M-Health Innovations for Patient-Centered Care 9781466698611, 9781466698628, 1466698616

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M-Health Innovations for Patient-Centered Care Anastasius Moumtzoglou P&A Kyriakou Children’s Hospital, Greece

A volume in the Advances in Healthcare Information Systems and Administration (AHISA) Book Series

Published in the United States of America by Medical Information Science Reference (an imprint of IGI Global) 701 E. Chocolate Avenue Hershey PA, USA 17033 Tel: 717-533-8845 Fax: 717-533-8661 E-mail: [email protected] Web site: http://www.igi-global.com Copyright © 2016 by IGI Global. All rights reserved. No part of this publication may be reproduced, stored or distributed in any form or by any means, electronic or mechanical, including photocopying, without written permission from the publisher. Product or company names used in this set are for identification purposes only. Inclusion of the names of the products or companies does not indicate a claim of ownership by IGI Global of the trademark or registered trademark. Library of Congress Cataloging-in-Publication Data Names: Moumtzoglou, Anastasius, 1959- editor. Title: M-health innovations for patient-centered care / Anastasius Moumtzoglou, editor. Description: Hershey, PA : Medical Information Science Reference, [2016] | Includes bibliographical references and index. Identifiers: LCCN 2015046937| ISBN 9781466698611 (hardcover) | ISBN 9781466698628 (ebook) Subjects: LCSH: Medical informatics. | Medical personnel and patient. | Medical care--Quality control. Classification: LCC R858 .M463 2016 | DDC 610.285--dc23 LC record available at http://lccn.loc.gov/2015046937 This book is published in the IGI Global book series Advances in Healthcare Information Systems and Administration (AHISA) (ISSN: 2328-1243; eISSN: 2328-126X) British Cataloguing in Publication Data A Cataloguing in Publication record for this book is available from the British Library. All work contributed to this book is new, previously-unpublished material. The views expressed in this book are those of the authors, but not necessarily of the publisher. For electronic access to this publication, please contact: [email protected].

Advances in Healthcare Information Systems and Administration (AHISA) Book Series Anastasius Moumtzoglou Hellenic Society for Quality & Safety in Healthcare and P. & A. Kyriakou Children’s Hospital, Greece Mission

ISSN: 2328-1243 EISSN: 2328-126X

The Advances in Healthcare Information Systems and Administration (AHISA) Book Series aims to provide a channel for international researchers to progress the field of study on technology and its implications on healthcare and health information systems. With the growing focus on healthcare and the importance of enhancing this industry to tend to the expanding population, the book series seeks to accelerate the awareness of technological advancements of health information systems and expand awareness and implementation. Driven by advancing technologies and their clinical applications, the emerging field of health information systems and informatics is still searching for coherent directing frameworks to advance health care and clinical practices and research. Conducting research in these areas is both promising and challenging due to a host of factors, including rapidly evolving technologies and their application complexity. At the same time, organizational issues, including technology adoption, diffusion and acceptance as well as cost benefits and cost effectiveness of advancing health information systems and informatics applications as innovative forms of investment in healthcare are gaining attention as well. AHISA addresses these concepts and critical issues.

Coverage

• • • • • • • • • •

Measurements and Impact of HISA on Public and Social Policy Nursing Expert Systems Clinical Decision Support Design, Development and Implementation Pharmaceutical and Home Healthcare Informatics IS in Healthcare Rehabilitative Technologies Telemedicine Virtual Health Technologies IT Applications in Physical Therapeutic Treatments Decision Support Systems

IGI Global is currently accepting manuscripts for publication within this series. To submit a proposal for a volume in this series, please contact our Acquisition Editors at [email protected] or visit: http://www.igi-global.com/publish/.

The Advances in Healthcare Information Systems and Administration (AHISA) Book Series (ISSN 2328-1243) is published by IGI Global, 701 E. Chocolate Avenue, Hershey, PA 17033-1240, USA, www.igi-global.com. This series is composed of titles available for purchase individually; each title is edited to be contextually exclusive from any other title within the series. For pricing and ordering information please visit http://www.igi-global.com/book-series/advances-healthcare-information-systems-administration/37156. Postmaster: Send all address changes to above address. Copyright © 2016 IGI Global. All rights, including translation in other languages reserved by the publisher. No part of this series may be reproduced or used in any form or by any means – graphics, electronic, or mechanical, including photocopying, recording, taping, or information and retrieval systems – without written permission from the publisher, except for non commercial, educational use, including classroom teaching purposes. The views expressed in this series are those of the authors, but not necessarily of IGI Global.

Titles in this Series

For a list of additional titles in this series, please visit: www.igi-global.com

Improving Health Management through Clinical Decision Support Systems Jane D. Moon (The University of Melbourne, Australia) and Mary P. Galea (The University of Melbourne, Australia) Medical Information Science Reference • copyright 2016 • 425pp • H/C (ISBN: 9781466694323) • US $225.00 (our price) Maximizing Healthcare Delivery and Management through Technology Integration Tiko Iyamu (Cape Peninsula University of Technology, South Africa) and Arthur Tatnall (Victoria University, Australia) Medical Information Science Reference • copyright 2016 • 378pp • H/C (ISBN: 9781466694460) • US $235.00 (our price) Flipping Health Care through Retail Clinics and Convenient Care Models Amer Kaissi (Trinity University, USA) Medical Information Science Reference • copyright 2015 • 306pp • H/C (ISBN: 9781466663558) • US $245.00 (our price) Healthcare Informatics and Analytics Emerging Issues and Trends Madjid Tavana (La Salle University, USA) Amir Hossein Ghapanchi (Griffith University, Australia) and Amir Talaei-Khoei (University of Technology, Sydney, Australia) Medical Information Science Reference • copyright 2015 • 414pp • H/C (ISBN: 9781466663169) • US $235.00 (our price) Laboratory Management Information Systems Current Requirements and Future Perspectives Anastasius Moumtzoglou (Hellenic Society for Quality and Safety in Healthcare, Greece & P. & A. Kyriakou Children’s Hospital, Greece) Anastasia Kastania (Athens University of Economics and Business, Greece) and Stavros Archondakis (Military Hospital of Athens, Greece) Medical Information Science Reference • copyright 2015 • 354pp • H/C (ISBN: 9781466663206) • US $245.00 (our price) Cloud Computing Applications for Quality Health Care Delivery Anastasius Moumtzoglou (Hellenic Society for Quality and Safety in Healthcare, Greece & P. & A. Kyriakou Children’s Hospital, Greece) and Anastasia N. Kastania (Athens University of Economics and Business, Greece) Medical Information Science Reference • copyright 2014 • 342pp • H/C (ISBN: 9781466661189) • US $245.00 (our price)

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In memory of my aunt Vasiliki Birtachas – A.M.



Editorial Advisory Board George Bohoris, University of Piraeus, Greece Ales Bourek, Masaryk University, Czech Republic Dimitrios Fotiadis, University of Ioannina, Greece Susan Frampton, Planetree, USA Petros Karakitsos, University of Athens, Greece Vahe Kazandjian, ARALEZ Health LLC, USA & The Johns Hopkins University, USA Dimitrios Koutsouris, National Technical University of Athens, Greece

 

Table of Contents

Foreword.............................................................................................................................................. xvi Preface................................................................................................................................................xviii Acknowledgment............................................................................................................................... xxvi Chapter 1 mHealth: History, Analysis, and Implementation.................................................................................... 1 C. Peter Waegemann, Independent Consultant and Speaker, Berlin, Germany and Boston, USA Chapter 2 mHealth R&D Activities in Europe....................................................................................................... 20 Yiannis Koumpouros, Technological Educational Institute of Athens, Greece Aggelos Georgoulas, Technological Educational Institute of Athens, Greece Chapter 3 A Collaborative m-Health Platform for Evidence-Based Self-Management and Detection of Chronic Multimorbidity Development and Progression........................................................................ 52 Kostas Giokas, National Technical University of Athens, Greece Panagiotis Katrakazas, National Technical University of Athens, Greece Dimitris Koutsouris, National Technical University of Athens, Greece Chapter 4 Integrated Platform for the Lifestyle Change and Holistic Approach to Personalized Prevention and Self-Management of Patients with High Blood Pressure................................................................ 72 Kostas Giokas, National Technical University of Athens, Greece Vassilia Costarides, National Technical University of Athens, Greece Dimitris Koutsouris, National Technical University of Athens, Greece Chapter 5 Mobile Platforms Supporting Health Professionals: Need, Technical Requirements, and Applications........................................................................................................................................... 91 Ioannis Tamposis, OraSys New Technologies S.A., Greece Abraham Pouliakis, University of Athens, Greece Ioannis Fezoulidis, University of Thessaly, Greece Petros Karakitsos, University of Athens, Greece 



Chapter 6 mHealth: Sleeping Disorders Diagnosis.............................................................................................. 115 Assim Sagahyroon, American University of Sharjah, UAE Chapter 7 The Contribution of mHealth in the Care of Obese Pediatric Patients................................................ 126 Elpis Vlachopapadopoulou, Children’s Hosp. P. A. Kyriakou, Greece Dimitrios I. Fotiadis, University of Ioannina, Greece Chapter 8 MoBip Project: To Raise Awareness about Bipolar Disorder through an 3D Pop-Up Book.............. 147 Hakan Altinpulluk, Anadolu University, Turkey Gulsun Eby, Anadolu University, Turkey Chapter 9 Mobile Health Applications Assisting Patients with Chronic Diseases: Examples from Asthma Care...................................................................................................................................................... 170 Petre Iltchev, Medical University of Lodz, Poland Andrzej Śliwczyński, Medical University of Lodz, Poland Potr Szynkiewicz, Prometriq Ltd., Poland Michał Marczak, Medical University of Lodz, Poland Chapter 10 M-Health in Prehospital Emergency Medicine: Experiences from the EU funded Project  LiveCity............................................................................................................................................... 197 Bibiana Metelmann, Greifswald University, Germany Camilla Metelmann, Greifswald University, Germany Chapter 11 Mobile Telemedicine Systems for Remote Patient’s Chronic Wound Monitoring.............................. 213 Chinmay Chakraborty, BIT Mesra, India Bharat Gupta, BIT Mesra, India Soumya K. Ghosh, Indian Institute of Technology Kharagpur, India Chapter 12 From Telecytology to Mobile Cytopathology: Past, Present, and Future............................................ 240 Abraham Pouliakis, National and Kapodistrian University of Athens, Greece Stavros Archondakis, 401 Military Hospital, Greece Niki Margari, National and Kapodistrian University of Athens, Greece Petros Karakitsos, National and Kapodistrian University of Athens, Greece



Chapter 13 The Use of Mobile Health Applications for Quality Control and Accreditational Purposes in a Cytopathology Laboratory................................................................................................................... 262 Archondakis Stavros, 401 General Military Hospital of Athens, Greece Eleftherios Vavoulidis, Medical School Aristotle University of Thessaloniki, Greece & Hippokration General Hospital of Thessaloniki, Greece Maria Nasioutziki, Medical School Aristotle University of Thessaloniki, Greece & Hippokration General Hospital of Thessaloniki, Greece Chapter 14 Computer Virus Models and Analysis in M-Health IT Systems: Computer Virus Models................ 284 Stelios Zimeras, University of the Aegean, Greece Chapter 15 Information Security Threats in Patient-Centred Healthcare............................................................... 298 Shada Alsalamah, King Saud University, Saudi Arabia Hessah Alsalamah, King Saud University, Saudi Arabia Alex W. Gray, Cardiff University, UK Jeremy Hilton, Cranfield University, UK Chapter 16 Tailored M-Health Communication in Patient-Centered Care............................................................ 319 Anastasius S. Moumtzoglou, P. & A. Kyriakou Children’s Hospital, Greece Chapter 17 The Nexus of M-Health and Self-Efficacy........................................................................................... 341 Anastasius S. Moumtzoglou, P. & A. Kyriakou Children’s Hospital, Greece Compilation of References................................................................................................................ 366 About the Contributors..................................................................................................................... 424 Index.................................................................................................................................................... 435

Detailed Table of Contents

Foreword.............................................................................................................................................. xvi Preface................................................................................................................................................xviii Acknowledgment............................................................................................................................... xxvi Chapter 1 mHealth: History, Analysis, and Implementation.................................................................................... 1 C. Peter Waegemann, Independent Consultant and Speaker, Berlin, Germany and Boston, USA The author argues that mHealth systems have been maturing since 1995, yet there remains no common definition. The widest definition encompasses not only mobile devices and digital communication systems but also the multitudes of apps and add-ons for those mobile devices and systems. Accordingly, mHealth is an indicator of emerging communication-based healthcare and an enabler of participatory health. mHealth implementation and user acceptance varies by geographical region. In the most advanced regions, mobile device and new communication systems lead to disruptive changes that improve the quality of care and reduce healthcare costs. At the same time, providers and public authorities are challenged with designing and implementing mHealth policies and security measures. Ultimately, mHealth will change healthcare procedures, the structures of healthcare, and the roles of patients and healthcare professionals. Chapter 2 mHealth R&D Activities in Europe....................................................................................................... 20 Yiannis Koumpouros, Technological Educational Institute of Athens, Greece Aggelos Georgoulas, Technological Educational Institute of Athens, Greece The authors present a thorough review on the most up to date research and development activities funded by the European Union in the m-health sector. The review brings to light the latest research directions and trends that are taking place in Europe and the world. The mhealth market is analyzed along with the focusing on the main apps and their classification. Moreover, they discuss the trends of the research topics addressed and what are the plans and future activities pushed. The obstacles faced, the pros and cons and the proposed actions, and their match to real life situations are also discussed. The chapter concludes with the current trends and the potential market for m-health solutions and innovations and how they are trying to address the global need for patient-centered care.  



Chapter 3 A Collaborative m-Health Platform for Evidence-Based Self-Management and Detection of Chronic Multimorbidity Development and Progression........................................................................ 52 Kostas Giokas, National Technical University of Athens, Greece Panagiotis Katrakazas, National Technical University of Athens, Greece Dimitris Koutsouris, National Technical University of Athens, Greece The authors argue that the ageing process of EU population has played a key role raising the prevalence of chronic diseases, with more than 80% of people in the last age group (65-74) reported to be having three or more long-term Multimorbidity or Multiple Chronic Conditions (MCCs). The main problem is that currently, clinicians have limited guidance, as well as evidence of how to approach care decisions for such patients. As a consequence, the understanding of how to best take care of patients with multimorbidity conditions may lead to improvements in Quality of Life (QoL), utilization of healthcare, safety, morbidity and mortality. The root of this problem is not narrowly confined to guidelines development and application but is inherent throughout the translational path from the generation of evidence to the synthesis of the evidence upon which guidelines depend on. Chapter 4 Integrated Platform for the Lifestyle Change and Holistic Approach to Personalized Prevention and Self-Management of Patients with High Blood Pressure................................................................ 72 Kostas Giokas, National Technical University of Athens, Greece Vassilia Costarides, National Technical University of Athens, Greece Dimitris Koutsouris, National Technical University of Athens, Greece The authors aim to address preventive solutions for high Blood Pressure (BP) by improving adherence to lifestyle changes as well as therapy compliance by patients’ education and monitoring of compliance. They aim to create a systemic solution for health promotion and disease prevention to support hypertensive citizens and healthcare professionals in co-producing healthy management and preventive care actions leading to behavioral changes. They try to join the concept of prevention centered on a) promotion of subject empowerment, b) engagement of citizen at risk, c) provision of physicians with user-friendly devices, d) supporting behavioral changes of citizens in the adherence of lifestyle protocols, e) introduction of innovative organizational models to improve healthcare system performance Chapter 5 Mobile Platforms Supporting Health Professionals: Need, Technical Requirements, and Applications........................................................................................................................................... 91 Ioannis Tamposis, OraSys New Technologies S.A., Greece Abraham Pouliakis, University of Athens, Greece Ioannis Fezoulidis, University of Thessaly, Greece Petros Karakitsos, University of Athens, Greece The authors analyze the background of applications related to medical imaging and clinical and laboratory medicine. They introduce a technological framework supporting mHealth applications in an agnostic manner. Within this framework, they present two application examples. The first application (ImaginX) supports a health ecosystem (hospitals, radiologists, clinicians, patients) medical image management. The second application (HPVGuard) supports a divergent but cooperating environment of the laboratory and clinical doctors and patients involved in cervical cancer prevention and control. The two applications are analyzed, and issues related to user acceptance and future directions are presented.



Chapter 6 mHealth: Sleeping Disorders Diagnosis.............................................................................................. 115 Assim Sagahyroon, American University of Sharjah, UAE The author discusses the use of mHealth in the monitoring and diagnosis of sleep-related diseases with a particular emphasis on sleep apnea since it is considered to be one of the most prevalent disorders. Apnea symptoms and the physiological signals associated with it are described with an overview of the current sensing technology used to capture and record these signals. The chapter continues to discuss the integration of sensors with today’s’ mobile devices to offer mhealth platforms that allow for the monitoring, diagnosis and management of sleep apnea. Chapter 7 The Contribution of mHealth in the Care of Obese Pediatric Patients................................................ 126 Elpis Vlachopapadopoulou, Children’s Hosp. P. A. Kyriakou, Greece Dimitrios I. Fotiadis, University of Ioannina, Greece The authors argue that mhealth solutions are already used for self-management, remote monitoring and counseling of several chronic conditions, including diabetes mellitus, heart failure, Parkinson’s disease, etc. Today, these solutions can result in closed loops, which support health self-management for chronic diseases, in a personalized manner. Concerning childhood obesity, those solutions can combine targeted games and motivational approaches towards both physical activity and diet. In this context, they could help in addressing this serious and global health issue, in the direction of minimizing co-morbidities and eventually preventing serious, life-threatening events. Chapter 8 MoBip Project: To Raise Awareness about Bipolar Disorder through an 3D Pop-Up Book.............. 147 Hakan Altinpulluk, Anadolu University, Turkey Gulsun Eby, Anadolu University, Turkey The authors explain how the mHealth ecosystem and Universal Design principles could be used in designing an “interactive augmented reality 3-D pop-up book” that can be viewed on mobile devices. The book addresses bipolar disorder and is the first mHealth study in the literature. Chapter 9 Mobile Health Applications Assisting Patients with Chronic Diseases: Examples from Asthma Care...................................................................................................................................................... 170 Petre Iltchev, Medical University of Lodz, Poland Andrzej Śliwczyński, Medical University of Lodz, Poland Potr Szynkiewicz, Prometriq Ltd., Poland Michał Marczak, Medical University of Lodz, Poland The authors analyze the role of m-health applications supporting patients with chronic diseases, based on examples from asthma care.



Chapter 10 M-Health in Prehospital Emergency Medicine: Experiences from the EU funded Project  LiveCity............................................................................................................................................... 197 Bibiana Metelmann, Greifswald University, Germany Camilla Metelmann, Greifswald University, Germany The authors show that smartphone applications that allow retrieval of data or real-time communication with a remote medical expert can be brought to the emergency site. In this context, high definition video communication offers the highest amount of mHealth communication currently available in prehospital emergency medicine. In the LiveCity EU funded project, a special video camera was developed and tested showing an improvement of the quality of patient care. Chapter 11 Mobile Telemedicine Systems for Remote Patient’s Chronic Wound Monitoring.............................. 213 Chinmay Chakraborty, BIT Mesra, India Bharat Gupta, BIT Mesra, India Soumya K. Ghosh, Indian Institute of Technology Kharagpur, India The authors describe the implementation of a mobile telemedicine system for the monitoring of chronic wounds. The main objective of their work is to design and develop a tele-wound technology network (TWTN) that acquires, and processes information in monitoring chronic wounds. Chapter 12 From Telecytology to Mobile Cytopathology: Past, Present, and Future............................................ 240 Abraham Pouliakis, National and Kapodistrian University of Athens, Greece Stavros Archondakis, 401 Military Hospital, Greece Niki Margari, National and Kapodistrian University of Athens, Greece Petros Karakitsos, National and Kapodistrian University of Athens, Greece The authors argue that there are limited mobile applications relevant to cytopathology. However, mobile applications could be used in numerous activities of the cytopathology laboratory, including and not limited to: training, reporting, diagnosis and consultation, laboratory management, whole slide imaging, interactions between patient-doctor, doctor-doctor and within the laboratory personnel, quality control and assurance. Chapter 13 The Use of Mobile Health Applications for Quality Control and Accreditational Purposes in a Cytopathology Laboratory................................................................................................................... 262 Archondakis Stavros, 401 General Military Hospital of Athens, Greece Eleftherios Vavoulidis, Medical School Aristotle University of Thessaloniki, Greece & Hippokration General Hospital of Thessaloniki, Greece Maria Nasioutziki, Medical School Aristotle University of Thessaloniki, Greece & Hippokration General Hospital of Thessaloniki, Greece The authors present a thorough research of mobile applications related to cytopathology and try to foresee applications that may benefit the modern cytopathology laboratory and its clients. The feasibility of adopting mobile applications for inter-laboratory comparisons, proficiency testing, and diagnostic accuracy validation is also examined. Finally, the role of mobile applications for providing or/and enhancing the existing laboratory capabilities through educational training and other research activities is investigated.



Chapter 14 Computer Virus Models and Analysis in M-Health IT Systems: Computer Virus Models................ 284 Stelios Zimeras, University of the Aegean, Greece The author argues that viruses quickly spread through the Internet exploiting security holes. Epidemiological models have traditionally been used to understand and predict the outcome of virus outbreaks either in human or animal populations. However, the same models were recently applied to the analysis of computer virus epidemics. Chapter 15 Information Security Threats in Patient-Centred Healthcare............................................................... 298 Shada Alsalamah, King Saud University, Saudi Arabia Hessah Alsalamah, King Saud University, Saudi Arabia Alex W. Gray, Cardiff University, UK Jeremy Hilton, Cranfield University, UK The authors define a common collaboration-driven information security while identifying requirements in Legacy Information Systems to address the inconsistent policies in modern PC collaborative environments that would help improve the quality of care. Chapter 16 Tailored M-Health Communication in Patient-Centered Care............................................................ 319 Anastasius S. Moumtzoglou, P. & A. Kyriakou Children’s Hospital, Greece The author argues that emerging M-Health technologies provide fundamentally different ways of looking at tailored communication technology. As a result, tailored communications research is poised at a crossroads. It needs to both build on and break away from existing frameworks into new territory, realizing the necessary commitment to theory-driven research at basic, methodological, clinical, and applied levels. In this context, the revolution of M-Health holds great promise in both health care and public health. The chapter envisions tailored M-Health communication in the context of patient-centered care, as it remains to be seen whether the revolution in M-Health will provide the tools to engineer sufficient impact on patient-centered care and tailored communication. Chapter 17 The Nexus of M-Health and Self-Efficacy........................................................................................... 341 Anastasius S. Moumtzoglou, P. & A. Kyriakou Children’s Hospital, Greece The author explains that self-care emerged from the concept of health promotion in the 1970s while from 2000 onwards the term ‘self-management’ gained popularity, with a greater focus on long-term conditions and the trend towards more holistic models of care. Although ‘self-management’ and ‘selfcare’ are often used interchangeably, a distinction between the two concepts can be made. Both can be considered in terms of a continuum, with self-care at one end as ‘normal activity’ and self-management an extension of this. Self-management support is the assistance given to patients in order to encourage daily decisions that improve health-related behaviors and clinical outcomes. Self-efficacy, which is grounded in social cognitive theory, is defined as confidence in one’s ability to perform given tasks. The chapter envisions these concepts on a continuum with one pole representing mobile health and the other self-efficacy. It concludes that self-management support is the nexus of mobile health and self-efficacy.



Compilation of References................................................................................................................ 366 About the Contributors..................................................................................................................... 424 Index.................................................................................................................................................... 435

xvi

Foreword

The emerging field of mHealth (mobile health) is the practice of medicine and public health with the use of mobile devices. The explosion versatility of mobile devices in recent years, such as smartphones, tablets, smart wrist watches and intelligent sensors gave the platform to rapid deployment of mHealth in healthcare systems across the world. mHealth has become now an important field within the eHealth domain. The mHealth implementations can reach countries which are less developed due to the diminishing cost of mobile devices and the variety of health applications that may be installed with. Third world countries are already taking advantage of the benefits of mHealth applications and populations in rural areas may reach healthcare services which were not possible before with the traditional means. Besides the clinical environment where mobile and monitoring devices and sensors are utilized, the community medicine and public health may find that mHealth applications are quite appropriate to minimize cost and improve quality of care for the patient and in prevention for the citizens. This book is an excellent compilation of chapters providing an insight to the mHealth principles, applications and assessment. In the beginning, definitions and principles of mHealth are explained. Later, a review chapter brings to light the latest research directions and trends that are taking place in Europe and the world. The mhealth market is analyzed along with the focusing on the main apps and their classification. Moreover, the authors discuss the trends of the research topics addressed and what are the plans and future activities pushed. Collaborative platform of mHealh is presented to increase the quality of life in a disease specific environment. Personalized prevention and lifestyle management is facilitated by mHealth monitoring approaches. It is also well documented that by providing support to healthcare professionals by means of integrated environments that mHealth applications may easily provide, can really increase the quality of healthcare services. A number of chapters focus on important mHealth applications in the clinical domain from sleeping disorders to treating bipolar patients in the psychiatric specialty. Paediatric applications also exemplify the importance of mHealth, whereas laboratory medicine receives a special attention since through its evolvement has now reached the point where it can become mobile and reach the patient at the point of care. Epidemiological models in public health are reviewed for the mHealth field. The mHealth environment is a special area where the security of individual’s data should be treated with the utmost secrecy. Special attention is given to security in as specific chapter. Finally, the mHealth facilitates the patientcentred approach which is required in the healthcare services. The chapter envisions tailored M-Health communication in the context of patient-centered care, as it remains to be seen whether the revolution in M-Health will provide the tools to engineer sufficient impact on patient-centered care and tailored communication. Furthermore, the final chapter envisions the concepts of self-management and self-care  

Foreword

on a continuum with one pole representing mobile health and the other self-efficacy. It concludes that self-management support is the nexus of mobile health and self-efficacy. As it is stated by WHO, the mHealth field operates on the premise that technology integration within the health sector has the great potential to promote a better health communication to achieve healthy lifestyles, improve decision-making by health professionals (and patients) and enhance healthcare quality by improving access to medical and health information and facilitating instantaneous communication in places where this was not previously possible. It follows that the increased use of technology can help reduce health care costs by improving efficiencies in the health care system and promoting prevention through the communication of behavior change communication. The mHealth field has proved a powerful potential to advance clinical care and public health services by facilitating health professional practice and communication through the use of mobile technology. This book is a good step in achieving the objective of disseminating the benefits and challenges of the mHealth applications. John Mantas University of Athens, Greece

John Mantas is Professor of Health Informatics and Director of the Laboratory of Health Informatics at the University of Athens, Greece. His current research interests are in health information systems, patient safety, biomedical informatics, and management of healthcare. He is the organiser for more than ten years of the International Conference on Informatics, Management, and Technology in Healthcare. Professor Mantas is author of more than 250 scientific publications. He has supervised more than 200 Master’s theses and 30 doctoral dissertations. He currently lectures on Introduction to Informatics, Health Informatics, Hospital Information Systems, Biomedical Informatics and Technology, and Special Issues in Biomedical Informatics Research. He is the author and the main editor of ten international published books in English. He is serving in many international scientific journals as associate editor and reviewer. For many years served as advisor and expert in European Commission panels of experts. He led many European initiatives in the educational field of Health Informatics. From 1990, he is the Coordinator and Director of the Inter-University Master’s programme in Health Informatics. He was the President of the Faculty of Nursing and Head of Department of Public Health at University of Athens, and Vice-President of the Board of Trustees and Dean of the School of Health Sciences of the Cyprus University of Technology. He was the President of the European Federation for Medical Informatics from 2010 to 2012, and Vice-President of IMIA from 2012 to 2014.

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xviii

Preface

Person-centeredness is widely recognized as a multidimensional concept that advocates patients’ informed decisions, successful management of their own health and care, and choice when to invite others to act on their behalf (Silva, 2014). It is a conception that comprehends patients as peer partners in planning, developing and assessing care. In other words, person-centered care is about co-production rather than consumerism. Moreover, the Institute of Medicine prioritizes six dimensions of patient-centeredness as decisive to supporting quality healthcare. These are (US Institute of Medicine, 2001): • • • • • •

Being respectful of patients’ values, preferences, and expressed needs Being coordinated and integrated Providing information, communication and education Ensuring physical comfort Providing emotional support and easing fear and anxiety, and Involving family and friends

However, reviewers have argued that the model of person-centered care is somewhat rhetorical and equates to ‘consumer-based’ model rather than a psychosocial approach. Moreover, they also contend that there is no unopposed definition of person-centered care in the empirical literature (Silva, 2014). As a result, the complexity of the theory raises the need to articulate its shared meaning and explicate how it can be put into use. Additionally, the term ‘patient-centered care’ which is more frequently used than person-centered care and tends to describe a much wider range of disease areas has often been analyzed as a multifaceted construct (Ishikawa, Hashimoto, Kiuchi, 2013) with no single theory that can sufficiently define the whole idea or lacking a unified definition and operationalized measurement (Silva, 2014). On the other hand, notwithstanding patient-centered care may be considered of modern origin, its essence can unquestionably be found in the Hippocratic Oath. Respect and broad-mindedness to the patient needs, relevant ethics, and concern for community well-being are prominently evident in Hippocrates. However, this inclination to the origins of medicine has been long discontinued. Beneficence as a bioethical teaching has lost part of its radiance, dominated by the belief of autonomy and by the current emphasis on defending the medical commonality. As a consequence, medicine has missed its holistic focal point, which patient-centered philosophy aims to regenerate for patients. The holistic notion upholds that each aspect of patient’s needs including corporeal, social and mental should be taken into account and perceived as a whole.  

Preface

How exactly, do you do that? What does ‘emotional, spiritual and mental needs’ look like in a doctorpatient encounter? The doctor-patient relationship can be seen as a social mechanism for salubrious impact on the patient’s well-being (Benedetti, 2011). The important point is to realize why this social interplay is necessary to stimulate the endogenous mechanisms that handle expectation and placebo outcomes. However, the reason a social mechanism of that kind surfaced in the course of evolution appears to be considerably reasonable. There are numerous benefits of altruism and social partnership. Suppression of psychological uneasiness by human interactions warrants a robust mechanism to recover, at least in part. In this context, following evolutionary theory, the healthcare system can be more complicated and can acquire the qualities of an actual endogenous system. According to Humphrey (2002), the ability to stimulate expectation in addition to placebo mechanisms following the doctor-patient encounter is an emergent issue and essential feature of the ‘natural healthcare service’. Humphrey (2002) claims that patient’s body together with the brain have a considerable role in healing themselves but that capacity for self-cure is not revealed spontaneously, but can be triggered by the influence of the doctor. Therefore, the pivotal point is to realize why the patient-doctor encounter is needed to initiate the self-cure mechanisms. The conceptualization of an endogenous healthcare system by Humphrey (2002) is extremely useful to know why the doctor-patient encounter is necessary in order to trigger expectation in addition to placebo mechanisms in the patient’s brain. Doctors and health professionals represent environmental variables that act on the patient’s brain by inducing expectancies of benefit and hope. Health professionals are crucial actors in this process, as they promise treatment and induce expectations and hope for the patient’s future well-being. The patient’s expectations also play a key role. If the patient wants to consult a physician, this is because of his beliefs about the doctor’s healing skills. Therefore, the ‘healer’ is the environmental variable that triggers endogenous mechanisms of self-cure. From both an evolutionary, neuroscientific and patient-centered care perspective, it is obvious that the therapist belongs to the system and has a pivotal role in triggering all mechanisms that take place in the patient’s brain. Conclusively, patient-centered care should be defined as the symmetry of the artful and the perfunctory element that is represented by the Ancient Greek word ‘techne’. As a result, patient-centered care is the competence to produce a preconceived outcome using consciously controlled and directed action, which involves (Moumtzoglou, 2014): • • •

The undivided completeness and universality of human health defined as the state of being free of physical or psychological malfunction The rational and ethical principles used by health professionals to distinguish between different procedures, and observe the correct diagnosis and action in each case The environmental variables that act on the patient’s brain by inducing expectancies of benefit and hope

THE CHALLENGES The health care environment is currently changing to meet technology and societal trends which converge to bring into being new communication patterns that connect and coordinate the roles of healthcare stakeholders. At the same time, the healthcare industry is steering inexorably toward a distributed-service design in which essential decision-making occurs at the point of care. One of the central engines of this xix

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shift towards decentralization and reorientation of healthcare services is mobile healthcare (mHealth). mHealth describes the use of a broad range of telecommunication and multimedia technologies within a wireless care delivery design and can be broadly defined as the delivery of healthcare services via mobile communication devices. mHealth establishes healthcare communities in which every stakeholder can participate. However, it disrupts the traditional service model where healthcare information, security and access is centrally managed, maintained and limited, transforming the healthcare sector and destroying components that are slow to adapt. mHealth interventions range from simple to complex applications and systems that remotely coordinate and actively manage patient care. In this context, it offers an elegant solution to the problem of accessing the right information where and when it is needed within highly fluid, distributed organizations. Moreover, it removes geography and time as barriers to care by establishing connectivity with remote locations and remote workers, creates new points of contact with patients, and changes the frequency and intensity of healthcare delivery. It also establishes effective new treatment modalities like telehealth, remote patient monitoring, self-care and home health while it blurs the boundaries between professional medical advice and self-care. Overall, mHealth blends three bodies of knowledge: high technology, life sciences, and human factors. On current trends, mHealth embraces medical and public health practice sustained by mobile phones, patient monitoring devices, personal digital assistants (PDAs), and tablet PCs. The spread of 3G and 4G networks has boosted the use of mobile applications offering healthcare services. 4G is a mobile network, IP-based, providing a connection via the best network using seamless roaming and independent radio access technologies. In 4G mobile systems, different access technologies are combined in the best possible way for different radio environments and service requirements. They promise much larger data rates supporting full mobility while enabling wireless connection and access to multimedia services with high-quality voice and high-definition video. In addition, the availability of satellite navigation technologies in mobile devices supports safety and autonomy of patients. Through sensors and mobile applications, mHealth permits the accumulation of extensive medical, physiological, lifestyle, daily activity and environmental data. Consequently, mHealth serves evidence-driven care practice and research activities while expediting patients’ access to health information and accommodating lifestyle and wellbeing applications, counseling systems, health information and medication reminders. However, beyond clinical connectivity, mHealth is a field that came to light holding the promise of quality improvement, cost reduction, wholesale gains in population health, access to care and better allocation of health-delivery resources. With mHealth, healthcare professionals can continuously monitor and manage health conditions. As a result, mHealth becomes embedded in some care delivery strategies, including the medical home, a health information exchange, the care team and patient-centric healthcare. In its fullest flowering, mHealth it is expected to address the most intractable problems of healthcare quality and cost, chronic disease management, public health, wellness, and prevention. However, the impact of mHealth is just beginning to be felt as it results in more personalized medication and treatment and contributes to patient-centered care.

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SEARCHING FOR A SOLUTION There is no standardized definition of mHealth. However, in most cases, mobile health or mHealth is defined as medical and public health practice supported by mobile devices involving: • •

The use and capitalization on a mobile phone’s core utility of voice and short messaging service Applications including general packet radio service (GPRS), third and fourth generation mobile telecommunications (3G and 4G systems), global positioning system (GPS), and Bluetooth technology

Mobile health became functional in biomedical engineering and started with looking at wireless and sensor technologies that could be incorporated to monitor people’s health at a distance. mHealth implementation came out in developing countries out of access necessity. Moreover, mobile phones had been around for years, but it was not until 1976 that mobile phones first appeared in Japan. However, a lot of work happened predominantly in the early millennium when mHealth started to develop mobile health applications for cellphones. The early days there were things like remote cardiac monitors that evolved to look at diabetes monitoring and other types of sensor technologies. The early programs provided support tools for supply chain management while mobile communications gave access to areas that people never had using fixed line telephones. More recently, mHealth evolvement provided access to emergency medical transportation services, facilitated patient-doctor encounter, and there was a movement to personal digital assistants use. mHealth is increasingly being used in the healthcare field since its use is becoming a cost-effective method of identifying and monitoring health issues, as well as guiding the formulation of health policies. Programs to support the professional development of people in the health field, using mHealth technology, are becoming readily available. mHealth also provides health professionals with access to patient data as well as access to various information sources, both of which provide valuable assistance in the diagnosis and formulation of treatment. Individuals can use mHealth to access resource materials on health issues. Patients can self-monitor and transmit information to their health care provider making mHealth particularly important to people living in remote areas or those who are physically impaired. While the timely emergence of mHealth did not resolve the myriad problems, it offers unique opportunities to reduce cost, increase efficiencies and improve the quality and access to care. Home-based monitoring helps hospitals track patient recovery and compliance, thereby minimizing costly episodes of re-admission. Coordination between departments and providers reduces wasteful spending and improves the quality of care. Moreover, with rapid consumer adoption of smartphones, physicians can perform two-way videoconferencing while patients and physicians have access to medical records and vital signs. Finally, wireless technology allows physicians to serve more patients despite geographical limitations. In this context, the book explores the emergence of mHealth in the healthcare setting by: • • • •

Focusing on the broad range of technologies available Tackling the effects of mHealth on the industry and stakeholders exploring the infrastructure and architecture needed to support these technologies Discussing the evolvement of various stakeholders and the impact of mHealth on existing technology Analyzing the transformation of the business model xxi

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Looking forward, it explores how mHealth reshapes access, quality, and treatment and demystifies the impact of mHealth on patient-centered care. Conclusively, it intends to: • • • •

Support students understand the effect of mHealth technologies on quality in healthcare Help healthcare professionals better understand the needs of their patients Act as an assistant for patients to derive more benefits from their healthcare Encourage e-health systems designers and managers to ground everyday practice on mHealth technologies

The prospective audience includes undergraduate and extended degree programs students, graduate students of health care quality and health services management, executive education and continuing education, health care managers and health professionals.

ORGANIZATION OF THE BOOK In Chapter 1, Peter Waegemann argues that mHealth systems have been maturing since 1995, yet there remains no common definition. The widest definition encompasses not only mobile devices and digital communication systems but also the multitudes of apps and add-ons for those mobile devices and systems. Accordingly, mHealth is an indicator of emerging communication-based healthcare and an enabler of participatory health. mHealth implementation and user acceptance varies by geographical region. In the most advanced regions, mobile device and new communication systems lead to disruptive changes that improve the quality of care and reduce healthcare costs. At the same time, providers and public authorities are challenged with designing and implementing mHealth policies and security measures. Ultimately, mHealth will change healthcare procedures, the structures of healthcare, and the roles of patients and healthcare professionals. In Chapter 2, Yiannis Koumpouros & Aggelos Georgoulas present a thorough review on the most up to date research and development activities funded by the European Union in the m-health sector. The review brings to light the latest research directions and trends that are taking place in Europe and the world. The mhealth market is analyzed along with the focusing on the main apps and their classification. Moreover, they discuss the trends of the research topics addressed and what are the plans and future activities pushed. The obstacles faced, the pros and cons and the proposed actions, and their match to real life situations are also discussed. The chapter concludes with the current trends and the potential market for m-health solutions and innovations and how they are trying to address the global need for patient-centered care. In Chapter 3, Kostas Giokas, Panagiotis Katrakazas & Dimitris Koutsouris argue that the ageing process of EU population has played a key role raising the prevalence of chronic diseases, with more than 80% of people in the last age group (65-74) reported to be having three or more long-term Multimorbidity or Multiple Chronic Conditions (MCCs). The main problem is that currently, clinicians have limited guidance, as well as evidence of how to approach care decisions for such patients. As a consequence, the understanding of how to best take care of patients with multimorbidity conditions may lead to improvements in Quality of Life (QoL), utilization of healthcare, safety, morbidity and mortality. The root of this problem is not narrowly confined to guidelines development and application but is inherent

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throughout the translational path from the generation of evidence to the synthesis of the evidence upon which guidelines depend on. In Chapter 4, Kostas Giokas, Vassilia Costarides & Dimitris Koutsouris aim to address preventive solutions for high Blood Pressure (BP) by improving adherence to lifestyle changes as well as therapy compliance by patients’ education and monitoring of compliance. They aim to create a systemic solution for health promotion and disease prevention to support hypertensive citizens and healthcare professionals in co-producing healthy management and preventive care actions leading to behavioral changes. They try to join the concept of prevention centered on a) promotion of subject empowerment, b) engagement of citizen at risk, c) provision of physicians with user-friendly devices, d) supporting behavioral changes of citizens in the adherence of lifestyle protocols, e) introduction of innovative organizational models to improve healthcare system performance In Chapter 5, Ioannis Tamposis, Abraham Pouliakis, Ioannis Fezoulidis & Petros Karakitsos analyze the background of applications related to medical imaging and clinical and laboratory medicine. They introduce a technological framework supporting mHealth applications in an agnostic manner. Within this framework, they present two application examples. The first application (ImaginX) supports a health ecosystem (hospitals, radiologists, clinicians, patients) medical image management. The second application (HPVGuard) supports a divergent but cooperating environment of the laboratory and clinical doctors and patients involved in cervical cancer prevention and control. The two applications are analyzed, and issues related to user acceptance and future directions are presented. In Chapter 6, Assim Sagahyroon discusses the use of mHealth in the monitoring and diagnosis of sleep-related diseases with a particular emphasis on sleep apnea since it is considered to be one of the most prevalent disorders. Apnea symptoms and the physiological signals associated with it are described with an overview of the current sensing technology used to capture and record these signals. The chapter continues to discuss the integration of sensors with today’s’ mobile devices to offer mhealth platforms that allow for the monitoring, diagnosis and management of sleep apnea. In Chapter 7, Elpis Vlachopapadopoulou & Dimitrios I Fotiadis argue that mhealth solutions are already used for self-management, remote monitoring and counseling of several chronic conditions, including diabetes mellitus, heart failure, Parkinson’s disease, etc. Today, these solutions can result in closed loops, which support health self-management for chronic diseases, in a personalized manner. Concerning childhood obesity, those solutions can combine targeted games and motivational approaches towards both physical activity and diet. In this context, they could help in addressing this serious and global health issue, in the direction of minimizing co-morbidities and eventually preventing serious, life-threatening events. In Chapter 8, Hakan Altinpulluk & Gulsun Eby explain how the mHealth ecosystem and Universal Design principles could be used in designing an “interactive augmented reality 3-D pop-up book” that can be viewed on mobile devices. The book addresses bipolar disorder and is the first mHealth study in the literature. In Chapter 9, Petre Iltchev, Andrzej Śliwczyński, Potr Szynkiewicz & Michał Marczak analyze the role of m-health applications supporting patients with chronic diseases, based on examples from asthma care. In Chapter 10, Bibiana Metelmann & Camilla Metelmann show that smartphone applications that allow retrieval of data or real-time communication with a remote medical expert can be brought to the emergency site. In this context, high definition video communication offers the highest amount of mHealth communication currently available in prehospital emergency medicine. In the LiveCity EU funded project, a special video camera was developed and tested showing an improvement of the quality of patient care. xxiii

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In Chapter 11, Chinmay Chakraborty, Bharat Gupta & Soumya K. Ghosh describe the implementation of a mobile telemedicine system for the monitoring of chronic wounds. The main objective of their work is to design and develop a tele-wound technology network (TWTN) that acquires, and processes information in monitoring chronic wounds. In Chapter 12, Abraham Pouliakis, Stavros Archondakis, Niki Margari & Petros Karakitsos argue that there are limited mobile applications relevant to cytopathology. However, mobile applications could be used in numerous activities of the cytopathology laboratory, including and not limited to: training, reporting, diagnosis and consultation, laboratory management, whole slide imaging, interactions between patient-doctor, doctor-doctor and within the laboratory personnel, quality control and assurance. In Chapter 13, Archondakis Stavros, Eleftherios Vavoulidis & Maria Nasioutziki present a thorough research of mobile applications related to cytopathology and try to foresee applications that may benefit the modern cytopathology laboratory and its clients. The feasibility of adopting mobile applications for inter-laboratory comparisons, proficiency testing, and diagnostic accuracy validation is also examined. Finally, the role of mobile applications for providing or/and enhancing the existing laboratory capabilities through educational training and other research activities is investigated. In Chapter 14, Stelios Zimeras argues that viruses quickly spread through the Internet exploiting security holes. Epidemiological models have traditionally been used to understand and predict the outcome of virus outbreaks either in human or animal populations. However, the same models were recently applied to the analysis of computer virus epidemics. In Chapter 15, Shada Alsalamah, Hessah Abdullah Alsalamah, Alex W. Gray & Jeremy Hilton define a common collaboration-driven information security while identifying requirements in Legacy Information Systems to address the inconsistent policies in modern PC collaborative environments that would help improve the quality of care. In Chapter 16, Anastasius Moumtzoglou argues that emerging M-Health technologies provide fundamentally different ways of looking at tailored communication technology. As a result, tailored communications research is poised at a crossroads. It needs to both build on and break away from existing frameworks into new territory, realizing the necessary commitment to theory-driven research at basic, methodological, clinical, and applied levels. In this context, the revolution of M-Health holds great promise in both health care and public health. The chapter envisions tailored M-Health communication in the context of patient-centered care, as it remains to be seen whether the revolution in M-Health will provide the tools to engineer sufficient impact on patient-centered care and tailored communication. In Chapter 17,Anastasius Moumtzoglou explains that self-care emerged from the concept of health promotion in the 1970s while from 2000 onwards the term ‘self-management’ gained popularity, with a greater focus on long-term conditions and the trend towards more holistic models of care. Although ‘self-management’ and ‘self-care’ are often used interchangeably, a distinction between the two concepts can be made. Both can be considered in terms of a continuum, with self-care at one end as ‘normal activity’ and self-management an extension of this. Self-management support is the assistance given to patients in order to encourage daily decisions that improve health-related behaviors and clinical outcomes. Self-efficacy, which is grounded in social cognitive theory, is defined as confidence in one’s ability to perform given tasks. The chapter envisions these concepts on a continuum with one pole representing mobile health and the other self-efficacy. It concludes that self-management support is the nexus of mobile health and self-efficacy. xxiv

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REFERENCES Benedetti, F. (2011). The patient’s brain: The neuroscience behind the doctor-patient relationship. Oxford, UK: Oxford University Press. Humphrey, N. (2002). The mind made flesh. Oxford, UK: Oxford University Press. Ishikawa, H., Hashimoto, H., & Kiuchi, T. (2013). The evolving concept of ‘patient-centeredness’ in patient-physician communication research. Social Science & Medicine, 96, 147–153. doi:10.1016/j. socscimed.2013.07.026 PMID:24034962 Moumtzoglou, A. (2014). Redefining patient-centered care. International Journal of Reliable and Quality E-Healthcare, 3(2), iv–v. Silva, D. (2014). Helping measure person-centred care: A review of evidence about commonly used approaches and tools used to help measure person-centred care. London: The Health Foundation. US Institute of Medicine. (2001). Crossing the quality chasm: A new health system for the 21st century. Washington, DC: National Academy Press.

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I would like to thank the editorial advisory board for their invaluable advice, the reviewers for the care with which they reviewed the manuscripts and all the authors for their diverse and outstanding contributions to this book.

 

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mHealth:

History, Analysis, and Implementation C. Peter Waegemann Independent Consultant and Speaker, Berlin, Germany and Boston, USA

ABSTRACT mHealth systems have been maturing since 1995, yet there remains no common definition. The widest definition encompasses not only mobile devices and digital communication systems, but also the multitudes of apps and add-ons for those mobile devices and systems. Accordingly, mHealth is an indicator of emerging communication-based healthcare and an enabler of participatory health. mHealth implementation and user acceptance vary by geographical region. In the most advanced regions, mobile devices and new communication systems lead to disruptive changes that improve the quality of care and reduce healthcare costs. At the same time, providers and public authorities are challenged with designing and implementing mHealth policies and security measures. Ultimately, mHealth will change healthcare policies and procedures, the structures of healthcare, and the roles of patients and healthcare professionals.

INTRODUCTION Mobile phones are replacing wallets, credit cards, some printed books, travel and admission tickets, cameras, CDs and DVDs, printed newspapers, magazines, photo albums, game sets, landline telephones, keys, and much more. Yet the biggest change is in the expanded access to knowledge. Ask a smartphone any question, and it will give an answer (in most cases). Additionally, it will help when one has lost her way, and it will provide reminders and support in professional or life tasks. Mobile devices increase intelligence by enabling research or ‘looking up’ what a person does not know, and they help people perform better in their professions. Think of lawyers, engineers, accountants and many other professionals for whom the smartphone is the daily tool that enhances their work and makes them knowledge workers. One obvious sign of expanded knowledge through artificial intelligence (AI) is evidenced in geographical information systems. The navigation system embedded in a mobile device was unthinkable just a few decades ago. Today, people rely on its support wherever they go, drive, or bike. DOI: 10.4018/978-1-4666-9861-1.ch001

Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 mHealth

In this context, one has to admit that both cultural and technological achievements are not based solely on intellect. Since the beginnings of writing, knowledge (i.e. memory) has had to be supplemented by books, notes, and other written material. Now, digital systems expand the potential range of knowledge beyond that which can be memorized. In medicine, the doctors of the 21st century cannot keep all the information of the scientific body of medicine between their ears. The volume is simply too much. Access to ‘outsourced’ knowledge through the Internet is essential for good healthcare delivery. The mobile device enables this at any time and anywhere. In addition, customized apps for mobile devices expand many professional and medical functionalities. It is important to remember that intelligence is the main element of all living beings on this planet (Waegemann, 2012). Mobile phones are a means of support to human intelligence. In the past, access to knowledge and information was limited to the books people had available at home, in the office, at a library, or other place. Through the connectivity of the Internet, anyone can access information with a computer device, whether it is a desktop computer, a tablet, watch, intelligent glasses, or a smartphone. For thousands of years, the main human occupation was physical labor. As more and more humans evolve from laborers to knowledge workers, information access and information management defines their work and life. Most industries are changing. Look at education, for example. The smart kid of the past was good in memorizing. The smart kid of the future is good at navigating the digital information system, accessing information, putting it in context, and distinguishing ‘false’ information from that which fits into one’s belief system and is therefore deemed correct. The digital society is leading to dramatic changes on our society and the future of mankind. In healthcare, these changes are currently barely visible. While other industries are experiencing a rapid revolution, healthcare is going through a slow and rocky transition that began with its attempt to convert paper medical records into electronic health records and moved into the beginnings of mHealth in 2002. As mHealth matures, mobile devices and their capabilities are stimulating disruptive changes.

BACKGROUND mHealth technologies are built on the ‘miracle’ of sending information through the air. Wireless transmission of information goes back almost 150 years when radio transmission was first envisioned, and the first patent for a ‘photophone’ was issued in 1880. The one-to-many wireless communication capability of the radio established it as a major information source during the first half of the 20th century, supplemented soon by TV services. The 20th century was the era of wired communication as telephones and telecommunication advanced. For thousands of years, people have been trying to develop devices that could process mathematical tasks. With the help of electricity, sophisticated computing devices could process complex mathematical tasks such as deciphering a secret code; hence the term computer for devices that now can do much more than computing functions. For almost 50 years, those computers have been able to perform most human intelligence functions, including converting speech to text and vice versa, decision-making according to algorithms, learning from experience (machine learning), and logical thinking by using data to extrapolate knowledge. Integrated circuits can quickly process information, and software quickly enables most general arithmetic and logical operations and in very small devices. The Internet provides unprecedented connectivity and functional interoperability between these devices, although semantic interoperability between electronic medical record systems and with mobile devices has not yet been fully achieved.

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Figure 1. Three Levels of mHealth Implementation

So could a small phone achieve the same tasks as a full sized computer? Since the room-sized computers of the 1940s, there have been many innovative techniques for packing increased amounts of computational circuitry into smaller and denser spaces. The miniature computer processors of today contain millions of closely packed transistor components of near atomic size that can be packed into mobile computing devices. The birth of mobile phones occurred in the 1970s, when computer functions merged with wireless communication, enabling any digital device connected to the system to send and receive information. Devices of various sizes have similar features, including pocket-sized smartphones and page-sized tablets, as well as portable notebooks. The lines are blurring between full-sized personal computer devices (PCs), (old-fashioned) cellphones, smartphones, tablets, notebooks, and tablets with phone capabilities (phablets). The early decades of the 21st century are focusing on the mobile device that became a digital companion because of its value to the owner. However, there are developments that move most functions from the mobile device to the people and their immediate environment, such as the glasses or “intelligent” shirts one wears, which have integrated computers. In healthcare, the “Internet of Medical Things” (IoMT) includes basic items, such as mirrors, toothbrushes, scales, and even pills containing very small computing devices and artificial intelligence. In the 1990s, the Mobile Healthcare Alliance (MoHCA) was one of the first organizations to recognize the potential of handheld computers in healthcare. It was followed by the Center for (cell) Phone Applications in HealthCare (CPAHC) and later by the mHealth Initiative and other mHealth organizations. mHealth implementations vary in 2015 according to geographic regions. In developing countries (level 3 in Figure 1) with insufficient infrastructure conditions, mHealth provides basic life-saving and critical options through verbal advice from healthcare professionals who are geographically removed from rural areas where care is needed. Level 2 includes advanced countries that are able to use the Internet and other digital communication methods, but whose mHealth implementations are restricted by “anti-digital communication” cultures, excessive data protection requirements, and other cultural impediments. A research2guidance survey (2015) ranks the market readiness and maturity of mHealth implementation in Europe. Denmark, Finland, The Netherlands, Sweden, and the United Kingdom are shown to have the highest market readiness and provider willingness to implement mHealth (called market maturity). Five factors are taken into account to measure mHealth readiness: eHealth adoption, level of digitization, market potential, the ease of starting a new business (for Health IT), and regulations. (In level 3 countries, mHealth serves a

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public health role as well as a resource tool for healthcare information and remote healthcare delivery, allowing access to providers in areas where medical professionals are not on site. North America has the most advanced implementations of mHealth systems. In 2015, the average US hospital reported the deployment of 169 smartphones while the larger university hospitals talk of thousands. Hospitals report the positive impact of smartphones and tablets (HIMSS Analytics, 2014). However, one has to distinguish between patients’ functions of daily life and medical professional activities by physicians and nurses (the two main user groups). “Functions of daily life” include email, texting family members, appointment calendars, scheduling of meetings, navigating assistance while driving, note taking, taking pictures for personal reasons (for instance, of grandchildren), checking the weather, taking voice or written notes, reading books or newspapers on tablets, or making purchases online. Professional (medical) functions include accessing patient or medication data, researching scientific, medical knowledge, using the mobile device for specific care functions, etc. The smartphone can be, and is for many healthcare professionals, an essential tool for delivering healthcare in the 21st century.

WHAT IS mHEALTH? There is no widely agreed-upon definition of mHealth. Some organizations focus on the tremendous potential that mobile phones offer for promoting health in Africa, Asia, South America, and other less developed regions. Others consider mHealth mainly as the field of fitness and medical apps. Moreover, some organizations consider mHealth and telemedicine as synonymous. Even the term connected health is sometimes confused with mHealth. The mHealth Initiative, however, supported the broadest view of mHealth that involved seven information management clusters (see Figure 2). This vision has gained wide international acceptance.

mHealth and Telemedicine Telemedicine is the bilateral connectivity between two providers. Information is shared between providers, and expertise is transferred between them. Telemedicine consists of one-to-one communication between clinics or hospitals, sometimes also between doctors’ offices and hospitals or universities. Connected

Figure 2. Seven functionalities of mHealth

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health, on the other hand, can include multiple systems’ information interchange. In contrast, mHealth is not limited to provider information sharing. It also involves multiuser connectivity, the professional use of medical apps, the inclusion of artificial intelligence in small, mobile computers, i.e., it enables a new structure of healthcare. This structure will eventually move healthcare from the doctor’s office (or hospital or clinic) to a virtual space between the home, the medical offices, and other wellness and fitness providers. mHealth has seven distinct functionalities: 1. Empowering patients with access to Internet health resources specifically designed for patients and consumers. 2. Providing Internet information access for clinicians, thereby enabling them to research the Internet while with the patient and thereafter. 3. Offering new tools for physicians (apps and smartphone features as well as add-ons). 4. Enabling new communication patterns among patients and providers as well as among healthcare professionals. 5. Providing new solutions for research, financial, and administrative systems. 6. Enabling sensing, tracking, and therapeutic tools. 7. Allowing documentation (anywhere, anytime). Another, more detailed view of mHealth describes twelve application clusters (Tessier, 2010). 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12.

Patient communication Access to Web-based resources Point-of-care documentation Disease management Education programs and telemedicine Professional communication Administrative applications Financial applications Ambulatory/EMS services Public health Pharma/clinical trials Body area networks

This expanded view gives special attention to three areas not mentioned in other mHealth analyses, namely disease management, ambulatory/emergency medical services (EMS), and body area networks (BANs). mHealth in disease management guides a patient to manage his symptoms better. EMS allows the real-time collection and transmission of data by emergency health professionals from the time they pick up a patient and throughout patient transport, thus allowing the hospital personnel to be fully prepared to begin treatment immediately upon the patient’s arrival. Body area networks allow communication between implanted or wearable computer devices and nearby IT devices, monitoring of body functions

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such as heart rate and rhythm, liver functioning, blood glucose, etc. Although this field is still in its infancy, it is expected to be a major factor in treatment planning, delivery, and monitoring in the future. An analysis of mHealth must include considerations of the stakeholders throughout the selection, implementation, and review processes in order to minimize resistance and optimize success (Tessier, 2012).

Internet Health Resources for Patients and Consumers In countries with the greatest Internet use, patients use the Internet as a resource for health issues. A 2013 survey showed that between 77% and 80% of US patients research medical conditions or drug information on the Internet, 34% to 62% search for information about doctors, and 22% to 37% visit websites seeking personal health information (Catalyst Healthcare Research, 2014). With add-on sensing devices the mobile phone can collect data such as blood pressure, blood sugar levels, and heart rate, and can even monitor implants. For the chronically ill, appropriate accessories take blood pressure, measure blood values, check on asthma symptoms, capture nutritional information, and other health data. In other words, the device is becoming the dashboard for critical information about our bodies. In addition, there are thousands of fitness apps worldwide that help patients with nutrition, exercise, and behavioral issues. They offer inspiration, guidance, or information concerning health and fitness. They encourage people to exercise more, lose weight, or simply have fun with a specific sport or exercise program. Fitness apps also record sports and general activities, for instance, how many steps a person has taken or how fast she runs or bicycles. They support communication-based disease management and provide first-hand assistance. Wellness apps track a person’s general well being, body flexibility, back exercises, balancing, and more.

Internet Access for Physicians The scientific body of medicine has grown so much that any physician is well advised to use the resources accessible through the Internet. When the physician is in the exam room or away from his desk, the mobile phone is the primary research device. In 2013, the Orca Team (2012) reported that the smartphone and tablet are trending to be the most popular tech device for doctors since the stethoscope. Patients reported to mHealth Initiative (Anonymous, personal communication, September 1, 2008) that they feel better about the competence of a physician if he answers a question with “Let me research this. I’ll get back to you.” Yet the general search on medical sites on the Internet is often limited. The trend increasingly goes toward using apps.

New Tools for Clinicians Apps for clinicians provide primarily large amounts of data that a user cannot easily remember, by using algorithms that combine such rich data with functions. They are part of a long-term movement toward transferring the scientific body of medicine into “bite-sized” working apps that help doctors, nurses, and others in their daily professional work. In other words, what they have traditionally learned in medical school is offered in updated and easy-to-use-formats at the workplace. Apps extend, support, and partly replace the clinician’s professional knowledge.

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Categories of Clinical Applications • • • •

• • •

Drug Formulary apps Reference programs Educational apps Medical tools ◦◦ Patient information and documenting tools ◦◦ Patient monitoring apps ◦◦ Nursing apps ◦◦ Imaging apps ◦◦ Clinical apps Payer tools Decision support tools Patient support tools

Looking up data in books creates problems when such books have become outdated. Also, it means that the clinician has to lug books around or store multiple copies at various places of work, i.e., in the exam room, at the desk, in other rooms, etc. The lightweight, small portable smartphone is easily carried and allows updating from a central location.

Drug Formulary Apps There are tens of thousands of apps in healthcare. Drug formulary management was the most successful early use of an mHealth application. Healthcare plans in the United States and many other countries have established a formulary for clinicians that identifies which medications are allowed and which are not. Drug formulary management enables patients and physicians to choose clinically appropriate and cost-effective drugs for a given condition and is considered by the World Health Organization an essential part of good healthcare. In most cases, the lists are far too large for any physician to memorize.

Reference Programs The scientific body of medicine has grown in size so much that it is not possible for a clinician to memorize what was learned in medical school and what has developed since. Healthcare professionals are dependent on ways to quickly search and access further information, often while still with the patient. There are several avenues for accessing medical information. In medical schools, for instance, mobile devices are often used for searching literature in addition to other information sources (Wallace, Clark, & White, 2012). Also, most medical journals provide access to their journals through mobile phones (Joo, 2013). MEDLINE/PubMed is probably one of the most used platforms as it facilitates smartphone searches of medical databases to find published information. Various apps such as PubSearch, PubMed on Tap, Medscape, MEDLINE Database on Tap (MD on Tap or MDoT), Docphin, Docwise, Read by QxMD, ask MEDLINE and PICO also provide easy, quick access to information by the healthcare practitioner.

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Educational Apps From 2005 onwards, mobile phones, tablets, and other mobile computing and communication devices have been playing an important role in medical education. Students can learn anywhere, log their experiences, access information about medical conditions and drug treatment, perform calculations, make notes, test their knowledge in a quiz-app, access online text books, work with online text books, use medical calculators, search for unfamiliar terms, manage their tests, and communicate. The role of mobile devices for continuing education of healthcare professionals includes continuing medical education (CME) apps.

Medical Tools As mentioned above, the mobile phone is competing with the stethoscope as the most important tool for physicians, nurses, and most healthcare professionals (Waegemann, 2002). There are thousands of apps, and the number is increasing monthly. For the purpose of providing the reader an overview, apps are divided into a number of categories. Patient Information and Documenting Tools Documenting healthcare is a major challenge for healthcare as it is essential (1) to create a history of a patient’s health (previously the patient’s medical record), (2) to satisfy legal and regulatory documentation, and (3) to use such information for decision-making processes. Documentation, originally done by a physician with handwriting and dictation has moved during the last decades to a mixture of dictation, computer-generated, and system-generated entries. This created a number of problems because the professional had to return to her desk for recording information. For instance, a physician had to make notes or memorize data while in the exam room. Later in the office (or in front of his desk top computer), she had to recall the information in order to document it. Alternatively, in another example, nurses had to memorize information gathered at the bedside until they reached the nursing station to record it. Naturally, this led to a loss, and sometimes a distortion, of information. In contrast, mobile devices allow healthcare professionals to record information at the point of care, providing much better accuracy in addition to convenience. They also allow speech recording, sometimes coupled with speech to text conversion. This is implemented in a number of special healthcare apps. Yet the benefits remain limited to date. That is, a mobile app has not yet been developed that allows easy general healthcare documentation that is accurate and goes through an essential quality assurance process. When this becomes available, a major hurdle in healthcare will be overcome. The advantage of receiving urgent information anywhere is a major contributor to the quality of care. With mobile devices, physicians can check the status of a critical patient and receive and act on lifecritical lab data at home, outside the office, and so on. Rather than receiving an old-fashioned phone call or waiting for a written report, physicians can see graphics such as blood pressure charts, fetal heart tracing “strips”, ECG tracings, and other timely representation of patient data. As speech recognition becomes more refined in the medical field and natural language processing is integrated, one can expect more sophisticated mobile devices with appropriate apps. Some of the documentation will be done by the system itself. For instance, instead of documenting an order for a test, then documenting the test results and creating a report on the findings, there will be an integrated system in which the clinician documents the test order and interprets the test results. Everything else can be done automatically. 8

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Patient Monitoring Apps Mobile devices are a great help for remotely monitoring the health, medical behavior (compliance with instructions, medications, etc.), or even the location of patients. For instance, an ECG can be connected to a mobile phone in order to diagnose and follow the treatment of patients with sleep apnea (Ozdalga, Ozdalga & Ahuja, 2012). Special pillboxes that show when a patient is taking his medication have been tested since 2010 but have not yet received mainstream adoption. In behavioral health, mobile devices monitor patients’ movements, particularly when they tend to stray off the property, such as Alzheimer patients. These are just a few examples of an expanding range of apps in the patient monitoring field. Nursing apps Much of the literature is neglecting the impact of mHealth on nursing. The use of mobile phones has reached mainstream use in nursing in North America. The majority of nurses use smartphones for many personal and daily activities, just as doctors do. For nursing apps, a Wolters Kluwer survey (2012) showed that 71% use a smartphone for their job, 66% use it in education. 89% were keen on a drug guide for nursing.1 Nursing apps fall into several categories, each category with a multitude of competing apps. Nursing coordination is probably the most complex app. Typically, the app connects frontline clinicians and the care team to their patients and interacts with the EHR infrastructure. It provides workflows, clinically contextual communications, and configurable care interventions. Drug management for nurses ranges from a simple app that displays pills to determine in triage what medication a patient is taking (when the name of the medication is not known) to complex systems for filtration rates and dosage calculation. Another nursing-specific app is a digital immunization reference manager. Apps for disease management help nurses in point-of-need situations. Some apps help to manage lab test results and also understand commonly performed lab tests. Many apps contain solutions for sharing information with other caregivers in the hospital by sending messages with clinical images. Imaging Apps Some FDA-approved apps allow radiologists to “interpret images on the go.” The limitations of the small screen are substantial, but there are occasions when a radiologist does not have access to a traditional viewing station and in such situations, the app can help. Other apps include communication by fax, creating “standard” reports on the mobile device, searching medical journals, brushing up on common (and not so common) cases, and helping fulfill CME requirements. Interesting apps in ophthalmology have the ability to combine some commonly used clinical evaluation tools, such as near vision cards, color vision plates, a pupil gauge and ruler, a pen light, a fluorescein light, pediatric fixation targets, Amsler grids, a Worth 4 Dot test, red desaturation tests, and even an OKN drum simulator. These apps can be helpful in inpatient consultant settings and at emergency room visits. As with many other specialties, there are also educational apps for ophthalmologists that provide among other things, a list of diagnoses not to miss. In situations outside the ophthalmologist’s office, photos can be taken in various situations.

