citations is only the first step in obtaining information on a particular topic. After carefully evaluating the citations, either from the title and/or the abstracts, or after using one of the review processes described previously, the nurse will need to get the full text of the sources retrieved. Many articles are available in full text directly through the bibliographic databases searched. If not, a local library or academic institution would be a place to go to locate the items retrieved in a search. Document Delivery Services.  Document delivery services are secondary sources through which full text of items can be obtained for a fee. Fees differ depending on the

ch50.indd 834

service, the urgency of the request, and the publisher’s charges. Copy is usually sent via fax or electronic delivery. Many libraries provide document delivery for each other through services such as DocLine, an automated interlibrary loan (ILL) request routing and referral system provided through the National Library of Medicine. Electronic Publishers.  Many publications are now being published electronically, either as an “e-journal” only or as a print journal with electronic supplements. There are several advantages to this form of publication such as speed, ease of availability, and space required for publication. Searching for information in these journals is relatively easy. The Morbidity and Mortality Weekly Report (MMWR), published by the Centers for Disease Control and Prevention, is one such electronic publication that can be subscribed to and provided by email. The credibility and accuracy of the source of electronically published material must always be considered just as it is in print publications. The criteria mentioned along with additional criteria discussed later can be useful in evaluating this material. Two examples of electronic-only nursing journals are the Online Journal of Issues in Nursing, published by the American Nurses Association, and the Online Journal of Nursing Informatics, published by Healthcare Information and Management Systems Society (HIMSS). Other journals such as Nursing Standard Online, published by the Royal College of Nursing, have print counterparts but may have portions that are only electronic. Nursing publishers and organizations have their own Web sites, which have details about new publications, sometimes full text of some of the latest journal articles, official position statements of organizations, and/or practice guidelines. To identify the Web sites of nursing publishers and organizations, search Web site indexes such as Yahoo! (www.yahoo.com) or Google (www.google.com), or browse Web site lists on Web sites such as that of the University of Buffalo Library (http://library.buffalo.edu) or the Allnurses.com site (https://allnurses.com). On a Web site index such as Yahoo! or Google, do a general search for “nursing and publishers,” “nursing and organizations,” or “nursing and associations” or under the specific names of the publishers and organizations (e.g., Sage, Sigma Theta Tau). Advanced search options are also available. Lippincott Williams & Wilkins (https://www.nursingcenter.com) has placed nearly 70 journals including the AJN, American Journal of Nursing; Nursing Research; CIN: Computers, Informatics, Nursing; and JONA: Journal of Nursing Administration, among others, on their journals page with issues of some from January 1996 to the present. The site has search capability that allows key word searching

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Chapter 50 • Information Literacy and Computerized Information Resources 

of the contents of the journals on the site. There are both free and fee-based articles available on the site along with more than 1900 continuing education activities in CE Connection. Many nursing organizations provide a significant amount of support to practicing nurses. They publish journals and provide these as a member benefit. They also provide access to the full text of their position statements and/or practicing guidelines. Some of these resources are the American Nurses Association’s Web site Nursing World (www.nursingworld.org) and the Web sites of the American Academy of Nurse Practitioners (www.aanp.org), the Association of PeriOperative Registered Nurses (https://www.aorn.org), the Academy of Medical-Surgical Nurses, and many others. Details regarding new publications and ordering items can be found on the Web sites of most publishers. Since there is so much information on the World Wide Web, identification and evaluation of Web sites is very important to determine which provide valid information. At minimum, the nurse should consider the following: 1. Who created the site?

2. Is its purpose and intention clear?

3. Is the information accurate and current? 4. Is the site well designed and stable? 5. How frequently is it updated?

Also consider who sponsors or benefits from the site; is there a fee involved; is its foundation evidence based? In addition, Web sites providing information or discussions concerning specific diseases should be ­evaluated in the same way (e.g., the Web sites of the American Diabetes Association at www.diabetes.org and the American Heart Association at https://www.heart.org).

DEVELOPING AND MAINTAINING A LIST OF SOURCES FOR RESEARCH/ PRACTICE/EDUCATION Essential Computerized Resources The purpose of the information retrieved from these information resources is to enable nurses to answer specific questions that relate to research, practice, and/or education. For example:

• •

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A staff nurse needs to find information to share with her or his colleagues on oral care and the prevention of pneumonia. A nursing student has to finish a term paper and needs to find five nursing research studies on

•

  835

caring for a Hispanic patient with a myocardial infarction. A nurse manager needs to find research studies and anecdotal material showing the best way to prevent patient falls in her or his health facility.

Bibliographic Retrieval Systems.  Resources essential in answering these types of questions again include bibliographic databases as well as various Web sites. Once again, the resources need to be carefully evaluated for coverage and currency. Once a resource has been selected, the nurse breaks down her or his needs into a search statement such as, “I need information on oral care and prevention of pneumonia.” The information on this topic would best be found in a bibliographic database. On such a database, the best method of searching is to do a subject search using a controlled vocabulary (MeSH headings in MEDLINE, CINAHL subject headings in the CINAHL database, and so forth). Search Strategies.  One of the most important aspects of searching the literature is formulating the exact strategy to obtain the information from a resource, whether from a bibliographic retrieval system or a Web site. There are six steps in planning the search strategy. 1. Plan the search strategy ahead of time.

2. Break down the search topic into components. To find information on oral care and the prevention of pneumonia, remember to include synonyms or related terms. The components of the above search would be oral hygiene or mouth care and prevention of pneumonia. Sometimes the terms for the search will be subject headings in the database’s subject heading list (often called a thesaurus); in other cases, they will not be (Fig. 50.4). 3. Check for terms in a subject heading list, if available. If the concept is new and there are no subject headings, a text word or key word search is necessary. For example, before the term critical path or critical pathways was added to the CINAHL or MeSH Subject Heading List, respectively, it was necessary to do a text word search for this concept. A search using the broad term case management would have retrieved many articles that would not necessarily discuss or include critical paths. Combining the two concepts results in a more specific result: articles on case management that include critical paths. 4. Select operators, which are words used to connect different or synonymous components of the search.

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836    P art 9 • R esearch A pplications Strategy for a Successful Literature Search Reconsider Topic

DECIDE TOPIC

Rephrase Heading

Check for Subject Heading

NO

Available

Check Permuted

NO

Suggested Headings

YES

Check Alphabetic for Tree Numbers and Related Headings YES

SELECT

Check Alphabetic for Tree Numbers and Related Headings SELECT

CONDUCT SEARCH •  FIGURE 50.4.  Strategy for a Successful Literature Search. (Reproduced with permission from EBSCO Information Services.) The AND operator, for example, makes the search narrower or more specific as the results of the search for two different terms will only result in records that include both terms as subject headings (Fig. 50.5). The OR operator can be used to connect synonymous or related terms, which broadens the search (Fig. 50.6). An example combining subject headings using OR and AND operators is shown in Fig. 50.7. The NOT operator can be used to exclude terms (Fig. 50.8).

5. Run the search. For the search on oral care and pneumonia, select the option explode for the subject headings oral hygiene and mouth care. This would ensure the retrieval of articles on the

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broad heading and the more specific headings. For example, the specific heading under oral hygiene is “toothbrushing.”

6. View the results.

PracticeGuidelinesandPositionStatements.  Organizationspecific practice guidelines, position statements, and standards of practice can often be accessed and obtained from the Web site of an individual’s professional organization. These are extremely useful documents that present information on scope of practice, qualifications, and education among other important details. In addition, Cinahl Information Systems currently includes nurse practice acts

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Chapter 50 • Information Literacy and Computerized Information Resources 

AND

OR

Concept 1 “AND” Concept 2 = This means that only articles with both concept 1 and concept 2 are searched for.

•  FIGURE 50.5.  Venn Diagram AND.

Subject Heading: ORAL HYGIENE

  837

Concept 1 “OR” Concept 2 = This means that articles with either concept 1 or concept 2 are searched for.

•  FIGURE 50.6.  Venn Diagram OR.

Subject Heading: MOUTH CARE

Subject Heading: PNEUMONIA

Topical Subheading: PREVENTION & CONTROL

Use Operator “OR”

Use Operator “AND”

•  FIGURE 50.7.  Subject Headings Using OR and AND Operators. (Reproduced with permission from EBSCO Information Services.)

as one of its publication types in the CINAHL database. These may appear in full text and can be read online or printed.

NOT Concept 1 “NOT” Concept 2 = This means that articles with concept 1 that do not include concept 2 are searched for.

•  FIGURE 50.8.  Venn Diagram NOT.

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Continuing Education and Computer-Assisted Learning.  Many nurses do not have the time or money to attend conferences and workshops to keep abreast of the latest information in their specialties or to complete the necessary units or credits for continuing education (CE) for relicensure or recertification. The World Wide Web is a wonderful source for nurses that can be used to satisfy their requirements for CE. To identify CE Web sites visit the Nurse-friendly National Consumer Health Directories (http://www.nursefriendly.com/ceu/), or use one of several search engines, Google at www.google.com,Yahoo! at www.yahoo.com, or Ask at www.ask.com to obtain CE nursing sites. There are many nursing sites or point-ofcare resources that offer online CE and CEU certificates,

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838    P art 9 • R esearch A pplications such as Nursing Reference Center Plus through EBSCO, RnCeus.com, and the CEConnection at Lippincott Williams & Wilkins site at https://nursing.ceconnection. com. A directory of free online continuing education opportunities for nurses can be found at nurseCEU.com. As mentioned at the beginning of this chapter, nurses use computers for many purposes. Computer-assisted instruction (CAI), computer-assisted learning (CAL), and interactive videodisc (IVD) provide easy learning experiences using a computer.

Supportive Computerized Resources Supportive computerized resources that assist in practice, research, and education contain all types of health information including drug and treatment information, anatomy, and physiology. Specific products such as the Merck Manual of Diagnosis and Therapy (www.merck. com) or the Prescribers’ Digital Reference (formerly the Physician’s Desk Reference), available at https://www. pdr.net/, are also available on the World Wide Web. The Visible Human Project (https://www.nlm.nih.gov/ research/visible/visible_human.html) includes complete, anatomically detailed, three-dimensional representations of the male and female human bodies. The National Library of Medicine itself claims to be the “largest health science library in the world.” Other Web sites of particular interest in this category include the Nursing Theory Page (https://www.sandiego. edu/nursing/research/nursing_theory_research.php) and the Virginia Henderson Global Nursing e-Repository (https://www.nursinglibrary.org/about).

COLLABORATION AND NETWORKING REGARDING ISSUES OF PROFESSIONAL PRACTICE Nurses frequently gather information from their personal networks—either at the worksite or at professional meetings. The increased availability of computers makes contact with other professionals much easier, resulting in networking and collaboration possibilities heretofore impossible. Information retrieved by this method enables nurses to learn from their colleagues’ experiences. When considering with whom to network, the specialty of the person should be evaluated along with experience, the material they have published in their field, and the research undertaken by the institution with which they are affiliated. Most of this information is not published and would be unavailable through traditional information resources.

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Computerized resources for collaboration and networking vary in several technical details (e.g., their focus, the presence or absence of a moderator to monitor messages, the number of participants, and their level of interactivity).

Essential Computerized Resources Electronic Mail, Instant Messaging, and Text Messaging.  An important fundamental computerized resource for collaboration and networking is email, which is at the core of almost any electronic communication. Necessary components for email are access to Internet services (often provided by cable television and local telephone companies) and email viewing software, such as Internet Explorer, Google Chrome, or Firefox. Email allows one-to-one communication between individuals and can provide immediate response to practice-related questions. Instant messaging using an application such as Skype, Messenger, or What’sApp allows individuals to communicate with others in real time over the Internet. Text messaging or texting involves the use of a cellular network or an Internet connection. Both of these methods also allow one-to-one communication between individuals—or groups of individuals.

Supportive Computerized Resources Social Networking and Social Media.  Social ­networking and social media are used for interaction between individuals and sharing of information. These sites are continually being created and come in various formats including electronic bulletin boards forums, chat rooms, newsgroups, and are all designed to allow individuals to ask questions, provide answers, or simply state opinions. Electronic networking sites have become more and more sophisticated in their interactivity and design. The premise behind each of them is similar. An individual posts a message concerning a topic (known as a thread) for others to read and respond to. Allnurses.com has a “Break Room,” a general topic area in which nurses are invited to discuss anything of particular interest to them. Newsgroups operate in much the same way. Nursezone.com is an online news magazine that offers blogs, a job center, and various nurserelated information. All of these resources are interactive but on a delayed basis. An individual may respond to a message immediately or wait several days. Chat rooms, on the other hand, are interactive in real time. Conversations in chat rooms can be compared to telephone conversations. “Nursing Chat Room” in LinkedIn is an example focused especially on psychiatric nursing.

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Each of these methods of collaboration and networking provides an option for nurses to contact and build relationships with other professionals concerning issues important to them. Social networking sites such as LinkedIn, Facebook, and Twitter offer great potential for sharing experiences and ideas about practice issues. Using features of Smartphones such as texting also enhance communication opportunities. All of the above sites offer end users the ability to disseminate information about practice and/or products that they have found useful. By “liking” a particular site, an end-user can be included in any notifications about new information on that site. For example, a commercial entity that produces an insulin pump can create a Facebook account, post information about its operations or products, and promote itself to other account holders. Another supportive electronic resource includes videoconferencing using WebEx, Skype, Zoom, GoToMeeting, or several others. These offer Webinars and group meetings and sharing of information. Most require mobile and Internet connections.

SUMMARY While these three categories of information needs have been discussed as if they were independent of one another, a nurse might often find that she or he has needs that transcend all three categories or that fall under a different category each time, depending on the task. For example, a staff nurse may need to investigate the best methods to assess and manage pain. The process of retrieving appropriate information would be to first search for research studies and anecdotal material on the topic of pain measurement and pain management. This would involve a search for pain measurement or pain with therapy, drug therapy, and diagnosis using essential computerized resources such as bibliographic retrieval systems like MEDLINE or the CINAHL database. The nurse would also consult a point-of-care tool such as Lippincott Advisor or Nursing Reference Center Plus to locate the latest evidence on a particular patient care topic. Networking with other professionals facing the same task would be an additional step in this process. The nursing listservs mentioned under the “Collaboration and Networking Regarding Issues of Professional Practice” section would be an important and essential resource, while emailing or texting colleagues who are specialists in the field of pain would be a supportive resource. To locate specialists, a bibliographic retrieval system could be searched for research studies on pain measurement or management. The author affiliation field in the records retrieved would help track the institution with which the author is affiliated.

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Chapter 50 • Information Literacy and Computerized Information Resources 

Social networking sites can be useful in identifying and reviewing specialists in a given area of practice. Making sure to keep current on any new material published on pain measurement and pain management, by using current awareness services or Web sites, would also be vital in locating information on this topic. Bibliographic retrieval systems, already used as an essential resource, could be searched each month to assess what new material had been published on the topic. Supportive computerized resources might include a similar search for papers on the Cochrane Library’s Database of Systematic Reviews. An important part of identifying and using these essential and supportive computerized resources is the evaluation of each of them to assess whether or not they contain the information needed. Therefore, the nurse must determine what she or he is looking for, identify the most appropriate resources to locate the information needed, and, using the criteria discussed throughout this chapter, evaluate the resources to assess if they are valid, current, and accurate. Finally, it is important to realize that computerized information resources are like a “moving target,” in that technology is changing so quickly that resources used today may be gone, unavailable, or outdated tomorrow. The use of bibliographic retrieval systems and search engines encourages searching by subject or concept, which is the most reliable way to cope with the ever-changing nature of technology. This is vital to maintaining currency with the published literature, developing and maintaining a list of sources of topics of interest for practice, research, and/or education, and collaboration and networking with colleagues regarding issues of professional practice.

Test Questions 1. To identify best evidence and apply it in the care of the patient, the nurse must apply the information literacy process. Which of the following steps are included in this process? A. Recognize the need for evidence.

B. Know how to search for information.

C. Access, utilize, and evaluate information. D. All of the above.

2. Research on readiness of nurses to utilize evidencebased practice has demonstrated: A. Nurses value research as a basis for practice.

B. Nurses are taught how to use online databases. C. Lack of time, skill, and nonsupportive organizational culture are barriers to evidence-based practice.

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840    P art 9 • R esearch A pplications 3. Which of the following are necessary to maintain professional credibility? A. Keep current with the published literature.

B. Develop and maintain a list of bibliographic and other sources on specific topics of interest for practice, research, and/or education.

C. Collaborate and network with colleagues regarding specifics of professional practice. D. All of the above.

4. Indexing in the CINAHL database covers which of the following professions? A. Nursing

B. Business/finance C. Optometry

D. Biomedicine

E. All of the above F. A, C, D

5. Which of the following is not an accurate statement about an appropriate information resource? A. The resource is peer-reviewed.

B. The resource is updated irregularly.

C. The resource thoroughly covers the required specialty.

D. The primary journals and peripheral material in the field are included. 6. A search strategy includes which of the following steps? A. Plan the search ahead of time.

B. Break down the search into components. C. Check for terms in a subject heading list. D. All of the above.

7. Essential computerized resources useful for collaboration and networking include which of the following? A. E-mail

A. Bibliographic retrieval systems B. Current awareness services

C. Document delivery services D. A, B

9. Critical evaluation of Web sites includes which of the following considerations? A. Purpose and intention of the site

B. Currency and accuracy of information C. Stability of the site

D. Creator/sponsor of the site E. All of the above

10. Which of the following is an accurate description of an AND operator? A. It makes the search narrower. B. It is used to exclude terms.

C. It connects synonyms or related terms. D. All of the above.

Test Answers 1. Answer: D 2. Answer: C

3. Answer: D 4. Answer: F

5. Answer: B

6. Answer: D 7. Answer: E

8. Answer: D 9. Answer: E

10. Answer: A

B. Text messaging

REFERENCES

D. Social media

Alving, B. E., Christensen, J. B., & Thrysøe, L. (2018). Hospital nurses’ information retrieval behaviours in relation to evidence based nursing: A literature review. Health Information & Libraries Journal, 35, 3.

C. Instant messaging E. A, B, C

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8. Essential computerized resources for maintaining currency in nursing practice include which of the following?

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Chapter 50 • Information Literacy and Computerized Information Resources 

American Library Association. (1989). Presidential Committee on Information Literacy. Final Report. Retrieved from http://www.ala.org/acrl/publications/ whitepapers/presidential. Accessed date May 20, 2019. American Library Association. (2018). Information literacy competency standards for higher education. Retrieved from http://www.ala.org/Template. cfm?Section=Home&template=/ContentManagement/ ContentDisplay.cfm&ContentID=33553. American Nurses Association. (2015). Nursing : Scope and standards of practice. Silver Spring, MD, ANA. American Psychological Association: PsychInfo. (2018). Retrieved from https//www.apa.org/pubs/databases/psycinfo/index.aspx. Accessed date May 20, 2019. Badke, W. (2013). Coming back to Google Scholar. Online Searcher, 37, 65. Boden, C., Neilson, C. J., & Seaton, J. X. (2013). Efficacy of screen-capture tutorials in literature search training: A pilot study of a research method. Medical Reference Services Quarterly, 32, 314. Bramer, W. M. (2016). Variation in number of hits for complex searches in Google Scholar. Journal of the Medical Library Association, 104, 143. Clapp, M. J., Johnson, M., Schwieder, D., et al. (2013). Innovation in the academy: Creating an online information literacy course. Journal of Library & Information Services in Distance Learning,7, 247. Clarivate Analytics: Social Sciences Citation Index. (2018). Retrieved from http://wokinfo.com/products_tools/multidisciplinary/webofscience/ssci/ Accessed date May 20, 2019. Doyle, G. J., Furlong, K. E., & Secco, L. (2016). Information literacy in a digital era: Understanding the impact of mobile information for undergraduate nursing students. Studies in Health Technology and Informatics, 225, 297. Educational Resources Information Center. (2018). Who contributes to ERIC? Retrieved from https://eric.ed.gov/?faq. Friesen, M. A., Brady, J. M., Milligan, R., et al. (2017). Findings from a pilot study: Bringing evidence-based practice to the bedside. Worldviews on Evidence Based Nursing, 14, 22. Majid, S., Foo, S., Luyt, B., et al. (2011). Adopting evidencebased practice in clinical decision making: Nurses’ perceptions, knowledge, and barriers. Journal of the Medical Library Association, 99, 229. Marshall, A. P., West, S. H., & Aitken, L. M. (2011). Preferred information sources for clinical decision making: Critical care nurses’ perceptions of information accessibility and usefulness. Worldviews on Evidence Based Nursing, 8, 224. Melnyk, B. M., Fineout-Overholt, E., Gallagher-Ford, L., et al. (2012). The state of evidence-based practice in US nurses: Critical implications for nurse leaders and educators. The Journal of Nursing Administration, 42, 410.

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Melnyk, B. M., Gallagher-Ford, L., Thomas, B. K., et al. (2016). A study of chief nurse executives indicates low prioritization of evidence-based practice and shortcomings in hospital performance metrics across the United States. Worldviews on Evidence Based Nursing, 13, 6. Accessed date May 20, 2019. Miglus, J. D., & Froman, R. D. (2016). Evaluation of an evidence-based practice tutorial for nurses: A useful tool and some lessons learned. The Journal of Continuing Education in Nursing, 47, 266. Moreton, E. (2013). Embedding information literacy into evidence-based practice. Nursing and Allied Health Resources Section (NAHRS) Newsletter, 33, 5. O’Leary, D. F., & Mhaolrunaigh, S. N. (2011). Informationseeking behaviour of nurses: Where is information sought and what processes are followed? Journal of Advanced Nursing, 68, 379. Powell, C. A., & Ginier, E. C. (2013). Lessons learned: Year-byyear improvement of a required information competency course. Medical Reference Services Quarterly, 32, 290. Pravikoff, D. S., Pierce, S. T., & Tanner, A. B. (2005). Readiness of U.S. nurses for evidence-based practice. American Journal of Nursing, 105, 40. Ross, J. (2010). Information literacy for evidence-based practice in perianesthesia nurses: Readiness for evidencebased practice. Journal of PeriAnesthesia Nursing, 25, 64. Sadoughi, F., Azadi, T., & Azadi, T. (2017). Barriers to using electronic evidence based literature in nursing practice: A systematised review. Health Information & Libraries Journal, 34, 187. Sleutel, M., Bullion, J. W., & Sullivan, R. (2018). Tools of the trade: Improving nurses’ ability to access and evaluate research. Journal of Nursing Management, 26, 167. SocINDEX: EBSCO. (2018). Retrieved from https://www. ebscohost.com/nursing/products/socindex. Accessed date May 20, 2019. Stombaugh, A., Sperstad, R., Van Wormer, A., et al. (2013). Using lesson study to integrate information literacy throughout the curriculum. Nurse Educator, 38, 173. Sullivan, D. (2013). Google still world’s most popular search engine by far, but share of unique searchers dips slightly. Retrieved from http://searchengineland.com/googleworlds-most-popular-search-engine-148089. Accessed date May 20, 2019. Web of Science platform: Current Contents Connect. (2018). Retrieved from https://clarivate.libguides.com/webofscienceplatform/ccc. Accessed date May 20, 2019. Yadav, B. L., & Fealy, G. M. (2012). Irish psychiatric nurses’ self-reported barriers, facilitators and skills for developing evidence-based practice. Journal of Psychiatric and Mental Health Nursing, 19, 116.

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APPENDIX Clinical Care Classification (CCC) System: Overview, Applications, and Analyses Virginia K. Saba / Luann Whittenburg

• OBJECTIVES . Describe the characteristics of the Clinical Care Classification (CCC) System. 1 2. List the benefits of the Clinical Care Classification (CCC) System coding structure and Information Model to aggregate and parse Plan of Care data. 3. Highlight the HHS-recognized, national nursing terminology standard for the electronic capture of discrete coded data to advance evidence-based practice. 4. Define the benefits of a coded Nursing Plan of Care to measure and predict clinical care workload and care cost in electronic health records. 5. Identify the two core WAMM© processes used to calculate Relative Value Units (RVUs) and Workload Value Units for nursing and allied health services.

• KEY WORDS Analyses Applications Clinical Care Classification (CCC) System Nursing Plan of Care (POC) Workload Actions Measures Method (WAMM©)

INTRODUCTION An introduction to the Clinical Care Classification (CCC) System is presented in this Appendix which consists of several sections. The Appendix begins with a description of the CCC System which includes two CCC interrelated terminologies: (a) the CCC of Nursing Diagnoses and Outcomes and (b) the CCC of Nursing Interventions and Actions. Each is classified by a Care Component to form a single system. This is followed by a review of the CCC System Information Model which serves as the framework for the documentation of a Nursing Plan of Care using the six steps of the Nursing Process (ANA, 2010). The next section presents a sample case study for a three-day inpatient

episode of illness. The case study is a coded Nursing Plan of Care using the CCC System terminologies with examples of data frequencies to provide evidence-based care and other new information about nursing services. The Appendix also provides an analytics section to illustrate how basic nursing care data frequencies, when combined with new formulas, can generate new information such as patient Workload and Care Requirements data, called the Workload Action Measures Method (WAMM©). The Appendix concludes with a CCC System “Readyto-Use Guide” (Fig. A.6). The Guide is a resource for the use of standardized data and the use of the CCC System to achieve the exchange of nursing data. The CCC System provides the evidence to improve patient care and health 843

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844    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses outcomes. The Guide provides a listing of the CCC System terminologies as a foundation for conducting research, guiding the documentation of Nursing Practice and/or the development of a Nursing Plan of Care for electronic health record (EHR) systems. Also see Chapter 27, Nursing Plan of Care Framework for HIT.

STATUS The urgent need for coded, structured, and standardized nursing concepts in an EHR system is more critical than ever. Nursing can no longer rely on the empirical evidence-based trial and error methodologies and/or narrative nursing notes based on the intuition of senior nurses. As a profession, nursing must harness the current healthcare information technology (HIT) systems to investigate nursing practice, generate new nursing knowledge, and validate clinical evidence to improve healthcare outcomes. Nursing also needs to demonstrate to stakeholders why appropriate nursing staffing levels are important, as well as influence local, state, national, and international policies regarding the “Scope of Nursing Practice.” Nursing can accomplish these goals by documenting nursing practice using a standardized coded nursing terminology to study the “essence of nursing care.” “Only” nurses know that medical diagnoses alone cannot explain nor generate a complete understanding of a patient’s care (Saba and Taylor, 2007).

BACKGROUND In the 1990s, the Clinical Care Classification (CCC) System was developed from federally funded research. The CCC was empirically developed from live patient care data (Saba, 1992). The research collected data from a survey of recently discharged patients from healthcare facilities nation-wide. One major research result was the development of two clinical CCC terminologies. Once validated, the two clinical CCC terminologies were statistically sorted to form Care Component classes and structured for computer-based processing. The CCC System was recognized in 1992 by the American Nurses Association (ANA) as a standardized nursing terminology designed for the electronic documentation of nursing practice (ANA, 1994, 1998, 2008, 2014). In 2007/2008, the CCC System was selected by the U.S. Secretary of the Department of Health and Human Services (HHS) as the first interoperable national nursing terminology standard satisfying the required and rigorous usability criteria of the Office of the National Coordinator for Health Information Technology (ONC) for the

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interoperable documentation of patient care services in an EHR (HHS, 2008, p. 3975). The CCC System is currently the only nursing terminology standard with coded concepts and an Information Model. The CCC System Information Model adapted the six steps of the Nursing Process (ANA, 2010) for the electronic documentation of nursing practice. The CCC System’s framework consists of coded, standardized terminology concepts, including the CCC Information Model, that allow nursing services to be efficiently and effectively processed in an interdisciplinary Plan of Care (POC). The two clinical CCC terminologies are also mapped to the Systematized Nomenclature of Medicine—Clinical Terms (SNOMED-CT) and the Logical Observation Identifiers Names and Codes (LOINC) reference terminologies as required for interoperability by ONC. The ONC recommended for Interoperability Standards Advisory (ISA) process is a publication used for the coordination, identification, assessment, and public awareness of implementation specifications for use to address healthcare interoperability for clinical, public health, and research purposes (https://www.healthit.gov/isa/). The CCC is recognized by Health Level Seven (HL7) as HL7 compliant and meets the major criteria identified for an electronic health record terminology in the “Desiderata for Controlled Medical Terminologies” (Cimino, 1998). The CCC has been indexed in several nursing databases such the National Library of Medicine (NLM) Metathesaurus, 3M (MMM), the Cumulative Index of Nursing and Allied Health Literature (CINAHL), etc. The CCC System has also been translated from English into multiple foreign languages for the respective countries who are using the CCC System to document nursing practice. As examples, the CCC System is translated into Italian, Spanish, Korean, Norwegian, German, Slovenian, Portuguese, Persian, and Chinese. In 2019/2020, HCA Healthcare, of Nashville, TN, became the custodian of the Clinical Care Classification (CCC) System and is continuing the extraordinary legacy of Dr. Virginia K. Saba and the CCC System. HCA Healthcare has implemented the CCC System into the vast majority of the 180 HCA hospitals and is investing time, resources, and commitment to the CCC System. HCA Healthcare is creating a CCC System Expert Panel for the ongoing maintenance and development of the CCC. The CCC is copyrighted to protect the integrity of the terminology and no license is required for CCC System use. The CCC System combined with technology enables nursing collaboration and data sharing within and across healthcare setting to advance nursing practice, education, and research. For future CCC System requests, suggestions, and copyright use, contact HCA Healthcare at https:// careclassification.org/contact. HCA Healthcare is also a

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Appendix • Clinical Care Classification (CCC) System: Overview, Applications, and Analyses  partner in hosting the CCC website at https://careclassification.org.

FRAMEWORK The CCC System consists of two interrelated clinical nursing terminologies: CCC of Nursing Diagnoses and Outcomes and the CCC of Nursing Interventions/Actions, both of which are classified by Care Components that represent one single system (Saba, 2012; Saba & Taylor, 2007). The CCC System consists of a four-level framework that allows the coded nursing data to be aggregated upward as well as parsed downward to atomic level data (concepts) (Saba & McCormick, 2015, p. 835) (Fig. A.1).

First Level The first level of the CCC System’s framework consists of four Healthcare Patterns: (a) Physiological, (b) Psychological, (c) Functional, and (d) Health Behavior. The four Healthcare Patterns are used to categorize the second level of the CCC System which consists of 21 Care Components.

Second Level The second-level Care Component is defined as follows: “A Care Component represents a cluster of data elements that depict four healthcare patterns and represent a holistic approach to patient care” (Saba, 2012, p. 97).The Care Components provide the standardized framework for the electronic processing of a plan of care, which is based on the adaption of the six steps of the nursing process: (1) Signs/Symptoms (Assessment), (2) Diagnosis (Diagnosis), (3) Expected Outcome/Goal (Outcome Identification), (4) Intervention (Planning), (5) Action Type (Implementation), and (6) Actual Outcome (Evaluation) (ANA, 2010).

4 Healthcare Patterns

21 Care Component Classes 176 Diagnoses

804 Interventions

528 Outcomes

201 Interventions x

3 Outcomes

4 Action Types

•  FIGURE A.1.  CCC System Four-Level Framework.

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Third Level The third level consists of the two clinical, CCC Terminologies: (1) nursing diagnoses and (2) nursing interventions. Nursing Diagnoses CCC of 176 Nursing Diagnoses (3rd level) and 528 Outcomes (4th level) (176 Diagnoses—each combined with one of three Outcomes) (Table A.1) Nursing Diagnosis is defined by the ANA as follows: “A clinical judgment about the healthcare’s consumer response to actual or potential health conditions or needs. The diagnosis provides the basis for the determination of a plan to achieve expected outcomes” (ANA, 2010, p. 64).

Nursing Interventions CCC of 201 Nursing Interventions (3rd level) and 804 Actions (4th level) (201 Nursing Intervention—each combined with one of four Action Types) (Table A.2) A Nursing Intervention/Action is defined as follows: “A single nursing action designed to achieve an outcome for a diagnosis (medical/nursing) for which the nurse is accountable” (Saba, 2012, p. 99).

Fourth Level The fourth level represents the concepts used to modify each of the 176 CCC Nursing Diagnosis and each of the 201 Nursing Intervention concepts. The strategy of combining Nursing Diagnosis with an Outcome and Nursing Intervention with an Action Type makes the terminology flexible and expandable. The CCC System is easy to use and straightforward to code, document, classify, retrieve, and analyze patient care. By combining each third-level concept with a fourth-level concept, a new unique concept with a distinct meaning and code is formed. The specificity of the new concept provides new data and, when added to a time factor, can be used to measure actual outcomes, workload, and ultimately cost. Expected and/or Actual Outcomes The three CCC Expected Outcomes are linked to the patient’s Diagnosis or Healthcare Condition and represent the goals of the planned nursing services (also called nursing interventions) and/or medical treatment regime. Actual Outcomes represent the results of the patient/nursing services’ and/or medical treatments of the patient’s Diagnosis or Healthcare Condition. Each of the 176 CCC Nursing Diagnoses must be combined with at least one of the three

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846    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses

  TABLE A.1    Three Outcomes: CCC Nursing Diagnoses Expected Outcomes (Goals) and/or Actual Outcomes Expected Outcome Qualifiers (Goals)

Actual Outcome Qualifiers

To Improve: Change/Resolve

Improved: Changed/ Resolved

To Stabilize: Do Not Change

Stabilized: Did Not Change

To Deteriorate: Worsen/Die

Deteriorated: Worsened/ Died

  TABLE A.2   Four Action Types: CCC Nursing Intervention Action Types Action Types

Definition

Assess or Monitor

Collect, analyze, and monitor data on health status

Perform or Provide Care

Perform a treatment/therapeutic action

Teach or Instruct

Provide knowledge and/or education

Manage or Refer

Coordinate care process

Outcomes totaling 528 unique Expected and/or Actual Outcomes (Table A.1). The comparison of the Expected to an Actual Outcome provides evidence that care was effective. Action Types The coupling of a CCC System Nursing Intervention (also called Nursing Service) with an Action Type represents a unique coded concept or data structure. Each of the four Action Types expands the scope of a single Intervention. Of the CCC Action Types—Assess, Perform, Teach, and Manage—one is always combined with an Intervention. Each Intervention/Action Type unique coupling creates a new specific code. The Action Types provide the measures used (a) to determine status of the care processes and/or (b) the evidence for clinical-decision making. Each Nursing Intervention MUST be coupled with one of the four Action Types totaling 804 CCC of Nursing Interventions Actions (Saba, 2012) (Table A.2). Nursing Intervention Action Types are always required and used to document a nursing service (Intervention Action) for a specific Nursing Diagnosis that represents a patient care service. Nursing Intervention Action Types should NOT be used to document reminders of potential or possible nursing services. Research has indicated that there are generally twice as many Nursing Interventions

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than Nursing Diagnoses. This occurs because more than one Nursing Service is often required to provide care for a single Nursing Diagnosis. In the original CCC research, every study patient listed three to five Nursing Diagnoses with eight to ten Nursing Services (and/or Nursing Interventions) (Saba, 1992). In review, the CCC System framework consists of two clinical nursing terminologies. Each of the two terminologies is classified by Care Components to form one unique system. Together the four levels form the framework for the design, development, and documentation of nursing practice.

CODING STRUCTURE The CCC System uses a five-character alpha-numeric structure to code the concepts of the two terminologies allowing standardized, coded nursing documentation to link and track the patient care processes for an episode of care and/or health condition (Saba, 2012) (Table A.3). The CCC coding structure is based on the alpha-numeric coding structure of the International Statistical Classification of Diseases and Related Health Problems: Tenth Revision: Volume 1: ICD-10 (WHO, 1992). The CCC coding structure could be empirically applied to represent a Statistical Classification of Nursing Practice.

NURSING PLAN OF CARE Inpatient Care The typical admission of a patient to a hospital or healthcare facility is examined and assessed by the admitting nurse and admitting physician. Both healthcare professionals write patient care orders for medical treatments and/or nursing services during the hospitalization which are used to develop a Nursing Plan of Care (POC). The patient’s plan of care with the medical and/or nursing orders are converted into executable ones and are input into the computer by time and frequency. These orders are carried out during the hospital stay by nursing and other professional staff and are documented electronically in an EHR. A nursing POC of coded patient care data processed and/or stored generates descriptive data and information. The POC can also be used to provide data on the basic frequencies of care or used for other basic analyses. The frequencies/data can be aggregated to generate acuity, workload, care requirements, and ultimately nursing cost.

Sample Case Study The Sample Case Study illustrates an integrated Nursing Plan of Care that documents the “essence of care” using

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Appendix • Clinical Care Classification (CCC) System: Overview, Applications, and Analyses 

  TABLE A.3   Five Alpha-Numeric Characters Coding Structure for CCC System: Nursing Diagnoses, Intervention Action Types, and Outcomes Code Position

Alpha/Numeric

Level Name

Example

First

Alpha Character

Care Component

A to U

Second/Third/Fourth

Three-Numeric Digits (two digits and one decimal point)

a. Nursing Diagnoses or b. Nursing Interventions

01.0

Fifth

One Numeric Digit

Action Type or Outcome

01.0.1

Completed CCC code

Five Alpha-Numeric Characters

a. Diagnosis/Outcome or b. Intervention/Action Type

A01.0.1

the CCC System’s nursing terminologies and follows the six steps of the CCC System Information Model (Fig. A.2). Case Study A postsurgical patient is admitted from the emergency room with a Medical Diagnosis of an Abdominal Wound. The admitting nurse assessment includes several assessed signs and symptoms which represent the Care Component—“Skin Integrity.” The admitting nurse also develops an integrated nursing POC by combining the medical orders with the nursing orders and inputting the orders into the EHR using the CCC System terminologies. The orders are used to carry out the nursing services for each of the three inpatient hospitalized days for the patient’s episode of illness. On the fourth day, the patient was discharged and a summary of the data elements for each of the three days were generated as output with descriptive frequencies, percents, and other analytics.

SAMPLE PLAN OF CARE DESCRIPTION The Sample Plan of Care discussed above illustrates the six steps of the CCC Information Model as well as other information that supports the POC. The sample includes (a) Assessed Signs and Symptoms (S/S), (b) Nursing Diagnoses, each with its Care Component determined from S/S, (c) Expected Outcomes/Goals for the Nursing Diagnoses, (d) List of Actual Medical and Nursing Orders for patient care services, (e) Converted medical/ nursing orders to the coded nursing services, including when and how administered, and (f ) Actual Outcomes of Nursing Diagnoses. Data are also generated to provide (g) Evidence that measures Patient Outcomes, and (h) Action Type Encounters that generate nursing services for analysis. The retrieved coded POC data elements are used to generate coded output for review and analysis as well as

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descriptive and inferential analytics. The coded data elements generate nursing services frequency data that can be applied to measure care outcomes. The data elements are also data available for analytic tests to describe the scope of the nursing services which, when coupled with workload and care requirement formulas, create nursing service algorithms and protocols. The CCC-coded nursing services are summarized on discharge and the coded data aggregated and analyzed for the episode of illness. The coded data are mapped to SNOMED-CT producing reference terminology data available for information exchange with other healthcare facilities and/or professional entities for the continuity of care. Aggregated coded nursing data can be studied through descriptive and inferential statistics tests such as t-tests, analysis of variance (ANOVA), chi-square, Tukey– Kramer, Dunn’s, and others describe or summarize for a larger sample or population.

SAMPLE CASE STUDY ANALYSES Data Elements A summary of the nursing services for the “Assessed Skin Integrity” for an Abdominal Wound is presented in Table A.4. The table lists four Nursing Diagnoses by CCC code for “Skin Integrity” (Skin Incision, Infection, Pain, and Anxiety) and illustrates the patient’s three inpatient days of nursing services. The patient received individualized care with six unique Nursing Interventions each with an Action Type creating a specific code. The Action Types included four Perform Action Type (.2), one Assess Action Type (.1), and one Teach Action Type (.3).

Diagnoses and Interventions/Action Type Data The Nursing Services were performed by the nurses as ordered (three and/or four times each day), totaling 48 nursing encounters with the patient for

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CCC Assessed Signs and Symptoms

CCC Care Component Nursing Diagnosis/ Code

• Wound: Draining • Odor: Foul • Wound exudates: Purulent

Skin Integrity: R Skin Incision: R46.4

CCC Expected Outcome/ Goal

R46.4.1 To Improve Skin Incision

• Wound: Open

• Small amount of purulent drainage

Physical K25.6.2 Regulation: K To Stabilize Infection: K25.6 Infection

Medical/ Nursing Orders

Conversion of Orders into CCC Intervention and CCC Action Types

CCC Actual Outcome/ Evaluation

Irrigate with Normal Saline: – q8h.

Perform Wound Care: R55.0.2 • 3 × per day × 3 days

R46.4.1 Skin Incision/ Wound Improved

Lightly pack wound bed – q8h

Perform Dressing Change R55.2.2 • 3/ day × 3 days

Check for increased drainage – q8h

Assess Infection Control: K30.0.1 • 3/day × 3 days

Give Cefazolin (antibiotic) –q8h

Perform Medication Treatment: H24.4.2 • 3/day × 3 days

K25.6.2 Infection Stabilized

Patient Outcome Evidence

Action Type Encounters

• Wound: No drainage • Odor: None • Wound exudates: None

Performed Wound Care: 3/day × 3 days = 9

Wound: Closed

Performed Dressing Change: 3/day × 3 days = 9

• No purulent drainage

Assessed Infection Control: 3/ day × 3 days = 9 Performed Medication Treatment: 3/ day × 3 days = 9

• Pain Scale—10

Sensory: Q Pain: Q63.0

Q63.0.1 To Improve Pain

Administer Pain Medication when Pain Scale > 3 –q8h

Perform Pain Control: Q47.0.2 • 3/day × 3 days

Q63.0.2 Pain Stabilized

Pain scale result: 0

Assessed Pain Control: 3/day × 3 days = 9

• Affect: Worried

Self-Concept: P Anxiety: P40.4

P40.4.1 To Improve Anxiety

Check patient well-being q-day

Teach Mental Health Promotion: P45.2.3 1 x per day x 3 days

P40.4.1 Anxiety Improved

Patient no longer anxious

Teach Mental Health Promotion: 1/day × 3 days = 3

Note: Column headings starting and/or using CCC represents one of the six steps in the Information Model; whereas the other columns support and expand the CCC findings.

•  FIGURE A.2.  Nursing Plan of Care for an Inpatient with Skin Integrity for an Abdominal Wound the entire hospitalization (Table A.4). The frequency of Intervention Action Types can be used, with validation, to predict a nursing protocol of care for patient populations with the same or similar condition requiring nursing services (interventions). The frequencies of the Intervention Action Types on discharge provides evidence-based research for a nursing protocol to depict the optimal interventions and action types for patient care.

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Frequency Data In the sample, there were nine nursing encounters to “Perform Wound Care” and nine encounters to “Perform Dressing Change” for the Nursing Diagnosis—“Skin Incision.” The nursing services frequency data for the Skin Integrity for an Abdominal Wound during the three days of inpatient care are shown in Table A.4 and Table A.5. The Total Number of Nursing Interventions and Action

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  TABLE A.4   Frequencies of Nursing Intervention Action Types for a Patient with Nursing Diagnoses: Skin Incision, Infection, Pain, and Anxiety for a Three-Day Hospitalization Nursing Diagnoses

Intervention/Action Type

Intervention/Action Type Code

Skin Incision (R46.4)

Perform—Wound Care

R55.0.2

Perform—Dressing Change

R55.2.2

Total Skin Incision

2

Infection (K25.6)

Assess—Infection Control

K30.0.1

9

Perform—Medication Treatment

H24.4.2

9

Total Infection

2

Pain (Q63.0)

Perform—Pain Control

Total Pain

1

Anxiety (P40.0)

Teach Mental Health Promotion

Encounters by Intervention/Action Type 9 9 18

18 Q47.0.2

9 9

P45.2.3

3

Total Self-Concept

1

3

GRAND TOTALS: Total = 4

Total = 6

Total = 48

Types for each Nursing Diagnosis provides an EvidenceBased Protocol. The CCC evidence of nursing services may support an evidenced-based protocol about the effectiveness of the frequency of dressing changes. An evidence-based protocol based on frequency information may promote faster wound healing and could contribute to identifying the optimal frequency of wound dressings to prevent infection.

  TABLE A.5   Frequency Number and Percent of Intervention Action Types for a Three   Day Hospitalization of a Patient with Skin Integrity for an Abdominal Wound Frequency Number

Frequency Percentage

Assess

9

18.8%

Perform

36

75.0%

Action Type Data

Teach

3

6.3%

Action Types data are also listed by Number and Type and provide descriptive measures for the four Nursing Diagnoses in the POC. The Nursing Interventions’ four Action Types were analyzed separately from the POC data for the inpatient hospitalization (Table A.5). The frequencies of the nurse encounters with the patient during the three-day inpatient stay were as follows: “Assess” S/S—9 nursing encounters or 18.8%, “Perform” a nursing care service—36 encounters or 75%, “Teach” a specific process or procedure—3 encounters or 6.3%, and “Manage” no encounters required to provide indirect care on behalf of the patient. The data are illustrated in Fig. A.3.

Manage

0

0.0%

TOTAL

48

100%

Expected Outcome (Goal) and Actual Outcome Data A comparison of the Expected Outcomes (Goals) to Actual Outcomes is shown in Table A.6. The Outcome data demonstrate the Impact and Value of the Nursing Services. Two of the four Nursing Diagnoses “Skin Incision” and

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Intervention Action Type

“Anxiety” were “Improved” as Actual Outcomes. While each Nursing Diagnosis Expected Outcome (Goal) was “To Improve,” the Actual Outcome measured a 50% improvement for the patient’s Nursing Diagnoses. The Nursing Diagnoses “Infection” and “Pain” indicated care must be continued following hospitalization with the Actual Outcome of the “Infection” and “Pain” identified as “Stabilized.” With a coded plan of care nurses will have the “evidence-based” indicators to support and verify their empirical observations. To summarize, the CCC data elements analyzed as frequencies will provide a variety of information for nursing staff, nursing administrators, and other administrative stakeholders. And, if frequency data is coupled with other statistical formulas, coded nursing data can provide

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850    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses Nursing Care by Intervention Action Type Manage Action 0% Teach Action 6%

Assess Action 19%

Assess Action Perform Action Teach Action Manage Action

(Whittenburg, Lekdumrongsak, Klaikaew, & Meetim, 2017). As the world moves toward the global sharing of health information, nurses are able to exchange nursing data and use nursing concepts to synthesize data to improve patient care. Gordon (2005) noted that clinical documentation may be the most critical factor in a patient’s treatment and recovery and understanding the impact of nursing care on patient outcomes may be the key to improving care quality in healthcare systems.

Description Perform Action 75%

•  FIGURE A.3.  Frequency and Percent of Nursing Intervention Action Types for a Three-Day Hospitalization of a Patient with Skin Integrity for an Abdominal Wound.

new knowledge and other analytics. An example of a new analytic method is described below: Workload Actions Measures Method (WAMM©).

WORKLOAD AND CARE REQUIREMENTS Workload Actions Measures Method (WAMM©) When EHRs integrate the CCC System into multidisciplinary and intraprofessional care plans, CCC Nursing Intervention/Action Types may be used to determine nursing Workload Measures and Care Requirements and, ultimately, basic Nursing Cost. Research has shown obtaining an accurate Workload Measure requires not only CCC Action Types but also combining the Action Types with the “Time” to perform the Action Type. This finding is coupled with a measure and intensity of health status or healthcare condition of the patient to obtain the projected Workload Measure (Bureau of Health Manpower, Division of Nursing DHEW, 1978). A new technological solution called the Workload Actions Measures Method (WAMM©) was developed to consistently and systematically measure Workload. The method uses the nursing documentation of a POC with the coded, standardized nursing terminology of the CCC System in an EHR. For example, a patient with a specific healthcare condition such as the Coronavirus (COVID19) during an episode of illness with coded documentation allows for the exchange of data for continuity of care

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The WAMM© is designed to generate valid and reliable data for a specific patient for a specific time frame (shift or day) for an episode of illness. The WAMM© is based on two unique clinical values: “care value” and “acuity value.” The first is the clinical “care value,” which is a weighted relative value unit (RVU) based on the frequency and summary of CCC Nursing Services (Intervention Action Type) encounters’ total actual “time” aggregated to carry out the Nursing Intervention Action Types. The second is the clinical “acuity value” derived from a weighted “indicator” (formula/ measure that represents the care requirements for the patient’s healthcare condition during an episode of illness. These two clinical values are derived from POC data sets that are collected separately and coupled together to provide Workload Time and Care Requirements. 

Care Value The first clinical “care value” is a weighted Relative Value Unit (RVU) based on the frequency and summary of nurse–patient Action Type encounters “TIME” for all the CCC Nursing Interventions (Services) Care (Actions). The “TIME” for the documented encounters (estimated or actual) is aggregated for an entire episode of illness and applied to a pre-established Relative Value Unit (RVU) formula to obtain the nursing RVU or “care value.” Relative Value Unit (RVU) The pre-established Relative Value Units (RVUs) used for determining the “care value” were designed for clinician use. The RVUs are a coded listing of interactive healthcare services, interventions, or procedures with unit values to indicate the relative effort of each service provided to a patient. The RVUs were initially developed by RVS Inc. (RVSI) by grouping the Nursing Intervention Action Types from another nursing study to address the actual cost of patient care for physician “Pay for Performance” services to Medicaid patients (ABC Codes, 2019; Painter & FitzGerald, 1980). The Action Type values were averaged to determine relative values. Each RVU value is adapted for use by a survey

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  TABLE A.6   Comparison of Expected Outcomes (Goals) to Actual Outcomes for Four Nursing Diagnoses for a Patient with Skin Integrity for an Abdominal Wound Nursing Diagnoses

Expected Outcomes (Goals)

Expected Outcome Code

Number of Intervention Action Types

Actual Outcomes

Actual Outcome Code

Skin Incision (R46.4)

To Improve (.1)

R46.4.1

2

Condition Improved (.1)

R46.4.1

Infection (K25.6)

To Improve (.1)

K25.6.1

2

Condition Stabilized (.2)

K25.6.2

Pain (Q63.0)

To Improve (.1)

Q63.0.1

1

Condition Stabilized (.2)

Q63.0.2

Anxiety (P 40.0)

To Improve (.1)

P40.0.1

1

Condition Improved (2.)

P40.0.1

Physiological Pattern: Represents the Indicator for Physical Body Systems Serves as—Major Complex/Intensive “acuity value”: Includes Nursing Diagnoses in the Bowel/Gastric, Cardiac, Metabolic, Physical Regulation, Respiratory, Skin Integrity, Tissue Perfusion, Urinary, or Life Cycle—Care Components Psychological Pattern: Represents the Indicator for Mental Health Conditions Serves as—Above Average/Moderate “acuity value”: Includes Nursing Diagnoses in the Cognitive/Neuro, Coping, Role Relationship, or Self Concept—Care Components Functional Pattern: Represents the Indicator for Daily Living Conditions Serves as—Average “acuity value”: Includes Nursing Diagnoses in the Activity, Fluid Volume, Nutritional, Self-Care, or Sensory—Care Components Health Behavior Pattern: Represents the Indicator for Specialized Approaches to Patient Care Conditions Serves as—Minimal “acuity value”: Includes Nursing Diagnoses in the Medication, Safety, or Health Behavior—Care Components

•  FIGURE A.4.  Four Healthcare Pattern Indicators for “Acuity Value.” (From Saba, V. K. (2007). Clinical Care Classification (CCC) System manual: A guide to nursing documentation (p. 155). New York, NY: Springer; Saba, V. K. (2012). Clinical Care Classification (CCC) System: Version 2.5 (2nd ed., p. 89). New York, NY: Springer.)

of providers and based on five factors: (a) time, (b) skill, (c) risk to the patient, (d) risk to the nurse, and (e) severity of illness. These findings led to the RVU formula for WAMM© to determine the estimated Workload hours and/or minutes (time).

Acuity Value The second clinical is an “acuity value,” a weighted Base Value Unit (BVU) which is based on the evidence-based “indicator” representing the healthcare condition of a patient requiring hospital, clinic, and/or nursing services. The BVU “indicator” represents the degree of care required for the patient’s physical condition or healthcare status. The “indicator” is derived from an evidence-based Nursing Diagnosis that describes the patient’s healthcare condition and is classified by one of four Healthcare Patterns. The “indicator” is applied to a pre-established

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Base Value Unit (BVU) formula to obtain the patient’s BVU “acuity value.” Base Value Unit The pre-established Base Value Unit (BVU) used to determine the “Acuity Value” is based on the four CCC Healthcare Patterns: Physiological, Psychological, Functional, and/or Health Behavior (Fig. A.4). The Healthcare Patterns are four “indicators” which were derived from a research study and identified as classifying the patient’s assessed Nursing Diagnoses (Bureau of Health Manpower, Division of Nursing DHEW, 1978; Saba, 1992). Each of the 176 CCC Nursing Diagnosis represents a specific “level of care” based on the patient’s specific signs and symptoms. Each diagnosis is represented by one of the four Healthcare Patterns and one of 21 Care Components as an indicator of the specific level of care (Fig. A.5). Listed below is a description of the four CCC Healthcare Patterns, Care Components, and Nursing

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852    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses CCC System Framework 4 Care Patterns Health Behaviors

Psychological

Medication Safety Health Behavior

Cognitive Coping Role Relationship Self-Concept

Functional

Activity Fluid Volume Nutritional Self-Care Sensory

21 Care Components

Physiological

Cardiac Respiratory Metabolic Physical Regulation Skin Integrity Tissue Perfusion Bowel/Gastric Urinary Life Cycle

•  FIGURE A.5.  CCC System Framework’s Relationships between Healthcare Patterns, Care Components, and Nursing Diagnoses for Determination of Base Value Unit (BVU). (From Saba, V. K. (2012). Clinical Care Classification (CCC) System: Version 2.5 (2nd ed., p. 7). New York, NY: Springer.)   TABLE A.7   Example of WAMM© Number and Percent, Estimated Time (Minutes), RVU Score, and “Care Value” for Four Action-Type Nursing Service Encounters for Episode Action Type

Count

Frequency Percent

Assess

36

35%

Perform

43

42%

Estimated Time

RVU Score Time/Minutes

Care Value (h)

6.50 × RVU

31.20

0.52

16.25 × RVU

207.03

3.45

Teach

12

12%

4.75 × RVU

5.89

0.10

Manage

11

11%

18.50 × RVU

25.90

0.43

102

100%

Total Minutes X

207.02

4.5

SUMMARY

Diagnosis that represent the level of care “indicator” required by the patient. With the clinical RVU “care value” and the BVU “acuity value” established, the final method step is the coupling of the two sets of nursing service values to correlate the relationship between the “care value” and BVU “acuity value.” Together, the “care value” and “acuity value” provide an aggregated “time” Workload (hours/minutes) of all the Nursing Interventions (Services) Action Types (Care) that were administered or carried out by nurses for a specific patient, time, and shift during the episode of illness (Tables A.7 and A.8). To summarize, the Workload Actions Measures Method (WAMM©) uses two nursing clinical values, the RVU “care value” and the BVU “acuity value” coupled sequentially to achieve quantifying a specific healthcare condition to determine the nursing workload and care

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requirements for a patient’s episode of illness (Saba, 1988, 2012). Ultimately, once the workload “times” for patients with the same diagnosis and/or medical/ physical conditions have been validated, the “care values” can be used to predict the WAMM© “care value” (workload “time”) and “care requirements” needed for future patients with similar healthcare conditions. The “care values” and “acuity values” can also be used to calculate the “Costs” of the nursing services for patients with similar healthcare conditions using nursing salary information (Dykes, Wantland, Whittenburg, Lipsitz, & Saba, 2013). The Workload Actions Measures Method (WAMM©) is innovative in any setting, including home, across all nurse specialties and considered to be universally applicable for any patient, at any age, with any clinical diagnosis, and on any point on the health continuum.

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  TABLE A.8    “Acuity Value” Example of WAMM©: Four Healthcare Patterns for BVU Acuity Value Formula Healthcare Pattern

Care Component Name

Care Component Class

Interventions

Physiological

Skin Integrity

R

Perform Wound Care

Physiological

Skin Integrity

R

Perform Dressing Change

Physiological

Physical Regulation

K

Assess Infection Control

Health Behaviors

Medication

H

Perform Medication Treatment

Functional

Sensory

Q

Perform Pain Control

Psychological

Self-Concept

P

Teach Mental Health Promotion

“READY-TO-USE” GUIDE The “Ready-to-Use” Guide (Fig. A.6) provides the coded data elements used to document a Nursing Plan of Care (POC) following the CCC System Information Model. The CCC terminologies are unified as a single system to document and code the “essence of care” and track the documentation of the care process in all care settings. The Care Component links the CCC of Nursing Diagnoses and Outcomes and the CCC of Nursing Interventions/ Actions. The linkages help developers of an EHR to consider suggested Diagnoses and/or Interventions based on assessed signs and symptoms. The three Expected Outcomes and the four Action Types are listed in the headings for ease in combining Outcomes with an appropriate Diagnosis and combining Action Types with an Intervention.