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Clinical Apps Some hardware features of mobile devices can be put to clinical use. Here are just a few examples. The Otoscope uses the iPhone to view the inner ear with magnification. It also can take pictures that can be sent to the general physician or a specialist. Another is the AliveECG, a single-lead electrocardiogram reader that attaches to the smartphone to the patient and can be used by intensive care units and others outside of the traditional ECG setting. There also are a number of competing smartphones that act as glucometers. They provide non-invasive (not having to prick one’s finger) ways to measure serum glucose. Yet those mobile devices serve more than just as a glucometer; they also track blood glucose over time, show carbohydrate intake, and insulin dose, thereby enabling a patient to manage the regimen of diabetes care by posting alerts, keeping a log, and offering reminders and convenient communication paths. Dermatology is another field where mobile devices have shown great benefits both to patients and physicians. A patient can take a photo of a rash, wound, or dermatological irregularity and send the picture to a specialist for inspection. An advanced model called “Dermatoscope” fits over an iPhone and takes up to 20x magnification. It also has a polarized light to better show the skin problem, such as a skin lesion. To make ultrasound available for medical applications when a professional high-quality ultrasound is not available, systems have been developed that use the phone’s screen as a display. The developers foresee that every internist will be able to use this “entry-level” ultrasound device to detect vascular problems, gallstones, kidney stones, abdominal masses, and other problems. Other current uses of mobile devices include the use of a smartphone as Petri Dish, Eyepiece Digital Adapter, spirometer, breathalyzer, and even brain scanner. The app to measure tremor frequency has been reported to match the sophisticated and expensive devices used for electromyogram analysis. These are just some examples of mobile devices and apps providing alternatives to traditional devices.

Payer Tools Of course, healthcare financial systems vary from country to country. It is common in many countries that a need exists for both healthcare practitioners and patients to have real-time access to financial information about therapies and procedures. At present, in only isolated cases are costs accessible before or when the patient leaves the doctor’s office, the clinic or the hospital. This field is expected to expand in the near future.

Decision Support Tools A new generation of decision support tools helps at the point of care. Such apps allow the clinician to input patient-specific information along with reference material to receive diagnosis information or details on the patient’s condition. A study of 21 disease diagnosis applications published by BioMed Central provides details on the advantages of decision-supporting apps (Mosa, Yoo, & Sheets, 2012).

Patient Support Tools Patient support tools are part of disease management systems, allowing the treatment care team to manage or at least closely monitor the patient. They are most often used in diabetes cases, heart symptom management, and other chronic diseases. Apps to help patients manage their fitness and health are described further below. 10

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New Communication Systems As impressive as the impact of mobile devices and apps are for care settings, the opportunities they offer for new communication systems are even more promising. Communication-based healthcare enabled through mHealth marks a major evolution point in healthcare. The key changes are •

•

Traditionally, patients have been treated in doctors’ offices, clinics or hospitals or other facilities. Only in isolated, rare cases have physicians visited patients in their home. The place of care has mainly been the exam room, the doctor’s office, the ward in the hospital, etc. This is changing. The place of care in a fully developed mHealth environment is a virtual place encompassing the primary care physician’s office, the home, the offices of other healthcare providers (specialists, imaging, and other related service providers including PTs and OTs), and fitness providers (massage therapists, fitness trainers, yoga instructors, etc.), as well as pharmacies. Some of the health measurements, such as blood pressure and other vitals, are more easily and reliably measured in the home, at the office, or workplace. The coordination of these places of care and their direct interaction will enhance the quality of care. Online video visits are increasing as a virtual replacement of the traditional patient-physician encounter. An American Well survey (2015) found that the majority of US residents are willing to use an online video for a physician visit; 61% of the respondents found that this is more convenient to them. Online patient-physician communication is most wanted for prescription refills, birth control prescriptions, antibiotics in emergency situations, and routine prescriptions for chronic conditions. Online video visits are on the increase in many regions.

Instead of periodic patient visits, communication-based healthcare offers non-structured communication between the primary care physician and the patient. When a patient is trying to explain symptoms in the physician’s office several days after they occurred, his memory is likely to exclude some experiences and emotions may motive him to exaggerate others. mHealth offers constant digital communication; that is, the patient can describe how he felt after taken his medication or after a special meal, when the pain subsided or increased, or when he was stressed, etc. This leads to a better understanding of the medical problem. Many patients report that they cannot relate everything relevant in the traditional, emotionladen 10 or 15-minute encounter. Likewise, many patients report that, after they get home, they cannot remember what the physician said. Digital communication allows a patient to express her concerns in a quiet setting and to review the physician’s reply. Such patient documentation may contain observations of daily living (ODLs), personal notes that document increasing or decreasing pain, symptoms that may occur and then disappear, reactions to activities or intake of food or medications. They can likewise provide information about the quantity and quality of sleep or even daily changes in stress levels. mHealth systems allow the easy, reliable sharing of such data and the management of the information flow at the provider (Project Health Design, 2012). •

An evolution in the patient/physician relationship is underway in many countries. Patients are increasingly being encouraged to understand their health issues and to actively and consciously participate in their care. In the past, patriarchal care treated the patient like a child who does not understand her health problems. In participatory health, the patient owns copies of the full documentation of her medical records, namely a copy of, or parts of the reports kept by the primary

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care physician, as well as copies of records from specialists. In addition, the patient is encouraged to keep a personal health diary, which is a personal patient record in which the patient documents her view of her health status and personal health observations that she may or may not share with her care providers. Traditionally, a doctor worked in his office, had patients visit him there, and had little communication with other healthcare providers treating the same patient. mHealth enables active communication among all professionals involved in a patient’s health, wellness, and fitness issues. Various apps, as well as software programs, enable secure and convenient communication among various professionals such as the triage nurse, the primary care physician, specialists, rehab therapists, etc. •

•

It is envisioned that in a fully implemented mHealth system patients will visit their primary care physicians less frequently. Patients will send their symptoms, sometimes with pictures or readings from devices they keep at home, to the physician’s office. The provider may reply with instructions for further tests to be taken before the patient takes up valuable time at the office. This system will help to address the anticipated future shortage of primary care physicians and will cut costs in the healthcare system. Additionally, trials by laboratories allow patients to order lab tests directly, that is, they do not have to go through the traditional physician ordering. Apps and other systems allow better communication among healthcare professionals through video calls in which images and charts can be shared. This mHealth communication is particularly important as providers begin to have professional conference calls in which triage nurses, emergency technicians, primary care physicians, and specialists provide input. This will end traditional clinical silo thinking, where primary physicians and specialists practiced medicine in the past with little or no communication among themselves and even less with the support staff.

These new communication methods will create a new healthcare structure that both improves the quality of care and reduces costs.

Research and Financial Systems In some countries, healthcare analytics are done with the use of mobile devices, thus enabling large-scale research. Also, patients are being given custom smartphones for recording their personal health data for use in research by pharmaceutical companies. In Africa and other regions, mobile devices provide a framework for public health to assess the health system challenges (Leon, Schneider, & Daviaud, 2012). Numerous projects use such devices to collect data at the patient or provider level, providing valuable public health information. The collection of patient data, previously called data mining, sometimes reaches extensive proportions, particularly in level 1 countries in Figure 1. Called Big Data, they now provide such information as the medications physicians prescribe in response to specific symptoms, where patients buy their medications, success rates of procedures and surgery in different hospitals, the risk-taking level of physicians who recommend invasive therapies, financial data by physician or provider organizations, etc.

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As described above, there are beginnings of implementing real-time financial systems by using mHealth devices and systems. Health knowledge analytics represent the new approach of collecting data on patients’ conditions, behavior, medication use, visits to providers, and other fields and allowing cost comparisons at the same time.

Sensing, Tracking and Therapeutic Tools A mobile phone or tablet can be equipped with add-on equipment that senses body symptoms or takes measurements, monitors activities, and aims at motivating a person toward a certain behavior, such as smoke cessation. Some apps have been described above.

Patient Apps The ultimate success of mHealth depends on educating patients on the benefits of participating in their healthcare processes and on motivating them to keep their own records. For this purpose, it is important to understand the history of patients’ roles in healthcare. In many regions, cultural custom had it that an illness was a divine punishment. As a result, people would not talk about their health problems and wanted to keep a cloud of privacy around their health issues. They were embarrassed and protective about their health problems. The oath of Hippocrates also contributed to a sense of privacy, as did the belief that the doctor knows best: A patient should follow the doctor’s advice ‘blindly’. The emerging information society of the 20th century brought some changes. Beginning in the 1980s, concerned patients started to collect their own health information. The Internet opened up a new window into medicine. Patients were able to look up symptoms and/or side effects of the medications prescribed by their doctors. They could see discussions addressing whether to consent to surgery or to seek alternative therapies. However, in the early days of the 1990s, the official warning was, “Don’t trust the Internet.” By 2005, this cautionary message had widely disappeared. By 2010, patients were warned of physicians who would not use the Internet when they did not know the answer to a medical problem. At present, more than three-quarters of the North American population uses the Internet for a second opinion, often through their mobile device as they leave the doctors office (for instance, to check whether a newly described medication could cause adverse reactions in respect to the medications prescribed by another specialist). With the arrival of the digital age, many companies, standards committees, and other organizations have worked on a digital version of a patient health record. Some were software-based databases to be used on the patient’s computer; others used USB sticks to be carried by the patient to the provider office. Although a USB stick is not the “traditional” mobile device, such as a cell phone, it has a role as an intermittent communication tool. Both a lack of trust in the technology and confidentiality concerns made these attempts fail. For instance, in a project in Greater Boston, MA, USA, more than 200 parents were given their children’s patient data on a USB stick for use when their children sought treatment in the local hospitals/clinics/doctors’ offices. However, hospitals or clinics would not use the USB stick because of fear that a computer virus might be embedded on the USB stick. (E. Marcus, personal communication, January 4, 2007)

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The Medical Records Institute followed more than 100 start-up operations that provided various forms of patient record systems; almost all failed, with the exception of a few, such as Google Health, Apple Health, and some others. A company in Pennsylvania, USA, developed a safe personal health record system that was based on smartphones, particularly on the iPhone, but this project failed due to a lack of acceptance. The problem of loading the initial set of patient data onto a mobile device seemed insurmountable at that time. (Anonymous, personal communication, March 10, 2007) Although the number of patients who keep their own patient record has remained small, the use of Internet searches and personal health applications has blossomed. Literally thousands of apps motivate people to exercise. Other apps guide and motivate patients in regards to nutrition and sleep. Many apps, of course, go beyond a general fitness purpose. Some offer initial diagnoses. People can input symptoms and find some clues as to what is ailing them, and according to the initial diagnosis, they will be referred to specialist doctors in their area. Others offer guidelines for dealing with emergency situations. The range of apps covers initial advice to patients, detailed instructions for some chronic diseases, analyzing patients’ data, coaching patients according to nutritional or disease-specific guidelines, and communicating with providers. Taken all together, these new mHealth options represent a drastic disruptive change in healthcare. Patient communities have sprung up in which hundreds, in some cases thousands of patients, discuss how they experience their diseases, analyze specific diagnoses, share side effects of their medications, describe pain levels, and explain side effects of their therapies (chemotherapy, for example). Patients share their experiences openly and address personal experiences at a depth for which doctors or nurses do not have the time (or sometimes, the experience). Although many of these group discussions are not taking place on mobile devices, more and more are, and they represent a significant component of mHealth.

mHealth Systems It must be remembered that mHealth systems are neither uniform nor standardized, and many are homegrown. As individual healthcare providers learn about a new app, they are often willing to try it. Decisions on the organizational level are made by CIOs or department heads, technical personnel, or other leaders within an organization. Only in rare cases of coherent IT systems, such as Kaiser Permanente in the United States, can one find a cohesive approach to mHealth. Many CIOs do not know exactly which mobile devices are being used in their organization, nor do they know who deploys which apps. They do not have control over mHealth apps because physicians, nurses, technicians, and others have purchased their own mobile devices and software. In addition, users delete apps when they are found to be inconvenient or not helpful. These variations and fluctuations make the management of mHealth systems difficult. Shortly after the iPhone was introduced to the public, several hospitals in the United States and in Canada decided to install a documentation system based on these smartphones and to enable professionals to use them for accessing patient data and documenting during the encounter. In addition to purchasing hundreds of smartphones, they had to develop an information system that linked these smartphones to their electronic health record system, resulting in costs of millions. The result had a mixed success rate. (Anonymous, personal communication, May 15, 2007) Many vendors of electronic medical record (EMR) systems have included smartphones for a variety of applications. Although they may be considered add-ons to the EMR, they do represent mHealth solutions. Also mHealth devices and applications are being integrated into health information systems in hospitals or other provider organizations. When such devices are added to HIS system, the buyers

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and/or implementers of mHealth systems are individuals, such as doctors, nurses, technicians, systems administrators, CIOs, department heads, and information system professionals. There is no consistent system design, except (according to hearsay) in some international state-run systems or major health plans in North America. Because of the variety in device and system use, it is important that one consider necessary policies and procedures for mHealth implementation.

Policies In many clinics and hospitals, particularly in the United States, nurses, physicians, and others are using apps that they became aware of and download on their own, that is, without consideration of how they will fit into the organizational IT ecosystem. Some chief information officers (CIOs) have at least started surveys to understand how many apps are used in their organization and for which purpose. Yet keeping an actual inventory is not easy as individuals learn about apps, download them in seconds or minutes (cost is no issue because of the very low price tag), and if they don’t see benefits from an app, they are likely to delete them and try something else

Keeping an Inventory of Apps Is Recommended for any Healthcare Organization Yet there are department heads and others who do not approve of the logic of certain apps. They discourage those working in their organization from using specific apps. There have been anecdotal reports of instructions to not use mobile devices at all, or, in other cases, not to use specific apps. It has also been reported that some of the critics’ attitude toward the use of mobile devices must be seen as remnants of decades-old policies of hospitals and clinics that did not allow cell phone usage in fear of radioactive interference with medical devices. Except in special situations, there is no reason to ban any device for this reason. Since 2010, the general consensus is that modern smartphones are not a risk in regard to electromagnetic interference.

Who Should Own the Devices? In the large majority of cases, physicians, nurses, and other healthcare professionals bought their smartphones. First, the Blackberry was considered the most secure smartphone for healthcare use but it was also considered hopelessly outdated. Then the iPhone was purchased by the majority of healthcare providers because of its intuitive use. Finally, a group of smartphones entered on the Android platform. While most of the healthcare professionals in the United States are using iPhones and iPads, people in other countries also use Android-based smartphones in large numbers. Mobile devices from Apple, Inc., offer a comprehensive, secure eco-system that even allows user identification via biometric fingerprint identification and Apple’s system does not allow apps and add-ons that are not compatible with its infrastructure. On the other hand, Android-based systems offer more flexibility and allow approaches that Apple will not tolerate. Therefore, CIOs and decision makers have to decide which platform is best for integration into their HIS system. During recent years, it has been shown that it is often difficult to impose a single system and to rule out other devices. If, for instance, a physician prefers an Android device but the HIS system is generally in tune with Apple’s ecosystem, then this physician may be asked to use another system for “professional use,” that is, for apps to be used professionally at work. Some physicians have expressed their

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disapproval and dissatisfaction with having to carry five or six different phones with them, each one of them integrated with a specific hospital information system. As users mix personal and professional data and applications on their mobile device, there are potential conflicts and legal liabilities. For this reason, many hospitals have provided mobile devices to their staff and accredited healthcare providers. Others still allow the users to buy and maintain their own. In any case, a dual security zone should be installed on the device that separates professional from personal use and safeguards and syncs professional data.

Who Makes These Decisions? From 2005 to 2012, there was a debate about who should make these decisions. In some cases, the hospital/clinic administrator tried to govern mHealth use in her organization; in other cases department heads decided what devices and apps were used within their department; and in other instances, the chief information officers felt that such decisions should be their domain. In many cases, a committee was established to recommend apps, to determine whether some apps should or should not be used in their specific organization and to decide who should pay for devices, as well as for fees from telephone carriers, maintenance, and replacement costs.

Security and Data Protection In many countries, particularly in Europe, many mHealth features are not acceptable due to data protection rules or laws. This has created an inconsistent and what might be considered an irrational approach to communications. For example, a fax transmission is considered safe, although the risks of the fax getting to the wrong receiver and being disclosed at the fax machine are substantial. Similarly, telephone conversations are considered safe, even as non-authorized parties can break into the telephone system to listen in (as secret service and other security professionals routinely do). However, emails and text messages are considered unsafe, although billions of messages are transmitted every day with infrequent major security breaks. Every organization should have a security policy in regard to mHealth. The policy should not allow any picture taking of patients as this violates the patient’s right for privacy. Family members or friends of healthcare providers should not be able to look at the professional data or professional communication of a provider’s mobile device.

Integration of mHealth Applications into HIS/EMR Systems The main problem of mHealth activities is their lack of integration into the hospital information system (HIS) as well as into the electronic medical record (EMR) system. In this regard, mHealth is still in its first generation (approximately 1995-2020), during which individual apps are downloaded and used without regard to their interconnectivity or integration into other systems. There are first signs that this will change in the next generation (anticipated 2020-2040) when true interoperability and the era of the Internet of Medical Things will take over. (See below.)

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THE DIGITAL mHEALTH DIVIDE: INTERNET USE AND LANGUAGE mHealth is part of the digital society that is changing the role of human beings, our economy, human thinking. It involves the change from laborers to knowledge workers (Waegemann, 2012). While mHealth brings higher efficiency to the healthcare system, it also leads to fewer personal encounters, thereby reducing healthcare costs. A country’s openness toward the Internet and its acceptance of the changes that the digital society is bringing affect its willingness to implement mHealth systems. It must also be acknowledged that the digital divide in healthcare is also caused by language problems. As most apps are developed in English, English-speaking countries have a major advantage in using apps and implementing mHealth features.

FUTURE PERSPECTIVES: INTERNET OF MEDICAL THINGS AND RELATION TO INTERNET OF THINGS Just as the Internet of Things (IoT) will bring connected-life features to people, mHealth will enable the Internet of Medical Things (IoMT). Sensors will use artificial intelligence to process the data obtained through the device. A bathroom mirror will track one’s ECG with diagnostic accuracy. The watch or wearable device will monitor the blood pressure. The nanosensor within a pill will monitor and control specific cell movements. The toilet of the future will analyze a host of body chemistries, from glucose to blood. The sensor of a steering wheel can already detect the driver’s emotional or alcohol-influenced driving habits. The toothbrush of the future will look for cavities and monitor potential systemic bacterial contamination. The comb will monitor the biotome of hair and head. The bathroom scale measures will weigh and analyze body fat. The mobile phone will identify a person’s activities in steps and energy use and calculate caloric intake and metabolism. Tableware, that is, plates with sensors, will identify the nutritional value of a meal and calorie intake as well as the chemical interactions of various food elements. Shoes will have body sensors that track the function of various organs. Glasses will remind patients to take medications. Wearables such as socks or shirts with built in sensors and information processors will track dermatological data. Many new developments are in the microworld where microscopic devices are perfected. Nanobots and beebots can swim through the bloodstream and directly target the site of a tumor or disease, providing new ways of treatment. This is where body area networks come in. In a decade or two, nanomots will augment our immune system to fight disease, another example how communication-based medicine is enabled by mHealth. The future is on the horizon: The new world of artificial intelligence combined with sensors will change traditional medicine in many ways. mHealth will be the enabler of this new era.

REFERENCES American Well. (2015). Telehealth index: 2015 consumer survey. Retrieved May 1, 2015, from http:// cdn2.hubspot.net/hub/214366/file-2374840622-pdf/TelehealthConsumerSurvey_

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Catalyst Healthcare Research. (2014). Catalyst Healthcare Research finds that nine out of ten (93%) adults want email communication with their doctor. Retrieved April 21, 2015, from http://catalysthcr. com/news/catalyst-healthcare-research-finds-that-nine-out-of-ten-93-adults-want-email-communicationwith-their-doctor/ HIMSS Analytics. (2014). Mobile Devices Study. Retrieved April 20, 2015, from https://capsite.com/ assets/Uploads/2014-Mobile-Essentials-Brief-TOC12914.pdf Joo, J. H. (2013). The meaning of information technology (IT) mobile devices to me, the infectious disease physician. Infect Chemothr, 45(2), 244–251. doi:10.3947/ic.2013.45.2.244 PMID:24265976 Leon, N., Schneider, H., & Daviaud, E. (2012). Applying a framework for assessing the health system challenges to scaling up mHealth in South Africa. Retrieved May 4, 2013, from http://www.biomedcentral.com/content/pdf/1472-6947-12-123.pdf Mosa, A. S. M., Yoo, I., & Sheets, L. (2012). A systematic review of healthcare applications for smartphones. Retrieved April 29, 2015, from http://www.biomedcentral.com/1472-6947/12/67 Orca Team. (2012). Do smartphones (& tablets) mean smart healthcare? Retrieved April 13, 2015, from http://healthdecide.orcahealth.com/orcatheme_imagepost/smartphones-tablets-smart-healthcare/ Ozdalga, E., Ozdalga, A., & Ahuja, N. (2012). The smartphone in medicine: A review of current and potential use among physicians and students. Journal of Medical Internet Research, 14(5), e128. doi:10.2196/jmir.1994 PMID:23017375 Project Health Design. (2012). Observations of daily living. Retrieved April 15, 2015, from http://www. projecthealthdesign.org/resources/observations-of-daily-living research2guidance. (2015). EU Countries mHealth App Market Ranking 2015. Retrieved June 6, 2015, from http://research2guidance.com/r2g/research2guidance-EU-Country-mHealth-App-Market-Ranking-2015.pdf Tessier, C. (2010). Management and Security of Health Information on Mobile Devices. Chicago, IL: American Health Information Management Association. Tessier, C. (2012). The mHealth Stakeholder. In R. Krohn & D. Metcalf (Eds.), mHealth from Smartphones to Smart Systems. Chicago, IL: Health Information Management and Systems Society. Waegemann, C. P. (2002). Keynote Address. Paper presented at TEPR Conference, Seattle, WA. Waegemann, C. P. (2012). Knowledge Capital in the Digital Society. Boston, MA: Amazon. Wallace, S., Clark, M., & White, J. (2012). ‘It’s on my iPhone’: attitudes to the use of mobile computing devices in medical education, a mixed-methods study. Retrieved April 20, 2015, from bmjopen.bmj. com/content/2/4/e001099.full Wolters Kluwer. (2012). As Smartphone Usage Expands, Survey Says Nurses an Nursing Students Want Mobile Access to Credible Drug Data. Retrieved April 28, 2015, from http://www.wolterskluwerhealth. com/News/Pages/As-Smartphone-Usage-Expands,-Survey-Says-Nurses-and-Nursing-Students-WantMobile-Access-to-Credible-Drug-Data.aspx

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KEY TERMS AND DEFINITIONS App: A self-contained program or piece of software designed to fulfill a particular purpose on the mobile device. Electronic Health Record (EHR): A patient information system that is independent of healthcare enterprises (such as hospitals, clinics, specialists, wellness and fitness providers) and provides full access to all health information concerning one patient on a national or international basis. Electronic Medical Record (EMR): A patient information system that is interoperable within an enterprise (for instance, a hospital, or a clinic, as well as associated facilities) and that allows any provider within the enterprise access to a patient’s health record information. Internet of Medical Things: The network of medically related physical objects or “things” embedded within electronics, software, sensors, and connectivity in order to enable objects to exchange data with patients, healthcare providers, other stakeholders, and/or other connected devices based on the healthcare ecosystem. The term is derived from the Internet of Things (IoT). mHealth: The use of communication with or without mobile devices to provide Internet access to health information for patients and clinicians, offer new communication patterns and systems for care, research, financial and administrative solutions, allow tracking, sensing, and other therapeutic tools, enable documentation and include new tools for clinicians based on apps and add-ons to devices. (Note: The term mHealth was coined by the author, C. P. Waegemann, in 2001.) Mobile Device: A portable computer, or digital device, such as a cell phone (mobile phone), tablet, notebook, watch, glasses, etc. Participatory Health: Is a system based on the efforts of the Society for Participatory Medicine, which defines participatory medicine as “…a movement in which networked patients shift from being mere passengers to responsible drivers of their health, and in which [medical care] providers encourage and value them as full partners.” Smartphones: A cellular telephone with an integrated computer and other features not originally associated with telephones, such as an operating system, Web browsing, and the ability to run software applications. Telemedicine: A system that allows healthcare professionals to evaluate, diagnose, and treat patients in remote locations using telecommunications technology. It enables bilateral communication.

19

20

Chapter 2

mHealth R&D Activities in Europe Yiannis Koumpouros Technological Educational Institute of Athens, Greece Aggelos Georgoulas Technological Educational Institute of Athens, Greece

ABSTRACT The scope of the chapter is to present a thorough review on the most up to date research and development activities funded by the European Union in the m-health sector and more specifically in the domain of m-Health Innovations for Patient-Centered Care. This review brings to light the latest research directions and trends that are taking place around Europe and the world. The mhealth market is analyzed along with the focusing on the main apps and their classification. Moreover, it presents the trends of the research topics addressed and what are the plans and future activities pushed. The obstacles faced, the pros and cons and the proposed actions, and their match to real life situations are also discussed. The chapter concludes on the current trends and the potential market on m-health solutions and innovations and how they are trying to address the global need for patient-centered care.

INTRODUCTION Health care is the industry that leads the technological developments, while adopting first the Information and Communication Technologies (ICTs) innovations (Koumpouros I., 2012). The complexity and the individualities of the health care sector constitute a fertile soil for any technological innovation. The issues faced are numerous, i.e. the exponential growth of data produced, the financial viability of the system, security and privacy issues, the ageing of the population, etc. (Department of Economic and Social Affairs, Population Division, 2007), (Department of Economic and Social Affairs, Population Division, 2001), (Koumpouros Y., 2014). Technology can be proved a valuable asset in the hands of all involved key actors (i.e. health professionals, hospitals, ministry of health, stakeholders, patients, insurance companies, etc.). There is a great need for well-established solutions able to manage effectively the flow of information between the different parties and actors. ICTs are used in many ways to serve DOI: 10.4018/978-1-4666-9861-1.ch002

Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 mHealth R&D Activities in Europe

this purpose. Hospital Information Systems (HIS), Laboratory Information Systems (LIS), Picture Archiving and Communication Systems (PACS) are only some of the existing solutions already adopted effectively by almost any hospital around the globe. More recent technologies, like cloud computing and big data, are also used due to the need of better, more accurate and immediate results. Nevertheless, several problems still exist. Interoperability is one of the major obstacles faced for many years now. The absence of common protocols, standards, etc., makes interoperability still a goal for the long future. On the other hand, the explosion of new technological solutions, along with the appearance of the net generation, force to find new ways to provide the health care services (PWC, 2012), (Jones & Shao, 2011), (Bayne & Ross, 2007). In the new era, according to (IMS, Patient Apps for Improved Healthcare: From Novelty to Mainstream, 2013), the main stakeholders of the health domain in the short future will be the patients, while nowadays physicians and payers play the key role, and in the past physicians were considered as the only stakeholders. Health consumers have a totally different profile than the ones of the previous years. Patients are more updated and most of the times they may discuss further or even argue with their physicians. This is because they may have found some information on the Internet or they may have discussed with some others (e.g. close friends that faced a similar condition, etc.) and thus, have already formed some opinions for their certain case. Nowadays, patients are looking for information related to their interests and health condition before visiting a doctor. The main source of information is the Internet. A big question arises of course whether this piece of information is reliable or not. Today’s problem is the overflow of information found on the Internet and the lack of a quick and easy way to locate the right information, for the right person, from a reliable source. Misleading can be very easy. Simultaneously, the need for personalized solutions is of utmost importance for the end users. Patients are seeking answers for their specific individual needs from any place, at any time. Mobile technology is proved to be a valuable tool in many domains. The health care industry is one of the domains that are penetrated with many such solutions. The use of the term mHealth is widely used the past years for such a purpose. However, there is no standardized definition of mHealth. Some of the most common used definitions are presented below: • • •

• •

The World Health Organization -WHO (Youssef, MacCallum, McDonald, Crane, & Jackman, 2012) refers to mHealth as “the spread of mobile technologies as well as advancements in their innovative application to address health priorities”. The National Institutes of Health (NIH) defines mHealth as “the use of mobile and wireless technologies along with wearable and fixed sensors for the improvement of health outcomes, healthcare services, and health research”. The Global Observatory for eHealth - GOe (WHO, mHealth. New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth, 2011) and the European Commission (EC, COM(2014) 219 final, 2014) defined mHealth as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices”. United Nations Foundation (VitalWaveConsulting, 2009) refers to mHealth as “the provision of health-related services via mobile communications”. HIMSS - Healthcare Information and Management Systems Society (HIMSS, Healtcare Information and Management Systems Society, 2015) supports that “mHealth is the generation, aggregation, and dissemination of health information via mobile and wireless devices.”

21

 mHealth R&D Activities in Europe

The main objective of the chapter is to provide a clear view and understanding of the mHealth evolution that emerges nowadays in the health care sector, the current level of adoption, actual implementation, funding and impact. The health care industry and its characteristics are examined in relation to the needs for mHealth solutions, from the point of view of almost all potential stakeholders (i.e. patients, health care professionals, pharmaceutical companies, etc.). ICTs and more specifically, the mobile technologies, can support effectively the changes that are taking place in the health industry. The chapter maps and presents the major existing solutions, technologies and research activities in the mHealth domain. The case studies and applications presented reveal the valuable benefits that derive from the capitalization of the mobile technologies. Incorporating mHealth into health care clearly has the ability to transform the industry and improve quality of services for the benefit of all involved parties. The reader, after studying the chapter, will have a clear view on the challenges faced and opportunities given by mHealth, the potential of the specific market, while recognizing the key players and the latest approaches in the specific domain. The focus is on the latest research and development activities funded by the European Union for patient-centered care.

BACKGROUND The chaotic environment in health care enables the adoption of innovative applications in order to meet the needs of different stakeholders, which in some cases may be opposite to each other. For example, the improvement of the quality of the provided services may be in contrary to the financial viability of an organization (e.g. a private hospital). The landscape is changing dramatically, driven by governmental strategic decisions, policy, and advancements in technology. In parallel, within the global financial crisis faced, the continuous quest for better and high-quality care is the driving need. There is an urge therefore for a rethinking and redesigning of the whole health care system or as an alternative for exploring new ways to improve it. Ageing of population (Department of Economic and Social Affairs, Population Division, 2007), (Department of Economic and Social Affairs, Population Division, 2001) is another huge problem that is facing the system that will further depress the financial outcomes in health care. mHealth applications propose solutions to the emerging needs in the health care industry. It is critical that such implementations focus on the public health principles underlying mHealth initiatives, rather than on specific technologies (Fraser, Bailey, Sinha, Mehl, & Labrique, 2011), (WHO, mHealth. New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth, 2011). The latest technological breakthroughs led to smaller, “smarter” and “all-in-one” devices. Globalization, mobility of populations and the quality of life of the contemporary human (i.e. less free time, more time wasted in transportation, need for immediate access to valuable information and data, etc.) require a mobile approach. Cloud computing solutions, big data analytics, along with social networking and the use of modern mobile devices enable the provision of heath care services everywhere and at any time. The benefits are numerous and very significant (i.e. extended availability, improved services and satisfaction, containment of costs, etc.). The health care ecosystem is in the midst of a dramatic transformation, but still has a long way to go. Mobile broadband penetrates the market with frenetic pace. With almost 7 billion mobile cellular subscriptions (2.3 billion active mobile-broadband subscriptions), 75% of the world population has access to mobile communication (see Table 1 and Figures 1, 2) (TheWorldBank, 2012 Information and

22

 mHealth R&D Activities in Europe

Communications for Development: Maximizing Mobile, 2012), (International Telecommunication Union (ITU), 2014), (TheWorldBank, Indicators, 2015), while the number of devices with broadband capabilities reached more than 1 billion worldwide (PriceWaterhouseCoopers, 2014). As shown in Table 1, and according to (International Telecommunication Union (ITU), 2014), (TheWorldBank, Indicators, 2015) mobile-cellular penetration should reach 90% in developing countries by end 2014, compared with 121% in the developed ones. The same study reveals that Africa leads in mobile broadband growth (20% penetration in 2014), while the developing countries officiate mobilecellular subscriptions. Almost half a century ago, Gordon Moore envisaged the evolution of ICTs in our lives (Moore, 1965). The advent of smart phones formed a new “app-based economy” where interactivity is one of the core features. This new market affected many industries, as the one of health care. According to several studies the mHealth market size is booming (mHealthCompetenceCentre, 2014), (Statista, 2015). More than 100.000 mHealth applications are listed on app stores. It is estimated that by the end of 2017, there will be a revenue by the global mHealth market around to US$26 billion, growing by 61% (CAGRCompound Annual Growth Rate) (research2guidance, Mobile Health Market Report 2013-2017. The commercialization of mHealth applications (vol. 3), 2013). The interesting thing is that the major source of revenue will be generated by the services and hardware sales. All stakeholders (patients, providers, payers, organizations, government, etc.) will be affected by the utilization of mHealth apps due to the improved access to the delivery of health services, and the engagement and empowerment of patients and their relatives in the care process (van Heerden, Tomlinson, & Swartz, 2012). mHealth penetration differs between the developed and the developing countries (WHO, mHealth. New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth, 2011). Even though there are many countries that already incorporated in national programming mHealth, there is still a great need to increase the number of such projects towards the Millennium Development Goals (MDGs) 4, 5 and 6. To this end, another crucial issue is to increase awareness among policy-makers in countries where there is no mHealth strategy (mHealthAlliance, Baseline Evaluation of the mHealth Ecosystem and the Performance of the mHealth Alliance, 2012). The mHealth market focuses on specific areas, anticipating great benefits for almost any stakeholder in the health care arena. More specifically, the European Commission (EC, COM(2014) 219 final, 2014) strives to the direction of mobile solutions in order to: • • •

Increase prevention and improve quality of life by promoting healthy behaviors. Help in the direction of more efficient and sustainable practices in health care by reducing unnecessary visits and consultations, etc. Empower patients and enhance their responsibility over their own health.

The gathering of valid information about individuals can provide a valuable feedback on the elements of the intervention, thus yielding new information from which to act. Wearable devices and sensors are utilized in this direction to collect several data (e.g. biological, psychological, behavioral, etc.). The capabilities of the new generation of smart phones and tablets are numerous. Global Position Systems (GPS), accelerometers, gyroscopes, pedometers, fingertip recognition, etc., are only some of the features already integrated in the most common cell phones, broadening the options for more health related services to the end user. In parallel, wireless connectivity, along with the given level of computing capacity available, offer tremendous possibilities. These new services require a multidisciplinary approach. The 23

 mHealth R&D Activities in Europe

Table 1. Statistics on ICT indicators Key ICT indicators for developed and developing countries and the world (totals and penetration rates) (millions) 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014*

Mobile-cellular subscriptions Developed

992

1,127

1,243

1,325

1,383

1,404

1,411

1,447

1,490

1,515

Developing

1,213

1,618

2,125

2,705

3,257

3,887

4,453

4,785

5,171

5,400

World

2,205

2,745

3,368

4,030

4,640

5,290

5,863

6,232

6,662

6,915

Active mobile-broadband subscriptions Developed

N/A

N/A

225

336

450

554

707

828

939

1,050

Developing

N/A

N/A

43

86

165

253

475

726

991

1,265

World

N/A

N/A

268

422

615

807

1,182

1,554

1,930

2,315

2011

2012

2013

2014*

(millions) 2005

2006

2007

2008

2009

2010

Individuals using the Internet Developed

616

649

719

753

776

832

876

912

947

981

Developing

408

502

645

808

974

1,201

1,395

1,598

1,763

1,942

1,024

1,151

1,365

1,561

1,751

2,032

2,271

2,510

2,710

2,923

World

Rounded values. N/A: Not available. The developed/developing country classifications are based on the UN M49, see: http://www.itu.int/en/ITU-D/Statistics/Pages/ definitions/regions.aspx Key ICT indicators for the ITU/BDT1 regions (totals and penetration rates) (millions) 2005

2006

2007

2008

2009

2010

2011

2012

2013

2014*

Mobile-cellular subscriptions Africa

87

129

174

246

298

366

438

507

582

629

Arab States

84

125

173

213

263

310

349

379

399

410

Asia & Pacific

833

1,074

1,398

1,773

2,164

2,614

3,000

3,205

3,457

3,604

CIS2

166

227

267

312

355

377

358

368

386

397

Europe

550

610

677

714

717

709

730

743

766

780

The Americas

459

552

649

741

809

881

952

994

1,036

1,059

Active mobile-broadband subscriptions Africa

N/A

N/A

N/A

N/A

N/A

14

38

74

117

172

Arab States

N/A

N/A

N/A

N/A

N/A

18

46

58

75

92

Asia & Pacific

N/A

N/A

N/A

N/A

N/A

286

432

605

753

920

CIS

N/A

N/A

N/A

N/A

N/A

62

88

99

120

138

Europe

N/A

N/A

N/A

N/A

N/A

188

244

305

356

399

The Americas

N/A

N/A

N/A

N/A

N/A

230

323

401

494

577

continued on following page 24

 mHealth R&D Activities in Europe

Table 1. Continued Individuals using the Internet Africa

17

24

29

45

58

79

105

125

148

172

Arab States

26

36

44

55

66

81

94

121

137

152

Asia & Pacific

344

394

503

615

726

872

988

1,113

1,205

1,310

CIS

29

35

47

55

67

95

115

128

143

158

Europe

277

300

340

368

388

410

428

443

456

467

The Americas

316

346

385

405

428

473

519

556

597

639

Note: * Estimate. Rounded values. N/A: Not available. Regions in this table are based on the ITU BDT Regions, see: http://www.itu.int/en/ITU-D/Statistics/Pages/definitions/regions.aspx Source: ITU World Telecommunication/ICT Indicators database.

Figure 1. Mobile cellular subscriptions (per 100 people)

25

 mHealth R&D Activities in Europe

Figure 2. Internet users (per 100 people)

combination of the latest ICTs with the apps developed is having a knock-on effect on the communication and gathering of care related information. The health care industry addresses a host of challenges related to big data analytics, cloud computing, social networks, etc. Most mHealth applications are targeted towards remote data collection and monitoring, continuous vocational training of health care workers, diagnosis and treatment support, awareness raising, and tracking of disease and epidemic outbreaks (VitalWaveConsulting, 2009). The strategic objectives behind all these efforts are to: • • • •

Enhance the quality of the provided health services, Improve convenience, Extend reach to health care, Reduce the costs of health care provision.

Another study (Qiang, Yamamichi, Hausman, & Altman, 2011), based on World Bank categorization, reports that the perceived potential of mHealth solutions is based on the following axes: •

26

Improved health care quality and access ◦◦ Treatment support ◦◦ Patient tracking ◦◦ Supply chain management ◦◦ Health financing

 mHealth R&D Activities in Europe

• •

•

◦◦ Emergency services Making health sector human resources more efficient ◦◦ Support for clinical decision making ◦◦ Better record keeping Capture and use real-time health information ◦◦ Surveillance ◦◦ Disaster management ◦◦ Accountability for health care delivery Prevent disease and promote public health ◦◦ Disease prevention ◦◦ Education and awareness

The following section analyses the most recent or/and widely used mHealth apps, solutions and R&D efforts around Europe and the world, focusing on patient-centered care.

TOOLS AND TECHNOLOGIES Over the last seven years, the EU has funded various projects on Personal Health Systems and Patient Guidance Services, involving the use of smartphones and other mobile devices in order to support the users’ health and wellbeing. The main programme, under which these projects were funded, has been FP7-ICT (EU FP7-ICT, 2014), and especially subprogrammes ICT-2007.5.1 - Personal health systems for monitoring and pointof-care diagnostics, ICT-2009.5.1 - Personal Health Systems, ICT-2011.5.1 - Personal Health Systems (PHS), ICT-2011.5.3 - Patient Guidance Services (PGS), safety and healthcare record information reuse, and ICT-2013.5.1 - Personalised health, active ageing, and independent living. Within the following paragraphs, we attempt an overview of the most current (on-going or recently finished) EU funded projects in the field of mHealth, focused on the technical aspects of each solution. The projects of this period (2008-2015) have focused on proof of concept of small-scale systems expected to lead to new care paths triggered by mHealth. Monitoring of chronic conditions has been very popular among the developed solutions, with mental disorders, diabetes, COPD (Chronic Obstructive Pulmonary Disease), and CVD (Cardiovascular Disease) being the most common of them. The developed solutions follow modular and flexible system design that focuses on integration of existing technologies and re-uses previous experience and products, while at the same time advances current information processing technologies. The majority of these projects share a common patient-centric architecture, usually employing a Personal or Body Area Network of sensors (PAN or BAN) which communicates with a mobile device (Personal Digital Assistant or smartphone) via low power short-distance wireless protocols, such as Bluetooth and Zigbee. A remote back-end system provides database for data storage, web interfaces for communication with health professionals and -in some cases- algorithms and Decision Support System (DSS) in order to help the patients and the health professionals to monitor each condition and provide medical support. Communication between the patient’s side and the remote system is achieved through standard wireless Internet connection technologies, including 3G, GPRS and wifi.

27

 mHealth R&D Activities in Europe

A variety of wearable and/or individual mobile sensors is used, in order to monitor patients’ biosignals and physical activity indicators (including ECG/heart rate, respiration, pulse, blood pressure, glucose), as well as special ones such as accelerometers, body weight scales, food weight scales and chewing sensors. A limited number of systems also employ wearable artificial organs (i.e. kidney) and other equipment such as smart pillbox. A smartphone with a dedicated mHealth application is responsible for collecting the information from the sensors, acting both as a gateway for the remote system and a personal medical assistant for the patient. In most cases the applications also allow for manually entered data (e.g. in the form of questionnaires). Regarding the data collection and the communication with the sensors and medical devices, a very few projects have reported the usage of medical-specific standards, such as the ISO/IEEE11073. Moreover, a limited number of projects reported using or intended to use other international standards for health data communication, such as HL7 (Health Level 7). Detailed information on the projects mentioned below (including duration, funding information, etc.), is presented in the cumulative Table 2. METABO project (METABO, 2008) developed an ICT system to support metabolic management in diabetes (Figure 3a). The patient’s system comprises of mobile and compact wearable sensors (such as glucose sensors, physical activity sensors and a sphygmomanometer), a tabletop blood pressure monitor, a weight scale, and a Personal Medical Device (PMD), acting as data concentrator to enable gathering the physiological data from the aforementioned devices. Apart from these devices, the system has been also tested to store manually inserted data, directly from the patient in the PMD (e.g. a glucose level diary). The data collected by the PMD are transmitted over Internet and/or 3G connection to a central system, which host all patients’ databases and manages communication between patients and physicians. Doctors on the other side are provided with a complete data management environment which allows them to evaluate patient’s performance and prescribe personalized treatment. CHRONIOUS (CHRONIOUS, 2008) developed a wearable platform, based on multi-parametric sensor data processing, for monitoring people suffering from chronic diseases in long-stay setting (Figure 3b). The system comprises of a lightweight T-shirt, which is equipped with wearable heart, respiratory and activity monitoring sensors, and a set of additional (external) devices such as a digital weight scale, glucometer, blood pressure monitor, spirometer and air quality sensor. Data collected by these sensors is sent to a mobile device such as a smartphone or Personal Digital Assistant (PDA). All information are automatically transmitted via IP/3G/GPRS to the Central System, that using a web-interface and ruled based algorithms allows clinicians to monitor patients’ status and give suggestions for acting in case of worsening trend or risk situation. ICT4Depression project (ICT4Depression, 2015) developed a mobile solution called Moodbuster, for the treatment of depression (Figure 3c). The system includes sensors and devices for monitoring activities and biosignals in a non-intrusive and continuous way, as well as treatments for depression and automatic assessment of the patient using mobile phone and web based communication. The biomedical sensors consist of a hand worn device for the measurement of heart rate and sympathetic nervous system responses and a chest strap that provides heart rate, respiration rate and an acceleration data that can be used to infer the trunk orientation of the user. Both devices transmit the collected data wirelessly over Bluetooth connection. It also includes a smart pillbox which is used to monitor medication intake. The mobile application serves both as an aggregator for the physiological sensors as well as a location and activity sensor itself. It also provides the patient access to the treatment modules and allows them to perform mood-ratings and exercises. 28

 mHealth R&D Activities in Europe

Table 2. EU funded mHealth projects of the period 2008-2015 Project Short Name

Start Date

End Date

Subprogram

Funding Scheme

METABO

1/ 1/ 2008

30/ 6/ 2011

FP7-ICT

ICT-2007.5.13

CP4

11.419.530,00 €

8.100.000,00 €

Diabetes

Chronious

1/ 2/ 2008

31/ 5/ 2012

FP7-ICT

ICT-2007.5.1

CP

10.284.963,00 €

10.284.963,00 €

COPD and renal insufficiency

ICT4 Depression

1/ 1/ 2010

30/ 4/ 2013

FP7-ICT

ICT-2009.5.15

CP

3.706.506,00 €

2.701.845,00 €

Mental health / Depression

PSYCHE

1/ 1/ 2010

31/ 12/ 2013

FP7-ICT

ICT-2009.5.1

CP

3.903.007,00 €

2.909.969,00 €

Mental health / Bipolar dissorder

MONARCA

1/ 2/ 2010

31/ 7/ 2013

FP7-ICT

ICT-2009.5.1

CP

5.134.929,00 €

3.670.000,00 €

Mental health / Bipolar dissorder

REACTION

1/ 3/ 2010

28/ 2/ 2014

FP7-ICT

ICT-2009.5.1

CP

16.318.147,00 €

11.800.000,00 €

Diabetes

InterStress

1/ 3/ 2010

30/ 11/ 2013

FP7-ICT

ICT-2009.5.1

CP

4.438.842,00 €

3.009.653,00 €

Mental health / psychological stress

Bravehealth

1/ 3/ 2010

31/ 7/ 2012

FP7-ICT

ICT-2009.5.1

CP

10.382.905,00 €

6.999.546,00 €

Cardiovascular disorders

Nephron+

1/ 4/ 2010

31/ 12/ 2014

FP7-ICT

ICT-2009.5.1

CP

6.931.031,00 €

4.999.640,00 €

Lungs/Kidneys / Renal insufficiency

Help4mood

1/ 1/ 2011

30/ 6/ 2014

FP7-ICT

ICT-2009.5.1

CP

3.599.074,00 €

2.819.993,00 €

Mental health/ Depression

COMMODITY12

1/ 10/ 2011

31/ 12/ 2014

FP7-ICT

ICT-2011.5.16

CP

5.478.076,00 €

4.051.000,00 €

Diabetes

MobiGuide

1/ 11/ 2011

31/ 10/ 2015

FP7-ICT

ICT-2011.5.37

CP

7.068.682,00 €

5.386.996,00 €

Personal Health/ managing chronic illness

RemPark

1/ 11/ 2011

30/ 4/ 2015

FP7-ICT

ICT-2011.5.1

CP

4.735.804,00 €

3.282.912,00 €

Neurological disorders / Parkinson disease

Empower

1/ 2/ 2012

31/ 1/ 2015

FP7-ICT

ICT-2011.5.3

CP

4.276.946,00 €

3.024.340,00 €

Diabetes

Splendid

1/ 10/ 2013

30/ 9/ 2016

FP7-ICT

ICT-2013.5.18

CP

3.597.959,00 €

2.747.000,00 €

Personal Health/ prevent obesity

Welcome

1/ 11/ 2013

31/ 10/ 2017

FP7-ICT

ICT-2013.5.1

CP

8.272.080,00 €

6.170.975,00 €

COPD

PegasoF4F

1/ 12/ 2013

31/ 5/ 2017

FP7-ICT

ICT-2013.5.1

CP

11.639.121,00 €

8.934.000,00 €

Personal Health/ healthy lifestyle &food awareness

Program

Total Cost

EU Contribution

Comments:

PSYCHE project (PSYCHE, 2010) developed a personal, multi-parametric monitoring system based on textile platforms and portable sensing devices for the long term and short term acquisition of data from selected class of patients affected by mood disorders (Figure 3d). The wearable unit consists of a garment (a T-shirt for men or women, or a bra for women) connected to a portable electronic device, the SEW (Side Electronics Wearable). The garment with the portable electronic device is able to collect the following parameters: heart rate, breathing rate, breathing amplitude, Heart Rate Variability (HRV), posture and/ or activity classification (lying, standing, walking, and running), and estimation of energy expenditure. A smartphone with a dedicated mHealth application collects these data (via Bluetooth connection) and transmits them via the patient’s 3G or WiFi connection to the PSYCHE database management system. The mHealth app also supports digital agendas (sleep agenda, mood agenda), digitized questionnaires, voice analysis, and scheduling of dedicated affective elicitation protocols. The data are encrypted using secure connections (HTTPS with trusted certificate), and all the information circulated is anonymous. 29

 mHealth R&D Activities in Europe

Figure 3. METABO. CHRONIOUS, ICT4Depression, PSYCHE, projects

MONARCA (MONARCA, 2010) developed and validated a system for multi-parametric long term monitoring of behavioral and physiological information relevant to bipolar disorder. The system consists of a sensor enabled mobile phone, a wrist worn activity monitor, a “sock integrated” physiological (Galvanic Skin Response - GSR, pulse) sensor, a stationary electroencephalogram (EEG) system for periodic measurements, and a home gateway. A mobile application, continuously running in the background, samples these sensors via Bluetooth connection, records data on the phone memory and transmits it periodically to a dedicated server, where this information is combined with patients’ medical records. The medical staff is provided with interfaces for interpreting the data, therapy assessment and therapy planning tools (scheduling visits, planning medication). The mobile app also provides an interface for self assessment (on the basis of the above information), provision of warnings and risk profiles and a coaching concept for self treatment (Grunerbl, et al., 2014). The REACTION project (REACTION, 2010) has developed GlucoTab, a mobile system that uses sensors to monitor parameters such as blood glucose levels, nutritional intake, administered drugs and insulin sensitivity, and gives therapy advice (Figure 4a). The data are locally collected via ZigBee protocol and then transmitted over 3G/GPRS and stored on a back-end server. The system provides a mobile, tablet-based workflow support platform for nurses and physicians, including a validated basal/ bolus insulin titration protocol (REACTION algorithm) to provide decision support for daily glucose

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management of patients with diabetes. At the patients’ side, the system provides a smartphone app called Nutrition App, which is used for entering nutrition information, providing access to and selection of nutrition related data such as carbohydrates and calories. Finally, the system includes several other software components, i.e. the REACTION Patient Portal, Clinical Portal, the REACTION Database, the SMS Notification Component, and Network Monitoring Service for Mobile Devices. The GlucoTab system was CE marked in November 2013. BRAVEHEALTH (Canale, et al., 2013) developed a patient-centric system to cardiovascular disease (CVD) management and treatment, providing people already diagnosed as subjects at risk with a mobile solution for continuous and remote monitoring and real time prevention of emergency events (Figure 4b). The main system component is a wearable unit which is able to capture basic physiological data from the patient, including systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), respiratory rate (rr/min), SPO2 (%), OptiVol fluid index (Ohm), change of weight (%) and temperature (°C), together with a full electrocardiogram if required. These data are collected by a smartphone (i.e. Patient Gateway), running a dedicated mobile application called LIFE! GATEWAY, and further transmitted to a back-end server, in order for experts to access them and provide the necessary remote support. The system provides both automated support, in the form of text messages with information Figure 4. REACTION, BRAVEHEALTH, Nephron+, HELP4MOOD projects

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or suggestions to the patient directly generated by the system, and doctor managed supervision, allowing direct communication with the patients with voice/text/chat messages. In addition to visualization, the platform provides a route into the decision support system developed for the project and it can be interfaced with existing National Health Records and Physiological Data Banks in order to generate and verify risk prediction models using advanced data mining approaches (Dhukaram, Baber, Elloumi, van Beijnum, & De Stefanis, 2011). Nephron+ (Nephron+, 2010) developed a wearable artificial kidney device, whose indications can be remotely monitored by patients on their smartphone, and by medical staff too (Figure 4c). The whole system is built around the wearable dialysis device, which for safety reasons is able to run fully automatic as stand-alone machine. A range of sensors has also been developed in order to monitor the blood clearance and important process parameters. The patient’s smartphone, running the dedicated mobile app, is used to control and read the device, but it also serves as ICT hub to the Nephron+ web portal. The app also has a menu for giving feedback about health condition (questionnaires) and automatic retrieval of weight and blood pressure (medical devices) via Bluetooth connected devices. The Nephron+ dialysis system is remotely monitored by a medical center. Messages, alarms and sensor data are sent via Bluetooth (Continua HDP) to the smartphone and directed further to the web portal via wifi or GS3/4. Both doctor and patient have access to the history and actual status of the device and the current treatment. This includes sensor readings, clearance rate, battery status and operation mode. HELP4MOOD (Help4Mood, 2011) provides computerized support for people with Major Depression by monitoring mood, thoughts, physical activity and voice characteristics, prompting adherence and promoting behaviors in response to monitored inputs (Figure 4d). It is a distributed system with its three main components deployed at patient’s site. The personal monitoring system combines existing (movement sensor, psychological ratings) and novel (voice analysis) technologies in order to keep track of important aspects of behavior such as sleep or activity levels (Martnez-Miranda, Breso, & GarcaGomez, 2014). The interactive virtual agent has a dual role: (i) it can interact with the patient through a combination of enriched prompts, dialogue, body movements and facial expressions, and (ii) it provides a portal to trusted health information, and feeds back information collected through monitoring and questionnaires. The decision support system tailors each session with the virtual agent to the individual needs of the person with depression, and supports clinicians in interpreting the data collected through the virtual agent and the personal monitoring system. The MobiGuide project (MobiGuide, 2011) develops an intelligent decision-support system for patients with chronic illnesses, such as cardiac arrhythmias, diabetes, and high blood pressure (Figure 5a). The system comprises a set of wearable biosensors (the kind of sensors used varies depending on which illness the patient is suffering from, measuring e.g., heart rate, blood pressure, etc.) and a smartphone that includes the patient’s user interface as well as the signal analysis algorithms and the mobile decision support system that run on the smartphone’s processor (Marcos, González-Ferrer, Peleg, & Cavero, 2015). The data collected are transmitted to a powerful “backend” system consisting of a set of servers performing various advanced artificial intelligence functions in order to provide high quality intelligent guidance services to the patient and his care providers to help manage the patient’s conditions, follow their treatment and to allow timely clinical decisions. The analysis concerns signal data, hospital data, abstractions identified in the data and events generated by the decision support system. When an Internet connection is not available, the patient’s lightweight mobile device can still provide some modicum of support, based on the bodily sensors and on the local mobile device’s computational capabilities. Thus, decision support is provided to the patients and to their care providers anytime, anywhere (Peleg, Shahar, & Quaglini, 2013). 32

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Figure 5. MobiGuide, REMPARK, WELCOME projects

REMPARK system (RemPark, 2011) utilizes a small waist-worn module and headset controlled by a smartphone that will allow doctors to observe and manage the symptoms of Parkinson’s in real time (Figure 5b). The wearables that make up the system are controlled, and feed data back and forth, via the patient’s smartphone. The smartphone also acts as GPS (Global Positioning System) providing context-aware information and as an interface for the patient. A phone-sized waist module containing accelerometer and gyroscope sensors detects the patient’s cadence as they walk. This allows the device to constantly record ambulatory characteristics such as freezing of gait and falls, both of which are common occurrences for those suffering from the disease, and conveys the data to the smartphone via Bluetooth. The system will be able to send data to a back-end server of the relevant health service provider, allowing the patient’s neurologist to regularly follow the evolution of the patient’s disease in a more effective manner, as well as being able to make better informed decisions about the adjustment of the pharmacological treatment of the patient, a key issue in management of this disease. WELCOME project (Welcome, 2013) aims to develop an integrated care platform using wearable sensors and smart cloud computing for COPD patients with co-morbidities (Figure 5c). The system includes a light vest with a large number of non-invasive chest sensors for monitoring various relevant parameters. These data will be collected by the patient’s smartphone, which will also be equipped with

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interactive mobile applications to monitor and manage diabetes, anxiety and lifestyle issues. All patient’s information will be further transmitted to a dedicated smart cloud platform, where all the medical records and the monitoring data will be managed and processed via the decision support system. Healthcare professionals will be able to securely access the WELCOME applications to monitor and manage the patient’s conditions and respond to alerts on personalized level. The INTERSTRESS project (INTERSTRESS, 2010) developed an ICT-based mobile solution for self-management and treatment of psychological stress in professional and social life. The main system component is a mobile application which provides the user with a 3D virtual reality tropical island environment, where people can learn and practice effective relaxation techniques. Dedicated sensors (within a lycra-based wearable band) detect the user’s heart rate and respiration and are used to control the features of the virtual world – for example, decreasing the size of a virtual campfire based on heartbeat. The sensors’ network is further integrated with a Camera and Accelerometer-Based Activity Recognition (CBAR) system, which is able to detect specific stress-related gestures and also extract qualitative characteristics of the patient’s activity within the clinical setting. The app can also send alerts when the user is too stressed. The whole system is supported by a Decision Support System (DSS) for automatic classification of stress levels during exposure to VR environments as well as a Patient Management System (PMS) which allows the clinicians and patients to set and control the INTERSTRESS system based on the different relevant parameters required for managing patients’ therapy. The Positive Technology app is available for free on the iTunes app store for iOS devices where it is highly rated (4+). The Positive Technology App won the World Summit Award-Mobile prize for best mHealth app. COMMODITY12 (Commodity12, 2011) developed a multi-parametric system for continuous monitoring of diabetes type 1 and 2. The system includes a set of Bluetooth-enabled sensors exploited in different body area networks. Data from the sensors are collected to the user’s smartphone and transmitted over the cell-phone network to the back-end system where machine learning techniques are applied to classify the signals and provide indications about abnormalities in the curves. The system also includes a set of standard interfaces with external systems (as hospital system, laboratory systems, etc.) and the care management system as well as a database (called MGRID) which is exploited independently of all the other systems (Kafali, et al., 2013). EMPOWER (Empower, 2011) developed a modular system which facilitates the self-management of diabetes patients based on PHRs (Personal Health Records) and on context-aware, personalized services. The system includes Personal Health Applications which are usually operated by patients in a local environment, such as Desktop PCs, Smartphones or Tablets. Although patient data are directly managed by the patients, modern Personal Health Applications often provide data upload to web-based cloud services. The system also integrates Electronic Health Record, Personal Health Record and Practice Information System. SPLENDID project (Splendid, 2013) aims to provide personalised services guiding adolescents and young adults to healthy eating and activity behaviours, preventing the onset of obesity and eating disorders. It proposes an interactive system that accurately tracks eating and physical activity behavior, and provides goal oriented feedback to the user, such as to eat more slowly or adopt more activity, while the sensors monitor whether the individual succeeds. At the heart of the system we find a set of sensors (i.e. the Mandometer® a personal scale for recording eating behavior, a chewing sensor for recording chewing and swallowing and an accelerometer for recording physical activity) used to measure the speed at which food is eaten as well as how food is chewed. Users can also input their own data, including how full they feel after a meal as well as daily intake and activity logs. These data are collected to the 34

 mHealth R&D Activities in Europe

Table 3. Most recent mHealth research EU funded projects Project Short Name

Start Date

End Date

Program

Subprogram

Funding Scheme

Decipher PCP

1/ 2/ 2012

31/ 5/ 2016

FP7-ICT

ICT-2011.5.3

CPCSA9

3.551.931,00 €

2.524.974,00 €

mHealth procurement

Unwired Health

1/ 1/ 2014

31/ 12/ 2016

FP7-ICT

ICT2013.5.110

CPCSA

3.842.262,00 €

2.826.950,00 €

mHealth procurement

NYMPHA-MD

1/ 1/ 2014

30/ 6/ 2017

FP7-ICT

ICT-2013.5.1

CPCSA

2.589.981,00 €

1.884.000,00 €

Mental health/Mood disorders

m-Resist

1/ 1/ 2015

31/ 12/ 2017

H202011

H2020PHC-201412

RIA13

4.034.222,00 €

4.034.222,00 €

Mental health/ Schizophrenia

PD_Manager

1/ 1/ 2015

31/ 12/ 2017

H2020

H2020PHC-2014

RIA

4.345.500,00 €

4.345.500,00 €

Neurological disorders/Parkinson disease

HEARTEN

1/ 12/ 2014

1/ 1/ 2018

H2020

H2020PHC-2014

RIA

4.589.507,49 €

4.589.507,00 €

Cardiovascular disorders/heart failure

PAL

1/ 3/ 2015

1/ 3/ 2019

H2020

H2020PHC-2014

RIA

4.515.460,00 €

4.515.460,00 €

Diabetes

Total Cost

EU Contribution

Comments:

user’s smartphone and/or other portable computer device, and further transmitted to a remote server. An integrated software platform, partially running on the mobile device and partially located on a remote server, provide three main functions: (a) communication with all the sensors, (b) running of the algorithms used to identify behavior, (c) feedback provision and guidance to the user. For the students and the young adults, dedicated smartphone and web-based interfaces will be developed. In both platforms, the interfaces emphasize on the presentation of behavioral recordings and feedback notifications, while allowing the user to manually add their input when required. More elaborate interfaces will be designed for the health professionals that will be using the system. These interfaces will mostly be web-based and will handle the presentation of information, as well as receive feedback from the professional. PEGASO (Pegasof4f, 2014) aims to develop an ICT-based system that will motivate behavioral changes towards healthy lifestyles thus preventing overweight and obesity in the younger population. The proposed system includes a monitoring platform comprised of wearable sensors, mobile phone as well as multimedia diaries for the acquisition of physical, behavioral and emotional attitude of adolescents. The data collected are transmitted to a back-end server in order to provide feedback in terms of “health status” changes, required actions to undertake, etc., proposing personalized healthy options for alternative lifestyles. Since PEGASO targets teenagers, it attempts to exploit technologies and approaches they are familiar with, such as gaming strategies, leveraging social networks and communities of interest, in order to motivate them adopting healthy lifestyles.

FUTURE RESEARCH DIRECTIONS AND ISSUES mHealth applications, as presented in the previous sections, evolve every day. Many companies are trying to leverage prior experiences to provide a better solution. While the paradigms are numerous, there’s no question that mHealth is still in its infancy.

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Devices that monitor personal health, headsets that measure brainwaves and clothes that sense respiratory rates, are forming a vital part of the healthcare market. A growing number of medical devices and sensors are becoming wearable. However, there is not an obvious answer yet on how do we optimize innovation and safety. The market of mobile health applications is focused in three main sectors: fitness, medical references and wellness. According to a study of the National Center for Chronic Disease Prevention and Health Promotion (CDC, 2009), approximately 75% of health care spending is on patients suffering from one or more chronic diseases (i.e. diabetes, obesity, heart disease, lung disease, high blood pressure and cancer). mHealth solutions can be used to monitor biomarkers of these patients in order to enable effective management of those diseases. This has a multiple effect on the quality of life of the patient and the containment of the cost of health services due to the reduced number of care facility visits (McKinsey & GSMA, 2010). The main opportunities and obstacles faced in both the developed and the developing world are well presented in (Krohn & Metcalf, 2012) (see Table 4, 5). Data security and citizen privacy are areas that require legal and policy attention to ensure that mHealth users’ data are properly protected. Certain security concerns should therefore be taken into account in future developments of mHealth solutions: protection of data, unwanted sharing of sensitive information with third parties (Bielecki, 2012), accidental exposure or leaking of health data, etc. As mobile solutions depend on high capacity and ubiquitous networks, the European Commission, recognizing the need for high-speed networks, recently adopted a legislative package for a “Connected Continent: Building a Telecoms Single Market” (EC, European Commission, Digital Agenda for Europe. A Europe 2020 Initiative, 2015). Table 4. Opportunities and Obstacles of mHealth in Developed Nations (Krohn & Metcalf, 2012) Opportunities

Obstacles

Enhancing Quality

Consumer Awareness

• Chronic disease management • Emergency medicine • Medical compliance • Wellness and prevention • Health information exchange • Research and registry data

Retail adoption

Convenience

Regulation

• Streamlining care processes • Persistence and pervasiveness • Patient-friendly care modalities • Staff productivity • Telemedicine • Remote patient management • Integration with social networks

Approval and time to market

Cost Reduction

Reimbursement

• Clinical resource efficiency • Clinical collaboration • Wellness, prevention and pre-emption • Risk management

Lagging incentives

Workflow Disruption Resistance to innovation

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Table 5. Opportunities and Obstacles of mHealth in Developing Nations (Krohn & Metcalf, 2012) Opportunities

Challenges

Enhancing Quality

Resource Capacity

• Population health • Telemedicine • Wellness and prevention

• Clinical resource availability • Infrastructure • Technology • IT sophistication

Convenience

Cost

• Geographic reach • Resource utilization

Technology acquisition and total cost of ownership

Cost Reduction

Access

Capital efficiency

• Patient education • Geographic reach

Some of the most recent mHealth research EU funded projects, under FP7-ICT and the HORIZON 2020 (EC, Horizon 2020. The EU Framework Programme for Research and Innovation, 2015) initiatives, are presented in the following paragraphs (Table 3). The m-RESIST project (m-Resist, 2015) focuses on developing an innovative disease management system for patients suffering from resistant schizophrenia. The main target of the research effort is to empower the patients in order to participate actively in the therapeutic process and self-manage their condition. Thus, m-RESIST will design the related services and care pathways, while trying to create a predictive model based on a wide range of relevant data gathered by the system in order to identify risks and gaps in the treatments which will enable the prescription of personalized treatment and tools for patients, for managing co-morbidities and healthcare. This project was funded with 4 million euros. With 4,345 million euros, the PD_Manager project (PD_Manager, 2015) aims to assess motor and non-motor symptoms in Parkinson’s patients, evaluate their adherence to medical prescriptions, conduct a dedicated nutritional study to follow up patients’ life style, empower game-based physiotherapy at home and provide personalized suggestions. The proposed system will integrate a set of unobtrusive, simple-inuse, co-operative, mobile devices that will be used for symptoms monitoring and collection of adherence data (smartphone, sensor insole, smart pillbox, wristband with several sensors for temperature, heart rate, etc.). A smart watch is considered to be used to collect valuable data, using its embedded sensors, such as: activity tracker (number of steps, distance, calories), accelerometer (tremor, dyskinesia), heart rate, etc. Notifications and voice controls will also be used to advise the patient whenever a symptom is detected in order to cope with it, etc. The HEARTEN project (HEARTEN, 2015) tries to monitor breath, saliva and other symptoms of heart failure (HF) and send smartphone alerts to HF patients every time they find themselves in a critical situation. It will develop biosensors that detect and quantify novel breath and saliva HF biomarkers that reflect the health status of the patient and identify whether the patient adheres to the administered drugs. The breath biosensor will be integrated into the smartphone while the saliva biosensor will be integrated into the patient’s cup. Some more data (ECG, blood pressure, physical activity) will be recorded using the appropriate sensors. Nutritional data will complement the whole process. These data are then transmitted to the cloud reference architecture, where a knowledge management system analyses them

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Table 6. MNCH topics Maternal health:

Sexual and reproductive health Family planning Antenatal, perinatal, intrapartum and postnatal care Delivery (midwifery) Maternal depression (psychological issues) Maternal mortality, related to: - Hemorrhage - Hypertensive disorder - HIV - Sepsis/Infections - Abortion - Obstructed labor - Anemia - Ectopic pregnancy - PMTCT

Newborn and child health:

All health conditions relating to neonates, newborns and children age five and under.

and delivers critical information, provides alerts, guidelines, trends and predictive models to the patient and the ecosystem actors. The whole effort is funded with almost 4,6 million euros. UNWIRED Health (UnwiredHealth, 2014) is dealing with mHealth procurement for the transformation of health care services. More specifically, it focuses on apps offering services to (i) improve vaccination coverage and adherence, as well as (ii) coach patients with heart failures enabling education, motivation, remote monitoring and other functionalities, integrating and coordinating care provided by a hospital and the primary care physician. The final services will be on an open platform, suitable to any smartphone and any participating operator. The project started in 2014 and will end in 2016. Another EU funded project, the PAL - Personal Assistant for healthy Lifestyle (PAL, 2015), with 4,5 million euros budget, targets the development of a personalized assistant (PA) that will assist children, health professionals and parents to advance the self-management of the diabetic child, so that an adequate level is established before adolescence. The final solution is based on a social robot (NAO), its (mobile) avatar and an extendable set of (mobile) health applications (diabetes diary, educational quizzes, sorting games, etc.), which all connect to a common knowledge base and reasoning mechanism. DECIPHER PCP (Decipher PCP, 2013) deals with mHealth procurement. It is designing a mobile solution that enables secure cross-border access to existing patient healthcare portals. The project started in February 2013 and ends in 2016. Finally, NYMPHA-MD project (NYMPHA-MD, 2014) aims to define the framework of pre-commercial procurement for the provision of next generation services advocated for mental health treatment with a special focus on bipolar disorder based on the use of new technologies, open standards and open platforms. The monitoring model of such type of approach would be based on a portable data acquisition system able to obtain continuous objective measurements of patients behavior related to their clinical state, also giving feedbacks and visualizing data to patients, thus enhancing patients’ awareness and empowering attitude and supporting their self-management, with the support of ICT.

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In another domain, a research conducted by the mHealth Alliance and the UN Foundation (Philbrick, 2013) reported some interesting findings on mHealth solutions, on international level, related to maternal health, newborn, and child health. The basic research topics behind this effort are presented in Table 6. The aforementioned study concluded, based on a previous research (WHO & UNICEF, Countdown to 2015: Taking stock of maternal, newborn and child survival. 2000-2010 Decade Report, 2010), that the MNCH areas that need more attention, in terms of efficient mHealth solutions, are: • • • • • •

Increasing contraception prevalence Intermittent prevention treatment of malaria in pregnant women Prevention of mother-to-child transmission Children sleeping under insecticide treated net Antibiotics for pneumonia Malaria treatment

The same study dictates a more careful approach in order to fulfill the needs of crosscutting stakeholders of the health industry. Finally, it concludes that there is a great need for more evidence linking mHealth with health outcomes. Another interesting issue is the confluence of evidence-based medicine and social science. Prevention and monitoring of treatment are of great importance for any stakeholder. The real time collection of behavioral and physical data can lead to many mobile health applications in order to promote and preserve healthy living. The latest ICTs will improve existing methodologies and approaches for data collection and evidence gathering. The data collected by mHealth applications can yield new insights into the factors that lead to disease, while, after appropriate analysis, prompting changes in behavior. Behavioral intervention technologies (BITs) is another area of application of mHealth solutions (Burns & Mohr, eHealth, telehealth, and Telemedicine, in Encyclopedia of Behavioral Medicine, in Press). It constitutes the application of behavioral and psychological intervention strategies through the use of mobile technology to address behavioral, cognitive and affective targets that support physical, behavioral and mental health. Self-assessment, self-monitoring, psycho-education, goal setting, and feedback are some of the interventions addressed by BITs (Mohr, Burns, Schueller, Clarke, & Klinkman, 2013). The sensor data collected can be used either with algorithms that predict patient states (Miluzzo, et al., 2008), (Morris & Guilak, 2009) or with machine learning techniques (Witten & Eibe, 2005), (Burns, et al., 2011) that are applied to collected sensor data and patient self-reports. Despite the strong growth prospects of the mHealth sector, a multitude inhibitory factor exists. Interoperability (eHGI, 2012) between mHealth solutions and devices is one of the main barriers. The European Interoperability Framework for eHealth (ISA) proposes a process on how to proceed on this issue in order to ensure the required exchange and communication between mHealth systems across Europe. Of primary concern are also the privacy and security of data transmitted and accessed wirelessly. Another obstacle is the chaos established in the sector of mHealth due to the number of apps available. More than 16,000 apps confuse the patients on which one: (i) fit their needs better, and (ii) is medically sound. Ease of use and usefulness are also taken into account upon decision. On the other hand, physicians report on a recent survey (IMS, Patient Apps for Improved Healthcare: From Novelty to Mainstream, 2013) six main hurdles to recommend an app:

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 mHealth R&D Activities in Europe

• • •

• • •

Legal: They could recommend the prescription of an app to their patients only after receiving the endorsement of their organization’s legal team. Liability from any medical repercussions as a result of app use is also a concern for physicians. Choice: They need more help and evidence to choose an app in order to meet the specific needs of the individual patient. Security and Data Privacy: Patient awareness and consent about collection, use and transmission of data is necessary prior using the app. HIPAA (Healthcare Information Portability and Accountability Act) regulations or any other applicable laws should be followed when necessary. Any stakeholders involved should take care of the protection of data and any security mechanism when dealing with patient sensitive data. Regulations: They need to be confident that the apps they may recommend to their patients are receiving appropriate levels of regulatory scrutiny (e.g. FDA, etc.). Infrastructure to Recommend: Appropriate formularies are needed in order for physicians to be able to recommend/prescribe an app to the patient’s phone, as well as follow up whether the app was downloaded. Reimbursement: It is not clear to physicians how the patient will pay for the app. Payment of physicians for monitoring remotely generated patient data is also a consideration.

The lack of trust in some mHealth solutions is a significant issue when choosing an app. Sometimes app may even endanger people’s safety (Sharp, 2012). Safety could be ensured by using for example the draft standard IEC 82304-1 of the International Electrotechnical Commission (IEC) (http://www. iec.ch), that details requirements for software that are medical devices, while intended to be used with a broader scope, such as for health and wellbeing purposes. In the United Kingdom there are currently some certification programmes for apps (e.g. the NHS choices health apps library) (http://apps.nhs.uk). According to this initiative, all apps have to be reviewed and prove their safety and compliance with data protection rules. However, in the EU there are no clear regulations yet for software developers and manufacturers of mHealth apps. Even though a lot of progress has been witnessed in the last years, still considerable work is needed to have better mHealth solutions and more effective interventions. A recent report projects the life in the future (2020) using affordable wearables and biosensors (Deloitte, Healthcare and Life Sciences Predictions 2020: A bold future?, 2014). According to the study, the patients will be more engaged and more willing to share their data. Interoperability will not be a problem at that time. However, privacy concerns will still exist. To achieve all the above a lot of research needs to produce ready-to-use commercialized solutions that can be adopted in everyday activities. The authors argue that wearables are now entering the commercialization phase. As new mHealth solutions continuously appear, the need for appropriate regulations and legislations is revealed. To this end, several organizations are working to produce guidelines and regulations for mHealth applications. The FDA (U.S. Food and Drug Administration) has already proceeded in the classification of mHealth apps that need to be regulated (Table 7) (Thomson). The unregulated mobile apps, according to the FDA report, belong to some general categories: • • • 40

Electronic copies of medical reference materials Educational tools Apps that a patient may use to get medical information

 mHealth R&D Activities in Europe

Table 7. FDA categories of regulated apps

• •

Class

Risk

Regulatory Requirements

Class I

Low

Class II

Medium

The class I requirements plus often an obligation to seek FDA clearance before marketing. FDA clearance involves showing that your product is substantially equivalent to others in the market, and often involves 90 or more days of FDA review.

Class III

High

The class I requirements plus an obligation to seek FDA approval before marketing. Approval, in contrast to clearance, involves showing fundamentally that the product is safe and effective. Often this requires clinical studies and takes much longer in terms of FDA review.

Typically, mostly observance of the quality system and reporting of adverse events.

Automate general office functions General purpose products

Finally, the apps that should be regulated but is still on the vendor’s choice whether to do it or not can be categorized as follows: • • • • • •

Patient-self management Patient trackers Access to contextually relevant information Patient communication and telemedicine Simple, professional calculators Connections to Electronic Health Records (EHRs)

Other issues that are of great importance for the future of the mHealth industry are related to consumer privacy and security requirements. These are well addressed by the Federal Trade Commission (FTC) in the United States of America (FTC, Privacy and Security, 2015). FTC worked in order to offer suggestions to improve mobile privacy disclosures (FTC, Mobile Privacy Disclosures. Building Trust Through Transparency, 2013).

CONCLUSION The proliferation of mHealth applications holds promise for better results in almost any stakeholder. However, the benefits till now are limited for both health care professionals and patients. The new services bring together a variety of disciplines sometimes lacking understanding of each other’s perspective. The current chapter examined the most recent research efforts and innovations in Europe and the world, with a dual focus on the opportunities and challenges faced by companies producing mHealth applications. It is crucial to first identify key obstacles and constraints in order for someone to be able to deliver proven health interventions effectively (Travis, et al., 2004), based on appropriate mHealth strategies. The promise of mobile health is profound, but yet unrealized. It is critical to bridge the gap from opportunity to adoption. Information and Communication Technologies are already presenting new ways of interaction between patients and health professionals, as well as the way conditions are diagnosed, treated and billed.

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Cloud computing, big data analytics, social networks, smart phones, tablets, etc., are working in joint action, while completing the puzzle of the new health service arena. The health care transformation that is taking place is being led by the current and future needs for reining the costs, engaging patients in the care process and providing ubiquitous access to everyone. According to a study (Bhargave & Johnmar) innovations in ICTs are helping to make health care more efficient, safe, and effective for all patients. However, serious concerns exist about the privacy and ethics of these new tools. Technology driven consumer demand along with the globalization and the mobility of population are some of the main reasons mobility has been so readily adopted in the healthcare industry. Moreover, the limited time drives clinicians to desire access health data and communicate with patients and colleagues at any time and from any place. This forces productivity while providing better services to their patients. A study (GSMA & PwC, 2012) forecasts that the global mHealth market will reach US$ 23 billion in 2017, distributed in Europe by 30%, while Asia-Pacific will account for 29.56% and North American for 28.26%. The same study reports that remote monitoring treatment solutions will dominate the market of mHealth deployments in Europe. Wellbeing and health apps will count for 15% of the market alongside with solutions focusing on the efficiency of health care workforce and system. Apart from the obstacles faced, the mHealth industry progresses well. More than 2.8 million patients are remotely monitored nowadays (Fagerberg & Kurkinen). EU countries’ ranking in the mHealth market is analyzed thoroughly in (research2guidance, EU Countries’ mHealth App Market Ranking 2015: Which EU countries are best for doing mHealth business, 2015). The study reveals that Denmark, Finland, the Netherlands, Sweden and the UK offer the best market conditions for mHealth app companies. The Table 8. Classification and examples of mHealth apps Category Prevention and Healthy Lifestyle

Selfdiagnosis

App

Description

Comments

CalorieCount

Counts calories for a healthier lifestyle. Preserves a database with numerous foods

http://www.caloriecount.com

OneSportsMan

Real time precise charts of speed and altitude

https://itunes.apple.com/om/app/ onesportsman-basic-sport-running/ id400634401?mt=8

Weight and BMI Diary

Tracks weight and calculates Body Mass Index

https://itunes.apple.com/us/ app/weight-and-bmi-diary/ id591327756?mt=8

Calorie Counter & Diet Tracker

Calorie counter with food database and exercise entry to help lose weight

https://itunes.apple.com/us/app/ calorie-counter-diet-tracker/ id341232718?mt=8

HealthTap

Personalized answers, tips, news, and app recommendations from doctors

www.healthtap.com

NHS Health and Symptom Checker

Allows users in UK to check their symptoms when feeling unwell. They can get an assessment, information about their condition and advice on how to look after themselves.

https://itunes.apple.com/gb/app/ nhs-health-symptom-checker/ id439637433?mt=8

Symptom Checker

It gives information regarding treatment options for minor injuries and illnesses, while recommending to seek help or professional treatment. It helps access emergency numbers and store health providers’ details.

http://www.medibank.com.au/healthinsurance/mobile-apps/symptomchecker/

Health Buddy

Improves convenience and accessibility to health information and services (e.g. health tips, GP listing, etc.).

https://itunes.apple.com/sg/app/ health-buddy/id448572997?mt=8

continued on following page 42

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Table 8. Continued Category Locate a physician

Education

Pharmacy and Prescription

Compliance

Therapy

App

Description

Comments

BetterDoctor

Search doctors rated by education and experience.

www.betterdoctor.com

US Hospitals Lite

It gives hospital specific performance data for over 7,000 health care institutions in the US.

https://itunes.apple.com/us/app/ushospitals-lite/id413438396?mt=8

Patient Fusion

Find a doctor and book an appointment. See lab orders, medications, etc., during a visit.

https://id.patientfusion.com/signin

ZocDoc

Map of doctors. Patient reviews and doctor’s available times for booking

https://www.zocdoc.com

Family Drug Guide

A drug resource that educates consumers about medications and provides cost comparisons, safety facts, and track records for effectiveness.

http://www.familydrugguide.com/ family/ub

Emergency Info 4Family

Quick access to all medical information for each member of the family. Emergency contacts. List for allergies and for medication information

http://apple.vshare.com/352726412. html

iTriage

Gives access to a healthcare & medical database (symptoms, diseases, etc.). The medical content is reviewed by Harvard Medical School. It also helps to find a doctor.

https://play.google.com/store/apps/ details?id=com.healthagen.iTriage

CVS Pharmacy

Refill, transfer and manage prescriptions. Access your prescription history. Find a CVS/pharmacy store and check hours, etc.

https://play.google.com/store/apps/ details?id=com.cvs.launchers.cvs

Walgreens

Prescription refills, health info and services, pill reminders, etc.

http://www.walgreens.com

MediSafe Meds & Pill Reminder

Medication manager and pill reminder.

https://play.google.com/store/apps/ details?id=com.medisafe.android. client&hl=el

Medicine Reminder HD - with Local Notifications Lite

Sends notifications when is the time to take a medication.

https://itunes.apple.com/app/ id407330433

Dosecast

Medication reminder with customizable dose amounts and instructions.

http://www.montunosoftware.com/ products/dosecast/about/

Glucose Buddy

Data storage utility for people with diabetes. Patients can enter data and activities

http://www.glucosebuddy.com

T2 Mood Tracker

Patients monitor their moods on six preloaded scales (anxiety, stress, depression, brain injury, post-traumatic stress, general wellbeing).

https://play.google.com/store/apps/ details?id=com.t2.vas

Zimmer Arthritis 411

Education resource for people who suffer from osteoarthritis.

https://itunes.apple.com/us/ app/zimmer-arthritis-411/ id496317851?mt=8

Dr K’s Breast Checker

Helps women keep track of change in breasts using interactive tools. It provides practical information and reminders

http://doctor-k.net

Young Epilepsy

For young people with epilepsy, and parents or carers of a child with epilepsy. Contains information portal, video and diary that helps track and manage seizures and symptoms

https://itunes.apple.com/gb/app/ young-epilepsy/id564205130?mt=8

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convenience that mHealth apps provide seems really valuable to all parties involved (PriceWaterhouseCoopers, 2014), (HIMSS, 3rd Annual HIMSS Analytics Mobile Survey, 2014). Another survey conducted by research2guidance (research2guidance, 3rd mHealth Expert Survey, 2012) reports that chronically ill is and will remain the main target group for mHealth solutions. Diabetes is the therapeutic area with the highest business potential, followed by fitness and obesity. The same study supports that most patients (52.1%) and health professionals (59.5%) will use mHealth apps by 2017. The main barriers reported are the lack of business models, regulation and standardization. A classification of mHealth apps along with some examples is presented in Table 8. It is obvious from what has been detailed in the current chapter that, apart from the several barriers, the promise of mHealth is driving demand. Many well known organizations follow this trend in a way to ensure the best possible results for their clients. For example, the Children’s Hospital of Philadelphia established a multidisciplinary committee that examines all mobile initiatives under consideration at the facility. At the same time, the IT department of Johns Hopkins Hospital purchases any new mobile device in order to test it thoroughly and determine its capabilities and limitations. As a conclusion we can support that mHealth solutions will be disseminated widely in the health market with multiple benefits but also problems. However, their usefulness is already recognized and well documented by several studies in large populations, both for the developed and developing countries (WHO, mHealth. New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth, 2011), (CDC, 2009), (mHealthAlliance & VitalWaveConsulting, Sustainable Financing for Mobile Health (mHealth): Options and opportunities for mHealth financial models in low and middle-income countries, 2013), (Deloitte, The four dimensions of effective health: People, places, payment, purpose, 2014).

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Frost & Sullivan. (2012). U.S. Hospital Health Data Analytics Market: Growing EHR Adoption Fuels A New Era in Analytics. Retrieved 11 18, 2013, from Frost & Sullivan: http://www.frost.com/c/10046/ sublib/display-report.do?id=NA03-01-00-00-00 FTC. (2013). Mobile Privacy Disclosures. Building Trust Through Transparency. Federal trade Commission. FTC. (2015). Privacy and Security. Retrieved 5 15, 2015, from Federal trade Commission: http://business.ftc.gov/privacy-and-security Gens, F. (2011). Top 10 Predictions. IDC Predictions 2012: Competing for 2020. IDC. Retrieved December 5, 2013, from IDC: http://cdn.idc.com/research/Predictions12/Main/downloads/IDCTOP10Predictions2012.pdf Grunerbl, A., Muaremi, A., Osmani, V., Bahle, G., Ohler, S., Troster, G., . . . Lukowicz, P. (2014, Jan). Smartphone Based Recognition of States and State Changes in Bipolar Disorder Patients. IEEE Journal of Biomedical and Health Informatics, 19(1), 140-148. GSMA, & PwC. (2012). Touching lives through mobile health - Assessment of the global market opportunity. PriceWaterhouseCoopers. HEARTEN. (2015). A co-operative mHEALTH environment targeting adherence and management of patients suffering from Heart Failure Table of Content. Retrieved from www.hearten.eu Help4Mood. (2011). A Computational Distributed System to Support the Treatment of Patients with Major Depression. Retrieved from help4mood.info HIMSS. (2014). 3rd Annual HIMSS Analytics Mobile Survey. HIMSS Analytics. HIMSS. (2015). Healtcare Information and Management Systems Society. Retrieved 5 13, 2015, from HIMSS transforming health through IT: http://www.himss.org/ ICT4Depression. (2015). User-friendly ICT Tools to Enhance Self-Management and Effective. Retrieved from www.ICT4DEPRESSION.eu IMS. (2013). Patient Apps for Improved Healthcare: From Novelty to Mainstream. IMS Institute for Healthcare Informatics. International Telecommunication Union (ITU). (2014). The World in 2014. ICT facts and Figures. Geneva: International Telecommunication Union. INTERSTRESS. (2010). Interreality in the Management and Treatment of Stress‐Related Disorders. Retrieved from www.interstress.eu ISA. (n.d.). eHealth European Interoperability Framework. ISA - Interoperability Solutions for European Pyblic Administration. Jones, C., & Shao, B. (2011). The Net Generation and Digital Natives. Implications for Higher Education. The Open University.

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Kafali, O., Bromuri, S., Sindlar, M., van der Weide, T., Aguilar Pelaez, E., Schaechtle, U., & Stathis, K. et al. (2013). COMMODITY12: A smart e-health environment for diabetes management. Journal of Ambient Intelligence and Smart Environments, 5, 479–502. Koumpouros, I. (2012). Information and Communication Technologies & Society (1st ed.). Athens: New Technologies Publications. Koumpouros, Y. (2014). Big Data in Healthcare. In A. Moumtzoglou & A. Kastania (Eds.), Cloud Computing Applications for Quality Health care Delivery. IGI Global. Krohn, R., & Metcalf, D. (2012). mHealth: From Smartphones to Smart Systems. HIMSS. m-Resist. (2015). Mobile Therapeutic Attention for Patients with Treatment Resistant Schizophrenia. Retrieved from www.mresist.eu Marcos, C., González-Ferrer, A., Peleg, M., & Cavero, C. (2015). Solving the interoperability challenge of a distributed complex patient guidance system: A data integrator based on HL7’s Virtual Medical Record standard. J Am Med Inform Assoc. Martnez-Miranda, J., Breso, A., & Garca-Gomez, J. M. (2014). Look on the Bright Side: A Model of Cognitive Change in Virtual Agents. Intelligent Virtual Agents - 14th International Conference, IVA 2014, Boston, MA. McKinsey, & GSMA. (2010). mHealth: A new vision for healthcare . McKinsey & Company, Inc., and GSMA. McKnight, J., Babineau, B., & Gahm, J. (2011). North American Health Care Provider Information Market Size & Forecast. ESG-Enterprise Strategy Group. METABO. (2008). Controlling Chronic Diseases related to Metabolic Disorders. Retrieved from www. metabo-eu.org mHealthAlliance, & VitalWaveConsulting. (2013). Sustainable Financing for Mobile Health (mHealth): Options and opportunities for mHealth financial models in low and middle-income countries. mHealth Alliance. mHealthAlliance. (2012). Baseline Evaluation of the mHealth Ecosystem and the Performance of the mHealth Alliance. mHealth Alliance. mHealthCompetenceCentre. (2014). News. Retrieved 5 10, 2015, from mHealth Competence Centre: http://www.mobilehealthglobal.com/in-the-news/news/41/the-mhealth-app-market-will-reach-26-bilionusd-revenue-by-2017-says-himss-europe-report Miluzzo, E., Lane, N., Fodor, K., Peterson, R., Lu, H., Musolesi, M., . . . Campbell, A. (2008). Sensing meets mobile social networks: The design, implementation and evaluation of the CenceMe application. SenSys ‘08 Proceedings of the 6th ACM conference on Embedded network sensor systems (pp. 337-350). ACM. MobiGuide. (2011). Guiding Patients Anytime, Everywhere. Retrieved from www.mobiguide-project.eu

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Mohr, D., Burns, M., Schueller, S., Clarke, G., & Klinkman, M. (2013). Behavioral Intervention Technologies: Evidence review and recommendations for future research in mental health. General Hospital Psychiatry, Special Section: Health Information Technology and Mental Health Services Research: A Path Forward, 35, 332-338. MONARCA. (2010). MONitoring, treAtment and pRediCtion of bipolAr Disorder Episodes. Retrieved from www.monarca-project.eu Moore, G. E. (1965). Cramming more components onto integrated circuits. Electronics Magazine, 4. Morris, M., & Guilak, F. (2009). Mobile heart health: Project highlight. IEEE Pervasive Computing / IEEE Computer Society [and] IEEE Communications Society, 8(2), 57–61. doi:10.1109/MPRV.2009.31 Moving Life. (2011). MObile eHealth for the VINdication of Global LIFEstyle change and disease management solutions. Retrieved from www.moving-life.eu Nephron+. (2010). ICT enabled Wearable Artificial Kidney and Personal Renal Care System. Retrieved from www.nephronplus.eu NIH. (n.d.). National Institutes of Health. Office of Behavioral and Social Sciences Research. Retrieved 5 14, 2015, from NIH. Office of Behavioral and Social Sciences Research: http://obssr.od.nih.gov/scientific_areas/methodology/mhealth/ NYMPHA-MD. (2014). Next Generation Mobile Platforms for HeAlth, in Mental Disorders. Retrieved from www.nympha-md-project.eu PAL. (2015). Personal Assistant for a healthy Lifestyle. Retrieved from www.pal4u.eu PD_Manager. (2015). mHealth platform for Parkinson’s Disease management. Retrieved from www. parkinson-manager.eu Pegasof4f. (2014). Personalised Guidance Services for optimising lifestyle in teen-agers through awareness, motivation and engagement. Retrieved from www.pegasof4f.eu Peleg, M., Shahar, Y., & Quaglini, S. (2013). Making healthcare more accessible, better, faster, and cheaper: the MobiGuide Project. European Journal of ePractice, (20), 5-20. Philbrick, W. C. (2013). mHealth and MNCH: State of the Evidence. Trends, Gaps, Stakeholder Needs, and Opportunities For Future Research on the Use of Mobile Technology to Improve Maternal, Newborn, and Child Health. mHealth Alliance, UN Foundation. mHealth Alliance, UN Foundation. PriceWaterhouseCoopers. (2014). Emerging mHealth: paths for growth. PriceWaterhouseCoopers. PSYCHE. (2010). Personalised monitoring SYstems for Care in mental HEalth. Retrieved from www. psyche-project.org PWC. (2012). The power of the net generation. PWC. Qiang, C., Yamamichi, M., Hausman, V., & Altman, D. (2011). Mobile Applications for the Health Sector. ICT Sector Unit. The World Bank.

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REACTION. (2010). Remote Accessibility to Diabetes Management and Therapy in Operational healthcare Networks. Retrieved from www.reaction-project.eu RemPark. (2011). Personal Health Device for the Remote and Autonomous Management of Parkinson’s Disease. Retrieved from www.rempark.eu research2guidance. (2012). 3rd mHealth Expert Survey. research2guidance. research2guidance. (2013). Mobile Health Market Report 2013-2017. The commercialization of mHealth applications (vol. 3). research2guidance. research2guidance. (2015). EU Countries’ mHealth App Market Ranking 2015: Which EU countries are best for doing mHealth business. research2guidance in partnership with mHealth Summit. research2guidance. Sharp, R. (2012). Lacking regulation, many medical apps questionable at best. Retrieved 5 15, 2015, from The New England Center for Investigative Reporting, Boston University: necir.org/2012/11/18/ medical-apps/ Splendid. (2013). Personalised Guide for Eating and Activity Behaviour for the Prevention of Obesity and Eating Disorders. Retrieved from splendid-program.eu Statista. (2015). Value of the global mHealth (mobile health) market 2012-2020 (fee-based). Retrieved 5 7, 2015, from Statista. The Statistics Portal: http://www.statista.com/statistics/295771/mhealth-globalmarket-size/ TheWorldBank. (2012). 2012 Information and Communications for Development: Maximizing Mobile. The World Bank. TheWorldBank. (2015). Indicators. Retrieved 5 11, 2015, from The World Bank. IBRD-IDA: http://data. worldbank.org/indicator?display=graph Thomson, B. (n.d.). FDA Regulation of Mobile Health (2nd ed.). Chester Street Publishing, Inc. Travis, P., Bennett, S., Haines, A., Pang, T., Butta, Z., Hyder, A., & Evans, T. et al. (2004). Overcoming health-systems constraints to achieve the Millennium Development Goals. Lancet, 364(9437), 900–906. doi:10.1016/S0140-6736(04)16987-0 PMID:15351199 UnwiredHealth. (2014). UnwiredHealth EU Project Official web page. Retrieved from http://www. unwiredhealth.eu van Heerden, A., Tomlinson, M., & Swartz, L. (2012). Point of care in your pocket: A research agenda for the field of m-health. The World Health Organization. Bulletin of the World Health Organization, 90(5), 393–394. doi:10.2471/BLT.11.099788 VitalWaveConsulting. (2009). mHealth for Development: The Opportunity of Mobile Technology for Healthcare in the Developing World. UN Foundation-Vodafone Foundation Partnership. Washington, D.C. and Berkshire, UK: UN Foundation-Vodafone Foundation Partnership. Welcome. (2013). Wearable Sensing and Smart Cloud Computing for Integrated Care to COPD Patients with Comorbidities. Retrieved from www.welcome-project.eu

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WHO, & UNICEF. (2010). Countdown to 2015: Taking stock of maternal, newborn and child survival. 2000-2010 Decade Report. World Health Organization and Unicef. WHO. (2011). mHealth. New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth (Vol. 3). World Health Organization. Witten, I., & Eibe, F. (2005). Data mining: Practical machine learning tools and techniques, 2nd edition. San Francisco, CA: Morgan Kaufmann Publishers Inc. Youssef, A., MacCallum, T., McDonald, D., Crane, R., & Jackman, M. S. (2012). Healthcare Information and Management Systems Society. Retrieved May 15, 2014, from HIMSS transforming health through IT: http://www.himss.org/ResourceLibrary/mHimssRoadmapContent.aspx?ItemNumber=30392

KEY TERMS AND DEFINITIONS Behavioral Intervention Technologies (BITs): It includes mHealth and eHealth interventions to support people in changing behaviors and cognitions related to mental health, health and wellness. It is a multidisciplinary field, including psychologists, physicians, software engineers, human factors engineers, computer scientists, etc. BITs apply behavioral and psychological intervention strategies by using the latest ICTs. Biosensor: It refers to an analytical device able to detect, record and transmit information regarding physiological/biological change or process (e.g. blood pressure, etc.). Some other indicative applications relate to the detection of body movement, temperature and fluid analysis, which are turned into electrical signals. In summary, it converts a biological response into an electrical signal. The biosensor is made up of a biological recognition element (e.g. an enzyme, a nucleic acid or an antibody) and a transducer (to convert the recognition event into a measurable signal). Body Area Network (BAN): A Body Area Network (BAN) or a Body Sensor Network (BSN) or a Wireless Body Area Network (WBAN) can be defined as a system of low power devices/sensors in close proximity to the body of the user that cooperate using a wireless network for the benefit of the subject/ end user. The devices may be wearable, implants, etc., and through gateway devices, it is possible to connect them to the Internet and transmit data to a local base station and to remote places. A WBAN system can use WPAN wireless technologies as gateways to reach longer ranges. In the medical sector, they are used to help medical professionals to monitor patients’ data and activities remotely. Chronic Care Management (CCM): The term is interchanged with disease management and is referred to activities (e.g. motivating patients to persist in therapies, etc.) carried out by health care professionals to help patients with chronic diseases (e.g. diabetes, high blood pressure, multiple sclerosis, etc.) learn and understand their condition and live with it having reasonable quality of life. eHealth: refers to the use of information and communication technologies for the support of healthcare practice. It covers electronic exchange of health related data, while may be used for clinical, educational, research and administrative purposes. Information and Communication Technologies (ICTs): The term is referred in the technologies used from the telecommunication and informatics sectors as well as any possible combination of them. It may includes any communication device (e.g. telephone, tv, radio, cell phones, computers, satellite systems, wireless networks, etc.), as well as the software, applications and services associated with them

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which enable users to exchange information. The term covers also the audio-visual sector in combination with computer networks and telecommunication networks. mHealth: The term is referred to the use of mobile technologies combined with wearable and fixed sensors to provide health-related services. Patient-Centered: Patient-centered refers to any case (process, operation, system, etc.) that is focused, designed and developed based on the patient’s needs and the patient’s pathway, while trying to fulfill his/her expectations. The term is usually used in patient-centered care. Personal Area Network (PAN): A Personal Area Network (PAN) or a Wireless Personal Network (WPAN) is a computer network for data transmission among devices (e.g. telephones, cell phones, computers, PDAs, wearable computer devices, etc.) organized around a person’s workspace. When using wireless technologies (e.g. irDA, Bluetooth, ZigBee, etc.) for data transmission the term WPAN is used, while PAN can also use computer buses (e.g. USB or FireWire). PANs can cover a range of almost 10 meters. Wearable Devices: The term can also be found as wearables, wearable technology, fashionable technology, etc. It refers to accessories that can be worn on the body and clothing incorporating electronic technologies and computer. The main idea behind wearables is the ability to connect to the Internet, thus enabling data exchange between the device and the network. Wearables can provide biofeedback and tracking of physiological function and transmit the information. Biosnesors are also used for wearables. Glasses, watches, headbands, bracelets, and others, are some examples of wearable devices. Smart tattoos and implanted devices are a more invasive version of wearables.

ENDNOTES 3 4 5 6 7 8 9 1 2

12 10 11



13

International Telecommunication Union / Telecommunication Development Bureau Commonwealth of Independent States ICT-2007.5.1 - Personal health systems for monitoring and point-of-care diagnostics Collaborative project ICT-2009.5.1 - Personal Health Systems ICT-2011.5.1 - Personal Health Systems (PHS) ICT-2011.5.3 - Patient Guidance Services (PGS), safety and healthcare record information reuse ICT-2013.5.1 - Personalised health, active ageing, and independent living Combination of Collaborative Projects and Coordination and Support Actions for Pre-Commercial Procurement (PCP) ICT-2013.5.1 - Personalised health, active ageing, and independent living EU Horizon 2020 R&I FP H2020-PHC-2014-single-stage / PHC-26-2014 (Self management of health and disease: citizen engagement and mHealth) Research and Innovation action

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Chapter 3

A Collaborative m-Health Platform for Evidence-Based Self-Management and Detection of Chronic Multimorbidity Development and Progression Kostas Giokas National Technical University of Athens, Greece Panagiotis Katrakazas National Technical University of Athens, Greece Dimitris Koutsouris National Technical University of Athens, Greece

ABSTRACT The ageing process of EU population has played a key role raising the prevalence of chronic disease, with more than 80% of people in the last age group (65-74) reported to be having three or more long-term Multimorbidity or Multiple Chronic Conditions (MCCs). The main problem is that currently, clinicians have limited guidance, as well as evidence of how to approach care decisions for such patients. As a consequence, the understanding of how to best take care of patients with multimorbidity conditions, may lead to improvements in Quality of Life (QoL), utilization of healthcare, safety, morbidity and mortality. The root of this problem is not narrowly confined to guidelines development and application, but is inherent throughout the translational path from the generation of evidence to the synthesis of the evidence upon which guidelines depend.

DOI: 10.4018/978-1-4666-9861-1.ch003

Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 A Collaborative m-Health Platform for Evidence-Based Self-Management

INTRODUCTION: OBJECTIVES OF PRESENT RESEARCH The vision of the proposed research is to develop an m-health ecosystem, leading to an evidence-based self-management and detection of chronic multimorbidity development and progression, where clinical data will be periodically collected by an engaged and empowered chronic patient through an extensive on–intrusive use of market available and ad hoc made m-health apps that co-produce additional clinical data. The ecosystem above systematically interoperates and integrates into the Electronic Health Records available in the private cloud environment of local or national European healthcare organizations. This challenging approach is expected to highly contribute and increase the self-management attitude of the patient, as well as the research conducted on multimorbidity, by supporting clinicians and researchers to understand the clinical course of disease in detail and improve clinical outcomes. Three main layers of Scientific and Technological Objectives represent the load-bearing pillars of the proposed m-health ecosystem including knowledge, applications and services that will enable more effective and efficient: 1. Health Promotion: Improve self-management, patient management and patient-patient/patientdoctor collaboration a. Promote self-management pathways for chronic elderly patients and increase the level of awareness of their health condition. b. Create and Test an m-health ecosystem enabled, personalized, patient – centric care model in different European healthcare systems leading up to a step forward in the cross-border harmonization c. Increase the level of patient-patient and patient-doctor interaction by encouraging the patient to have a more active role in changing their behaviour by reaching healthcare goals. 2. Public Health: Combine the benefits of self-management with the need of increased research evidence on multimorbidity d. Create additional insights to increase the level of knowledge in the estimation of the occurrence and distribution of multimorbidity e. Contribute to and Support the development of consensus on self - management and care of multimorbidity, engaging it as a subject on expert panels focused on the care of older adults f. Increase the daily evidence of the role of contextual and lifestyle-related factors in the development of multimorbidity. g. Aggregate and analyse the informative asset generated by the platform to enrich and better describe the natural history of multimorbidity both on a patient and community level 3. Standard, business models & Regulations: Increase the patients and doctor confidence in technology as a foundation to create an holistic care process driven as a patient-centric healthcare system h. Design innovative care models supported by an effective combination of disruptive technologies like cloud, social, mobile and analytics, which are developed in full respect of patient privacy and safety. i. Deploy a set of application and services contributing to the widespread of most relevant standards and protocols for interoperability of personal health systems (e.g. Continua Health Alliance, HL7, IHE, etc.) as well as IEEE based connectivity standards j. Contribute to the implementation of the results reached by the Working Party set up under Article 29 of Directive 95/46/EC, dealing with Data Protection Directive applied to the use of apps on smart devices. 53

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BACKGROUND: THE CO-OCCURRENCE OF CHRONIC DISORDERS - MULTIMORBIDITY The ageing process of EU population has played a key role raising the prevalence of chronic disease, with more than 80% of people in the last age group (65-74) reported having three or more long-term conditions. This condition is defined as Multimorbidity or Multiple Chronic Condition (MCC). Multimorbidity is defined as the co-existence of two or more chronic conditions, where one is not necessarily more central than the others. Multimorbidity affects the quality of life, ability to work and employability, disability and mortality. The number of individuals with MCC is expected to increase dramatically in coming years (Anderson & Horvath, 2002). The term multimorbidity, captures multiple, potentially interacting, medical and psychiatric conditions, so it may be more appropriate and more patient-centered for the older population than considering it from the perspective of a single index condition, which is the traditional approach. The main problem is that currently clinicians have limited guidance or evidence as to how to approach care decisions for such patients (Boyd et al., 2005). As a consequence the understanding of how to best care patients with multimorbidity may lead to improvements in quality of life (QOL), utilization of healthcare, safety, morbidity and mortality. According to a recent study (Wallace & Salive, 2013) the suggestion for these signs, symptoms, and syndrome, is to be carefully and systematically addressed, since many never reach the level of a specific diagnosable “disease” with an ICD code; however, they can cause considerable suffering and require extensive health care. With rare exceptions, nowadays clinical practice guidelines focus on the management of a single disease, and do not address how to optimally integrate care for individuals whose multiple problems may make guideline-recommended management of any single disease impractical, irrelevant or even harmful. The root of this problem, however, is not narrowly confined to guidelines development and application, but is inherent throughout the translational path from the generation of the evidence to the synthesis of the evidence on which the aforementioned guidelines depend on. Recently, the emphasis has been placed on the role of “pragmatic clinical trials” to lead the care of real world populations. It is essential to note that without appropriate standardized data management and analytic techniques to account for heterogeneity of treatment effects, the results of such trials may be misleading about whether specific patients benefit more or less from therapies than the average patient. The generation of evidence is related to the possibility of collecting and analysing data from daily routine care settings including the strong collaboration of the patient themselves that, if duly empowered to self-management, can maximize the synergies with the healthcare actors. Multimorbidity deals with complex clinical manifestations of conditions, such as signs (visually observable patient abnormalities), symptoms (abnormal perceptions of illness that only patients can report, such as pain, itching, fatigue, depressive feelings), and syndromes (clusters of signs, symptoms, and other clinical phenomena that may or may not be indicative of a specific underlying disease).

MAIN FOCUS OF THE CHAPTER (STATE OF THE ART) Issues, Controversies, Problems: Proposed Concept Disease management is a healthcare model that could help physicians, patients and managed care organizations to improve outcomes and control costs through coordinated and proactive interventions.

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To effectively implement this model, a trans-disciplinary approach is needed posing inevitable challenges for complex healthcare systems. There are several core issues that disease management professionals deal with: engaging patients in their health management, handling multiple and coexisting chronic disease states, supporting physician decision-making, and using data and decision making supporting technologies to early identify and propose appropriate interventions. All of these aforementioned issues, if well implanted, need to lead to a more efficient utilization of resources and, eventually, cost savings. Recently some Disease-Management Programmes (DMPs) have produced better results than in the past according to a study made by McKinsey (Brandt, Hartmann, & Hehner, 2010). The study analysed a wide range of DMPs from countries around the world to determine the characteristics that differentiate successful and unsuccessful programs. Five traits seemed to be the most important in ensuring that DMPs meet their goals: program size, simplicity of design, a focus on patients’ needs, the ability to collect data easily and analyse results (Brandt et al., 2010), and the presence of incentives that encourage all stakeholders to comply with the program. Out of the five characteristics that can help ensure that a disease-management program achieves its clinical and financial goals, the proposed research concept aims at contributing to: •

•

Patient focus: the interventions they include apply to the vast majority of enrolled patients, as well as simple and straightforward to implement. In line with the Self-management technology support proposed in our research, the patients are given ongoing, disease-specific coaching and resources to access the most relevant information to maximize their ability to care for themselves Information transparency: Many early DMPs did not have good mechanisms to prove their effectiveness because they did not have systems in place to monitor what patients were doing and what results were being achieved. In some cases, there were no clearly defined measures to gauge the programs’ success and no established methods for data collection (Brandt et al., 2010). Starting from these assumptions, the four factors connected in the disease management process envisaged to be supported by the proposed technology are listed below and inherited by the successful DMPs mentioned before. All the four factors will be taken into consideration in the absence of any one of them can reduce the likelihood of achieving an optimal outcome. ◦◦ Factor 1: The monitoring must be timely conducted on a regular basis, ◦◦ Factor 2: The monitoring must be comprehensive covering all necessary (disease and patient specific) vital signs and symptoms, ◦◦ Factor 3: The data must be shared appropriately with the care team, ◦◦ Factor 4: The data must be meaningful and actionable for the patient or the relatives in case of cognitive impairment or dementia present in the subject.

Involving patients actively in their care is critical for controlling disease management costs associated with monitoring and assessment. Encouraging and supporting patients to become more engaged in their healthcare will simultaneously encourage and support the goals of disease management (Intel Health, 2007). In this approach to healthcare, the more a patient becomes an equal partner in his or her care team, the more efficiently and effectively programs are likely to run. The following set of proposed enabling services is expected to highly impact the routine care management of chronic patients by supporting clinicians and researchers to better understanding the underlying mechanisms of multimorbidity: 55

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A Patient-Specific Content Management Suite (PS-CMS) available for healthcare operators in charge for the management of primary chronic condition, to “drag and drop” specific services and generate zero-code mobile apps to be “prescribed to the patient. These apps are needed to collect specific data from patients and complete the possible lack of information retrieved from the Data Management and Orchestration Service (DMOS). The list of services will include interoperability modules based on the widely used IEEE 11073 Health information protocol1 to easily connect the set of point of care sensors (e.g. Continua Alliance2), as these are defined by the doctor and may also be needed to complement information related to the patient, to the mobile devices. A set of personalized content management technologies and services based on intelligent tools and techniques, able to provide functionality for assessing relevance and reliability of web content. These will offer personalised, intelligent interventions (e.g. prompts, recommendations, remarks) to promote the self-engagement of patients. They will also compare and estimate how new, surprising and valuable different associations in the form of similarity and analogy may be in a particular context to increase the effectiveness of recommendations. Finally, they will offer personalised search and exploration for the users to focus on the aspects most relevant to their condition. A Data Management and Orchestration Service (DMOS) founded on the hybrid cloud paradigm, able to manage the enormous variety of structured and unstructured data available for the same subject and collected from disparate data sources, e.g. the m-health ecosystem interacting with the EHRs/PHRs. The DMOS will act as a single point of truth to manage unstructured data (collected from m-health apps) available on a local level in the smartphone or public cloud (e.g. back end of the Health APP provider), as well as more structured data from private clouds of healthcare data sources (such as patient information managed and stored in local, regional or national private clouds where EHRs are accessible by the network of healthcare providers). Instead of duplicating mobile apps already existing in the market and generating highly relevant data such as BMI calculators, calories counter, diet and food tracker, insulin calculators, the DMOS will tie the data generated by a meaningful healthcare driven process aimed at transforming those data into valuable information. Issues related to privacy, security and health information communication related to safety and quality of data generated through mobile applications, will contribute to the progress in establishing regulations and definitions of standards, as expected by the European Commission’s eHealth Action Plan 2012 – 2020. A HL7 categorization system, included in the hybrid cloud-based DMOS environment, able to clean, normalize and translate data collected from mobile applications in HL7 format with the purpose of producing standardized and comparable data regardless the source of data generation (e.g. regardless if produced in a structured healthcare IT environment or in daily life environment such as home settings, and collected by mobile applications). Detailed analysis and predictive models running in the hybrid cloud analytics tools, able to create common clusters of primary chronic conditions associated with physiological parameters, lifestyle as well as social determinants and their interactions to early recognise signs, syndromes and symptoms leading to a possible arise of secondary chronic diseases before multimorbidity. The proposed model is addressing different type of healthcare actors interested in analysing and connecting clinical and patient information across varied settings and time periods to generate longitudinal and comprehensive views of patient care, identify unknown risk factors combination affecting the generation of multimorbidity, or aggregating data for epidemiological and public health use.

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SOLUTIONS AND RECOMMENDATIONS: APPROACH AND METHODOLOGY The proposed research will follow an iterative, collaborative and cooperative approach at a very early stage. From a technological development perspective, the design and development methodology will be focused on the core of the system, the patient. The methodology, called Mixed Methodology in literature (Daraghmi, Cullen, & Goh, 2008), will be based on a co-design principle including Participatory Design (Schuler & Namioka, 1993), User Centred Design (Noyes & Baber, 2012) to develop a usable interface and to develop a reliable and acceptable system by eliciting users participation in the design phase, integrated with Rapid Application Development (Beynon-Davies & Holmes, 1998; Guelfi & Savidis, 2006) for early deployment. This type of methodology includes usability engineering approaches to evaluate the new system by validating each development stage against old and emergent requirements. The implemented methodology will consist of three stages with each stage iteratively performed. It will also follow a new generation of Participatory Design based on three modules (Third Generation of Participatory Design, 3GPD (Pilemalm & Timpka, 2008)). The first module covers the pre-design stage when the project plan, schedules, and contacts with stakeholders are set up. The second module represents requirements analyses, design and prototyping stages. Finally, the third module is the post-design stage including full implementation and completion of requirements’ specifications. The new generation of 3GPD is resource-effective, can be integrated with other methods such as rapid methods, and provides full documentation (Pilemalm & Timpka, 2008). From a Health Research perspective, the availability of large databases in UNIPG (Università degli Studi di Perugia3) and ARC (Aging Research Center4), which are based on a national sample of 60+ old adults and include information on medical, social and psychological states (cross-sectional assessment) and events (follow-up assessments), allows the proposed research to carry out a retrospective study whose results will be implemented in the knowledge management system (DSS and predictive models), following these steps: 1. Development of a Health Index which can summarise the health status of older adults and trace the time-related changes: medical as well as functional aspects will be integrated. This index will characterise the subjects with multimorbidity and trace their progression to unfavourable health outcomes. 2. Validation of the index in other populations as a predictive tool for health changes from the complete absence of disease and functional impairment to the development of chronic multimorbidity and disability. 3. Identification of the biological and medical factors that lead to a higher risk of developing multimorbidity and consequent disability. 4. Identification of the social and environmental factors that can modulate the onset and progression of chronic multimorbidity. Once the platform is developed and integrated, a small-scale clinical trial will be set-up in (possibly three) different and appropriate pilot sites, whose main objectives will be: 1. The development and validation of a Health Index to be used as a predictive tool for health changes, 2. The characterisation of the target users enrolled for the trial and risk stratification, 3. The design of personalised interventional program models for patients living in senior houses,

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4. The monitoring of the patients (half acting as a test group with apps and sensors; half as a control group without them) living in their own homes, with particular attention to those with higher level of multimorbidity severity, and the regular updating of the model according to newly collected data, 5. The verification of the proposed system’s effectiveness, stability and reliability as a tool to empower patients in self-management through validation and comparison tests, 6. The refinement of the developed solutions, applications and approaches on the basis of the research findings, and finally 7. The evaluation of clinical and non-clinical outcomes.

CLINICAL PROTOCOL The clinical protocol included in the pilot study is devoted to the validation of the proposed methodology in young-old subjects (age 65-75 years) suffering from two or more of the following index diseases (hypertension, diabetes, monitoring, heart failure, arthritis, impairment in cognitive functions) and with a score 90 mmHg DBP, according to evidence from RCTs, treatment-induced BP reductions are beneficial. The same classification is used in young, middle-aged and elderly subjects. Engage the citizen at risk and defeat reluctance in compliance by helping them realize the importance of the treatment, as well as its impact on their condition and to improve the general attitude towards Blood pressure self-measurement (BPSM) through a co-operative set of ICT ecosystem aiming at stimulation, sharing of experiences and information, collaboration (citizen-physician; citizen-citizen/s etc.) between all the stakeholders addressing the Hypertension related direct, collateral, long or short terms effects and impact on the population. Provide physicians with user-friendly devices for collecting 24-hours continuous accurate and contextual blood pressure related monitoring symptoms during the daily life of the citizen. The citizen will have just to wear a smart watch or a bracelet provided by the local healthcare system in charge for the compliance and blood pressure monitoring healthcare programmes. The same smart watch or bracelet can be used by the citizen to track other activities of his/her personal interest, as the most widespread bracelet monitoring systems available in the market (step counter, activity tracking for fitness etc.). The availability of wellness monitoring features mixed up with professional clinical wise parameters monitoring in the same device will result in a higher level of acceptance by the citizen. The clinical monitoring features in the smart watch or a bracelet allows a 24-hours beat by beat monitoring of blood pressure in order to reveal boosts of blood pressure of very short duration (from a few seconds to minutes), not detectable by traditional 24 hour monitoring. Introduce innovative organizational models to improve healthcare system performance through cost-effective predictive personalized health (pHealth) models embedded in an holistic supporting environment aiming at optimize the costs and budget available for healthcare prevention campaigns (= costs > efficacy) with the use of smart technologies and reduce the long-term indirect costs of the healthcare and overall community, thanks to a reduction of acute and chronic diseases directly connected to a poor prevention or therapy compliance Support behavioural changes of the citizens at risk of hypertension in the adherence to better lifestyle management protocols. The citizens participating in the initiative will be subject to a various combination of stimuli (e.g. audio-visual stimuli; game-based stimuli; social interaction based stimuli) that will be personalized according to their degree of risk of hypertension. The system will be able to track any behavioural changes in the life style management (e.g. increase of intensity and quality of physical exercises proposed by the system, increase of the game-based interactions with the other participants in the on-line community; adherence to dietary advises proposed by the system; increase of participation in the on-line community activities). This will allow the people at risk to improve their self-management capability of health and disease prevention

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•

Support a migration path towards innovative services supporting at the same time more effective healthcare strategies and sustainable business models. In the initial phase of the proposed research, the system will be recording all the changes in behaviours of the participant taking into account the risk of hypertension as well as the vital signs values monitored through the smart watch and the bracelet. At the same time, the system will be able to track all the types of stimuli to which the persons have been exposed. In parallel, the vital signals and the risk of hypertension of a group of persons with a similar risk of hypertension characteristics of the people participating in the pilot stage will be monitored as well. These two parallel sets of data will allow the production of statistical evidence of any causality relationships between the various stimuli and changes in the degree of risk of hypertension in the target population. This will help the health care authority to tailor better preventive campaigns on the population at risk of hypertension as well as to propose to the health care practitioners more effective instruments and strategies to address the prevention of cardiovascular chronic disease and related comorbidities. The suggested approach will establish the evidence’s strength and improve knowledge about individual’s behaviours related to wellbeing, disease prevention. Furthermore, it will facilitate the creation of new more personalized behavioural health intervention.

The scope of this research is to reduce and/or postpone the risk event of chronic cardiovascular disease and related comorbidities for this segment of population (population at risk), through different and conjoint stimuli (audio-visual stimuli; game-based stimuli; social interaction-based stimuli) aimed at producing significant behavioural changes in the diet and lifestyle of such population and engaging the citizens tin becoming the first actors of their care. The proposed solution aims at achieving two mutually interrelated final impacts: on one side is to improve the quality of life of the citizens for a longer period of time by increasing their healthy life expectancy (through prevention of cardiovascular events such as stroke, acute myocardial infarction, thus preventing cardiovascular death) and improving quality of life, on the other side is to reduce the cost and the burden on the national health care systems by reducing the hospitalization times and treatments needs of the population by increasing the preventive actions before the occurrence of chronic diseases. The proposed environment will be shaped as a health information system which combines individualized stimuli modules (e.g. audiovisual, serious games, social networking,), personalized non-intrusive diet monitoring modules and multiplatform blood pressure tracking systems (e.g. from traditional to innovative ones) specifically designed for people at various level of risk of hypertension low, moderate, high and very high risk. The platform will be organized in a way to allow both a self-use of its components by each user and the cooperation amongst users and health professionals. All the modules and services offered by the proposed environment will be designed to help progressively the citizen to decrease the level of risk. The supportive environment that is managed by the platform will subconsciously and effortlessly engineer patient awareness to engage in healthy behaviours, offer personalised guidance in regards to the patients personal history, medical records and vital signs and provide support to behavioural change through innovative models for ICT-enabled disease prevention and promotion related to hypertension risk reduction and cardiovascular chronic disease reduction of complications and comorbidities. The innovative aspects of the proposed solution could be focused initially on the proposed platform that will be based on mining, profile-matching, crowd-sourcing, advanced searching and profiling based on 24-hours continuous monitoring with linked clinical protocols through collecting, analysing, and 75

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explaining visual data and people’s experiences of health and illness and focusing mainly on hypertensive or people at risk of hypertension. Innovative lifestyle matching self-trained algorithms will be also developed during the project to support the formation of cluster groups of individuals belonging to the same network and sharing same problems and experiences. Early advice about the bad-lifestyle code of conducts will be provided. Surveys among users will also help us to gather information about motivation, aptitudes and incentives to tailor health promotion and prevention message.

BACKGROUND The project idea is to develop a health promotion and disease prevention platform (HMP) to support individuals and health care professionals in coproducing healthy management preventive care actions for hypertensive people. The Health Management Platform (HMP) will be embedding people’s experiences of health and illness while mainly focusing on people suffering from hypertension along with their personal health data and health information to support them and the health care professionals in coproducing healthy management guidance and preventive care actions against hypertension for ageing well. The HMP will be a health information system with social networking components that will be user-friendly and designed specifically for the elderly to use on their own and in cooperation with other individuals and health professionals. This supportive environment will promote awareness regarding healthy behaviours, self – management of diseases, will offer personalised guidance and provide support to behavioural change through innovative models for ICT-enabled disease prevention and promotion. Hypertension, otherwise called the “silent killer” is one of the most common worldwide non –communicable diseases afflicting humans and is a major risk factor for stroke, aneurysms, myocardial infarction, vascular disease, and chronic kidney disease. According to a WHO report (2009), around 1 billion adults had hypertension – that is around 26% of adults worldwide. Predictions and estimations bring the number up to 1.5 billion adults with hypertension by 2025 (WHO, 2009). The WHO estimates that 36% of Europeans aged 30 or older have high blood pressure, which means that around 200m in greater Europe (i.e. not just the EU) will have one or another type of hypertension. In most developed societies, the proportion of elderly people is steadily growing and aging is considered the leading cause of morbidity and mortality. Aging is increasingly regarded as an independent risk factor for the development of cardiovascular disorders such as atherosclerosis and hypertension and their complications. With increasing age, the vasculature undergoes functional and structural impairment and furthermore, antithrombotic and vasodilatory properties of the endothelium of the blood vessels are reduced with aging, whereas the inflammatory activity and oxidative stress increase (Minamino & Komuro 2007). Ageing is related to depression and stress, which have not yet been linked to long-term blood pressure rise, but nevertheless mental health problems, affect our bodies. When the organism is under stress, adrenaline and cortisol are released into the blood and prepare the body for a flight or fight response by an increase in the sympathetic nervous stimulus, making the heart beat faster and constricting the blood vessels to get more blood to the core of the body instead of the extremities. Even though the physical reaction of the body ends with the stressful stimulus, chronic stress and depression, do play a role in general wellness and add up to hypertension-related issues. Despite extensive research over the past several decades, the aetiology of most cases of adult hypertension is still unknown, and control of blood pressure is suboptimal in the general population. This

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can be attributed to the fact that patients either don’t know they suffer from hypertension or they do not adhere to the prescribed medication and / or health regime. This failure is multifactorial and only speculative, not only because of the lack of awareness, or lack of effective pharmacologic agents or lack of understanding of the role of lifestyle modification. Due to the associated morbidity and mortality and cost to society, preventing and treating hypertension is an important public health challenge. Hypertension, as mentioned above, which is mostly defined as systolic blood pressure exceeding (BP) ≥140 mm Hg, diastolic BP ≥90 mm Hg, increases with age and is affecting more than 50% of patients aged ≥60 years with approximately 66% of those aged ≥65 years. The total direct and indirect costs are very high, and drug expenditure on antihypertensive medications is around 10% of the total annual drug expenditure, in high-income countries (Nguyen et al 2010). Despite the costs, only approximately 1/3 of hypertensive patients, are at the recommended blood pressure goal. Hypertension will develop in the majority of the population in their lifetime, so early preventive measures and prompt management, including lifestyle and pharmacologic options, are essential to minimize complications associated with this condition. Since the specific project proposes a prevention plan for hypertensive people or people that might be hypertensive in the next decades, it is decided that the target population will be people aged between 45 and 65 years old.

THE HEALTH ECOSYSTEM AND EMERGING BUSINESS MODE: SOLUTIONS AND RECOMMENDATIONS We envision the proposed system to emerge as an ecosystem, with its components abiding as standalone entities, but at the same time being characterized and defined by the network of interactions among the patients, their caregivers and the health care professionals. The involvement of the hypertensive person, in the design, development and testing activity of the solution of the whole ecosystem will advance the quality of the ecosystem per se and at the same time will enhance the Quality of Experience and advance adherence. As described in Figure 1 this ecosystem is constituted by two main groups of stakeholders. The stakeholders that interact with hypertensive subjects as potential patients are: •

•

The wide spectrum of Healthcare institutions (e.g. GPs, Hospitals, Healthcare authorities) that are pushed by macroeconomic factors impacting the next future efficiency of the welfare, need to promote in the EU citizens a new approach in behavioural change management by proposing personalized and proactive services based on the principles of citizens engagement (self-management; self- determination; self-efficacy; enhancement of knowledge regarding risk on health) in therapy and management of their hypertension risks factors for a better quality of life. By engaging in such dynamics, it will be possible to achieve better health care for the financially challenged, to increase investments in health and progressively eliminating non-financial co Health insurances, they represent an emerging trend in the health services landscape. Their importance is growing in all the EU Member States even in the one where the health care services are mainly managed by the public authorities e.g. the Mediterranean countries. As described in Figure 1 the health insurance belongs to both circles constituting the ecosystem around the citizens at risk of hypertension, since, on one side they see them as potential clients of the products, on the other side, they are important stakeholders in supporting and promoting preventive actions of the health

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Figure 1. Multi – stakeholder ecosystem

•

care authorities and in guaranteeing the whole sustainability of the health and social care service. Finally, the insurance companies can play an important role for the take-up and then up-scaling the proposed solution. On the other hand for countries with prevailing public insurance, it would be beneficiary to incorporate preventive projects and platforms in primary health care facilities and settings, in order to lower the number of ER visits and hospitalization costs. Integrated care systems are to be seen as organisations that are in between providers and insurance or national health systems. On the one hand they are connected to the providers and aim at changing attitudes and behaviours (often they are connected primarily with physician networks sometimes as well with sports clubs, nutritionists, social workers), on the other hand they are contractually connected to the insurances and may have their data and the possibility of identifying persons in need and under risk using predictive modelling and other techniques and data analysis.

Other possible stakeholders that interact with the hypertensive citizens and are to be treated as potential clients of health services and would benefit from being offered a higher level of awareness in regards to citizen’s health risk profile is for example the food industry. Food industry trends show how the products that are launching on the market are more and more personalized on the characteristics of their clients. In this perspective, the persons at risk of hypertension are an interesting emerging segment of consumers that the food industry is approaching. To the end of this industry, the proposed product and related services can provide important insight in terms of preference of their potential clients and type of dietary recommendations that need to be followed to reduce risk of hypertension. As described in Figure 1, as the Health insurance industry, they too can play a double role in the ecosystem that surrounds the citizens at risk of hypertension: on one side, as discussed above, they see such citizens as a potential client for their products, on the other side, however they can contribute to support the health

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care authorities in more proactive and preventive campaigns against obesity and risky lifestyles. Figure 1 Moreover, they can play an important role for take up and up-scaling the proposed solution with appropriate advertisement, dissemination and exploitation strategies (Table 1).