Implications The CCC-coded data, retrieved and analyzed, may be used to predict the ”care time” “workload time” for nurses and other healthcare professionals. With the use of the CCC System, nurses have the quantitative “care time” evidence (shown as 4.5 hours estimated in Table A.7) for the care of a patient with a Nursing Diagnosis—“Skin Incision” for an Abdominal Wound. This standardized data are needed to measure the workload relationship of Nursing Intervention Actions to the nurse’s impact on patient care for evidence-based practice using a scientific foundation. The CCC codes applied as “Severity of Illness” and/ or “Intensity of Service” indicators can be calculated to determine the time and level of nursing care required by an individual patient. Once a common measure of nursing is established, the CCC System can be used to calculate the values—“care value” and “acuity value”—for each nursing service. As the nation moves toward fully operational health information record systems, nurses must know and understand how nursing care “Action Types” relate to other electronic data elements in the synthesis of information to improve patient care. The use of the CCC Systems gives nursing the data urgently needed to generate the nursing evidence to improve patient care and healthcare outcomes. The CCC

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System nursing terminology standard is an integrator to achieve the exchange of nursing outcomes, resources, and workload. For diverse health systems and patient care settings, the CCC System is an interoperability standard for the analysis of nursing and ancillary healthcare data to advance care coordination, continuity, patient safety, and the documentation of professional care quality. As a nurse begins to develop a POC based on the identification of the CCC Nursing Diagnosis derived from the patient’s Assessed Signs and Symptoms and guided by the Care Component, each selected Nursing Diagnosis requires a Care Goal or Expected Outcomes. Each Nursing Diagnosis also requires the section of one or more Nursing Interventions from the list of CCC of Nursing Interventions/Actions. Nursing Interventions do not exist without a Nursing Diagnosis. Without a Nursing Diagnosis there is no logical rationale for implementing a Nursing Intervention following the Nursing Process. The Ready-to-Use Guide supports the identification of Nursing Interventions/Action Types for nursing and interprofessional patient care. The selection of Nursing Interventions/Actions is repeated for each identified Nursing Diagnosis in the POC for a given patient. Once a POC is developed, nurses use the POC to document the care provided by selecting the planned/implemented Interventions/Actions at each encounter during each shift

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854    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses

FIGURE A.6

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b.

Care Components (A–U)

Nursing Diagnoes and Outcomes

Nursing Interventions/Actions

Coding structure consists of

Expected Outcomes and Actual Outcomes To improve (.1) or improved (.1) To stabilize (.2) or stabilized (.2) Deterioration (.3) or deteriorated (.3)

Nursing Intervention—Action Types Assess or monitor (.1) Perform or care (.2) Teach or instruct (.3) Manage or refer (.4)

Example: Expected Outcome: Activity Alteration—To Improve—A01.0.1 Example: Actual Outcome: Activity Alteration Improved—A01.0.1

Example: Assess Activity Care—A01.0.1 Example: Perform Activity Care—A01.0.2 Example: Teach Activity Care—A01.0.3 Example: Manage Activity Care—A01.0.4

Activity Alteration—A01.0

Activity Care—A01.0 Activities performed to carry out physiological or psychological daily activities Energy Conservation—A01.2 Actions performed to preserve energy Fracture Care—A02.0 Actions performed to control broken bones Cast Care—A02.1 Actions performed to control a rigid dressing Immobilizer Care—A02.2 Actions performed to control a splint, cast, or prescribed bed rest Mobility Therapy—A03.0 Actions performed to advise and instruct on

First—A to U: CC Second/third—major category Fourth—subcategory

A. Activity Component Cluster of elements that involve the use of energy in carrying out musculoskeletal and bodily actions

Activity Intolerance—A01.1 Incapacity to carry out physiological or psychological daily activities Activity Intolerance Risk—A01.2 Increased chance of an incapacity to carry out physiological or psychological daily activities Lack of interest or engagement in leisure activities Fatigue—A01.4 Exhaustion that interferes with physical and mental activities Physical Mobility Impairment—A01.5 Diminished ability to perform independent movement Sleep Pattern Disturbance—A01.6 Imbalance in the normal sleep/wake cycle Sleep Deprivation—A01.7 Lack of a normal sleep/wake cycle Musculoskeletal Alteration—A02.0 or support structures

Ambulation Therapy—A03.1 Actions performed to promote walking Assistive Device Therapy—A03.2 Actions performed to support the use of products to aid in caring for oneself Transfer Care—A03.3 Actions performed to assist in moving from one place to another Sleep Pattern Control—A04.0 Actions performed to support the sleep/wake cycle Musculoskeletal Care—A05.0 Actions performed to restore physical functioning Range of Motion—A05.1 Actions performed to provide active and passive exercises to maintain joint function Rehabilitation Exercise—A05.2 Actions performed to promote physical functioning Bedbound Care—A61.0 Actions performed to support an individual conPositioning Therapy—A61.1 Process to support changes in body positioning Diversional Care—A77.0 Actions performed to support interest in leisure activities or play

(continued)

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Appendix • Clinical Care Classification (CCC) System: Overview, Applications, and Analyses 

FIGURE A.6

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

B. Bowel/Gastric Component Cluster of elements that involve the gastrointestinal system

Bowel Elimination Alteration—B03.0

Bowel Care—B06.0 Actions performed to control and restore the functioning of the bowel Bowel Training—B06.1 Actions performed to provide instruction on bowel elimination conditions Disimpaction—B06.2 Actions performed to manually remove feces Enema—B06.3

system Bowel Incontinence—B03.1 Involuntary defecation Diarrhea—B03.3 Fecal Impaction—B03.4 Feces wedged in intestines Perceived Constipation—B03.5 of hard, dry feces without cause Constipation—B03.6

Diarrhea Care—B06.4 Actions performed to control the abnormal Bowel Ostomy Care—B07.0

Gastrointestinal Alteration—B04.0 intestines Nausea—B04.1

C. Cardiac Component Cluster of elements that involve the heart and blood vessels

ing that removes bowel waste products Bowel Ostomy Irrigation—B07.1

Vomiting—B04.2 Expulsion of stomach contents through

waste products Gastric Care—B62.0 Actions performed to control changes in the stomach and intestines Nausea Care—B62.1 Actions performed to control the distaste for food and desire to vomit

Cardiac Output Alteration—C05.0

Cardiac Care—C08.0 Actions performed to control changes in the heart or blood vessels Cardiac Rehabilitation—C08.1 Actions performed to restore cardiac health Pacemaker Care—C09.0 Actions performed to control the use of an electronic device that provides a normal heartbeat

of the heart Cardiovascular Alteration—C06.0 vessels Blood Pressure Alteration—C06.1 diastolic pressure Bleeding Risk—C06.2 Increased chance of loss of blood volume

D. Cognitive/Neuro Component Cluster of elements involving the cognitive, mental, cerebral, and neurological processes

Cerebral Alteration—D07.0 Confusion—D07.1 State of being disoriented (mixed up) Lack of information, understanding, or comprehension

Behavior Care—D10.0 Actions performed to support observable responses to internal and external stimuli Reality Orientation—D11.0 Actions performed to promote the ability to locate oneself in an environment

Test—D08.1 Lack of information on test(s) to identify disease or assess health condition

(continued)

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FIGURE A.6

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

Regimen—D08.2 Lack of information on the prescribed diet/ food intake Process—D08.3 Lack of information on the morbidity, course, or treatment of the health condition

Wandering Control—D63.0 Actions performed to control abnormal movability Memory Loss Care—D64.0 Actions performed to control a person’s inability to recall ideas and/or events Neurological System Care—D78.0 Actions performed to control problems of the neurological system

Volume—D08.4 requirements Regimen—D08.5 Lack of information on prescribed regulated course of medicinal substances Precautions—D08.6 Lack of information on measures to prevent injury, danger, or loss Regimen—D08.7 Lack of information on regulated course of treating disease Thought Process Alteration—D09.0 tive processes Memory Impairment—D09.1 Diminished ability or inability to recall E. Coping Component Cluster of elements that involve the ability to deal with responsibilities, prob-

Dying Process—E10.0 Physical and behavioral responses associated with death Community Coping Impairment—E52.0 Inadequate community response to problems or Family Coping Impairment—E11.0 Inadequate family response to problems or Disabled Family Coping—E11.2 Inability of family to function optimally Individual Coping Impairment—E12.0 Inadequate personal response to problems or Adjustment Impairment—E12.1 Inadequate adjustment to condition or change in health status Struggle related to determining a course

Counseling Service—E12.0 Actions performed to provide advice or instruction to help another Coping Support—E12.1 Actions performed to sustain a person dealing with responsibilities, problems, or Stress Control—E12.2 Actions performed to support the physiological response of the body to a stimulus Crisis Therapy—E12.3 Actions performed to sustain a person dealing with a condition, event, or radical Emotional Support—E13.0 Actions performed to maintain a positive Spiritual Comfort—E13.1 Actions performed to console, restore, or promote spiritual health

(continued)

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Appendix • Clinical Care Classification (CCC) System: Overview, Applications, and Analyses 

FIGURE A.6

  857

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

Defensive Coping—E12.3 Self-protective strategies to guard against threats to self Denial—E12.4 Attempt to reduce anxiety by refusal to accept thoughts, feelings, or facts Post-trauma Response—E13.0 Sustained behavior related to a traumatic event Rape Trauma Syndrome—E13.1 Group of symptoms related to a forced sexual act Spiritual State Alteration—E14.0

Terminal Care—E14.0 Actions performed in the period surrounding death Bereavement Support—E14.1 Actions performed to provide comfort to the family/friends of the person who died Dying/Death Measures—E14.2 Actions performed to support the dying process Funeral Arrangements—E14.3 Actions performed to direct the preparation

Spiritual Distress—E14.1 Anguish related to the spirit or soul Grieving—E53.0 Feeling of great sorrow Anticipatory Grieving—E53.1 Feeling great sorrow before the event or loss Dysfunctional Grieving—E53.2 Prolonged feeling of great sorrow F. Fluid Volume Component Cluster of elements that involve liquid consumption

Fluid Volume Alteration—F15.0

Increased chance of dehydration or Fluid Volume Excess—F15.3 Fluid retention, overload, or edema Fluid Volume Excess Risk—F15.4 or edema Electrolyte Imbalance—F62.0 Higher or lower body electrolyte levels

Fluid Therapy—F15.0 Actions performed to provide liquid volume intake Hydration Control—F15.1 balance Intake—F15.3 Actions performed to measure the the body Output—F15.4 Actions performed to measure the the body Hemodynamic Care—F79.0 Actions performed to support the movement of solutions through the blood Intravenous Care—F79.1 Actions performed to support the use of infusion equipment Venous Catheter Care—F79.2 Actions performed to support the use of a venous infusion site Arterial Catheter Care—F79.3 Actions performed to support the use of an arterial infusion site

(continued)

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858    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses

FIGURE A.6

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

G. Health Behavior Component Cluster of elements that involve actions to sustain, maintain, or regain health

Health Maintenance Alteration—G17.0

Community Special Services—G17.0 Actions performed to provide advice or information about special community services Adult Day Center—G17.1 Actions performed to direct the provision of a

health-related needs. Failure to Thrive—G17.1 Inability to grow and develop normally Health-Seeking Behavior Alteration—G18.0 improve health state Home Maintenance Alteration—G19.0 Inability to sustain a safe, healthy environment Noncompliance—G20.0 Failure to follow therapeutic recommendations Noncompliance of Diagnostic Test—G20.1 Failure to follow therapeutic recommendations on tests to identify disease or assess health condition Noncompliance of Dietary Regimen—G20.2 Failure to follow the prescribed diet/food intake Noncompliance of Fluid Volume—G20.3 requirements Noncompliance of Medication Regimen—G20.4 Failure to follow prescribed regulated course of medicinal substances Noncompliance of Safety Precautions—G20.5 Failure to follow measures to prevent injury, danger, or loss Noncompliance of Therapeutic Regimen—G20.6 Failure to follow regulated course of treating disease or health condition

Hospice—G17.2 Actions performed to support the provision nally ill persons Meals on Wheels—G17.3 Actions performed to direct the provision of a community program of delivering meals to the home Compliance Care—G18.0 Actions performed to encourage adherence to care regimen Compliance with Diet—G18.1 Actions performed to encourage adherence to diet/food intake Compliance with Fluid Volume—G18.2 Actions performed to encourage adherence to therapeutic intake of liquids Compliance with Medical Regimen—G18.3 Actions performed to encourage adherence to physician’s/provider’s treatment plan Compliance with Medication Regimen—G18.4 Actions performed to encourage adherence to prescribed course of medicinal substances Compliance with Safety Precaution—G18.5 Actions performed to encourage adherence with measures to protect self or others from injury, danger, or loss Compliance with Therapeutic Regimen— G18.6 Actions performed to encourage adherence with plan of care Nursing Contact—G19.0 Actions performed to communicate with another nurse Bill of Rights—G19.1 Statements related to entitlements during an episode of illness Nursing Care Coordination—G19.2 Actions performed to synthesize all plans of care by a nurse Nursing Status Report—G19.3 Actions performed to document patient condition by a nurse

(continued)

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Appendix • Clinical Care Classification (CCC) System: Overview, Applications, and Analyses 

FIGURE A.6

  859

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions Physician Contact—G20.0 Actions performed to communicate with a physician/provider Medical Regimen Orders—G20.1 Actions performed to support the physician’s/ provider’s plan of treatment Physician Status Report—G20.2 Actions performed to document patient condition by a physician/provider Professional/Ancillary Services—G21.0 Actions performed to support the duties performed by health team members Health Aide Service—G21.1 Actions performed to support care services by a health aide Social Worker Service—G21.2 Actions performed to provide advice or instruction by a social worker Nurse Specialist Service—G21.3 Actions performed to provide advice or instruction by an advanced practice nurse or nurse practitioner Occupational Therapist Service—G21.4 Actions performed to provide advice or instruction by an occupational therapist Physical Therapist Service—G21.5 Actions performed to provide advice or instruction by a physical therapist Speech Therapist Service—G21.6 Actions performed to provide advice or instruction by a speech therapist Respiratory Therapist Service—G21.7 Actions performed to provide advice or instruction by a respiratory therapist

H. Medication Component Cluster of elements that involve medicinal substances

Medication Risk—H21.0 Increased chance of negative response to medicinal substances Polypharmacy—H21.1 Use of two or more drugs together

Chemotherapy Care—H22.0 Actions performed to control and monitor antineoplastic agents Injection Administration—H23.0 Actions performed to dispense medication by a hypodermic Medication Care—H24.0 Actions performed to support use of prescribed drugs or remedies regardless of route Medication Actions—H24.1 Actions performed to support and monitor the intended responses to prescribed drugs Actions performed to ensure the continued supply of prescribed drugs

(continued)

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860    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses

FIGURE A.6

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions Actions performed to control adverse untoward reactions or conditions to prescribed drugs Medication Treatment—H24.4 Actions performed to administer/give drugs or remedies regardless of route Radiation Therapy Care—H25.0 Actions performed to control and monitor radiation therapy

I. Metabolic Component Cluster of elements that involve the endocrine and immunologic processes

J. Nutritional Component Cluster of elements that involve the intake of food and nutrients

Endocrine Alteration—I22.0 or hormones Immunologic Alteration—I23.0

Nutrition Alteration—J24.0 Less than adequate intake or absorption of food or nutrients Increased chance of less than adequate intake or absorption of food or nutrients Body Nutrition Excess—J24.3 More than adequate intake or absorption of food or nutrients Body Nutrition Excess Risk—J24.4 Increased chance of more than adequate intake or absorption of food or nutrients Swallowing Impairment—J24.5 Inability to move food from mouth to stomach Infant Feeding Pattern Impairment—J54.0 Imbalance in the normal feeding habits of an infant Breastfeeding Impairment—J55.0 Diminished ability to nourish infant at the breast

Allergic Reaction Care—I26.0 Actions performed to reduce symptoms or precautions to reduce allergies Diabetic Care—I27.0 Actions performed to support the control of diabetic conditions Immunologic Care—I65.0 Actions performed to protect against a particular disease Enteral Tube Care—J28.0 Actions performed to control the use of an enteral drainage tube Enteral Tube Insertion—J28.1 Actions performed to support the placement of an enteral drainage tube Enteral Tube Irrigation—J28.2 enteral tube Nutrition Care—J29.0 Actions performed to support the intake of food and nutrients Feeding Technique—J29.2 Actions performed to provide special measures to provide nourishment Regular Diet—J29.3 Actions performed to support the ingestion of food and nutrients from established nutrition standards Special Diet—J29.4 Actions performed to support the ingestion of food Enteral Feeding—J29.5 Actions performed to provide nourishment through a gastrointestinal route Parenteral Feeding—J29.6 Actions performed to provide nourishment through intravenous or subcutaneous routes Breastfeeding Support—J66.0 Actions performed to provide nourishment of an infant at the breast Weight Control—J67.0 Actions performed to control obesity or debilitation

(continued)

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FIGURE A.6

  861

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

K. Physical Regulation Component Cluster of elements that involve bodily processes

Physical Regulation Alteration—K25.0

Infection Control—K30.0 Actions performed to contain a communicable disease Universal Precautions—K30.1 Practices to prevent the spread of infections and infectious diseases Physical Healthcare—K31.0 Actions performed to support somatic problems Health History—K31.1 Actions performed to obtain information about past illness and health status Health Promotion—K31.2 Actions performed to encourage behaviors to enhance health state Physical Examination—K31.3 Actions performed to observe somatic events Clinical Measurements—K31.4 Actions performed to conduct procedures to evaluate somatic events Specimen Care—K32.0 Actions performed to direct the collection and/or the examination of a bodily specimen Blood Specimen Care—K32.1 Actions performed to collect and/or examine a sample of blood Stool Specimen Care—K32.2 Actions performed to collect and/or examine a sample of feces Urine Specimen Care—K32.3 Actions performed to collect and/or examine a sample of urine Sputum Specimen Care—K32.5 Actions performed to collect and/or examine a sample of sputum Vital Signs—K33.0 Actions performed to measure temperature, pulse, respiration, and blood pressure Blood Pressure—K33.1 Actions performed to measure the diastolic and systolic pressure of the blood Temperature—K33.2 Actions performed to measure the body temperature Pulse—K33.3 Actions performed to measure rhythmic beats of the heart Respiration—K33.4 Actions performed to measure the function of breathing

Life-threatening inhibited sympathetic response to noxious stimuli in a person with a spinal cord injury at or above T7 Hyperthermia—K25.2 Abnormally high body temperature Hypothermia—K25.3 Abnormally low body temperature Thermoregulation Impairment—K25.4 Fluctuation of temperature between hypothermia and hyperthermia Infection Risk—K25.5 Increased chance of contamination with disease-producing germs Infection—K25.6 Contamination with disease-producing germs Intracranial Adaptive Capacity Impairment—K25.7

(continued)

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862    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses

FIGURE A.6

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

L. Respiratory Component Cluster of elements that involve breathing and the pulmonary system

Respiration Alteration—L26.0

Oxygen Therapy Care—L35.0 Actions performed to support the administration of oxygen treatment Pulmonary Care—L36.0 Actions performed to support pulmonary hygiene Breathing Exercises—L36.1 Actions performed to provide therapy on respiratory or lung exertion Chest Physiotherapy—L36.2 Actions performed to provide exercises for postural drainage of lungs Inhalation Therapy—L36.3 Actions performed to support breathing treatments Ventilator Care—L36.4 Actions performed to control and monitor the use of a ventilator Tracheostomy Care—L37.0 Actions performed to support a tracheostomy

M. Role Relationship Component Cluster of elements involving interpersonal work, social, family, and sexual interactions

function Airway Clearance Impairment—L26.1 Inability to clear secretions/obstructions in airway Breathing Pattern Impairment—L26.2 Inadequate inhalation or exhalation Gas Exchange Impairment—L26.3 Imbalance of oxygen and carbon dioxide transfer between lung and vascular system Ventilatory Weaning Impairment—L56.0 Inability to tolerate decreased levels of ventilator support

Role Performance Alteration—M27.0 responsibilities Struggle with parental position and responsibilities Parenting Alteration—M27.2 ure’s ability to promote growth Sexual Dysfunction—M27.3 Deleterious change in sexual response Caregiver Role Strain—M27.4 Excessive tension of one who gives physical or emotional care and support to another person or patient Communication Impairment—M28.0 Diminished ability to exchange thoughts, opinions, or information Verbal Impairment—M28.1 Diminished ability to exchange thoughts, opinions, or information through speech Family Process Alteration—M29.0

Communication Care—M38.0 Actions performed to exchange verbal/nonverbal and/or translation information Psychosocial Care—M39.0 Actions performed to support the study of psychological and social factors Home Situation Analysis—M39.1 Actions performed to analyze the living environment Interpersonal Dynamics Analysis—M39.2 Actions performed to support the analysis of the driving forces in a relationship between people Family Process Analysis—M39.3 Actions performed to support the change Sexual Behavior Analysis—M39.4 Actions performed to support the change response Social Network Analysis—M39.5 Actions performed to improve the quantity or quality of personal relationships

a related group Sexuality Pattern Alteration—M31.0 response Socialization Alteration—M32.0

(continued)

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FIGURE A.6

  863

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

Social Interaction Alteration—M32.1 quantity or quality of personal relations Social Isolation—M32.2 State of aloneness, lack of interaction with others Relocation Stress Syndrome—M32.3 Excessive tension from moving to a new location N. Safety Component Cluster of elements that involve prevention of injury, danger, loss,

Injury Risk—N33.0 Increased chance of danger or loss Aspiration Risk—N33.1 Increased chance of material into trachea– bronchial passage. Disuse Syndrome—N33.2 immobility Poisoning Risk—N33.3 Exposure to or ingestion of dangerous products Increased chance of inadequate air for breathing Trauma Risk—N33.5 Increased chance of accidental tissue processes Fall Risk—N33.6 Increased chance of conditions that result in falls Violence Risk—N34.0 Increased chance of harming self or others Suicide Risk—N34.1 Increased chance of taking one’s life intentionally Self-Mutilation Risk—N34.2 Increased chance of destroying a limb or essential part of the body Perioperative Injury Risk—N57.0 Increased chance of injury during the operative processes Perioperative Positioning Injury—N57.1 Damages from operative process positioning Surgical Recovery Delay—N57.2 Slow or delayed recovery from a surgical procedure Substance Abuse—N58.0 Excessive use of harmful bodily materials Tobacco Abuse—N58.1 Excessive use of tobacco products Alcohol Abuse—N58.2 Excessive use of distilled liquors Drug Abuse—N58.3 Excessive use of habit-forming medications

Substance Abuse Control—N40.0 Actions performed to control substances to avoid, detect, or minimize harm Tobacco Abuse Control—N40.1 Actions performed to avoid, minimize, or control the use of tobacco Alcohol Abuse Control—N40.2 Actions performed to avoid, minimize, or control the use of distilled liquors Drug Abuse Control—N40.3 Actions performed to avoid, minimize, or control the use of any habit-forming medication Emergency Care—N41.0 Actions performed to support a sudden or unexpected occurrence Safety Precautions—N42.0 Actions performed to advance measures to avoid danger or harm Environmental Safety—N42.1 Precautions recommended to prevent or reduce environmental injury Equipment Safety—N42.2 Precautions recommended to prevent or reduce equipment injury Individual Safety—N42.3 Precautions to reduce individual injury Violence Control—N68.0 Actions performed to control behaviors that may cause harm to oneself or others Perioperative Injury Care—N80.0 Actions performed to support perioperative care requirements

(continued)

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864    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses

FIGURE A.6

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U) O. Self-Care Component Cluster of elements that involve the ability to carry out activities to maintain oneself

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions Personal Care—O43.0 Actions performed to care for oneself Activities of Daily Living—O43.1 Actions performed to support personal activities to maintain oneself Instrumental Activities of Daily Living—O43.2 Complex activities performed to support basic life skills

Impaired ability to cleanse oneself Inability to clothe and groom oneself Impaired ability to feed oneself Impaired ability to maintain oneself Activities of Daily Living Alteration—O38.1 tain oneself Instrumental Activities of Daily Living Alteration—O38.2 activities than those needed to maintain oneself Impaired ability to urinate or defecate for oneself

P. Self-Concept Component Cluster of elements that involve an individual’s mental image of oneself

Anxiety—P40.0 Feeling of distress or apprehension whose source is unknown Fear—P41.0 Feeling of dread or distress whose cause can be Meaningfulness Alteration—P42.0

Hopelessness—P42.1 Feeling of despair or futility and passive involvement Powerlessness—P42.2 Feeling of helplessness or inability to act Self-Concept Alteration—P43.0

Mental Healthcare—P45.0 Actions taken to promote emotional well-being Mental Health History—P45.1 Actions performed to obtain information about past or present emotional well-being Mental Health Promotion—P45.2 Actions performed to encourage or further emotional well-being Mental Health Screening—P45.3 Actions performed to systematically examine emotional well-being Mental Health Treatment—P45.4 Actions performed to support protocols used to treat emotional problems

one’s image of self Body Image Disturbance—P43.1 Imbalance in the perception of the way one’s body looks Personal Identity Disturbance—P43.2 Imbalance in the ability to distinguish between the self and the nonself Chronic Low Self-Esteem Disturbance—P43.3 Persistent negative evaluation of oneself Situational Self-Esteem Disturbance—P43.4 Negative evaluation of oneself in response to a loss or change

(continued)

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FIGURE A.6

  865

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

Q. Sensory Component Cluster of elements that involve the senses, including pain

Sensory Perceptual Alteration—Q44.0

Pain Control—Q47.0 Actions performed to support responses to injury or damage Acute Pain Control—Q47.1 ing, hurting, or distress Chronic Pain Control—Q47.2 Actions performed to control physical suffering, hurting, or distress that continues longer than expected Comfort Care—Q48.0 Actions performed to enhance or improve well-being Ear Care—Q49.0 Actions performed to support ear problems Hearing Aid Care—Q49.1 Actions performed to control the use of a hearing aid Wax Removal—Q49.2 Actions performed to remove cerumen from ear Eye Care—Q50.0 Actions performed to support eye problems Cataract Care—Q50.1 Actions performed to control cataract conditions Vision Care—Q50.2 Actions performed to control vision problems

stimuli Auditory Alteration—Q44.1 ability to hear Gustatory Alteration—Q44.2 ability to taste Kinesthetic Alteration—Q44.3 balance Olfactory Alteration—Q44.4 ability to smell Tactile Alteration—Q44.5 ability to feel Unilateral Neglect—Q44.6 Lack of awareness of one side of the body Visual Alteration—Q44.7 ability to see Comfort Alteration—Q45.0 distressing Pain—Q63.0 Acute Pain—Q63.1 Severe pain of limited duration Chronic Pain—Q63.2 Pain that persists over time

R. Skin Integrity Component Cluster of elements that involve the mucous membrane, corneal, integumentary, or subcutaneous structures of the body

Skin Integrity Alteration—R46.0 Oral Mucous Membrane Impairment—R46.1 Diminished ability to maintain the tissues of the oral cavity Skin Integrity Impairment—R46.2 Decreased ability to maintain the integument Skin Integrity Impairment Risk—R46.3 Increased chance of skin breakdown Skin Incision—R46.4 Cutting of the integument/skin Latex Allergy Response—R46.5 Pathological reaction to latex products

Pressure Ulcer Care—R51.0 Actions performed to prevent, detect, and treat skin integrity breakdown caused by pressure Pressure Ulcer Stage 1 Care—R51.1 Actions performed to prevent, detect, and treat stage 1 skin breakdown Pressure Ulcer Stage 2 Care—R51.2 Actions performed to prevent, detect, and treat stage 2 skin breakdown Pressure Ulcer Stage 3 Care—R51.3 Actions performed to prevent, detect, and treat stage 3 skin breakdown Pressure Ulcer Stage 4 Care—R51.4 Actions performed to prevent, detect, and treat stage 4 skin breakdown

(continued)

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866    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses

FIGURE A.6

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

Peripheral Alteration—R47.0 ization of the extremities

Mouth Care—R53.0 Actions performed to support oral cavity problems Denture Care—R53.1 Actions performed to control the use of Skin Care—R54.0 Actions to control the integument/skin Skin Breakdown Control—R54.1 Actions performed to support tissue integrity problems Wound Care—R55.0 Actions performed to support open skin areas Drainage Tube Care—R55.1 Actions performed to support wound drainage from body tubes Dressing Change—R55.2 Actions performed to remove and replace a new bandage on a wound Incision Care—R55.3 Actions performed to support a surgical wound Burn Care—R81.0 Actions performed to support burned areas

S. Tissue Perfusion Component Cluster of elements that involve the oxygenation of tissues, including the circulatory and vascular systems

Tissue Perfusion Alteration—S48.0

T. Urinary Elimination Component Cluster of elements that involve the genitourinary systems

Urinary Elimination Alteration—T49.0

tissues

Foot Care—S56.0 Actions performed to support foot problems Perineal Care—S57.0 Actions performed to support perineal problems Edema Control—S69.0 Circulatory Care—S70.0 Actions performed to support the circulation of the blood (blood vessels) Vascular System Care—S82.0 Actions performed to control problems of the vascular system

waste matter of the kidneys Functional Urinary Incontinence—T49.1 Involuntary, unpredictable passage of urine Involuntary passage of urine occurring at predictable intervals Stress Urinary Incontinence—T49.3 Loss of urine occurring with increased abdominal pressure

Bladder Care—T58.0 Actions performed to control urinary drainage problems Bladder Instillation—T58.1 Actions performed to pour liquid through a catheter into the bladder Bladder Training—T58.2 Actions performed to provide instruction on the care of urinary drainage

(continued)

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Appendix • Clinical Care Classification (CCC) System: Overview, Applications, and Analyses 

FIGURE A.6

  867

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

Urge Urinary Incontinence—T49.5 Involuntary passage of urine following a sense of urgency to void Urinary Retention—T49.6 Incomplete emptying of the bladder Renal Alteration—T50.0

Dialysis Care—T59.0 Actions performed to support the removal of waste products from the body Hemodialysis Care—T59.1 Actions performed to support the mechanical removal of waste products from the blood Peritoneal Dialysis Care—T59.2 Actions performed to support the osmotic removal of waste products from the blood Urinary Catheter Care—T60.0 Actions performed to control the use of a urinary catheter Urinary Catheter Insertion—T60.1 Actions performed to place a urinary catheter in bladder Urinary Catheter Irrigation—T60.2 Urinary Incontinence Care—T72.0 Actions performed to control the inability to retain and/or involuntarily retain urine Renal Care—T73.0 Actions performed to control problems pertaining to the kidney Bladder Ostomy Care—T83.0 ing to remove urine Bladder Ostomy Irrigation—T83.1

U. Life Cycle Component Cluster of elements that involve the life span of individuals

Reproductive Risk—U59.0 Increased chance of harm in the process of repli-

Reproductive Care—U74.0 Actions performed to support the production of

Fertility Risk—U59.1 Increased chance of conception to develop

Fertility Care—U74.1 Actions performed to increase the chance of

Infertility Risk—U59.2 Decreased chance of conception to develop

Infertility Care—U74.2 Actions performed to promote conception by

Contraception Risk—U59.3 Increased chance of harm preventing the

Contraception Care—U74.3 Actions performed to prevent conception of

Perinatal Risk—U60.0 Increased chance of harm before, during, and

Perinatal Care—U75.0 Actions performed to support the period before, during, and immediately after the creation of

child Pregnancy Risk—U60.1 Increased chance of harm during the gestational period of the formation of an

Pregnancy Care—U75.1 Actions performed to support the gestation child (being with child)

(continued)

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Table A.4

Clinical Care Classification System, Version 2.5: CCC Nursing Diagnoses with 3 Outcome Qualifiers and CCC Nursing Interventions with 4 Action Types, with Definitions and Classified by 21 Care Componentsa,b. (continued)

Care Components (A–U)

Nursing Diagnoses and Outcomes

Nursing Interventions/Actions

Labor Risk—U60.2 Increased chance of harm during the period supporting the bringing forth of an

Labor Care—U75.2 Actions performed to support the bringing Delivery Care—U75.3 Actions performed to support the expulsion

Delivery Risk—U60.3 Increased chance of harm during the

Postpartum Risk—U60.4 Increased chance of harm during the period immediately following the delivery of an Growth and Development Alteration—U61.0

Postpartum Care—U75.4 Actions performed to support the period immediately after the delivery of an Growth and Development Care—U76.0 growth standards and/or developmental skills

growth standards and/or developmental skills a

FACMI, LL, may be used ONLY with written Permission. (Permission Form available from Web site http://careclassification.org). Revised 1992, 1994, 2004, 2006, and 2011.

b

or workday. The reader using the Ready-to-Use Guide below determines an Actual Outcome for each Nursing Diagnosis, meaning the reader determines a point at which the Nursing Diagnosis was Improved, Stabilized, or Deteriorated on or before Discharge. The CCC codes used for the POC are now in the background of an electronic system and available to be summarized for outcomes and analysis.

provides the evidence of nursing care. Using the Workload Actions Measures Method (WAMM©) provides the evidence of nursing time and cost associated with the patient clinical care requirements. These unique processes based on a POC for a specific healthcare condition can and will advance the profession of nursing to a higher level.

SUMMARY

1. Which best describes why a nursing terminology is needed for documenting nursing care?

The Appendix provides the design strategy for an electronic POC and begins with an understanding of principles of nursing practice. The Plan of Care is configured to document and code the “essence of care” provided by nurses and allied health professionals. This means nurses begin to develop a POC based on the identification of the Clinical Care Classification (CCC) Nursing Diagnoses derived from the patient’s Assessed Signs and Symptoms guided by the Care Component. Each selected Nursing Diagnosis requires a Nursing Care Goal or Expected Outcome. Each Nursing Diagnosis requires the selection of one or more Nursing Interventions from the list of CCC of Nursing Interventions for the same Care Component Class as the basis of proposed care. Using the CCC coding structure

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Test Questions

A. Nursing care based on the intuition of senior nurses and outdated B. Nursing care based on trial and error

C. Nursing care based on narrative nursing notes D. Coded nursing care data designed to improve patient care and generate outcomes 2. What are the four levels of the CCC System Framework? A. Healthcare Patterns

B. Care Components Classes

C. Nursing Diagnoses and Outcomes

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Appendix • Clinical Care Classification (CCC) System: Overview, Applications, and Analyses  D. Nursing Interventions and Actions

D. Only Acuity Value

F. None of the above

F. All of the above

E. All of the above

3. Which of the six steps of ANA Nursing Process is the foundation of the CCC Information Model? A. Evaluation

B. Signs and Symptoms C. Nursing Diagnoses D. Implementation

E. Outcome Identification F. Planning

G. All of the above

H. None of the above 4. What activity is considered a CCC System’s Nursing Intervention Action Type Qualifier? A. Perform Urinary Catheter Care B. Assess Acute Pain Control

C. Manage Non-Nursing Activities

D. Teach Nursing Care of Disease Condition E. Supervise Health Aide Services F. Provide Hemodialysis Care G. All of the above

H. Only A, B, E, and F 5. Who prepares a Plan of Care (POC) for a patient being admitted into a hospital? A. Physical Therapist and/or Social Worker B. Social Worker and/or Registered Nurse

C. Registered Nurse and/or Medical Doctor

D. Medical Doctor and/or Physical Therapist 6. What statistics are provided from using the CCC System? A. Evaluate provider care cost

B. Percent of patient care time by provider

C. Frequencies of patient encounters by provider D. Teaching Time by provider E. All of the above F. All except A

7. What are the Care Values for WAMM©? A. Care Value and Acuity Value B. Only Care Value

C. Only Relative Value Unit

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E. Only A

8. What does WAMM© Measure? A. Workload Time

B. Action Type Time C. Cost of Workload

D. Workload Action Time E. All of the above

F. None of the above 9. What is the value of WAMM©? A. Measured Action Times

B. Measures Workload Times

C. Measures Intensity of Patient’s Health State D. Determines Cost of Action Types E. All of the above

F. None of the above 10. How does the Ready-to-Use-Guide assist POC Users? A. Links Nursing Diagnoses to Nursing Interventions

B. Links Assessed Signs and Symptoms to Outcomes C. Links Nursing Interventions to Outcomes D. Links Nursing Diagnoses to Action Types E. All of the above F. Only A

Test Answers 1. Answer: D  Nurses remain responsible and legally accountable for care and never fully record many nursing interventions, actions, and other services which are written as narrative progress notes, based on intuition or trial and error. The documentation of nursing actions in not “visible” in health information technology applications and EHRs without coded nursing terminology.

2. Answer: E  The Clinical Care Classification (CCC) System is a unified information framework approach for documenting nursing in the electronic health record and health information systems. The CCC System consists of two interrelated terminologies, the CCC of Nursing Diagnoses and Outcomes and the CCC of Nursing Interventions and Actions,

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870    Appendix • C linical Care Classification (CCC) System: Overview, Applications, and Analyses which are classified by 21 Care Components within four Healthcare Patterns to form a single nursing terminology standard recognized by the American Nurses Association (1991) and the Department of Health and Human Services (2007).

3. Answer: G  The CCC System framework uses the ANA nursing process as a theoretical framework for documenting nursing care. This framework allows CCC-coded concepts to document, link, and track the six steps of the nursing process for professional decision-making during an episode of care.

4. Answer: H  Four CCC System action types: Assess, Perform, Teach, and Manage along with CCCapproved synonyms are used to modify each of the 201 Nursing Intervention concepts. The strategy of combining a Nursing Intervention with an Action Type makes the CCC terminology flexible and expandable to document, classify, retrieve, and analyze patient care.

5. Answer: C  Nurses are legally responsible for patients, 24 hours a day, seven days per week, and 365 days a year in many clinical settings. Consistent with current standards of practice, admission assessments and orders must be completed as a condition of admission. A nursing admission assessment provides information for making clinical judgments required for nursing care. 6. Answer: F  A coded POC provides data on the basic frequencies of care and percent data.

7. Answer: E  The WAMM© is based on two unique “care values” and “acuity values.” The first “care value” is a weighted relative value unit (RVU) based on the frequency and summary of CCC Nursing Services (Intervention Action Type) encounters’ total actual “time” aggregated to carry out the Nursing Intervention Action Types. The second is the patient’s “acuity value” derived from a weighted “indicator” (formula) that represents the care requirements for the patient’s healthcare condition during an episode of illness.

8. Answer: E  With the WAMM© RVU “care value” and the BVU “acuity value” established, the final method step in WAMM© is the coupling of the two sets of nursing service values to correlate the relationship between the “care value” and BVU “acuity value.” Together, the “care value” and “acuity value” provide an aggregated “time” Workload (hours/minutes) of all the Nursing Interventions (Services) Action Types (Care) that were administered or carried out by nurses for a specific patient, time, and shift during the episode of illness.

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9. Answer: E  The Workload Actions Measures Method (WAMM©) is innovative in any setting, including home, across all nurse specialties and considered to be universally applicable for any patient, at any age, with any clinical diagnosis, and on any point on the health continuum. Once workload “times” for patients with the same diagnosis and/or medical/ physical conditions have been validated, the “care values” can be used to predict the WAMM© “care value” (workload “time”) and “care requirements” needed for future patients with similar healthcare conditions. The “care values” and “acuity values” can also be used to calculate the “Costs” of the nursing services for patients with similar healthcare conditions using nursing salary information (Dykes, Wantland, Whittenburg, Lipsitz, & Saba, 2013).

10. Answer: E  The “Ready-to-Use” Guide provides 176 Nursing Diagnoses and 201 Nursing Interventions based on assessed signs and symptoms classified by 21 Care Components. The three Expected Outcomes and the four Action Types are listed in the headings for ease in combining Outcomes with an appropriate Diagnosis and combining Action Types with an Intervention.

REFERENCES ABC Codes. (2019). ABC coding solutions. Accessed June 6, 2020. Retrieved from https://abccodes.com/. American Nurses Association (ANA). (1994, 1998, 2008, 2014). Scope and Standards of Nursing Practice: Nursing Informatics. Silver Springs, MD: American Nurses Association. American Nurses Association (ANA). (2010) Nursing: Nursing scope and standards of practice. Silver Spring, MD: ANA. Bureau of Health Manpower, Division of Nursing DHEW (1978). Methods for studying nurse staffing in a patient unit: A manual to aid hospitals in making use of personnel (Pub # HRA 78-3). Hyattsville, MD: U.S. DHEW. Cimino, J. J. (1998) Desiderate for controlled medical vocabularies in the twenty-first century. Method of Information in Medicine, 37, 394-403. Department of Health and Human Services (HHS) (2008). Notice of Availability: Secretarial Recognition of Certain Healthcare Information Technology Standards Panel (HITSP) Interoperability Specifications as Interoperability Standards for Health Information Technology (Recognition as the 1st National Nursing Terminology, the Clinical Care Classification System (CCC) by the Office of the National Coordinator, Health Information Technology Standards Panel (HITSP), Bio-surveillance

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Appendix • Clinical Care Classification (CCC) System: Overview, Applications, and Analyses  Use Case). Federal Register, 73(15):3973–3977. Accessed June 6, 2020. Retrieved fromhttps://www.govinfo.gov/ content/pkg/FR-2008-01-23/html/08-234.htm. Dykes, P. C., Wantland, D., Whittenburg, L., Lipsitz, S., & Saba, V. K. (2013). A pilot study to explore the feasibility of using the clinical care classification system for developing a reliable costing method for nursing services. In AMIA 2013 Annual Symposium Proceedings (Vol. 2013, p. 364). Chicago, IL: American Medical Informatics Association. Gordon, S. (2005). Nursing against the odds: How healthcare cost cutting, media stereotypes, and medical hubris undermine nurses and patient care. Ithaca, NY: Cornell University Press. Painter, R., & FitzGerald, R. M. (1980). Relative Value Studies, Incoporated (RVSI) Retrieved from http://www.rvsdata. com. Accessed June 6, 2020. Saba, V. K. (1988). Overview of nursing information systems. Identification of the nursing minimum data set (pp. 89–102). New York, NY: Springer. Saba, V. K. (1992). The classification of home health care nursing diagnoses and interventions. Caring, 10(3), 50–57. Saba, V. K. (2012). Clinical Care Classification (CCC) System: Version 2.5 (2nd ed.). New York, NY: Springer. Saba, V. K. & McCormick, K. A. (2015). Essentials of nursing informatics (6th ed.). New York, NY: McGraw-Hill. Saba, V.K., & Taylor, S. L. (2007). Moving past theory: Use of a standardized coded nursing terminology to enhance nursing visibility. Computers, Informatics, Nursing, 25(6), 324–331. Whittenburg, L., Lekdumrongsak, J., Klaikaew, A., & Meetim, A. (2017). The IHE® Patient Plan of Care profile implementation in an Electronic Nursing Documentation System in Bangkok, Thailand. In L. Bright & J. Goderre (Eds.), Underlying standards that support population health improvement. Batavia, IL: Taylor & Francis. World Health Organization (WHO) (1992). International statistical classification of diseases and related health

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problems: Tenth revision: Volume 1 (ICD-10). Geneva, Switzerland: WHO.

RECOMMENDED READINGS AHRQ. (2020). National quality strategy annual reports. Rockville, MD: Agency for Healthcare Research and Quality (AHRQ). Retrieved from https://www.ahrq.gov/ workingforquality/reports/index.html. Accessed June 6, 2020. American Nurses Association. (2019). Nursing process. Retrieved from https://www.nursingworld.org/practicepolicy/workforce/what-is-nursing/the-nursing-process/. Accessed on June 3, 2020. Brief Description of the Problem (2008, September 10). IHE PCC profile proposal, clinical documentation of patient assessments using a coded nursing terminology. Retrieved from Wiki.ihe.net/images/7/75/IHE_Clinical_ Documentation_Proposal_10Sep08_vks_law_(2)_(3).doc. Accessed on June 3, 2020. Moss, J., Andison, M, & Sobko, H. (2007, November). An analysis of narrative nursing documentation in an otherwise structured intensive care clinical documentation system. Paper presented at the meeting of the American Medical Informatics Association, Washington, DC. Nightingale, F. (1860). Notes on nursing: What it is, and what it is not. New York, NY: D. Appleton & Company. Whittenburg, L. (2009). Nursing terminology documentation of quality outcomes. Journal of Healthcare Information Management, 23(3):51–55. Yura, H. & Walsh, M. B. (1978). Human needs and nursing process. New York, NY: Appleton-Century-Crofts.  Yura, H. & Walsh, M. B. (1983). The nursing process: Assessing, planning, implementing, evaluating. New York, NY: Appleton-Century-Crofts.

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Appendix.indd 872

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INDEX Page numbers followed by f or t indicate figures or tables, respectively.

A

AACN. See American Association of Colleges of Nursing; American Association of Critical Care Nurses AAN. See American Academy of Neurology; American Academy of Nursing Abacavir, 645 ACA. See Affordable Care Act Academic integrity, 748 ACBs. See Authorized Certification Bodies Access to data, 110 to health services, 358 Accessibility, 746 Access-control services, 176 Accommodations, reasonable, 746 Accountability, 348, 525, 746 Accountable care organizations (ACOs), 368, 528, 619 Accountable Health Communities models, 184 Accredited Standards Committee (ASC) X12N/Insurance, 123 ACR. See American College of Radiologists Acute ischemic stroke (AIS), 621–623 Adapter cards, 41–42 Adaptive Biotechnologies, 594–595 Adaptive learning, 751 Adobe, 796, 797 Advanced health informatics certification (AHIC), 716, 717t–718t Advanced practice registered nurses (APRNs) competencies of, 425, 426t, 427, 711–720 COVID19 pandemic, 472–474 curricula structuring/reform for, 470–472, 471f, 711–720

emergent knowledge and, 472–474 in retail clinics, 597 social medica and, 463–468 structured data and, 460, 462 telehealth and, 470, 618 wearable devices and, 468 Adverse events, 347–348, 644–645, 636, 701. See also Safety Advisory groups, 482 Aetna, 596, 597 Affordable Care Act (ACA) as driver of mHealth, 49 overview of, 181, 288 patient engagement and, 370 population health informatics and, 522 recent legislation built upon, 288 Web site for, 63 Affordable Care Together Movement, 589 Agency for Healthcare Research and Quality (AHRQ) comparative effectiveness reviews of, 802 data collection programs of, 334 as evidence-based practice resource, 415 on nurse fatigue/burnout, 386 research by, 304–305 role of, 494 safety resources from, 558 SDOH data collection and, 186 on telehealth, 623 usability framework and, 21 Aggregation, of data, 146 Agile design, 221 AHA. See American Heart Association AHIC. See Advanced health informatics certification; American Health Information Community

AHIMA. See American Health Information Management Association AHIP. See America’s Health Insurance Plans AHLTA. See Armed Forces Health Longitudinal Technology Application AHRQ. See Agency for Healthcare Research and Quality AI. See Artificial intelligence Air Force, 495 AIS. See Acute ischemic stroke ALA. See American Library Association Alarms, 211, 435, 516, 517, 701 Alert systems, 577 Alexa platform, 591, 598 All of Us Research, 636 Alliance for Nursing Informatics (ANI), 278, 296, 298, 589 Allscripts, 643 Alpha Go, 606 Alpha testing, 226t, 230 Alphabet, 587, 592–593, 595 Alternative payment models, 331, 332 AMA. See American Medical Association Amazon, 587, 588, 591, 598, 760 American Academy of Ambulatory Care Nurses (AACN), 618–619 American Academy of Neurology (AAN), 622 American Academy of Nursing (AAN), 268, 279, 636, 698 American Association of Colleges of Nursing (AACN), 394, 711, 712–713, 713t–714f American Association of Critical Care Nurses (AACN), 620 American College of Epidemiology, 525

873

index.indd 873

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874    I ndex American College of Radiologists (ACR), 123 American Health Information Community (AHIC), 802–803 American Health Information Management Association (AHIMA), 198, 269, 293 American Heart Association (AHA), 592, 622 American Hospital Association, 622 American Library Association (ALA), 826 American Medical Association (AMA), 124 American Medical Informatics Association (AMIA), 86–87 evidence-based practice and, 436 legislative influence of, 309 in nursing informatics curricula reform, 716 nursing informatics working group in, 22, 23, 268, 277 overview of, 297 system life cycle resources from, 198 training programs of, 292 American National Standards Institute (ANSI), 48, 126, 293 American Nurses Association (ANA) code of ethics of, 164, 184, 266, 279, 748 credentialing exam from, 18 on genomics, 636–638 on improving nursing process, 393–395 Nursing Informatics: Scope and Standards of Practice, 1, 18, 266, 268, 282–283, 503 in nursing informatics curricula reform, 714–715 nursing informatics definition by, 268, 715 in nursing informatics history, 9t, 11t, 12t, 18–19 on nursing process, 443–445 nursing terminologies and, 22, 138–140, 139t overview of, 277 population health informatics and, 526 Principles for Nursing Documentation: Guidance for Registered Nurses, 636

index.indd 874

project management stance of, 254 on research terminologies, 802 scheduling/staffing concerns and, 383–384 Scope and Standards of Practice for Nursing Informatics, 102, 266, 268, 279 supportive computerized resources from, 835 vocabulary standards of, 124 workflow analysis and, 237 American Nurses Credentialing Center (ANCC) nursing credentials of, 269, 280, 382–383, 716 in nursing informatics curricula reform, 716 on system life cycle, 193, 196, 235 American Nursing Informatics Association (ANIA), 268, 277, 436, 562 American Paradox, 182–183 American Psychiatric Association (APA), 622, 623 American Recovery and Reinvestment Act (ARRA) committees created by, 291 education/training funded by, 292 EHR adoption and, 553 EHR certification/training and, 293–294 evidence-based practice and, 435 interoperability and, 295, 335–336 in nursing informatics history, 19–20, 307 overview of, 300–301, 363 policy foundation started with, 320 in quality measurement, 331 recent legislation built upon, 288 research funding from, 304–305 security/privacy and, 164 American Red Cross, 545 American Society for Quality (ASQ), 344 American Society for Testing and Materials (ASTM), 125 American Stroke Association (ASA), 622 Americans with Disabilities Act (ADA), 746 America’s Health Insurance Plans (AHIP), 307, 333

AMIA. See American Medical Informatics Association Amos, 805 ANA. See American Nurses Association Analytics after data mining, 113 benefits of, 112 definition of, 412 in population health informatics, 527 purpose of, 111–112, 122 in quality measurement, 350 research trends involving, 794, 795 ANCC. See American Nurses Credentialing Center Ancestry DNA, 592 Android, 81 ANI. See Alliance for Nursing Informatics ANIA. See American Nursing Informatics Association Anonymous collective, 167–168 ANSI. See American National Standards Institute Antibody testing, 472–474 Anti-virus software, 59 APA. See American Psychiatric Association APACHE IV system, 801 Apache servers, 70, 81 Apache Software Foundation, 74 API. See Application programming interface APRNs. See Advanced practice registered nurses Apple, 587, 589–591 Apple Heart Study, 590 Apple Maps, 591 Apple Watch apps, 572, 589, 590 Application programming interface (API), 325, 511 Appointment reminders, 569 Appropriateness, 679 Apps. See also Software; specific apps with artificial intelligence, 608–610 backup copies of, 59 definition of, 57, 58 from disruptive technology companies, 590–591, 597 for eHealth, 696–698 in evidence-based practice infrastructure, 412

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to improve lives of healthcare providers, 259–260 overview of, 39, 60–61 patient engagement and, 569, 570–571 to reduce healthcare costs, 360 for rural areas, 359–360 in system design/build, 207–208, 209, 210f ArcGIS Online system, 807 Arch Collaborative, 489 Architectural safeguards, 169f, 174–175 Archival software, 60 Armed Forces Health Longitudinal Technology Application (AHLTA), 496, 497 ARRA. See American Recovery and Reinvestment Act Artificial intelligence (AI) in adaptive learning, 751 apps with, 608–610 challenges of, 610–611, 612 definition of, 605–606 disruptive technology companies and, 592, 594–595 foundational concepts in, 606–607 healthcare functions of, 607–608 history of, 606 for staffing/scheduling, 386–387 in telehealth programs, 625 Artificial neural networks, 607 ASA. See American Stroke Association ASC. See Accredited Standards Committee X12N/Insurance ASQ. See American Society for Quality Assembly language, 61, 62f Assessments, nursing, 323–324 Assessments, of learning, 745 ASTM. See American Society for Testing and Materials Asymmetric encryption, 176 Asynchronous communication, 745 ATLs. See Authorized Test Labs Attain by AetnaSM App, 597 Audacity, 808 Audits, 111, 176, 225 Augmented intelligence, 607 Augmented reality, 783 Australia, 278, 411

index.indd 875

I ndex   Authentication and authorization infrastructure (AAI), 172, 176 Authorized Certification Bodies (ACBs), 294 Authorized Test Labs (ATLs), 294 Auto-ID technologies, 514 Automated databases, 106 Automated performance measures, 413, 414 Availability, 175 Awards, 21

B

Baccalaureate programs, 712, 713t Back ends, 106 Backloading, 240, 244t–245t Backup utilities, 59 Bar Code Medication Administration (BCMA), 497 Barcode technology, 19, 49, 497 Basic Input/Output System (BIOS). See BIOS Battery capacity, 46, 51 BCS. See British Computer Society Beddit, 590 Behavioral change, 271, 655 Behavioral sciences, 274 Benchmarking, 113 Benefits identification, 205 Berkeley Internet Name Domain (BIND), 82 Berkeley system distribution (BSD), 79 Best Care at Lower Cost: The Path to Continuously Learning Health Care in America (IOM), 290–291 Beta testing, 226t, 230 Bias, 465–466 Bibliographic retrieval systems, 827–832, 835–837, 836f, 837f Bibliography management tools, 797 Bidirectional connectivity, 515 Bi-directional Health Information Exchange (BHIE), 499–500 Bidirectional interface, 208 BidShift, 816 Big bang activation approach, 213, 485 Big data. See also Data analysis of, 655–659 components of, 654, 655 confusion surrounding, 653 definition of, 105, 653–655, 665 digital disrupters and, 588t evidence-based practice and, 115

  875

in federal healthcare sector, 501–502 future of, 672 goal of, 102 government projects related to, 102 history of, 653–655 legislation regarding, 102 management cycle of, 665, 665f patient-generated data for, 574–575 principles for, 654 research trends involving, 794, 802 sizes of, 105t trends in, 666–668 uses for, 655 workgroups for, 16t, 22 Big Data and Analytics Hub, 654 Big Data Institute, 667 Big data science, 665–666, 666f Binary language, 61 BIND. See Berkeley Internet Name Domain Biomedical text mining (BioNLP), 63–64 BIOS, 32, 42, 58 Bipartisan Budget Act, 619 Biosurveillance, 537–540 Bioterrorism, 536, 541 Bits, 40 Blockchain technology, 516 Blue Button 2.0, 305, 325 Bluetooth, 47, 591 Blu-Ray discs, 35 Bombings, 536 Boolean logic, 93–94 Boots Alliance, 597 Boston Children’s Hospital, 167–168 Botswana, 699 Boxes, computer, 30 Brazil, 77 Bring Your Own Device (BYOD). See BYOD British Computer Society (BCS), 277 Broad Institute, 592 Browsers. See Web browsers BSD. See Berkeley system distribution Buck Institute, 592 Bundles, 413 Burnout, 383, 386 Bush administration, 291, 295, 543, 726 Business agreements, 171–172 Business intelligence, 413, 434 BYOD (Bring Your Own Device), 51 Bytes, 40

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876    I ndex

C

C programming language, 62, 63 Cabling, 41 Cache, 33, 38 CAHIMS. See Certified Associate in Healthcare Information and Management Systems CAHPS. See Consumer Assessment of Healthcare Providers and Systems Calico, 592 California Nursing Outcomes Coalition (CalNOC), 384 Canada data standards in, 664, 668–672 evidence-based practice models in, 411 health literacy approach in, 575–576 nursing data science in, 664, 666–672 pharmacogenetics guidelines in, 641 professional organizations in, 279 Canada Health Infoway, 667, 668–669 Canadian Health Outcomes for Better Information and Care (C-HOBIC), 670–671, 670t Canadian Institute for Health Information (CIHI), 666–667, 668 Canadian Nursing Informatics Association (CNIA), 279 Canadian Pharmacogenomics Network for Drug Safety (CPNDS), 641 Cancer, 645–646 C&BI. See Clinical and business intelligence Candidate Status data class, 442 CAN-IMPLEMENT, 411 CAP. See College of American Pathologists Caradigm, 594 Care coordination, 413–414, 598, 698 Care delivery models, 588–599 Care Equality, 299 Care Everywhere, 298 CareKit, 589, 590 Carequality, 130 CARIN Alliance, 130, 299, 589 Case Fatality Rate (CFR), 536–537 Case management, 413–414, 523 Causal inference process, 609

index.indd 876

CCAN. See Council on Computer Applications in Nursing CCC. See Clinical Care Classification System CCG. See Clinical Commissioning Group CCHIT. See Certification Commission for Healthcare Information Technology CCLF. See Claim and Claim Line Feed CCOW. See Clinical Content Object Workgroup CDC. See Centers for Disease and Prevention CDISC. See Clinical Data Interchange Standards Consortium CD-ROMs, 34–35 CDS. See Clinical decision support CEDS. See Certified E-Discovery Specialist Certification CEH. See Certified Ethical Hacker certification Cellular networks, 51–52 CEN. See European Committee for Standardization Center for Connected Health Policy, 470 Centers for Disease and Prevention (CDC), 523–524, 526, 540–543, 626, 761 Centers for Medicare and Medicaid (CMS) community-level communications and, 368 COVID19 and, 337 data element library of, 323 disruptive care models and, 589 establishment of, 320, 363 evidence-based practice and, 435 interoperability and, 296, 321, 325 legislation affecting, 288, 321 in Meaningful Use history, 301–302 in nursing informatics history, 20 overview of, 373–374 patient safety initiatives and, 556 in population health informatics, 528 quality measurement and, 302–303, 331, 332t, 335, 337 reimbursement from, 20, 302 social determinants of health and, 184 system life cycle and, 195, 196 on telehealth, 396, 616, 619