PROPOSED PLATFORM ARCHITECTURE AND CONCEPT The architecture of the proposed system is based on the utilization of sensor and stimuli generators and recording devices that will collect the signals coming from the 24-hours continuous BP measurement device or several audio-visual or game-based stimuli, and correspond them to the patients’ responses. The high-level components of the platform are described in Figure 2. In particular the four elements in the upper part of the figure represent the four sets of stimuli managed by the platform: •

•

•

Audio-visual stimuli, that are representing a set of audio-visual documents where people with different CV risk or/and additional chronic disease complication talk about their health conditions / difficulties and their related experiences in regards to how they were addressing these issues and changing their lifestyles and behaviours. Positive and negative experiences, as well as success stories and reinforcement, will be available on the platform repository and the “audio-visual stimuli management component” of the platform will guide each user in selecting them according to their risk profile and lifestyle choices and behaviours. A data tracking system will register per each user the relevant parameters that are related to the use of the audio-visual stimuli and other behaviours that could be induced by the use of the “audio-visual stimuli management component.” Serious game-based stimuli will represent a group of competition and emulation stimuli that will be designed, based and derived from games aimed at inducing the users to increase their physical training, level of activity and healthy lifestyle. This component of the platform will stimulate the practices of gym exercises, physical movements as well as changes in diet regimes and lifestyle (e.g. use of stairs instead of the elevator). To this end the component will manage a gamified environment stimulating competition/emulation amongst the other participants where good practices will be awarded with an increase of the total “healthy lifestyle score”, while bad behaviours and/ or deviation from the suggested code of conducts shaped according to each user’s risk profile (e.g. deviation from the recommended and planned diet or program of physical exercise) will result in a significant reduction of the overall “healthy lifestyle score”. The data tracking system of the platform will also register changes in behaviours and choices in the diet regime and physical exercises. Serious game based stimuli will test and challenge the patients to learn how best to handle their own health conditions. This stimulus will be a valuable tool for the physicians, as they can also engage in a competition of their own, to see who can provide better results for their patients. Diet Monitoring and stimuli. An interactive and totally non-invasive module will be offered to the user, capable of continuous recording of food intake, processing data and presenting them to the specialist in a useful way. This interaction will allow the specialist to have access to complete information on amount and quality of the dietary intake. This way it will be possible to return appropriate corrective diet actions or changes that lead the subject to a motivated change in the approach towards healthier food consumption. The system will educate the user to balance his/her diet by daily suggestions of appropriate and recommended food quantity to be consumed as well as preferable food categories. The left side module of Figure 2 is the most important since it aims

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Table 1. Indicators and expected benefits for the target groups Indicators

Expected Benefit

Selected Scale or Metrics for Quantification

Matching Objectives and Impacts

Unhealthy lifestyles favouring CVD

Healthier lifestyle choices through adherence to beneficiary changes, promoting prevention of CVD, by incorporating proposed platform in everyday activities.

During the randomized control Trial in four Pilots Centres:    • Metrics regarding assessment of:    • Smoking    • Nutrition habits    • Sedentariness    • Stress stimuli    • Lower average daytime ABP levels and lower BPV as assessed by ABPM    • Questionnaires of QoL    • Risk score assessment    • BP response during ECG effort testing

• Improvement of risk factors awareness and self-management of health leading to disease prevention. • Creation of new personalized behavioural health interventions

Visit time too tight (ESH/ESC 2013 Guidelines)

Visits schedule would be defined according to the healthcare status evolution monitored by our solution. It will be proposed to the specialist by the system according to the lifestyle protocol adoption and compliance and will be adjusted to patient’s changes and needs in real time.

Each subject has personalized cognitive –behavioural strategies and multimodal interventions, according to the degree of CV risk that will be calculated from the platform, in conjunction with family history. Improvement will be evaluated as    • Reduction of visit’s (number/year)and    • Amelioration of the abnormal bio-humoral tests with respect to baseline values (e.g. creatinemia from 1.6 mg/dl to 1.1 mg/dl, reduction of microalbuminuria).

Improvement of disease management and/or expenditure. Quality of life (less stress during physician’s visits – white coat hypertension)

Lack of malnutrition related alert in-between visits

Changes in nutritional behaviour and diversion from recommended dietary plan detected by our system and information provided by the in-between specialist visits

The Mini-Nutritional Assessment (MNA®) scale will be used to measure the improvement on nutritional habits. Anthropometric measurement: estimation of BMI variance in overweight (> 24.9) conditions and measurements of waist circumference

Economic benefits from the use of ICT in the new care models

Complexity of drug treatment levels

Simplification of drug regimen under our proposed supportive environment for healthy behaviour and rise in adherence statistics

Reduction of number of drugs. Reduction of dosages at equivalent PB control, as assessed by ABPM. Reduction of counter reactions between drugs Less adverse effects

Ecosystem and new business models for promotion and coproduction of health. Healthcare professionals and relevant stakeholders macroeconomic benefits.

Unsatisfactory control of BP under antihypertensive treatment

Better detection of BP variability (BPV) thank the BP monitoring multiplatform integration. Precise monitoring and estimation of false alarms related to physical stimuli

• Lower average daytime ABP levels and lower BPV as assessed by ABPM after our interventions, with respect to the baseline measurements (results from the four pilot centres) • 24/7 Pulse wave analysis by the watch: reduction of sudden boosts of abnormal BP during daily life and everyday activities • Reduction of BP response at peak ECG stress effort test

Increased confidence in decision support systems for wellbeing and disease/ patient management from Patients perspective Health Care Professionals point of view

at managing the individual risk profile of each user (Mancia et al 2013). This risk is related to the hypertension condition / stage and the pre-existing asymptomatic chronic damage and disease, for each user of the platform. Its quantification will be done on a dynamic base and it will be related

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Figure 2. High-level structure of proposed platform

to hypertension guidelines (see left side module of Figure 2), to the habits and the behaviours of the users. The lower part of the figures contains three further components of the platform: •

•

The advice services on behavioural changes. It represents a rule base system that aims at proposing to the user proactive advice on required changes in the diet regime and in his/her habits, based upon the evidences of the monitoring of the user’s vital signs (e.g. blood pressure; daily physical activity etc.) and the mining of the information registered by the data tracking system. It is an intelligent system that will keep being “educated” as it will incorporate and include more data from everyday activities and habits, and it will eventually educate the users and act preventively instead of therapeutically. The health policy statistics management. It is a platform component that helps in extracting and producing statistical data from observing the users’ behavioural changes, preference structures patterns in diet regimes (in relation to contents, quantity, frequency and time), level of physical activity and exercises and related variations in their vital signs and risk profiles. These evidence-

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•

based information and statistics can be used, for example, by policy makers to design their health policies and targeted lifestyle campaigns. It will be the knowledge base that builds personalized services in the ecosystem, but it will be also used in comparison with other users so that valuable statistical indicators are extracted and estimated in regards to average, The health and social services management. It will be a platform component that helps users have at all times a direct contact of communication with health care professionals, nurses, and social services personnel. This component will contribute to the co-production of a healthy lifestyle code of conducts with the parallel contribution of the users. The health and social services component will also be able to inform the user about news as a call for vaccination of sensitive groups, new guidelines and it will be able to inform only specific profiles and will ask a feedback.

To address the preliminary functional specifications of the platform described above, the key technological elements of the platform will allow to develop the several functionalities. Innovative lifestyle matching self-trained algorithms will be also developed during the project to support the formation of cluster groups of individuals belonging to the same network and sharing same problems and experiences. It will also be possible to manage and deliver on the platform, in an early stage, personalised advice related to negative vital sign input information (e.g. high blood pressure; high heart rate), serving as an early stage educative feedback service. Surveys among users will also help us to gather information about motivation, attitudes and incentives so that health promotion and prevention messages can be tailored and customizer as well as a way to produce statistics useful for public health authorities to better address their health policies and enhance lifestyle campaigns management for tackling the population at large. In order to collect all desired information (24/7) an elaborate network of mobile devices based recording systems, such as smartphone and tablet embedded MEMS sensors, accelerometers, gyroscopes, GPS, as well as watch/bracelets activity tracking and game based enabled recording systems will be activated.. Regardless of the technical maturity and BP measurement devices already available at citizen’s premises, through the adaptive platform it will be possible to directly track or infer about the user’s health status. Tracking scales will inform the user about his/her big picture of health and with the use of algorithms that will be developed, proactive and preventive advice will be provided regarding lifestyle conditions and lifestyle and activity choices that negatively contribute to high BP and at the same time suggestions regarding behavioural changes will be delivered. This supportive environment for healthy behaviour will be developed and based on models that will be the outcome of a research result of the project (namely: audiovisual stimuli management; serious game based stimuli management; social networking stimuli management). Also with the use of activity tracking tools, exergames based physical training programmes will be developed and serious game knowledge along with intelligent software will stimulate the user to exercise indoors or outdoors, according to their registered risk profile.

OVERALL APPROACH AND METHODOLOGY Our proposed research and innovation activities aim at the monitoring and management of hypertensive citizens or people at risk of hypertension in order to provide them with a platform that offers useful advice for better and proactive management of their own health, aiming at prevention rather than intervention. The outcome of this research will result in the design and development of a novel ICT platform that will create a multi-disciplinary supportive environment targeted at health promotion and disease

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prevention that supports population at risk of hypertension, or hypertensive population, exploiting the advantages of new and evolving IT solutions adapted to the different tasks and levels of specific patient’s management. Additionally, the system will be beneficial to health care professionals and institutions in coproducing healthy management preventive care actions that will be leading to behavioral changes. So that the initial goal of this research is satisfied, and the specific platform is developed, the architecture needs to be designed according to technical standards and software architecture principles that are: • • • •

Mapping of its functionality onto hardware and software components Mapping of the software architecture onto the hardware architecture Representation of the human interaction with its components and Interface specifications of the systems components. The main parts of the architecture, as depicted in Figure 3, are:

Figure 3. Platform architecture

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•

•

•

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The signal and stimuli-recording module that includes several entities that will be used for the collection and the recording of different sets of data from the patient’s body and the environment. An innovative part of this module is the 24/7 Blood Pressure (BP) Monitoring device that includes a functional BP digital watch and an activity tracker device that serves to collect data during the whole day. These data after they are collected, they are stored temporarily in the specific devices and then they get transmitted, at least once a day, to the tablet/smart phone device or in its absence to a local PC via Bluetooth connectivity. The traditional BP measuring part could include three different methodologies for BP monitoring. Manual blood pressure monitors use a stethoscope and an inflatable arm cuff connected by a rubber tube to a gauge that records the pressure. Hybrid (digital) monitors have a cuff and a gauge that records the pressure. The digital monitors have the same functionality with the hybrid monitors and have an additional module that transmits automatically stored and recorded data to the receiver unit that in the specific case is the home / mobile environment. ◦◦ The input signal module combines the Stimuli recordings module (audio visual, serious games, social networking), and a personalized non-intrusive diet-monitoring module. The several audio-visual stimuli modules, represent a set of audio-visual documents where people talk about their health conditions, the difficulties that they face in their everyday lives and their experiences in addressing their health issues and the challenge of changing their lifestyles and behaviors. ◦◦ This module will be accompanied by a serious game-based stimuli group with competition and emulation stimuli that are based on games and that aim at inducing the users to increase their physical training and healthy lifestyle, through self-challenges and competition. Together with the social network stimuli group all the stimuli related to the participation of a social network of people that share the same needs and problems. The Home / Mobile Environment module (H/ME): This module will be established either for each person’s/family indoor home environment, but it will also have a mobile part, to secure, continuous collection of data outdoors. It will communicate with the rest modules through the Bluetooth channel (with the signal and stimuli recording module) and through an Internet connection to the system’s server. Additionally, it also will be feasible to utilize the GPRS/3G network connection of the patient’s tablet/smart phone device. The Home / Mobile Environment module can be further described in detail, with its respective parts as follows: ◦◦ The contextual info part, which will handle the insertion of the measurements that the patient will record manually. ◦◦ The modeling process that will utilize the collected data to build a light model of the person’s status, his profile and his behavioral class. ◦◦ The quick Expert system that represents the intelligent part of the (H/ME) that contains a feature extraction sequence and a light data processing for a rapid evaluation of the collected signals. The Server Module: The server module will consist mainly of all the needed parts that are existed in the H/ME but at a more demanding level since the computational power and the different sources of the server will provide a better environment of in-depth analysis and more effective processing of the existed data. Additionally, the context awareness part will handle the categorization and parameterization of diet monitoring, information that will be utilized by the expert system and the modeling process.

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The modeling process will utilize the collected data to build the full model of the person’s status, his profile and his behavioral class. This outcome will describe clearly and accurately the person as an entity and all the recordings that will be collected will then be compared with this initial pattern so that the emerging alterations can be validated properly through the profile adaptation part. Thus, apart from the parts that have already described above, the new parts of the server module are the Patient profiler and the Model Validation parts. In the patient profiler module, the patient’s initial data will be gathered, and each patient will be categorized and introduced in a specific profile class. These profiles and templates will feed the intelligent engine that will classify each patient accordingly and regarding his/her condition and his/her overall health status. In the model validation component, the clinician groups will evaluate the performance of the system by the realization of the comparisons between the traditional categorization of patients’ conditions/status and the extracted outcomes of the system. In the case that new exportable correlations appear among the extracted parameters, new medical knowledge might be extracted so that updated forms of the existing guidelines might be proposed. •

•

The Collaborating and Beneficiary Groups: The Collaborating and Beneficiary groups are the clusters of experts that will be involved in the project and benefited from the development of the system. They will provide the connecting links between the system, the medical area, the health insurance companies and the multi-stakeholder ecosystem. For each of the above-mentioned interconnections, the profit will be on several extents and the development of this innovative idea will return the maximum feedback to the whole community. The Interconnectivity, Security and Interfaces Section: This component involves mainly the technical contributions in the system. Although that the system is composed of elementary technological solutions, its design should include all the needed parts to overcome the technical difficulties that might appear during the project. The technical partners that will handle the implementation of these tasks will have the knowhow and will be ready to provide all the possible solutions for the overcoming of the problematic situations.

AMBITION OF THIS RESEARCH The proposed research on an integrated platform for the lifestyle change and holistic approach to personalized prevention and self-management of patients with high arterial blood pressure addresses both clinical and several technological and research challenges in various multidisciplinary fields. In particular in the following section the various covered fields will be discussed, as well as the current state of the art of the approaches currently used for preventing and reducing the risk of hypertension in the target population. The actual major technological developments will be described and the proposed advancements and innovations that will be addressed during the project lifecycle. During the past few years, the recent ICT solutions that support hypertensive and ageing population have taken the shape of online support groups and separate user groups and communities. Current research suggests that the development of cognitive and behavioural methods, to support patients in adopting a healthy lifestyle, could improve the life of hypertensive patients, and it would elongate the years of independent living. Through subtle behavioural reinforcement, social exclusion of this part of the population that is currently a phenomenon would be avoided with the extra advantage of the economic positive flow to the health system resources. Evidence from the field has demonstrated that amongst the tested methods the most effective seem to be the following: 85

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• • •

Methods that aim at establishing cognitive-behavioural strategies (e.g. motivational interviewing; gaming; audio-video stimuli) to facilitate lifestyle changes; Methods that foresee the involvement of specialized healthcare professionals (e.g. nurses, dieticians, psychologists, etc.) in communication and awareness creation actions; Methods that are based on multimodal interventions integrating educational activity on a healthy lifestyle, medical resources, exercise training, stress management, and counselling on psychosocial risk factors.

Our proposed research intends to go beyond the state of the art, by developing a technological platform and a risk management support platform, targeted at people between 45 and 70 years old that will combine different possible risk strategies and personalized preventive interventions on the individual at different levels of HT. The core innovation proposed is based on the engagement of the HT people on the concept of continuously monitoring their risk factors and on the stimulation of their behavioural changes through personalized and multimodal interventions that are constituted by a different mix of stimuli (audio-visual, social gaming, social interaction), information (risk factors updating; physical activity data; etc.) and personalized recommendations (dietary suggestions, physical exercises plans, lifestyle indications, personalized health care professionals advices). To be clinically useful and medically accurate, BP measurement must be obtained following a range of recommendations, in regards to the method used, anatomy, positioning of the cuff or the sensor, level of activity, time of the day etc. This principle applies to measurements performed both at home and in the physicians’ office. Due to the high prevalence of “white-coat hypertension” (an increase of BP values observed only during office measurements, affecting between 20% to 40% of all patients visiting a clinic (Helvaci & Seyhanli 2006) (Pickering et al., 2010), to obtain a complete blood pressure profile of the subject, out of office - ambulatory measurements (e.g. ABPM or self-measurements) are recommended, according to pre-defined directions. Indeed, it has been demonstrated that self-blood pressure monitoring at home helps patients to keep their BP values under control, at least over a short period (Uhlig, et al 2013). It is not completely clear how self-measurement helps in maintaining BP values within normal range, but the main hypothesis is that keeping a continuous recording of BP changes over time may on one side offer a feedback to physicians and patients on the effectiveness of the strategies (medicine, diet, activity etc) undertaken to lower BP, while, on the other side, the information regularly obtained in daily life on BP. trends over time may help increasing patients’ compliance with prescribed treatment. Successful self-management of hypertension, through continuous monitoring, may equip patients with further self – accomplishment feeling, resulting in improved confidence and feelings of self-control, that lower the effects of stress. An empowered patient, understanding the benefits and witnessing him/ herself the positive results, through home monitoring, will likely be willing to engage even more in his/ her treatment with exercise and dieting. National and international guidelines, including those from the American Heart Association and the European Society of Hypertension, recommend that patients with hypertension measure and monitor their BP in home setting, as this can result in better BP control. Self-monitoring includes keeping a record of the readings so a physician can determine if intervention is needed, or if current intervention is working effectively or if there is a need to change and improve. Left uncontrolled, high BP can lead to stroke, eye and kidney damage, heart disease and disability. The beneficial effects of home BP measurement are stronger and longer when accompanied by constant health care personnel contacts, or with educational stimuli when compared to self-management of BP. A study (Parati et al, 2009) showed that 86

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combining HBPM with remote telemonitoring of home BP values led to a significantly better control of ambulatory daytime BP. It is important to notice the improved outcomes that result from the collection of data from self-monitored BP used in conjunction with clinicians and/or nurses who are using and monitoring the values. Clear indications of use of HBPM have been provided by the ESH Guidelines on Home BP Monitoring, the preparation of which was led by AUX (Parati et al 2008). Engaging European Citizens to increase the attitude to the Self-monitoring is one of the major features of the proposed research. By using a combination of stimuli, tracking and contextual information monitoring, the proposed research will progress the state of the art not only from a technological perspective but also from the clinical research perspective by increasing the evidence of the better management of blood pressure, stimulating and actively engaging patients in the measuring and monitoring of their blood pressure. In addition, it will further engage them goal achieving and goal maintenance. In relation to trials applied on the subject and results it will be interesting to establish the evidence of self-measurement effectiveness, for the part of the population that is not treated by medical professionals but that is engaging in self-awareness and self-management through a set of ad hoc stimuli such as serious games, social networks, interactive videos, etc. Thanks to the flexibility of the proposed architecture, most of the devices validated according to ESH HBPM guidelines can potentially be used in a prospective study (O’ Brien et al, 2010). Furthermore, it would be feasible to perform a complete assessment through 24h Ambulatory Blood Pressure Monitoring, by focusing on the reduction of 24h, daytime and nighttime average BP levels and the reduction of 24h BP variability. The validated measurement system will thus range from: •

• •

Automatic blood pressure monitors used in the physician’s office: Automatic monitors, also called electronic or digital monitors, which are battery-operated monitors that use a microphone to detect blood pulsing in the artery. The cuff, which is wrapped around patients upper arm, automatically inflates and deflates when activated (when the cuff is inflated to a certain extent, the artery collapses and by gradual release, the blood flows through, producing the known as Korotkoff sounds) Automatic blood pressure monitors used at patients’ home for HBPM, according to ESH Guidelines (Parati et al 2008), with appropriate education of the patients. 24 hours Ambulatory blood pressure monitoring (O’Brien E. et al, 2013), which offers a view of BP pattern during day time and night time and during strenuous activities, and is also helping in correlating particular conditions (e.g. physical or mental stress, time length of drug efficacy, white coat effect) with BP fluctuations, which may otherwise be missed by sporadic measurements over the 24h (Lefebvre et al, 2002).

In fact, ABPM can reveal relationships between plasma drug levels and therapeutic effect, identify times of day during which a medication is less effective, provide insight into the impact of missed doses, and detect variability in BP (BPV) during the night and early morning (at the end of the dosing interval in most treated patients), which could have a considerable influence on cardiovascular (CV) outcomes (White 1999) (Giles 2005). BPV has gained interest in recent years on the background of the evidence indicating that the adverse CV consequences of high BP may not only depend on absolute BP values but also on BPV. When assessed either in the short or in the long term and independently of mean BP levels, an increasing BPV has been shown to be associated with development, progression and severity of cardiac, vascular and renal organ damage and with an increased risk of CV events and mortality. Recently (Parati et al 2013), post hoc analyses of large intervention trials in hypertension have raised 87

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concern that preventive interventions against CV consequences of hypertension should be targeted to stabilize BPV in addition to control of mean BP values.

CONCLUSION The proposed research will include the validation of a novel cuff-less technology for 24-h ambulatory blood pressure monitoring (ABPM), according to the ESH International protocol validation for new devices. Indeed, one of the added values of the proposed research is to achieve innovation in the way hypertensive diagnostic and prognostic risk profiles are evaluated. If the validation ends up positively, the novel device will provide information of beat-by-beat information in real life setting (information not available). From a technological point of view, use of the novel device may allow to: • • •

Detect, through the 24/7 ABPM continuous monitoring prototype the occurrence of “spikes” in BP over 24h, Detect contextual information that may be associated with BP “spikes” thus clarifying possible pathophysiologic mechanisms of these phenomena; Contribute to defining subjects’ risk profile in addition to the information provided by validated BPM devices.

The main value added of the proposed research is to achieve innovation in the way hypertensive diagnostic and prognostic risk profiles are evaluated. In a constantly evolving world, where the dynamics are ever changing, technology is being incorporated into everyday lives and people are living longer, it is important to maintain a level of independence and quality of life, by being self-aware and proactive, through the use of personalized algorithms via simple user friendly interfaces, that incorporate a vast amount of statistically significant and available data. Novel ICT technologies, now allow us not only to monitor and treat diseases, but also to promote a healthier proactive lifestyle, through cognitive reward methods, that include serious games, involvement in Social Groups, sharing of experiences, fun competition and in the end, taking our lives into our own hands.

REFERENCES Giles, T. (2005). Relevance of blood pressure variation in the circadian onset of cardiovascular events. Journal of Hypertension. Supplement, 23(Suppl 1), S35–S39. doi:10.1097/01.hjh.0000165626.57063. b1 PMID:15821450 Helvaci, M. R., & Seyhanli, M. (2006). What a high prevalence of white coat hypertension in society! Internal Medicine (Tokyo, Japan), 45(10), 671–674. doi:10.2169/internalmedicine.45.1650 PMID:16778338 Jennings, H., & Cook, T. (2010). Hypertension: Clinical Practice updates, PSAP-VII, Cardiology. Retrieved April 20, 2015, from https://www.accp.com/docs/bookstore/psap/p7b01sample01.pdf Lefebvre, J., Poirier, L., & Lacourcière, Y. (2002). Methodology to determine duration of action for antihypertensive drugs. The Annals of Pharmacotherapy, 36, 874–881. doi:10.1345/aph.10367 PMID:11978167 88

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Mancia, G., Fagard, R., & Narkiewicz, K., Redón, J., Zanchetti, A., Bohm, M., … Zannad, F. (2013). ESH/ESC Guidelines for the management of arterial hypertension: The Task Force for the management of arterial hypertension of the European Society of Hypertension (ESH) and of the European Society of Cardiology (ESC). Journal of Hypertension, 31, 1281–1357. doi:10.1097/01.hjh.0000431740.32696. cc PMID:23817082 Minamino, T., & Komuro, I. (2007). Vascular Cell Senescence, Contribution to Atherosclerosis. Circulation Research, 100(1), 15–26. doi:10.1161/01.RES.0000256837.40544.4a PMID:17204661 Nguyen, Q., Dominguez, J., Gullapalli, N., & Nguyen, L. (2010). Hypertension Management: An update. Retrieved April 20, 2015, from http://www.ahdbonline.com/issues/2010/january-february-2010-vol-3no-1/108-feature-108 O’Brien, E., Atkinsb, N., Stergiouc, G., Karpettas, N., Parati, G., Asmar, R., & Shennan, A. et al. (2010). Working Group on Blood Pressure Monitoring of the European Society of Hypertension. Blood Pressure Monitoring, 15, 23–39. PMID:20110786 O’Brien, E., Parati, G., Stergiou, G., Asmar, R., Beilin, L., Bilo, G., & Zhang, Y. et al. (2013). European Society of Hypertension Working Group on Blood Pressure Monitoring European Society of Hypertension position paper on ambulatory blood pressure monitoring. Journal of Hypertension, 11(31), 1731–1768. PMID:24029863 Parati, G., Ochoa, J. E., Lombardi, C., & Bilo, G. (2013). Assessment and management of blood-pressure variability. Nature Reviews. Cardiology, 10(3), 143–155. doi:10.1038/nrcardio.2013.1 PMID:23399972 Parati, G., Omboni, S., Albini, F., Piantoni, L., Giuliano, A., Revera, M., & Mancia, G. et al. (2009). Home blood pressure telemonitoring improves hypertension control in general practice. The TeleBPCare study. Journal of Hypertension, 27(1), 198–203. doi:10.1097/HJH.0b013e3283163caf PMID:19145785 Parati, G., Stergiou, G. S., Asmar, R., Bilo, G., de Leeuw, P., Imai, Y., . . . Mancia, G. (2008). ESH Working Group on Blood Pressure Monitoring. Retrieved April 20, 2015, from http://jbr.org/articles. html http://www.heart.org/HEARTORG/ Parati, G., Stergiou, G. S., & Mancia, G. (2008). ESH Working Group on Blood Pressure Monitoring. European Society of Hypertension guidelines for blood pressure monitoring at home: a summary report of the Second International Consensus Conference on Home Blood Pressure Monitoring. J. Hypertens, 26(8), 1505-26. Pickering, T. G., White, W. B., Giles, T. D., Black, H. R., Izzo, J. L., Materson, B. J., & Weber, M. A. et al. (2010). When and how to use self (home) and ambulatory BP monitoring. Journal of the American Society of Hypertension, 4(2), 56–61. doi:10.1016/j.jash.2010.03.003 PMID:20400049 Ravisankar, P., & Shajeeya Amren, S.K., Devadasu, C., & Devala Rao, G. (2014). Controlling Hypertension: A brief review. Journal of Chemical and Pharmaceutical Sciences, 7(2), 122–136. Uhlig, K., Patel, K., Ip, S., Kitsios, G. D., & Ethan Balk, E. (2013). Self-Measured Blood Pressure Monitoring in the Management of Hypertension. A Systematic Review and Meta-analysis. Annals of Internal Medicine, 159(3), 158–194. doi:10.7326/0003-4819-159-3-201308060-00008 PMID:23922064

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White, W. B. (1999). How well does ambulatory blood pressure predict target-organ disease and clinical outcome in patients with hypertension? Blood Pressure Monitoring, 4(Suppl 2), S17–S21. PMID:10822418 WHO. (2009). Global Health Risks, Mortality and burden of disease attributable to selected major risks. WHO Library Cataloguing-in-Publication Data.

KEY TERMS AND DEFINITIONS Compliance: The degree to which a patient correctly follows medical advice. Empowerment: Is the granting of patients to take an active role in the decisions made about his or her own healthcare. Health Management Platform: A system that focuses on the entire care cycle by proactively engaging patients from disease prevention, surveillance and screening; disease management for periods of illness; care transitions; health promotion; or wellness and education. Healthcare Ecosystem: Collection of medical and clinically related activities supporting the healthcare continuum. Hypertension: Is also known as elevated blood pressure or arterial hypertension, is a chronic medical condition in which the blood pressure in the arteries is elevated. pHealth: Also eHealth, ubiquitous care delivery independent of time and location of the resources involved. Serious Games: Games designed for a primary purpose other than pure entertainment.

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Mobile Platforms Supporting Health Professionals: Need, Technical Requirements, and Applications Ioannis Tamposis OraSys New Technologies S.A., Greece Abraham Pouliakis University of Athens, Greece Ioannis Fezoulidis University of Thessaly, Greece Petros Karakitsos University of Athens, Greece

ABSTRACT Mobile computing is beginning defining the future of healthcare. The vast majority of mHealth applications are related to fitness, training and self-monitoring; limited applications are targeting physicians and doctor-patient interactions. However this can change. In this chapter the background of applications related to medical imaging and clinical and laboratory medicine is analyzed. A technological framework supporting mHealth applications in an agnostic manner is also introduced. Within this framework there are implemented two application examples, one application (ImaginX) supporting a health ecosystem (hospitals, radiologists, clinicians, patients) for medical image management. The second application (HPVGuard) supports a divergent but cooperating environment of laboratory and clinical doctors and patients involved in cervical cancer prevention and control. The two applications are analyzed and issues related to user acceptance and future directions are presented. mHealth has the potential to shape health future not by just translating existing applications but by inspiring new ideas.

DOI: 10.4018/978-1-4666-9861-1.ch005

Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Mobile Platforms Supporting Health Professionals

INTRODUCTION Mobile Health (mHealth) is the application of medicine and public health through mobile devices. The last two decades the use of mobile communication devices has rapidly grown. Nowadays, the computing capacity, the large displays with high resolution, the capability to have a communication channel always connected to the internet and the www, along with the concept of smartphone as this is implemented by enhanced and user friendly operating systems and applications, has converted the mobile phone practically to a mobile computer. This gives tremendous opportunities for applications into numerous sectors, among them mHealth (Kastania et al., 2012; Nilsen et al., 2012; Steinhubl et al., 2013). Mobile computing is nowadays beginning to define the future of healthcare (Kastania et al., 2012) and is foreseen as a hot topic in the business world and healthcare the forthcoming years. mHealth is not just a replacement and “translation” of health care applications from fixed stations to mobile devices, mobile computing is an enabling technology for new mHealth applications such as 24x7 healthcare support and especially ambulatory medicine (K. Banitsas et al., 2005; K. A. Banitsas et al., 2006; Kiselev et al., 2012; Pavlopoulos et al., 1998; Rosales Saurer et al., 2009; Zerth et al., 2012). mHealth applications are already available in a variety of health-related domains: diabetes management (Quinn et al., 2011), asthma management (Gupta et al., 2011), obesity control (Patrick et al., 2009), smoking cessation (Ghorai et al., 2014; Ybarra et al., 2014), stress management (Clarke et al., 2014) and depression treatment (Burns et al., 2011). The vast majority (43%) of applications are related to fitness and training and self-monitoring (74.8%) (Sama et al., 2014), despite currently there are rather limited applications targeting physicians and doctor-patient interactions (Martin, 2012), this picture has the potential to change quickly. In this chapter, we analyze the background for medical imaging as well as clinical and laboratory medicine, is highlighted the role of cloud computing for the various involved actors in mHealth applications, security issues, and privacy protection are raised and interoperability problems and standards are mentioned. Subsequently, a technological framework capable to support the health care application in an agnostic manner is introduced and described. Specifically, a platform capable to host new but diverging applications, to support different health care sectors and being capable to support applications for mobile devices. Within this framework two examples are described. The first application (ImaginX) supports an ecosystem of health (hospitals, radiologists, clinicians, patients) having the need for medical imaging management, and the second application to support a divergent but cooperating environment of laboratory doctors, clinical doctors and patients related to cervical cancer prevention and control (HPVGuard). Finally, matters relating to the acceptance of mobile health applications by the users and future directions are highlighted.

BACKGROUND Medical Imaging It is a common opinion of all physicians, that imaging is a key component of the patient health record and is often critical for diagnosis and treatment. Currently, the rapid development of medical research and new technologies produce a continuous stream of knowledge about medical imaging and a constantly growing volume of image data. It is forecasted that there will be an increase in the US cloud computing market for medical images approximately 27% by 2018 at a Compounded Annual Growth

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Rate (CAGR). This is mainly due to the growing volume of medical images and the increasing costs of owning and maintaining Picture Archiving and Communication Systems (PACS) (GlobalData, 2012). Access to medical images via mobile devices is today possible as both size of displays, their resolution and communication channels bandwidth have increased. Medical imaging is not just restricted to radiology, as other medical specialties can take advantage of it. For example, ophthalmology and surgery need imaging informatics to improve the efficiency, the accuracy, the usability and the reliability of their decisions within the healthcare ecosystem.

Clinical and Laboratory Medicine Medical imaging is an extremely useful tool for medical practice. However, there are numerous examinations providing information for patient management performed within laboratories. Today it is impossible to imagine people are not carrying a mobile device usually always connected (due to low costs of 3G and 4G and availability of WiFi). This device is the end point that patients can be informed of the availability of the laboratory results or receive a reminder for scheduled appointments. Similarly, clinicians may receive informative messages on their mobile devices for the availability of an urgent examination result. Today mobile technology provides a continuously open channel between laboratories, clinical doctors, and patients.

The Role of the Cloud for Mobility in mHealth The volume of data in nowadays medicine is enormous. Cloud computing is an enabling technology, as it can catalyze the problem of storage (Moumtzoglou et al., 2014; A. Pouliakis et al., 2014a; A. Pouliakis et al., 2014b), and additionally is a technology enabling the always connected concept. Cloud computing provides Internet accessed storage that can today be exploited any time, any place, any device, actually for medical imaging there is a continuous growing trend to move towards cloud-based storage and computing (Kagadis et al., 2013; Marques Godinho et al., 2014; Neves Tafula et al., 2014; Schoenhagen et al., 2013). A cloud system provides a communication channel between patients, providers, laboratories and researchers.As a result, a new health ecosystem is created. By using mobile applications, ecosystem users could benefit to access remotely their medical data (either as owner [for example a patient] or as service provider [for example a medical radiology laboratory]). The advantages and benefits offered by mobile applications can be categorized according to the services recipients as follows: •

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Patients: Patients can store, control and share their personal health data; as a result they can benefit from faster and more efficient care. Nowadays, more and more people own smart devices such as mobile phones and tablets with Internet access. Thus, there is a new and growing mass of users; therefore, IT experts should aim to provide more services via mobile technologies. These services will eventually give patients the opportunity to access medical images wherever they are (for instance while traveling in any place waving Internet connectivity) or to be informed via SMSs and email Notifications. For example, when a study report is available, medical examination results are released, or an appointment is approaching. Radiologists: The mobile functionality is valuable in critical situations where radiologists are away from their office, and they need to perform a diagnosis. Radiologists need more efficient workflows and tools to help them improve the accuracy and reliability of medical diagnosis and

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•

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increase productivity. It is important for them to compare recent examinations with previous medical record data (usually imaging data), which were performed in the same body region or to obtain immediately a second opinion from an expert. Clinicians and Laboratory Doctors: Clinicians need Real-Time Access to Images and examination results anytime, anyplace (for example, during a critical operation a surgeon might need immediate access to Computed Tomography images performed before operation). Moreover, it is easier for clinicians to diagnose an unusual case if they have similar medical cases that can be accessed through the mobile device. Such capabilities lead to patient safety and better quality of care; additionally allow clinicians to make a faster diagnosis and give better medical advice, potentially from anywhere in the world. Other applications include: easy, instant and convenient ordering of examinations via the mobile device during a patient examination. Other medical specialties may benefit as well, for example, dermatologists may diagnose remotely, on the basis of pictures grabbed from the patients themselves, using their mobile device camera. Laboratories can easily receive examination orders and send examination results online. Gynecologists supported by the HPVGuard platform may obtain immediately examination results on their mobile devices, and laboratories have the benefit to reduce workload, as examination results are delivered via the platform immediately after their release. Society: Mobile technology may result in reduced total cost of health care, flexibility, reduction of patient’s examinations and visits and more effective prevention and treatment among others. Thus, mobile applications may create a HealthCare ecosystem that has value for money. Research and Education: Access to medical data across various locations for academic research and education purposes provides the ability to: a) continuous medical training b) availability of clinical cases for students study, c) improvement of medical practice and training d) instant access to knowledge when and where needed.

Privacy Protection All the above services have to be provided and applied by the law. As a reference, two laws are related to his issue, the first is the Health Insurance Portability and Accountability Act (HIPAA), a U.S. based law of 1996 and the second is the European Directive 95/46/EC adopted by the European Union. In short, HIPAA describes the rules that covered entities need to follow when sharing and managing personal health information of the patients who use their services (Office For Civil Rights, 2003). Further, by European Directive, member states shall protect the fundamental rights and freedoms of citizens, and, in particular, their right to privacy, with respect to the processing of personal data. Also, the European Directive is allowing the free flow of personal data between Member States for particular reasons (European Union Agency for Network and Information Security, 2014). Therefore, healthcare providers should take every precaution to ensure the privacy and safety of shared information, because in cases that private medical data fall into wrong hands, there is severe risk for medical professionals, patients and personnel responsible for data handling; often accompanied with severe legal and financial penalties. IT systems, therefore, should share information using only secure services and always encrypted. It is recommended to avoid some in common and convenient methods; this includes email, CDs, memory sticks and online file sharing services that may communicate data in a non-encrypted format. Email is the most common means of communication that many users all over the world use, as it is easy to use for a doctor or patient and facilitates sharing of medical information.

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However, can be guaranteed that the data is encrypted? Furthermore, the e-mail provider varies from person to person, making it difficult to keep track of the security measures in place and even track the transmission path of the exchanged data. Another common method of exchanging medical images is based on CDs and USB sticks, this method involves the risk that the data media may be lost or stolen; thus, sensitive data on such media should always be encrypted. A more recent method is cloud-based data storage services, in this case it is not determined where and how data is being stored and transferred; especially in occasions that data centers exist in different countries, and each country has different regulatory and legal requirements. So there is a variety of reasons that make privacy compliance, patient and physician protection more complicated; thus this generates many questions: (Theoharidou et al., 2013) • • • •

Who has created/read/write/modify permissions, and why? Who owns the data? Who owns the service? What happens if the bill is not paid, or the service becomes unavailable?

A health provider can choose to deploy its applications on Public, Private, Hybrid or the nowadays proposed Community Cloud. In a public cloud, a health provider offers resources such as services and information to the general public over the internet. In a Private Cloud, health providers can take similar advantages and services as in the public cloud but there is a limited number of people involved, and the system is secured (usually via a firewall). A private cloud is dedicated to a single organization where remote users can access services via a Virtual Private Network (VPN). A hybrid cloud is a composition of two or more clouds (private, community or public), and finally the community Cloud is a multi-tenant service that is shared among several facilities.

Interoperability in Health Communicating and exchanging information, inevitably raises a linguistic issue, namely a common language among the communicants should be established, in the IT world this is called interoperability and is coupled with the concepts of standards and protocols. The reason that interoperability is so important is related to the numerous heterogeneous information systems that need to communicate, exchange information and make data available when and where needed. Nowadays, connectivity and interoperability helps modern healthcare facilities around the world to provide optimal patient care and lower healthcare costs (Barr et al., 2012; Tamposis et al., 2014b; Zurovac et al., 2012). Interoperability is the enabling technology that makes integration among all types of equipment and applications feasible. A system should be compliant with International Standards for medical data exchange, such as HL7, DICOM, IHE, and agent-based methods (Mauricio, 2014) such as those proposed by the Agency for Integration, Diffusion, and Archiving of medical information (AIDA) (Luciana et al., 2014), otherwise it will be extremely difficult to communicate and be integrated. Obviously, a mHealth application is not an exception, indeed as the number of potential users is expanding, new mHealth systems and applications are created, a stressing factor to adopt those standards.

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Industry Standards: DICOM, HL7, and IHE The two most common standards used in the communication of medical data and medical imaging are HL7 and DICOM. HL7 (Health Level Seven) (Goossen et al., 2014) is a standard for medical data exchange. It was introduced aiming to solve the communication problem, and provides the specifications that provide organizations (Hospitals, Health Centres, insurance organizations, etc.) the ability to set specific standards and clearly identify how to interconnect both existing and new information systems; in a manner that these operate under a single framework and as a unified information system. Additionally provides to all Health Contractors, the ability to standardize their daily operations and procedures, to ensure the communication between organization and suppliers, to formalize the organization processes, facilitate purchasing and installation of IT systems and eventually provide significant economic benefits for standardization and interoperability of systems. HL7 was founded in 1987 according to the declaration on the organization website “HL7 is a not-for-profit, ANSI-accredited standards developing 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”. DICOM (Digital Imaging and Communications in Medicine) (Bidgood et al., 1997; Flanders et al., 2003) has grown over years as the standard for the encoding, the management and the exchange of medical imaging. DICOM is implemented in almost every radiology, cardiology imaging, and radiotherapy device (X-ray, CT, MRI, ultrasound, etc.), and increasingly in devices in other medical domains such as ophthalmology and dentistry. Hospitals, clinics, imaging centers and specialists use the DICOM standard to produce, manage and distribute their medical images. IHE (Integrating the Healthcare Enterprise) (Flanders et al., 2003) is an organization consisting of health professionals and industry organizations; aims to improve IT systems to exchange healthcare information. IHE promotes the coordinated use of established standards such as DICOM and HL7 to address specific clinical needs and eventually support optimal patient care. Systems that support the profile of IHE operate more efficiently, are easier to implement, and enable health care providers to use information more effectively, focusing on the medical tasks rather than technical details.

REST (Representational State Transfer) Evolution Other followed standards are relevant to the system architecture as a system should be based on contemporary architectures (for example RESTful API (Wikipedia, 2015)) and may be interconnected to other systems through standardized protocols such as HTTP/HTTPS. WADO (Web Access DICOM Object) is a standard for distribution of results and images to healthcare professionals. FHIR® (Fast Healthcare Interoperability Resources) is another approach, being the latest standard under development by the HL7 organization as mentioned earlier.

Integration with Equipment A system that stores and manages medical images must integrate with available medical modalities. According to IHE, the system should provide the DICOM Modality Work List (MWL) (Gale et al., 2000; Yoshimura et al., 2003), so the modality can automatically be updated and therefore inserting data by

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technologists in the modality console is not required. When an examination is completed, the modality sends a Modality Performed Procedure Step (MPPS) DICOM message and the images (Noumeir, 2005). When an order is completed, it will be included in the Patient Record with its results (a medical report accompanied with medical data). All these protocols and standards obviously facilitate everyday routine on imaging devices and most importantly reduce the errors that may be attributed to the human factor. Most of the modern imaging modalities support DICOM including (Bidgood et al., 1997) but not limited to: angioscopy, arthroscopy, bronchoscopy, colposcopy, cystoscopy, fetoscopy, hysteroscopy, gastrointestinal endoscopy, laparoscopy, naso- pharyngoscopy, sinoscopy, microscopy for cytology and histology, images produced by operating microscopes used in cardiothoracic surgery, general surgery, neurologic surgery, obstetrics and gynecology, ophthalmologic surgery, oral and maxillofacial surgery, orthopedic surgery, otorhinolaryngology, pediatric surgery, plastic surgery, urologic and vascular surgery, anatomic pathology, dermatology, dentistry, forensic pathology and ophthalmology among others. This incomplete list is continuously expanding and is representative that DICOM is everywhere in medical imaging.

Semantic Annotations: Ontologies The search for a tremendous number of medical reports and even worst in medical image banks is a complicated and time-consuming task; the outcome is not always successful. The use of semantic technologies provides easy retrieval of information for healthcare professionals and fast access to patient information. Those data are derived from several sources; they are intelligently indexed with semantic ontology annotation features using international vocabularies (e.g. ICD10). The primary objective is to facilitate search, thus annotation data have to be available in the resources, these are used for navigation by other systems that use semantic technologies; this approach helps physicians to understand better radiological images and reports. Additionally, physicians may easily compare similar studies and images and have a better comprehension of the medical history and eventually improve diagnosis procedures. Similarly, organizations conducting research, can organize more efficiently and effectively their data (Grätzel von Grätz et al., 2013). mHealth application can benefit from this approach when the cloud bases approach is followed, as searches are performed on remote computers with potentially unlimited processing power and storage.

Interconnection with Governmental Services An mHealth system must confirm patient identity and demographics. For that reason, if a country provides to the citizens an SSN (Social Security Number), the medical systems should be interconnected with National Registries in order to receive accurate patient data through the social security number and to the national prescription system to allow physicians to perform prescriptions of examinations in an organized, controlled and recorded manner. In Greece, a governmental organization (under the name E-governance in Social Insurance) handles SSNs for all population. This organization provides web services for this purpose. Thus systems can be securely interconnected and use SSN data. Furthermore, another governmental agency (the Greek National Health Service) provides web services to receive and process electronic appointments to meet physicians as well as electronic services for prescriptions, these services can be integrated into relevant systems that may exploit their capabilities and use accurate data.

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Integration and Communication between Medical Centers The interconnection of medical centers can provide solutions among regional healthcare units to support small units that lack the necessary medical equipment and specialized medical staff. For example, a small health center in a remote island or mountain region may own an X-Ray or another modality but doesn’t have a radiologist. In fact, in that case, all diagnoses can be performed remotely by radiologists at higher level hospitals manned by appropriate personnel while the modality can be locally operated by technologists. This is a typical case of telemedicine application that has many benefits: a) the communication time can be considerably reduced, especially when specialized mHealth applications are used either by medical staff and by the patient as well, b) local medical services can be upgraded by provisioning of advanced techniques/examinations, c) there is a wider population coverage by smaller health centers, d) advanced services are offered in a cost-effective and efficient manner, e) transfer of patients is avoided, thus reducing expenses due to travel costs and lost manpower and reducing as well transfer complications and hazards. Nowadays Internet-based, wired and wireless data networks are the highways of communication among all those distributed health facilities.

A TECHNOLOGICAL ARCHITECTURE FOR mHEALTH APPLICATIONS Nowadays there are two main types of mobile applications envisioned: a) on-device or otherwise called native applications and b) web-based. Native mobile applications are those that are built for a specific mobile operating system, such as iOS, Android, Windows Mobile, or BlackBerry. Native mobile applications are written in the target operating system’s application development language: Objective-C for iOS, Java for Android, and so forth. Mobile web-based applications, on the other hand, are written as web applications and are accessed using the mobile device’s browser. Each approach has advantages and disadvantages, native applications are not browser dependent and can use the mobile device resources and capabilities, for example cameras, microphone and speaker, compass and GPS or operating system functionalities such as reading and sending SMSs, additionally graphics handling is more robust and flexible. On the other hand native applications should be downloaded and installed by the user and even worst should be adapted to each different operating system and device capabilities, thus it is required serious development effort according to each device type, model, and capabilities. Web based applications lack the advantage of exploiting the device capabilities, they consume more bandwidth than is truly required (due to the overhead of HTTP protocol) but have the benefit of being centralized, thus they are developed once and used everywhere.however, taking care of the used browser and the limited device capabilities, in addition, they have the advantage of installation requirement; therefore, latest enhanced software versions are readily available. The basis of the platform software described in this section is based on numerous components, combined they create a framework capable to host divergent medical applications designed for specific medical sectors. The key components include the front end (i.e. user screens) and their adaptation to device capabilities, the database used to store data, specific frameworks capable to exploit device capabilities and specialized frameworks, for example to render DICOM images.

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Responsive Web Design Responsive Web Design provides the ability to design web applications across a broad range of devices (ranging from desktop computer monitors to mobile phones). If an application is used via a mobile device, then web pages designed for the mobile device will be presented; if the applications are used on a desktop computer, then a different user interface designed for larger displays is shown. This capability has been made available by new web based tools, techniques and technologies. Specifically via a technology named CSS (Cascaded Style Sheets), by CSS, on the server side, there are performed capability and media related queries relevant to the mobile device; by the response the server side is aware of the user device capabilities and subsequentlly different styling is applied and user pages are generated and served for small or large displays respectivelly. By these tools; the application developers have more time to focus on the design of the business logic and the environment of their applications instead of designing user screens for the various displays. Some pages are difficult to be designed as responsive pages because of their complexity. The cost of designing a responsive page with multi and complex processes are much higher and usually require higher developing skills.

Platform Architectural Design The proposed platform is built as web-based mobile application based on technologies such as HTML5, CSS3, and JavaScript; these frameworks are now mature and have the functionality to produce rich user interfaces. In contrast, as described earlier, native applications require expertise in multiple computer languages and environments and leads to the development of numerous different applications. This requires enormous effort both in the development stage as well as for application maintenance as well. Specifically, and as used in the proposed framework, the Oracle Application Express is extremely flexible to design and build web-based applications as the mobile theme and operations in applications are built using jQuery Mobile. However the problem of accessing device resources is still an issue, a framework such as the Apache Cordova can be utilized to deliver a native application, but underneath, it is still a web-based application (allowing delivering a web application into the device as native application). With this approach is possible to exploit both the mobile device resources and capabilities, such as cameras, microphone and speaker, compass and GPS or operating system functionalities such as reading and sending SMSs and simultaneously users can have a web core; however this is performed by downloading an application through specific marketplaces (for example iTunes Store, Google Play, Amazon Market, BlackBerry App World, Windows Phone Store, etc.). Another architectural component, used by the platform, is Cornerstone, it is a lightweight JavaScript library for displaying medical images in modern web browsers that support the HTML5 canvas element. Via this component complete web-based medical imaging functionalities to the application developers can be delivered. Thus, they have the ability retrieve and parse a DICOM object and to design their DICOM Web Viewer that provides to users the basic functionalities. Cornerstone consists of three sub-libraries:

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• • •

The WADO (Web Access to DICOM Objects) Image Loader: Integrates cornerstone with WADO servers or any other HTTP-based server that returns DICOM images The dicomParser: Used to integrate with DICOM Servers that provide WADO services The Cornerstone Tools: A library that provides a set of common tools needed in medical imaging to work with images.

Application Design Mobile applications are presented with a different user environment (see Figure 1, Figure 2), business goal and audience from desktop or web applications. Therefore, developers must take into account the minuscule screen compared to a desktop monitor, and that there is no mouse, instead users use the touch screen and usually their fingers. In mobile applications, the environment must be easy to use and friendly because many operations should be performed via the touch screen often by using one hand. The user requirements for mobile applications requested the availability of a Home button (Figure 2) or icon on every page, therefore users can readily get back to the first page of applications. A homepage on a desktop computer often serves many purposes; in contrast the homepage button on mobile devices should focus on connecting users

Figure 1. Mobile application menu

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to the content they are looking for. Desktop users are used to operating complex and multi-level menus, but mobile users will not have the patience to scroll through a long list of options and try and find the required function. Thus, developers should consider how they can present the menu items in levels in a simple, efficient and quick way.

System Architecture Security mHealth can provide effective health services among people and health providers potentially from anywhere in the world. On the other side of the spectrum, health information is very sensitive because citizens value their privacy. These two values may come into conflict because applying tight security measures may reduce application functionality or, for example, make applications appear more complex to the users. Finding the right balance, always considering the legal framework, between these two values will be an effective approach. IT systems must protect medical data using solutions for physical security, end-to-end encryption, and auditing. Thus, the proposed platform is protected by an embedded firewall, and all web traffic is SSL/TLS encrypted as an additional measure of security because wireless transmitted data packets can be captured easily. The communication between the mobile devices and the system units is done using TCP/IP over mobile telephony networks (e.g. 3G, 4G) or WiFi networks. VPN (Virtual Private Network) can be used through private institutions to create a closed secured corporate network over the internet. Other measures to tighten security include: • • • • • •

Authentication: User authentication is accomplished by applying complexity requirements in usernames and passwords. Session Expiration: Users, are automatically logged out after a determined time of inactivity. Password Expiration: All users are forced to change their passwords on initial login and every determined time interval after that. Authorization: a role based access control approach is used to restrict access to particular system functionalities to authorized users. Thus, authenticated users are provided with different web interfaces, menu items, system operations and data access, based on their respective user role rights. Anonymization: Furthermore, ingenious anonymization mechanisms are in place to remove or replace sensitive personal information from the data, for users with limited rights either to view medical data or to export data for research purposes. Audit: Last but not least the system should incorporate an audit mechanism, this provides visibility into which data has been accessed and shared by each user.

Patient Registration It would be useful if health platforms could provide patients, with a variety of mobile applications and services, control of their health data. So, a commitment to privacy and security of the users’ information is the foundation upon which the relationship between the provider and the patient will be built. In this case, a mobile application can be activated by an authorized user such as a doctor who is responsible for activating the patient’s account, to select the services provided to the patient and providing the patient an activation code that can be used for any device.

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After that, the patient will be able to download an application depending on the device used. After successful activation, the patient can take advantage of all available services, information, and the doctor’s instructions. If the patient loses the device or it is stolen, the patient must immediately inform the doctor to deactivate the particular device. Such operations can be easily performed by non-specialized users, with no prior training, thus medical doctors, as proposed, can be easily involved, the primary reason for this engagement, is the trusted relationship between doctors and patients.

Local Storage and Offline Operation When activated a device application stores configuration data and settings. Also, when a user is in an environment without an Internet connection, applications should already have stored data offline, and synchronize that data when there is available an internet connection. For that purpose the application uses Cordova SQLitePlugin to store data local into Mobile SQLite Database, encryption of this information should be performed.

mHealth Applications On the basis of the principles presented in the previous section, in this section is the analysis of two implemented applications based on the described platform are introduced.

ImaginX The ImaginX Platform (Tamposis et al., 2014b) is a software platform that supports medical imaging workflow management and provides a high level of functionality with reduced costs and user effort. The platform is developed by OraSys since 2013. ImaginX is mobile oriented by design. All features can be easily accessed from mobile devices such as Smartphones and tablets. ImaginX offers as well a mobile application for providing users with basic functions. The Patient Record is the basic component for recording patients’ health events, like allergies, diseases, surgical operations, medications and medical examinations. The primary use of such data is to provide physicians the ability to have immediately available the necessary clinical information. In addition, this component encapsulates an essential feature: Patient Timeline, a modern technology that gives to the doctors, the opportunity to make fast clinical decisions, because it presents the patient’s medical history in a visual form easily comprehensible in a timeline. The application interacts as well with national registries using web services so as to obtain certified patient data based on national identification codes (e.g. SSN). ImaginX is designed to operate paperless in all the tasks that supports. There is available a Paperless Based Order Schedule System that enables clinicians to effortlessly order exams and include referrals (Figure 3, Figure 4). ImaginX mostly requires clinicians to fill up a simple form (from their office or during patient visit or examination) and submit the request to the registration desk. Furthermore, clinicians receive annotations when an examination is scheduled or completed and eventually receive examination results including medical images and reports. A second functionality is the Mobile Appointment Calendar, which informs doctors for daily, weekly and monthly examinations scheduled and provide them the ability, to view upcoming, canceled or rescheduled physical examinations of patients. For the clinic directors (Leaders), the system completely monitors all steps of a review from ordering and scheduling

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to the patient arriving and reporting. Also, there is a functionality to send an appointment confirmation. Therefore, by including all the workflow functions in the mobile device, the system users are facilitated to perform tasks anytime, any place. The mobile application has embedded a DICOM mobile viewer that is a fast, responsive and userfriendly DICOM viewer for accessing imaging data through smartphones and tablets. This functionality can serve emergency cases where a workstation equipped with a more powerful image viewer is out of reach or in cases that doctors are on the move. Today mobile devices have high resolution and examining of medical images on the mobile display is rather safe. ImaginX can display DICOM images (Figure 5), and reversely supports the ability to “DICOMISE” (Figure 6) digital images captured by the mobile device camera and subsequently to uploading them to the archive; and finally, embed them in patient medical record. This capability represents a novelty provided by mHealth, because “pushes” the use of health services out of the clinics. In fact, patients themselves can send captured images, instead of complex, lengthy and time-consuming outpatient visits to physicians or clinics. This functionality is flexible, supporting primary health care that includes more accurate triage of referrals or “advice only” service, and reduces the need for patients to visit a doctor or a hospital for simple incidents. On the other side, it reduces the time in waiting rooms of medical centers. For instance, a patient would have a dermatological problem at hand, may capture one or more

Figure 3. Mobile calendar: Monthly view

Figure 4. Mobile calendar: Daily view

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images, with his/her mobile device, from the disease area and send them to a dermatologist, in order to be advised. Such services could be provided by physicians as mobile or web services. Finally, this system is able to support pre-hospital care before a patient is decided by doctor to visit a hospital, especially as real-time two way video services have the potential to provide the ability of Virtual Video Visits. In several cases, non-experienced doctors may make some common mistakes: not identify a real disease (False Negative), conclude that a healthy patient is suffering from a disease (False positive) or not sure whether a disease is present (Inconclusive results). In, these circumstances, mHealth can easily serve this situation and be the mechanism to obtain a Second Opinion; this helps doctors be sure that they are making the right choices. Especially as Telehealth – Teleradiology provides health care services from a distance, regardless of geographic location.

HPVGuard HPVGuard (Tamposis et al., 2014a) is a software application capable to store and handle a multitude of medical examination data along with non-medical information. HPVGuard integrates artificial intelligence models (Bountris et al., 2014; Karakitsos et al., 2012; A Pouliakis et al.) combining data from different medical examinations and producing an estimation of women’s risk to develop Cervical Cancer (CxCa).

Figure 5. Mobile DICOM viewer with tools

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Figure 6. Dicomizing

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The system embeds features supporting mobile functionality and online access to specific tasks via mobile devices. Currently, HPVGuard supports access via smartphone/tablet to patient medical records (Figure 7), to details of women visits as well as functionality to exploit the risk estimation models. Considerations of the platform involve conditional and automated risk estimation model execution and dispatching of e-mails and SMSs to inform responsible gynecologists and cytopathologists on their mobile devices. These functionalities have reduced patient’s visit time and clinician’s time as well by 2-5 minutes per visit, because clinicians may order examinations with less effort (using simply the computer which has preloaded patient identification data) and because referral forms are produced in a paperless manner. As a result, more patient cases are handled at the same time. The system facilitates the return of the examination report back to the requesting facility based on web technologies and health standards — including HL7, secure emailing and faxing– to the hospital and/or to the referring physician. Access to the patient history (Figure 8) is essential and is facilitated by timeline (see fig. 7). History is organized via visits, these are subsequently separated as diagnostic visits or therapeutic visits. Physicians can easily select in the timeline the requested visit and upon click appears a comprehensive list of all examination results. Thus, gynecologists can have immediately a snapshot on woman’s record and, therefore, make critical decisions. Additional system functionalities include transmission of informative SMS texts to the mobile telephone of the women; this is performed only after the gynecologist managing the woman requests to release such messages. This functionality is important to gynecologists, the other option would be a telephone call to the woman, to inform them about the availability of results.This is now simply replaced by the click of a button. An additional option may involve the transmission of the examination results to the woman, in case that they are negative, and thus no additional visit to the gynecological office is required. Considering the application of HVPGuard in the laboratory environment (i.e. a cytopathology laboratory), the mobile application has a special value. The requested examinations are added in a queue (task Figure 7. Patient history timeline: Tablet view

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Figure 8. Patient history report: Tablet view

list) available through the platform; responsible personnel (cytopathologists and molecular biologists) can easily have this task list on their mobile phones, this brings the application on the bench. Cytopathologists and molecular biologists can easily consult their mobile phones and see the pending examinations; also they can immediately add the examination results, upon their availability.

User Acceptance User acceptance is crucial to the implementation of a modern system of many new technologies and user feedback to the development and the improvement of these systems. To evaluate user acceptance, two already running projects involving over 100 users were employed. The first involves the University Hospital of Larissa (UHL), the University of Thessaly and a private radiological laboratory. All three parties were located in the city of Larissa, Greece, a medium-sized city that covers a wide area and range of incidents in Thessaly region. The second involves the University Hospital of Athens, the University Hospital of Ioannina and “IPPOKRATIO” hospital in Thessaloniki, Greece. With the vision of creating a modernized public hospital offering quality services at the cutting edge of technology, the focus was to organize this department into a technologically advanced hospital with skilled medical personnel to provide the required health care services to its fellow citizens. When using the IT system in clinical practice and education of students and doctors, we observed a lot of interesting facts and points. With regard to groups of employees (Doctors, Secretaries, Nurses, Technologists) and their learning competencies of Mobile Platforms’ capabilities, it is noted that different users get familiar and use with ease or difficulty this technology, depending on their familiarity with mobile devices and the use of the internet and the various social media. In the case of the hospital, while mostly young people are employed, it is noted that only a small percentage holds onset the basic knowledge to directly accept applications of Mobile Platforms. Thus, the position of those leaders who

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have relevant knowledge as users and vision for new technologies proves to be critical. Usually, in a group of 50 people there is one to two people, usually doctors, while the ideal case would be one of them to be the Director of the department or a senior medical specialist and a junior partner. Μedical laboratory personnel: Starting from the recording of diagnoses with a mobile smartphone it is noted that after a few initial difficulties the method gets soon accepted and becomes irreplaceable. Using a smartphone as a medium for recording and sending the recorded diagnosis to the Secretariat for transcription by e-mail or via FTP, becomes indisputable for both private practices and hospitals. The same applies to tablets and laptops that are also used as means for recording and sending recorded diagnoses. The final review of the transcripted text, the correction, and the final diagnosis are made by the doctor with less effort required on the desktop PC and more on laptops, tablets, and smartphones. Also, we encouraged radiologists to access images remotely by providing remote access through VPN. Nevertheless, mobile applications provide the user - physician considerable discretion in managing the diagnosis remotely and thus, offers him more flexibility and time. Apart from the initial study of imaging examinations where a computer system of high standards is required in the workplace or at home, all the remaining steps from the recording of diagnosis to the order for printing or its’ electronic submission, can be made with the use of Mobile Platforms. It is extremely useful for radiologists to look at a patient’s file directly from their hospital and offer their advice in critical cases. A major influence on the user to use the mobile application is the limitations of the device. The limitations include small screen, small and limited keyboard, limited battery power and the absence of fast connections. For the laboratory scientific personnel, the mobile application brings the computer to the bench, either to have in hand a ToDo list or to immediately release examination results and avoid much paperwork. Furthermore, smartphones and tablets improved the collaboration between the specialist and the resident doctor, where the diagnoses are going through the necessary exchanges, corrections, final prints and archiving with great easiness. In the private medical laboratory, these applications for mobile devices become even more useful since the exchange and collaboration between the secretariat and the medical and technological staff is more coordinated easier and in a more flexible manner. They are used for sending diagnoses and images to doctors and patients who embrace the new prospects and potentials with great pleasure and satisfaction. The major advantage for clinical doctors is that they know all scheduled appointments anytime, anyplace. •

•

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Clinicians: It is extremely useful to order an examination in a paperless manner and have access to images and diagnosis report anytime, anyplace. Because clinicians have already been impressed the services and solutions provided by the system, they request for more services such as ordering examinations via their mobile during the patient visit for examination. For Research Purposes: Mobile Platforms are used by Health Professionals as teaching means within the University and the Hospital, replacing other older systems. It is now easy to collect data in a single repository and have the system configured so that each player has access only to required data. This single repository forces the use of a common standard format to report examination results, also allows involvement of numerous dispersed medical centers. Therefore multicentre medical studied are facilitated, more data are collected, and safer conclusions can be extracted. Rare medical cases occurred in a small number of centers, can now be used in other centers and serve either as use-cases for inexperienced doctors or as training material for trainees. For the Physician’s Office: Sending results via e-mail and/or SMSs, is crucial in order to save time for the office personnel (physicians, secretary, nurses), thus the physician’s office becomes more productive. 107

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In conclusion, the capabilities of Mobile Platforms in supporting health professionals are already substantial and are expected to become even more in the near and distant future for both health professionals and health users.

FUTURE RESEARCH DIRECTIONS Despite the availability of various mHealth applications, there is rather a lack of evidence for their efficacy, research is needed to assess when, where, and for whom mHealth devices, apps, and systems are efficacious (Brown-Johnson et al., 2015; Déglise et al., 2012; Kumar et al., 2013; Zapata et al., 2015). The general aspects evaluated during the assessment according to the Health IT Usability Evaluation Model (Health-ITUEM) are: error prevention, completeness, memorability, information needs, flexibility/customizability, learnability, performance/speed, competency, and other (Brown et al., 2013). For the presented applications, it is important to identify how all above technologies and services are usable and acceptable from users. Two major groups of users, patients, and health experts are identified. Patients: In the next years, is foreseen that patients will become more autonomous and health educated and will take their healthcare into their hands by using smart devices to control some usual measurements that warn them of potential health problems. Therefore, the forthcoming years it is expected to have “mobile educated” patients that are ready to accept mobile health applications. Additionally, as more and more people use social media, in the future we expect the development of patient portal platforms, where people will be able to make their complete patient profile with pictures, personalized information about diseases and treatments. These portals with the more mobile capability will provide faster response times when patients are in need of care. Health Experts: Doctors believe that mHealth is a destruction of the traditional health. The truth is that healthcare is about five to seven years behind the rest of the service industry. Even more, while there is significant progress in web and mobile technologies that offer new tools and potentials for healthcare providers, the spreading and utilization of those technologies by medical experts remains at a relatively low level and is used several years later. However, medical experts that are using the described applications are impressed by the services and solutions provided by today’s technologies. In the near future the current situation is expected to change dramatically and the penetration of new technologies in healthcare will be increased and hopefully, this technology will soon create new health strategies replacing the practice used today by health providers.

CONCLUSION New technologies take more than five years to be adopted in health care, probably due to the reluctance of health professionals. Mobile telephony is now more than 20 years in use; the recent advances in the last decade have converted mobile telephones into smart telephones via sophisticated operating systems. Especially, during the last five years, hardware components enriching mobile phones with processing power (4 or 8 processors), high definition cameras (more than 10Mpixel nowadays) and large displays resolutions (nowadays more than 1500x2500) became commercially available. Additionally the 4G and WiFi connectivity created an always open, rather inexpensive, communication channel with the Internet. Today there is no more the trend to shrink the telephone, in contrast mobile phones are becoming larger

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and compete tablets in terms of size and weight, they are easy to use and have capabilities similar to modern computers available five years ago. It seems that those devices have now the maturity of computers being used in the health sector five years ago and are ready to be adopted. The presented framework constitutes an environment capable to host almost every type of medical application, to target simultaneously both the desktop computer and the mobile phone as well. Thus, the creation of new applications is easier than ever. The security issues are not a barrier if appropriate technologies are in place because users are now well educated and familiarized. The two mobile applications presented, proved that there are advantages for the benefit of almost all health actors: primarily the patient, medical doctors of different specialties, technologists, biologists, hospital, and clinic administrative staff among others. It seems that mHealth has the capability to shape the future of health not by just translating existing applications but by inspiring new ones.

ACKNOWLEDGMENT Part of this study was funded by the Greek Ministry of Development (General Secretariat for Research and Technology-GSRT), Project “HPVGuard”, Cooperation 2011-2013 (code: 11ΣYN_10_250). http:// HPVGuard.org

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KEY TERMS AND DEFINITIONS Cloud Computing: A type of computing, comparable to grid computing that relies on sharing computing resources rather than having local servers or personal devices to handle applications. Digital Imaging and Communications in Medicine (DICOM): It is a standard for handling, storing, printing, and transmitting medical imaging information. It composed of a file format definition and a network communications protocol, the first providing information about the data (including images) and the later for communicating them. Health Level-7 (HL7): Is a set of international standards used for the transfer of clinical and administrative data between health related software applications. IHE: an international organization involving healthcare professionals and industry representatives working together to improve the way computer systems in healthcare share information. IHE provides a common framework for building effective solutions to close the communication gaps between systems and foster their interoperability. Interoperability: A property of a product or system, that has interfaces allowing them to work with other products or systems. Interoperability in software systems is related to data format and communication protocols, see for example HL7 and DICOM standards designed to exchange health relevant images and health record or administrative data. Medical Imaging: Techniques or processes related to the creation of visual representations of the interior of a body. Medical imaging is used for clinical analysis (diagnosis) and medical intervention (treatment). Medical imaging reveals internal structures hidden by the skin. A relevant term is digital microscopy which should not be confused by medical imaging. Mobile Application Development: the set of processes and procedures involved in writing software for small, wireless computing devices such as smartphones or tablets. Mobile Health (mHealth): The practice of medicine and public health supported by mobile devices. Responsive Web Design (RWD): An approach to web design aimed at crafting sites to provide an optimal viewing and interaction experience—easy reading and navigation with a minimum of resizing, panning, and scrolling—across a wide range of devices (from desktop computer monitors to mobile phones) Security Architecture: A unified security design that addresses the necessities and potential risks involved in a certain scenario or environment.

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mHealth:

Sleeping Disorders Diagnosis Assim Sagahyroon American University of Sharjah, UAE

ABSTRACT The increasing computing power of mobile electronic devices coupled with advances in sensing and wireless technology have paved the way for mobile health (mHealth) to play a major and innovative role in the health sector. This chapter discusses the use of mHealth in the monitoring and diagnosis of sleep-related diseases with a particular emphasis on sleep apnea since it is considered to be one of the most prevalent disorders. Apnea symptoms and the physiological signals associated with it are described with an overview of the current sensing technology used to capture and record these signals. The chapter continues to discuss the integration of sensors with todays’ mobile devices to offer mhealth platforms that allow for the monitoring, diagnosis and management of sleep apnea. We conclude by discussing the current limitations of the mHealth technology and discuss possible future enhancements.

INTRODUCTION Mobile Health or mHealth is the use of mobile and wireless devices such as smartphones, tablets, and other patient monitoring devices to support various medical and health practices. mHealth has the potential to turn mobile devices into personal labs that continuously assess a person’s physiology, behavior, social context, and environment exposure (Kumar, 2013). mHealth based techniques have been applied in different domains of the health sector. In recent years, some novel approaches (where there were serious attempts to maximize the benefits offered by this new paradigm shift in healthcare delivery) are reported in the literature. Examples of such efforts include the use of mHealth and related technologies in assessing and promoting physical activity (O’Reilly, 2013). Smartphones integrated cameras coupled with an intelligent system running on the mobile are used in the skin disease analysis (Bourouis, 2013). mHealth intervention techniques are successfully utilized in enhancing the physical activity of patients with cardiovascular disease (Carter, 2013). Phippard (2012) examined the use of mobile phones as tools to support and advance HIV/AIDS work in sub-Saharan DOI: 10.4018/978-1-4666-9861-1.ch006

Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 mHealth

Africa. An overview of smartphones’ use in behavioral healthcare and the options available to integrate this technology into real life clinical practice is provided in (Luxton et al.). Brian and Ben-Zeev (2014) examined the integration and utilization of mobile technologies into the diagnosis and treatment of mental disorders in the Asian region. mHealth techniques proved to offer valuable opportunities in service delivery for the mentally ill in parts of India (Jain, 2015). mHealth methods are recently used to identify patterns of high-risk illicit drug use in a study of drug users in Baltimore, Maryland (Linas, 2015). Recently, IBM has collaborated with Telecom companies in Africa to populate an Ebola diseasemapping system; mHealth based strategies are then used as educational tools during the Ebola epidemic to spread awareness. Sleeping disorders play a significant role in individual activities during the daytime, and can lead to complications that make the patient suffers from other diseases. Estimates indicate that approximately 70 million Americans experience some form of sleep disorder (Abidi, 2015). mHealth oriented intervention with the aim of diagnosing and improving sleeping patterns of individuals has been an open and active area of research with some related mobile applications developed in recent areas. A case in point is the recent announcement of ‘Johns Hopkins Center for Sleep’ the use of a mobile application in a pilot study to assess its feasibility in alleviating the anxiety of many Americans who suffer from sleep-related issues (Motti, 2015). One of the most common and prevalent sleep disorders is Obstructive Sleep Apnea (OSA); according to the World Health Organization, around 100 million people worldwide have OSA (Alqassim, 2012). In this chapter, we discuss the role of the mHealth innovations as it relates to OSA. We will first provide a brief overview of OSA then proceed to discuss the application of mobile technology for managing and diagnosing OSA, and conclude by highlighting current limitations and pointing out possible future directions. Throughout the chapter the terms apnea and OSA are used interchangeably.

BACKGROUND Obstructive Sleep Apnea (OSA) OSA is a sleeping disorder characterized by the repetitive reduction of airflow during sleep where air is physically blocked from flowing into lungs intermittently. A phase of apnea is accompanied by an initial decrease in heart rate and a drop in oxygen saturation after a few seconds. This phase is followed by an awakening signal of the central nervous system and is characterized by arousal, short-term EEG activation, acceleration of the heart rate, heavy breathing, as well as an increase in blood oxygen saturation (Hoffmann, 2011). The National Sleep Foundation concluded that for adults to function in a healthy and productive manner, they should have seven to eight sleeping hours every night. Frequent obstructions of airflow during this period have a considerable influence on the performance of humans during the daytime. OSA may cause excessive sleepiness, non-restorative sleep, high blood pressure, diminished neurocognitive performance, cardiovascular diseases, memory loss problems, erectile dysfunction, personality changes, and depression. Besides daytime tiredness, OSA patients may also experience job impairment and automobile accidents (Ancoli-israel, 2003; Al-Mardini, 2014). The current approach to diagnosing OSA is attended overnight polysomnogram or PSG. It is the golden standard for OSA diagnosis but considered to be involved, time-consuming and costly in time and hospital use. During a PSG session, sleep is examined under laboratory conditions with the goal of

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recording and eventually analyzing various biosignals. It requires overnight monitoring of the patient in a confined setting. Sensors and different transducers are used to record eye movements; airflow, respiratory effort and cardiac rhythm. Pulse oximetry is also recorded to assess the severity of oxygen desaturations (Hauser, 2012).

OSA Physiological Sensors OSA screening and diagnosis is based on first acquiring physiological signals such as ECG, pulse oximetry, snoring, or nasal airflow, and the use of different algorithms to analyze the collected data and make inferences based on medical rules or observations. During an overnight PSG session, tests are performed on the patients, and some physiological variables are measured and recorded during the sleep period. Physiologic sensor leads are placed on the patient’s body to record some or all of the following parameters: • • • • • • •

Brain electrical activity Oxygen saturation Airflow ECG Eye and jaw muscle movement Leg muscle movement Respiratory effort (chest and abdominal excursion)

Readings from these sensors are then collected and analyzed by a computer program that outputs different waveforms representing the variation of values during the sleep period. The medical team uses these waveforms and other possible test outputs to assist in the diagnostic process. Furthermore, sleep centers are sometimes equipped with video recording cameras in each patient room; this allows the medical team to watch the tape if needed, and determine whether an anomaly in the graph is caused by a normal move in bed, or perhaps a period of wake, etc. Table 1 depicts the different types of sensors that may be used to sense and report readings related to critical physiological parameters that may be used for OSA assessment. Some sensors are available in today’s smartphones, and others are easy to interface (wirelessly) to mobiles using analog-to-digital converters and communication protocols such as Bluetooth or ZigBee. A substantial amount of data is generated by a sleep study, but the most important parameters are the apnea-hypopnea index, or AHI and oxygen desaturation levels. An apnea attack causes a complete cessation of breathing for 10 seconds or more. A hypopnea occurs when the subject experiences constricted breath (more than one-fourth, less than three-fourths) that lasts 10 seconds or longer. The total number of apneas and hypopneas the sleeper experiences each hour is the index number or AHI. A thershold AHI is defined above which the subjcet is ocnisdered to suffer from sleep apnea as follows: an AHI of 5 to15 is classified as mild obstructive sleep apnea; 15 to 30 is moderate OSA; 30 or more is severe OSA. Also, PSG examines the heart rhythm, snoring levels, and determines if there are any abnormalities. Another important part of the assessment is limb movement since leg movement can constitute another sleep disorder (American Sleep Apnea Association). Reductions in blood oxygen levels (desaturation) are typically noted and recorded during polysomnography.

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Table 1. Physiological sensors Type of Bio-Signal

Type of Sensor

Description of Measured Data

Built-In Mobile

Body Movement

Accelerometer

Acceleration forces in the 3D space

Yes

Snoring level

Microphone or audio recorder

Recording of inhalation and exhalation sounds

Yes

Respiration rate

Piezoelectric sensor

Used to count the number of inspiration and expiration per unit time (breathing rate)

No

Electrocardiogram

Skin/chest electrodes

Graphic record of the Electrical activity of the heart

No

Airflow

Thermistor or thermocouple

senses the amount of air moving into and out of the airwaysused to determine the presence and extent of apneic episodesoutput voltage proportional to airflow

No

Oxygen Saturation

Pulse Oximeter

Amount of oxygents in the subject blood

No

Heart rate

Skin electrodes or microphone

Frequency of cardiac cycle

Yes (if mic is used to record pulse rate)

The normal level of oxygen saturation in the blood ranges from 96% to 97%. If the saturation level is lower but not less than 90% then it is a mild case of desaturation. If the level is reduced to below 80% then it is considered to be as severe case of desaturation (Harvard medical school).

MOBILE TECHNOLOGY AND OBSTRUCTIVE SLEEP APNEA In recent years, the global penetration of mobile-based technologies has been more than remarkable. The future promises a rapid increase in the use of mHealth to facilitate the monitoring, diagnosis, and management of various symptoms and illnesses. As discussed earlier, PSG has several disadvantages, for example, it requires patients to be confined to hospitals or laboratory settings for at least one night, many sensors are to be attached to the body, very few places can provide these specialized tests, and last, it is very difficult for subjects under test to assume normal sleeping patterns under such a stressful environment and this might in turn affect the results of the test. Gathering sleep-related data in a comfortable environment such as a home setting for disease assessment is a major advantage no doubt. This involved and costly approach has tempted healthcare professional and researchers to seek diagnostic alternatives to improve and ease the diagnosis of OSA while simultaneously providing needed comfort to patients and reducing cost. mHealth technology fills this need by providing a simple and reliable mean to perform OSA diagnosis. Advances in mobile devices and communications infrastructure can contribute significantly in identifying future approaches to the monitoring and diagnosis of OSA; proposed and implemented diagnostic alternatives related to sleep disorders and discussed in the literature indicate that mHealth has enormous potential in improving the lives of many when it comes to sleep-related disorders.

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mHealth Based Diagnosis Various approaches to addressing OSA and its related symptoms have been discussed in the literature, however, in this chapter we will only highlight those attempts made where the smartphone or another mobile device is used as an integrated component of the proposed method to diagnose the disease. The primary objective of this section is to provide the reader with an overall view of diagnostic apnea techniques within the context of mHealth; however, a comprehensive critique of each presented diagnosis alternative is beyond the scope of this chapter. The various techniques differ mainly in the types of sensors used and hence the nature of the collected data for processing. In some work, only ECG data was of interest, in others heart rate and oxygen level in the blood, and some combined accelerometers with heart rate, respiratory as well as oxygen level sensors. Oliver and Mangas (2007) presented one of the earliest work in using mobile phones to detect sleep apnea. They argue that the lack of airflow during apneic periods can lead to recurrent episodes of hypoxemia that can be detected by oximetry as fluctuations in oxyhemoglobin saturation (SPO2). Hence, they used a blood oximeter to monitor the subjects blood oxygen level and pulse while sleeping. Collected data is transmitted to the mobile phone of the subject using the Bluetooth communication protocol. The authors developed two algorithms, one based on multi-threshold time analysis and the other on spectral analysis to confirm the occurrence of apneic episodes during sleep in real time. A sleep study with 20 participants with ages between 25 and 65 is conducted. The OSA algorithms detected all the known cases of OSA among the participants. Part of their suggested future work incorporates comparing their results with those obtained using polysomnography in a sleep clinic. Burgos et al (2009), discuss SAMON, a mobile-based apnea monitoring system that uses a classifier program running on the mobile to identify the presence of sleep apnea from blood oxygen saturation signal fragments recorded using an SpO2 sensor. The proposed system can also forward collected data to hospital servers where it can be analyzed by pneumologists using tested and proven techniques. According to the authors, the system may be considered as a clinical decision support system to aid pneumologists in diagnosing apnea. No conclusive experimental results were reported. Apnea MedAssist is a real-time system developed for Android based smartphones. The fully automated system uses patient’s single channel nocturnal ECG to extract feature sets and uses a support vector classifier (SVC) to detect apnea episodes. For feature extraction and classification, 1-min segment of ECG epochs are used in real time. Furthermore, the optimization of the ECG processing together with the reduction of SVC model complexity using techniques such as reduction of the dimension of the extracted features, and the minimization of the number of support vectors has led to a simpler design of the ‘Apnea MedAssist’ platform. The device and algorithms were tested with encouraging results using Physionet Apnea-ECG Database (Bsoul, 2011). The database (Penzel, 2000) has a total of 35 subjects’ sleep studies. The recordings were visually scored by an expert for sleep apnea/hypopnea events on the basis of respiration and oxygen on a per minute basis. Sannino et al. (2013) also carried out ECG data collection using a wearable sensor. Data is collected and sent to the mobile device for processing in real time each minute, and Heart Rate Variability (HRV) related parameters are computed from it. If calculated values activate a subset of stored if -then rules describing occurrence of OSA, the system can immediately wake up the patient and/or send an alert to medical personnel. Authors claim that their proposed system has the following main features: clear explanation of the reasons supporting the detection of each OSA episode using a set of rules, ease of use (it just needs one ECG lead), detection of occurrence of an OSA episode in real time, consequent 119

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real-time action, execution of the complete processing phase on a mobile, and personalized healthcare due to the fact that for different patients different sets of rules will be found. Testing is carried out using a well-known literature database (Penzel, 2000) of OSA patients and the researchers indicated that future work will involve testing their approach on real OSA subjects. Patil et al. (2012) discussed the analysis of HRV and SPO2 signals to monitor apneic events. Their system will detect the apneic attack by analyzing the signals (SpO2 & HRV) and calculating the risk factor for alarm activation at the apneic period. The classifier that is able of identifying the presence of sleep apneas (in real time) from blood oxygen saturation signal fragments taken from pulse oximetry systems (SpO2 & HRV) is implemented on the smartphone of the patient. Testing of the approach again here is carried out using a database of stored signals. In an effort to identify new criteria for diagnosing apnea, Karci et al. (2011) studied post apnea snoring episodes. Emphasis is placed on identifying post apnea episodes and the apnea periods. Initially segmentation is done to eliminate the silence parts. Then, episodes are represented by distinctive features and are classified using supervised methods. False alarm rates are reduced by adding additional constraints into the detection algorithm. These methods are applied to snoring sound signals recorded by three patients. They then evaluated their classification algorithm using randomly selected fifteen-minute segments from whole night sleep sound recordings of these three different patients. These fifteen-minute segments form the test data. Four different training sets are generated, and the test data are classified according to these four training sets. In their conclusion the authors argue that post apnea sounds can be used to detect apnea periods by introducing new features that were extracted from the LPC (Linear Predictive Coding) error curve, and presenting a different property, high energy variation of post apnea sounds through the use of the entropy of energy variation. Although all the apnea periods do not end with a “post apnea sound,” they claim that ones that end with such sounds may be used to quantify the severity of the OSA, which could facilitate the development of algorithms to diagnose apnea from only snoring sound recordings in the future. Zhu et al. (2014) developed a Body-Sensor-Network portable device (mobile or PDA) to monitor respiratory parameters using a micro thermal flow sensor to monitor respiratory airflow, a tri-axis micro accelerometer to monitor body posture, and a micro photoelectric sensor to monitor blood oxygen saturation. In their set up, the subject wears the respiration-posture node on the upper body and collects the respiratory airflow using a nasal cannula. The oximeter clip is worn on the fingertip. All sensor data are transmitted wirelessly through Bluetooth to a mobile phone nearby where data is processed and analyzed for diagnosing OSA. The authors did not specify the number of testers or subjects used to validate the performance of the system. Rofouei et al. (2011) proposed the use of a wearable non-invasive neck-cuff equipped with different physiological sensors for real-time monitoring during sleep. The sensors used are an oximetry sensor for oxygen monitoring, a small microphone integrated with a large stethoscope-like diaphragm inside the neck cuff that is placed against the neck and records breathing sounds, an accelerometer, body movement can cause variation in the pulse oximetry readings, subjects go through many involuntary movements throughout their sleep which affect the accuracy of the pulse oximetry readings and in turn the precision of sleep analysis. Using an accelerometer most movements can be detected, and their nature be identified. Three volunteers wore the neck cuff overnight at their homes for sleep monitoring. A multi-signal algorithm for probable sleep apnea detection is programmed on the mobile of these subjects. Future efforts include the expansion of the testing phase and comparison against diagnostic results from sleep centers where PSG is used. 120

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In another effort, a mobile application that uses three main sensors to extract physiological signals from patients which are an oximeter to measure the oxygen level, a microphone to record the respiratory effort, and an accelerometer to detect the body’s movement is developed and results obtained are compared with PSG results using a sample of 15 patients (Al-Mardini, 2014). Analysis showed that 100% of patients were correctly identified as having the disease, and 85.7% of patients were correctly identified as not having the disease. The authors also studied the impact of each physiological signal on the accuracy of their system. Oxygen saturation and body movement are considered one set, and respiratory effort represents the other set, while the complete data set represents the case when all three physiological signals are used. The comparison yielded that all the signals are critical especially in assessing the severity of the case. Doukas et al. (2012) discussed the use of sleep breath as the only physiological signal to record and analyze. The idea here is to assess if snore sounds analysis leads to an acceptable diagnosis of apnea . Snore signals are recorded on the device and snore events, along with apneic events identified. To evaluate the proposed algorithmic technique for sleep sound analysis, a number of 30 sound samples have been collected. Each sound sample corresponds to a complete sleep study (duration up to 6 h) of patients that either suffered from sleep apnea or were examined for symptoms of sleep breath disorders with acceptable accuracy. Few interesting but non-mobile based approaches are targeting OSA that could, however, be ported to mobiles in the future include the use of formant frequencies as a differentiator between apneic and benign snores (Ng, 2008). Snoring sounds from different subjects were recorded processed and modeled using linear predictive coding. Formant frequencies F1, F2 and F3, were extracted for the LPC spectrum analysis and compared. Results indicate that apneic snores exhibit higher formant frequencies than benign snores, especially F1. Diagnosing Apnea based on respiratory signal classification is discussed by Almazaydeh et al. (2013). They used a voice detection algorithm to characterize the breathing sound and measure the energy of the acoustic respiratory signal during breath and breath hold. The performance of the algorithm is tested on real respiratory signals with acceptable outcomes. Again, the processing here can easily be implemented on today’s powerful smartphones.

FUTURE RESEARCH DIRECTIONS Energy and battery limitation is a major challenge. The realistic lifetime of smartphones, when engaged in continuous sensing and analysis, can be very short. At present scientists and application developers are using the current sensors built in today’s mobiles to run smart algorithms and application targeting mHealth related issues. With the ever increasing use of mHealth, we expect the introduction of dedicated built-in sensory units in mobiles, and supporting architecture with high performance but optimized power consumption for medical applications. We also anticipate some level of support from future operating systems. The lack of performance standards is also problematic. There is a need for establishing standards founded on the practical experiences of mHealth, and on any research output about its different aspects. Furthermore, at present, there are no rigorous and comprehensive studies that evaluate the impact of mHealth on the life of individuals’ and the health of a given population at large. Thus, more studies are

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needed to conclude convincingly that virtual visits are a viable substitute for the physical visits to our physicians. Electronic health information stored on mobile devices is vulnerable to a wide range of security threats. Additionally, mHealth systems may use the internet to share remotely patient’s information with their physicians and are thus vulnerable for attacks. Current deployed or proposed mHealth applications and solutions donot necessarily conform to the HIPAA (Health Insurance Portability and Accountability Act) security requirements. HIPAA (Pieper, 2004) enforces administrative, physical, and technical safeguards to ensure confidentiality, integrity and availability of electronic healthcare information that is stored or transferred electronically. Studies that reflect on the current situation of mHealth security levels and outline security requirements of mHealth systems are of paramount importance. Security experts still have to propose a framework that leads to the design and implementation of a secure and private mHealth environment.

CONCLUSION The goal of this chapter was to provide the reader with an insight into the emergence of mhealth as a viable alternative to traditional medical diagnostic approaches using sleep apnea as a case study. Starting from a general introduction where we illustrated the application of mobile health technology in various medical applications, we moved to define sleep apnea, it causes and current diagnostic procedures. We followed that with a comprehensive review and discussion of the mhealth-centered diagnosis methods to sleep apnea, and highlighted the characteristics of each proposed technique. We concluded by focusing on areas of improvement and research that would eventually allow for the effective and efficient future use of this promising myriad of technologies.

REFERENCES Abidi, S. (Ed.). (2015). Mobile Health: A Technology Roadmap. Switzerland: Springer International. Al-Mardini, M., Aloul, F., Sagahyroon, A., & Al-Huseini, L. (2014). Classifying Obstructive Sleep Apnea using smartphones. Journal of Biomedical Informatics, 52, 251–259. doi:10.1016/j.jbi.2014.07.004 PMID:25038556 Almazaydeh, L., Elleithy, K., Feazipour, M., & Abushakra, A. (2013). Apnea Detection Based on Respiratory Signals Classification. Procedia Computer Science, 21, 310–316. doi:10.1016/j.procs.2013.09.041 Alqassim, S., Ganesh, M., Khoja, S., Zaidi, M., Aloul, F., & Sagahyroon, A. (2012). Sleep Apnea Monitoring Using Mobile Phones. In Proceedings of the IEEE 14th International Conference on e-Health Networking, Applications and Services. Bejing, China: IEEE Publishers. American Sleep Apnea Association. (2015). Getting a Diagnosis. Retrieved March 2015. from http:// www.sleepapnea.org/treat/diagnosis.html

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Ancoli-Israel, E. R., Stepnowsky, C., Engler, R., Cohen-Zion, M., & Marler, M. (2003). The relationship between congestive heart failure, sleep apnea, and mortality in older men. CHEST Journal, 124(4), 1400–1405. doi:10.1378/chest.124.4.1400 PMID:14555572 Bourouis, A., Zerdazi, A., Feham, M., & Bouchachia, A. (2013). M-Health Skin Disease Analysis System Using Smartphone’s Camera. Procedia Computer Science, 19, 116–1120. doi:10.1016/j.procs.2013.06.157 Brian, R., & Ben-Zeev, D. (2014). Mobile Health for Mental Health in Asia: Objectives, Strategies and Limitations. Asian Journal of Psychiatry, 10, 96–100. doi:10.1016/j.ajp.2014.04.006 PMID:25042960 Bsoul, M., Minn, H., & Tamil, L. (2011). Apnea MedAssist: Real-time Sleep Apnea Monitor Using single-Lead ECG. IEEE Transactions on Information Technology in Biomedicine, 15(3), 416–427. doi:10.1109/TITB.2010.2087386 PMID:20952340 Burgos, A., Goni, A., Illaramendi, A., & Bermudez, J. (2009). SAMON: Sleep Apnea Monitoring. In Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine. Washington, DC: IEEE Publishers. Carter, K., Maddison, R., Whittaker, R., Stewart, R., Kerr, A., Jiang, Y., & Rawstorn, J. et al. (2013). Heart: Efficiency of mHealth Exercise-based cardiac Rehabilitation Program. Heart Lung and Circulation, 22(7), 553. doi:10.1016/j.hlc.2013.04.015 Doukas, C., Petsatodis, T., Boukis, C., & Maglogiannis, I. (2012). Automated Sleep Breath Disorders Detection utilizing Patient Sound Analysis. Journal of Biomedical Signal Processing and Control, 7(3), 256–264. doi:10.1016/j.bspc.2012.03.002 Harvard Medical School. (2011). Apnea: Understanding the Results. Retrieved March, 2015 from http:// healthysleep.med.harvard.edu/sleep-apnea/diagnosing-osa/understanding-results Hauser, R., & Carlucci, C. (2012). Sleep Apnea, Current Clinical Neurology Series. Switzerland: Springer International. Hoffmann, K. P., & Pozos, R. (2011). Sleep Diagnostic Systems. In Springer Handbook of Medical Technology. Switzerland: Springer International. Jain, N., Singh, H., Koolwal, G. D., Kumar, S., & Gupta, A. (2015). Opportunities and barriers in service delivery through mobile phones (mHealth) for Severe Mental Illnesses in Rajasthan, India: A multi-site study. Asian Journal of Psychiatry, 14, 31–35. PMID:25701069 Karci, E., Dogrusoz, Y., & Ciloglu, T. (2011). Detection of Post Apnea Sounds and Apnea Periods from Sleep Sounds. In Proceedings of the 33rd International Conference of the IEEE EMBS. Boston, MA: IEEE Publishers. doi:10.1109/IEMBS.2011.6091501 Kumar, S., Nilsen, W., Pavel, M., & Srivastava, M. (2013). Mobile Health: Revolutionizing Healthcare through Transdisciplinary Research. IEEE Computer Magazine, 46(1), 28–35. doi:10.1109/MC.2012.392 Linas, B., Latkin, C., Genz, A., Westergaard, R. P., Chang, L. W., Bollinger, R. C., & Kirk, G. D. (2015). Utilizing mHealth Methods to identify patterns of High Risk Illicit Drug Use. Journal of Alcohol and Drug Dependence, 151, 250–257. doi:10.1016/j.drugalcdep.2015.03.031 PMID:25920799

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Luxton, D., McCann, R., Bush, N., Mishkind, M., Matthew, C., & Reger, G. M. (2011). mHealth for mental health: Integrating smartphone technology into behavioral healthcare. Professional Psychology, Research and Practice, 42(6), 505–512. doi:10.1037/a0024485 Motti, J. (2015). John Hopkins turns to mHealth to identify sleep disorders. Retrieved April 5, 2015 from http://www.fiercemobilehealthcare.com/story/johns-hopkins-turns-mhealth-identify-sleep-disorders/2015-04-05 Ng, A., Koh, T., Baey, E., Lee, T., Abeyratne, R., & Puvanendrau, K. (2008). Could Formant Frequencies of Snore Signals be an alternative means for the Diagnosis of Obstructive Sleep Apnea? Sleep Medicine Journal, 9(8), 894–898. doi:10.1016/j.sleep.2007.07.010 PMID:17825609 O’Reilly, G., & Sprujit-Metz, D. (2013). Current mHealth Technologies for Physical Activity Assessment and Promotion. American Journal of Preventive Medicine, 45(4), 501–507. doi:10.1016/j. amepre.2013.05.012 PMID:24050427 Oliver, N., & Flores-Mangas, F. (2007). Health gear: Automatic Sleep Apnea Detection and Monitoring with a Mobile Phone. Journal of Communication, 2(2), 1–9. Patil, D., & Wadhai, V. (2012). Apnea Detection on Smartphone. International Journal of Computers and Applications, 59(7), 15–18. doi:10.5120/9559-4022 Penzel, T. (2000). The Apnea–ECG database. Computers in Cardiology, 27, 255–258. Phippard, T. (2012). The (m)Health Connection: An Examination of the Promise of Mobile Phones for HIV/AIDS Intervention in Sub-Saharan Africa. (Master Thesis), Available from Electronic Thesis and Dissertation Repository, University of Western Ontario. Pieper, B. (2004). An overview of the HIPAA security rule. Journal of the American Optometric Association, 75(10), 654–657. doi:10.1016/S1529-1839(04)70213-4 PMID:15508867 Rofouei, M. (2011). A Non-invasive Wearable Neck-cuff System for Real Time Sleep Monitoring. In Proceedings of the International Conference on Body Sensor Networks. Dallas, TX: IEEE Publishers. doi:10.1109/BSN.2011.38 Sannino, G., De Falco, I., & De Pierto, G. (2013). Detecting Obstructive Sleep Apnea events in a real time monitoring system through automatically extracted set of rules. In Proceedings of the IEEE International Conference on Service Science for e-Health. Lisbon, Portugal: IEEE Publishers. doi:10.1109/ HealthCom.2013.6720630 Zhu, R., Cao, Z., & Que, R. (2014). Integration of Micro-sensors with Mobile Devices for Monitoring Vital Signs of Sleep Apnea Patients. In Proceedings of the 9th IEEE International Conference on Nano/Micro Engineered and Molecular Systems. Waikiki Beach, HI: IEEE Publishers. doi:10.1109/ NEMS.2014.6908850

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KEY TERMS AND DEFINITIONS mHealth: Mobile health signifies the adoption of mobile technology in the health sector. Mobile Apps: Computer programs that run on mobile devices and are intended for different applications. Mobile Devices: A relatively small electronic computing and mobile device that can be hand held and used for various computing and telecommunication needs; examples include the smartphone and the iPad. OSA: Obstructive Sleep Apnea. Physiological Sensors: Miniature sensing devices that are connected to parts of the body to sense and record its vital signs. Polysomnography: A medical test used to diagnose sleep diorders in humans. Sleep Apnea: A sleep disorder that hampers healthy breathing during sleep and may lead to serious health consequences.

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The Contribution of mHealth in the Care of Obese Pediatric Patients Elpis Vlachopapadopoulou Children’s Hosp. P. A. Kyriakou, Greece Dimitrios I. Fotiadis University of Ioannina, Greece

ABSTRACT In this chapter the evolution of mHealth solutions for monitoring and treatment of children suffering from obesity is discussed. Nowadays, obesity emerges as a major chronic health condition that affects the general population, both children and adults. mhealth solutions are already used for self-management, remote monitoring and counseling of several chronic conditions, including diabetes mellitus, heart failure, Parkinson’s disease, etc. Today, those solutions can result to closed loops, which support health self-management for chronic diseases, in a personalized manner. Concerning childhood obesity, those solutions can combine targeted games and motivational approaches towards both physical activity and diet, which could help in addressing this serious and global health issue, in the direction of minimizing co-morbidities and eventually preventing serious, life threatening events.

INTRODUCTION During 1990s, in association with the extensive use of the internet, a variety of applications have developed using e-technology. The introduction of e-Health promised to improve health care delivery and health care access through increased availability of information and enhanced communication. Although the word used is “health” it refers to healthcare. There are several definitions for e-Health. Most of the definitions use technology as a tool to enable a process or improve function and as effective means to enhance human activities (Hans, 2005). The most commonly used definition is that of Eysenbach (2001). mHealth is an abbreviation for mobile health, a term coined for the practice of medicine and public Health interventions, using mobile devices. mHealth broadly encompasses the use of mobile telecommuDOI: 10.4018/978-1-4666-9861-1.ch007

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nications and multimedia technologies, within or along with conventional health care delivery systems. mHealth, today, is closely related to smartphones, which can provide connectivity to various devices and provide a means for receiving the feedback from the healthcare professional. A definition formulated during the 2010 mHealth Summit for the Foundation of the National Institute of Health defines mHealth as “the delivery of health care services via mobile communication devices”. We could differentiate mHealth and eHealth by elaborating on their fundamental functions, as eHealth supports health systems while mHealth provides healthcare access. mHealth offers an unparalleled opportunity to reach individuals and implement changes. With mHealth applications, the individual is at the center and the most important link: the technology responds to an individual’s needs. Everything starts with the need of an individual, either a healthcare worker or a patient, in our case the patient, and the mobile technology application is viewed as the potential lever of the solution. Effective technologies are those that will undergo extensive modifications based on users needs. Patient groups that can benefit include, but are not limited, to patients with asthma, diabetes, obesity, heart failure, neurogenerative diseases, multiple sclerosis, malignancies and bipolar disorders. In this chapter, we will review and elaborate on the published experience regarding interventions using mobile technology targeting childhood obesity, as well as, on the future potential, benefits and limitations of this emerging technology. Childhood obesity is considered by the WHO as a new epidemic and has been characterized as the number one health problem worldwide (Ng, 2013). The American Academy of Pediatrics guidelines target the reduction of total and abdominal obesity through increased physical activity and healthy nutrition (American Academy of Pediatrics, 2011). Although, recent research has demonstrated the efficacy of these lifestyle changes on weight loss and weight maintenance as well as on the prevention of comorbidities, promotion and maintenance of such changes continues to be a challenge (Teixeira & Yun, 2015). It appears that there is a significant and growing opportunity for eHealth obesity intervention designers to leverage the widespread public adoption of rapidly converging information and communication technologies—most notably the World Wide Web, wireless PDAs and cellular telephones (Tufano, 2005). Communication technologies such as smartphones offer a potentially powerful approach to support and maintain behavior changes, through delivering of convenient individually tailored, in line with the guidelines, behavioral interventions (Allen 2013). There is research evidence suggesting that mobile phones provide a powerful tool for interventions seeking to improve and maintain health outcomes (Allen, 2013; Krishna, 2009, Core- Lewis, 2010). This is supported by a multitude of applications ranging from graphical outputs, diaries, games, motivational platforms, etc. and of course connectivity to social media. More importantly, the child in this framework, is placed at the center of the mHealth solution, but at the same time it is connected with other players who are involved in his/her healthcare activities, such as caregivers, mainly family, paediatrician, another medical doctor, the psychologist, the nutritionist, the hospital, the school, etc. By those means, there is hope and expectation that the positive effect can be maximal.

BACKGROUND The number of smartphone users worldwide will surpass 2 billion in 2016, according to new figures from eMarketer—after nearly getting there in 2015. In 2016, there will be over 1.91 billion smartphone

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users across the globe, a figure that will increase another 12.6% to near 2.16 billion at the end of 2016 citizens of Middle East and Africa are included in the users. Mobile applications (apps) can help people manage their health and wellness, promote healthy living, and gain access to useful information when and where they need it. These tools are being adopted almost as quickly as they can be developed. According to industry estimates, 500 million smartphone users worldwide will be using a healthcare application by 2015 and by 2018, 50 percent of the more than 3.4 billion smartphone and tablet users will have downloaded mobile health applications (Jahns, 2010). These users include healthcare professionals, consumers, and of course patients. Thus, it would appear that smartphones would be the preferred hardware platform for mHealth obesity interventions for reasons of both enabling effective intervention design features and for promoting rapid public adoption and acceptance (Tuffano, 2005). The trend is to merge mobile phones with tablets. FDA encourages the development of mobile medical apps that improve healthcare and provide consumers and healthcare professionals with valuable health information. The FDA also has a public health responsibility to oversee the safety and effectiveness of medical devices – including mobile medical apps. The FDA intends to exercise enforcement discretion for mobile medical apps that help patients/ users self-manage their disease or condition without providing specific treatment suggestions; provide patients with simple tools to organize and track their health information; provide easy access to information related to health conditions or treatments; help patients document, show or communicate potential medical conditions to health care providers; automate simple tasks for health care providers (U.S.Food and Drug Administration, 2013) Childhood obesity is considered by WHO as a new epidemic, and it has been characterized as the number one health problem worldwide. It has been on the rise for the past decades, specifically, the prevalence of combined overweight and obesity rose by 47.1% for children between 1980 and 2013 (Ng, 2014). Researchers have associated the increased body mass index (BMI) in childhood with comorbidities during childhood and adolescence, but also with comorbidities in adult life. Childhood obesity is associated with increased blood pressure, hyperlipidemia and insulin resistance, triad that constitutes the metabolic syndrome (Litwin, 2014), diabetes type 2, sleep apnea, increased likelihood of asthma, increased risk for orthopedic problems, psychological problems and social maladjustment. A correlation of childhood obesity with the premature development of diabetes and cardiovascular disease is well established (Litwin, 2014). Of special concern is the recently established association of childhood obesity with increased cancer risk during adulthood. The prevalence of several cancers is increased including thyroid cancer, colon, endometrial, and breast cancer. Contrary with previous findings, promising recent data provide supportive evidence that weight loss and attainment of normal weight during childhood or adolescence decreases the risk of malignancy later on in life. However, there are longitudinal studies that suggest that morbidity and mortality from complications of diabetes and cardiovascular disease is increased independently of the adult BMI (Kelsey, 2014).

mHEALTH SOLUTIONS FOR PATIENTS WITH OBESITY The need for interventions, in order to achieve primary prevention (preventing normal weight children to develop obesity) as well as secondary prevention (treating obese children and adolescents in order to revert to a normal BMI) to minimize health consequences, is obvious. Due to cost and resource limitations, effective obesity interventions can be challenging to deliver to the adolescent population in need of

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care. The low cost and availability of mobile phones make mHealth an important player in encouraging change and sustaining healthy behaviors (Dijkstra, 1999, Kreuter, 2003). One serious limitation of the traditional approach to childhood obesity is the difficulty parents have to adhere to the healthy lifestyle habits as suggested by the physicians and the difficulty to keep the medical appointments, due to work or school obligations. Additionally that parents and children recognize a burden in self-monitoring practices. Self-monitoring of energy expenditure, caloric intake and weight are hallmarks of long-term weight control (Wing, 2000). Despite the evidence that self-monitoring is very important for weight control the majority of adults and children find it quite difficult to adhere to a specific program. Previous studies have reported that in the first month of a weight control intervention for morbidly overweight children, 44% self-monitored at least 3.5 days per week (German, 2007, Kirschenbaum, 2005); however, only 25% continued at six months (German, 2007). Compliance can be improved if a diary system can be used. Electronic communication systems also provide electronic diaries. Electronic diaries may increase adherence via immediate feedback as well as time recording (Bartlet, 2002, Stone, 2003). There are reports documenting, that the use of a personal digital assistant (PDA), significantly increased the rate of adherence from 11% to 94% (Stone, 2003). Children use electronic devices regularly; 45% of US teenagers ages 12–17 own a mobile phone and 33% use the short message service regularly (SMS; text messaging) (Shapiro, 2008). Researchers and physicians should look for protocols that will involve more modern ways of communication. Cost-effectiveness as compared to traditional therapy, telephone, email, web-based intervention or SMS should also be examined. Globally, an estimated 43 million preschool children (under age 5) were overweight or obese in 2010, a 60 percent increase since 1990. The problem affects both countries rich and poor. Of the world’s 43 million overweight and obese preschoolers, 35 million live in developing countries. By 2020, if the current epidemic continues unabated, 9 percent of all preschoolers will be overweight or obese—nearly 60 million children (De Onis, 2010) Over 200 million school-age children are overweight, making this generation the first one predicted to have a shorter lifespan than their parents, as stated by the World Obesity Federation (2014). The necessity of implicating secondary prevention programs accessible to the large population of patients can be met by the development of web-based programs. To implicate such programs, the field of digital therapeutics has emerged. Furthermore, the vast majority of children are tentatively candidates for developing obesity in an older age and therefore in need of having accessible information regarding healthy nutrition, physical activity and other daily habits proven to be important in maintaining a normal weight. Digital therapeutics is evidence-based behavioral treatments delivered online that can increase accessibility and effectiveness of healthcare. Those approaches offer a new opportunity for healthcare delivery, which can enrich the range of applications in mHealth. To date, several systematic reviews have found that Web-based interventions may be a moderately effective way to facilitate lifestyle change and weight loss. Many people don’t read books, they might not explore the internet, but 90% of the people in most developed countries have a mobile phone, usually smartphone, which they carry all the time with them. These mobile phones have camera and microphone, they can send and receive text, they can provide information about location, they make applications always available and they are handy and chargeable anywhere and anytime. Mobile technology offers access; access offers knowledge and mHealth applications empower individuals to take an active role for their health and promote their well-being. Thus, there is a substantial need for credible, trustworthy, understandable, concise but simple health information that is relevant, culturally sensitive and easy to 129

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apply. mHealth expands the definition of personalized medicine while maintaining and enhancing the integrity of the individual, delivering to the right person, the right information, at the right place, on the right time. Mobile means anytime and anywhere, at the discretion of the individual. Health issues and queries do not only arise when we are in front of a computer screen, they can arise anytime, anywhere. Via mobile technology it is feasible to adapt face to face obesity interventions to a mobile platform and deliver effective remote care (Tate 2013, Krishna, 2009). Given the chronic health conditions associated with obesity, physicians, and other health providers are faced with the challenge to implement programs for both immediate and long-term weight loss. Predictive models have been published for adults that can predict weight change based on the daily energy imbalance. Based on historical controls they have estimated that an excess intake of 30kcal per day can lead to a weight gain of 1.8-2.0 lbs per year (Hill, 2003). Another model used the method of doubly labeled water to measure total energy expenditure and introduces equation to estimate the difference in energy flux between two different weights (Swinburn, 2009). Hall and Jordan developed a spreadsheetbased model of the change in steady-state body weight that included the effect of both total energy intake together with physical activity changes and metabolic adaptation (Hall, 2008). The application of predictive models for children is more complex as the energy needed for growth and the changing energy expenditure of the growing organism need to be considered. At present, there are no validated predictive models for childhood obesity. Predictive models can be implicated in intervention strategies, and they can increase the acceptance and accuracy of weight loss intervention plans, implicating nutrition, physical activity, and behavior changes. There are several behavioral interventions that have demonstrated encouraging short-term results, but they do not have consistent results as far as the long-term maintenance of weight loss (Tate, 2001). Thus, research for better models that can easily be delivered and sustained overtime, is needed. To that end, mobile health development intervention could greatly benefit from the application of the health behavior theory (Khaylis, 2010). Smartphones can be easily connected to sensors that measure physiological parameters.-Several protocols are available and continuous or not measurements can be collected. Features of those signals can be extracted, and those, or the raw signals can be uploaded in the cloud for storage and processing. Mainly the processing is related to the fusion of signals that characterize activity, lifestyle, physiological measurements, etc. Recently, those data have been enriched with the collection of environmental data, e.g. temperature, humidity, etc. which are also fused. The idea is to create decision support systems that can provide monitoring of the patient and support his/her treatment and well being. As technology progresses, several players participate in this monitoring procedure and provide and exchange data. For example, caregivers can play a role in patient monitoring, and they can receive data even from patients otherwise unable to do so, concerning their vital signs, health status, activity, and medications. So parents, medical and school personnel become active and interactive players for the care of children with obesity and related disorders. The smartphone becomes a tool for data collection, data pre-processing and sometimes data processing and decision making. The old fashion telemedicine approach provides those data to the medical doctor, who is responsible to take decisions regarding the patient. Those decisions can be transferred to the mobile, and the patient can receive an SMS, or an alert on his/her smartphone. In that regard, we say that the loop is closed. In some cases, and this soon will become a reality, readings of the sensors and decisions made by the system can be fed to an actuator, e.g. a pump, in the case of diabetes an insulin pump and the system is again closed, meaning that the artificial pancreas is operational.

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Figure 1. ­

To that Internet of Things can be used to provide additional measurements which are related to blood pressure, weight, fat, etc, which can be combined in the same framework with other environmental and lifestyle measurements, which might come from sensors which can be placed in a refrigerator, in the ground, inside the house, etc. This results in a wider environment, which acts in the direction of collecting continuous measurements, which easily can be transferred to the cloud for storage, processing and decision making. The general framework of developing such an approach within the healthcare ecosystem for obese children is given in Figure 1. In that framework, the participation of other healthcare ecosystem stakeholders becomes more evident and active. Parents, teachers, relatives and, in general,, formal and informal caregivers become more informed and aware of the status of the patient and can participate in an active process, which is patient-centric, highly personalised, but at the same time holistic since the medical doctor, the patient, the caregiver, the nurse and other actors can communicate and keep active in the ecosystem. The mHealth systems now are driven by the concept of self-management. Self-management can be defined as the decisions and behaviors that patients with chronic illness engage in that affect their health. Selfmanagement support is the care. Moreover, encouragement provided to people with chronic conditions and their families to help them understand their central role in managing their illness, make informed decisions about care, and engage in healthy behaviors (Group Health Research Institute, 2015). The question is how this can be achieved through an mHealth system.

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Figure 2. ­

Figure 2 presents the evolution of self-management devices from 2004 to today in terms of connectivity, measurements and functionalities (Georga, 2014). The first row presents the functionalities, the second the measurements and the third the connectivity technology. Lately, to those platforms apart from the use of sensors, several biosensors have been included. A biosensor is an analytical device incorporating a biological or biologically derived sensing element either intimately associated with or integrated within a physicochemical transducer. The usual aim is to produce a electronic digital signal that is proportional to the concentration of a chemical or set of chemicals (Turner, 1987). At present those sensors can become small. Key enabling technologies, such as microfluidics, nanotechnology, and flexible electronics can be utilized to provide us with miniature sensors, which can analyze blood, saliva or other biological fluids and communicate the result to the smartphone (see Figure 2)

Previous Experience Behavioral weight loss interventions consist of diet, exercise and behavior therapy (Wadden, 2003). Behavioral modification strategies usually include the following: self-monitoring, goal-setting, shaping, reinforcement and stimulus control (Wadden, 2003). Behavior modification strategies usually include self-monitoring, goal-setting, reinforcement, and stimulus control (Wadden, 2003). The use of the web or mobile technology to support these strategies can augment the results. To date, several systematic reviews have found that Web-based interventions may be a moderately effective way to facilitate lifestyle change and weight loss (Neve, 2010, Norman, 2007). Khaylis et al. have reviewed 21 studies and concluded that five elements are the key components in technology base behavior interventions to be successful in weight loss (Khaylis, 2010). These features

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are self-monitoring; counselor feedback and communication; social support; structured program; and individually tailored program (Khaylis, 2010). The authors comment that a technology based model of behavior change for weight management using the above mentioned five key components is advantageous over traditional methods in several aspects. The internet is a cost-effective way to deliver relevant information and stepwise instructions to individuals of different backgrounds, age range, and location. Using their mobile phone or digital music players, participants can have an interactive communication with the physician, dietitian or counselor via online diaries and self-reports. They can also have access to lessons and healthy lifestyle information. A factor that is highly appreciated by the participants is that this approach is time-saving and can be incorporated into busy lifestyles, thus reducing resistance to engage in a weight reduction program. One of the most important reasons of drop outs of long-term programs in outpatient clinics is time restraint. On the other hand, portable devices such as handheld PDAs or pedometers, give the opportunity for continuous monitoring that have been shown to increase weight loss when compared with traditional methods. Of utmost importance is that technology-based interventions can provide individuals with a sense of control that is essential for developing and implementing short- and long-term behavioral change (Khaylis, 2010). The program “Prevent” was created to apply the principles of Diabetes Prevention Program (DPP) into a digital form and make it available to millions of people. The effectiveness of “Prevent” when used by adults with obesity and pre-diabetes in lowering BMI and hemoglobin A1c was assessed. Participants were enrolled in the “ Prevent” program that they were able to access via any Internet-enabled desktop or mobile device (prevent by Omada Health) (Salber, 2014). “Prevent” is an Internet-based translation of the DPP lifestyle intervention, which includes small group support, personalized health coaching, a weekly DPP-based curriculum, and digital tracking tools. Participants were demographically matched into groups of 10-15 participants and placed into a private online social network resembling Facebook where they could discuss goal progress and provide social support to one another. At any convenient time or place using Internet-enabled devices, they could asynchronously complete weekly DPP-based health education lessons, privately message and call a health coach for individual counselling, track weight loss, and physical activity using a wireless weight scale and pedometer, and monitor their engagement and weight loss progress. The participants were enrolled for one year, and they were followed for two years. The program was proven to be successful in weight reduction and amelioration of HbA1c even one year after the program discontinuation (Sepah 2014; Sepah, 2015). Shapiro et al.(2008) used SMS texting to assure compliance with a program and adherence to selfmonitoring. Children and parents participated in a total of three group education sessions (one session weekly for three weeks) to encourage increasing physical activity and decreasing screen time, and sugar-sweetened beverage consumption. All randomized children received a brief psychoeducational intervention and then were randomized into three groups either monitored target behaviors via SMS with feedback, via paper diaries or participated in a no monitoring control group for eight weeks. Children who were randomized to the SMS group were given one phone each to share for the duration of the study. They were instructed not to use the phone for anything except study-related SMS. They were instructed to send two SMS per day (one for parent and one for child), daily for the full eight weeks of the study, and for each SMS sent, they would each receive an immediate, automated SMS feedback message from the program hosted on a secure server. The feedback message was automated to provide instant responses to the participants regardless of the time of day. This group of children had significantly higher adherence to self-monitoring than children who kept a paper diary, 43% vs. 19%. The children 133

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appear to prefer the assignment using mobile technology (Shapiro, 2008). The authors concluded that according to the cognitive, social learning theory, perhaps the support and positive reinforcement led to adherence and acceptability of the self-monitoring program (Shapiro, 2008). Moreover, SMS is more reliable regarding the date and time that is recorded automatically as opposed to paper diaries that are possible to be backfilled (Stone, 2003). A similar study in adults was performed that utilized text messaging. A text was sent to all participants daily. Participants were adults who have already lost 5% of their body weight and owned a mobile phone and the program aimed at weight loss maintenance. The acceptability of the intervention was also evaluated. To eliminate the possible confounders of simply receiving a daily message regardless of content and the novelty effect of technology, participants randomized to the control group received general health messages over a standard-of-care control group. Participants find it useful and wellaccepted this way of intervention. There was a clinically significant decrease in mean weight, and a greater proportion of people sustained weight loss in the promotion and prevention message groups. The authors state that this mHealth intervention was feasible, acceptable, and efficacious on sustaining recent weight loss (Shaw, 2013). A randomized controlled pilot trial of a weight loss intervention using a smartphone application for self-monitoring as an adjunct to behavioural counselling, in adults, has showed high satisfaction of the participants and a tendency towards higher weight loss in the self-monitoring smartphone group (Allen, 2013). There have been 12 behavior modifying studies using mobile technology targeting diet and exercise. Four of these used PDA and eight mobile phones sending SMS. Bauer et al. (Bauer, 2010) generated text messages based on weekly input from the overweight children in the study, but these messages were reviewed and modified by staff before sending. Diet, weight, and exercise data were provided via self-report for all interventions except the one by Hurling et al. (Hurling, 2007) which used accelerometer data wirelessly transmitted via Bluetooth to the mobile phone. Output from these interventions was predominately text although tabular and graphic comparisons to targets/goals were provided by some interventions (Burke, 2011, Beasley,2008, Haapala, 2009) intervention used a mobile phone program that adjusted music tempo to encourage an appropriate walking pace (Liu, 2008). Riley WT et al criticized these studies for the fact that although they have based the protocol on a theoretical basis they did not evaluate the changes in the targeted factors (Riley, 2014a, Riley, 2014b). On the other hand, a positive outcome can be foreseen if there is a dynamical system model with algorithms that are used in real time and adapt the timing and the dose of the intervention (Riley, 2014a, Riley 2014b). A smartphone intervention protocol for adolescent obesity was recently published. The program is ongoing with the primary aim to assess the impact of a smartphone application compared with usual care on body mass index SDS over 12 months in adolescents (age range from 12-17 years) who are obese. Secondary outcome measures are waist circumference, insulin sensitivity, quality of life, physical activity and psychosocial health (O Malley, 2014a). It is an investigator blinded protocol of one-year duration. The obese adolescents, after the initial evaluation by a multidisciplinary team, are randomized into two groups one receiving standard care and the other following the experimental smartphone intervention protocol. The smartphone application incorporates evidence-based behavioral change tools such as self-monitoring, goal setting, and peer support. Evidence-based tips are sent to the user in the form of a text tip, a video tip or an image tip. The tips aim to increase the knowledge of the participant concerning healthy eating, physical activity, physical fitness, and sleep. The user is encouraged to engage in daily goal setting to 134

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increase the physical activity level and sleep, increase water intake, reduce intake of sugar and fat and to increase intake of fiber, fruits and vegetables. Also, the users are encouraged to monitor their progress by reviewing their goals daily and by entering their height and weight measurements. The findings are expected to guide further development of a telemedicine system for the management of clinical obesity in adolescence. (O Malley, 2014a) Preliminary results of a pilot study of the above intervention project reveal that the majority of the adolescents were satisfied with the ease of use the benefit of the weight tracking and reward systems and the appealing look and feel of the app (O Malley, 2014b). The use of social media and especially Facebook was integrated with a pilot study targeting college students. The study examined the feasibility, acceptability, and initial efficacy of a technology-based 8-week weight loss intervention among college students. Students were randomly assigned to one of the three arms: Facebook, Facebook Plus text messaging and personalized feedback. Waiting List control with assessments at four weeks and eight weeks post treatment. At 8 weeks, the Facebook Plus group had significantly greater weight loss than the Facebook alone and control group, indicating the potential use of social media in weight control interventions, particularly when targeting large groups of adolescents and young adults (Napolitano, 2013) The term Ecological Momentary Interventions [EMI] was used for treatments that are provided to people during their everyday lives (i.e., in real time) and in natural settings (i.e., real world). King and colleagues (King, 2008) developed a palmtop computer delivered intervention aimed at increasing middle- and older-aged adults’ physical activity using a palmtop computer-based EMI. The researchers demonstrated that a relatively short-term, low-intensity palmtop computer-based EMI could increase middle and older-aged adults physical activity. More, it provides an example of an automated, palmtopcomputer based EMI that requires limited clinician time during the intervention (Heron, 2010; King, 2008). The first RTC using smartphone technology aiming to increase daily physical activity was published in 2013. The smart move intervention program included obese patients older than 16 years of age who owned a smartphone and were randomly assigned to the intervention or controlled group. The intervention group was instructed in the usability features of the smartphone application and encouraged to try to achieve 10,000 steps per day as an exercise goal and was given an exercise promotion leaflet. The control group was encouraged to try to walk an additional 30 minutes per day along with their normal activity (the equivalent of 10,000 steps) as an exercise goal and was given an exercise promotion leaflet. The primary outcome was the mean difference in daily step count between baseline and follow-up (Glynn, 2013).

Future Implications for Weight Management Several systematic reviews have found that web-based interventions are moderately effective in promoting weight loss. A personalized approach and intervention, with a two-way feedback, sets a much more effective plan of action. Current technology offers the ability to measure and track several biological and environmental factors. At present, there are devices that can record weight, calories, sleep time, physical fitness, heart rate, sports performance. Shortly, we expect them to be able to measure and record nutrients, microbiome, sweat and tear production, medication concentrations in the blood, comprehensive stresses of the spine and many other variables. Some of these functions are already incorporated into smartphones while other measurements come from sensors that transmit data through USB or wireless connections. The sensors needed to capture these data have become miniscule, durable and highly effective. Currently, the data are personalized, and they can be exported in raw numbers or in the form

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of graphs. Cloud sourcing and remote data accumulation and manipulation is already feasible and applicable. There are several health devices, usually sensors that connect to the body and they are used to acquire the recordings needed. In more details, we already have devices that can track calorie intake, activity and sleep round the clock using multiple sensors, others that can track weight, body fat, and BMI, or measure steps, distance and calories burned. Furthermore, other devices can monitor sleep or track distance covered, pace and calories. The use of GPS-enabled data recording, and modulation enhances such a fitness watch. Blood pressure, heart rate, and pulse rate can be monitored and tracked, as well. iHealth Smart gluck monitoring system measures and records blood glucose levels, as this is measured and recorded using a portable testing kit. Similar sensors and apparatuses to collect similar biological data measurements are being developed at an enhanced pace. Furthermore, of special interest and with fast development are the smart clothes. The world of electronics and textiles is collaborating, and they are creating smart clothes and e-textiles. These new materials are promising to detect an amazing number of physiological parameters, including thermal, mechanical, chemical, electrical, optical and magnetic signals. The availability of such diverse and relevant measurements and parameters highly enhances our ability to establish algorithms and models for health status analysis, prompt and effective intervention, with ability of real-time data feedback and the ability to further fine tune our actions. Once the sensors detect the signal, it is collected, processed, stored and transmitted appropriately. There are several other developments, as the industry is currently researching extensively, to manufacture small, state-of-the art health tools and useful gadgets. The main theory behind all that excitement is that by measuring and tracking your everyday activities, you can help improve your quality of life and health. Specifically, all of the above mentioned traceable parameters and factors are of great interest and significance for weight management and weight loss interventions. All the information captured can be easily transferred to a smartphone and can be associated with location, date, time and other information contained on the phone such as calendar events, phone calls or mails. Thus, individuals and their physician can develop a good understanding of the real factors influencing their health and health-related behaviors. Based on that, a reaction plan can be available to lead decisions and actions, mainly regarding food choices and nutrition but also sleep and stress management techniques. In order to promote self-management, predictive personalised models can be combined with captured data (clinical, biological, lifestyle, diet) and empower the patient to participate in the management of his or her health, with application in lifestyle changes and disease monitoring. This will improve patient compliance, adherence to intervention plan and possibly improve the quality of life and prevent later advert events. Today’s technology offers various ways to encourage children to adopt a certain type of behavior. Mobile technology is one of them since several alerts can be provided based on the understanding of unhealthy behavior. Also, messages can encourage the child to change the unhealthy behavior. In that respect, serious games provide with a means to entertain children, and in that way it is guaranteed that the children will use it, and, on the other hand, to provide with a means to change their behavior. The game must have a balance between “fun – ness” and “serious – ness” elements which can entertain and at the same time impose rules on their behavior (Thompson, 2014). For diabetes and self-management tools, several video games are distributed commercially to cover all ages (Lieberman, 2012). They are experimental and interactive, permitting the player to immerse in worlds offering challenges and progress feedback (Lieberman, 2012). In that sense, a game enhances learning about his/her health and stimulates behavioral change. To enhance access to those games, the cloud has been employed to provide content, 136

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recommendation, and evaluation and in that sense with guidance to change behavior (Hassan, 2012). A game can be used to fight obesity in children if energy expenditure can be increased, education on nutrition is provided and healthy eating physical activity is promoted (Selmanovic, 2010). A serious game can be combined with a mhealth solution. Smartphones can be utilized to deliver to children the high quality and attractive in that sense interfaces. From the point of view of frequent use, this is a real dangerous situation since the child can be addicted to the use of such a game. This somehow can be restricted, if mechanisms for delivering the game permit access only for certain time periods. The game must be carefully designed to include characters, which are familiar and trustable to the child. The game must be viewed as a two edges utility. In one edge the child can be placed, on the other the caregiver, especially the parents. Both sides must be motivated in healthy behavior: The child to avoid any unhealthy behaviors, and the parents to be motivated in a healthy diet, activity, etc. The child in everyday life eats whatever is offered by his/her parents. He/she exercises if the parents exercise. In general he/she mimics the parent behavior. In that sense, both participate in an active and self-management “game”, which entertains, trains and motivates. The game design must be performed by experts, who have a better understanding of the life of an obese child. Providing strict rules does not help, does not really motivate. The game must entertain and at the same time gain the child and convince on healthy attitudes. The solution must be straightforward and not complicated, the healthy eating messages must be presented in an easy to understand way, and above all the provided solution to be attractive and feasible. Serious games might also involve the school community, but the advice and training provided must be followed by the school personnel itself. Working with a virtual environment and living in a real world, those two actions must be compatible. If the motivation for healthy lifestyle and weight decrease cannot be followed in the school environment, e.g. if the school canteen does not have, or it is not oriented to the suggestion given by the game, this is not going to work. The same happen with the family and the home. The child must be motivated, but this must be reflectedto the surrounding world, and the solutions must be there, other ways such games or motivation approaches do not work. Future developments and implications include the following: 1. Text messaging is an easy and cheap way to obtain greater adherence to treatment and monitoring. It is also very well accepted by parents and children. 2. A theory-driven model of mobile intervention development promises improvement of these interventions. 3. Applications with the end or another involved user in mind that are age appropriate and easy to use will be of greater success. 4. The more interactive the interface is, the more efficacious and approved by the users it will prove to be. The ability to have advice on day to day changing internal and external circumstances can increase compliance and have the much needed sustainable results 5. Use of social support networks may augment the impact. Adolescents are already engaged in social media so they can move to another platform that of weight management and belong to a closed group. They retrieve information, set goals and have peer support. They can also download and upload materials. An application can request data from the user’s profile (e.g., his “likes” and interests). The moderators post the task of the week. The members of the group can access the app themselves and be presented with a list of completed and uncompleted tasks. The participants can send material to the moderator through mail or Facebook message. 137

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6. Serious games can be engaged and implemented in order to provide an interaction between the children and their teachers and / or parents. The games offer the opportunity to “play” (really engage) with their chronic condition, to receive advice and alerts and in case they succeed to receive points to reach a certain goal. Those serious games have been transferred to a smartphone/ tablet platform and are part of any behavioral monitoring or motivation platform. The development of those games is guided by behavioral science principles and it is a real lead for future developments (Thomson, 2010).

DISCUSSION The options that technology can offer to us are unlimited. However the evaluation of their clinical effectiveness, as well as the users’ satisfaction, is very important to improve outcomes and establish effective and sustained progress. At present few developers report whether their apps have been in line with best practice guidelines (Tate 2013, Mobile medical applications guidance, 2013). It is important to have the needs of end users in mind. Kushniruk et al. published cognitive approaches for evaluation of information systems, and users interface (Kushniruk, 1997). Usability of a computer system can be defined as the capacity of the system to allow users to carry out their tasks safely, effectively, efficiently and enjoyably. Results indicated that subjects tended to rate the system in a very positive way on the questionnaire, despite the fact that video recording of their interaction with the system showed that they had encountered considerable problems in using the system, ranging from inability to navigate through the information contained in the program, to comments indicating that the program’s content was out of date (Kushniruk, 1997). The practice of evaluating the usability of the electronic application in a small cohort, as O’ Malley et al did, is a valuable, important and necessary tool before applying the application in larger patient groups (O’Malley, 2014b) Another study explored the usability of the self-management program for youth with juvenile idiopathic arthritis and their parents to refine the health portal prototype (Stinsonthe usability of the self-management program for youth with juvenile idiopathic arthritis and their parents to refine the health portal prototype (Stinson, 2010, 2010). Adolescents and parents provided similar as well as differing suggestions on how the website user interface could be improved in terms of improving its usability. Many participants responded that the interactive features made them feel supported and “not alone” in their illness (Stinson). Adolescents and parents provided similar as well as differing suggestions on how the website user interface could be improved in terms of improving its usability. Many participants responded that the interactive features made them feel supported and “not alone” in their illness (Stinson, 2010, 2010). This creates a very positive attitude for the whole process and enhances effective interventions.). This creates a very positive attitude for the whole process and enhances effective interventions. Additionally, one very important issue is that the content and timing of the intervention can be tailored to patients. By integrating the assessment and intervention capacities of the mobile technology, applications can be developed that are sensitive to the participants’ internal states (e.g., mood, cravings, physiological responses) and external cues and contexts (e.g., social interaction, location) (Heron, 2010). This feature is of particular importance when dealing with adolescents, but of course it is welcomed for any population group.