Centers of Excellence Program, 596 CentOS, 80t Central processing units (CPUs), 32, 41, 42, 58, 62, 207 Centralized databases, 107 Centralized telehealth, 615 CER. See Comparative effectiveness research; Comparative Effectiveness Research Certification Commission for Healthcare Information Technology (CCHIT), 293–294 Certifications. See also specific certifications in nursing informatics, 269, 716, 717t–718t in staffing/scheduling, 383 from TIGER virtual learning, 732 Certified Associate in Healthcare Information and Management Systems (CAHIMS), 732 Certified E-Discovery Specialist (CEDS) Certification, 269 Certified Ethical Hacker (CEH) certification, 269 Certified Information Systems Security Professional (CISSP), 269 C4Therapuetics, 592 CFR. See Case Fatality Rate CGM. See Continuous Glucose Monitoring Change management in communications planning, 370–372 in optimization, 488–489 overview of, 247–248 preparation for, 502 theories of, 271–273, 272t tools for, 248t Change, system, 274 Chaos theory, 272t, 275 Chart abstraction, 333 Charter documents, 483–484 CHCS. See Composite Health Care System Chief Medical Informatics Officer (CMIO), 495 Chief Nursing Informatics Officer (CNIO), 495, 658 Children’s Health Insurance Program (CHIP), 185, 331

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Children’s Medical Center (Dallas), 324 Chile, 699 CHIME. See College of Healthcare Information Management Executives China, 542, 571 Chips, 42 C-HOBIC. See Canadian Health Outcomes for Better Information and Care CHP. See Community Health Partner Chrome browser, 70, 81 Chronic diseases. See also specific diseases alert systems for, 577 disruptive technology initiatives for, 590–593, 597–598 genomics in, 637, 638f patient engagement and, 569, 572, 573, 577 telehealth for, 623 Chronic pain, 816 Cigna, 596 CIHI. See Canadian Institute for Health Information Cinahl Information Systems, 828, 832 CIS. See Clinical Information Systems CISCs. See Complex instruction set computers CISSP. See Certified Information Systems Security Professional Cityblock Health, 593 Claim and Claim Line Feed (CCLF), 528 Claims processing, 125, 126 Claims-derived measures, 333 Class II medical devices, 512 Classification, 137 Classifiers, 607 Cleveland Clinic, 624 Client-server configurations, 106–108, 110 Clinical analytics, 415 Clinical and business intelligence (C&BI), 434 Clinical and Translational Science Award (CTSA), 306 Clinical Care Classification (CCC) System case study of, 144–145, 145f, 146f Finnish documentation system and, 681

index.indd 877

I ndex   in nursing informatics history, 20 in Nursing Plan of Care, 446–450 for nursing process, 445f overview of, 349, 446 for research studies, 802, 816 Clinical Commissioning Group (CCG), 190 Clinical Content Object Workgroup (CCOW), 49 Clinical Data Interchange Standards Consortium (CDISC), 125 Clinical data registries, 334, 528 Clinical decision support (CDS) artificial intelligence in, 608, 609 definition of, 211, 430 eHealth and, 700–701 in evidence-based practice, 412, 429–432, 434–435 forms of, 211, 296 origin of, 291 overview of, 609, 700–701 for social determinants of health effectiveness, 189 for staffing/scheduling, 387 success of, 431, 435 Clinical Health Data Repository (CHDR), 499, 500 Clinical Information System Evaluation Scale, 214 Clinical Information Systems (CIS), 237–241, 246–247, 269, 431, 496 Clinical outcomes. See Nursing outcomes Clinical Pharmacogenetics Implementation Consortium (CPIC), 639, 640t–641t, 641–642, 644 Clinical practice guidelines, 411–412 Clinical trials management software (CTMS), 797, 798t–799t Clinical video telemedicine (CVT), 469 Clinical workflow analysis. See Workflow analysis Clinical Workflow, analysis, 195, 238 Clock speeds, 41, 62 Clocks, 42 Clopidogrel, 644 Closing Process Group (CPG), 260–261, 261t Closing the Gap (WHO), 182 Cloud services advanced hardware for, 46

  877

benefits of, 510–511 business models of, 171 in client-server configurations, 107 disruptive technology companies and, 594 growth of, 511 overview of, 35–36, 35f redundancy in, 36 for research studies, 794, 796–797 security and, 171, 172, 174, 175 CMIO. See Chief Medical Informatics Officer CMS. See Centers for Medicare and Medicaid; Content management systems CNIA. See Canadian Nursing Informatics Association CNIO. See Chief Medical Informatics Officer CNPII. See Committee for Nursing Practice Information Infrastructure Coast Guard, 494, 497 Cochrane Collaboration, 415–416 Cocreation, 346–347 Code freeze, 228, 230 Code of Ethics for Nurses with Interpretive Statements (ANA), 164, 266, 268, 279 Codeine, 644 Codman, E., 330 Cognitive aids, 413 Cognitive computing, 607 Cognitive mapping, 813–814, 813f Cognitive science, 272t, 275 Cognitive task analysis, 158–159 Cognitive walkthrough, 160 Collaboration of the Health IT Policy and Standards Committees, 304 Collaborative workgroup. See specific workgroups College of American Pathologists (CAP), 140 College of Healthcare Information Management Executives (CHIME), 324 Collegiality, 348 Command centers, 213–214, 215 Commercial off-the-shelf (COTS) software, 73 Commercial Systems (COTS), 226t-227t

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878    I ndex Committee for Nursing Practice Information Infrastructure (CNPII), 18 Committees, project, 199, 199f, 200f, 261, 482 Common Formats for reporting, 558 Common Payer Consumer Data Set (CPCDS), 589 Common Terminology Criteria for Adverse Events (CTCAE), 645 Commonwealth of Learning, 751 CommonWell Health Alliance, 130, 298 Communication channels, 273 Communication media, 41 Communication theories, 273–274 Communications, healthcare challenges to, 364 complexity of, 365–366 COVID19 and, 376 as driver of patient engagement, 568 in evidence-based practice infrastructure, 412 future of, 375–376 governance of, 366–368, 367f, 368t health information exchange in, 365, 375 importance of, 364–365 industry considerations for, 372–373 legislation regarding, 363–364 planning for, 370–372, 371t role of federal agencies in, 373–375 stakeholders in, 364, 368–370, 368f Communications plans, 210–211 Communications protocols, 123–124 Community health information exchanges, 298 Community Health Partner (CHP), 593 Community-level SDOH data, 186–187, 186t Comparative effectiveness research (CER), 304–305, 802 Comparison, of data, 146 Compatibility, 39–40, 123 Competencies content indicators, 181, 186 educational, 636 Competitive benchmarking, 113 Complex adaptive systems, 365–366 Complex instruction set computers (CISCs), 41

index.indd 878

Complexity, 66 Composite Health Care System (CHCS), 495–496, 497 Comprehensive Meta-Analysis application, 805, 806f Compressing data, 59, 60 Computer chips, 42 Computer literacy, 308, 699–700 Computer systems. See Networks The Computer-Based Patient Record: An Essential Technology for Healthcare (IOM), 290 Computer-Based Patient Record Institute (CPRI), 21 Computerized Patient Record System (CPRS), 497 Computerized patient records. See Electronic health records Computerized physician order entry (CPOE) eHealth and, 701 in nursing informatics history, 17, 20 overview of, 701 patient safety and, 554, 559, 560t, 561, 701 software testing of, 222, 224–225, 238–239, 239f, 240f, 243t–244t Computerized resources, 825–839 Computers. See also Personal computers basic components of, 32 common storage devices for, 34–36 current functions of, 4, 825 definition of, 29 development of, 4 everyday influence of, 29–30 importance of, 3 learning capabilities of, 606 major types of, 36–39 in nursing informatics history, 3–19 power of, 40–41 Concept-oriented terminologies, 141–142, 142f, 142t, 146–147 Concepts, 141 Concierge services, 515, 595 Concurrent languages, 62–63 Conferences, 17–18, 24t–25t Configuration management, 172 Configurations, 30 Confirmatory analysis, 804–805 Conformity assessment programs, 128, 128f

Connected Age, 757, 759–762 Connected Care, 759, 759f Connecting for Health collaboration, 166 Connecting Nurses forum, 694 Consensus-based standards, 122 Consent management, 172–173 Consortium of College and University Media Centers, 747 Consumer Assessment of Healthcare Providers and Systems (CAHPS), 334 Consumer-centric healthcare system, 21 Consumer-generated data, 529, 542 Content exchange standards, 123, 125–126 Content management systems (CMS), 82–83, 740–741 Contextual inquiry, 158 Contingency planning, 174, 230 Continuing education, 360, 647–648, 648t, 834, 837–838 Continuity of operations, 174 Continuous Glucose Monitoring (CGM), 590 Continuous networked devices, 512 Continuous Quality Improvement (CQI), 242, 245–247, 414–415 Continuous stand-alone medical devices, 513 Continuum of care, 385, 413–414, 637–638, 638f Contracted services, 171 Controllers, 33 Conversions, system, 234, 240 Copyleft, 78 Copyright, 64–65, 73, 78, 747, 764 Copyright Act, 747 Core data elements, 125 Core Quality Measure Collaborative (CQMC), 307, 332–333 Correctional facilities, 624 COTS. See Commercial off-the-shelf software Council on Computer Applications in Nursing (CCAN), 18 COVID19 pandemic advanced practice nursing and, 472–474 artificial intelligence apps during, 610 controversies surrounding, 537

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diagnostic testing during, 472–474 disruptive technology apps and, 591 distance learning during, 751 healthcare communications and, 376 misinformation about, 763–764 quality measurement in, 337 R0 of, 536 spread of, 536 surveillance during, 530 telehealth in, 396, 626–627, 628t CPCDS. See Common Payer Consumer Data Set CPG. See Closing Process Group CPIC. See Clinical Pharmacogenetics Implementation Consortium CPNDS. See Canadian Pharmacogenomics Network for Drug Safety CPOE. See Computerized physician order entry CPOE Design Checklist, 561, 562t CPR Project Evaluation Criteria, 21 CPRI. See Computer-Based Patient Record Institute CPRS. See Computerized Patient Record System CPT. See Current Procedural Terminology code set CPUs. See Central processing units CQI. See Continuous Quality Improvement CQMC. See Core Quality Measure Collaborative Credentials, nursing, 18, 269, 280, 382–384, 716 Credentials, software, 172 CRIB database, 459–462, 460f, 461f CRMs. See Customer Relations Managers Crossing the Quality Chasm (IOM), 290, 330–331, 368, 430, 567 Cross-references, 91 Cryptographic hash functions, 176 CSF. See Cybersecurity Framework CTCAE. See Common Terminology Criteria for Adverse Events CTSA. See Clinical and Translational Science Award Culture. See Organizational culture Curating, 110-111 Cures Act. See 21st Century Cures Act Curo Healthcare Services, 596

index.indd 879

I ndex   Current awareness services, 832, 833f Current Procedural Terminology (CPT) code set, 124, 138, 597, 623 Curricula, nursing education/ informatics, 470–472, 471f, 711–720 Curriculum in nursing education, 710-711 Customer Relations Managers (CRMs), 486 Cutover plans, 212–215, 213f CVS, 588, 591, 596–597 Cybersecurity. See Security Cybersecurity Framework (CSF), 510 Cycles, 41

D

DAD. See Discharge Abstract Database Dartmouth AI Workshop, 606 DAS. See Distributed Antenna Systems Dashboards, 65 definition of, 396 in evidence-based practice infrastructure, 412 examples of, 397, 398f for ONC strategic plan, 300 overview of, 112, 397, 412 for quality improvement, 247, 247f resources for, 112t Data. See also Big data access to, 110 artificial intelligence functions for, 607–608 to change nursing practice, 393–397 common uses of, 441–442 conversion testing of, 224 curation of, 110–111 in data to wisdom continuum, 103 definition of, 103, 104, 122, 273 displays of, 397, 399f, 400t in expert systems, 114f generating, 104–105 interpretation of, 111–113 legislation to improve sharing of, 322–325 machine-readable, 430 manipulation of, 396 in population health informatics, 525–527

  879

redundancy and inconsistency in, 106 repositories for, 106–110, 349 retrieval of, 111 reuse of, 680, 683t sources of, 104, 105, 527–530 states of, 104 in translational science, 407–408, 407f validation of, 224 in value-based healthcare, 115, 131 Data analysis, 803–807, 808–814 Data analytics. See Analytics Data centers, 35–36, 35f Data classes, 442–443 Data coding, 803, 808 Data collection electronic health records for, 137 for HCI usability testing, 157–161 informatics nurses’ role in, 281 in mHealth public policy, 53 in nursing practice, 266–267 in patient engagement, 570, 573–575 purpose of, 669f for quality measurement, 333–335, 347–350 in research studies, 800–803, 807–808 for social determinants of health, 185–189 in system life cycle, 202–204 Data dictionaries, 205–206, 211 Data discovery, 412 Data element libraries, 323 Data entry, 104–105, 110, 230–231 Data, Information, Knowledge, Wisdom (DIKW) model, 103–103, 103f, 432–433, 434t Data integrity services, 176 Data lakes, 109 Data marts, 109 Data mining versus big data analysis, 658 for data reuse, 683t in evidence-based practice, 412–413, 434 overview of, 112–113, 683 for research studies, 802 Data registries, 334, 528, 529 Data review, 205 Data rich and information poor (DRIP) phenomenon, 668

11-11-2020 21:47:19

880    I ndex Data science, 665–666 Data sharing, 332, 526 Data standards. See also Health data standards in Canada, 664, 668–672 in evidence-based practice, 412, 430–431 importance of, 664, 669 for interoperability, 122, 146 legislation regarding, 122, 130, 322–324 Data stewardship, 110, 525 Data stores, 109 Data transfers, 41–42, 47, 110–111, 177 Data types, 204 Data verification, 514–515 Data warehouses, 108–110, 224, 501 Database management systems (DBMSs), 106–108, 797 Databases artificial intelligence functions and, 608 of bibliographic details, 827–832 case study of, 91–94 as component of expert systems, 114t data types in, 204 for evidence-based practice, 433t importance of, 102 of medical device reports, 558 multidimensional searches of, 456–458, 456f, 457f in population health informatics, 529 unstructured versus structured data, 458–462 Database theory courses, 774, 775f Data-driven clinical decision support systems, 430 Datatypes, 91, 92 Davies Award of Excellence Program, 21 DaVINCI. See DoD VA Infrastructure for Clinical Intelligence project DaVita Medical Group, 596 DBMS. See Database management systems DCMO. See Deputy Chief Management Officer DDoS. See Distributed denial-ofservice Debian, 80t, 81

index.indd 880

Debugging programs, 60 Decentralized Hospital Computer Program (DHCP), 497 Decentralized telehealth, 615 Decision Explorer, 813, 813f Decision making artificial intelligence functions for, 608, 609 collaboration for, 346–347 at Department of Defense, 498 in evidence-based practice, 429–432, 434–435 genomics in, 637, 638f for optimization, 482–483, 483f, 484f Decision support systems, 113, 114f, 429–435 Decoding, 273 Deep Blue, 606 Deep learning, 607 Deep Mind, 592, 606 Defect resolution, 221–222, 230 Defragmenting, 59 Delphi technique, 161 Department of Defense (DoD), 77, 90 big data work at, 501 decision making at, 498 funding of, 498–499 health system of, 494–503 interoperability at, 499–501, 510 security at, 499 technology evolution at, 495–497 VA’s interagency link to, 499, 501 Department of Health and Human Services (HHS) Cures Act requirements of, 287–288 emergency planning/response of, 537, 543 in health IT–public policy link, 320 in history of telehealth, 617 on improving nursing practice, 394 in Meaningful Use history, 301 overview of, 494 personal information breaches and, 167 population health informatics and, 526 in precision medicine/health, 636 Department of Health Resources and Services Administration Office of Rural Health Policy, 470 Department of Homeland Security (DHS), 168, 494, 537

Department of Veterans Affairs (VA) approval processes at, 498 big data work at, 501 DoD’s interagency link to, 499, 501 enrollment in, 494 funding of, 498–499 health exchange project of, 129 interoperability at, 499–501, 510 nursing informatics role in, 495 nursing outcome database of, 384, 395 opens source case study of, 89–91 optimization at, 498 overview of, 494 patient engagement at, 502 patient portals at, 359, 497 remote monitoring and, 358 size and complexity of, 497, 498 staffing/scheduling and, 384 technology evolution at, 497 telehealth in, 469, 617 Departmental teams, 200 Deputy Chief Management Officer (DCMO), 498 Descriptive analysis, 804 Descriptive analytics, 112 Desktop computers, 30, 37 Devices. See Medical devices Dexcom, 590 DHCP. See Decentralized Hospital Computer Program DHIS2. See District Health Information Software 2 DHS. See Department of Homeland Security Diabetes disruptive technologies for, 573, 590, 591, 593, 597 telehealth for, 623 Diagnostic programs, 59, 608 Diagnostic tests in continuum of care, 637, 638f for COVID19, 472–474 direct-to-consumer strategy for, 572 genomics in, 637, 638f, 646 and patient engagement, 569, 572 DICOM (Digital Imaging and Communications in Medicine), 123 Dictation apps, 808, 809t–811t Diffusion of innovation theory, 271–273, 272t, 488–489 Digital care delivery, 595, 598

11-11-2020 21:47:19

Digital divide, 764–765 Digital exhaust, 529–530 Digital Imaging and Communications in Medicine. See DICOM Digital literacy, 699–700 Digital media, 374 Digital Millennium Copyright Act, 747 Digital patient engagement (DPE), 335 Digital signatures, 176 Digitalization, 678 DIKW model. See Data, Information, Knowledge, Wisdom model Direct-to-consumer (DTC) strategy, 572, 623–624 Direct-to-provider agreements, 596 DirectTrust, 298 Disaggregated faculty, 743 Disaster medical assistance teams (DMATs), 543, 545 Disaster response. See Emergency planning/response Discharge Abstract Database (DAD), 668 Discrete data, 204 Disease mapping, 527 Disease surveillance, 358 Disk cleaners, 59 DispatchHealth, 598 Disruptive technologies big four companies of, 583, 587–595, 598, 760 effects of, 589–599, 760 in new care models, 598–599 nurses’ role in, 597, 598 to reduce healthcare costs, 588–589 types of, 589 Distance education, 360, 744, 746, 751 Distant sites, 616 Distributed Antenna Systems (DAS), 52 Distributed databases, 107 Distributed denial-of-service (DDoS), 167–168 District Health Information Software 2 (DHIS2), 85 DMATs. See Disaster medical assistance teams DNorm, 64 DNS. See Domain name system Doctoral programs, 712, 714t, 720, 777, 778f Document delivery services, 834

index.indd 881

I ndex   Documentation, clinical artificial intelligence apps for, 610 decreased burden of, 296 importance of, 443 legislation regarding, 435 nonstandard data in, 323 pharmacogenetics and, 639, 640t–641t, 641–642 in precision medicine/health, 642–645 quality of, 342 research examples of, 815–816 software testing for, 222 terminologies for, 443, 681 Documentation, system, 212 DOD. See Department of Defense DoD VA Infrastructure for Clinical Intelligence (DaVINCI) project, 501 Domain name system (DNS), 82 Donabedian, A., 330, 331 Doody’s Review Service, 832 Downtime procedures, 212, 213 DPE. See Digital patient engagement DPWG. See Royal Dutch Association for the Advancement of Pharmacy–Pharmacogenetics Working Group Draft Trusted Exchange Framework, 295–296, 320 DrChrono, 590 DRIP. See Data rich and information poor phenomenon Drones, 784, 786, 787 Drug guides, 838 Drupal, 82 DTC. See Direct-to-consumer strategy Dual core processors, 62–63 Duplicate records, 324 DVDs, 35 Dynamic Health (EBSCO), 833 Dynamic homeostasis, 274 Dynamic reports, 224 Dynamics 365, 595

E

EAP. See Extensible Authentication Protocol Early Warning Systems, 516 Ebola outbreak, 542 EBP. See Evidence-based practice EBSCO, 831–832, 833, 833f

  881

ECDL. See European Computer Driving License competencies ECHO. See Expanding Capacity for Health Outcomes Act eConsult, 616 eCQMs. See Electronic clinical quality measures ECRI Institute, 412, 558, 559–561, 701 Edge computing, 52 Effective Health Care Program, 802 EFMI. See European Federation for Medical Informatics eHealth advanced hardware for, 45–53 applications for, 696–698 definition of, 678, 693, 734 Exchange, 159, 294, 295, 298 global trends in, 698–701 health equity and, 700 health literacy and, 699 organizations involved in, 694–696, 697t overview of, 356, 693 research in, 698 in rural areas, 356–358 versus telehealth/mHealth, 698 TIGER initiatives on, 734 EHI Export. See Electronic Health Information Export EHR Incentive Programs. See Meaningful Use EHRs. See Electronic health records EIDS. See Enterprise Intelligence and Data Solutions EIF. See Empowerment Informatics Framework E-learning, 83 Electronic clinical quality measures (eCQMs), 329, 333 Electronic Health Information (EHI) Export, 288 Electronic health records (EHRs) award programs for, 21 big data from, 656–658 certification of, 129, 293–294 classification of, 678 common uses of data from, 441–442 communications regarding, 364–375 for data collection, 137, 333 definition of, 196 eHealth and, 697–698, 700

11-11-2020 21:47:19

882    I ndex Electronic health records (EHRs) (Cont’d.) emergency planning/response and, 545–546 in evidence-based practice, 412, 414 genomics and, 643 growth of, 290, 481, 553, 697, 710 legislation affecting, 288, 291, 293–294, 363, 553, 710 medical device connectivity to, 510–517 Medicare incentive program and, 20 in military health system, 495–501 nonstandard nursing data in, 323 in nursing informatics history, 3–24 for Nursing Plan of Care, 445–446, 446f, 447f, 448f nursing terminologies and, 138, 141, 146 open source software for, 83–86 optimization of, 481–489 overview of, 196, 288–289 patient access to, 21, 570 patient safety and, 553–561, 700 pharmacogenetics and, 642, 644 research trends involving, 794, 795, 815 in simulation-based learning, 716, 719 social determinants of health in, 185 stakeholders’ adoption of, 368–370 steering committee in planning, 199 system life cycle for, 196–215 usability of, 21, 159 in workload management systems, 386–389 Electronic incident reporting systems, 701 Electronic publishers, 834–835 Electronic subscriptions, 360 Elsevier Clinical Skills, 833 E-mail, 48, 167, 745–746, 838 EmblemHealth, 593 Emergency departments, 622 Emergency Operations Center (EOC), 543 Emergency planning/response case studies of, 540–542 challenges to, 536 disasters/emergencies warranting, 535–536 electronic health records and, 545 federal system for, 537–540

index.indd 882

framework for, 626, 627f future of, 546 rationale for, 535–537 security/privacy and, 174 telehealth and, 626–627 victim tracking in, 545–546 Emergency Support Functions (ESF), 537, 538t–539t Emergency System for Advance Registration of Volunteer Health Professionals (ESAR-VHP), 543, 545 Emergency Use Authorization (EUA), 472, 474 Emerging data classes, 442 EMORY NELL Project, 658 Empowerment Informatics Framework (EIF), 270 Encryption, 47, 48, 59, 176 Encoding, 273 End-user licensing agreement (EULA), 77–78, 79 End-users satisfaction of, 241–242, 435 software testing by, 225, 227t support for, 213–214, 215 training for, 212 England Patient and Public Participation Policy, 568 Enterprise Intelligence and Data Solutions (EIDS), 501 Enterprisewide connectivity, 512 Entity Relationship (ER) Model, 108 Entrepreneurial mindset, 348–349, 349t Entropy, 274 Environmental assessments, 201–202 EOC. See Emergency Operations Center EPG. See Executing Process Group EPHS. See National Essential Public Health Service Episodic devices, 512 EpiWatch, 572 Equifinality, 274 ER Model. See Entity Relationship Model ERIC database, 831 Errors, nurse. See also Medication administration errors artificial intelligence to reduce, 608 in history of nursing informatics, 4, 17, 19

nurse fatigue and, 383, 389 terminologies for, 558 ESAR-VHP. See Emergency System for Advance Registration of Volunteer Health Professionals Escape Fire: The Fight to Rescue Healthcare (documentary), 49 ESRI, 807 Essential computerized resources, 825–826, 827, 835–838 Essentris, 496 Ethics of genomics, 646–647 online learning and, 748 of population health informatics, 524–525 of social media, 466, 764 Ethics, nursing codes of, 164, 170, 184, 748 regarding social determinants of health, 184 security/privacy concerns and, 164, 170 Ethnicity, 642–643 EUA. See Emergency Use Authorization EULA. See End-user licensing agreement Europe, 77, 78, 85–86, 164 European Commission, 75–76, 77 European Committee for Standardization (CEN), 126, 143 European Computer Driving License (ECDL) competencies, 715 European Federation for Medical Informatics (EFMI), 86, 87 EU*US eHealth Work Project, 733–735, 734f, 735f Evaluations, in system life cycle, 214–215 Evidence Communication Innovation Collaborative, 364 Evidence-based healthcare, 679–680 Evidence-based practice (EBP) appraisal tools for, 408, 409t barriers to, 427, 432 benefits of, 406, 424 clinical and business intelligence in, 434 clinical information system selection in, 431

11-11-2020 21:47:19

data standards in, 430–431 decision support systems in, 429–432, 434–435 definition of, 196, 406, 424 facilitators of, 427 health information technology’s role in, 427–429 implementation phase of, 406–407 informatics tools for, 411–412 information literacy and, 826 infrastructure to support, 412–415 legislation regarding, 395, 435 nurse competencies for, 425–427, 426t, 429, 429t nurses’ role in, 416–417 nurse training in, 427 nursing data science and, 663, 669–671 optimizing existing systems for, 435–436 organizational culture for, 425f, 427 professional organizations for, 410t, 415–416 rationale for, 395 research results in, 408 resources for, 410t, 415–416, 427, 428t, 432, 433t for scheduling/staffing, 385–386 steps of, 424–425 terminologies for, 412, 430–431 theories/models related to, 408–411, 432–433, 434t translational science in, 407–410, 410t vendor collaboration in, 431–432 E-visits, 21 Executing Process Group (EPG), 258–259 Executive dashboards, 247, 247f Expanding Capacity for Health Outcomes (ECHO) Act, 359–360 Expert systems, 114–115, 114f, 114t Exploratory analysis, 804 Express Care® Online, 624 Express Scripts, 596 Extensible Authentication Protocol (EAP), 47 Extensible Markup Language. See XML External evidence, 424 Extreme Programming (XP), 221

index.indd 883

I ndex  

F

FACAs. See Federal Advisory Committees and Agencies Facebook, 53, 465–466, 467 Faculty, nursing informatics development of, 742–743 in online learning, 719, 742–744, 747 in simulation-based learning, 716, 719 in virtual-world learning, 784–786, 785t workload of, 743–744 Fail-safe designs, 175 Failure Mode and Effects Analysis (FMEA), 245–246 Fair information practice principles, 166 FAIR principles, 299 Fair use, 747 Falls, 623, 774 Family history, 642–643 Fast Healthcare Interoperability Resources (FHIR), 125, 130, 173, 288, 296, 510, 511 Faxing, 222 FCC. See Federal Communications Commission FCC-CER. See Federal Coordinating Council for Comparative Effectiveness Research FDA. See Food and Drug Administration FDASIA. See Food and Drug Administration Safety and Innovation Act Feasibility, 201–202, 205, 679 Federal Advisory Committee Act, 306 Federal Advisory Committees and Agencies (FACAs), 306–307 Federal Communications Commission (FCC), 50, 559 Federal Coordinating Council for Comparative Effectiveness Research (FCC-CER), 304 Federal Health Information Exchange (FHIE), 499 Federal Health IT Strategic Plans (ONC), 300, 304 Federal healthcare sector, 493–503. See also specific health systems Federal Patient Movement system, 546 Federal Register, 325–326, 326f

  883

Federal Trade Commission (FTC), 53 Fedora, 80t, 81 Feedback, 160 Fee-for-service (FFS) payment model, 332, 359, 385 FHIE. See Federal Health Information Exchange FHIR. See Fast Healthcare Interoperability Resources Field studies, 159 Fifth-generation (5G) networks, 47 Fifth-generation languages, 63–64 File Transfer Protocol (FTP), 48, 124 Find Care Program, 597 Finnish classification system, 681–685, 682f, 686f Firefox browser, 70, 81 Firewalls, 59 Firmware, 32, 58 5G networks. See Fifth generation networks 5S methodology, 344 Flash drives. See USB drives Florida government, 396 FLOSS (free/libre/OSS). See also Open source software acronyms related to, 70t benefits of, 74–75, 83 case studies related to, 89–94 common types of, 79–86 development of, 73–74 guidelines for choosing, 76–77 issues related to, 75–76 licensing of, 77–79 organizations and resources related to, 86–87, 88t overview of, 72–73 Flow charting, 237, 344 Flu pandemics, 536, 540–546, 540t, 541t FMEA. See Failure Mode and Effects Analysis Focus groups, 160–161 Food and Drug Administration (FDA), 52 adverse event reports to, 645 COVID19 pandemic and, 472 direct-to-consumer strategy and, 572 medical device connectivity and, 512 medical device queries/regulations and, 463–465, 464t, 510

11-11-2020 21:47:19

884    I ndex Food and Drug Administration (FDA) (Cont’d.) on pharmacogenetics, 641–642 safety resources from, 558–559 Food and Drug Administration Safety and Innovation Act (FDASIA), 558–559 FORTRAN, 63 Foundational interoperability, 320 FoundationOne CDx, 646 Fourth-generation (4G) networks, 47 Fourth-generation languages, 63 Fourth Industrial Revolution, 647–648 Framework for Responsible Sharing of Genomic and Health-Related Data (Global Alliance for Genomics and Health), 525 Free software, 71t, 73 Free Software Foundation (FSF), 70, 73, 79 Free text data, 204 Freedoms, software, 73–74 Free/libre/OSS (FLOSS). See FLOSS Freeware, 73 Front ends, 106 FSF. See Free Software Foundation FTC. See Federal Trade Commission FTP. See File Transfer Protocol Fully online courses, 744, 744t Functional benchmarking, 113 Functional design documents, 204 Functional specifications, 206–207 Functional tests, 211, 226t The Future of Nursing: Leading Change, Advancing Health (IOM), 289

G

Gantt chart, 259, 259t, 260 Gap analysis, 203 Gaps in care, 523 Gartner Research, 384 Gateways, 512 GE Healthcare, 594 General Data Protection Regulation (GDPR), 164 General purpose machines, 36 Generic benchmarking, 113 GENESIS Patient Portal, 499. 496–497 Genetic Information Nondiscrimination Act, 646–647 Genetic tests, 572, 642 Genomics

index.indd 884

databases for, 529, 594, 595 ethics of, 646–647 history of, 636–638 and medication administration, 644–645 nurses’ engagement in, 637–638, 638f nursing standards and, 636 pharmacogenetics and, 638–639, 644 research of, 636 training in, 643, 647, 647t–648t Geographical Information Systems (GIS), 358, 413, 806–807, 807f Geolocation data, 528–529 Getting to Affordability Initiative: Regional Total Cost of Care project, 588–589 Ghidra, 76 GHz. See Gigahertz Gigahertz (GHz), 41 GIS. See Geographical Information Systems Global Alliance for Genomics and Health, 525 Global workforce development, 713, 734 Glucose monitoring, 590, 591 GNU GPL license, 78t, 79, 80, 81 GNU/Linux, 80–81 GNUmed, 84 GNU software, 48, 70, 78t Go Live plans, 212–215 Go-No Go decisions, 212 GOOD. See Graph-Oriented Object Data Model Google, 81 artificial intelligence and, 606 augmented reality technology of, 783, 787 as digital disrupter, 587, 592–593 in emergency planning/response, 543 for information literacy, 831 misinformation and, 465 as research tool, 467, 796, 797 Google Scholar, 831 Governance of eHealth Exchange, 295 in healthcare communications, 366–368, 367f, 368t in healthcare organizations, 366–368, 367f, 368t

in military health system, 498 of optimization requests, 482–483, 483f in project management, 199, 199f, 261 public health informatics and, 525, 526 rules for, 367–368 in work ecosystem, 346 Governance Framework, 295 Government-developed standards, 122 Grand theories, 270 Graphical data displays, 805–807 Graphics cards, 40, 63 Graph-Oriented Object Data (GOOD) Model, 108 Grassroots efforts, 726 Great Clips Incident, 537 Group dynamics, 274, 275 Guardrails, 515 Guide to the Project Management Body of Knowledge (PMBOK® Guide) (PMI), 262 Guiding principles, 483

H

Hacking, 167 Haiti earthquake, 546 Handheld computers, 39 Hard drives, 34, 34f, 40–41 Hardware. See also specific components advanced systems of, 45–53 advances in, 30–31 cloud services for, 171 for networks, 41–42 in nursing informatics history, 17, 18, 19 overview of, 30 research trends involving, 795 security/privacy regarding, 171, 174–175 in system design, 207 versus software, 57 Harvard Center of Informatics for Integrating Biology and the Bedside (i2b2), 462 Haven, 591 HCI. See Human–Computer Interaction HCPCS. See Healthcare Common Procedure Coding System

11-11-2020 21:47:19

HCPLAN. See Health Care Payment Learning and Action Network HCUP databases, 815 HCV. See Hepatitis C virus Health Atlas Ireland, 86 HealthCareCAN, 667 Healthcare Common Procedure Coding System (HCPCS), 616 Healthcare costs American Paradox of, 182–183 apps to reduce, 360 as driver of care delivery models, 588 factors affecting, 196–197 initiatives for reducing, 588–589, 591 pharmacogenetics and, 642 of telehealth, 621 U.S. totals of, 588 Healthcare delivery models, 595–599 Healthcare Effectiveness Data and Information Set (HEDIS), 335 Healthcare industry as complex adaptive system, 365–366 computer advances affecting, 30–31 future of, 309 leadership of, 370 ongoing transformation of, 319 Healthcare Information Technology Standards Panel (HITSP), 20, 294 Healthcare NExT, 594 Health Care Payment Learning and Action Network (HCPLAN), 331, 332 Healthcare policy. See Public policy Health Care Quality Initiative, 431 Health data standards, 122–131. See also Data standards Health equity, 700 Healthify, 189 Health informatics, 267, 428, 428f, 710 competencies, 733 Health Information and Technology, Evaluation and Quality (HITEQ) Center, 188 Health information exchanges (HIEs), 129, 331, 335, 365 Health Information Management Systems Society (HIMSS) legislative influence of, 309 nursing informatics community in, 268, 696

index.indd 885

I ndex   in nursing informatics curricula reform, 713–714 overview of, 277–278, 297–298, 696, 730–731 support of CCHIT by, 293–294 system life cycle and, 198 TIGER initiative and, 429, 730–731 usability principles of, 21 Health Information Systems Program (HISP), 85 Health information technology (HIT) challenges of, 70, 320, 363–364 consumers’ wariness of, 166 definition of, 3, 678 drivers of change in, 290–309 in evidence-based practice, 427–429 factors affecting implementation of, 196–197 future of, 309 goal of, 320 growth of, 431, 432f history of, 70, 290–291, 394 importance of, 3, 166 job growth in, 289, 290 military health system’s leadership in, 495–501 open source software for, 70, 83–86 patients’ literacy in, 575–577 public policy’s link to, 320–322 recent disruptions to, 163–164 security risks of, 164 trust framework for, 168–177, 169f Health Information Technology Advisory Committee (HITAC), 126–127, 291, 303–304, 374–375 Health Information Technology Competencies. See HITComp Health Information Technology for Economic and Clinical Health (HITECH) Act audit requirements of, 225 effects of, 288 eHealth Exchange and, 294 EHR adoption and, 553, 554, 710 EHR certification/testing and, 293–294 evidence-based practice and, 435 federal healthcare sector and, 501 interoperability requirements of, 40, 295, 336 in nursing informatics history, 19–20 overview of, 196, 300–301, 363, 435 in quality measurement, 331

  885

recent legislation built upon, 288 research funding from, 304–305 security/privacy and, 164–165, 166, 167 state/regional health IT programs and, 306 system life cycle and, 195, 206 Health Information Technology Patient Safety and Action & Surveillance Plan (ONC), 556 Health information technology system. See Hospital Information System (HIS) Health insurance. See Insurance plans Health Insurance Portability and Accountability Act (HIPAA) audit requirements of, 225 genomics ethics and, 647 mHealth and, 52 NCVHS responsibilities under, 307 in nursing informatics history, 18 patient identification and, 324 privacy/security issues in, 126, 164–167, 170–174, 292–293, 710 public health informatics and, 524 social media and, 373 USB flash drives and, 34 Health IT and Patient Safety Report (IOM), 555–556, 555t Health IT Hazard Manager, 558 Health IT Policy Committee, 291, 292, 373–374 Health IT Safety Center, 559 Health IT Standards Committee, 291, 374 Health IT Workforce Development Program (ONC), 305 Health IT Workforce Development workgroup, 292 Health Level Seven (HL7), 48–49 consent management and, 172–173 interface testing and, 224 interoperability and, 296, 510 nurses’ group in, 696 overview of, 511, 571, 696 vocabulary standards of, 125 Health literacy Canada’s approach to, 575–576 definition of, 575–576 eHealth and, 699 nurses’ influence on, 699

11-11-2020 21:47:19

886    I ndex Health literacy (Cont’d.) patient engagement and, 570, 575–577 for quality care, 685 strategies for increasing, 576 types of interventions for, 699 Health policy. See Public policy Health Records API, 589 Health reference database, 527, 797, 825 Health Resources and Services Administration (HRSA), 102, 616 Health Vault, 593 Healthy Nevada Project, 529 Healthy People initiative, 528 Heartland Telehealth Resource Center, 470 Heatsink, 42 HEDIS. See Healthcare Effectiveness Data and Information Set Helene Fuld Health Trust National Institute for Evidence-based Practice in Nursing and Healthcare, 427 Henderson, V., 138 Hepatitis C virus (HCV), 359 Heuristics, 159–160 HHCC. See Home Health Care Classification HHS Office of Civil Rights, 165–166 HICS. See Hospital Incident Command System High Care Coordination Need Model, 462, 463f Higher Education Act, 746 HIMSS. See Health Information Management Systems Society HIMSS Privacy and Security Forum, 167–168 HIPAA. See Health Insurance Portability and Accountability Act HISP. See Health Information Systems Program HISs. See Hospital information systems HIT. See Health information technology HIT Policy Committee (HITPC), 304 HIT Standards Committee (HITSC), 304 HITAC. See Health Information Technology Advisory Committee

index.indd 886

HITComp (Health Information Technology Competencies), 276 HITECH. See Health Information Technology for Economic and Clinical Health Act HITEQ. See Health Information and Technology, Evaluation and Quality Center HITPC. See HIT Policy Committee HITSC. See HIT Standards Committee HITSP. See Healthcare Information Technology Standards Panel HL7. See Health Level Seven HoloLens, 783 Home computers, 38, 40 Home healthcare, 19, 52, 681 Home Health Care Classification (HHCC), 681 Homeland Security Presidential Directive 5 (HSPD5), 537 H1N1 pandemic, 536, 540–546 Hospital Incident Command System (HICS), 543 Hospital information systems (HISs), 37, 60–61, 65, 196–197 Hospital Readmissions Reduction Program (HRRP), 386 Hospital-branded apps, 571–572 Hotspots, 47 HRSA. See Health Resources and Services Administration HSPD5. See Homeland Security Presidential Directive 5 HTML (HyperText Markup Language), 64 HTTP. See HyperText Transfer Protocol HTTPS. See Hypertext Transfer Protocol Secure Humana, 596 Human–Computer Interaction (HCI), 153–161, 154t, 156f, 157t Human factors, 153, 154, 154t, 155t Human rights, 524–525 Hurricanes, 545, 546 Hybrid courses, 744, 744t Hybrid quality measures, 333 HyperText Markup Language (HTML). See HTML HyperText Transfer Protocol (HTTP), 48

Hypertext Transfer Protocol Secure (HTTPS), 59 Hypervisors, 175 Hypothesis testing, 804–805

I

IA. See Information assurance IaaS. See Infrastructure-as-a-Service IBM, 606, 654, 665 ICD-9/10. See International Classification of Diseases ICF. See International Classification of Functioning, Disability and Health iChart, 546 ICHI. See International Classification of Health Interventions ICN. See International Council of Nurses ICN Telenursing Network, 694 ICNP. See International Classification for Nursing Practice ICSP. See International Competency Synthesis Project ICUs, 619–621, 621t Identifiable health information, 165 Identity federation, 177 Identity management, 172 IDS. See Intrusion detection system IEC. See International Electrotechnical Commission IEEE. See Institute of Electrical and Electronics Engineers IETF. See Internet Engineering Task Force IHE. See Integrating the Healthcare Enterprise IHI. See Institute for Healthcare Improvement IHS. See Indian Health Service IHTSDO. See International Health Terminology Standards Development Organization IISSB. See Immunization Information System Support Branch Image analysis, 607, 609 IMIA. See International Medical Informatics Association IMIA-NI. See International Medical Informatics Association— Nursing Informatics Working Group Imitation game, 606

11-11-2020 21:47:19

Immersive learning, 750–751 Immunization Information System Support Branch (IISSB), 526 IMPACT Program, 366–367, 367f Implantable devices, 46 Implementation strategy, 75, 485 Implementation science, 406–407 Imprivata, 594 Improving Medicare Post-Acute Care Transformation (IMPACT) Act, 322–323 IMT-Advanced. See International Mobile TelecommunicationsAdvanced Incident Command Center Framework, 626, 627f Incident management system (IMS), 543, 544t Incident reporting systems, 701 Incident response, 173–174 Income, personal, 655–656, 700 Incompatibility issues, 39–40 Indian Health Service (IHS), 494, 497, 501, 617 Individual-level SDOH data, 187–188 Indivo, 84 Inference engine, 115t Informatics, 267 Informaticist, 364, 366, 482, 487 Informatics and Technology Expert Panel (ITEP), 279 Informatics nurse specialists (INSs), 715–716 Informatics nurses in big data analysis, 658 in change management, 247 competencies of, 275–276, 308, 394, 714–720, 733–736 in Continuous Process Improvement Process, 242 credentials of, 18, 269, 280, 716 definition of, 266 disruptive technologies and, 598 as early pioneers of NI, 22–24 education of, 268–269, 291–292, 394, 668, 709–720 in federal sector, 494, 502–503 importance of, 289, 307, 587 versus informatics nurse specialists, 715 in nursing informatics history, 22–23 in optimization process, 486–487

index.indd 887

I ndex   in policy development, 308 in population health informatics, 524 in precision medicine/health, 642–645 in project planning, 199 roles of, 1, 269, 280–281, 308 scope/standards of practice of, 279–283 in system design/build, 207 in testing process, 231 titles of, 280–281 Informatics Research Organizing Model, 154 Information in data to wisdom continuum, 103 definition of, 273 in evidence-based practice, 432– 433, 434t in project management, 253 in translational science, 407–408, 407f Information assurance (IA), 169f, 170, 499 Information blocking, 321–322, 336–337 Information literacy, 825–839 Information models, 349 Information resources, 773 Information retrieval, 37, 63 Information science, 66, 273 Information systems technology, 129 Information theory, 273 Informed consent, 172 Infrastructure-as-a-Service (IaaS), 171 Initiating Process Group (IPG), 256, 256t Initiative on the Future of Nursing, 289 i-NMDS. See International Nursing Minimum Data Set Innovation adoption of, 271–273, 489 collaboration for, 346–347 culture of, 348–349 evidence-based, 679–680, 679f nursing informatics in, 677–678 quality-related, 679–680 in virtual-world technology, 783–786 Input devices, 33 Insider threat detection, 173 Inspire.com, 466, 468

  887

INSs. See Informatics nurse specialists Instant messaging, 838 Institute for Healthcare Improvement (IHI), 355, 386, 413, 521 Institute for Safe Practice of Medicine (ISMP), 558–559, 560t Institute of Electrical and Electronics Engineers (IEEE), 47, 123, 511 Institute of Medicine (IOM) Best Care at Lower Cost: The Path to Continuously Learning Health Care in America, 290–291 on clinical decision support systems, 431 The Computer-Based Patient Record: An Essential Technology for Healthcare, 290 Crossing the Quality Chasm, 290, 330–331, 368, 430, 567 on data standardization, 430, 431 digital literacy and, 699 To Err Is Human, 290, 330, 710 The Future of Nursing: Leading Change, Advancing Health, 289 in healthcare communications, 364 healthcare terminologies and, 138 in HIT history, 290 on nursing curricula reform, 711 in nursing informatics history, 20–21 on patient engagement, 567 quality measurement history and, 330–331 safety initiatives of, 555–556, 555t on social determinants of health, 184–185, 185f on telehealth, 625 Institutional Review Boards (IRBs), 165, 797, 800 Insulin pumps, 395–396 Insurance plans, 591, 595–596 Integrated testing example of, 240–241, 241t, 242t of legacy systems, 211–212 process of, 220, 222, 225–231, 227t timing of, 239–240 Integrating the Healthcare Enterprise (IHE), 127–128, 128f, 296, 698 Integration of a Reference Terminology Model for Nursing (ISO), 128, 143 Integration profiles, 127 Intellectual property, 64, 78, 747 Interaction designers, 156

11-11-2020 21:47:19

888    I ndex Interdisciplinary, 726 Interfaces, 58, 140, 208, 222, 224 Intermountain Healthcare, 324 Internal benchmarking, 113 Internal evidence, 424 Internal hard drives, 34, 40 International Classification for Nursing Practice (ICNP), 142, 697 International Classification of Diseases (ICD-9/10) definition of, 694 driving force behind, 307 functions of, 138 in population health informatics, 527–528 as quality measurement tool, 348 social determinants of health in, 188, 188t transfer standards and, 124 International Classification of Functioning, Disability and Health (ICF), 694 International Classification of Health Interventions (ICHI), 694 International Competency Synthesis Project (ICSP), 733 International Council of Nurses (ICN), 143, 164, 170, 694, 695 International Electrotechnical Commission (IEC), 48 International Health Terminology Standards Development Organization (IHTSDO), 64, 500, 695 International Labor Organization (ILO), 345 International Medical Informatics Association (IMIA), 86–87, 268, 694–695 International Medical Informatics Association—Nursing Informatics Working Group (IMIA-NI), 143, 277, 278, 695 International Mobile Telecommunications-Advanced (IMT-Advanced), 47 International Nursing Minimum Data Set (i-NMDS), 139 International Open Source Network (IOSN), 87 International Organization for Standardization (ISO), 48

index.indd 888

on advanced terminologies, 141, 143 eHealth initiatives of, 695 overview of, 128–129, 695 usability testing and, 159, 230 International Society for Telemedicine and eHealth (ISfTeH), 695–696 International Statistical Classification of Diseases and Related Health Problems: Tenth Revision. See ICD-10 International Telecommunications Union (ITU), 83 International Telecommunications Union-Radio (ITU-R), 47 Internet addresses on, 82 benefits of, 757 definition of, 756 in digital literacy, 699–700 history of, 757–758 home access to, 38 in nursing informatics history, 19 patient health education and, 685 protocols and standards for, 48 in rural areas, 358 telehealth implications of, 627 transport standards and, 123 versus World Wide Web, 758 Internet Engineering Task Force (IETF), 48 Internet of Things (IoT), 529, 572–573, 609, 759 Internet Protocol (IP), 48, 177 Internet Protocol security (IPsec), 177 Interoperability for data transfers, 110–111 definition of, 122, 321, 442 eHealth and, 697 in emergency planning/response, 546 in evidence-based practice infrastructure, 412 health data standards and, 122, 129–130, 146 importance of, 320 industry initiatives supporting, 298–299 issues in, 40 legislative/policy history regarding, 40, 294–296, 320–325, 335–336, 442 levels of, 320–321

in military health systems, 499–501, 510 in nursing informatics history, 20 overview of, 509–510, 509f patient identification and, 324 patient-generated data and, 574 procurement, 507 in public health informatics, 526 in quality measurement, 335–337 security/privacy and, 175 terminology standards and, 22, 146 trusted exchange principles in, 321, 526 Interoperability Standards Advisory (ISA), 323–324, 324f Interprofessional, 524 Interprofessional collaboration (IPC), 719 interRAI, 668 Intrusion detection system (IDS), 167, 173 Invitational Summit, 727-728 IOM. See Institute of Medicine IOSN. See International Open Source Network IoT. See Internet of Things IP. See Internet Protocol IPC. See Interprofessional collaboration IPG. See Initiating Process Group IPsec. See Internet Protocol security IRBs. See Institutional Review Boards ISA. See Interoperability Standards Advisory ISfTeH. See International Society for Telemedicine and eHealth ISMP. See Institute for Safe Practice of Medicine ISO. See International Organization for Standardization ISO 18104:2003 standard, 143–144 ISO 18104:2014 standard, 144. 143f, 144f, 695 ISO Technical Committee 215 Working Group, 143, 695 ISO/CEI 17025 standard, 128 ISO/IEC 27002:2013 standard, 126 Issues lists, 197 IT departments, 369–370 IT Help Desk, 215 ITEP. See Informatics and Technology Expert Panel

11-11-2020 21:47:20

ITU. See International Telecommunications Union ITU-R. See International Telecommunications UnionRadio i2b2. See Harvard Center of Informatics for Integrating Biology and the Bedside (i2b2) I-Wall, 752

J

Jagger, M., 508 Japan, 77, 577 Java, 62, 63 Jayhawk Community Living Center (JCLC), 773–776, 774f, 775f Joanna Briggs Institute, 415–416, 680 Job growth, 289, 290 Joint Commission, 225, 514, 517, 623, 701 Joint Initiative Council (JIC), 129 Journals, 814, 827–832, 834 JSON language, 511 Jumpstart Program, 743

K

Kaiser Permanente, 129, 617 Kanta services, 682–683, 682f KatrinaHealth, 545 KDD. See Knowledge discovery and data mining Kettering Health Network, 592 Keys, 176 Kindred Healthcare, 596 KLAS Research, 489 KNOPPIX, 80t Knowledge in data to wisdom continuum, 103 definition of, 273 to gain wisdom, 113–115, 114t in nursing process, 444 production of, 111–113 in translational science, 407–408, 407f Knowledge, skills, and attitudes (KSAs), 712 Knowledge discovery and data mining (KDD), 112–113 Knowledge objects, 456f, 457 Knowledge Representation, 121, 124 Knowledge Translation+ (KT+), 415 KUMC. See University of Kansas Medical Center

index.indd 889

I ndex  

L

LabCorp, 597 Laboratory information system (LIS), 222 LAMP. See Linux, Apache, MySQL, PHP/Perl/Python architecture LANs. See Local area networks Laptop computers, 38 Leading Edge Acceleration Projects (LEAP), 306 Lean, methodology, 242, 344–348, 347f, 350 Leapfrog Group, 561 Learning Health System series (National Academy of Medicine), 291 Learning management systems (LMSs), 740, 741t, 751 Learning process, 749 Lee Health, 396 Legacy systems, 208–212, 240 Legislation. See also specific legislation on evidence-based practice, 395 to improve security/privacy, 164–167, 292–293 to improve sharing of health data, 322–325 and information assurance policy, 170 IT professionals’ influence on, 309 job growth and, 289 for Meaningful Use, 288, 293–294, 300–301, 320, 336 in nursing informatics history, 18, 19, 20 on paper versus electronic documentation, 435 on public health surveillance, 528 public’s involvement in, 325–326 regarding big data, 102 regarding health data standards, 122, 130, 322–324 regarding online learning, 746, 747 regarding telehealth, 469–470 regarding value-based payments, 288, 301, 336–337, 710–711 reports required by, 281 Licensing, software of commercial software, 73 of FLOSS, 75, 77–79 open source software distribution and, 71t–72t

  889

overview of, 65, 77–78 of VistA, 89–90 Light pens, 33 Link analysis, 158 Linux, 80–81 Linux, Apache, MySQL, PHP/Perl/ Python (LAMP) architecture, 82 Lippincott Williams & Wilkins, 834–835 LIS. See Laboratory information system Literature databases, 63–64, 608 Livongo Health, 597–598 Loansome Doc, 828 Local area networks (LANs), 41–42, 47, 65–66 Location-centric association, 514 Lockheed Missile and Space Company, 616–617 Logical Observation Identifiers Names and Codes (LOINC), 22, 125, 138, 140–141, 188, 188t Long-term care facilities, 337 Long-Term Evolution (LTE), 47

M

Machine language, 61 Machine learning, 607 Machine readable data, 430 Machine-to-machine (M2M) technology, 52 MACRA. See Medicare Access and CHIP Reauthorization Act Main memory, 40–41 Mainframe computers, 17, 18, 36–37, 36f Maintenance, of systems, 215, 241 Malware, 167, 176–177 Managed Care Organizations, 184, 335 Management science, 274–275 Managers, 342, 381–389, 427 Mandriva, 80t, 81 MANs. See Metropolitan area networks Manufacturer and User Facility Device Experience (MAUDE), 558 Mapping techniques, 527 Markle Foundation, 166 Mass shootings, 536 Massachusetts General Hospital Utility Multi-Programming System. See MUMPS

11-11-2020 21:47:20

890    I ndex Massive open online course (MOOC), 741–742, 751 Master’s programs, 712, 714t MAUDE. See Manufacturer and User Facility Device Experience Mayo Clinic, 324, 761 MCPG. See Monitoring and Controlling Process Group MDDS. See Medical device data system MDLive platform, 597 MDRs. See Medical device reports Meaningful Measures Framework, 331, 332t Meaningfulness, 679 Meaningful Use (MU) audit requirements of, 225 development of, 20 evidence-based practice and, 435 goals of, 710 growth of, 290, 431, 432f history of, 20, 295, 301–302, 363 incentives for, 435 legislation regarding, 288, 293–294, 300–301, 320, 336 overview of, 196–197, 331 quality measurement and, 302–303, 331 stages of, 295, 301 in system design/build, 208 Meaningful Use. See also Promoting Interoperability program Media, 309 Medicaid, 184, 185, 331, 335, 588 Medical Community of Interest (Med-COI) architecture, 499 Medical device data system (MDDS), 512, 512t Medical device reports (MDRs), 558 Medical devices alarms on, 516–517, 701 associations/standards related to, 517t challenges related to, 517–518 connectivity of, 510–517 FDA queries regarding, 463–465, 464t patient engagement and, 569 regulation of, 510 safety resources for, 558 security of, 168, 171, 510, 517 testing of, 224

index.indd 890

Medical Expenditure Panel Survey (MEPS), 515 Medical informatics, 267 Medical Information Bus (MIB), 123 Medical language processing, 607 Medical Reserve Corps (MRC), 543 Medicare cost of, 588 data sharing applications of, 325 EHR incentive program for, 20 genomics challenges related to, 645–646 in nursing informatics history, 20 in population health informatics, 528 quality measurement for, 331, 335 telehealth for, 396, 469, 619, 621 Medicare Access and CHIP Reauthorization Act (MACRA) evidence-based practice and, 435 in nursing informatics history, 20 overview of, 288, 321, 710–711 patient safety initiatives and, 556 payment model of, 301, 710–711 in quality management, 336 Medication administration errors. See also Errors, nurse; Safety EHR adoption and, 553–554 EHR/CPOE’s cause of, 554 in history of nursing informatics, 4, 17, 19 nurse fatigue and, 383, 389 pharmacogenetics and, 636, 644–645 safety resources for, 559, 561 smart pumps and, 515, 515t Medication Errors Reporting Program (MERP), 559 Medication Event Monitoring System (MEMS), 800, 801f, 817 MEDLINE, 406, 608, 828, 830f MedlinePlus, 828 Medtronic, 168 MedVirginia, 129 Megahertz (MHz), 41 Memory, 32–33, 38, 40–41. See also specific types MEMS. See Medication Event Monitoring System Mental health disorders, 622, 623, 656 MEPS. See Medical Expenditure Panel Survey