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Strengths mHealth-based child obesity interventions can capitalize on flexibility, speed, and power participant burden offered by mobile technology. The following can be considered: •

• • • • • • • • • •

The prediction for wide use of smartphones as well as the availability of inexpensive smartphones, are opening new opportunities for health applications and particularly for obesity prevention program reaching larger number of patients including lower socioeconomic status children, adolescents and their families. The use of mobile phone apps may be a motivational tool for sustainable weight control Individualized intervention can be an aspect of success for the intervention Sensors and IoT devices offer a great flexibility in the collection of data related to weight and other behavioral/ lifestyle patterns. Cloud services can be used as platform for data gathering, analysis, and decision support. mHealth advances may provide a new channel through which large audiences of obese young people can be reached and engaged. mHealth solutions improve the participation of the patient in the care process Use of predictive models augments preventive management The major actors in this healthcare process can be easily integrated through the use of mobile applications. Serious games can be developed based on the principles of behavioral science. Such an approach offers flexibility and is enjoyable for the young people.

Limitations Still there are many limitations, which must be faced in self-management systems, which limit the use of such systems and do not permit the wide spread of those approaches: •

• • • • • •

Mobile health technologies are rapidly progressing, and they can support health behavior interventions that can be delivered in a personalized manner. A possible drawback is that if the concept behind the intervention is not based on behavior theory, then the content and the timing of the intervention may not be as effective. Researchers and practitioners should consider the theoretical basis of health interventions. Data protection is a major concern. The time that the child or adolescent spends in sedentary behavior and the screen time have to be accounted. Increased social isolation, addiction to gaming, or electromagnetic radiation exposure with high cell phone use should be monitored. There is a need for interdisciplinary collaboration and development of appropriate assessment tools. The relatively slower speed of the scientific research versus the high development speed of the mobile technology industry.

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CONCLUSION Communication technologies such as mobile phones may serve as an effective medium to deliver affordable health promotion and disease prevention care to an array of people, due to their ubiquity and penetration into people’s everyday lives. However, emerging and promising technologies must be matched with such content that can successfully motivate people to change and sustain healthy behaviors. mHealth systems offer opportunities for surveillance and research in childhood obesity as well as development, delivery and dissemination of treatment and prevention programs. Usability testing is a critical step in the development of Internet interventions and solicits end-user feedback in order to learn what really works, what does not work, and where gaps in information or functionality exist, using iterative cycles to refine the prototype (Curie, 2005 Wichansky, 2000). These factors may impact on the frequency of use, understanding, and the likelihood that a user will implement the recommendations (Curien, 2005, Wichansky, 2000). Usability testing can also help determine the appropriateness of the website interface and content (Gustafson, 2004), especially when it is designed for different audiences (e.g., youth and parents) and delivered in different languages.

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KEY TERMS AND DEFINITIONS Behavior Interventions: Protocols targeting body mass index reduction implementing behavior modification and/or diet change and increased physical activity. The strategy as well as the personnel involved, the duration and the means used vary. Biosensors: A biosensor is a device, based on a biological sensing element, whose output signal is proportional to a concentration of a chemical or chemicals. Childhood Obesity: Childhood obesity: Increased body weight associated with increased adiposity which is best reflected by increased body mass index. Body mass index cut offs points differ according to age and sex. There is no universal agreement regarding the cut offs. Cloud: Cloud computing refers to a model which permits users to access servers and storage resources through internet using a web browser or a client code. Comorbidities: Childhood obesity is potentially associated with a number of complications that include insulin resistance, glucose intolerance, diabetes mellitus, fatty liver disease, dyslipidemia, hypertension, metabolic syndrome, sleep apnea, depression. Internet of Things (IoT): Is a concept which describes a network of “things” (sensors, electronic devices, other hardware, etc) and their capability to exchange data.

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Lifestyle: The sets of daily habits that include meal frequency and composition, exercise frequency and duration, hours of sleep, hours of sedentary activities, hours of physical activity mainly walking. Sensors: A sensor is a device which can detect an input from its physical environment, in our case from the human body. The sensor output is an electronic signal which can be transmitted and displayed. Smartphones: A smartphone is a small size device, which can be used as cellphone and personal computer having the advantage of mobility. The disadvantage of the small screen has been overcome by its high resolution screen.

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MoBip Project:

To Raise Awareness about Bipolar Disorder through an 3D Pop-Up Book Hakan Altinpulluk Anadolu University, Turkey Gulsun Eby Anadolu University, Turkey

ABSTRACT This study aims to set forth a framework for how the design and scenarios should be handled, and how mHealth ecosystem and Universal Design principles should be used in the designing of an “interactive augmented reality 3-D pop-up book” that can be viewed on mobile devices. This book, which will pursue the goal of increasing university college professors’ awareness about students with bipolar disorder, will be the first mHealth study handled in this scope in the literature. In the background section of the study, the authors first elaborate on the rapid advancement of mobile devices, their proliferation and their reflections on mHealth projects in the healthcare sector. Then the authors include mHealth-related applications that raise awareness, the authors analyze the importance of social awareness about mental health, and finally, the authors get to the core of bipolar disorder and present the current situation. Within the scope of this study, the authors construct a theoretical framework that will assume the guiding role in the completion of an interactive 3-D pop-up book.

INTRODUCTION This study aims to develop a framework for designing an interactive augmented reality 3-D pop-up book and increase the awareness of university teaching staff about learners with bipolar disorder. With the help of this book, it will be possible in the future to act out scenarios that are very hard to actualize in real life. In addition, the spatial adequacy of the college professors who use this book rather than interacting with a 2-dimensional printed book will be positively enhanced. DOI: 10.4018/978-1-4666-9861-1.ch008

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 MoBip Project

In the book which is to be designed, scenarios will be formed corresponding to the daily problems of people with bipolar disorder and will be acted out in an augmented reality pop-up book which will be presented to university teaching staff through a digital storytelling technique. Individuals with bipolar disorder who spend significant amounts of time at educational institutions tend to have frequent interactions with the professors there. However, due to a lack of empathy on the part of these academicians, the bipolar individuals often become isolated and feel excluded. Mood swings are a large proportion of the lives of persons with bipolar disorder, their quality of life should be enhanced, and they should be provided with equal opportunities. Support for these individuals, which can be quite productive when steered in the right direction, is of utmost importance to reintegrate them into society. A review of the literature shows that although there are many mobile applications for people with bipolar disorder, there is no study involving augmented reality interaction, edutainment, and scenarios enhanced with usability to increase awareness about these individuals. Among the analyzed mHealth projects that target increased social awareness, there is no augmented-reality-supported project that seeks to reintegrate bipolar individuals into society. Despite the development of various mobile applications for people with bipolar disorder and efforts to raise social awareness on special days in recent years, it is apparent that these efforts are inadequate. This study aims to alleviate this deficiency in the literature, and a novel study will be conducted. The seven principles of Universal Design and the “health,” “technology” and “finance” components of the mHealth ecosystem will be combined to construct the theoretical matrix. Scenarios prepared with ZooBurst software will be acted out to create interactive AR 3-D pop-up books; these books will be implemented to academicians at the universities. In the final stage, the efficiency of the study will be evaluated.

BACKGROUND Development of Mobile Technologies The revolutionary developments in information and telecommunication technologies have a great impact on people’s lives. Mobile technology has transformed many aspects of life. Cellular mobile phone technology is regarded as one of the fastest adopted technologies in the history of humanity (Brian & Ben-Zeev, 2014). According to International Telecommunication Union [ITU] (2012), in more than 100 countries, mobile cellular penetration has already exceeded 100%. This simply shows that mobile cellular subscribers have outnumbered the people living in those countries. Likewise, according to an ITU (2014) report, the number of mobile broadband subscriptions is 2.3 billion, whereas the number of mobile cellular subscriptions is 7 billion. According to Cisco (2012), the number of mobile devices is expected to exceed the world population and reach 10 billion, and there will be 1.4 mobile devices per person in 2016. These data show that use of mobile devices will increase even further in the future and that they will be an indispensable part of daily life. In addition to the SMS, GPRS and Bluetooth features of mobile devices (World Health Organization [WHO], 2011) their functionality has increased with new added features such as GPS, accelerometer, and many different sensors; their mobile data speed has increased with 3G and 4G; touch screen technologies have advanced; processor capacity and memory have increased. Also, mobile software and applications

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have improved in functionality—all these developments have caused the proliferation of smart phones (Greenspun & Coughlin, 2012). Being independent of time and space, these ubiquitous technologies allow individuals to obtain information, to communicate with others, to collaborate, to play games and to get connected to social networks. They have in many ways made people’s lives easier. From among the super-users subcategory, people called “smartphonatics” have emerged; these relatively young individuals use smartphones in every aspect of life, from shopping to payments (Greenspun & Coughlin, 2012; Shevlin, 2012). Mobile devices and wireless systems do have limitations, such as battery and storage capacity, broadcast constraints, interference, disconnection, limited bandwidth and network delays (Silva et al., 2013). The mobile world (mWorld) is increasingly becoming smaller, faster, and smarter (Greenspun & Coughlin, 2012). It is clear that mobile technologies are effectively used in every aspect of life and lots of different industries. Although there are applications in many different fields, such as online learning, mobile banking services and mobile agricultural support to farmers, adaptation of mobile applications’ use in the healthcare sector has been progressing quite slowly, particularly in emerging countries (WHO, 2011). At this point, the term mobile health (mHealth) has been set forth as a sub-component of electronic health (eHealth) and adopts an approach that targets integration of mobile technologies and getting better health outcomes.

Definition of mHealth and Its Features Mobile technologies provide simultaneous multi-functionality and communication that is independent of time and space, and they supply information. Low income, hard-to-reach populations especially tend to benefit from this worldwide growth. Especially in these societies, it is observed that mobile technologies have the potential to overcome traditional barriers in the delivery of healthcare services and can provide new opportunities, especially from the perspectives of access, quality, time and resources (Schweitzer & Synowiec, 2012). Vishwanath et al. (2012) takes into account the fact that the needs of developed countries are different from those of developing countries in healthcare delivery, and classifies them as follows: For developed countries it lists increased healthcare spending, pressures experienced in healthcare delivery, and elderly care for the aging population. For developing countries, it mentions the weakness of the healthcare services infrastructure and problems in finding trained healthcare personnel. The McKinsey and GSMA (2010) report, however, underlines that worldwide healthcare spending costs are increasing day by day. The report lists the reasons for this as the increasing number of elderly people, the cost of medical procedures, resource limitations, increased patient consciousness and their demands for patient-centric services. These problems experienced in the field of healthcare bring forth needs, and various initiatives and approaches are developed to meet those needs. The mHealth term, which encompasses benefiting from mobile technologies regarding the problems experienced in the field of healthcare, particularly in low-income countries, has been set forth. We see that there are many definitions of mHealth. According to Hamel et al. (2014) mHealth is defined as the use of portable devices such as phones, PDAs or tablets for medical purposes such as diagnosis, treatment, and general healthcare support and well-being. According to another definition, outside of formal and traditional clinical environments, mHealth is the use of personal wireless communication tools such as smartphones, smart watches, wear-

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able wireless equipment with sensors, and point-of-care devices (Samples, Ni & Shaw, 2014). mHealth is also referred to as “wireless telemedicine” (Istepanian & Lacal, 2013). Although mHealth is regarded as an intersection of technology and healthcare services (Greenspun & Coughlin, 2012), it should not be considered only as technology integrated with healthcare, but perceived as an approach to find new and more efficient ways to manage healthcare data (The World Bank, 2012). Despite the fact that access to healthcare services is still limited in many countries, access to mobile network services and mobile devices has been continuously increasing day by day (Vishwanath et al., 2012). Moreover, this situation makes the use of these devices in healthcare inevitable. Network-connected mobile devices enable physicians and nurses to follow their patients and better manage medical conditions. They also allow for improvement of healthcare outputs, decreasing medical errors and expanding the access to healthcare services (Hamel et al., 2014). At this juncture, particularly doctors’ interest in mobile technologies plays a very important role. Research shows that doctors are among the professional groups that adopt new technologies fastest. In the use of mobile technologies, and particularly in their quick adoption of smartphones and tablet computers, medical professionals are among the forerunners. To illustrate this point, 81% of doctors in the US use smartphones (Dolan, 2011; Ernst Young, 2012). In addition, it is observed that doctors adopt smart mobile devices approximately twice as much as members of the general population (Turisco & Garzone, 2013). In addition to providing opportunities to doctors and other healthcare professionals, mHealth provides patients with information about their health and makes management of the process easier. Thus, it allows for the patient to spend less time in the hospital or in visiting the doctor, decreasing the costs accordingly (Kotz, 2011). Users/patients use mHealth applications for fitness, health content, self-management, and social networking purposes (Turisco & Garzone, 2013). This potential of mHealth increases the transformation to citizen-centric social healthcare implementations (WHO, 2011). Particularly in the implementation of healthcare services in developing countries, a transformation is occurring, from traditional physician-centric implementations that take place in clinics to patient-centric implementations (Taga et al., 2011; mHealth Alliance and Stop TB Partnership, 2012). While services were being provided to specific individuals of specific societies in the past, now they are provided to a wider population, including doctors, healthcare professionals and patients (Curioso & Mechael, 2010). As of the first quarter of 2014, in the two prominent mobile application stores, Android and IOS, there are more than 100,000 mHealth applications, and a majority of them are fitness applications (30.9%) (Research2guidance, 2014). In today’s world, it is apparent that users of healthcare services are more knowledgeable, conscious, and tech-savvy, and their expectations from healthcare services are higher (Ernst Young, 2012). This situation is reflected in the data of some research reports. According to a study conducted in 2013, in the past five years, mHealth applications were downloaded to 50% of 3.4 billion smartphones and tablets that had access to mobile applications (Research2guidance, 2013). While smartphones rank first among the devices using mHealth applications (75%), experts mention that in the next five years wearable technologies (smart watches, smart eyeglasses) will have high potential (Research2guidance, 2014). Mobile technologies will be effective in chronic diseases, elderly care, pregnancy, reminding patients to take their pills on time, rural places where healthcare services are not adequately delivered, improving healthcare services and increasing efficiency (West, 2012). When considered within this context, mHealth presents cheap, fast and simple solutions (Akter, Ambra and Ray, 2013) and provides “equality of opportunity” in delivering healthcare services (Istepanian & Lacal, 2003). 150

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The factors having an effect on the development of mHealth are listed as follows (mHealth Alliance and Stop TB Partnership, 2012): • • • • •

It provides low-cost, high-performance service It provides society-based technology, which is accessible from anywhere and at any time It provides on-time and high-quality healthcare services It raises awareness among people about early diagnosis and disease prevention It compensates for the deficiency in the number of healthcare personnel

Still, mHealth cannot be used effectively enough, particularly in underdeveloped and developing countries. Developed countries have strong infrastructures and foundations in both healthcare services and technology fields. Moreover, mobile devices such as smartphones and tablet computers have been quickly adopted by those societies. As a result, healthcare service providers have made great progress in the use of mHealth applications (Taga et al., 2011). Access to mobile devices and networks has been increasing steadily not only in developed countries but also in developing countries (McKinsey & GSMA, 2010). However, developing countries are still behind developed countries in penetration rates. According to the WHO (2011) report, the usage rate of mHealth in high-income countries in Southeast Asia, America and Europe is high, whereas use of mHealth applications in low-income countries in the Western Pacific and Africa is low. The United Nations and the World Health Organization are the two institutions that particularly noticed the great potential of using mHealth in low-income countries, which experience great trouble in accessing healthcare services (WHO, 2011). One of the many objectives that came out of the United Nations meeting in 2011 was developing action plans to increase health literacy and awareness and targeting improvement of healthcare outputs (Miron-Shatz & Ratzan, 2011; United Nations, 2011). Likewise, it was mentioned that in order to reach United Nations Millennium Development Goals, which include healthcare and development targets, mHealth can be instrumental in preventing child deaths, improving mothers’ health, and in the fight against HIV/AIDS, tuberculosis, malaria, and epidemics and diseases stemming from poverty (Sloninsky & Mechael, 2008; Tamrat & Kachnowski, 2012; United Nations, 2015; WHO, 2011).. When we consider that mHealth penetration will increase in developed countries and will be on the rise in underdeveloped and developing countries in the coming years, it is easy to envision that mHealth will become an ever-increasing market. By the end of 2017, total mHealth revenues are expected to reach $26 billion (Research2guidance, 2014). It is safe to assume that many components, from government policies to application developers, will be influential in this process. A worldwide problem particularly experienced in developed countries is that an aging population with its accompanying diseases increases the healthcare service costs and constitutes a heavy socioeconomic burden (Hung, 2014). While the proportion of people aged 60 and over was 11.5% in 2012, that number is expected to rise to 22% by 2050 (Kwan, 2013; UNFPA and Help Age International, 2012). To prevent the problems emerging with an increasing average age, mHealth’s unique attributes such as accessibility, personalized solutions, immediacy, provision of location-based information, interactivity and mobility (Akter, Ambra & Ray, 2013) should be benefited from, and the development of “best solutions” should be targeted. A one-size-fits-all approach does not accord with mHealth and does not constitute a solution for mHealth (Taga et al., 2011). At this point, personalized and adaptable mHealth solutions are needed. There have been various studies proposing classifications of mHealth services. According to the Vishwanath et al. (2012) report, mHealth services can be classified into two broad fields: solutions 151

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across the patient pathway and healthcare systems strengthening. While the solutions across the patient pathway are mentioned as wellness, prevention, diagnosis, treatment, monitoring and entailing direct touch-points with patients, the healthcare system strengthening implementations are described as emergency response, healthcare practitioner support, healthcare surveillance and healthcare administration. According to the WHO (2011) report, however, mHealth services are divided into 14 categories: • • • • • • • • • • • • • •

Health call centers Emergency toll-free telephone services Managing emergencies and disasters Mobile telemedicine Appointment reminders Community mobilization and health promotion Treatment compliance Mobile patient records Information access Patient monitoring Health surveys and data collection Surveillance Health awareness raising Decision support systems

According to the same report, the most frequently encountered mHealth initiatives are ranked as follows: health call centers (59%), emergency toll-free telephone services (55%), managing emergencies and disasters (54%), mobile telemedicine (49%). The least implemented mHealth applications are ranked as follows: surveillance, raising public awareness and decision support systems (WHO, 2011). Vital Wave Consulting (2009) ranks the mHealth applications that play key roles in developing countries as follows: • • • • • •

Education and awareness Remote data collection Remote monitoring Communication and training for healthcare workers Disease and epidemic outbreak tracking Diagnostic and treatment support

Despite having many advantages, there are also some challenges experienced in mHealth implementations. A worldwide ranking of the barriers confronted in mHealth implementation is as follows (WHO, 2011): • • • • •

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• • • •

Operating costs Demand Lack of technical expertise Infrastructure problems

However, it can be said that with the advancements to come in mobile technologies and the healthcare field in future years, these challenges will be minimized.

mHealth and Awareness-Raising Implementations Raising social awareness through mHealth implementations seems to be one of the least implemented categories (23%) and these are usually awareness-raising implementations regarding diseases like HIV/ AIDS, which can cause social exclusion (WHO, 2011). Basic awareness-raising subjects rank as women’s health, drug and alcohol addiction, smoking cessation and HIV/AIDS (WHO, 2011). Awareness-raising implementations have the aim of increasing people’s medical knowledge and teaching them about diseases like HIV/AIDS using games and quizzes. While these programs may be games that can be downloaded to telephones, they may also be text messages making up stories to raise medical awareness (WHO, 2011). One of the most frequently used mHealth functions used to raise awareness is SMS. Because of its low cost, ability to reach long distances and wide popularity, SMS has some advantages over other communication environments (Vital Wave Consulting, 2009). In many studies, it is emphasized that, particularly in education and awareness-based projects, SMS is more influential than radio and TV campaigns. While it can be used in raising awareness about diseases like HIV/AIDS which are considered taboo in most societies, it is also helpful in providing information to individuals with insufficient medical knowledge in distant rural regions where clinics and workers are fewer in number (Vital Wave Consulting, 2009). Although SMS is one of the most frequently implemented mHealth functions for awareness, it has many limitations. Among them are its length being limited to 160 characters, language barriers, illiteracy and technical support shortcomings in rural regions (WHO, 2011). According to the WHO report, only 15 countries have been implementing a program related to raising awareness (WHO, 2011). Although awareness-raising initiatives are implemented at a higher rate in high-income countries (42%) (WHO, 2011) many projects having the aim of raising awareness and providing general health-related education have been put into use by underdeveloped or developing countries. One of these projects, The Mobile Alliance for Maternal Action (MAMA) project, was aiming to send sensible and adaptable medical information messages to expectant mothers through mobile telephones. The pilot implementation of this program was conducted in Bangladesh, India and South Africa, three countries where mobile phone use was widespread and maternal-infant mortality were at high rates. MAMA has gained support from local governments, mobile operators and other non-governmental organizations in these countries and is expected to reach more expectant mothers (mHealth Alliance and Stop TB Partnership, 2012). In Bangladesh, with the project initiated by the Ministry of Health and Family Welfare of Bangladesh in 2007, the objective was to enhance the general medical knowledge of the community and raise awareness about health issues. Within the scope of this health campaign, SMS messages in different health matters were sent to every phone number in the country (WHO, 2011).

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In addition to the above, mHealth projects have been implemented aiming to raise awareness regarding HIV/AIDS, particularly in countries where rates were high. Some of these educational and awarenessbased projects are listed below (Vital Wave Consulting, 2009): • • • • • •

Freedom HIV/AIDS Project, India Learning about Living, Nigeria HIV/AIDS Video Distribution by Mobile Phone, Georgia HIV Confidant, South Africa Project Masiluleke, South Africa Text to Change (TTC) – HIV Prevention through SMS Quiz, Uganda

Mental Health and Awareness-Raising Implementations It can be argued that, except for diseases such as HIV/AIDS which are considered taboo in society and cause stigmatization of patients, preventing them from interacting with society, mental health related information provision, consciousness raising and awareness raising projects are inadequate. Because of today’s lifestyles, and the fact that the average age is steadily on the rise and that as age increases cognitive abilities decrease, susceptibility to diseases based on mental problems will increase. Therefore, because patient care costs will inevitably rise, use of mHealth implementations in early diagnosis, prevention and treatment of mental problems becomes much more important (Kwan, 2013). Treatment of individuals with mental illnesses sometimes cannot be achieved due to inadequate awareness, stigmatization and exclusion from the community. While mHealth applications are used particularly in the prevention of mental illnesses such as depression, epilepsy, and schizophrenia, they are used for raising social awareness as well (Kwan, 2013). The motto in the WHO report was “No health without mental health.” (Herrman et al., 2005) The necessity of raising awareness about mental illnesses bears significant importance (Kwan, 2013). Projects about mental illnesses have been put into practice in many countries. Some examples of these projects are the Companion-SMS Project fighting depression in the US, the M-Kifafa Project against epilepsy, the Mobile Assessment and Treatment of Schizophrenia (MATS) Program against schizophrenia in Kenya and the Schizophrenia Research Foundation (SCARF) in India. As an example of an educationand-awareness-purposed program, the MINDS Foundation in India put into use a three-stage process in rural regions of the country. With this program, they aimed to raise awareness about supporting people with mental illnesses and reintegrating them into society (Kwan, 2013). The Australian Department of Health and Ageing organized campaigns that focused on sending text messages with the purpose of increasing mental health literacy, raising awareness and early diagnosis/treatment (Australian Department of Health and Ageing, 2009; Brian & Ben-Zeev, 2014).

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BIPOLAR DISORDER THROUGH A 3D POP-UP BOOK What is Bipolar Disorder? Mood disorders have plagued humanity for at least as long as recorded history, and likely from the beginning of human existence (Marohn, 2011). It was identified many hundreds of years ago, forgotten, and then rediscovered in the 20th century (Ferrier, MacMillan & Young, 2001). The DSM–IV–TR classifies mood disorders into two main types, unipolar and bipolar disorders. Even though this illnesses are both considered mood disorders, they are dissimilar (Otto et al., 2011). Bipolar disorder occurs between two poles, high and low, as opposed to unipolar disorder, where mood swings occur along only one polar-the lows (Miklowitz, 2011). Bipolar disorder is a chronic mental illness that is also called manic-depressive illness (National Institute of Mental Health [NIMH], 2012a). The prefix “bi”, which means two, refers to the two extreme moods of bipolar disorder. The ending “polar” refers to the opposite states, or poles, that describe the disease (Leonard & Jovinelli, 2012). No one knows the exact cause of bipolar disorder. Perhaps brain chemistry, genetics, and environments all seem to have a part (Peacock, 2000). It is characterized as a mood disorder within DSM-IV. The criteria specify first the mood episodes that can be included within a diagnosis of bipolar disorder. These are (1) major depressive episode, (2) manic episode, (3) mixed episode and (4) hypomanic episode (Lam, Jones & Hayward, 2010). Major depressive episode is a medical disorder that lasts at least two weeks and produces a combination of physical and emotional symptoms that make it very difficult to function in life (Otto et al., 2011). It is characterized by depressed mood or loss of interest or pleasure along with symptoms including changes in weight and sleep, problems with concentration and decision making, reduced energy, and either agitated or slowed psychomotor activation (Lam, Jones & Hayward, 2010). In contrast, a manic episode mood is required to be abnormally and persistently elevated, expansive or irritable for a period of at least a week (Lam, Jones & Hayward, 2010). It can cause enormous problems in daily functioning and often leads to serious problems with a person’s relationships or work functioning (Otto et al., 2011). A mixed episode is described as one in which symptom criteria for both manic and major depressive episodes. A hypomanic episode has the same symptoms as those of manic episode delusions or hallucinations may not be present (Lam, Jones & Hayward, 2010). It does not cause problems to the same extent as mania, and for some patients hypomania can be a pleasant state of good humor and high productivity (Otto et al., 2011). The International Classification of Diseases (ICD) is the standard diagnostic tool for epidemiology, health management and clinical purposes (WHO, 2015). Bipolar disorder are specified under the heading -Mood (affective) disorders- between F30-F39 in ICD-10. This section contains, manic episode, bipolar affective disorder, depressive episode, recurrent depressive disorder, persistent mood (affective) disorders, other mood (affective) disorders, unspecified mood (affective) disorder (WHO, 1993). Sources state that (American Psychiatric Association, 1994; Otto et al., 2011) there are four subtypes of bipolar disorder: Bipolar I, Bipolar II, Cyclothymia, and Bipolar NOS (not otherwise specified). Bipolar I is the most serious form. High and low moods are clearly defined, and mood swings tend to be dramatic (Peacock, 2000). It is perhaps easiest to diagnose and the most predictable in terms of its future course (Miklowitz, 2011).

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In bipolar II, episodes of major depression alternate with episodes of hypomania, a milder from of mania (Peacock, 2000). When the patients become hypomanic, they experience euphoria, irritability, flight of ideas, or other sypmtoms, but do not become psychotic and do not need to be hospitalized (Miklowitz, 2011). In cyclothymia, episodes of mild depression alternate with episodes of hypomania. In all categories of bipolar disorder, episodes of mania and depression vary in pattern, length, and frequency. Cycling refers to the length of time between episodes (Peacock, 2000). Despite various studies showing that the lifelong prevalence of bipolar disorder is between 0.5% and 1.5%, recent clinical and epidemiological studies show that bipolar disorders affect approximately 6% of the population (Kessler, 2006; Ozalmete, 2009). Without exception, bipolar disorder affects every aspect of a person’s life, including his family relationships, marriage, and professional life, causing severe and quite permanent malfunctions and even leading to suicides (Saka et al., 2001; NIMH, 2012). Bipolar disorder usually comes to the surface near the end of youth or in the early stages of adulthood; at least half of all cases begin before the age of 25 (Kessler et al., 2005; NIMH, 2012). Some medications are used to treat bipolar disorder: Mood stabilizers, antidepressants, antipsychotics, antianxiety (anxiolytics) (Otto et al., 2011), lithium, anticonvulsants (Yatham, Kutcher & Kusumakar, 2002). These medications are the main tools for controlling bipolar disorder. Other medication tools also may be used to treat additional symptoms. (Otto et al., 2011). There are mHealth practices, too, regarding bipolar disorder, with various treatment approaches. Mobile applications are widely used. At present, some of the applications on Android and IOS platforms are used in the cure and treatment of people with bipolar disorder, and they provide various conveniences and opportunities for more comfortable lives for these individuals. Likewise, other applications aim to raise awareness about persons with bipolar disorder—for example, iMoodJournal, eMoods, T2 Mood Tracker, Bipolar Bear, Moody Me, iMind & Mood, and Mood Watch (Slabodkin, 2013; Pietrangelo, Rosecrans and Hirsch, 2014). Awareness-raising projects about bipolar disorder have begun to accelerate in recent years. March 30, the birthday of world-famous painter Vincent Van Gogh, whose pathology was analyzed and diagnosed with bipolar disorder, will be celebrated as World Bipolar Day starting in 2015. The vision of World Bipolar Day is to raise awareness about bipolar disorder across the world and eliminate social stigmatization. With international cooperation, the objective is to educate and inform everyone in the world and raise sensitivity for the illness (International Society for Bipolar Disorders, 2015). Like diabetes, heart conditions, and many other physical conditions, bipolar disorder is a lifelong illness that requires careful management with medication and other treatments, and when it is treated effectively, the patient can continue a healthy, productive life (Van Dijk, 2009). Accurate diagnosis, correct treatment, correct support and a correct environment are the most important components in this war.

3-D Pop-Up Book with Augmented Reality An augmented reality (AR) is a technological system that supplements the real world with virtual (computer-generated) objects that appear to coexist in the same space as the real world (Azuma et al., 2001). Augmented reality has three fundamental characteristics (Azuma, 1997): • •

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Combines real and virtual Interactive in real time

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Figure 1. Reality-Virtuality Continuum (Milgram and Kishino, 1994)

•

Registered in 3-D

Milgram and Kishino (1994) define and classify four types of environments regarding reality. They place the real environment that includes the physical world to the left of the continuum (Figure 1). On the extreme opposite side, they place the virtual environment (virtual reality) which is completely made up of artificial and synthetic components. Moreover, they place augmented reality and augmented virtuality between them. “Augmented Reality” means making virtual and digital enrichments to physical reality. “Augmented Virtuality,” on the other hand, is actualized by adding elements to virtual environments from real life. On top of Milgram and Kishino’s reality-virtuality continuum is the “Mixed Reality,” where elements of real and virtual environments are present. It is also an umbrella concept covering augmented reality and augmented virtuality. Within this context, it can be stated that augmented reality involves more of real environments than of virtual environments. Being different from augmented reality, virtual reality is completely realized in digital and virtual environments. AR is a collection of technological applications which is used in many diverse fields, from medicine to education and from games to museums. Augmented reality is separated into different categories in different studies. In their categorizations, Azuma (1997) mentions optical displays and video displays, Cheng and Tsai (2013) and Dunleavy (2014) use vision-based and position-based categorization, and Johnson et al. (2010) use marker-based and markerless AR categorizations. Frequent use of mobile smartphones and tablet computers in recent years has attracted the attention of augmented reality software developers, as well. Presently available smartphones and tablets have faster processors, more advanced graphical hardware and wider touch screen displays. Likewise, the addition of features such as GPS, compass, and accelerometer inspire interest in the usability of augmented reality in these devices (Billinghurst & Duenser, 2012). With the proliferation and increased variety of such mobile applications, it can be said that augmented reality applications are switching from desktop computer technologies or head-mounted displays to mobile applications. This inclination toward mobile applications will likely increase the opportunities for the use of augmented reality. In traditional learning processes, augmented reality has been finding usage areas in the following (Yuen, Yaoyuneyong & Johnson, 2011): • • • • •

AR Books AR Gaming Discovery-based Learning Skills Training Object Modeling

Augmented reality books are one of the best examples of augmented reality technology being used in educational processes. Using text, audio, graphics, animations and videos and facilitating interaction

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adds edutainment to learning processes. It also improves learners’ motivation, attracts their attention and gives a new point of view to printed books (Vate-U-Lan, 2011). The biggest advantage of augmented reality books over printed books is interaction. By the help AR book, users can develop different interactions with the virtual contents (Billinghurst & Dunser, 2012). It seems that augmented reality books will be the most important building block of the bridge between the digital and physical worlds (Yuen, Yaoyuneyong & Johnson, 2011) and they are expected to have even wider use in the future. One of the most attention-grabbing examples of augmented reality books is Magic Book. With this book, in which a vision-based tracking model is used, augmented reality scenes were created, and virtual additions were made onto the physical real book pages. Also, the interface supports multi-scale collaboration and allows for many users to have experiences from egocentric and exocentric perspectives (Billinghurst, Kato & Poupyrev, 2001). The AR books that have the aim of supporting the interaction deficiency in physical books with virtual and three-dimensional elements and adding value to printed books have many examples in academic and commercial domains. Some of those books that give a different point of view to commercial books, particularly for children, are The Future is Wild, Interactive Alien Book, Popar Books, Wonderbook, and boookApp. When we look at the academic studies, other than the Magic Book example of Billinghurst, Kato and Poupyrev (2001), other noteworthy studies are the AR pop-up book by Mahadzir and Phung (2013) with which they aim to increase motivation in English language education, Augmented Reality 3D Pop-up Children Book: Instructional Design for Hybrid Learning by Vate-U-Lan (2011) and An Augmented Reality 3D Pop-Up Book: The Development of a Multimedia Project for English Language Teaching by Vate-U-Lan (2012), which were AR 3-D Pop-up Book works designed by ZooBurst, and Virtual Pop-Up Book based on Augmented Reality by Taketa, Hayashi, Kato, and Noshida (2007).

The Theoretical Framework of the Study Research indicates that patients with bipolar disorder have a decreased life expectancy, with this being specially pronounced in younger patients (McDermid, 2015). More children and teenagers are being diagnosed with bipolar disorder (Marohn, 2011). The average age of diagnosis for bipolar used to be 32 years old but in the last decade this has dropped strikingly to an average of 21 years old, and is likely to go even lower (BipolarUK, 2013). People between the ages of 15–25 have the highest risk of developing this disorder (Otto et al., 2011). Symptoms of bipolar disorder often occur in the late teens or early adult years (NIMH, 2012b). There are some differences between young individuals and adults regarding the symptoms of disease. Young people with bipolar disorder may have symptoms more often and switch moods more frequently than adults with the illness (NIMH, 2011). Moreover, some young individuals with bipolar disorder try to hurt themselves or attempt suicide (NIMH, 2011). The solution of this problem depends on the true diagnosis. However, young individuals may be less likely to seek or accept help and may fail to engage with mental health services due to not believing there is a problem (Macneil, 2009). In the age of youth, individuals are more independent and self-confident. For this reason, bipolar disorder is difficult to diagnose in adolescence period. Youth with bipolar disorder may also experience difficulty with peer relationships, including social isolation, teasing, and frequent conflict with others (Otto et al., 2011).

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No blood tests or brain scans can diagnose bipolar disorder and, there is no cure for it, but it can be treated effectively over the long term (NIMH, 2012b). Some treatment methods are available such as medications, psychotherapy, school supports, planning around college, and helpful parenting strategies (Otto et al., 2011). More particularly, appropriate support at school or college can have a major positive impact. Making the college aware of how bipolar, and the possible side effects of medication, can affect performance in education will help university teaching staff to offer support and increase their understanding of an area they may not have much experience with (BipolarUK, 2013). The objective of this study is to design an interactive 3D pop-up book for college professors to gain awareness about the learners with bipolar disorder. This disease is observed at higher rates in the community. Many students are also fighting with bipolar disorder at the college. Due to the problem of empathy, the students may become excluded and isolated within the community. The quality of bipolar students’ life should be increased, and equal opportunities should be provided. Reintegration the bipolar students into the society is a great importance. With interactive 3D pop-up book, stigmatized bipolar learners are tried to be reintegrate into the society. In this book, the scenarios will be prepared in accordance with a bipolar students’ problems of daily life. Then, these scenarios will be vocalized, visualized, and animated with digital storytelling technique. They will be prepared with contributions from experts in the field such as physicians, nurses and also parents who have a child with bipolar disorder. With the help of the interactive 3D pop-up book to be prepared in this study the teaching staff will be able to: • • • • •

have basic information regarding syndromes and treatment of the disorder ensure that the treatment processes for bipolar learners will be more effective be more aware of and more knowledgeable about the effects and side effects of the medications that bipolar individuals take have a better idea about how they should behave to bipolar patients create opportunities through which bipolar learners can enhance their productivity and creativity.

In addition, it will be possible to develop measures that will prevent bipolar learners from harming themselves and people around them, as well as eliminating the high tendency for them to commit suicide. Hence teaching staff will gain awareness about learners with bipolar disorder, and will possess the knowledge needed to help make them useful and productive individuals within society. The interactive 3D pop-up book can be displayed via mobile devices. ZooBurst will be used at the stage of the preparation. The environment that will be used in this study is the tool that enables augmented reality visualization of the scenarios that are dubbed and acted out with “ZooBurst,” a digital storytelling technique. This software allows any computer-literate person to prepare a 3-D pop-up book and is available only in the App Store at present. ZooBurst has a free “Basic” version, but there are also “Premium” and “School License” versions of it for a cost. While ZooBurst books can be viewed on desktop and laptop computers, it is also possible to view them on iPads through a ZooBurst mobile application. Characters and props are arranged with over 10,000 free pictures and materials available in the database so that a 3-D world can be visualized. ZooBurst allows one to load voices and to create downloadable books. Also, ZooBurst enables sharing of created books in other websites or blogs. To enable visualization of books with augmented reality it is necessary to have a webcam, on desktop computers only. In order to view the book, visitors align

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the special symbol with the camera and enter the augmented reality world, and by introducing simple motions they can use the 3-D pop-up book interactively (ZooBurst, 2015). In the implementation stage, teaching staff will be determined with respect to certain conditions such as number of students with bipolar disorder that they teach, seniority in the profession, and willingness to take part in the study. When choosing the teaching staff, the ones who have students with bipolar disorder will be given priority. The teaching staff, who will be chosen on a volunteer basis, will either be provided with temporary technological support or will be able to view the application by means of their own devices. Although it is possible to use the books prepared by ZooBurst in different environments, teaching staff will be able to view the books in 3 dimensions via the camera of their computer, should they choose to do so. Since the objective of this study is to determine the components required for designing the book within a theoretical framework, the details of the implementation stage will be clarified later. The Universal Design principles and mHealth ecosystem will be utilized in the framework for the design of the book. To complete this study, various processes will be set to work. At the first stage, a theoretical matrix is constructed which will assume the role of a guide in the completion of the study. The theoretical framework that will shed light on future studies is formed by Universal Design principles and mHealth ecosystem components. These two approaches have been selected to be a guide in preparation of scenarios and stories, in the design of products and scenes, in financial expenses, in the technologies to be used and as a summary of all the work to be performed in the future. The most convenient framework for mHealth is the mHealth ecosystem outlined in the World Bank report. The “health”, “technology” and “finance” components, and government as an upper element, take a part in the ecosystem. With the formation of the ecosystem, the objective is to improve healthcare services by taking the mHealth-related stakeholders into consideration (Figure 2) (Qiang et al., 2012). In this study, a theoretical framework will be proposed by using Universal Design principles and an mHealth ecosystem. Universal Design is defined as the design of products and environments to be usable by all people, to the greatest extent possible, without the need for adaptation or specialized design (Story, 1998). When the Universal Design term was first used by Ron Mace (1991) in architecture, he underlined that it was not a new science or a style or a unique or unchanging way. It was an approach based on awareness of needs, which could be used not only by the disabled but by everybody, and that could be adaptable to different sectors (Mace, Hardy & Place, 1991). In 1997, a group of architects, designers and engineers from North Carolina State University came up with a series of wide-ranging design principles that included environmental planning, production and communication processes, and they also determined the seven principles of Universal Design (Connell et al., 1997). The Universal Design principles are as follows: 1. Equitable Use: The design should be usable and purchasable by individuals with very different skill levels, and equal means should be provided in this regard. 2. Flexibility in Use: The design should accommodate a wide range of individual preferences and abilities. 3. Simple and Intuitive Use: The design should be easy to understand, regardless of the user’s experience, knowledge, language skills, or current concentration level. 4. Perceptible Information: The design should communicate necessary information effectively to the user, regardless of ambient conditions or the user’s sensory abilities.

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Figure 2. mHealth Ecosystem (Qiang et al., 2012)

5. Tolerance for Error: The design should minimize hazards and the adverse consequences of accidental or unintended actions. 6. Low Physical Effort: The design should be used efficiently and comfortably and minimize fatigue. 7. Size and Space for Approach and Use: Appropriate size and space should be provided for approach, reach, manipulation, and use regardless of user’s body size, posture, or mobility. These principles provide a range of approaches to architects and designers to create accessible spaces and products (McGuire & Scoot, 2006) and they are adaptable to many different fields (Silver, Bourke & Strehorn, 1998; Elias, 2010; Elias, 2011). Within the scope of this study, these principles are crossed with the mHealth ecosystem to be a guide in the design of the AR 3D pop-up book (Table 1).

CONCLUSION It is observed that presently mobile technologies are being effectively used in many industries. Mobile devices play a fundamental role within the context of “universal accessibility” of information (Elias, 2011; Wellman, 2007). The use of mobile devices in the healthcare field and mHealth applications that target making healthcare services accessible have been gaining popularity. With the projects they develop, many countries have been looking for ways to deliver healthcare services indiscriminatingly to everywhere in their jurisdictions. Each country, and the mHealth projects that are targeted to respond to the challenges of that country have different characteristics and different needs.

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Table 1. Theoretical framework of the study Design of Interactive 3D Pop-Up Book Universal Design Principles

mHealth Ecosystem Finance

Health

Technology

Equitable Use

Use of application provides access to everybody

Provides equality of opportunity in access of college professors, physicians and patients (learners).

Design involves interaction that will be liked by all users

Flexibility in Use

Provides cost-effectiveness

Can be quickly adaptable to other projects related to mental health

Provides wide array of alternatives for different skills

Simple and Intuitive Use

Visuals and animations that will be used to enable easy understanding of scenarios do not cause a financial burden

Meets the needs of users’ senses and expectations

Incentive

Perceptible Information

Platforms on which the design will be developed are free of charge

Does not get affected by conditions in the environment

It involves the users with sensory limitations and includes techniques or interfaces to enable compatibility

Tolerance for Error

Reverting back from possible errors does not cause high financial costs

Clearly states the behaviors and design factors that can cause accidents and errors

Provides corrections and feedbacks for users’ individual and simple errors

Low Physical Effort

Be efficient

Environments in which scenarios will be displayed provide comfort in use

Easily materializes hardto-experience, abstract and dangerous concepts

Size and Space for Approach and Use

Can cheaply reach to even distant rural communities

Provides proper conditions regardless of user characteristics

Provides adequate space for auxiliary materials

In this study, we analyze the mHealth applications for people with bipolar disorder and draw the general theoretical framework of an interactive 3-D pop-up book to be prepared. Bipolar disorder is a psychological illness that is frequently encountered in the community and that causes individuals to lose connection with society. Despite the initiatives in recent years to raise awareness about people with bipolar disorder, it is obvious that they are not adequate. This study elaborates on the importance of college professors’ being aware of bipolar disorder patients who are students at the University. It then proposes a framework regarding the formation of a book by mHealth ecosystem and Universal Design principles. This framework constitutes a guiding perspective for preparing such a book; literature review does not reveal a previous attempt on the same matter. After this study, scenarios of the 3-D pop-up book will be prepared in line with the theoretical framework outlined in this study and with the contributions of field experts. Then it will be dubbed and completed with the interaction provided by augmented reality. The study then will be presented to university faculty staff, and its effectiveness will be determined and evaluated.

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KEY TERMS AND DEFINITIONS 3D Pop-Up Book: A type of book in which interactivity and 3 dimensional features are brought in to the printed book, which is enriched with augmented reality applications. Augmented Reality: The enrichment of real-life physical world objects and environments using artificial and virtual elements.

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Bipolar Disorder: A mental illness, known as manic depressive disorder, which creates mood swings in the sufferer. Edutainment: A word formed by combining the words “education” and “entertainment”, and which means “learning while entertaining”. Smartphonatics: A term used for consumers who do their banking transactions, shopping and all other payments via their smartphones. Telemedicine: The provision of medical information to healthcare personnel in an electronic environment using telecommunications technologies. Universal Design: The compilation of seven fundamental principles that first emerged in the field of architecture and that aim at designing products and environments which are accessible by everyone without any discrimination.

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Mobile Health Applications Assisting Patients with Chronic Diseases: Examples from Asthma Care Petre Iltchev Medical University of Lodz, Poland Andrzej Śliwczyński Medical University of Lodz, Poland Potr Szynkiewicz Prometriq Ltd., Poland Michał Marczak Medical University of Lodz, Poland

ABSTRACT This chapter analyzes the role of m-health applications supporting patients with chronic diseases (based on examples from asthma care). The purpose of the chapter is to describe the mobile health application development cycle. The chapter begins with a presentation of asthma as a chronic disease and its prevalence and costs for society, as a determinant of the role and place of m-health applications in chronic disease management. Subsequent sections analyze trends in the development of health care, information systems, and health care payment systems as components of the environment for the implementation of m-health applications. The chapter focuses on prerequisites for the introduction of this type of solutions, presents existing applications, and discusses how to define the key functionalities and benefits for patients, payers, and doctors. The financing cycle, barriers to implementation, and future trends are also addressed.

DOI: 10.4018/978-1-4666-9861-1.ch009

Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Mobile Health Applications Assisting Patients with Chronic Diseases

INTRODUCTION Based on data on morbidity, mortality, hospitalization, and social costs, one may select groups of chronic diseases for which mobile applications should be first developed to support patients, doctors, providers, and payers. The data quoted come from the World Health Organization, Organization for Economic Cooperation and Development, and, less often, from national sources. Assessments of asthma prevalence depend on the adopted criteria for clinical diagnosis (Masoli, Fabian, Holt, & Beasley, 2014). According to the report “Global Burden of Asthma” published by the Global Initiative for Asthma, 300 million people suffer from asthma around the world today (Masoli et al., 2014). In turn, the Global Asthma Report 2014 gives an estimate of 334 million patients (Global Asthma Network, 2014). It is thought that by 2025, the number of asthma patients will increase by 1/3 (Masoli et al., 2014). According to the Global Burden of Disease Study 2010, asthma ranks 34th among the leading causes of death (29th in developing countries and 60th in developed countries). Importantly, asthma-related mortality in children aged 1–4 years ranks 22nd (29th place in developed countries and 22nd in developing countries) and in persons over 70 years of age – 21st (17th in developing countries and 45th in developed countries). The above data delineate the target group of patients with asthma to be supported by mobile applications (in the case of children, the users will be their parents or caregivers). Morbidity and mortality vary greatly among countries. According to data from the European Lung White Book, mortality among adults with asthma in Europe, ranges from 8.7 per 100,000 in Portugal to 0.54 per 100,000 in the Netherlands (Gibson, 2013). Risk factors in patients with asthma include air pollution, smoking, and others. The patients’ health status is largely dependent on air pollution. A report by the European Environment Agency emphasizes that air quality in cities exerts a significant impact on the residents’ health. However, despite some improvements in air quality, air and noise pollution continue to cause serious health impacts, particularly in urban areas. In 2011, about 430 000 premature deaths in the EU were attributed to fine particulate matter (PM2.5)” (European Environment Agency, 2015, p. 12). Reports by environmental agencies and asthma studies show a growing need for air monitoring to prevent asthma attacks. The rising costs associated with this disease are a burden on health care systems, patients, their families, and governments. According to the US Centers for Disease Control and Prevention, “Asthma costs the United States $56 billion each year.” (Center for Disease Control and Prevention, 2012). One way of cutting down these costs is better management of the disease, including self-management by patients. This may reduce the frequency of life-threatening asthma attacks that are often associated with expensive hospitalization. According to David Van Sickle, president of the company Propeller: “Despite all we know about asthma as a disease and how to treat it, the majority of people with asthma are poorly controlled” (Lawrence, 2014). In the process of disease self-management, an important role may be played by mobile solutions assisting patients with asthma. The goal of this chapter is to analyze the development life cycle of m-health applications for supporting patients with chronic diseases on the example of asthma. This section presents the trends in health care, information technology, and health care system financing affecting the development of m-health applications, the prerequisites for their use in health care, the infrastructure facilitating their implementation, the objectives of mobile solutions for asthma care, the functionalities of selected m-health applications, the business model, and the financing and implementation of the application.

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BACKGROUND According to the World Health Organization, m-health is part of eHealth “supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants, and other wireless devices” (World Health Organization, 2011, p. 6). In turn, Zhenwei Qiang, Yamamichi, Hausman, Miller, and Altman in the paper “Mobile Applications for the Health Sector” define m-health as “any use of mobile technology to address healthcare challenges such as access, quality, affordability, matching to resources, and behavioral norms” (Zhenwei Qiang, Yamamichi, Hausman, Miller, & Altman, 2012, p. 15). m-Health applications are implemented through software used on computers and mobile devices (e.g., smartphones and tablets) and are concerned about the health of individual patients. Such solutions target selected groups of patients, e.g., asthmatics. m-Health applications may also support physicians, nurses, payers, insurers, and health care policy-makers. However, such functionalities are often not included in applications used by individual patients. (The terms application and solution are used interchangeably in this chapter.) The trends, rules, and conditions that define the development of mobile applications supporting patients with chronic diseases can be divided into three main groups: medical, IT, and financial (for payers such as the health fund and insurance companies). Please note that the following list should be adapted to the conditions of individual countries. Trends in health care facilitating the development of m-health are: • • • • • • • •

Patient-centric health care. Data-based health care (Fung, 2015; Lohr, 2015). Real-time patient monitoring. Replacement of direct interactions between patients and medical staff with telemedicine and remote diagnostics, where possible (Neville, 2015). Teaching patients self-management of chronic diseases. Personalized medicine, customized treatment, the Precision Medicine Initiative (Reardon, 2015). Entrusting patients with tasks that would have previously fallen on the clinical team (Wicklund, 2015). Development of cooperation platforms in the process of care provision (Wicklund, 2015).

Trends in the development of information technology that affect the prevalence and effectiveness of m-health solutions are: • • • • • •

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Development of devices such as the iPhone, Apple Watch, Google Glass, Application Development Environments, and Software Development Kits (Panettieri, 2015a). Encryption of communication and data stored on smartphones to meet the requirements of regulations such as Health Insurance Portability and Accountability Act. Software-as-a-service. Cloud computing. Data warehouses, data mining, big data. Internet of things (for example, an inhaler may be connected to a smartphone for monitoring and control (Lawrence, 2014)).

 Mobile Health Applications Assisting Patients with Chronic Diseases

One possible solution increasing the security of cloud computing is the storage and processing of depersonalized data in the cloud while storing personalized and sensitive data locally. Pay-for-performance and bundled payments are conducive to m-health technology trends in medical care payments. Prerequisites for the use of these payment models include the ability to measure direct medical costs associated with asthma and with preventing asthma attacks. There are also some trends that are common to information technology and medicine: • • • • • •

Electronic health records. Interoperability of electronic patient data by different physicians and health care providers. Health information exchange networks (World Health Organization, 2012, p. 18). Costs of hospitalization often many times exceed the costs of the development and use of mobile applications. Development of social networks to gather data on environmental quality, including air (Panettieri, 2015b), and then the use of big data for the purposes of public health care and the management of chronic conditions (Fung, Tse, & Fu, 2015). Technologies such as voice assistants (Microsoft Cortana, Apple Siri) are supporting the use of m-health applications and the management of chronic diseases including asthma.

Social networks collecting data about air pollution are especially helpful for patients in countries where such data are not widely available due to a lack of monitoring or other reasons (Kazmin, 2015). The integration of social networks with analytical tools can produce surprising results. Indeed, as Henri de Castries, president of the insurance company Axa, said in an interview “Big data ‘changes everything, it’s the equivalent of oil and electricity a century ago and printing five or six centuries ago’” (Stothard, 2015). The medical care objectives that can be achieved using mobile applications are derived from the reports of international organizations, associations, and foundations, such as the Global Initiative for Asthma. Medical goals can be divided into two groups, mainly focused on the health of individual chronic patients and their diseases. The first case concerns individual patient management while the other represents a public health perspective on diseases. The objectives of m-health solutions oriented towards individual patients include: • • • • • •

•

Increased ability to control and manage the disease by the patient to avoid life-threatening events, and reduce the number of costly hospitalizations and emergencies (ambulance calls). Greater patient involvement and motivation in disease management. Getting patients in the habit of taking medications according to schedule. Health checks and medication alerts. Monitoring the patient’s compliance with the physician’s recommendations, e.g., in terms of prescribed medications, physical activity, and lifestyle. Education tailored to patients in different age groups: children, adolescents, adults, and the elderly (as well as workers, parents of children with asthma, and caregivers of asthmatic patients) has a positive effect on the medical culture of the patients and improves the results of disease self-management. Implementation of coordinated and connected care.

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According to Simon Stevens, Chief Executive of the National Health Service of England “digital interaction will be the main way that people will interact with the health service” (Neville, 2015). From the point of view of solutions supporting patients with chronic diseases, the important steps include sending data to health care providers using m-health, analysis of those data by the medical staff, and delivering recommendations to the patients. In the traditional model of health care that process takes place mostly through doctors’ visits. Ideally, an expert system based on analysis of the patient’s historical data, environmental conditions, and location should be able to predict when the patient will be likely to need a medication (use an inhaler) to avoid a life-threatening asthma attack. Benefits to the public health care system from developing m-health solutions for chronic diseases include: • • • • • • • •

Decrease in disability-adjusted life years lost (DALYs) due to asthma. Improvement in the treatment of patients with chronic diseases, for example through a bundled payment method used in the care of patients with diabetes in the Netherlands (Struijs et al., 2010). Ensuring disease self-management in areas with few physicians per 1000 patients with a particular chronic disease, thus decreasing the need for doctor visits and the risk of asthma mismanagement, Mitigating the effects of the shortage of physicians – a paper published in the Annals of Family Medicine predicts that an additional 52,000 primary care physicians will be needed in the United States alone by 2025 (Petterson et al., 2012). Help with diagnosing new cases. Facilitating adequate long-term care and warning the patient early about the risk of an asthma attack. Better access to health care, better control of resources, including prescribed medications (Masoli et al., 2014). Reducing the cost of health care through the use of new technologies, including mobile solutions supporting patients with asthma – this is particularly important in view of the estimated 33% increase in the number of asthmatic patients by 2025 (Masoli et al., 2014).

Following a brief presentation of some of the health care goals that can be achieved using mobile technology, it is worth discussing the requirements on those applications. According to the “Global Burden of Asthma” report, mobile applications must be designed to handle increasingly younger patients (children) and the elderly (Masoli et al., 2014). Mobile applications for younger patients should involve learning through games (e.g., “Asthma for All”), while those for the elderly should take into account their limitations in terms of vision, hearing, upper limb mobility, etc. In determining further objectives to be met through the development of mobile applications supporting patients with chronic diseases, it would be helpful to organize a brainstorming session with the participation of medical staff, patients, and payers. The list of barriers preventing a reduction in the burden of asthma prepared by “Global Burden of Asthma” report could be a starting point for the identification and analysis of the possibilities of using mobile applications to support patients with asthma (Masoli et al., 2014). Therefore, we seek answers to questions such as: • •

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How can mobile applications reduce or even eliminate the barriers described in the “Global Burden of Asthma” report (Masoli et al., 2014, pp. 1-5)? How can mobile applications eliminate obstacles to better outcomes in chronic diseases?

 Mobile Health Applications Assisting Patients with Chronic Diseases

• •

How to improve the health status of patients with asthma using solutions such as m-health? How can the development of mobile applications for asthma care contribute to increasing the amount of data collected, new analyses, and detecting asthma patterns (Masoli et al., 2014, p. 1)?

Among the new trends in m-health use, models have been transferred from other sectors of the economy. An increase in the use of other applications for mobile devices will accelerate the adaptation of m-health solutions. Mobile technology can be a factor reducing health care costs, prioritizing prevention, and helping to eliminate barriers such as distance to the doctor and a lack of medical specialists in a given area. As asthma is not a top priority disease (ranking 25th) according to the “Global Burden of Asthma” report (Masoli et al., 2014, p. 12), the use of mobile solutions for this illness is not only desirable and can reduce patient treatment cost, but can also release health care resources for the treatment of more urgent diseases of the respiratory tract.

Infrastructure Supporting the Development of Mobile Solutions for Patients with Asthma The existence of the infrastructure and environment supporting the development of m-health applications is a prerequisite for the implementation of such innovations in health care. What is meant by infrastructure supporting the implementation of m-health applications for patients with asthma (as well as other chronic diseases) is the environment both outside and within the health care system. This infrastructure can be divided into: • • • • •

Legal framework: Regulations that govern the implementation of mobile solutions (such as the U.S. Food and Drug Administration in terms of approval of medical devices). IT infrastructure. Payer’s infrastructure. Social infrastructure: Development of digital culture in society. An environment supportive of innovation (Zhenwei Qiang et al., 2012, pp. 19-21).

Of paramount importance to the development of mobile applications supporting patients with chronic diseases are clear, transparent regulations stipulating the requirements the applications should meet to be approved for use (Gold, 2015). Without appropriate legislation, investors will be reluctant to finance projects in the field of m-health (Schulke, 2014). m-Health applications are not just about technology or medicine, but very often also law and ethics. For example, in the process of developing the concept of an application with the functionality of remote patient monitoring some of the important questions are: Does the application enable remote monitoring of the patient? Should such an application be turned on automatically or should it be up to the patient? Does the patient have to express in writing that he or she agrees to automatically turning the application on under certain conditions (in life-threatening situations)? In what situations should the application switch to automatic monitoring or alert emergency services? Applications that store user data and notes must comply with the regulations for the protection of sensitive personal data. Thus, the question is how this information is protected and how access to it should be authorized. The problem arises as to how to reconcile personal data protection regulations with the need to access personal data in life-threatening situations. Questions related to the sharing of medical records are:

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How to regulate issues related to accessing data by the physician responsible for the patient, emergency personnel, and researchers? Who is to decide about sharing patient data? Shared data can contribute to new studies based on big data (large data sets), such as analyses of the effectiveness of pharmaceutical products. Is the physician to act as a gatekeeper limiting access to the patient’s electronic medical records? Who should have access to the electronic health records (EHRs) of asthmatic patients? Information technology supporting m-health applications includes, amongst others, cloud storage and services and electronic health records. Basic problems concerning information technology and health care in the area of implementing m-health applications are how to integrate data from mobile applications in terms of processing the EHRs of asthmatic patients? It is also necessary to develop platforms that will make it possible to safely collect and analyze sensitive clinical data. Medical clusters around universities are a common type of infrastructure supporting innovation, including m-health applications. Social factors promoting m-health technologies are the following: • • • • •

Progressive digitization of all processes in society. Growing number of older people in the Organization for Economic Co-operation and Development countries, implying an increasing demand for medical services. Public payers are seeking to contain health care spending. The young generation is expecting new ways of interacting with medical personnel. Digital technologies are transforming not only traditional businesses, but also consumer behavior in terms of shopping habits, social networking, and entertainment.

According to a 2015 study conducted by Salesforce among 1,700 insured adults in the United States (“State of the Connected Patient”), “Americans primarily use antiquated methods to communicate with their doctors and manage their health. For example, less than 10% of those surveyed use the web, email or text to set up appointments”. Furthermore, only 21% access their medical records via the Internet. A study by Bitkom Research involving 1,000 German adults aged 65 years and more in the years 2010–2014 reported that the number of people searching the Internet for information related to health increased from 36% in 2010 to 68% in 2014. Nine percent of the study population used the Internet to communicate with their physicians (Insights, 2015). Financial models that focus on the responsibility of the health care provider and medical personnel for patient health are likely to increase patient–doctor communication. Examples of such models are accountable care organizations, bundled payments and pay for performance. Also, regional health policy-makers can facilitate the implementation and dissemination of m-health solutions by providing information on air quality in the region, preferably in real-time. We expect that these trends will be seen in developing countries as well. The main problem with the implementation of m-health applications is the identification of actors that can play the role of catalysts for change and support large-scale innovation in health care (GSMA, & A.T. Kearney, pp. 3-5). Without such an actor, one cannot expect widespread use of m-health. A good example is the Polish company Medicalgorithmics S. A., which is unknown in the Polish market to patients with cardiac diseases while having a presence in developed countries. In different countries, change in health care may be initiated by insurance companies, employers (staffing shortages due to an aging population force companies to invest in the health of their employees), retail companies, and organizations and associations of patients who appreciate the benefits of digital technology and are digital natives. 176

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Examples of organizations that can assist in the development and implementation of m-health solutions in Europe are the European Lung Foundation and the European Respiratory Society. The functions fulfilled by these organizations in the development of m-health applications may include: • • • • • • •

Participation in the development of requirements for mobile solutions supporting patients with selected chronic diseases. Evaluation of m-health projects. Providing information on m-health projects (e.g., distributing their web addresses) to national organizations and associations of patients. Participation in the process of project financing, for example through the dissemination of the project web address on crowdfunding web sites. Participation in parallel independent monitoring of risk factors, such as air pollution, based on social networks (India, 2015, 10). Forums for the exchange of experience in the use of mobile technology for asthma treatment. Promotion of patterns of m-health use, publication of criteria for mobile application selection, discussion of business models and practices, funding and using m-health applications.

The quickest way of overcoming the reluctance of medical professionals to use m-health and gaining their approval of this technology is the inclusion of m-health in the financial plans of the public payer. This is the first step towards mass deployment of this technology, with the next step being the training of medical personnel. It often happens that the medical consultant, financing organization, and the software developer are the same entity (as is the case with the application AsthmaPulse). However, the independence of the medical consultant and the financing organization from the software developer can increase the quality of solutions and costs. It is believed that the development of applications in one organization combining all the functions efficiently can deliver solution faster and at a lower cost. Best practices in the development of such applications call for active participation of physicians, nurses, patients, payers, and other organizations that can offer valuable contributions in defining requirements and commit to application development and testing (e.g., patients). Factors related to information and communication technology that may have an impact on the development of m-health applications include the cost of data transmission via cellular networks, the prices of sensors specific to particular diseases, safety regulations concerning the storage, transmission and processing of sensitive personal data, and the development and use of cloud computing in health care.

DESIGN, FINANCING, AND IMPLEMENTATION OF M-HEALTH APPLICATIONS FOR ASTHMA CARE The purpose of this section is to present an approach for defining and designing the functionality of mobile applications supporting patients with asthma. In the process of determining requirements for mobile solutions, the point of departure involves the perspectives of the physician, nurse, patient, and payer. It is important to show how mobile solutions can support asthma self-management by patients.

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Table 1. Example of the relationships between goals, processes, and functionalities of a mobile application Objectives \ Functions Patient Medical staff (doctor, nurse)

Goal

Process

Function

Disease self-management

Education

Case-based learning

Disease self-management

Education

Assessment

Disease self-management

Education

To prepare case study

Note

……. Source: Own work

Analysis of the Functionality of Mobile Applications Used by Patients with Asthma One can expect that mobile applications will be first available in Organization for Economic Co-operation and Development countries due to their high level of social and economic development and the fact that a particularly large proportion of their population suffers from asthma. After defining the objectives of mobile solutions for supporting patients with asthma, the next step is to determine concrete requirements. Therefore, the question arises as to how such requirements can be identified. In the process of defining requirements and functionalities of applications for asthma care, it would be helpful to create a table or matrix describing those functionalities. Such a table would offer a comparison of the goals, needs, and expectations of medical staff, patients, and payers concerning the planned functionalities (Table 1) (Martin, 1982). The functionalities of an application depend on what goals are to be met. The degree to which the functionalities reflect the desirable goals determines the extent to which the expectations of physicians, nurses, patients, and payers will be fulfilled. Methods of determining the functionalities of m-health applications can be divided into two groups: • •

Analysis of the functionalities and trends in existing applications; Analysis of the functionalities postulated by physicians, nurses, paramedics, payers, as well as patients and their families.

When defining the functionalities of m-health solutions, one should first analyze existing applications, and then possibly develop them to incorporate the functionalities postulated by the patients, physicians, and payers. Table 2 shows selected available mobile applications for patients with asthma, which are described on web page http://www.imedicalapps.com. Applications assisting patients with asthma can be divided according to the developer into: • • •

Those developed by foundations and associations; Those developed by pharmaceutical or medical manufacturers; Those developed by start-ups or IT companies, etc.