Merit-Based Incentive Payment System (MIPS) attestation for, 336–337 description of, 336 in nursing informatics history, 20 patient engagement and, 568 patient safety initiatives and, 556 for public health informatics, 528 purpose of, 20 MERP. See Medication Errors Reporting Program MERS-CoV. See Middle East Respiratory Syndrome Meta-analysis, 805, 806f Metadata, 349–350, 442 Method of Introducing a New Competency (MINC) Implementation Model, 643 Metropolitan area networks (MANs), 41, 65–66 Mexico, 699 MGH Perinatal Depression Scale (MGHPDS), 572 mHealth advanced hardware for, 45–53 BYOD trend in, 51 definition of, 49 drivers of, 49 in emergency planning/response, 546 future of, 51–52 history of, 49–50 infrastructure of, 50 legal and policy concerns regarding, 53 patient engagement and, 573 planning for/adoption of, 52 in population health informatics, 529 recent progress in, 45–46 research studies of, 817 in rural communities, 356–357 security/privacy considerations for, 51, 52–53 mHIMSS Roadmap, 49 MHz. See Megahertz MIB. See Medical Information Bus Microcomputers. See Personal computers Microlearning, 751 Microsoft as digital disrupter, 587, 593–595 in informatics nurses’ education, 394

11-11-2020 21:47:20

research proposal tools from, 796, 797 virtual reality technology of, 783 Web servers from, 81 Microsoft Azure, 594, 595 Microsoft Genomics, 594 Microsoft Intelligent Network for Eyecare, 594 Microsoft Office, 81 Microsoft Teams, 595 Microsoft 365, 595 Middle East Respiratory Syndrome (MERS-CoV), 542 Middle-range theories, 270 Middleware, 224, 511–512, 513f Migrant workers, 659 Migration, software, 75–76 Military Health System (MHS), 494–503 Military Nursing Outcomes Database (MilNOD), 384, 395 MINC. See Method of Introducing a New Competency Implementation Model Minimum data sets (MDSs), 125, 138–140, 394, 430 Minimum Redundancy–Maximum Relevancy (mRMR), 815 MiNOD. See Military Nursing Outcomes Database MinuteClinic, 596 MIPS. See Merit-Based Incentive Payment System Misinformation, 465–466, 610, 763–764 Missouri government, 470 Missouri tornado, 545 MIT Open Courseware, 747 Mixed reality, 783 MMWR. See Morbidity and Mortality Weekly Report Mobile devices, 742, 762. 751. See also specific devices Mobile media, 374 Mobile WiMAX. See Worldwide Interoperability for Microwave Access Models, of healthcare. See Healthcare delivery models Monitoring and Controlling Process Group (MCPG), 259–260, 260t Monitoring patients. See Remote monitoring

index.indd 891

I ndex   MOOC. See Massive open online course Moodle, 83 Moral duties, 525 Morale, nurse, 215 Morbidity and Mortality Weekly Report (MMWR), 834 Morris F. Collen Award of Excellence, 297 Motherboards, 30, 31–32, 31f, 32f, 42 Mount Sinai Health System, 617 Moving stage, 271 Mozilla Organization, 81 MRC. See Medical Reserve Corps mRMR. See Minimum RedundancyMaximum Relevancy M2M. See Machine-to-machine technology MU. See Meaningful Use Multidimensional modeling, 458 Multidimensional search systems, 456–458, 456f, 457f Multiprocessing, 62–63 Multithreading, 63 MUMPS (Massachusetts General Hospital Utility MultiProgramming System), 61–62 MVX codes, 526 MX Linux, 81 My Healthevet portal, 359, 497 MyHealthBank, 573–574 MyHealthEData initiative, 325 MyMediHealth, 817 mymobility app, 590 MyOpenSourcematrix, 82 MySQL, 82 MyStrength app, 598

N

NaaS. See Network-as-a-Service NAHIT. See National Alliance for Health Information Technology NATE. See National Association for Trusted Exchange National Academy of Medicine, 291, 411, 432 National Alliance for Health Information Technology (NAHIT), 294 National Association for Trusted Exchange (NATE), 299 National Association of Community Health Centers, 187

  891

National Center for Advancing Translational Sciences (NCATS), 306 National Center for Biomedical Computing, 462 National Center for Health Statistics (NCHS), 528 National Committee for Quality Assurance (NCQA), 335 National Committee on Health Policy, 20 National Committee on Health Standards, 20 National Committee on Vital and Health Statistics (NCVHS), 307 National Council for Prescription Drug Programs (NCPDP), 125, 293 National Council of State Boards of Nursing (NCSBN), 746 National Cyber Awareness Center (NCAS), 177 National Cybersecurity Center of Excellence (NCCoE), 53 National Data Warehouse (NDW), 501 National Database of Nursing Quality Indicators (NDNQI), 109, 334, 384 National Electrical Manufacturers Association (NEMA), 123 National Essential Public Health Service (EPHS), 571 National Guideline Clearinghouse, 411–412 Extent of Adherence to Trustworthy Standards (NEATS), 411–412 National Health Insurance Administration (NHIA), 574, 575 National Healthcare Safety Network (NHSN), 337 National Institute for Nursing Research (NINR), 19, 21, 299, 638 National Institute of Standards and Technology (NIST), 47, 48, 159, 320 National Institutes of Health (NIH), 49 in big data history, 653–654 education funding from, 292 genomics documentation and, 643 genomics research of, 636

11-11-2020 21:47:20

892    I ndex National Institutes of Health (NIH) (Cont’d.) in population health informatics, 529 research funding from, 305–306 research repository of, 501, 802 role of, 494 strategic plan of, 299–300 National League for Nursing (NLN), 276, 278, 394 National Library of Medicine (NLM), 22, 124, 299, 494 National Patient Information Reporting System (NPIRS), 501 National Quality Forum (NQF), 307, 334, 395 National Quality Measures Clearinghouse, 411 National Response Framework, 537 National Security Agency (NSA), 76 National technology infrastructure, 131 National Uniform Claim Committee (NUCC), 126 National Vital Statistics System (NVSS), 528 Nationwide Health Information Network. See eHealth Exchange Nationwide Privacy and Security Framework for Electronic Exchange of Individually Identifiable Health Information (ONC), 166 NATs. See Nursing Advisory Teams Natural disasters, 535, 536 Natural languages, 63–64, 114t, 430, 462, 607 Natural language processing (NLP), 63, 144, 606, 668, 679 Navy, 495, 497–498 NCAS. See National Cyber Awareness Center NCATS. See National Center for Advancing Translational Sciences NCCoE. See National Cybersecurity Center of Excellence NCHS. See National Center for Health Statistics NCPDP. See National Council for Prescription Drug Programs NCQA. See National Committee for Quality Assurance

index.indd 892

NCSBN. See National Council of State Boards of Nursing NCVIS. See National Committee on Vital and Health Statistics NDNQI. See National Database of Nursing Quality Indicators NDW. See National Data Warehouse NEATS. See National Guideline Clearinghouse Extent of Adherence to Trustworthy Standards Needs assessments, 203–204 Needs theory, 444 Negative testing, 225 Negentropy, 274 NEHI. See Network for Excellence in Health Innovation Neighborhood effect, 529 Nelson D-W continuum. See Data, Information, Knowledge and Wisdom Model NEMA. See National Electrical Manufacturers Association Network File System (NFS), 48 Network for Excellence in Health Innovation (NEHI), 617 Network for Regional Health Improvement (NRHI), 588–589 Network interface cards, 41–42 Network-as-a-Service (NaaS), 171 Networks advanced hardware for, 47–49 allocation strategies for, 66 cloud services for, 171 database management selection and, 110 definition of, 41, 65 hardware for, 41–42 in system design, 207 types of, 65–66 Neurology, 621–622 Nevada health study, 529 New York City, 593 New Zealand Health Strategy, 344 Newborns, 637, 638f Newsletters, 369–370, 371 Next Generation Sequencing (NGS), 646 NFS. See Network File System NGINX Web server, 81 NHIA. See National Health Insurance Administration

NHSN. See National Healthcare Safety Network NI. See Nursing informatics NIA. See Nursing Informatics Australia NICA L3/L4©. See Nursing Informatics Competencies Assessment Level 3 and Level 4 Nicholas E. Davies Award of Excellence Program, 21 NICUs, 620–621 Nightingale, F., 138, 330, 393, 436, 678 NIH. See National Institutes of Health NINR. See National Institute for Nursing Research NIPRNet. See Non-classified Internet Protocol Router Network NIST. See National Institute of Standards and Technology NIWG. See Nursing Informatics Working Group NLM. See National Library of Medicine NLN. See National League for Nursing NLP See Natural Language Processing NMDS. See Nursing Minimum Data Set NMMDS. See Nursing Management Minimum Data Set Non-classified Internet Protocol Router Network (NIPRNet), 499 Nonprofit organizations, 296–299 Non-Relational Databases, 107 Non-repudiation, 176 NoSQL, 107, 108t Notebook computers, 38 Notes on Nursing (Nightingale), 138 Novice-to-expert theory, 271, 272t, 275 NOWIKNOW application, 817 NowPow, 189 NPIRS. See National Patient Information Reporting System NQF. See National Quality Forum NRCP. See Nursing Reference Center Plus NRHI. See Network for Regional Health Improvement NSA. See National Security Agency NSG. See Nursing Specialist Group NUCC. See National Uniform Claim Committee Nurse anesthesia programs, 777, 779f

11-11-2020 21:47:20

Nurse education. See also specific types of education in big data analysis, 658–659 gaps in, 711 of informatics nurses, 268–269, 291–292, 394, 668, 709–720 IT-related mandates for, 711 in nursing informatics history, 17 Nurse–Patient Trajectory Framework, 154, 156, 156f Nurse Safety and Quality Initiative, 395 Nurses. See also specific types of nurses in advancement of evidence-based practice, 416–417 challenges of, 342–343 competencies/standards for, 266, 383, 425–427, 426t, 429, 429t, 618–619 computer literacy of, 308 credentials of, 18, 269, 280, 382–384 credibility of, 825 data model, 501 documentation burden of, 296 fatigue of, 383, 386 health literacy and, 699 importance of, 289, 309 information literacy of, 825–839 in new insurance models, 595 portfolios of, 382–383 in precision medicine/health, 635–639, 642–645 scheduling/staffing of, 381–389 self-evaluation by, 393–397 standardized language for, 131, 137–147 trustworthiness of, 163–164 turnover of, 383, 386 work environments of, 342–343, 343f, 346–347, 347f Nursing artificial intelligence apps for, 610 big data history/analysis in, 654, 656–658 communications of, 369 constrained documentation terminologies of, 443 data collection for quality improvement in, 347–348 data collection in, 266–267 definition of, 266, 308, 443 Lean philosophy in, 344

index.indd 893

I ndex   productivity concepts in, 344–346, 345f standards for, 4, 17, 18 telehealth for, 396, 619–622 theories of, 271, 272t using data to improve, 393–397 Nursing Advisory Teams (NATs), 369 Nursing and Big Data Working Group, 436 Nursing applications programs, 60–61 Nursing content standards, 681 Nursing data science, 663–672 Nursing diagnoses, 447, 449f Nursing documentation. See Documentation, clinical Nursing Informatics Australia (NIA), 278 Nursing Informatics Competencies Assessment Level 3 and Level 4 (NICA L3/L4©), 276 Nursing Informatics History Collection, 24 Nursing Informatics International Research Network, 231 Nursing informatics (NI) certifications for, 269 code of ethics for, 184 definition of, 267–268, 456, 677, 715 goal of, 265, 267 guiding documents for, 266–267, 266t within health informatics, 428, 428f history of, 3–24, 307, 444, 711 models for, 269–270, 272t as nurse specialty, 268–269, 280, 711 overview of, 267–268 professional organizations for, 87, 268, 276–279, 277t, 296–299 qualitative, 794 quantitative, 794 methodology, 794 process, 794 research applications, 794 secondary analytics, 794 scheduling/staffing challenges of, 388–389 scope of practice for, 102–103 theories supporting, 270–275, 272t in translational science, 407–408, 407f Nursing Informatics: Scope and Standards of Practice, Second

  893

Edition (American Nurses Association), 1, 18, 266, 268, 279, 282–283, 503 Nursing Informatics Test Content Outline (ANCC), 196 Nursing Informatics Working Group (NIWG), 22, 23, 277, 297 Nursing interventions, 447, 449f, 450t Nursing Knowledge Big Data Science Initiative, 711 Nursing Management Minimum Data Set (NMMDS), 140 Nursing Minimum Data Set (NMDS), 19, 138–139, 139t, 394, 430, 802 Nursing outcomes, 447–448 Nursing Plan of Care. See Plan of Care, Nursing Nursing process analytical model for, 448–450 architecture, 42 background of, 443 data quality in, 680–681 definition of, 138 as nursing foundation, 443–444 overview of, 266, 443–444, 444f, 445 in precision medicine/health, 643f quality of, 342 steps in, 444, 445 versus system life cycle, 235, 236t terminologies for, 445f, 446–448, 446f, 447f, 448f Nursing Reference Center Plus (NRCP), 834 Nursing research. See Research Nursing Specialist Group (NSG), 277 Nursing summaries, 682–683 Nutrition programs, 779–783, 780f–782f NVivo, 812–813, 812f NVSS. See National Vital Statistics System NwHIN Exchange. See eHealth Exchange

O

OAT. See Office for the Advancement of Telehealth Obama administration, 288, 295, 301, 320, 497 Object Management Group (OMG), 129 Objectives, statement of, 201 Objects, 141

11-11-2020 21:47:20

894    I ndex Occupational therapy programs, 777, 779 Ochsner Health Network, 596 OCR. See Office of Civil Rights; Optical character recognition program OECD. See Organization for Economic Co-operation and Development OERs. See Open educational resources Office applications, 60, 64, 81–82 Office-based physicians, 290 Office for Consumer eHealth, 305 Office for the Advancement of Telehealth (OAT), 470 Office of Civil Rights (OCR), 52, 174, 293 Office of the Inspector General (OIG), 168 Office of the National Coordinator (ONC) for Healthcare Information and Technology in communications endeavors, 368, 369, 373–374 data classes and, 442–443 eHealth Exchange and, 294 EHR testing/certification and, 293–294 emergency response/planning initiatives of, 545 federal sector role of, 494 goals of, 320 Health IT Workforce Development Program, 305 in health IT–public policy link, 320–322 HITAC establishment and, 303–304 on information blocking, 320–322 interoperability and, 295–296, 323–324, 336, 442, 510 medical device communications and, 511 in nursing informatics history, 19–20 origin of, 19. 726, 164, 291, 320 patient engagement and, 568. 305 patient identification and, 324, 325 research funding from, 306 safety initiatives/resources of, 556–558, 558t security/privacy and, 164–165, 166 social determinants of health and, 185

index.indd 894

standardized terminology and, 22 standards innovation/testing and, 129–130 steps for governance from, 368 strategic plans of, 300 system life cycle resources from, 198 trusted exchange and, 321–322 usability framework and, 21 workforce training programs from, 305 OIG. See Office of the Inspector General Older adults, 576–577 Omada Health, 597 Omaha System Partnership, 528 OMG. See Object Management Group Omics, 645, 646f, 654 ONC. See Office of the National Coordinator for Healthcare Information and Technology Onduo, 593, 597 OneDrop, 591 OneFifteen, 592–593 One-way connectivity, 515 One-way interface, 208 Online learning challenges of, 743–744, 748, 769–770 course development for, 744–745 definition of, 739 effectiveness of, 748–749 ethical/legal issues in, 746–748 evaluation/accreditation of, 749, 750t faculty of, 719, 742–744 future of, 749–752 history of, 740 learning assessments in, 745 mixed reality, 783 modes of, 743, 743t, 744t quality measures of, 749, 750t student support in, 745–746 technologies used in, 741t. 740–742, 751 of TIGER initiative, 731–733, 731f total enrollment in, 749 virtual worlds for, 770–786 Online Patient Community (OPC), 571 Online support groups, 466, 467–468, 571, 761

Ontologies, 142, 143t OPCq. See Oulu Patient Classification Open Courseware, 747 Open educational resources (OERs), 741, 748 Open Office, 81–82 Open Security Controls Assessment Language (OSCAL), 511 Open source initiative (OSI), 70, 72–73, 79 Open source medical image analysis (OSMIA), 85 Open Source Observatory and Repository, 85–86 Open source software (OSS). See also FLOSS acronyms related to, 70t benefits/drawbacks of, 83–84 definition of, 73 distribution of, 71t–72t for health information systems, 70 pervasiveness of, 69–70 Open System Interconnection (OSI) model, 177, 696. 511 openECG, 85 openEHR Foundation, 85 OpenID Connect, 177 OpenMRS, 84–85 Opera browser, 81 Operating systems for home computers, 38 open source software for, 80–81 overview of, 58–59 security/privacy and, 175 for smartphones, 39 Operational safeguards, 169f, 171–174 Operations plans, 207–208 Operations research (OR), 274 Optical character recognition (OCR) program, 801 Optical media, 34–35 Optimality, 66 Optimization, 241, 481–489 Optum Health, 596 Optum Labs, 524 OR. See Operations research Order entry testing, 222 Organization for Economic Cooperation and Development (OECD), 183 Organizational behavior, 274 Organizational culture, 342, 343f, 346, 348, 414, 425f, 427

11-11-2020 21:47:20

Originating sites, 616 OSCAL. See Open Security Controls Assessment Language Oscar, 595 OSI. See Open source initiative; Open System Interconnection model OSMIA. See Open source medical image analysis OSOR.eu, 86 OSS. See Open source software Oulu Patient Classification (OPCq), 683–684, 684f Outcome-based incentives, 131 Outcomes measures, 334, 347–348, 523 Output devices, 33–34 Ovid EmCare, 828 OWL. See Web Ontology Language Ownership rights, 64–65, 78

P

PaaS. See Platform-as-a-Service Packages, software, 57–58 PACS. See Picture archiving and communication systems Pandemic and All-Hazards Preparedness Act, 537 Pandemic Influenza Plan 2017 Update, 542 Pandemic Severity Index (PSI), 540–541 Parallel activation approach, 213, 226t, 230 PARiHS. See Promoting Action on Research Implementation in Health Services Partners In Health, 84–85 Partnership for Health IT Patient Safety, 561 Partnership for Patients initiative, 556 PAs. See Physician assistants Patient advocates, 502–503 Patient Acuity System, 65 Patient assignments, 383 Patient care in evaluation of new system, 214 increased complexity of, 4 staffing/scheduling and, 383–386, 387 surveys of, 334 Patient-centered care, 698 Patient-centered communications, 368 Patient-centered outcomes research, 304–305

index.indd 895

I ndex   Patient-Centered Outcomes Research Institute (PCORI), 305, 348 Patient-centric identification, 513–514 Patient engagement barriers to, 575–577 benefits of, 568–569, 577 chronic disease and, 569, 572 communications for, 370 definition of, 502, 523, 567–568, 577 drivers of, 577, 568 in federal healthcare sector, 502 health literacy and, 570, 575–577 importance of, 305 initiatives to increase, 305, 370 outcomes measures for, 334 overview of, 567–570 patient-generated data for, 570, 573–575 in population health informatics, 523 for quality care, 684–685, 686f in quality measurement, 335 safety/quality issues and, 577 technologies for, 569, 570–573 Patient Engagement Playbook (ONC), 305 Patient experience/satisfaction, 334, 343, 356–359, 685 Patient-generated data, 529, 573–575 Patient identification, 324–325, 513–514 Patient Portal. See Portals, patient Patient-Reported Outcomes Measurement Information System (PROMIS), 334 Patient-reported outcomes (PROs), 577 Patients Over Paperwork initiative, 325, 556 PatientsLikeMe community, 359, 529, 761 Patient safety. See Safety Patient Safety and Quality Improvement Act, 558 Patient safety indicators (PSIs), 413 Patient Safety Institute, 558 Patient safety organizations (PSOs), 556, 558, 559 Patient surveys, 334 Patient to device association (P2DA), 513–514 Pattern knowledge, 103

  895

Payment models. See Value-based payments PCHR. See Personally controlled health record PCORI. See Patient-Centered Outcome Research Institute PCORnet, 305 PCs. See Personal computers PDAs. See Personal digital assistants Pediatrics, 620–621, 622 Performance, 66, 413 metrics, 413 Periodic devices. See Episodic devices Peripherals, computer system, 30, 42, 58, 207, 222 Perl (Practical Extraction and Reporting Language), 82 Personal computers (PCs). See also Computers advantages of, 17 character sets in, 40 common software packages for, 64 development of, 17 hard drive storage of, 34, 35t hardware overview of, 30–31, 38 in nursing informatics history, 17, 19 various uses of, 37–38 Personal digital assistants (PDAs), 39, 49, 65 Personal health records (PHRs), 395, 571 Personal information, 167, 173–174, 292–293 Personal online profiles, 374 Personalized healthcare. See Precision medicine Personally controlled health record (PCHR), 84 Person-centered health system, 684–685 Peru, 77 PGP. See Pretty Good Privacy Pharmaceutical services artificial intelligence apps for, 610 direct-to-consumer strategy for, 572 disruptive technology companies and, 591 drug guides for, 838 insurance plans and, 591 pharmacy standards in, 125 telehealth practice and, 624 PharmaCloud system, 574

11-11-2020 21:47:20

896    I ndex Pharmacogenetics in continuum of care, 638, 638f genomics and, 638–639, 644 nursing documentation and, 639, 640t–641t, 641–642 nursing standards and, 636 safety and, 636, 644–645 PharmGKB, 641 Phased-in activation approach, 213 PHI. See Protected Health Information; Public health informatics PHIN. See Public Health Information Network Phishing, 167 Photographs, 39 PHP Hypertext Preprocessor, 82 PHRs. See Personal health records; Population health records Physical and occupational therapy programs, 777, 779 Physical safeguards, 169f, 170–171 Physician assistants (PAs), 618 Physician Quality Reporting System (PQRS), 20 Physicians apps for, 359–360 communications support for, 368–369 shortages of, 618 in UK healthcare, 190 Physitrack, 590 PI. See Promoting Interoperability Programs Pick-list Checklist, 561–562 PICNIC, 85 PICOT format, 424 Picture archiving and communication systems (PACS), 123 PICUs, 620–621 PillPack, 591 Pilot activation approach, 213 Pioneers, of nursing informatics, 22–24 Pirated programs, 59 Plagiarism, 748 Plan of Care, Nursing, 443–450, 815–816 Plan-Do-Check-Act cycle, 220, 242, 245 Plan-Do-Study Act, 242 Planned-change theory, 271 Planning Process Group (PPG), 256, 258, 258t

index.indd 896

Platform-as-a-Service (PaaS), 171 Pledge Community, 305 PMI. See Precision Medicine Initiative; Project Management Institute Point of care (POC) solutions, 512, 832–833 Policies. See Public policies P1073 Medical Information Bus (MIB), 123 Population-based payment models, 332 Population health apps to improve, 358–359 artificial intelligence for, 609 data reporting in, 523–524 data sources for, 527–530 income and, 655–656 management of, 522 in nursing informatics history, 17 outcomes, 523, 527 rural, 787 Population health informatics, 521–527 Population health records (PHRs), 522–523 Population identification, 523 Population, of interest, 522, 523 Portals, patient consumers’ wariness of, 166 data retrieval in, 111 for improved quality, 684–685 in Meaningful Use requirements, 208 in military health system, 359, 496, 497, 502 overview of, 21, 684–685 rationale for, 370, 502 in rural communities, 359 Portfolio, nurse, 382–383 Postacute care, 322–323 PowerPoint, 81–82 PPG. See Planning Process Group PQRS. See Physician Quality Reporting System Practical Extraction and Reporting Language (Perl). See Perl Practice theories, 270 PRAPARE. See Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences PreCINAHL records, 832 Precision Medicine Initiative (PMI), 319

Precision medicine/health, 319, 609, 636–649 Preconception period, 637, 638f Predictive analytics, 112, 113, 384, 751 Premera Blue Cross, 594 Premier Health, 592, 593 Prenatal period, 637, 638f Prescriptive analytics, 112 Presentation software, 81–82 Pretty Good Privacy (PGP), 48 Primo, 831 Principles for Nursing Documentation: Guidance for Registered Nurses (ANA), 636 Printers, 38, 222 Privacy. See also Security Alexa platform and, 591 artificial intelligence challenge of, 611 breaches of, 167, 173–174 emergency planning/response and, 174 FLOSS security issues and, 76 genomics ethics and, 646–647 mHealth and, 52–53 patient identification and, 324–325 in population health informatics, 524, 525 versus safety/security, 166, 169 social media concerns about, 53, 764 USB flash drives and, 34 Private keys, 176 Problem-solving, 198, 345–346 Procedural languages, 61, 63 Process groups, 255f, 256–261, 254 Process isolation, 175 Process mapping, 237 diagram, 237 Processing speed, 37, 38, 41 Productivity, 342, 344–346, 345f, 388 Professional online profiles, 374 Programming language, 57, 60–64 Programs. See Software Project Baseline, 592, 593 Project charters, 256, 258t Project ECHO, 359–360 Project InnerEye, 594 Project integration management, 256 Project management. See also System life cycle definition of, 253, 254. 251 future of, 262 importance of, 253–254

11-11-2020 21:47:20

knowledge areas of, 254, 255t-256t overview of, 197 methodology, 197, 254 phases of, 256–261 process groups for, 254, 255f, 256–261 project governance in, 199, 199f, 261 project scope, 200-202 in software development models, 221 versus system life cycle, 235, 236t Project Management Institute (PMI), 197, 198, 254, 262 Project management office, 262, 485 Project policy, 211 Projects failed, 260 managers of, 200, 254–255, 259– 262, 485 plans for, 256, 258 proposals for, 205, 209, 483–486 scope/purpose of, 200–202, 260, 484 teams for, 200, 369–370 work plans for, 197, 202, 209, 209f PROMIS. See Patient-Reported Outcomes Measurement Information System Promoting Action on Research Implementation in Health Services (PARiHS), 410–411 Promoting Interoperability (PI) program. See also Meaningful Use current requirements of, 336 history of, 301, 302, 710 objectives of, 336 system life cycle and, 196–198, 206 Proposals for projects, 205, 209, 483–486 for research, 796–800 Proprietary software, 73, 74, 75, 78 ProQuest, 797, 828–829, 831 PROs. See Patient-reported outcomes Protected Health Information (PHI), 292–293 Protocol for Responding to and Assessing Patients’ Assets, Risks, and Experiences (PRAPARE), 187 Protocols, 48–49, 123–124 Providence St. Joseph Health (PSJH), 595 PSI. See Pandemic Severity Index

index.indd 897

I ndex   PSIs. See Patient safety indicators PSJH. See Providence St. Joseph Health PSOs. See Patient safety organizations Psychiatry, 622 PsycINFO database, 831 PP. See Spanish Version Personal Patient Profile–Prostate (P3P) P2DA. See Patient to device association Public domain, 78t Public health informatics (PHI), 522 Public Health Information Network (PHIN), 526 Public Health Service Act, 537 Public Health Surveillance, 525, 528 Public keys, 176 Public Law No: 114-255. See 21st Century Cures Act Public policy ARRA’s influence on, 320 definition of, 308 as driver of patient engagement, 568 health IT’s link to, 320–322 informatics nurses’ role in, 308 mHealth and, 53 public health informatics and, 525, 526 public’s engagement in, 325–326, 570 related to FLOSS, 77 Public relations, 211 Public–private partnerships, 296–299 Publishers, 834–835 PubMed, 63–64, 456, 457f, 608, 828

Q

QCDR. See Quality Clinical Data Registry QHINs. See Qualified Health Information Networks QPP. See Quality Payment Program QSEN. See Quality and Safety Education for Nurses Quadruple Aim, 355–356, 625. 386 Qualified Health Information Networks (QHINs), 442 Qualitative research, 796, 807–814 Quality of data, 110, 680–684 indicators of, 20 of online education, 749, 750t Quality, of care big data analysis and, 656

  897

definition of, 342 dimensions of, 343–344 evidence-based practice for, 414–415 innovations related to, 679–680 patient engagement and, 577, 684–685, 686f staffing/scheduling and, 383, 384 work ecosystem and, 342–343, 343f, 346–347, 347f Quality and Safety Education for Nurses (QSEN), 394, 429, 429t, 711–712, 712t Quality assurance, 219–220, 220f Quality Clinical Data Registry (QCDR), 528 Quality management, 109 Quality measurement apps and devices for, 335 classification of measures in, 331 in COVID19 pandemic, 337 data analytics in, 350 data collection for, 333–335, 347–350 in evidence-based practice infrastructure, 413 history of, 330–331, 678 importance of, 329–330, 337 interoperability in, 335–337 lead agencies in, 307 in Meaningful Use, 302–303 nursing data in, 680–684 patient experience/satisfaction in, 334, 343, 685 payment models in, 331–333 quality improvement, 39, 224, 337, 712t, 804 reporting of, 303 staffing/scheduling and, 384 tools for, 343–349 workplace limitations affecting, 342 Quality Payment Program (QPP), 336–337, 435, 528, 556 Quantitative research, 796 Quantitative research, 800–807 Quasi-MOOC, 741 Queen’s University Research Roadmap for Knowledge Implementation (QuRKI), 411, 416, 417 QUERI model, 411 Queries, 93–94 Querying databases, 93-94 Questionnaires, 800

11-11-2020 21:47:20

898    I ndex

R

Radio-frequency identification (RFID), 19, 47–48, 50, 514t Radiology, 30, 33 RAIDs. See Redundant arrays of independent disks RAND Corporation, 359 Random access memory (RAM), 30, 32, 33, 42 Ransomware, 168 RAPID risk assessments, 642 RCE. See Recognized Coordinating Entity RDF. See Resource Description Framework RDF Schema, 142 Readmissions, 386, 462–463 Read-only memory (ROM), 30, 32, 43 Realist review and synthesis, 411 Real-time location solutions (RTLS), 50, 515 Real-time Outbreak Disease Surveillance (RODS), 541 Real-world data (RWD), 468, 680 Real-world evidence (RWE), 468, 680 Recognized Coordinating Entity (RCE), 321, 442 Records, Computers, and the Rights of Citizens (U.S. Department of Health, Education, and Welfare), 166 RECs. See Regional Extension Centers Red Hat, 80t Reduced instruction set computers (RISCs), 41 Redundancy, 36, 106, 174, 175 Redundant arrays of independent disks (RAIDs), 46 Reference resources, 360 Reference terminologies, 140–144, 143f, 144f Referential matching, 527 Refreezing, 271 RefWorks program, 797 Regenstrief Institute, Inc., 84–85, 125, 140 Regional Extension Centers (RECs), 370 Regional health IT programs, 306 Registered health information administrator (RHIA), 269 Registered health information technician (RHIT), 269

index.indd 898

Registered nurses. See Nurses Regulations, 510 Rehabilitation Act, 746 Reimbursements, 645–646 Relational databases, 107, 108t Relative advantage, 488–489 Reliability, 174 Remote monitoring device connectivity for, 511–517 mHealth planning/adoption and, 52 overview of, 571, 616 patient engagement and, 571 for rural communities, 357–358 in telehealth, 616, 623 Reports Boolean logic and, 94 to complete audits, 111 government-required, 281 informatics nurses’ role in, 281 for patient safety, 556, 558, 559–560 in population health informatics, 523–524 for research dissemination, 814 of safety incidents, 701 for system life cycle analysis/ implementation, 240–241, 244t–247t, 247 in systems testing, 224 Representational State Transfer (REST) architectural style, 123 Request for information (RFI), 209, 210f Requirements, 481, 482, 484 Request for proposal (RFP), 209, 210f Research artificial intelligence and, 608, 610 clinical management tools for, 797, 798t–799t computerized resources for, 835–837, 836f–837f of disruptive technology disruptors, 590, 592–593 dissemination of, 814 on documentation burdens, 296 in eHealth, 398 in evidence-based practice, 408 examples of, 814–818 in federal healthcare sector, 501 on genomics, 636 on implementation science, 406–407 NIH funding of, 305–306 versus nursing data science, 667–668

ONC funding for, 306 on patient-centered outcomes, 304–305 on precision medicine/health, 638 proposal development in, 796–800 qualitative approach in, 796, 807–814 quantitative approach in, 796, 800–807 in relationship to theories, 271 resources for, 822–823 retrieval of information for, 758 right to benefit from, 524–525 social media as tool for, 466–468, 467t, 572, 764 social media studies in, 762–763 trends in, 794–795 ResearchKit, 589 Research policy, 299 ResMed, 592 Resource assessments, 484–485, 485f Resource Description Framework (RDF), 142 REST. See Representational State Transfer architectural style Retail clinics, 583 Retailers, 588, 596–597, 617, 623–624 Reverberation, 274 Review councils, 486 Review services, 832 RFI. See Request for information RFID. See Radiofrequency identification RFP. See Request for proposal RHIA. See Registered health information administrator RHIT. See Registered health information technician RISCs. See Reduced instruction set computers Risk assessments, 637, 638f, 642 Risk management, 168–170, 169f, 281, 517 RiteAid, 588 R0, 536 Robert Wood Johnson Foundation (RWJF), 289, 394, 529 Robotics, 610, 624, 625t, 783–784 RODS. See Real-time Outbreak Disease Surveillance Rolling carts. See Workstations on wheels

11-11-2020 21:47:20

ROM. See Read-only memory Rooms, patient, 515–516 Royal Dutch Association for the Advancement of Pharmacy– Pharmacogenetics Working Group (DPWG), 641 RTLS. See Real-time location services; Real-time location solutions Rural areas, 290, 359–360, 469, 619 Ruth Lilly Nursing Informatics Scholar, 279 RWD. See Real-world data RWE. See Real-world evidence RWJF. See Robert Wood Johnson Foundation RxNorm, 124

S

SaaS. See Software-as-a-service Safari browser, 81 SAFER. See Safety Assurance Factors for EHR Resilience Safety. See also Adverse events; Medication administration errors alarm use and, 517, 701 clinical decision support for, 211 eHealth and, 700–701 EHR adoption and, 553–554, 700 evidence-based practice for, 413, 414–415 health IT history and, 554 health IT program for, 562 location-centric association, 514 national initiatives for, 555–557 patient engagement and, 577 patient identification and, 324–325 privacy/security and, 169, 171, 174 professional organizations/ resources for, 557–562 quality data for, 680 software testing and, 230–231 staffing mandates, 381 of telephone medicine, 617 virtual sitter programs for, 623 Safety Assurance Factors for EHR Resilience (SAFER), 557–558, 557t Sandboxing, 175 SARA. See State Authorization Reciprocity Agreement SAS. See Statistical Analysis Services Scalability, 174

index.indd 899

I ndex   SCCR. See Standing Committee on Copyright and Related Rights Scheduling/staffing nurses, 381–389, 816 Schemata, 142–143 ScienceDirect, 831 Scope and Standards of Practice for Nursing Informatics (ANA), 102, 266, 268, 279 Scoring tools, 483 Screen savers, 59–60 Screenings, 637, 638f Scrum method, 221 SDO. See Standards development organizations SDOH. See Social determinants of health Search engines, 465 Second Life (SL), 770, 771–773, 773–783, 787 Secret Internet Protocol Router Network (SIPRNet), 499 Secure Sockets Layer (SSL), 48 Securing Electronic Health Records on Mobile Devices (NCCoE), 53 Security. See also Privacy of Bluetooth, 47 of e-mail, 48 flash drives and, 34 FLOSS issues related to, 76 goal of, 53 increased concerns regarding, 289–290 of Internet of Things, 573 legislation regarding, 164–167, 170–174, 292–293, 710 of medical devices, 168, 171, 510, 517 in mHealth, 51, 52–53 of military health systems, 499 nurse informatics certifications in, 269 versus privacy, 166 of radio-frequency ID, 48 risks to, 164, 167–168 screen savers and, 60 social media drawbacks concerning, 373 software for, 59 standards for, 123, 126 testing of, 224–225 trust framework for, 168–177, 169f wireless standards for, 47, 48–49

  899

Security technology safeguards, 169f, 176–177 Segment-targeted communications plans, 211 Self-Assessment of Nursing Informatics Competencies Scale, 276 Self-management, 698–699 SEM. See Structural equation modeling Semantic interoperability, 321 Semantic network applications, 813 Semantic Web, 758 Semiotic triangle, 141–142, 141f Sensors, 21–22, 52, 529 September 11, 2001, attacks, 536 Sequoia Project, 129, 130, 294 SGML. See Standardized General Markup Language Shareware, 73 SHARP. See Strategic Health IT Advanced Research Projects SHF. See Super High Frequency waves SHIEC. See Strategic Health Information Exchange Collaborative Shootings, 536 Sidewalk Labs, 593 Sigma Theta Tau International (STTI), 279 Simplicity, 175 SimonSezIT, 394 Simple Mail Transfer Protocol (SMTP), 48, 124 Simple Object Access Protocol (SOAP) standard, 123 SimpleTrials, 797 Simulation-based learning electronic health records in, 716, 719 faculty of, 716, 719 rationale for, 716 virtual worlds for, 770–787 Single sign-on, 177 SIPRNet. See Secret Internet Protocol Router Network SIREN. See Social Interventions Research and Evaluation Network Site licenses, 65 Six Sigma, 242, 344–348, 347f, 349t, 350 SL. See Second Life SLC. See System life cycle

11-11-2020 21:47:20

900    I ndex Smart alarms, 517 Smart glasses, 783 SMART Platforms project, 84, 296 Smart pumps, 515 Smart rooms, 515–516 SMARTIE, 85 Smartphones benefits/drawbacks of, 39, 50–51 description of, 39, 46 in emergency planning/response initiatives, 545, 546 in mHealth adoption, 52 patient engagement and, 569, 571, 572 in population health informatics, 529 for quality improvement, 335 research studies of, 817 in virtual world innovations, 783 SMEs. See Subject matter experts SMTP. See Simple Mail Transfer Protocol SNOMED International, 64, 140, 340, 695 SNOMED-CT. See Systematized Nomenclature of MedicineClinical Terms SOAP. See Simple Object Access Protocol standard SOC. See Standard Occupational Classification codes Social determinants of health (SDOH) data sources for, 185–189 definition of, 181, 184, 522 description of, 184–185, 184t, 185f in electronic health records, 185 global use of, 760 importance of, 182 informaticists’ concerns regarding, 182 international recognition of, 182–184 measurement, 185 nursing informatics resources for, 189, 189t provider–referral loop in, 189 in the United Kingdom, 189–190 visual overview of, 183f Social distancing, 626, 627 Social Interventions Research and Evaluation Network (SIREN), 187 Social media

index.indd 900

advanced practice nurses and, 463–468 benefits/drawbacks of, 373, 762–765 as communication channel, 373 as computerized resource, 838–839 definition of, 374, 760 downsides of smartphones and, 39 ethical/legal issues regarding, 466, 764 mHealth concerns regarding, 53 mobile device links to, 762 patient engagement and, 569 in population health informatics, 529–530 primary uses of, 761 research of, 762–763 as research tool, 466–468, 467t, 572, 764 in rural communities, 358 Social networking, 760–761 Social sciences, 274 Social Sciences Citation Index (SSCI), 831 Social Security Administration, 129 SocINDEX, 831 Soft limits, 515 Software. See also Apps; specific software categories of, 58–61 cloud services for, 171 common PC packages of, 64 compatibility issues regarding, 40 credentials for, 172 definition of, 57 function of, 57–58 versus hardware, 57 for home computing, 38 for ontology management, 142 ownership rights of, 64–65 programming languages of, 61–64 research trends involving, 795 revision of, 90–91 security/privacy regarding, 171, 174–177 testing of, 157–158, 159, 208, 211– 212, 219–231 Software-as-a-service (SaaS), 171 Sound Physicians, 596 Source code, 72–73 South Africa, 77, 85 Spanish Version Personal Patient Profile–Prostate (P3P), 816

Spatial analytic methods, 527 Special purpose machines, 36 Specialization, 274 Speech analysis, 607, 609 Speed, processing, 37, 38, 41 SPIRIT nursing system, 109 Sponsors, 484 SPSS. See Statistical Package for Social Sciences SQL (Structured Query Language), 63, 82, 91–94, 111, 112t SSCI. See Social Sciences Citation Index SSL. See Secure Sockets Layer St. Jude Cloud, 594 Stakeholders adoption of EHR by, 368–370 in healthcare communications, 364, 368–370, 368f HIT literacy and, 576–577 in project management planning, 256, 257f project updates to, 240–241 types of, 40 Standard Occupational Classification (SOC) codes, 292 Standard order sets, 559, 560t Standardized General Markup Language (SGML), 64 Standards nursing information standards, 449 standardization, 185, 188 Standards development organizations (SDOs), 122, 125–129 Standards of professional performance, 282-283 Standing Committee on Copyright and Related Rights (SCCR), 747 Standish Group 2015 Chaos Report, 260 Start-ups, 597–598 State Authorization Reciprocity Agreement (SARA), 746 State health IT programs, 306 Static reports, 224 Statistical analysis, 803–805 Statistical Analysis Services (SAS), 804, 805 Statistical Package for Social Sciences (SPSS), 63, 804, 805 Steering committees, 199, 199f, 200f Stetler Model of Evidence-Based Practice, 411

11-11-2020 21:47:21

Storage. See also specific devices in computer cases, 30 in computer power, 40–41 data repositories for, 106–110, 349 history of, 3–4 for home computers, 38 system/utility software related to, 58–59 terminology related to, 35t types of, 34–36 Store and forward encounter, 616 Strategic Health Information Exchange Collaborative (SHIEC), 129, 299 Strategic Health IT Advanced Research Projects (SHARP), 306 Stratification, 523 Streams, 592 Strokes, 621–622 Structural equation modeling (SEM), 802 Structural interoperability, 321 Structure, 66 Structured data, 458–462, 459t, 460f–461f Structured Query Language. See SQL STTI. See Sigma Theta Tau International Student support services, 745–746 Subject matter experts (SMEs), 502 Subjective measures, 159 Suicides, 623 Super High Frequency (SHF) waves, 47 Super users, 4, 213 Supercomputers, 36 Supportive computerized resources, 826, 834–835. 831 Surescripts, 298 Surgeries, 30–31, 138 Surveillance artificial intelligence for, 608, 609–610 CDC’s role in, 541, 542–543 data sources for, 528 definition of, 522 ethics of, 525 during pandemics, 530, 571 tools for, 526 Surveys, 334, 800 SuSE, 80t, 81 Swim lane diagrams, 237, 238f, 344 SWOT analysis, 256

index.indd 901

I ndex   Symmetric encryption, 176 Symptom management, 637, 638f, 654 Synchronous communication, 745 Syndromic surveillance, 609–610 Syntax, 60 System Design, 206-207 System life cycle (SLC). See also Project management change management in, 247–248 definition of, 197, 235 for electronic health record implementation, 196 factors affecting, 196–197 failures in, 198 for legacy system conversions, 208–212, 240 versus nursing process, 235, 236t overview of, 195–196 phases of, 196, 197–215, 198f, 216f, 235–241 versus project management, 235, 236t resources for, 198, 202 testing in, 208, 211–212, 219–231 System management utilities, 59 System module interfaces, 224 System optimization, 241 System proposal documents, 205 System software, 58–59 System theory, 66 Systematized Nomenclature of Medicine-Clinical Terms (SNOMED-CT), 22, 64, 125, 140, 697, 803 Systemic reviews, 679–680 Systems theory, 274

T

Tables, database, 107 Tablet computers, 39, 46, 52 Taiwan, 573–575, 574f TANIC. See TIGER-based Assessment of Nursing Informatics Competencies Target, 588 Tasks, 160 TCO. See Total cost of ownership TCP. See Transmission Control Protocol TCP/IP. See Transmission Control Protocol/Internet Protocol Technical analysis, 203

  901

Technical Committee (TC) 251, 126, 128 Technical specifications, 207 Technology Informatics Guiding Educational Reform (TIGER). See TIGER initiative Technology trends, 51t, 253 TEFCA. See Trusted Exchange Framework and Common Agreement Teladoc, 596 Telecommunication, 3, 46, 615, 619, 627 Teleconsultation, 616 Telehealth in acute care setting, 622–623 advanced practice nurses and, 470, 618 BYOD trend in, 51 centers for, 469 for chronic disease intervention, 623 in correctional facilities, 624 costs associated with, 621 in COVID19 pandemic, 396, 626–627, 628t in critical care setting, 619–620 definition of, 468–469 by diagnosis, 618, 618f versus eHealth, 698 funding for, 396 future of, 624 growth of, 396, 469 hardware advances and, 31 healthcare systems using, 617 history of, 616–617 implementing program for, 624–626, 626f informatics nurses’ role in, 627, 628 Internet access/speed for, 627 legislation/regulations regarding, 469–470 location of, 615–616 for Medicare, 396, 469, 619, 621 nursing care delivered via, 396 in nursing informatics history, 19 nursing standards for, 618–619 overview of, 356, 615 payment for, 619 for pharmaceutical services, 624 provider-patient interaction types in, 616 retailers in, 617, 623–624

11-11-2020 21:47:21

902    I ndex Telehealth (Cont’d.) in rural communities, 356, 469 surveillance/monitoring in, 616 versus telemedicine, 615 usaBility and, 616 workforce solutions of, 618 Telehealth Network, 469 Telehealth Resource Centers (TRCs), 470 Tele-ICU units, 619–621, 621t Telemedicine, 571, 595, 615, 623–624 Telementoring, 616 Telephone medicine, 617 Telenursing, 694, 696, 702, 710 TeleStroke, 621–622 Temporary files, 59 Tenet Healthcare Corporation, 366–367, 367f, 369–370, 372 Terminologies for changing nursing practice, 395 classification systems for, 430t for clinical documentation, 443, 681 in data collection for quality measurement, 349–350 for database management, 106 development of, 138 in evidence-based practice, 412, 430–431 in functional design documents, 204 in general healthcare, 138 for informatics field, 267 in military health system, 500 models for, 142–144 in needs assessments, 203 in nursing informatics history, 18, 22 in Nursing Plan of Care, 446–448 for nursing process, 445f, 446–448, 446f, 447f, 448f for nursing standardization, 138–147, 323–324 for patient safety reports, 558 in population health informatics, 525–526 for research studies, 802–803 standards for, 124, 131, 137–147 Terms, 142 Terrorism, 536 Tesla Team, 468 Testing. See also specific types versus quality assurance, 219–220, 220f

index.indd 902

in system life cycle, 208, 211–212, 219–231 for usability, 157–158, 159 Texas Medical Center Library, 24 Text formatting languages, 64 Text messaging, 838 Text4baby program, 335 Theories, 270–275, 409–410 Therapeutic decisions, 637, 638f Third-generation languages, 61–62 TICC. See TIGER Informatics Competencies Collaborative TIGER Informatics Competencies Collaborative (TICC), 735–736 TIGER Initiative Foundation, 728, 730 TIGER International Task Force (TITF), 732–733 TIGER (Technology Informatics Guiding Educational Reform) initiative on attributes of successful implementation, 237 collaborative workgroups of, 728, 729t–730t competency model of, 715 in evidence-based practice, 429 informatics resources from, 736 international expansion of, 732–735, 734f, 735f nursing informatics competencies from, 276, 733–736 in nursing informatics curricula reform, 713–714, 715 origin of, 394, 429, 726 overview of, 278–279, 696, 713, 731 past work of, 727–731 rationale for, 725–726 summit of, 726–728 virtual learning program of, 731–733, 731f vision of, 727, 727f TIGER-based Assessment of Nursing Informatics Competencies (TANIC), 276 TigerPlace housing, 817 TIIKO software, 456–457, 458f, 463f Time constraints, 260 Timelines, for projects, 202, 222, 223f, 228–230 TITF. See TIGER International Task Force TLS. See Transport Layer Security To Err Is Human (IOM), 290, 330, 710

Total cost of ownership (TCO), 75 Touch screens, 33 TPP. See Translational Pharmacogenetic Program TPSs. See Transaction processing systems Training on artificial intelligence, 611 in evidence-based practice, 427 funding for, 305 in genomics, 643, 647, 647t–648t increased programs for, 291–292 in optimization process, 488, 488f, 488t in patient engagement, 570 in security/privacy, 171, 173 in system design, 206, 212 Transaction processing systems (TPSs), 36–37 Translation programs, 63 Translational Pharmacogenetic Program (TPP), 644 Translational science, 407–411, 410t Transmission Control Protocol (TCP), 48 Transmission Control Protocol/ Internet Protocol (TCP/IP), 124 Transport Layer Security (TLS), 48, 177 Transport standards, 122–123 TRCs. See Telehealth Resource Centers Trial By Fire Solutions, 797 Trialability, 489 TRICARE program, 494, 496 Tri-Council for Nursing, 712 Trigger tools, 413 TRIP initiatives, 411, 415 Triple Aim, 355, 368, 386, 521, 522 Triple constraint, 260 Triple S, of SDOH data, 188 Trump administration, 325 Trusted exchange, 321–322, 526 Trusted Exchange Framework and Common Agreement (TEFCA), 288, 321–322, 442 Trust Worthy Systems, 120, 208 Tuckman’s stages, 275 21st Century Cures Act, 130, 165 disruptive care models and, 589 eHealth Exchange and, 294 Health IT Advisory Committee and, 291

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in health IT–public policy link, 320, 321–322 on information blocking, 321–322 interoperability and, 294–296, 442 overview of, 252, 287–288 patient identification and, 324–325 precision medicine/health and, 636 requirements of, 287–288 Twitter, 465–466, 763 Type definitions, 143

U

UAT. See User acceptance testing UAV. See Unmanned aerial vehicles Ubicom, 415 Ubiquitous devices, 751, 783 Ubuntu, 80t, 81 UC Browser, 81 UCONN Health, 167 UCSF Walter Laboratory, 592 UK. See United Kingdom Ukraine, 699–700 Ultra High Frequency (UHF) waves, 47 UN. See United Nations Unfreezing, 271 Unified Medical Language System (UMLS), 124 Unintended consequences, 280, 554, 557, 559, 622 Unit testing, 227t, 238–239, 239f, 245t United Health, 596, 597 United Kingdom (UK) patient engagement drivers in, 568 professional organizations in, 277 security/privacy concerns in, 168 social determinants of health in, 189–190 terminologies in, 140 workforce changes in, 599 United Nations (UN), 182 University of Connecticut (UCONN), 167 University of Kansas Medical Center (KUMC), 773–783, 787 University of Maryland School of Nursing, 716 Unmanned aerial vehicles (UAV), 784, 786, 787 Unstructured data, 458–459, 459t, 462–463 Upgrade testing, 225, 227t, 228 Urgency, sense of, 726

index.indd 903

I ndex   U.S. Core Data for Interoperability (USCDI), 288, 442–443 U.S. Department of Health, Education, and Welfare, 166 U.S. Health Information Knowledgebase, 415 U.S. Public Health Service (USPHS), 494 U.S. Standards Strategy, 122 Usability as artificial intelligence challenge, 611 definition of, 21, 230 in Human–Computer Interaction, 157–161 principles of, 21 security services for, 169f, 177 telehealth and, 616 testing for, 157–158, 159, 227t, 230 USB drives, 33, 34 USCDI. See U.S. Core Data for Interoperability User acceptance testing (UAT), 225 User Centered Staffing, 386 User experience (UX), 157, 616 User’s manuals, 212 USPHS. See U.S. Public Health Service Utility software, 58, 59–60

V

VA. See Department of Veterans Affairs Vaccines, 526, 546, 817 VADS. See Vocabulary Access and Distribution System Validation, 611 Value, of data, 665 Value risk assessments, 256 Value Stream Mapping (VSM), 344–345 Value-based healthcare, 115, 131, 588–589 Value-based payments disruptive technology and, 599 as driver of patient engagement, 568 evidence-based practice and, 435 legislation regarding, 288, 301, 336–337, 435, 710–711 quality measurement for, 331–333 scheduling/staffing and, 385, 388 Variance analysis, 413 Variety, of data, 654, 655 VCU health system, 370

  903

Velocity, of data, 654, 665 Veracity, of data, 654, 665 Verily Life Sciences, 592–593, 597 Version 5010 standards, 293 Veterans Administration. See Department of Veterans Affairs Veterans Health Information systems and Technology Architecture. See VistA Victim Tracking and Tracing System (ViTTS), 545 Video conferencing, 52 Video libraries, 23 Video Remote Interpreting (VRI), 52 Virginia Henderson International Nursing Library, 279 Virginia K. Saba Nursing Informatics Leadership Award, 279 Virtual care interactions, 616, 625 Virtual Diabetes Clinic, 593, 597 Virtual learning. See Online learning Virtual Lifetime Electronic Record (VLER), 500 Virtual private network (VPN), 177 Virtual reality future of, 751 overview of, 742, 770, 783 resources for, 785f Virtual worlds benefits/drawbacks of, 769–770, 786 future of, 786 innovation in, 783–786 most popular, 770 teaching/learning in, 770–787 VistA (Veterans Health Information systems and Technology Architecture), 89–94, 497 Visual BASIC, 62 Visual C++, 62 Visual programming languages, 62 Visualization, 527, 805 Vital statistics, 528 ViTTS. See Victim Tracking and Tracing System VLER. See Virtual Lifetime Electronic Record Vocabulary Access and Distribution System (VADS), 526 Vocabulary standards, 122–123, 137–147, 526 Voice recognition, 63, 808, 809t–811t

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904    I ndex Voice-Over Internet Protocol (VOIP), 50 Volatile memory, 43 Volume, of data, 654, 665 Volunteerism, 543, 545 VPN. See Virtual private network VRI. See Video Remote Interpreting VSM. See Value Stream Mapping

W

WAE. See Wireless Application Environment Walgreens, 588, 597 Walmart, 588, 591, 596 WannaCry, 168 WANs. See Wide area networks WAP. See Wireless Application Protocol Warfarin, 644–645 Waterfall model, 220–221 We Can’t Wait initiative, 301 Wearable technology advanced practice nurses and, 468 in changing nursing practice, 395–396 classifications of, 468 examples of, 357–358 overview of, 21–22, 46, 516 patient engagement and, 572–573 in quality measurement, 335 Web browsers, 38, 70, 81 Web Ontology Language (OWL), 142 Web servers, 81 Web-enhanced face-to-face courses, 744, 744t WebMD, 359 Weight management programs, 779– 783, 780f–782f WHA. See World Health Assembly White House Office of American Innovation, 325 Wide area networks (WANs), 41–42, 65–66 Wi-Fi Protected Access (WPA), 47 Wi-Fi technology, 47, 50–51, 123 WIISARD. See Wireless Internet Information System for medical Response in Disasters

index.indd 904

Wireless Application Environment (WAE), 47 Wireless Application Protocol (WAP), 47 Wireless devices advanced hardware systems for, 45–53 major trends in, 51t in mHealth infrastructure, 50 in nursing informatics history, 19 patient engagement and, 569 in system design/build, 207 Wireless Internet Information System for Medical Response in Disasters (WIISARD), 545 Wireless local area networks (WLANs), 47 Wireless medical telemetry systems (WMTS), 50 Wireless networks, 41–42, 47–50 Wisdom in data to wisdom continuum, 103 definition of, 273 in evidence-based practice, 432–433, 434t gaining, 113–115 in translational science, 407–408, 407f WLANs. See Wireless local area networks WMSs. See Workload management systems WMTS. See Wireless medical telemetry systems Word processing, 81–82, 796–797 Work Breakdown Structure (WBS), 258, 258t, 260 Work ecosystem for evidence-based practice, 425f quality considerations in, 342–343, 343f, 346–347, 347f Workflow analysis definition of, 237 description of, 203, 204, 205 diagrams for, 237, 238f, 239f, 487f documents for, 204, 237, 238f, 239f for optimization, 484, 486–487

Workforce training. See Training Workload management systems (WMSs), 385–389 Workload measurement, 382 Workplan, 197, 209t Workstations on wheels (WOWs), 38, 50 World Health Assembly, 182 World Health Assembly (WHA), 694 World Health Organization (WHO), 308 digital technology classifications of, 762 eHealth and, 693, 694, 697 in emergency response/planning, 535, 542 on health literacy, 576 social determinants of health and, 182, 184, 184f on social media’s misinformation, 763–764 World Wide Web, 19, 123, 758–759 World Wide Web Consortium (W3C), 123 WorldVistA, 90 Worldwide Interoperability for Microwave Access (Mobile WiMAX), 47 WOWs. See Workstations on wheels WPA. See Wi-Fi Protected Access

X

XML (Extensible Markup Language), 123, 142, 143t, 511 XP. See Extreme Programming XSight, 812 X12 Standards, 293 X12N standards, 123

Y

YAML language, 511

Z

Zika virus, 542 Zimmer Biomet, 590

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