Analysis of the evolution of m-health applications is the basis for the development of a list of the main functional requirements for software supporting patients in the process of chronic disease man-

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Table 2. Functionalities of selected mobile applications assisting patients with asthma Application

Current air polution

airText

Yes, for Greater London, UK

AsthmaCoach

Asthma-Info

Air polution forecast Yes, for Greater London, UK

Yes

Education

Yes, for Switzerland

Yes, for Switzerland

Yes, for Switzerland

AsthmaPulse

Selfmonitoring

Health advice

Yes

Yes, for Switzerland

Reminders

Yes

Yes

Supported languages

Cost Free/ Payed

Android, Apple

English

Free

Yes

Apple, Web browser

English, French, German, Italian, etc.

Free

Yes

Android, Apple, Web browser

French, German, Italian

Free

Yes

Apple

English

Payed

Yes

Yes

Yes

Android, Apple, Web browser

English, French, German, Italian

Free

My Asthma

Yes

Yes

Yes

Android, Apple

English, Spanish

Free

My Asthma Log

Yes

Yes

Android

English

Free

Propeller Health

Yes

Yes

Android, Apple, Web browser

English, Spanish

Free

e-symptoms

Yes

Available on

Yes

Pollenvarsel

Pollen levels and dispersal in Norway

Android, Apple, Windows, Web browser

Norwegian

Payed

Sussex Air

Yes, for the county of Sussex, UK

Web browser

English

Free

Source: Own work based on m-health application from http://myhealthapps.net

agement. Subsequently, the list should be modified in the course of discussions with representatives of physicians, nurses, patients, and payers. Discussions or working sessions are the basis for deciding the requirements for mobile applications supporting asthmatic patients. Trends in “digital health” indicate possible directions of evolution of m-health functionalities for patients with chronic diseases (StartUpHealth, March, 25, 2015). These are: • • •

Supporting disease management (patient’s health status); Exchanging health information with health care providers, ensuring secure transmission, and processing of sensitive data (including identity and access management); Increasing patient involvement;

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•

Remote monitoring of the patient’s health status. Not every patient’s condition requires remote monitoring. As can be seen from Table 2 showing the functionalities of selected applications for asthma care, none of those solutions offers a possibility to remotely monitor the patient’s health; Using large data sets for disease prevention and better management of the patient’s health – important elements of the functionality of m-health applications include monitoring, collecting data, as well as transmitting and receiving information about risk factors. In the case of asthma, one of the main risk factors is air pollution (India, 2015, 10). The real-time analysis of asthmatic patients’ attacks may reveal areas with the most heavily polluted air. Information from other asthmatic patients about places or city districts with the highest air pollution (greatest number of asthma attacks) at a given time may allow patients to choose alternative routes with less air pollution and a lower likelihood of an asthma attack; Providing information about medical products that may cause adverse effects in asthmatic patients;

•

•

During discussions or workshops, the following questions should be addressed: • • • • • • • • • • • • •

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How can mobile solutions support patients with asthma? What functionalities are expected in mobile applications by patients? What is the role of disease self-management in asthma care? What is the nature of disease self-management in asthma? What functionalities can help patients with disease self-management? What are the specific needs of patients with chronic diseases, including asthma, in the case of mhealth applications? What kind of functionalities and improvements in disease management assistance are expected by patients, nurses, family doctors, specialist doctors, payers, and insurers from m-health applications? How to use mobile applications to enhance patient involvement in disease management, improve education, and achieve better discipline (e.g., in terms of diet)? What data are essential to chronic disease self-management? What data is the basis for doctors’ treatment decisions related to asthma? For example, air pollution is a factor with a major impact on asthma. What indicators describe such data? How can these data be shared and analyzed? How to integrate m-health in medical procedures, recommendations, care, and treatment? What development cycle is appropriate for application improvement? Which requirements can be implemented at a later stage of development? Who are the target users of the application? Patients of what age are to use the application? What is the preferred form of contact with the medical staff (Pennica, 2015)? How well can they use electronic devices (smartphones, tablets)? How to customize m-health technology for specific groups of patients with chronic diseases (by age or by disability, such as poor vision or hearing, or problems with using a touch screen or mouse)? What are the ease-of-use priorities of people experiencing various difficulties when using a computer, smartphone, or tablet? How can m-health applications be customized to the needs of each patient? It is rather obvious that the expectations of schoolchildren and college students will differ from those of older people.

 Mobile Health Applications Assisting Patients with Chronic Diseases

• •

Are there any mobile applications supporting child asthma care designed for use by children? What are the differences between applications adapted for children and those intended to assist adults? What is the preferred form of contact with the medical staff? For instance, 40% of respondents in the 2015 survey “State of the Connected Patient” said they did not receive any ongoing care recommendations from their physician (Salesforce, 2015). In the United States, the millennials pose new challenges in terms of communication with the medical staff (Salesforce, 2015). For 60% of them, the preferred form of contact involves new communication channels replacing traditional doctor visits. The possibility to arrange appointments using remote access to medical information, as well as preventive care and chronic disease management are the essential features of any application.

When designing m-health solutions as part of a platform of collaboration in the process of delivering care, one should not only focus on providers and medical staff, but also on the community of patients, their families, and supportive friends (Wicklund, 2015). The basic element of the collaboration platform is a care plan, which must be implemented in m-health applications. Having access to their plans of care, patients will not only monitor their health, but will also participate in the process with greater awareness. m-Health solutions for patients with asthma are among the first three applications that will use the platform of collaboration following a preliminary test involving patients with hypertension (Wicklund, 2015). A special place among the functionalities of mobile applications supporting patients with asthma is occupied by an educational module on disease self-management. In the process of designing this module, it may be helpful to address the following questions (Table 3). When analyzing functionalities, one needs to take into account the devices on which the application are run to make the best use of the features of those devices. The smartphone is the basic device for mobile health applications. According to The Economist, “one in five Americans use health apps on their smartphones” (The Economist, March 7th, 2015). Furthermore, smartphones can be connected to sensors using Bluetooth technology or a USB port. According to the number of users, there are four major mobile application platforms for smartphones: iOS, Android, Windows Phone, and Blackberry OS (IDC, 2015). Mobile applications that support patients with asthma can be divided into commercial and free of charge. To maximize the chances of success of the project, the teams developing m-health solutions should comprise medical staff (doctors, nurses), patients, medical researchers, and payers (the National Health Fund, insurance companies). When developing or selecting a mobile application, it is important to consult a broad spectrum of stakeholders.

Business Model for the Development and Use of m-health Applications The purpose of this section is to present business models useful in developing and operating m-health applications. This issue is important as it determines how the applications will be financed as well as what benefits the various stakeholders can attain. Indeed, an m-health project is unlikely to succeed unless all stakeholders realize they can benefit from it. It is not sufficient for an application to serve only patients; it must also offer certain benefits to medical personnel and payers, or else they will not be interested in its implementation (mHealth Alliance, 2013, pp. 12-21; GSMA, A.T. Kearney, pp. 15 – 20).

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Table 3. The questions to address in designing the educational module of asthma m-health applications Question What are the educational needs of different groups of patients (according to age, level of disease self-management, etc.)? Who can provide content related to the management of asthma (agencies, organizations, associations, foundations, research institutes)? How many levels of education or self-management knowledge should be defined? Who should prepare case studies of the most common life-threatening situations? (In asthmatic patients, asthma attacks are lifethreatening if the disease is poorly controlled). Which web sites (in English or other languages) are noteworthy and reliable enough to provide links to them in the application? Who should approve or review web sites and other resources to be recommended for patients with asthma? How to collect information about organizations, associations, and foundations that support patients with asthma? How to evaluate the self-management knowledge of patients with chronic diseases (asthma)? Source: Own work

Thus, the question arises as to what business model should be chosen from the point of view of the patients, physicians, and payers (health funds, insurance companies, or patients paying out-of-pocket) (Zhenwei Qiang et al., 2012, pp. 19-21). How to transfer best practices in the use of m-health applications for asthma care from countries with high GDP per capita to those with lower GDP levels, which also spend less per capita on health care (mHealth Alliance, 2013)? How to create the right conditions in the health care systems of poorer countries to adopt mobile technologies supporting the management of patients with asthma? Technological obstacles are by no means the most important ones in terms of integrating m-health applications for asthma care with the health care system. For this purpose, it is necessary to assess the business value of such mobile solutions, as otherwise it will be difficult to win payer support and motivate the physicians (Zhenwei Qiang et al., 2012, p. 50-54). In this process, special attention should be paid to difficulties in measuring outcomes, such as: • • • •

Reduced use of emergency care. Fewer hospitalizations due to life-threatening situations caused by asthma attacks. Making health services more accessible to patients in rural areas (Bowman, 2015). Improved quality of life of the patients.

Various estimation methods may prove m-health applications to be more or less efficient and should be the basis for pilot studies. In the development of mobile applications for patients with asthma, the central problem is how to create value in the health care triangle formed by the patient, the health care provider (medical personnel), and the payer. Without appreciable benefits, none of them will be interested in the development and implementation of such applications. The problem with value creation in health care results from the fact that the profits of one party (the patient) are the cost of another (the health fund or insurance company). Figure 1 shows the relationships associated with the implementation of m-health solutions. The report of the Working Group on m-health describes key stakeholders from Figure 1 (First Report, 2014, pp.3-6). Health care financing models consistent with m-health applications include bundled payments, accountable care organizations, pay for performance. New models of implementation of m-health solutions in the health care system can be interpreted as examples of the on-demand economy (The Economist

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Figure 1. Relationships associated with the implementation of m-health solutions Source: Own work

Briefing, 2015), software-as-a-service, or the application economy in the health care sector. In this model, some activities previously performed by the medical personnel may be transferred to IT solutions. Patients can access the application whenever they have health problems or need assistance. m-Health solutions are part of patient-centric health care. The question is why there are so few implementations of m-health solutions and finding an answer to this question may open the way for a wider use of these applications in chronic diseases (e.g., asthma). Undoubtedly, the basic problem lies in the fact that m-health technology changes the process of delivering health care, providing treatment, monitoring the patient’s health status, as well as disease self-management and the patient’s interactions with the medical staff. In traditional health care, the physician collects and analyzes information on the patient’s health status during consultations. In the case of m-health, the patient’s health can be monitored, and appropriate actions can be taken in real-time. m-Health technology can be understood as a disruptive innovation creating a new market, a novel model of patient–medical personnel interactions, and new value added to health care, so it may be difficult to incorporate it in the current health care system. Accountable care organizations (ACOs) represent a health care model that is most appropriate for m-health implementation as the reimbursement of the health care provider is correlated with the health status of the patient. Here, the provider and the medical staff are responsible for the health of the patient and at the same time are interested in cutting costs. Patients should also be vitally interested in using m-health solutions for asthma care, which can raise the quality of disease self-management, improve communication with the medical staff, and decrease the occurrence of life-threatening events. A combination of the patient’s willingness and the health fund’s (insurance company’s) savings will form a solid platform for the implementation of m-health technology. Importantly, the payers can encourage a transition to the ACO model by preferential treatment of this type of patient–provider–payer relationships, offering additional remuneration to health care providers following the ACO model. The business model and the effectiveness of m-health solutions are affected by factors such as the system of financing health care, the availability of medical personnel, and the cost of hospitalizations

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Table 4. Matrix for determining the model of m-health application use

Patient

Specialist physician

Family doctor

Nurse

Emergency department

Payer (health fund)

Payer, (insurance company)

Health policymakers

What does the m-health solution offer? How will the m-health application be used? What processes will be conducted using the proposed solution? How will the m-health solution impact work organization? Why did the stakeholder decide to use the m-health application? What benefits will be derived from the implementation of the m-health solution? What are the negative effects for each stakeholder? What risks are created by the m-health solution? How can m-health mitigate or eliminate inequalities in access to physicians by patients living in rural areas? How to monitor the outcomes of m-health applications and the progress of disease self-management? What is the revenue model of the m-health solution? How to make money on the m-health application (monthly fee, purchase of equipment and application by the patient, partial reimbursement by the health fund)? Source: Own work

due to life-threatening events caused by asthma attacks (Zhenwei Qiang et al., 2012, p. 58). The business model also largely depends on the degree of integration of m-health solutions with the process of provision of health care services. A working meeting using tools such as mind maps could facilitate the identification of solutions relevant for a given country. Answering the questions presented in Table 4 will help determine the directions of evolution of m-health solutions so that they better meet the expectations of all stakeholders. Those answers may be a point of departure for designing a business plan. To make it easier for each group of stakeholders (patients, physicians, and payers) to find the right solutions, the questions are presented in a matrix which organizes and visualizes the answers and shows relationships between the questions and the various stakeholders. To demonstrate the benefits of m-health applications in asthmatic patients, it is crucial to select the right region for application testing. m-Health solutions offer the greatest benefits to patients living in cities with the most polluted air, where residents are more likely to develop respiratory diseases. Such cities can be selected based on air pollution levels expressed as the number of days per year in which

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the limits of daily concentrations of PM10 particulate matter are exceeded. According to a report of the Supreme Audit Office, the most polluted regions of Poland are Małopolska and Silesia. Air pollution limits are exceeded for 150 and 123 days per year in Cracow and Katowice, respectively (Najwyższa Izba Kontroli, 2014, p. 20). Thus, in those cities the implementation of m-health applications for asthmatic patients will soon bring the best results and enable quick returns on investment. Another possible strategy is to test m-health solutions on patients with the highest number of hospitalizations due to asthma attacks. A study involving a control group would make it possible to determine to what extent the use of a given m-health application reduces the number, length, and cost of hospitalizations. The key to the development of the market for m-health applications supporting patients with chronic diseases (e.g., asthma) is to offer different levels of service at different prices, starting with free applications. Applications delivered free of charge create a future paid services market, give the patient an opportunity to test their usefulness, showcase the benefits of solutions supporting chronic disease management, and provide a platform for surveying user opinions and collecting the feedback necessary to improve the solutions and better adapt them to the needs of patients, medical personnel, and payers. Finally, new payment models should be sought to support the dissemination of m-health solutions in the management of chronic diseases (Small, 2015).

Financing of Mobile Applications Supporting Patients with Chronic Diseases When considering the financing of m-health applications, one should distinguish between two main stages: financing application development and funding application use. Depending on where the new products and services are developed and on the stage of software development, the following sources of funding may be applicable: • • • • • • • • • •

Research and development budgets of companies. Grants for research and development (Hall, 2013). Business angels. Crowdfunding (Bohineust, 2014). Associations, foundations (Zhenwei Qiang et al., 2012, p. 18). Venture capital funds, such as the $ 100 million West Health Investment Fund (Jackson, 2011). Corporations. Startup investment vehicles and venture capital trusts (Taylor, 2015a; Taylor, 2015b). Air protection as a source of financing for air pollution monitoring systems. Telecommunications companies, as they perceive this field as a promising market (Thomas, 2015).

Analysis of the development of mobile applications for cardiac patients by Medicalgorithmics S.A. shows that the company benefited from grants and then from a public offering of its shares on the Warsaw Stock Exchange. Figure 2 illustrates consecutive steps in raising capital by Medicalgorithmics S. A. Venture capitalists are interested in m-health companies due to the high growth potential of the market. Venture capital funds typically invest in companies that need capital for further development. Prototype testing is already completed, the business model has demonstrated and proved the value of the idea, and the company has a team of workers and wants to quickly gain a market share. m-Health companies typically develop prototypes at university labs using research grants. Health-related projects are increasingly financed by venture capital funds established by corporations such as Google, Apple,

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Figure 2. Stages in the financing of Medicalgorithmics S.A. Source: Own work

and Microsoft. By 2014, more than 1/3 of the $ 425 million dollar fund Google Venture was invested in health and life sciences; in 2013 the share of the health sector was 9% (Garde, 2014; Barr, 2014). Product presales through crowdfunding with the active participation of patients are a way not only of acquiring funds, but also creating a community that actively participates in the testing and development of m-health applications. Information appropriately targeted to patients with chronic diseases can turn them into investors in m-health applications through crowdfunding platforms. The patient community can be reached in the easiest, cheapest, and fastest way through non-governmental organizations. What is important here is to present the functionalities of future applications and consult projects with representatives of patient organizations. Patient participation in the financing of m-health solutions is not only financial. Patients who contribute to application financing are likely to: • • • •

Actively define desirable functionalities. Use the m-health application due to participation in its development and financing. Promote the solution due to their involvement. Recommend financing model (mHealth Alliance, 2013, pp. 38-39).

National associations of patients are examples of organizations that can take part in the process of raising finances for m-health projects. Apart from crowdfunding platforms for financing new product projects, there are also web sites assisting companies in the early stages of development with obtaining loans. Each company must present its financial documentation, and individual investors decide whether or not to get involved. The use of commercially available m-health applications by patients is usually financed by the national health care payer – the health fund (Bowman, 2015) or insurance companies. Some of the important factors that have an effect on financing are:

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Table 5. Questions for discussion related to financing m-health applications Question What are the important issues in the financing of m-health applications? What are the common problems faced by m-health companies? What is the nature of financing m-health projects in countries with a public payer (health fund)? Who evaluates the effectiveness of m-health technology? What regulations govern the assessment of m-health devices? How to finance the implementation of m-health solutions in a situation when spending on digital technology in the health sector is limited? What changes in the health care financing model would facilitate the introduction of m-health solutions? How to remunerate medical staff for communicating with patients via e-mail, Skype, text messaging, phone calls, and chat (in place of doctor visits)? Patients, physicians, and nurses may have different application-related needs depending on the nature and history the disease, the patient’s place of residence, etc. – how can the prices of m-health solutions be tailored to individual patients or service providers? What are the possibilities of using the pay for performance model in relation to m-health solutions? Source: Own work

• •

Payer’s policy (e.g., the health fund or insurance company may finance some telemedicine services). The policy of remunerating the medical personnel offering telemedicine – in the initial stage of telemedicine implementation, payments for services should include a bonus for novelty rewarding medical personnel for supporting patients through m-health services). Table 5 lists questions that may be helpful in discussing the financing of m-health projects. Table 6 presents websites that may assist in designing applications and finding investors.

Problems Related to the Implementation of Mobile Solutions for Patients with Asthma This section sets out to present problems arising in connection with the implementation of m-health solutions. The World Health Organization report describes barriers to m-health adoption (World Health Organization, 2011, pp. 63-69). Monrad Aas also analyzes the organizational challenge for health care from telemedicine and e-health (Monrad Aas, 2007). The introduction of digital interactions in health care requires changes in business processes, which remain insufficiently automated. For instance, patient records are often used simultaneously both in paper and electronic formats because patients’ histories have not been entered into the information system. Evolution in health care processes requires modifications in work organization and changes in the mentality of medical staff, who may be reluctant to accept new solutions. It is quite natural that some service providers are opposed to m-health technology as it brings about some major disruptive changes. As a result, it may be argued that we are witnessing a “future shock” in health care (Toffler, 1976). Unquestionably, digital interactions between patients and the medical staff may revolutionize the health care provision model (Zhenwei Qiang et al., 2012, p. 58). The key to success of m-health solutions convincing medical personnel that digital interactions with patients can save time, enhance their

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Table 6. Websites that may assist in designing applications and finding investors Website

Website address

American Telemedicine Association

http://www.americantelemed.org

Crowdrise

http://www.crowdrise.com

Crunchbase

https://www.crunchbase.com

FierceBiotech

http://www.fiercebiotech.com

FierceMobileHealthcare

http://www.fiercemobilehealthcare.com

Healthcare Information and Management Systems Society (HIMSS Europe)

http://himss.eu

Health 2.0

http://www.health2con.com

Health Data Management

http://www.healthdatamanagement.com

mHhealth News

http://www.mhealthnews.com

Kickstarter

http://www.kickstarter.com

Startuphealth.com

http://www.startuphealth.com

Startuphealth Twitter

https://twitter.com/startuphealth

Startuphealth Youtube

https://www.youtube.com/ playlist?list=PLkzfu6hnQEQEhMMaHJx3bjXfWsDPQrC-o

The list of top 10 crowdfunding platforms

http://www.crowdfunding.com

Source: Own work

productivity, increase treatment effectiveness, improve the quality of care, and, in the pay-for-performance model, also boost the revenues of health care service providers. To attain this goal, it is necessary to change the health care financing model. Without the introduction of a payment model rewarding the outcomes of interactions rather than the quantity of services provided, m-health applications will not attract the interest of the medical community. As a result, prior to the implementation of m-health solutions supporting patients with chronic diseases, including asthma, it is necessary to check whether the system of health care financing is outcome-based (e.g., involving bundled payments or accountable care organizations). An appropriate payment model, in conjunction with m-health technology integration in treatment systems for patients with chronic diseases, provides the foundation for the implementation of m-health applications. The next step involves the training of medical personnel and patients in the field of information and communication technology, not only to improve their skills, but most of all to change their thinking so that they would accept new forms of communication. A good example in this area is the activity of the Chief Executive Officer of National Health Service of England (Neville, 2015). Currently, providers tend to process patient data locally. Transition to cloud computing would require investment in technology to modify IT systems and to ensure the security of patient data. It is important how the payers (national health care payers (funds), health insurance companies) will reward health care providers for bringing health care closer to the patients and for real-time monitoring of their health status. This should result in higher revenues and force other health care providers to follow suit. It may be expected that the first provider to introduce m-health solutions will have a competitive advantage resulting from the higher quality of services, better performance, and more satisfied patients. The measures that can be undertaken at the national level to accelerate the development and implementation of m-health applications in chronic diseases include:

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Table 7. Key points for a discussion of factors influencing the implementation of mobile applications for patients with asthma Key points How to ensure the success of m-health applications? What are the determinants influencing physicians in terms of using m-health applications beneficial to patients with chronic diseases (Aydin, 2004)? What is the imperative driver of change that will initiate the introduction of m-health solutions? In what ways should the health care system be modified to put into practice the idea of digital interactions and communication between patients and physicians (health care providers)? How to eliminate boundaries between the real and digital health care systems? How to measure health outcomes in chronic diseases when m-health applications largely prevent adverse events such as medical emergencies or hospitalizations due to life-threatening situations? How to choose the right model of remunerating medical staff (doctors, nurses) to reflect assistance to patients using mobile applications? Are family doctors, nurses, and specialists willing to communicate with the patients via e-mail, Skype, text messaging, telephone, or chat to improve communication? How to prepare medical personnel (physicians, nurses) for a new health care model (digital interactions and communication with the patient)? What is the vision of the regional health policy-makers concerning the development of m-health applications? What are the most serious obstacles in the region to the implementation of m-health solutions for patients with chronic diseases? How can regional initiatives stimulate the implementation of m-health solutions assisting patients with chronic diseases? How to select patients for the pilot test of m-health solutions?

• • • • •

Adoption of appropriate regulations governing m-health applications. Issuing recommendations on how to use m-health solutions in chronic diseases. Launching pilot implementations to verify whether the technology is sufficiently mature and integrated into the existing health care infrastructure. Convincing providers as to the benefits of m-health. Valuation of the services provided under this model as well as a remuneration system promoting extensive use of this form of medical care.

Table 7 presents questions that can facilitate discussions with physicians, health care financing institutions, policy makers, and patients about how to streamline the implementation of m-health applications for asthmatic patients. Solutions to problems in this area depend on the nature of the health care system, the presence of the public payer, the existence of compulsory health insurance, competition among insurance companies offering health insurance, as well as the degree of economic and social development of the country.

FUTURE RESEARCH DIRECTIONS While the previous sections present the functionalities of mobile applications supporting patients with asthma, the business model, the financing process, and the problems related to the implementation, the present one sets out to identify possible future development. One promising direction includes mobile applications supporting children with asthma. Analysis of advances in innovative products (such as

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Apple Watch) and methods such as “the theory of inventive problem solving” and the Altshuller matrix (Altshuller, 2007) can be helpful for generating ideas on the future development of m-health solutions. Working on future m-health applications, it is worth considering how to integrate new technologies, such as wearables, smart watches, and the Internet of things to offer patients additional benefits. For example, wearables can act as smartphones, sensors, and applications supporting patients with asthma. Ideas that may lead to new functionalities and new directions of expansion of m-health include such technologies and solutions as: • • • • • • • •

Applications are assisting patients with multiple chronic diseases (comorbidities). Voice / natural language recognition. Voice-controlled assistants (e.g., Apple’s Siri, Microsoft’s Cortana). Internet of things, connected devices (e.g., an inhaler connected to a smartphone or tablet via Bluetooth, applications integrated with smart watches such as Apple Watch), smart homes (Clark, 2015). The functionality of checking asthma medications for adverse interactions with other pharmaceuticals. Open source platforms for the collection, aggregation, and dissemination of data on the state of air pollution, including means of public transport, such as trains, trams, and buses (Saxena, 2015). Data as the basis for gradual transformation of medicine (Lohr, 2015), the use of machine learning and data analysis to identify particularly dangerous places for asthmatic patients, similar to a project launched by Google (Taylor, 2015c). Integration of applications with activity trackers, enabling m-health applications to use data from such trackers.

One possible development direction for m-health applications is supporting patients with multiple comorbidities by one application. Even if in the beginning the application is focused on one chronic disease, such as asthma, incorporating the possibility to add comorbidities over time will facilitate future application development. In the process of analyzing the role of m-health solutions in addressing asthma attacks, one can use examples from other areas of medicine, such as cardiology (Kovic, Lulic, 2011). In the future, patients’ lives may be saved by an application using a social network identifying the closest person with a portable inhaler or life-saving medication for asthma attacks. Another possible direction for m-health applications is to combine monitoring with an expert system alerting users to the need of taking a medication. m-Health applications are gradually going to incorporate elements of artificial intelligence, such as real-time data analytics and rule-based expert systems analyzing data related to the health status of the patient and air pollution on the basis of the physician’s recommendations (Waters, 2015). Particle counters providing information about air pollution in different districts of the city may be employed for environmental monitoring. This information can be made available to the public through a web site. Patients can also use personal particle counters to measure air pollution in areas frequented by them, e.g., in the workplace or along frequently used routes. Airquality monitors (Metz, 2014) record air pollution data along with information about time, place (GPS coordinates), and the patient’s location (in the car, public transport, in the open air, inside a building). On the basis of the data collected, the application identifies areas with the highest air pollution for a given time and weather conditions. The patient’s inhaler may be connected to a smartphone with GPS collecting information as to when, where, and how many doses of medicine the patient should take 190

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(Lawrence, 2014). Along with information about air pollution, these data may be used to optimize the patient’s route (avoiding highly polluted areas) and to teach the expert system when to alert the patient to the need of taking a medicine (including dosage). Future directions for the development of mobile applications for patients with asthma will offer even more features supporting disease self-management. At the same time, they will be easier to use, more patient friendly, and more useful for the physicians and payers.

CONCLUSION m-Health solutions constitute disruptive innovations in the field of health care. They have given rise to novel processes and development trends in medical technology, cutting-edge ways of providing care, new markets, as well as value added for patients, payers, and health care providers. Over time, this can lead to major changes in the current health care model (Gamble, 2013). It may be expected that such factors as population aging, longer life expectancy, increasing health awareness, and greater numbers of patients with chronic diseases in conjunction with the widespread ability to use smartphones, as well as falling prices of electronic devices, software and telecommunications services will promote advances in m-health, as it was the case with the mobile phone market. Indeed, m-Health solutions may fundamentally alter health care services. This warrants a broader discussion, involving all stakeholders, about how to best integrate these innovations in health care and how to accelerate this process. Regulations concerning the approval of medical devices and software for the management of patients with chronic diseases should be crafted in such a way as not to undermine the benefits of m-health applications (Krisch, 2015). Furthermore, the participation of doctors and patients in the development of m-health will make it possible for the new medical applications to meet all the relevant requirements.

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Thomas, D. (2015, March 2). Telcos seek to redefine role as digital competition intensifies. Financial Times. Retrieved March 26, 2015, from http://www.ft.com/intl/cms/s/2/4bd12fd8-ac6b-11e4-9d3200144feab7de.html Toffler, A. (1984). Future Shock. Bantam. Waters, R. (2015, January 4). Investor rush to artifical intelligence is real deal. Financial Times. Retrieved March 26, 2015, from http://www.ft.com/intl/cms/s/2/019b3702-92a2-11e4-a1fd-00144feabdc0.html Wicklund, E. (2015, March 13). mHealth masters: The rise of collaborative care platforms. mHhealth News. Retrieved March 26, 2015, from http://www.mhealthnews.com/news/mhealth-masters-risecollaborative-care-platforms World Health Organization. (2011). mHealth New horizons for health through mobile technologies. Based on the findings of the second global survey on eHealth. Geneva: World Health Organization. Retrieved March 26, 2015, from http://www.who.int/goe/publications/goe_mhealth_web.pdf World Health Organization. (2012). Management of patient information. Trends and challenges in Member States. Based on the findings of the second global survey on eHealth. Geneva: World Health Organization. Retrieved March 26, 2015, from http://apps.who.int/iris/bitstream/10665/76794/1/9789241504645_eng. pdf Zhenwei Qiang, C., Yamamichi, M., Hausman, V., Miller, R., & Altman, D. (2012). Mobile Applications for the Health Sector. Washington, DC: ICT Sector Unit World Bank. Retrieved March 26, 2015, from http://siteresources.worldbank.org/INFORMATIONANDCOMMUNICATIONANDTECHNOLOGIES/ Resources/mHealth_report_(Apr_2012).pdf

KEY TERMS AND DEFINITIONS Global Asthma Network: The mission of the Global Asthma Network is to prevent asthma and improve globally health care for asthma patients. The website of the Global Asthma Network is: http:// www.globalasthmanetwork.org. Global Initiative for Asthma: The mission of the Global Initiative for Asthma is to reduce asthma prevalence and mortality. The website of the Global Initiative for Asthma is: http://www.ginasthma.org. Healthcare Information and Management Systems Society (HIMSS): HIMSS is a gobal organization not-for-profit promoting application of information technology in health care thought events sucha as eHealth week. The website of the HIMSS is: http://www.himss.org. K4Health Knowledge for Health: The Knowledge for Health project deliver the m-health Evidence database. The website of the K4Health is https://www.k4health.org. mHealth working group: The mission of the mHealth Working Group is to share knowledge, best practices in the implementation of the mHealth projects. The website of the mHealth Working Group is: https://www.mhealthworkinggroup.org. StartUp Health: The mission of Startup Health is to transforming health care by portfolio of new startups, delivering coaching program to health startups, linking investors and enrepreneurs providing resource, such as Crunchbase. The website of the Startup Health is: http://www.startuphealth.com.

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Chapter 10

M-Health in Prehospital Emergency Medicine: Experiences from the EU funded Project LiveCity Bibiana Metelmann Greifswald University, Germany Camilla Metelmann Greifswald University, Germany

ABSTRACT Prehospital emergency medicine strives to treat (potentially) life-threatening conditions as early as possible and thus reduce preventable disabilities and deaths. MHealth enables the transfer of knowledge to the emergency site. The purpose of this chapter is to display different approaches. Knowledge can be brought to the emergency site e.g. by smart phone applications allowing retrieval of data or by real-time communication with a remote medical expert. High definition video communication in real time offers the highest amount of mHealth communication currently available in prehospital emergency medicine. Projects, using such a video communication are discussed. In the European Union funded project LiveCity a special video camera was developed and tested. After having encountered simulated emergency scenarios, emergency doctors and paramedics rated the video connection as helpful, an improvement of the quality of patient care and could imagine working with such a video consultation. MHealth has huge potential for the application in prehospital emergency medicine.

INTRODUCTION Prehospital emergency medicine is an essential part of all health care systems worldwide. The goal of prehospital emergency medicine is to treat time-critical diseases and conditions as early as at the emergency site and thus reduce preventable disabilities and deaths. MHealth offers the opportunity to balance existing healthcare disparities by using mobile information and communication technologies. It DOI: 10.4018/978-1-4666-9861-1.ch010

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has huge advantages in emergency medicine, where the transfer of knowledge in a short time is critical and potentially lifesaving (Amadi-Obi, Gilligan, Owens, & O’Donnell, 2014). The purpose of this chapter is to display different approaches, how mHealth might be beneficial in prehospital emergency medicine. After introducing the role of paramedics and emergency doctors in the prehospital emergency medicine, the three key emergency cases myocardial infarction, stroke, and trauma are described. These life-threatening diseases belong to the leading causes of death worldwide and have a high economic impact. MHealth offers a possibility to increase the quality of treatment starting at the emergency site to potentially save lives. There are two main ways of applying mHealth in prehospital emergency medicine. One is to retrieve data, e.g. by using smart phone applications. Multiple uses of applications will be presented. The other way to use mHealth is a real-time communication with a medical expert. This communication could be the transmission of audio, vital signs, photos or video. Three projects using high-definition real-time video communication from the emergency site to a remote emergency doctor will be presented and discussed. Experiences in mHealth in the field of prehospital emergency medicine gained in the European Union FP7-funded project LiveCity (Grant Agreement No. 297291) will be described. Within the project a video camera was developed and tested in a medical simulation center. Key findings of this study will be presented and issues and problems, which arose, will be analyzed and possible solutions discussed. Future research is expected to solve some remaining technical challenges, making mHealth in prehospital emergency medicine very promising.

BACKGROUND Prehospital Emergency Medicine Prehospital emergency medicine summarizes all efforts made by medical professionals to treat acute illnesses, life-threatening conditions and pain at the emergency site and to transport the patient – if needed – to a hospital. Prehospital emergency medicine varies between countries (Roudsari et al., 2007). Most developed countries have an advanced life support system, which can be divided into the Anglo-American model and the Franco-German model. In the Anglo-American model, the prehospital emergency medicine is done by paramedics. The Franco-German model is similar to the Anglo-American model, but differs in life-threatening conditions. In those cases additionally to paramedics, there are also emergency physicians sent to the emergency site (Dick, 2003).

Three Key Emergencies: Myocardial Infarction, Stroke, Trauma Medical emergencies, which happen outside a hospital, contribute immensely to the global morbidity and mortality. The World Health Organization published a factsheet about “the top 10 causes of death”, where ischaemic heart disease and stroke are the two leading causes of death worldwide (WHO, 2014). Myocardial infarction, as the acute and life-threatening form of ischaemic heart disease, is a very frequent reason to alert the prehospital emergency system. The European Society of Cardiology emphasizes the importance of the prehospital phase because this is the most critical phase for the occurrence of cardiac arrest. Early treatment is proven to reduce morbidity and mortality (Steg et al., 2012). Stroke, which also is a common diagnosis in prehospital emergency medicine, depends on the early treatment, too. The

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European Stroke Organisation recommends priority treatment to reduce morbidity and mortality (The European Stroke Organisation Executive & Committee, 2008). A recent systematic review showed, that mHealth for stroke in prehospital emergency medicine is feasible and beneficial (Hubert, Müller-Barna, & Audebert, 2014). Trauma, as in road injuries, also ranks in “the top 10 causes of death” and attempts are made to reduce trauma-related deaths (Dagal, Greer, & McCunn, 2014). The burden of traffic-related traumas is likely to increase further with further motorization and the World Health Organization recognized the importance of prehospital emergency medicine in their developed action plans (Moroz & Spiegel, 2014). The prognosis for 2030 predicts ischaemic heart disease, stroke and trauma to be within the top five leading causes of death worldwide (WHO, 2010). This chapter will illustrate how different approaches in mHealth might improve the emergency treatment of these three severe emergencies.

MHealth in Prehospital Emergency Medicine As shown in the three examples, the early beginning of the right treatment is crucial to improving the outcome of the patient. There are several concepts to make expertise available at the emergency site within a minimum of time. One possible way is by using mHealth. MHealth offers the transfer of knowledge over geographical distances in real time. In contrast to the traditional telemedicine, mHealth has the huge advantage, that it is not restricted to stationary devices anymore. Thus, a connection to a moving partner is possible. MHealth, therefore, allows telemedicine in the prehospital emergency medicine. MHealth in prehospital emergency medicine can be grouped into two categories based on the kind of transmitted data: The first one is the retrieval of data, e.g. at the emergency site. This can, for instance, be made with smart phone applications (apps). The second one is a real-time communication with a medical expert. It allows the transfer of knowledge from a remote expert to the emergency site.

1. Retrieval of Data In January 2014, there were about 300 smartphone apps with a focus on emergency medicine available in the Apple App Store (Lin, Rezaie, & Husain, 2014). Looking at the dynamics of the past, this number is expected to grow rapidly. Smart phone apps are either designed for non-medical professionals or medical professionals or both; latter being the rarest. Apps for non-professionals aim to assist daily life and help to detect alarming symptoms. Apps for medical professionals enable retrieval of knowledge in multiple domains: While some apps offer textbooks and lookup tables, others offer interactive flowcharts and step-by-step instructions. As an example for helping with prehospital decision making, an app assisting with the allocation of trauma patients to the appropriate hospital, was successfully tested against a paper version (Freshwater & Crouch). Consistently it could be shown that the use of tablet PCs at the emergency site to help allocating patients, could reduce transportation times and costs (Yamada, Inoue, & Sakamoto, 2015). Several apps assist in finding the right medication dosage. Calculating medical scores can be very challenging, because of complex equations and numbers. Thus, apps assisting in these tasks were developed. For instance, it could be shown that an app calculating adequate fluid management for burn victims was feasible and accurate (Barnes et al., 2014). Another group of apps keeps the user up-to-date on the latest research to ensure high-quality medicine. Many medical professionals value apps because they support work by providing information in a structured way. The traditional textbooks, handwritten notes, and papers can be replaced by smartphone apps, which are ubiquitous available and allow fast retrieval of data. However, one has to be aware, that not all apps have been officially approved by medical

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committees and not all information displayed in apps is consistent with medical guidelines and current research. The Medicine and Healthcare Products Regulatory Agency of the United Kingdom published in August 2014 a “Guidance on what a software application medical device is and how to comply with the legal requirements” (MHRA, 2014).

2. Real-Time Communication with Medical Expert The second category of mHealth in prehospital emergency medicine enables the user to have a realtime communication with a medical expert. This allows a dynamic interaction and remote guidance. The kind of transmitted data differs between audio, vital signs (e.g. heart rate, blood pressure, oxygen saturation), photos, and video. One paramount example for transmission of audio to the emergency site is telephone-advised cardiopulmonary resuscitation (CPR). If a first aider calls the European emergency number “112” and the emergency dispatcher detects a cardiac arrest, the emergency dispatcher will then start assisting the layperson by explaining resuscitation. The European Resuscitation Council recommended in the 2010 guidelines this telephone-CPR, because of the wide distribution of mobile phones and the overwhelming benefit (Sandroni & Nolan, 2011). An example for the transmission of vital signs from the emergency site to a remote expert is the wireless transmission of a 12-lead electrocardiogram (ECG). In cases of acute transmural myocardial infarction, a change in the ECG in terms of ST-elevation is detectable. As mentioned above, treatment of this ST-elevation-myocardial infarction has to start as early as possible. If a pathological 12-lead-ECG is transmitted to a cardiologist in the hospital, he can start preparing the therapy. Thus, many studies showed a benefit of wireless transmission of 12-lead ECGs (Kerem et al., 2014), so that it is recommended by the European Society of Cardiology (Steg et al., 2012). Although many ambulance cars are equipped with a digital camera, there is little research, how the transmission of photos can increase emergency treatment. In a trial by BERGRATH and colleagues paramedics mostly transmitted photos of medical records, physician’s notes and medication lists (Bergrath, Rossaint, Lenssen, Fitzner, & Skorning, 2013). Even though the transmission of pictures is inferior to the transmission of videos to enable guidance by remote experts, there are still good reasons to take photos at the emergency site. For instance, in trauma patients, pictures of a traffic accident or wounds and injuries before emergency treatment can later help hospital doctors with further treatment and might be significant for forensic reasons. The video combines the two body senses hearing and seeing, which are essential for medical doctors to find the right diagnosis. While the patient’s history and symptom description are assessed acoustically, visual impressions are of the same importance. Highdefinition video communication in real time offers the highest amount of information-transfer currently available. It probably has the greatest potential for the application of mHealth in prehospital emergency medicine. Therefore, the following part of the chapter will focus on the use of video communication in prehospital emergency medicine.

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VIDEO COMMUNICATION IN PREHOSPITAL EMERGENCY MEDICINE Three Projects Using Real-Time Video Communication in Prehospital Emergency Medicine Several different mHealth concepts of video communication in prehospital emergency medicine are currently under study or already implemented. They use the mHealth video technology to transmit the expertise of emergency doctors to support and guide paramedics at the emergency site (Czaplik et al., 2014). Exemplarily, pioneering projects from Germany, Belgium and USA will be outlined: TemRas, based on the project Med-on-@ix (Aachen, Germany), developed a monitor, which automatically sends the vital signs of a patient in real time to an emergency doctor, who is stationed at the emergency dispatch centre. Additionally, the ambulance car is equipped with a video camera, which sends high-definition videos in real time to the emergency doctor. The remote emergency doctor can operate this camera, which is located at the ceiling of the ambulance car. For instance, the emergency doctor can zoom to analyze small details. This concept was successfully implemented as part of the emergency medical system in the city of Aachen in April 2014 (Buscher & Schilberg; Rortgen et al., 2013; Skorning et al., 2012). The FACT Study (Feasibility of AmbulanCe-based Telemedicine) (Brussels, Belgium), part of the PreSSUB Project, has a similar approach using real-time video connection between the ambulance car and a teleconsultant. In patients with suspected stroke, the teleconsultant examines the patient according to a standardized protocol, asking questiones and evaluating for example movements of facial muscles. It could be demonstrated that remote stroke assessment in moving ambulances is possible and reliable (Van Hooff et al., 2013; Yperzeele et al., 2014). The Tucson ER-link Project (Tucson, USA) combined a video link from inside an ambulance car with additional videos taken by cameras attached to the outside of the ambulance car and the existing highway cameras. The advantages of this approach lie especially in trauma management when the emergency doctor can also look at the accident scene. The system was successfully implemented in ambulance cars in the city of Tucson could reach approximately 95% of the city’s inhabitants (Latifi, 2010; Latifi et al., 2007). Unfortunately, this project was terminated due to shortage of funding. One of the limitations of this Aachen-system and Brussels-system for telemedical consulting is, that the video camera is fixed on the ceiling of the ambulance car (Thelen, Schneiders, Schilberg, & Jeschke, 2013; Yperzeele et al., 2014). That way, the first video connection between paramedics and the remote emergency doctor is not possible before the paramedics and the patient enter the ambulance car. For some emergency situations, this is too late. In a patient with severe bleeding, shock or coma, the blood circulation of the patient has to be stabilized or the airway secured, e.g. by endotracheal intubation, before it is possible to start transportation (Arbabi et al., 2004; Kleber et al., 2012). It could be shown, that paramedics not always perform endotracheal intubation in situations, where it is strongly recommended because they are not practiced enough (Franschman et al., 2009). This stabilization and the airway management can be difficult and in a meta-analysis it has been shown that emergency doctors have a significant higher success rate in emergency endotracheal intubation than paramedics (Lossius, Roislien, & Lockey, 2012). So, a video based assistance by an emergency doctor for those tasks could be helpful. Thus, the Tucson ER-link Project already integrated videolaryngoscopes into their prehospital telemedicine network (Sakles et al., 2011).Videolaryngoscopes are intubation-devices, which transmit a real-time video showing the vocal chords on a screen. 201

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Another lack of benefit when using a video camera system mounted on the ambulance car occurs at the other end of the emergency spectrum, where there are also situations, in which a video consultation before entering the ambulance car might be helpful. The decision, whether a patient has to be treated in a hospital or can be left at home, is a complex decision requiring consideration of a lot of additional surrounding facts, like age of the patient, living situation, accessibility of family, friends and neighbours. It is especially difficult to make a decision regarding hospital admission in cases of allergies and anaphylaxis and altered level of consciousness (Cummins et al., 2013). So it is a decision many paramedics want to be made by the emergency doctor. Emergency doctors are more confident than paramedics in deciding not to transport a patient to a hospital (Roberts, Blethyn, Foreman, & Bleetman, 2009). The moment a patient has been carried into the ambulance car just for video consultation, the emergency doctor and the paramedics will become reluctant to tell the patient, that he can be treated at home and can leave the ambulance car again.This could lead to a higher rate of patients admitted to hospital, which in consequence increases the workload and costs in the health system (Patton & Thakore, 2013). In all situations mentioned above, patients would benefit from a mobile camera that can build a video connection and can be brought directly to the emergency site. That way, the emergency doctor can get earlier visual information about the patient. In a pilot feasibility study, WU and co-workers tested a video camera attached to the stretcher, with which the patient can be transported in a lying position from the emergency site and the ambulance car to the hospital. They concluded that prehospital stroke evaluation using this camera was feasible and reliable (Wu et al., 2014).To further enhance the chance to obtain essential information, it might be important to see the surroundings of the emergency site and thus get a better picture of what might be the reason for the emergency. This requires a mobile camera, which is directed by the paramedics. Nonetheless, it is essential that the camera should not restrict the work of the paramedic while keeping both hands free to work. Such a kind of mobile video camera is google glass, which is at the moment extensively tested in numerous projects in different scientific areas. Among others, PORTER and co-workers test at the Rhode Island Hospital in Providence, USA, the use of google glass for a dermatology examination in the emergency department and HRONG and colleagues test a modified version of google glass in the emergency department at Beth Israel Deaconess Medical Center in Boston, USA (Friedman, 2014; Rojahn, 2014). Also, first trials in prehospital emergency medicine are made with google glass (Webster, 2014). Because of the wide distribution of mobile phones with the ability to do video calls, the idea often arose, that telemedicine could be realized with commercial off-the-shelf products. Moreover, especially in emergency medicine, the idea seems appealing, that the patient or the first-aider calling the emergency dispatcher uses a mobile phone video call and thus increases the amount of information the emergency dispatcher gets. For example, dispatcher-assisted cardio-pulmonary resuscitation with video-conference via mobile phones could be shown to be superior to audio-connection (Johnsen & Bolle, 2008; Yang et al., 2008). However, off-the-shelf products are not designed for this purpose and often technical issues regarding for instance light and audio-quality limit the success of such projects (Bolle, Johnsen, & Gilbert, 2011; Melbye, Hotvedt, & Bolle, 2014). Furthermore, the internet connection needs to “transport” a high amount of data within a short time in a stable and high quality, which limits the successful implementation of some telemedicine projects with off-the-shelf products (Mosier, Joseph, & Sakles, 2013). Additionally, the telemedicine connection has to meet the high standards regarding the security of vulnerable patient data. Concerns about failures in data security are one of the main obstacles in telemedicine (Klack, Ziefle, Wilkowska, & Kluge, 2013). Therefore, individual devices with specially

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designed software and hardware for the specific purposes have to be developed. The camera, developed in the LiveCity project, was built to meet those challenges.

Experiences Made in the LiveCity Project LiveCity Project Among the main goals of the European Union are the reduction of disparities and the sustainable gain of equal opportunities across borders and over geographical distances. The LiveCity Project (“Live Videoto-Video Supporting Interactive City Infrastructure”) examines the technical and structural basis of live communication between individuals or groups of individuals in distant places by using high-definition (HD) video communication in real time (I Chochliouros; I. Chochliouros, Stephanakis, Spiliopoulou, Sfakianakis, & Ladid, 2012). Such a concept is expected to positively contribute to the quality of life of citizens or communities within the European Union in various situations; some of them are to be examined representatively in the LiveCity Project (I. Chochliouros et al., 2012; Weerakkody, El-Haddadeh, Chochliouros, & Morris, 2012). One approach is to use mHealth in the prehospital emergency medicine by connecting paramedics at the emergency site with a remote emergency doctor through a video camera. For this purpose, a HD real-time video camera, called LiveCity camera, was developed within this project (Goncalves, Cordeiro, Batista, & Monteiro, 2012; Palma et al., 2013). This camera enables paramedics to demonstrate the emergency situation and vital signs of the patient to the emergency doctor, and the emergency doctor can assess the emergency situation and advice the paramedics at the emergency site (B. Metelmann, C. Metelmann, D. Morris, et al., 2014).

LiveCity Camera The LiveCity camera consists of (i) the video camera itself, worn with a headband above the right ear; (ii) a headphone with mouthpiece to enable audio connection in both ways; and (iii) the micro-PC, which builds the internet connection. The position of the camera above the right ear was chosen to transmit the same perspective the paramedic has. Since the emergency doctor sees the emergency “through the eyes” of the paramedic, he can access all relevant information needed to evaluate the situation and can then guide manual activities. The transmitted video is dynamic and follows the head movements of the paramedics. Another advantage of this camera position is that the paramedic still has both hands free to work, which is of great importance in emergency medicine. Since the work of paramedics requires much bending and kneeling, the microPC is placed in a small backpack. The transmitted video is received by the remote emergency doctor at a laptop provided with the special software. This software allows the emergency doctor to adapt the transmitted video according to the particular needs, e.g. regarding light, contrast and sound level (Metelmann, Metelmann, M.Wendt, K.Meissner, & Heyden, 2014). While the emphasis was put on reducing the time-lag to allow sufficient communication and guidance, the high legal standards regarding data security were met.

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Who Should Operate the Video Camera? All four projects mentioned above, which establish a video communication from the emergency site to a remote expert, share the idea, that the communication device is brought to the emergency site by the approaching paramedics. The prerequisite for mHealth is that two individuals or groups of individuals are connected with each other by the means of a communication technology. Often this connection is built beforehand during a face-to-face-meeting when both partners apportion the communication devices. When using mHealth in prehospital emergency medicine, there is no possibility to meet beforehand and establish a connection. If an emergency patient is not already part of a telemedicine project, the connection has to be newly created, and the devices to build the connection have to be brought to the patient. In the LiveCity Project this communication device, the LiveCity camera, was brought to the emergency site by the approaching paramedics (C. Metelmann, B. Metelmann, D. Morris, et al., 2014). There are several advantages and some disadvantages, if a paramedic operates the mHealth device and not the emergency patient: Because the paramedic is instructed into the operation of the LiveCity camera and practiced in using it, the telemedicine connection can be established fast. However, the connection can only be established after the paramedics arrived at the emergency site. Patients and relatives are in an exceptional situation. For different reasons, their thoughts and actions are focused and reduced to the essential. Because of pain, anxiety and different levels of consciousness the operation of a new device might be difficult or not possible. Therefore, it is of great value, when the paramedics establish the telemedicine connection, even if it is an additional burden for the paramedics. Another advantage is that the paramedics can integrate the maintenance of the device into their daily routine and, therefore, keep a high level of quality. Independent from the socioeconomic status of the emergency patient, this mHealth connection is accessible for all citizens. The patient does not need to have any prior knowledge in the use of computers or of similar equipment. This is especially important for elderly people, who form one of the main groups of emergency patients. Because the device can be used for several emergency patients, this concept also has the economic advantage of cost reduction (B. Metelmann, C. Metelmann, K. Meissner, et al., 2014).

Examining the LiveCity Camera in Simulated Emergency Scenarios In the LiveCityProject, the benefit of paramedics consulting an emergency doctor by use of the LiveCity camera was investigated in terms of professional workflow and outcome. To prevent potential harm to individuals, the initial phase of the study was performed in the fully equipped medical simulation center of the Department of Anesthesiology and Intensive Care Medicine at Greifswald UniversityMedicine. A medical simulation center creates dynamic realistic routine or emergency scenarios with computeroperated mannequins (Johannsson, Ayida, & Sadler, 2005). Simulation centers are commonly used for education and research in medicine worldwide and offer the possibility to learn and examine procedures without putting patients at risk (Cannon-Diehl, 2009; Levine, DeMaria, Schwartz, & Sim, 2013). Ten typical emergency scenarios from five different categories were standardized and structured for a randomized two-armed protocol. Three of the chosen categories were myocardial infarction, stroke, and trauma. They were selected to represent life-threatening diseases, which require fast treatment. Because these emergencies often occur, lead to a high number of disability, inability to work and death, an improvement in treatment could have a notable economic impact (Wolfe, 2000). Apart from these emergency categories, two additional groups of emergencies were chosen, one being rare diseases and

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the other complications during pregnancy. Rare diseases do not have such an economic impact, but they are often a challenge in diagnostics and treatment. Thus, a transfer of knowledge through mHealth might improve the outcome of the individual patient. Similarly, complications during pregnancy challenge the paramedic because special knowledge about changes in physiology and safe usage of medication in pregnancy is required. Hence, the five different emergency scenarios aimed to represent the broad spectrum of emergencies, which occur in prehospital emergency medicine. For each emergency category, two cases with a similar level of difficulty in terms of diagnosis and treatment were created. This allowed a cross-over design: Paramedics encountered corresponding scenarios (i) without a support by an emergency doctor and (ii) with support by a remote emergency doctor using the LiveCity camera. To assess the outcome in technical, practical and psychological aspects, paramedics and doctors were interviewed by the use of structured questionnaires.

Key Findings of the Simulated Emergency Scenarios More than 100 emergency scenarios were simulated and five key findings will be presented in the following. In line with the theoretical reflections made above, the vast majority of paramedics perceived the video guidance by the remote emergency doctor as helpful. Both, emergency doctors and paramedics agreed, at least partly, that the video connection with the LiveCity video camera improved the quality of patient care. At the same time, emergency doctors predominantly stated, that a sole transmission of vital signs without audio or video connection would not have been sufficient. Indeed, even a combined transmission of vital signs and audio would not have been sufficient for most emergency doctors. Though, many emergency doctors could imagine working as a doctor, consulting paramedics through a video connection. Similarly, after having encountered the simulated emergency scenarios without and with a video consultation by an emergency doctor, nearly all paramedics concluded, that they could imagine working with such a video camera.

Issues, Controversies, Problems in the LiveCity Project Although some technical hiccups occurred during the work with the LiveCity camera, there was a positive perception of the camera. The technical problems arose mostly due to interruptions of the data flow through the mobile network. This was intensified by the multiple demands on the data transmission, e.g. high definition video, real-time transmission and high requests on data security.

Discussion of Potential Solutions and Recommendations One possible solution to prevent technical hiccups and breakdowns could be a decreased amount of data transmission. This approach is difficult to realize. As mentioned above, data security regulations must be met at all times. Although complex encryption mechanism increase the amount of data transmission, they are fundamental. If users don’t trust telemedical systems, they will not be used. Another approach would be to permit greater time latency. But that would challenge a meaningful communication and could even make guidance by video communication impossible. To compare the LiveCity camera with off-the-shelf products, remote video guidance via the GoPro camera Hero 3® was also done. However, communication was severely hindered by a long time lag and additionally a handheld receiver had to be

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used to allow audio-communication in both ways. That made the Hero 3-camera, which wasn’t originally designed for such purposes, unusable for video communication in prehospital emergency medicine. Another strategy to decrease the amount of data transmission is the reduction of the video quality. This could make it impossible for the emergency doctor to detect important details. Moreover, a downgrade to mere photo and audio transmission instead of video transmission would make physical examinations from remote very challenging. For instance, the physical examination to detect a stroke includes the observation of facial movements. Thus, a reduction in video quality would strongly inhibit the potential of video communication in prehospital emergency medicine. In summary, the approach to decrease the amount of data transmission to prevent technical problems is currently not a feasible solution. Instead, the answer to this problem will be a further development of mobile networks.

FUTURE RESEARCH DIRECTIONS With the fast evolving technology and immense successes in computer science, mHealth is bound to change and improve constantly. Looking at the progress made in the last decades, it is safe to predict, that mHealth devices will get smaller, more compact and even more user-friendly. At the same time, the mobile networks will optimize, and next generations of mobile telecommunication technologies will ensure that transmission of the video will be more stable, of higher quality and faster. Thus, there is great promise in the use of video communication from the emergency site to a remote expert. It will be very interesting to see, how the future achievements in mHealth will contribute to prehospital emergency medicine and as a result potentially safe lives.

CONCLUSION Prehospital emergency medicine strives to treat (potentially) life-threatening conditions as early as possible. The three emergencies myocardial infarction, stroke, and trauma are salient because they are frequent and require immediate therapy. Thus, these emergencies would benefit from mHealth. MHealth offers the opportunity to transfer knowledge to the emergency site. This knowledge can be a retrieval of data, for instance with the help of smart phone applications. Alternatively, it can be a real-time communication with a remote medical expert by the transmission of audio, vital signs, photos, and/or video. Video communication combines two body senses (hearing and seeing) and is, therefore, superior to the transfer of either audio, vital signs or photo. High definition video communication in real time offers the highest amount of mHealth communication currently available in prehospital emergency medicine. Three projects from Germany, Belgium and USA are discussed and put into relation with the LiveCity project. LiveCity is a European Union project, which aims to increase the quality of life of European citizens by connecting them through high-quality video in real time, for instance in the field of prehospital emergency medicine. A special camera was developed and tested with realistic scenarios in a medical simulation center in a cross-over design. Paramedics were supported in diagnostics and treatment of emergencies by remote emergency doctors through the LiveCity camera. After having encountered the simulated scenarios, emergency doctors and paramedics rated the video connection as helpful, an improvement of the quality of patient care and could imagine working with such a video consultation. Based on the findings in the LiveCity project, the impact of high-quality real-time video communication

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on prehospital emergency medicine is clearly appreciated by potential users. MHealth has huge potential for the application in prehospital emergency medicine.

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Dagal, A., Greer, S. E., & McCunn, M. (2014). International disparities in trauma care. Current Opinion in Anaesthesiology, 27(2), 233–239. doi:10.1097/ACO.0000000000000049 PMID:24514036 Dick, W. F. (2003). Anglo-American vs. Franco-German emergency medical services system. Prehosp Disaster Med, 18(1), 29-35; discussion 35-27. Franschman, G., Peerdeman, S. M., Greuters, S., Vieveen, J., Brinkman, A. C. M., Christiaans, H. M. T., & Boer, C. et al. (2009). Prehospital endotracheal intubation in patients with severe traumatic brain injury: Guidelines versus reality. Resuscitation, 80(10), 1147–1151. doi:10.1016/j.resuscitation.2009.06.029 PMID:19632024 Freshwater, E., & Crouch, R. Technology for trauma: testing the validity of a smartphone app for prehospital clinicians. International Emergency Nursing, 23(1), 32-37. doi: 10.1016/j.ienj.2014.04.003 Friedman, J. (2014). Rhode Island Hospital launches country’s first Google Glass study in emergency department. R I Med J (2013), 97(4), 47. Goncalves, J., Cordeiro, L., Batista, P., & Monteiro, E. (2012). LiveCity: A Secure Live Video-to-Video Interactive City Infrastructure. In L. Iliadis, I. Maglogiannis, H. Papadopoulos, K. Karatzas, & S. Sioutas (Eds.), Artificial Intelligence Applications and Innovations (Vol. 382, pp. 260–267). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33412-2_27 Hubert, G., Müller-Barna, P., & Audebert, H. (2014). Recent advances in TeleStroke: A systematic review on applications in prehospital management and Stroke Unit treatment or TeleStroke networking in developing countries. International Journal of Stroke, 9(8), 968–973. doi:10.1111/ijs.12394 PMID:25381687 Johannsson, H., Ayida, G., & Sadler, C. (2005). Faking it? Simulation in the training of obstetricians and gynaecologists. Current Opinion in Obstetrics & Gynecology, 17(6), 557–561. doi:10.1097/01. gco.0000188726.45998.97 PMID:16258334 Johnsen, E., & Bolle, S. R. (2008). To see or not to see--better dispatcher-assisted CPR with video-calls? A qualitative study based on simulated trials. Resuscitation, 78(3), 320–326. doi:10.1016/j.resuscitation.2008.04.024 PMID:18583015 Kerem, Y., Eastvold, J. S., Faragoi, D., Strasburger, D., Motzny, S. E., & Kulstad, E. B. (2014). The role of prehospital electrocardiograms in the recognition of ST-segment elevation myocardial infarctions and reperfusion times. The Journal of Emergency Medicine, 46(2), 202–207. doi:10.1016/j. jemermed.2013.08.084 PMID:24268634 Klack, L., Ziefle, M., Wilkowska, W., & Kluge, J. (2013). Telemedical versus conventional heart patient monitoring: A survey study with German physicians. International Journal of Technology Assessment in Health Care, 29(4), 378–383. doi:10.1017/S026646231300041X PMID:24290330 Kleber, C., Giesecke, M., Tsokos, M., Haas, N., Schaser, K., Stefan, P., & Buschmann, C. (2012). Overall Distribution of Trauma-related Deaths in Berlin 2010: Advancement or Stagnation of German Trauma Management? World Journal of Surgery, 36(9), 2125–2130. doi:10.1007/s00268-012-1650-9 PMID:22610265

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Latifi, R. (2010). Telemedicine for Trauma, Emergencies, and Disaster Management. Norwood: Artech House. Latifi, R., Weinstein, R. S., Porter, J. M., Ziemba, M., Judkins, D., Ridings, D., & Leyva, F. et al. (2007). Telemedicine and telepresence for trauma and emergency care management. Scandinavian Journal of Surgery, 96(4), 281–289. PMID:18265854 Levine, A. I., DeMaria, S., Schwartz, A. D., & Sim, A. J. (2013). The Comprehensive Textbook of Healthcare Simulation. Springer. doi:10.1007/978-1-4614-5993-4 Lin, M., Rezaie, S., & Husain, I. (2014). Top 10 mobile apps in Emergency Medicine. Emergency Medicine Journal, 31(5), 432–433. doi:10.1136/emermed-2014-203607 PMID:24567149 Lossius, H. M., Roislien, J., & Lockey, D. J. (2012). Patient safety in pre-hospital emergency tracheal intubation: A comprehensive meta-analysis of the intubation success rates of EMS providers. Critical Care (London, England), 16(1), R24. doi:10.1186/cc11189 PMID:22325973 Melbye, S., Hotvedt, M., & Bolle, S. (2014). Mobile videoconferencing for enhanced emergency medical communication - a shot in the dark or a walk in the park? -- A simulation study. Scandinavian Journal of Trauma. Resuscitation and Emergency Medicine, 22(1), 35. doi:10.1186/1757-7241-22-35 Metelmann, B., Metelmann, C., Meissner, K., Wendt, M., Goncalves, J., Gilligan, P., & von der Heyden, M. (2014). The Potential of Telemedicine. In L. Iliadis, I. Maglogiannis, H. Papadopoulos, S. Sioutas, & C. Makris (Eds.), Artificial Intelligence Applications and Innovations (Vol. 437, pp. 30–37). Springer Berlin Heidelberg. Metelmann, B., Metelmann, C., Morris, D., Cordeiro, L., Chochliouros, I., von der Heyden, M., . . . Wendt, M. (2014). Live Video Transmission to Improve Emergency Medicine (Videoübertragung in Echtzeit zur Verbesserung der Notfallmedizin). Paper presented at the VDE-Kongress 2014 - Smart Cities Frankfurt am Main, Germany. Metelmann, C., Metelmann, B., Morris, D., Cordeiro, L., Chochliouros, I., von der Heyden, M., . . . Wendt, M. (2014). The Potential of Telemedicine for Patients at Home (Potenziale der Telemedizin bei Patienten in der Häuslichkeit). Paper presented at the VDE-Kongress 2014 - Smart Cities, Frankfurt am Main, Germany. Metelmann, C., Metelmann, B., Wendt, M., Meissner, K., & von der Heyden, M. (2014). LiveCity: The Impact of Video Communication on Emergency Medicine. International Journal of Electronic Government Research, 10(3), 47–65. doi:10.4018/ijegr.2014070104 MHRA, Medicines and Healthcare Products Regulatory Agency. (2014). Medical device stand-alone software including apps. Retrieved from https://www.gov.uk/government/publications/medical-devicessoftware-applications-apps Moroz, P. J., & Spiegel, D. A. (2014). The World Health Organization’s action plan on the road traffic injury pandemic: Is there any action for orthopaedic trauma surgeons? Journal of Orthopaedic Trauma, 28(Suppl 1), S11–S14. doi:10.1097/BOT.0000000000000105 PMID:24857989

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Mosier, J., Joseph, B., & Sakles, J. C. (2013). Telebation: Next-generation telemedicine in remote airway management using current wireless technologies. Telemedicine Journal and e-Health, 19(2), 95–98. doi:10.1089/tmj.2012.0093 PMID:23215736 Palma, D., Goncalves, J., Cordeiro, L., Simoes, P., Monteiro, E., Magdalinos, P., & Chochliouros, I. (2013). Tutamen: An Integrated Personal Mobile and Adaptable Video Platform for Health and Protection. In H. Papadopoulos, A. Andreou, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Intelligence Applications and Innovations (Vol. 412, pp. 442–451). Springer Berlin Heidelberg. doi:10.1007/978-3-642-41142-7_45 Patton, G. G., & Thakore, S. (2013). Reducing inappropriate emergency department attendances--a review of ambulance service attendances at a regional teaching hospital in Scotland. Emergency Medicine Journal, 30(6), 459–461. doi:10.1136/emermed-2012-201116 PMID:22802457 Roberts, K., Blethyn, K., Foreman, M., & Bleetman, A. (2009). Influence of air ambulance doctors on on-scene times, clinical interventions, decision-making and independent paramedic practice. Emergency Medicine Journal, 26(2), 128–134. doi:10.1136/emj.2008.059899 PMID:19164630 Rojahn, S. (2014). Why Some Doctors Like Google Glass So Much. MIT Technology Review. Rortgen, D., Bergrath, S., Rossaint, R., Beckers, S. K., Fischermann, H., Na, I. S., & Skorning, M. et  al. (2013). Comparison of physician staffed emergency teams with paramedic teams assisted by telemedicine--a randomized, controlled simulation study. Resuscitation, 84(1), 85–92. doi:10.1016/j. resuscitation.2012.06.012 PMID:22750663 Roudsari, B., Nathens, A., Cameron, P., Civil, I., Gruen, R., Koepsell, T., & Rivara, F. et al. (2007). International comparison of prehospital trauma care systems. Injury, 38(9), 993–1000. doi:10.1016/j. injury.2007.03.028 PMID:17640641 Sakles, J. C., Mosier, J., Hadeed, G., Hudson, M., Valenzuela, T., & Latifi, R. (2011). Telemedicine and telepresence for prehospital and remote hospital tracheal intubation using a GlideScope videolaryngoscope: A model for tele-intubation. Telemedicine Journal and e-Health, 17(3), 185–188. doi:10.1089/ tmj.2010.0119 PMID:21443441 Sandroni, C., & Nolan, J. (2011). ERC 2010 guidelines for adult and pediatric resuscitation: Summary of major changes. Minerva Anestesiologica, 77(2), 220–226. PMID:21368728 Skorning, M., Bergrath, S., Rortgen, D., Beckers, S. K., Brokmann, J. C., Gillmann, B., & Rossaint, R. et al. (2012). Teleconsultation in pre-hospital emergency medical services: Real-time telemedical support in a prospective controlled simulation study. Resuscitation, 83(5), 626–632. doi:10.1016/j. resuscitation.2011.10.029 PMID:22115932 Steg, P., James, S., Atar, D., Badano, L., Lundqvist, C., Borger, M., & Wallentin, L. et al. (2012). ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation: The Task Force on the management of ST-segment elevation acute myocardial infarction of the European Society of Cardiology (ESC). European Heart Journal. doi:10.1093/eurheartj/ehs215 The European Stroke Organisation Executive. (2008). Guidelines for Management of Ischaemic Stroke and Transient Ischaemic Attack 2008. Cerebrovascular Diseases (Basel, Switzerland), 25(5), 457–507. doi:10.1159/000131083 PMID:18477843

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Thelen, S., Schneiders, M., Schilberg, D., & Jeschke, S. (2013). A Multifunctional Telemedicine System for Pre-hospital Emergency Medical Services. Paper presented at the eTELEMED 2013, The Fifth International Conference on eHealth, Telemedicine, and Social Medicine. doi:10.1007/978-3-319-08816-7_14 Van Hooff, R. J., Cambron, M., Van Dyck, R., De Smedt, A., Moens, M., Espinoza, A. V., & Brouns, R. et al. (2013). Prehospital unassisted assessment of stroke severity using telemedicine: A feasibility study. Stroke, 44(10), 2907–2909. doi:10.1161/STROKEAHA.113.002079 PMID:23920013 Webster, C. (2014). Google Glass and Healthcare Information & Workflow. Paper presented at the Healthcare Systems Process Improvement 2014, Orlando, FL. Weerakkody, V., El-Haddadeh, R., Chochliouros, I., & Morris, D. (2012). Utilizing a High Definition Live Video Platform to Facilitate Public Service Delivery. In L. Iliadis, I. Maglogiannis, H. Papadopoulos, K. Karatzas, & S. Sioutas (Eds.), Artificial Intelligence Applications and Innovations (Vol. 382, pp. 290–299). Springer Berlin Heidelberg. doi:10.1007/978-3-642-33412-2_30 WHO. (2010). Injuries and violence: the facts. Geneva: WHO. WHO. (2014). The top 10 causes of death (Vol. Fact sheet N°310, pp. 5). World Health Organization. Wolfe, C. (2000). The impact of stroke. British Medical Bulletin, 56(2), 275–286. doi:10.1258/0007142001903120 PMID:11092079 Wu, T. C., Nguyen, C., Ankrom, C., Yang, J., Persse, D., Vahidy, F., & Savitz, S. I. et al. (2014). Prehospital utility of rapid stroke evaluation using in-ambulance telemedicine: A pilot feasibility study. Stroke, 45(8), 2342–2347. doi:10.1161/STROKEAHA.114.005193 PMID:24938842 Yamada, K. C., Inoue, S., & Sakamoto, Y. (2015). An effective support system of emergency medical services with tablet computers. JMIR Mhealth Uhealth, 3(1), e23. doi:10.2196/mhealth.3293 PMID:25803096 Yang, C. W., Wang, H. C., Chiang, W. C., Chang, W. T., Yen, Z. S., Chen, S. Y., & Lin, F. Y. et al. (2008). Impact of adding video communication to dispatch instructions on the quality of rescue breathing in simulated cardiac arrests--a randomized controlled study. Resuscitation, 78(3), 327–332. doi:10.1016/j. resuscitation.2008.03.232 PMID:18583016 Yperzeele, L., Van Hooff, R. J., De Smedt, A., Valenzuela Espinoza, A., Van Dyck, R., Van de Casseye, R., & Brouns, R. et al. (2014). Feasibility of AmbulanCe-Based Telemedicine (FACT) Study: Safety, Feasibility and Reliability of Third Generation In-Ambulance Telemedicine. PLoS ONE, 9(10), e110043. doi:10.1371/journal.pone.0110043 PMID:25343246

KEY TERMS AND DEFINITIONS Emergency Doctor: A doctor with special training in emergency medicine. In the Anglo-American model, the emergency doctor treats the patients in the emergency department of a hospital. In the FrancoGerman model, emergency doctors start the treatment of patients with (potentially) life-threatening conditions already at the emergency site.

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Medical Simulation Center: Creates dynamic, realistic routine or emergency scenarios with computeroperated mannequins to learn or examine procedures without putting patients at risk. Myocardial Infarction: Acute blockage of a coronary artery, stopping the blood flow to the heart muscle. This leads to severe damage of the heart and can result in cardiac arrest and death. Paramedic: Paramedics receive a one- to three-year education in handling emergency situations and are the basis of prehospital emergency systems worldwide. Prehospital Emergency Medicine: All efforts made by medical professionals to treat acute illnesses, life-threatening conditions and pain at the emergency site and to transport the patient – if needed – to a hospital. Stroke: Acute lack of oxygen in brain cells, caused by either bleeding or blockage of an artery. A stroke can lead to massive disability or even death. Trauma: Accidents, for example traffic accidents, leading to injuries. Severe accidents can result in massive bleeding, organ failure, and death.

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Mobile Telemedicine Systems for Remote Patient’s Chronic Wound Monitoring Chinmay Chakraborty BIT Mesra, India Bharat Gupta BIT Mesra, India Soumya K. Ghosh Indian Institute of Technology Kharagpur, India

ABSTRACT Telemedicine can be defined as the delivery of health care and sharing of medical information at a distance using telecommunication platforms. This chapter describes the implementation of a mobile telemedicine system for patient’s chronic wound (CW) monitoring using a smartphone. The system proved to be quick and reliable for providing health care at door step. The tele-wound technology network (TWTN) framework in telemedicine systems using smartphones for remote wound monitoring has been proposed. This framework is effective for both rural as well as urban people; it gives good performance in terms of wound monitoring and advanced diagnosis. The main objective of this work is to design and develop a TWTN system model that can acquire, process and monitor CW related problems with using a low cost smartphone to increase the overall performance of the system. Specifically, the TWTN system is developed for biomedical information like CW processing to monitor important patient information inexpensively and accurately.

INTRODUCTION Management of chronic wounds (CWs) is becoming a big challenge in medical health care globally. The skin is often referred as “the largest organ of the human body” as it stretches throughout the body parts. The skin is a first line of cover against infection, forms an insulating shield and also protects the body DOI: 10.4018/978-1-4666-9861-1.ch011

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against extremes of temperature, damaging sunlight, and harmful chemicals. As it forms the exterior of the human body, the skin is prone to be damaged by external and internal factors that lead to the formation of wounds. A wound is defined as a break in the skin due to an injury or damage, usually occurs when the integrity of the underlying tissue is compromised.

Chronic Wounds: Causes and Concerns Elderly people (over 60 years) are more prone to suffering due to chronic wounds. The non-healing wounds can persist for years, causing pain to patients and placing them at risk for secondary infections and loss of limb. All types of wounds may become chronic due to various conditions that stop or slow the wound healing process. These may include any of the following: • • •

•

•

•

Poor Blood Supply or Ischemia: The various conditions that may lead to tissue ischemia are low blood pressure, blocked or narrowed vessels, and various systemic diseases of the blood, heart, kidney, and lung. Infections: It may happen when large number of microorganisms gets into the wound by various foreign particles such as glass or metal. Devitalized necrotic tissue in the wound and underlying systemic disease such as diabetes can increase the risk of infection. Immune Suppression: The immune system helps in healing by reducing infection. Poor nutrition, radiation treatment, medical drugs used over a long period such as steroids and disease such as cancer and diabetes weakens the immune system. Emotional stress may also lower the immunity by increasing the cortisol levels (Snyder, 2005). Tissue Swelling: Tissue swelling decreases the blood flow in the wound area by increasing the pressure. Repeated physical trauma initiates the inflammatory process and leads to chronic wound formation. Swelling can also happen with conditions such as heart failure or blood vessel disease that cause decreased blood flow to the wound region. Age: Old age is also a contributing factor in the formation of chronic wounds (Mustoe, 2004). Aging skin of the older people is damaged more easily. Also an older cell does not proliferate as fast and lacks an adequate response to stress in terms of gene up-regulation of stress-related proteins. Malignancy: Cancerous tissue cam proliferates until the blood supply to the cells stops and tissue lead to the formation of an ulcer. Cancer may also develop as a result of an ulcer such as squamous cell carcinoma; probably repetitive tissue damage that stimulates cell proliferation may be the cause (Trent, 2007).

Patients are affected in many ways by various problems caused by the CWs such as pain, restricted mobility, economic burden and psychological stress all leads to the reduced quality of life. Pain varies with the type of wound as in diabetic ulcer there is no or diminished pain sensation, while in arterial ulcer there is a constant pain and in pressure ulcer intermittent pain is felt by the patient. Pain also varies from one patient to another. Wounds causing severe pain may reduce the quality of life by restricting the mobility of the patient that leads to the loss of earning. As CWs take long time to heal, therefore, combined with pain and inability to move the limb adversely affect the psychological state and behavior of the patient. A CW not only affects the individual, but the entire family gets suffered due to the enormous cost of wound care. Also it should be noted that without intensive medical care and treatment a wound

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will almost certainly never heal. The main effects of CWs are mainly delayed to repair or suspended skin re-growth, slow older cell proliferation rate, social isolation, depression, and a decrease in productivity, long-term and costly treatment. Wounds are caused by various acts such as a gunshot, surgical procedure or fall; by an infectious disease or by an underlying condition. Wounds can be classified in two types i.e. open and closed. Figure1 represents stages of open chronic wounds. The open wound is an injury, involves an internal or external break in body tissue. Most open wounds are minor and can be treated at residence. The five types of open wounds are abrasion (topmost layer of the skin is removed, painful), incision (smooth edges), laceration (irregular edges), puncture (deep, narrow wound) and avulsion (bleed heavily) respectively. In a closed wound, the skin surface is not broken so tissue damage and any bleeding occur below the surface.The types of close wounds are hematomas (damage to blood vessel) and crush injury (the extreme amount of force applied over a long period). Figure 2 represents wound creation and healing steps.

Figure 1. (a) abrasion, (b) incision, (c) laceration, (d) puncture and (e) avulsion wounds

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Figure 2. (a) Wound creation and (b) Wound healing

Classification of Wounds A wound can be defined as the interruption of continuity in a tissue usually following trauma. Wound healing also be defined as the physiological process by which the body replaces and restores the function of the damaged tissue. The wound tissue can be classified as three major categories like granulation, slough and necrosis. Granulation (G) tissue represents that growth of new tissue with a red color area that require moist wound healing products, slough (S) tissue indicates infection with yellow color area, requiring autolytic debridement and moist wound healing and necrotic (N) tissue represents the area of black dead tissue, needs to be removed respectively. Epithelization is defined as new epithelial tissue is a pink/white color and an infected wound is characterized by a green/yellow discharge and may have an offensive smell. The wound tissue classification systems can be considered from the aspects of severity Figure 3. Granulation, slough, and necrotic tissue

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(amount of tissue loss, infection is present), thickness, morphology (partial, deep and full thickness) and etiology (surgery, trauma, burns, pressure, venous, diabetic ulcer, radiation, amputation, disease-associated, immunosuppressive, pharmacological therapeutics and proliferative scars). The effects of acute wounds are bleeding, swelling, pain and fever, etc. The effect of this type of wounds is depression, pain, delayed or suspended skin re-growth, slow older cell proliferation rate, social isolation, decrease in productivity and Long term and costly treatment. The different types of wounds may become chronic due to various circumstances that stop or slow the wound healing process. These are infections, tissue swelling, immune suppression, age, malignancy and poor blood supply (Trent, 2007). Sibbald et al. (2000) proposed the wound bed preparation (WBP) model to control CWs and optimize practicable patient’s outcome. Janice et al. (1988) discussed that the clinicians are used to the red-yellow-black (RYB) color coding scheme for classifying the different wound tissue and digital image commonly represented as red-green-blue (RGB) color. Figure 3 depicts the different CW tissue types with color variation.

TYPES OF WOUNDS AND THEIR FEATURES The wound management aims at facilitating wound healing. Wounds may be classified by several different ways as the types and causes of wounds are wide ranging. In a broader way, wounds are classified in two types acute and CW as shown in Figure 4 (a) and (b) respectively. The two types of wounds are (a) acute wound and (b) chronic wound. Figure 4. (a) Image of acute wound: abrasion, (b) Image of chronic wound: pressure ulcer

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(a) Acute wound: heal within an expected time frame, there are several types of acute wounds like abrasions, burn injuries, penetrations or bites, avulsions and surgical incisions and traumatic injuries. This type of wound is defined as disruption in the integrity of skin and underlying tissue that progress through the healing process in a timely and uneventful manner that results in sustained restoration of anatomic and functional integrity. If this type of wound fails to heal within six weeks, it can become a chronic wound. (b) Chronic wound: according to Mustoe et al. (2006), chronic wound healing process is slow and stagnated; it does not heal through an orderly and timely reparative process over a period of three months to restore anatomic and functional integrity. The number of symptoms can be used to recognize the chronic wounds including the loss of skin and/or tissue surrounding the wound or by the amount of time it takes to heal. The author’s Moreo (2005), Mustoe (2004) discussed that CWs are mainly classified into four categories: diabetic ulcer (DU), pressure ulcer (PU), venous ulcer (VU) and ischemic or arterial ulcer (IU) respectively.

Status of Chronic Wound Treatment In India, there is a scarcity of wound data on the prevalence of diabetes, atherosclerosis, tuberculosis, leprosy, and trauma, all of which leads to development of CWs by one or other mechanism, also there is lack of adequate health facilities and appropriate treatment in rural areas where 70% of population lives, very few studies have been conducted to address the problem of CWs. The inappropriate treatment of acute traumatic wounds was the most widespread cause of the CW. One such study addressed the composition of the different type of tissue inside the wound by image processing based on color and pigmentation (Nayak et al., 2009).In rural India, a lot of patient’s can’t meet the expense of many of the wound related investigation. Indian patients especially for poor laborer who cannot give long periods of absence from their work with a large ulcer condition more than 5 cm in diameter, it may be suspicious for coverage to be effected with the help of a skin graft to reduce the morbidity and enhance faster healing. The different kinds of ulcers are debilitating and more painful, mainly reducing patient’s quality of life. Saraf et al. (2000) suggests a statistical performance like leprosy (40%), diabetes (23%), venous disease (11%), and trauma (13%) causes of lower extremity CWs. The wound assessment is a tedious assignment for clinicians as it requires periodic evaluation. According to the Indian epidemiological wounds, was reported as 4.5 per 1000 population whereas that of acute wounds was 10.5 per 1000 population (Gupta et al., 2004). The 5-7 million chronic or complex wounds occur each year in North America. An occurrence of patients with a wound was 3.55 per 1000 population in United Kingdom. The popularity of wounds were surgical/trauma (48%), leg/foot (28%), and pressure ulcers (21%). Prevalence of wounds among hospital inpatients was 30.7% (Vowden et al., 2009). In the United States, the healing cost of a unique pressure ulcer can be as much as $50 billion, and CWs affect around 6.4 million patients annually. The United Nations- 2009 report showed that it affects more than 2 billion European populations and the related cost of treatment is about 8 million Euros per year. In 2012, there were 24 million people who faced CWs problems worldwide. Also, it is anticipated that worldwide 380 million people will suffer from this highly branded disease by the year 2025 (Inter., 2006). In fact, the annual wound care management products market was valued $13 billion in 2008 and $15.3 billion in 2010 and also this is projected to reach $17 billion by 2015. The treating foot ulcers cost is estimated around $5 billion annually (Arthur, 2010). The International Diabetes Federation (IDF)

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Figure 5. BCC Research: Total Advanced Wound Care Market, 2009-2016. (BCC 2015)

Figure 6. Source: Medmarket Diligence, LLC - Statistical Chart for Global Prevalence (Idf’, 2009)

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estimates that the diabetes patients will reach almost 438 million by 2030 (Idf, 2009).The BCC research estimates that the total market for advanced wound care reached $3.4 billion in 2010, was $3.6 billion in 2011 and should reach $4.6 billion by 2016 at a Compound Annual Growth Rate (CAGR) of 4.9% (Figure 5). The correlation in between rapidly aging population and highest growth rates in occurrence with all ulcer types in global aspect are shown in Figure 6. CWs affect 6.5 million patients in the United States (Crovetti et al., 2004). In the Scandinavian countries, the associated cost accounts for 2- 4% of the total health care expenses (Guttrup et al., 2001). Although there is little variation in the treatment of the different CWs, appropriate management seeks to address the root cause of CWs, including ischemia, infection and imbalance of proteases (Mustoe, 2004). A typical treatment protocol is based on combination of the following interventions: • • • • • •

Proper cleansing of the wound with sterile water to reduce the possibility of infection. Debridement of dead necrotic tissue, foreign objects such as small stones, glass particles and dirt that can delay healing and lead to infection. Use of external compression bandages to reduce edema by encouraging movement of lymphatic fluids and blood through the veins. Use of wound dressings to absorb the wound exudate and maintain the moisture in wound area to promote and speed healing. Infection should be controlled by application of topical or oral antibiotics. If required wound should be treated surgically to promote wound healing.

There are limited accessing facilities to modern technology for managing the chronic wounds. According to clinical experience, high-quality regular care, especially for patients, self-management is necessary for accelerating wound healing. International statistics giving the complete depiction of the disability, occurrence and impairment of wounds are difficult to acquire. The etiologies of these conditions are numerous with regional, national and local specificities. The crisis of CWs is identified by other factors like poor access to the health care facility and infrastructure, low literacy rates, and insufficient clinical manpower. Also, inadequate education and clinical training in the basics of wound care significantly magnify the problem in India. If a wound is treated properly it will start healing and reduce in size in comparison to increased size of a deteriorating wound. CWs take a long time to heal over months or some times over a year or may not heal at all and become ‘indolent’. Wounds have a non-uniform mixture of red granulation tissue, yellow slough and black necrotic tissue as shown in Figure 7. Healing wounds have mostly red granulation tissue that shows proper blood circulation in the wound region and absence of any infection. Whereas yellow slough tissue is the due presence of pus, fibrous material and other cellular components accumulate as a result of infection in the wound. The black tissue is due to adherent necrotic or dead tissue in the wound which should be removed to improve the wound healing. Information about the size of wound area and percentage of each tissue in the wound area are important factors for determining the healing state of the wound and to evaluate and make necessary changes for the further treatment of the wound.

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Figure 7. Typical wound image with tissues of different colors

BACKGROUND Nowadays the internet is becoming one of the most preferred modes of communication. Figure 8represents the present patient’s situation like patient loses a day’s wage, has to pay for travel expenses, appointment with doctor is not guaranteed, many cases are extremely trivial and of non-emergency type and clinicians charge a lot of money. The CW affected patient’s information is collected by telemedicine systems through the faster network connectivity for processing and analysis. Rural people have been facing lot of problems like lack of education, gender inequality and explosive growth of population contribute to increasing burden of disease, poor educational status leads to non-utilisation of scanty health services and increase in avoidable health risk factors, the cost of treatment seems to raise everyday, which makes it unaffordable for a large chunk of the population, economic deprivation in a large segment of population results in poor access to health care and India faces high burden of disease because of lack of environmental sanitation and safe drinking water, under-nutrition, poor living conditions, and limited access to preventive and curative health services respectively. In India more than 75% patients are from villages and underprivileged states. They are poor and illness makes them poorer.

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Figure 8. Present situation for clinician’s checkup

Lack of awareness is a problem that is faced in building access to healthcare. Mass awareness is important since even if the treatment is free, unless the masses are educated and informed about the symptoms of the diseases, its repercussions and complications and finally the treatment available, there is no guarantee that people will avail these. The preventive measures can be taken to bring low-cost screenings to the patients. The major limitations of CW treatments are the lack of qualitative assessment, mostly dependent on color information, more ambiguities is there in detecting CW type, there is no information about percentage of wound tissue, inaccurate estimation of area and its monitoring over time, highly expert-dependent, limited access to modern technology for managing chronic wounds and there is no community database available in present days respectively. Moreover, in India, CW is compounded by demographic factors like low literacy rates, poor access to health care, inadequate clinical manpower, a poor healthcare infrastructure. There is however still a demand for a practical tool for automatic wound assessment, combining dimensional measurements and tissue classification in a single user-friendly system which is not only used in therapeutic follow-up in hospitals but could also be used for telemedicine purposes and clinical research, where repeatability and accuracy of wound assessment are critical. Automatic wound assessment tools integrated with the telemedicine facility will drastically reduce the health care cost as patient monitoring could be carried out from a distance, outside a hospital environment, in private homes properly equipped for telemedicine practice. CWs heal very slowly and the healing process may be further extended if an ineffective treatment is used. Accurate wound evaluation is a critical task for improving the efficacy and care of wound management. The clinician needs an objective wound characterization method to decide if the current treatment is adequate or require adjustments. The accurate wound measurement is an important task in CWs treatments, because changes in the wound size and proportions of different tissue types are indicators of the wound healing. Professionals dealing with wound patients need to make treatment decisions principally, but not solely based on their visual perception. Krouskop et al. (2002) stated that the wound assessment is based on two examinations (a) spatial measurements and (b) visual grading of the healing process.

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Traditionally only empirical traditional or manual methods are used by the health care professionals for spatial measurements such as rulers, sketches, transparency tracings and alginate moldings (Gethin, 2005). These methods give an only rough approximation of total wound area and no information about the proportion of different wound tissue types. Also these methods are time consuming, inaccurate and results in pain, infection and allergy to the patient. These evaluations depend on the experience of the clinician and is non-objective thus not eligible for validation of wound healing process. Digital color image processing is the most acceptable automatic method of wound assessment (Oduncu, 2004). Normal digital cameras and the smartphone could be used to acquire wound image quickly and store them for further processing. The different methods required for analyzing the wound image such as preprocessing, segmentation, feature extraction and classification are easily done by computers, although it is difficult task for a human (Albouy,2005). Digital image processing has many advantages over human assessment of wounds analysis. Evaluation is objective, more accurate, reproducible and faster. Affordability of high-speed computers makes possible the implementation of sophisticated classification algorithms. The wound analysis should include calculation of the wound area, and analysis of the color distribution within the wound. As manual methods of wound area calculation are invasive and inaccurate, the non-invasive digital planimetry method has the advantage of reducing the possibility of imparting infection as well as being faster and reproducible. In the digital planimetry wound region is manually delineated and area is calculated based on the total number of pixels in the region multiplied by a suitable scaling factor. Manual delineation of wound region is criticized as its performance is dependent on the human factor. By taking work load of the hospitals in consideration, the process needs to be accurate, reliable and faster. Also, any discrepancy in the assessment of wound area may increase the treatment duration that will extend the economic burden on the patient. Automation of manual digital planimetry by replacing the task of manual delineation with a wound measurement algorithm will exclude dependency on physician and reduce the probability of error due to the human factor.

Challenges in Chronic Wound Monitoring Methods: Conventional and Recent Advances Many people suffer from chronic wounds; it is a major problem in today’s healthcare systems worldwide. There is limited access to modern technology for managing CWs. The current traditional clinical wound assessments depend on simple spatial measurements like ruler-based methods (to measure the major and minor axes of the lesion), tape measurement method, transparency tracing and alginate cast etc. when the wound sample model is a rectangle shape, the area may be overestimated with less accuracy for smaller CWs. The 3D Kundin gauge (Kundin, 1985) ruler based method used to measure CW area and volume. The visual grading of healing process mainly depends on the area that is shown in Figure 9. Such types of subjective evaluations are inaccurate, inconsistent, inefficient and painful. The ambiguities and intra-observer variation affect wound classification. The manual errors include inconsistency of the data, loss of reliability and redundancy. The physical dangers to the data are ageing of the paper documents, water, and bugs respectively. Figure10 depicts the manual, error-prone patient monitoring systems. The treatments of CWs include monitoring color and size of the wound at regular intervals. The wound evaluation process is based on qualitative observation and manual measurements of wound. Most of the people reported re-occurrences even after a long period of treatment. The critical task is

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Figure 9. (a) Transparency sheet tracings, (b) (c) Saline injections

Figure 10. Manual, error-prone patient’s monitoring system

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to perform an accurate diagnosis and select a suitable treatment. The major parameter like color may provide relevant information about the tissue type and inflammation. The manual methods give higher inaccuracy. The transparent film is used for tracing the outline with a permanent marker. This transparent film approach is prone to human error. The number of partial squares of the grid inside the outline and the thickness of the marker may cause some less accuracy. The planimetric and volumetric methods are highly invasive and are not commonly used clinically. Table 1 depicts the features of different devices for CW healing monitoring. The semi-automatic and or automatic process gives better results with respect to traditional methods. Mankar et al. (2013) have discussed 2D and 3D techniques for wound measurement. 2D technique is easier to implement in favor of the 3D. 2D techniques are influenced by lighting conditions, camera position and angle of acquisition. 3D techniques could produce more metrics from the wound such as perimeter, depth, area, and volume. More specialized equipments are required. The new non-traditional techniques are required that increases accuracy in size measurements while reducing inter and intra-observer variability. They are low-cost, multispectral, hyper-spectral, hi-tech medical Table 1. Features of different medical devices for CWs Devices

Main Features

Limitation

Cost

Measurement of Area and Volume Instrument System (MAVIS) (Plassmann et al., 1998)

To measure area and volume of skin wounds, ulcers and pressure sores Small and deep wounds can be measured with greater precision It is fast and comparably more precise than existing traditional techniques Frequently monitoring is possible with higher consistency

It cannot measure the depth of wound or ulcer Lacking of interconnectivity between patients and clinics

Expensive

Stereophotogramm-etry (SPG) (Sprigle et al., 2006)

Multiple images are taken of the same wound with slightly different angle Easy to perform Gives an accurate and repeatable result

Lacks accuracy Complex systems

Expensive

Wound measurement device (WMD) (Nemeth et al., 2010)

Depth measurement is possible Increased reliability as compared to conventional methods with high precision

Poor accuracy

Moderate

ARANZ Medical Silhouette (Silhousette, 2010)

High-quality wound image handling capability Robust, portable, easy to use and gives an accurate outcome

Poor accuracy

Expensive

Advanced Wound Assessment and Measurement System (AWAMS) (Casas et al., 2011)

Interface of video camera with touch pad Calculate wound area and percentage of tissue types

----

Expensive

Medical Digital Photogrammetric System (MEDPHOS) (Malcolm, 2013)

Provide high speed and simple application Robustness and reliability Precision and accuracy

----

Low

DERMA device (Nila et al., 2013)

Measure the time evolution of CWs Provides uniform interface to manage data High precision

Complicated

Moderate

Verge Videometer (VeV) (Williams, 2009)

Determine wound measurement using accurate perimeter-based algorithm

Lack of accuracy

Quite costly

Stereophotogrammetry (SPG) (Thawer et al., 2002)

Wound volume determination performed Reliable and precise computer assisted technique

In accuracy

Low cost

Compression Therapy Device (Robson et al., 2006)

To reduce the swelling and aid in the healing of a CW Used for chronic venous insufficiency

Poor healing rate Complex

Moderate

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imaging combining with 3D surface and user-friendly. The computer-based diagnosis gives a good result with respect to manual observation. The wound shape and size are the vital parameters in clinical and basic research for test and effectiveness. The computerized wound documenting systems like digital VERG (Vista Medial Ltd., Winnipeg, Manitoba, Canada) and VISITRAK (Smith & Nephew Wound Management, Inc, Largo, FL) provide the capability to measure the CW length and width automatically (Haghpanah, 2006). The VERG and VISITRAK software provide estimates of the CW length, width, area, and volume. DigiSkin software uses digital photography for picture processing that provides CW length, width, depth, area, and volume (Korber et al., 2006).

Telemedicine for Remote Wound Monitoring According to Bashshur et al. (1997) comment “Telemedicine involves the use of modern information technology, especially two-way interactive audio/video communications, computers, and telemetry, to deliver health services to remote patients and to facilitate information exchange between primary care physicians and specialists at some distances from each other”. Telemedicine can be defined as the delivery of health care and sharing of medical information over a distance using telecommunication platforms. The main aim is to provide expert based medical care service to any place that health care is needed. The telemedicine concept was introduced about thirty years ago when telephone and fax machines were the first telecommunication means used. Telemedicine is used in various domains especially emergency health care unit, tele-cardiology, tele-radiology, tele-opthalmology, tele-dermatology, tele-ophtlalmology, tele-oncology, tele-psychiatry and remote monitoring (including monitoring at rural health centers, home monitoring and subject monitoring at distant or isolated locations) over wireless platforms.A telemedicine system offers reliable and accurate diagnostic services to patients at affordable prices.The crisis of good medical doctors, nurses, clinics or hospitals, and expensive expenditure incurred during medical treatment, increase the seriousness of the problem. Due to unavailability of these factors telemedicine is needed to collect the vital patient’s information remotely through computer added diagnostics and or high resolution camera based portable smartphones. The main goals of telemedicine are improved access to health care facilities for rural areas, gives clinicians better accessibility to tertiary consultation, gives clinicians access to conduct remote examinations respectively, improving patient care, reduces health-care costs and patient transfers to secondary and tertiary care centers respectively. Telemedicine allows new technologies in sensing, medical imaging and wireless data communications with much lower cost, enabling the development of new widespread remote medicine initiatives. A medical imaging system is used in the telemedicine system for capturing CW images of a patient’s skin. Skin conditions are common in the developing world, often showing up as symptoms of other diseases. The practice of tele-dermatology requires high resolution, properly illuminated color images and video. Choosing appropriate wound image quality for telemedicine is important to ensure accurate diagnoses on the part of the clinicians, while keeping the network traffic down. Advanced data compression methods and rapid growth of bandwidth are required for telemedicine systems. Telemedicine systems are broadly categories in two type’s i.e. real-time telemedicine system and store-and-forward approach respectively. In the real-time mode, the clinician is present during the capture process through a videoconferencing system and streaming live data can be sent to TMH. The store-and-forward approach in a telemedicine system consists of a tele-medical Agent (TMA) capturing patient data independently (images, text files, patient symptoms in a text file, etc.)

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and then sending them over the Internet to a clinician who can asynchronously and non-interactively perform a diagnosis(Chakraborty et al., 2014).The home-based telemedicine service provides TMA to monitor physiological changes, test results, images with proper clinical evidence. In this work, we have proposed the TWTN framework in telemedicine systems for remote wound monitoring. This framework is very much effective for rural peoples as well as urban people, it gives better performance in terms of wound data monitoring and advanced diagnostic. The different sensors are associated with telemedicine systems for acquiring the medical images from the patient. Oduncu et al. (2004) discussed that the most acceptable automatic or semiautomatic wound analysis is color image processing. The wound image could be quickly acquired using the ordinary cameras and or smartphones and store for further processing. Accurate and faster wound image analysis is possible with the help of a digital image processing technique rather than error-prone manual observation. The wound analysis includes estimation of wound area and color distribution within the infected portion of the wound. The area of the wound is measured based on the total number of pixels in the region multiplied by a suitable scaling factor.

Telemedicine Using Smartphone The CW problem is a serious issue, it can be solved using a digital platform like smart phone, and remote patient’s become benefited. The smartphone has been recognized as a possible tool for telemedicine system (Hung et al., 2003). The TMA can collect the wound data using a high-resolution camera based on a smartphone and send it to the TMH for better treatment consultation. When TMAs return to a medical health information exchange hub where the internet is available, the collected local information will be forwarded to a web-based multi-specialty telemedicine service. Appropriate returning messages will be routed to local providers through physical delivery to ensure information integrity. Phone calls or text messaging will be used only during a medical emergency. With lessons learned from this field study, we will establish a workable model of health information exchange and telemedicine to connect rural patients with medical services available only in the cities. In this work, the store-and-forward approach has been used for transmitting the patient’s wound image and other clinical information. The store-and-forward system eliminates the need to have the patient and clinician available at the same time. Telemedicine systems are becoming more demanding by providing advanced features of the smartphone and better computing services. Telemedicine systems are also maintained stored patient’s data in the database.

Wireless Body Sensor Networks (WBSN) The WBSNs related various issues and challenges were addressed here that’s are emergency medical care, reliable transmission of vital patient’s data, low cost and better quality of service, need for extremely low power operation, lightweight, avoidance of wearable/implantable sensors, maintain security and privacy, real-time connectivity to heterogeneous networks, low complexity, standardization and interoperability respectively. The Advanced Topometric sensor (ATOS-II) is a stereo-photographic system that measures a 3D map of the wound surface. The wound images captured are analyzed by software (Chulhyun et al., 2008). Present and emerging developments in today’s communications integrated with the developments in Microelectronics and Embedded system technologies will have a drastic impact on future patient monitoring and health information delivery systems. The important challenges are bandwidth limitations, power consumption and skin or tissue protection.The body sensor network is a set of several nodes distributed over the body to collect the physiological information. These networks are usually meant for the

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acquisition of data. The WBSN infrastructures are complex and need many functional support elements. Biosensors are attached to the human body for remote patient’s health monitoring with extremely high mobility. The wireless body area network consists of three types of nodes like (a) Implant-BAN – used for internal communication between the inside of the body where sensors and actuators are connected to the BAN coordinator that serves as a data acquisition centre (DAC), (b) External-BAN - for external communication between sensor nodes surrounding the body and the outside world, not contact with human skin, (c) Surface-BAN – placed on the surface of the human skin (Reinzo et al., 2009). These data is collected by telemedicine systems through the faster network connectivity for processing and analysis. Chen et al. (2011)present the difference between the wireless sensor network and BAN in terms of mobility, data rate, latency, node density, power supply, network topology, node replacement, security level etc and also compare with the existing body sensor nodes (Omeni et al., 2007),(Sofia et al., 2011),(Deena et al., 2012). Table 2. Detailed description of various projects Projects

Main Features

CodeBlue

Provides higher priority based scalable and robust patient’s medical monitoring.

MobiHealth

Allows continuous health monitoring fastly and reliably.

AlarmNet

Uses for medical data analysis and long-term health monitoring

AMON

Provides continuous collection and evaluation of various medical vital signs.

MERMOTH

Used to monitor patient’s vital signs comfortably using wearable interface and supports parallel data management service.

WiMoCA

Used to monitor patient’s movement with high flexibility.

CareNet

Monitors remote healthcare with highly reliable and secure way.

AID-N

Performs data delivery efficiently and treats a large number of patient’s.

SMART

Provides viable method for monitoring at risk patient’s in the waiting areas of an emergency department.

ASNET

Provides dynamic data query to allow the clinicians to monitor patients at any place via the web or smartphones.

MITHril

Provides healthware facility with highly flexible way and reduce medical costs.

LifeGuard

Monitors patient’s vital signs.

LifeShirt

Monitors patient’s health in real time basis.

HealthGear

Monitors, visualize and analyzes patient’s physiological data.

Ubimon)

Monitors patient’s physiological states continuously.

eWatch

Used in elderly monitoring and context aware notification.

(Blo’07)

Provides timely accessibility to the patient’s health status remotely. Improves patient’s quality care and life.

HeartToGo

Detects abnormality of cardiovascular disease.

MagIC

Detects ECG, respiratory activityand monitors patient data via wireless transmission scheme remotely.

Vital jacket

Monitors patient’s vital signs

M-Health

Continuously monitors ambulatory settings, early detection of abnormalities and supervised rehabilitation.

iSIM

Supports sensing of location; position; obstacles; range finding; temperature; sound; and vision.

WHMS

Monitors patient’s vital signs closely and provides optimal health status.

MIMOSA

Uses low-power, optimized for flexibility and smart sensor based architecture.

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A number of ongoing projects like firmware based CodeBlue(Shnayder et al., 2005), MobiHealth(Konstantas et al., 2003), AlarmNet(Wood et al., 2008), Advanced care and alert portable telemedical monitor (AMON) (Anliker et al., 2004), MagIC(Rienzo, 2005), Medical remote monitoring of clothes (MERMOTH) (Weber et al., 2006), Microsystems platform for mobile services and applications (MIMOSA) (Jantunen, 2008), Wireless sensor node for a motion capture systems with accelerometers (WiMoCA) (Farella et al., 2008), CareNet(Jiang et al., 2008), Advanced Health and Disaster Aid Network (AID-N) (Gao et al., 2007), SMART(Curtis et al.,2008), ASNET(Sheltami et al., 2006), MITHril(Pentland, 2004), Wearable health monitoring systems (WHMS) (Mienkovic et al., 2006), NASALifeGuard(Montgomery et al., 2004), non-invasive LifeShirt(VivoMetrics, 2002), iSIM(iSIM, 2015), HealthGear(Oliver et al., 2006), Ubiquitous Monitoring (Ubimon) (Jason et al., 2004), eWatch(Maurer et al., 2006), Vital jacket(Cunha et al., 2010), M-Health(Jovanov, 2005), Personal Care Connect (Blount, 2007) and HeartToGo(Jin et al., 2009) have contributed to establish a practical solution for WBAN that detailed described in Table 2. Sana et al. (2009) highlights theefficient power solutions towards in-body and on-body sensor networks. According to the World Health Organization (WHO’s) report, approximately 17.5 million people die due to heart attacks each year, more than 246 million people suffer from diabetes, it will be increasing up to 380 million by 2025 and almost 20 million people will die from cardiovascular disease in 2025 (World, 2010). So these deaths can be prevented with the help of the WBSN based telemedicine system. A typical WBSN consists of several sensor nodes with a low power constraint, each acquiring a specific physiological parameter from the human body. These nodes act as a bridge between the patient and technology enabled devices. We can easily diagnose the patient health status and early detection of abnormalities is also being possible using sensor nodes. So the mortality rate can be minimized. An efficient WBSN requires sensors with properties of portable, lightweight, low-power, miniature, and autonomous sensor nodes that monitor the health-related applications.

Tele-Wound Technology Networking (TWTN) Most of the population living in rural areasthey have been facing many problems due to shortages of healthcare capacity in India. The affordable connectivity is required in rural areas where connectivity is dense. Smartphones which integrated the TWTN system have the potential to provide cost-effective treatment to the rural area. The TWTN health will be more demanding by technology implementation, consumer demand and advancement of infrastructure and also providing the scope to add new diagnostic and interactive features. To design a TWTN system that efficiently provides the above-mentioned objective, challenges need to be addressed. The proposed system suggests the following high level requirements for the right solution: 1. Accessibility: to implement the methods to collect, process, analysis and monitor patient’s vital sign from remote to urban accessibility. 2. Scalability: to support efficient remote monitoring and computerized data analysis of large patient populations. 3. Reliability: to provide reliable communication between TMA and tele-medical hub (TMH). 4. Manageability: to manage all patient centric control information. 5. Flexibility: to be open, flexible and extensible. 6. Security: to protect the data when processing for getting best treatment over networks.

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7. Throughput: to provide high throughput in terms of data processed over heterogeneous networks. 8. Storage: to increase the large volume of patient’s data storage. 9. Cost: to minimize the cost. The cost is associated with network bandwidth, use of computation, storage space, monitoring, accounting and billing, software services and content delivery. The cost can be increased using of paid networks but the TWTN system minimizes the cost by the minimal use of network involvement. 10. Optimization: to provide network to the patient inexpensively. 11. Visualization: powerful visualization is required by the advanced processor for patients’ data analysis. 12. Easy up-gradation: the TWTN system can be upgraded easily with the changes in the medical information time to time. Our proposed TWTN system used to collect patient’s vital signs automatically via the internet from medical sensors to TMH for storage, processing, analyzing and distribution. TWTN used four parts like voice and video over internet protocol, the web-based electronic health records, cloud services, wireless broadband technology, advanced wound healing products. The CWs are not defined by size. The color digital image processing is the most commonly automated method for wound analysis. The accurate wound assessment in the TWTN system is an essential skill required by all practitioners to be able to plan effectively, implement and evaluate patient’s care. This system helps to develop a proper management plan for monitoring CW-related problem solving. Every CW should be taken to repair at least three to six months, so a regular medication related update is required. The TWTN system provides to patient’s such medication. The wound assessment is a complex process, the psychosocial needs of the patient. TWTN system maintained documentation accurately and carefully for identifying vital signs of improvement or deterioration. The TWTN system needed the smartphone solution to deliver CW care in a patient’s home. This system created its network-based tele-health CW management system to address the systematic issue in the provision of wound care. TWTN followed National Institutes of Health (NIH) and Agency for Health Research and Quality (AHRQ) guidelines for wound care and programmed them into its electronic health record system to standardize protocols for its TMA. The proper treatment of this type of wounds in the help of the TWTN system is very useful in minimizing morbidity and possible mortality.

Mobile Networking The electronic health (eHealth) services are driven by computers and other medical devices in the global market. Smartphone usability has increased drastically in recent years. Mobile health (mHealth) is a part of eHealth used to the delivery of healthcare services or medical information with a smartphone. Smartphones enable the TWTN heath care platform to provide cost-effective treatment, high-quality medical services and free public health care system. This TWTN system is used for acquiring, transmitting and monitoring CW status from rural to good facility added clinics by easy-to-use interfacing in smart phone technologies. It gives clinicians the possibility to monitor emergency home bound patients using telemedicine fastly, accurately and precisely. Telemedicine (Wood et al., 2008) is an emerging field in advanced communication systems and medical informatics, able to deliver the healthcare data and sharing of medical expertise using wireless technologies (GSM/WLAN/SATELLITE/2G/3G/4G) in the

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span of tele-oncology, tele-pathology, tele-radiology, emergency healthcare and tele-dermatology. The high featured smart phones like Xolo, APPLE, Nokia, SAMSUNG GALAXY TAB, Windows Phone, BlackBerry, iOS, Canon, Adobe, ANDROID platforms can be used for sensing, acquiring the variety of medical data collection and monitoring applications. The portable system like the smartphone used in telemedicine with inbuilt high-resolution camera capture digital images and computing and networking features allow direct interaction. Meum (2012) discussed the implementation and use of an Electronic Medication Management System (EMMS) using new technology to reduce the incidence of serious errors. The huge numbers of rural people in the world have been suffering from different types of wound. However, due to the lack of trained clinicians, this adds up in suffering populations. The huge improvement and development in mobile communication throughout the world reduce the problem up to some extent. Our medical experts with telecommunication engineering are trying to mitigate these issues. The portable, handheld device like the smartphone can be used to capturing high-quality wound images and acquiring patient’s demographic information and send it to TMH through secure, web based medium. The telemedicine based wireless body area networks can be used for continuous remote patient monitoring (Chinmay et al., 2013) where wound image collected by a high-resolution camera based on a smartphone through the image sensor. Tele-monitoring provides the patient’s vital signs regularly for reducing time and cost (Stephane et al., 2005). Given the prevalence of smartphones with high-resolution camera, monitoring CWs by real-time taken images would be an efficient and convenient method. The wound images are recorded with a pocket digital camera, SONY Cybershot, FujiFilms, Panasonic and CANNON camera etc. CW images are automatically converted to the JPEG format by the camera and transferred to a compatible computer. Some limitations of wound image acquisition are low image resolution, color quality and constancy, pose uncertainty due to target movement, reflections due to skin nature and illumination constraints, shading and noise and variable environment illumination respectively. The noise can be eliminated through appropriate filtering using median filtering to assist color information (Umbaugh et al., 1997). CW images are captured by a normal digital camera with varying lighting conditions in the examination room. This causes wounds to appear with different pixel intensities. The relative distance between camera and wound site vary significantly. Therefore images must be pre-processed to correct the variations in illumination. General illumination correction is based on the multiplicative relationship between illumination and reflectance (Land et al., 1971). White balance (Lam, 2005) adjustment and illumination correction for dermatological digital images (Glaister et al., 2012) have been reported in the literature. Automation of manual digital planimetry by replacing the task of manual delineation with a wound measurement technique will exclude dependency on clinician and reduce the probability of error due to the human factor. Results remain too dependent on image capture conditions, sample database building, region descriptor selection, tissue class learning protocol, etc., preventing reproducible results from being obtained within the complete image processing chain. Several features contribute to making automatic classification difficult. First, wound image acquisition requires technical skill, especially in the patient room where lighting is not controlled. At close range, the depth of field remains always limited in macro-mode, ambient light is insufficient and may easily result in fuzzy images. Moreover, the patient is rarely able to maintain a convenient posture for a snapshot. The TWTN system can collect remote CW related information and send it via communication platform to TMH for better treatment that shown in Figure 11.

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Figure 11. Tele-wound technology network for remote patient’s monitoring

FUTURE RESEARCH DIRECTIONS The tele-medical Agent (TMA) could capture the wound image with demographic (vital sign) information using smart phone and send it to tele-medial hub (TMH) to classify the wound tissue type. The clinician gives feedback to the patients based on a percentage of wound tissue. In future, a better authentication mechanism is required for sending the multiple numbers of patient’s information through the transmission medium. Availability of videoconferencing and tele-wound monitoring in problems provides more flexibility and improvement in rural, remote care. In future, we will propose an algorithm for the tele-truma case where patients can upload their images and videos of the wound for better treatment. However, advanced metadata protection mechanisms are required for sharing wound images. The big challenge is in managing large volumes of medical data that can be integrate with electronic health records for big data analytics purposes. This novel approach can be used in data mining application and public health monitoring systems.

CONCLUSION The patients of chronic wounds, especially in remote locations, often face various challenges like the unavailability of good clinicians and specialty care in rural area. However, many cases are extremely trivial and of no-emergency type. Telemedicine is becoming an effective means for providing fast and efficient diagnosis in the treatment of CWs, and several other diseases. The TWTN have been a break-

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through in providing quick and effective service to the patients. They can also maintain an e-prescription for referral case. Hence time and cost could be saved, and clinicians can work from anywhere using a smartphone. A handheld computing device like personal digital assistant is used to monitoring patient’s remotely. CWs represent not only a burden to individuals affected, but also a burden to the medical health care system. Information about the percentage of each wound area is a vital issue determining the factor for the healing state of the CW, allowing evaluation of the treatment efficiency and further treatment decisions. The clinical documentation in wound care is required for the recording of clinical information, communicating clinical data, treatment planning, billing, quality assurance, standardizing care and medico-legal reasons. The smartphone-based telemedicine system provides the following advantages: Increases the accessibility, quality of patient’s care and focuses on preventive medicine through early intervention, reduces the overall healthcare cost, provides the best services to remote people, reduces the need for transporting patients from house and specialized clinics, getting the best consultation by medical experts, makes specialty care more accessible to rural and medically underserved areas, improves communication between rural-to-urban providers, supports high patient and provider satisfaction.

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Sibbald, R. G., Williamson, D., & Orsted, H. L. (2000). Preparing the wound bed: Debridement, bacterial balance and moisture balance. Ostomy/Wound Management, 46, 14–35. PMID:11889735 Silhousette, T. M. (2010). Advanced wound assessment and management system. Retrieved March, 2014, from http://www.aranzmedical.com Snyder, R. J. (2005). Treatment of non-healing ulcers with allografts. Clinics in Dermatology, 23(4), 388–395. doi:10.1016/j.clindermatol.2004.07.020 PMID:16023934 Sofia, N., & Rabiah, A. (2011). Surveying the wireless body area network in the realm of wireless communication. IEEE 7th Int. Conf. on Information Assurance and Security. Sprigle, S., Nirmal, P., Aditya, J., & Thad, S. (2006). Handheld, non-contact wound measurement device. Clinical Symposium on Advances in Skin & Wound care, 379. Stephane, M. (2005). The current state of Telemonitoring: A comment on the literature. Telemedicine Journal and e-Health, 11(1). PMID:15785222 Thawer, H. A., Houghton, P. E., Woodbury, G., Keast, D., & Campbell, K. A. (2002). Comparison of computer-assisted and manual wound size measurement. Ostomy/Wound Management, 48, 46–53. PMID:12378003 Trent, J. T. (2007). Wounds and malignancy. Advances in Skin & Wound Care. PMID:12582304 Umbaugh, S. E., Wei, Y., & Zuke, M. (1997). Feature extraction in image analysis. IEEE Engineering in Medicine and Biology, 16(4), 62–73. doi:10.1109/51.603650 PMID:9241522 VivoMetrics. (2002). Three More Studies Use LifeShirt System to Assess Treatment Protocols, Ambulatory Intelligence. The Journal of Ambulatory Monitoring, 1(1). Retrieved from http://www. vivometrics. com/newsletter/columns.html#article_01 Vowden, K., Vowden, P., & Posnett, J. (2009). The resource costs of wound care in Bradford and Airedale primary care trust in the UK. Journal of Wound Care, 18(3), 93–98. doi:10.12968/jowc.2009.18.3.39814 PMID:19247229 Waldbusser S. (2000). Remote Network Monitoring Management Information Base. STD 59, RFC 2819. Weber, J., & Porotte, F. (2006). Medical remote monitoring with clothes. Int. Workshop on PHealth. Williams, C. (2009). The Verge Videometer wound measurement package. British Journal of Nursing (Mark Allen Publishing), 9(4), 237–239. doi:10.12968/bjon.2000.9.4.6383 PMID:11033643 Wood, A., Stankovic, J., Virone, G., Selavo, L., He, Z., Cao, Q., & Stoleru, R. et al. (2008). Context aware wireless sensor networks for assisted living and residential monitoring. IEEE Network, 22(4), 26–33. doi:10.1109/MNET.2008.4579768 World Health Organizations. (2010). Global status report on non communicable diseases. WHO. World Health Organization (WHO). (1998). A health telematics policy in support of WHO’s Health-ForAll strategy for global health development: report of the WHO group consultation on health telematics. WHO.

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KEY TERMS AND DEFINITIONS Chronic Wound: Chronic wounds are defined as wounds, which have failed to proceed through an orderly and timely reparative process to produce anatomic and functional integrity over a period of three months (Mustoe et al. 2006). Telemedicine: The delivery of health care services, where distance is a critical factor, by all health care professionals using information and communication technologies for the exchange of valid information for diagnosis, treatment and prevention of disease and injuries, research and evaluation, and for the continuing education of health care providers, all in the interests of advancing the health of individuals and their communities (Wor, 1998). Remote Monitoring: Remote network monitoring devices, often called monitors or probes, are instruments that exist for the purpose of managing and/or monitoring a network. Often these remote probes are stand-alone devices and devote significant internal resources for the sole purpose of managing a network. An organization may employ many of these devices, up to one per network segment, to manage its internet. In addition, these devices may be used to manage a geographically remote network such as for a network management support center of a service provider to manage a client network, or for the central support organization of an enterprise to manage a remote site (Waldbusser 2000). Smartphone: A Smartphone is a type of mobile device built on a mobile computing platform with more features, connectivity, and computing ability than a regular cell phone (Muto 2012). Tele-Wound Technology Network: Tele-wound technology network is a combination of networks that specifically used to monitor the wound status. The main purpose is to collecting the patient’s wound image using Smartphone under telemedicine system for remote monitoring. Wound Database: A large volume of patient’s wound images along with vital sign can be stored at particular place called wound database. Wound Tissue: Wound tissue is a biological cellular structure which has been injured. The wound tissue can be classified as three major categories like granulation, slough and necrotic.

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From Telecytology to Mobile Cytopathology: Past, Present, and Future Abraham Pouliakis National and Kapodistrian University of Athens, Greece Stavros Archondakis 401 Military Hospital, Greece Niki Margari National and Kapodistrian University of Athens, Greece Petros Karakitsos National and Kapodistrian University of Athens, Greece

ABSTRACT Cytopathology is a popular discipline since George Papanicolaou proposed the famous test pap about 60 years ago. Today modern cytopathology laboratories still use the microscope as the primary diagnostic device and additionally they host modalities performing medical tests and exchange data via networks and have imaging systems producing pictures and virtual slides; the volume of produced data nowadays is enormous. Simultaneously mobile phones and tablets have evolved; their capabilities compete desktop computers and have the advantage of being always connected and at the side of users. Despite there are rather limited applications relevant to cytopathology for the mobile device, there is potential for uses in numerous activities of the cytopathology laboratory, including and not limited to: training, reporting, diagnosis and consultation, laboratory management, whole slide imaging, interactions between patientdoctor, doctor-doctor and within the laboratory personnel, quality control and assurance. Mobile devices can offer important benefits to the modern cytopathology laboratory.

DOI: 10.4018/978-1-4666-9861-1.ch012

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 From Telecytology to Mobile Cytopathology

INTRODUCTION Mobile Health (mHealth) is the practice of medicine and public health supported by mobile devices. The use of mobile computing and communication devices, such as mobile phones, tablet computers and personal digital assistants (PDAs), by health professionals, is nowadays rapidly growing. The mHealth applications are mainly used for collecting community and clinical health data, delivery of healthcare information, real-time monitoring of patient vital signs, and direct healthcare provisioning. In more detail, handheld computing has applications such as ambulatory medicine (K. Banitsas et al., 2005; K. A. Banitsas et al., 2006; Kiselev et al., 2012; Pavlopoulos et al., 1998; Rosales Saurer et al., 2009; Zerth et al., 2012), diabetes management (Quinn et al., 2011; Ribu et al., 2013; Skrovseth et al., 2012; Spat et al., 2013), asthma management (Finkelstein et al., 1998; Gupta et al., 2011), control of obesity (Patrick et al., 2009), smoking control (Ghorai et al., 2014; Ybarra et al., 2014), seizure management (Pandher et al., 2014), stress management (Clarke et al., 2014) and treatment of depression (Burns et al., 2011) among others. In the field of mHealth the majority of applications are for fitness (43%) followed by health resource (15.0%) and diet/caloric intake (14.3%,), while the user angagement has the form of self-monitoring and training (74.8%) (Sama et al., 2014); in contrast, despite there are applications targeting patinets, currently, there are rather limited applications targeting physicians and doctor-patient interactions (T. Martin, 2012). Pioneering field seems to be radiology consultation for X-rays and mostly Computer Tomography (Choudhri et al., 2012; Choudhri et al., 2013; Johnson et al., 2012; Toomey et al., 2010) and ECG transmission (Vaisanen et al., 2003). The applications are very limited in the fields of pathology and even less in cytopathology despite both specialties deal with images. In relation to pathology, the most reported uses of handhelds, are limited to experimental endeavors in education and telemedicine (Park et al., 2012). For cytopathology, even after a thorough search, there were not found published articles or reports. However, pathology and cytopathology share many common characteristics. Actually, in most countries, cytopathology is considered as a subspecialty of pathology. Thus concepts and ideas can be useful to both specialties; therefore applications can be transferred from one domain to the other. Cytopathology is a medical sector/discipline that gained popularity when George Papanicolaou proposed the famous test Papanicolaou (known as Pap Test), about 60 years ago. Nowadays Pap Test is the most valuable tool for cervical cancer screening and prevention. Therefore, it is the reason that cytopathology is so popular. Additionally, cytopathology has the advantage of obtaining biological material using minimally invasive or not invasive at all methods. Cytopathology is a discipline that the diagnosis is mainly based on the examination of cells via the microscope, histopathology (or pathology) does the same; however using complete tissues (obtained via biopsy). The routine cytological examinations are performed, since the invention of the microscope, via the utilization of a glass slide and subsequent visual analysis. Today, the modern cytopathology laboratory is continuously changing. Nowadays, cytopathology laboratories perform additional examinations based on molecular techniques and immunocytochemistry methods. The modern cytopathology laboratory is equipped with a lot of modalities, these are capable of performing medical tests, as well as to exchange data via networks, there are available as well imaging systems, capable to create digital pictures of the slides or even virtual slides, which are complete slides in electronic format. The volume of data, in a cytopathology laboratory, nowadays is enormous; there are many applications that are available, and others that can be envisioned for the benefit of cytopathologists and the patients. The recent advances in handheld hardware and software; in parallel, with concurrent advances in whole slide imaging (WSI) and cloud computing, offer new opportunities 241

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and challenges for cytopathology. Cloud computing (one of the foundations required for mHealth) has already been proposed as a useful platform for modern cytopathology (Pouliakis et al., 2014a) as well as in other medical laboratories (Pouliakis et al., 2014d). In this chapter we address the current state of the art in handheld hardware and software, provide the history of handheld devices, with special focus on pathology and cytopathology, and present use cases that have the potential to be future applications. Various aspects of the modern cytopathology laboratory are analyzed as well, we present a thorough research of mobile applications, related to cytopathology and try to foresee applications, that, if enhanced with mobility and are available on mobile devices, they may provide an important benefit to the modern cytopathology laboratory, as well as to the patients. Wherever possible, we propose new mobile applications, having the potential to enhance the routine of the laboratory. Finally we highlight issues, controversies and problems and try to propose solutions and recommendations.

BACKGROUND In a two year old research (Park et al., 2012), an exhaustive search in PubMed revealed more than 6,500 articles on the topic of handheld computing, publication dates were from 1983. Of those, 55% were related to the usage of handhelds in medicine, rather than health risks caused by handheld devices (for example risk of cancer from radiation, likelihood of automobile accidents or electromagnetic interference in hospital devices). The vast majority of articles were published in the last decade; for instance, two articles were published in the year of 1983, compared to 61 articles in October 2011 alone (Park et al., 2012). A PubMed search for the terms cytology and handheld revealed 31 articles of which most are on miniaturized devices (for example mobile Flow Cytometer). However, only nine articles in the entire medical literature that deal directly with handheld computing in pathology there were found, ranging in publication date from 2004 to 2015. Based on the available literature, some conclusions may be extracted. Almost half of the scientific articles deal with static telepathology using mobile phone cameras, but there is only one paper that discusses whole slide imaging and about 25% of the articles deal with medical education. Despite not reported in the scientific medical literature, there are other innovative uses of handhelds in pathology. Nowadays, several reference laboratories, offer handheld applications that their clients can use to order tests and receive the results. There are available applications, that exploit the built-in phone camera, such as taking photographs through the eyepiece or interface a phone with a microscope directly (Breslauer et al., 2009). Concluding, the scientific literature, related to mobile pathology and cytopathology can be considered rather poor. Perhaps there are multiple possible reasons for this. Pathology, despite not being the sole medical specialty in need to manipulate data and/or images, it is unique in the scale of data that must handle. It is considered that over 70% of the data in a typical electronic medical record are generated within the clinical pathology laboratory (Pantanowitz et al., 2007). In anatomic pathology, a Whole Slide Image (WSI) can easily be in the range of gigabytes in size, even when heavy compression with losses is applied (Pouliakis et al., 2014a); comparing this to radiology, being a medical specialty with heavy use of images as well, in which it is rather rare for a digital image to be more than a few hundred megabytes in size. In addition, radiologists work primarily on grayscale images (8-bit and now 12 or 16 bits when measurements are required) with resolutions in the thousands of pixels, pathologists work with color (24-32 bit, which, simply triples the amount of data). In pathology the resolution of images in the range

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of tens to hundreds of thousands of pixels. This dictates the need for a large amount of computational power, high-quality and high-resolution screens with large bit depth (aka color range), and fast network access (sometimes with metropolitan or global aspects and not just for within building communications). All these characteristics, which were not available in handheld (and sometimes not even on desktop) computers until very recently, were a barrier to the adoption of electronic data interchange in pathology and even worst in mobile pathology and cytopathology. Nowadays as the characteristics of mobile devices have changed, it seems that it is the time for mobile pathology (and cytopathology). Mobile telephony is now more than 20 years in use; the recent advances, in the last decade, have contributed to the conversion of the mobile telephones into smart telephones; due to the sophisticated operating systems. Especially, during the last five years, there became available hardware components enriching mobile phones: more processing power (4 or 8 processors), high definition cameras (more than 10Mpixel nowadays) and large displays resolutions (nowadays more than 1500x2500). Moreover, the 4G and WiFi connectivity have created an always open, rather inexpensive, communication channel with the Internet. Today, there is no more the trend to shrink the telephone, in contrast, mobile phones are becoming larger and compete tablets in terms of size and weight, they are easy to use and have capabilities similar to computers available five years ago. It seems that the mobile devices have now the maturity of computers being used in health sector five years ago and are ready to be adopted. Some of the characteristics of mobile devices (most of them not relevant to the desktop computers) are summarized below: • • • • • • • • • • • • • • • • • •

Battery with capacity lasting for days CPU with processing power competing desktop and laptops if we consider that users rarely use the full processing power Excellent graphics capabilities Processor memory in the range of gigabytes Flash memory for storage in the range of hundreds of Gigabytes Embedded GPS chips to allow global positioning 3G and 4G connectivity WiFi connectivity (150Mbit) competing standard (1,000 Mbit) LAN connectivity if we consider that LAN connectivity is shared as well Bluetooth connectivity for the creation of personal area networks USB connectivity for coupling the device to other devices Accelerometer Gyroscope Compass Thermometer One or more high-resolution cameras Large displays of high resolution Capacitive touch screen allowing a rich user interaction Connectors to interface with displays on high-end models

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MOBILE CYTOPATHOLOGY Over the last years, new types of cameras and microscopes, connected to computers made possible cytological image capture and transmission, named as telecytology (Archondakis et al., 2009; Pantanowitz et al., 2009; Pinco et al., 2009). The wide implementation of telemedical systems in this field became a necessity dictated by the need of real-time results for therapeutic decisions (Briscoe et al., 2000; Jialdasani et al., 2006; Mun et al., 1995; Raab et al., 1996; Weinstein et al., 1997; Yamashiro et al., 2004) as well as physician training (Stergiou et al., 2009). However, telediagnosis and training are not the sole applications relevant to cytopathology. other applications, include but are not limited to the everyday routine tasks of the modern laboratory, specifically there are interesting applications for results reporting, day to day laboratory management, Whole Slide Imaging (WSI), various interactions between patient and doctor, doctor to doctor and between members of the same laboratory, finally there are applications relevant to quality control and assurance. In the sequel we analyse the available applications and try wherever possible pinpoint potential applications.

Mobile Applications Related to Teaching In 2006, a group of reasearchers, reported on the use of mobile phone cameras as a method of remote teaching in undergraduate pathology education (Sharma et al., 2006). Specifically, they noticed that students possessed photographs of nearly all the typical specimens they may encounter during examinations; these photos were considered by the departmental authorities harmless and similar to note-taking. Thus, they triggered a decission of the instutute to allow the mobile device during examinations, to be used as an aid to memory. Five years later, another researcher (Collins, 2011), reported on the usage of iPads for the online distribution of digital textbooks for cytopathology. The application was based on the epub 3.0 standard, which facilitates the creation of electronic books (e-books). The author of the application used the Adobe InDesign CS5 software, designed and created an electronic book with text and images; these were possible to be accessed from a variety of different mobile platforms. The major benefits of this cytopathology e-book was the searchable content, the interactive text and references with high resolution cytopathology photomicrographs, the capability to bookmark a page for future reference after exiting, video embedding to have a multimedia rich content, instant access to medical references with a single touch of a finger. Additionally, images as well as the relevant text; could be viewed on stand-alone in full screen because the iPad could be connected to a larger, external HDTV screen. In this setting the zoom function of the iPad permitted detailed examination of image details. Moreover, content could be copied, pasted, printed, and emailed. The 64 GB capacity up the mobile device was considered as ample storage space for such applications. Nowadays, the cloud based storage and the always connected capability provides virtually unlimited storage. Modern browsers, installed in the mobile phones today, are not proprietary anymore, in contrast they are identical to the browser installed on desktop PCs. This means that there is no need anymore, to develop web applications, specifically for the mobile device. Moreover, any advances in the usage of Web 2.0 technologies, in cytopathology related material, will be directly available to the mobile phones as well. Furthermore, handhelds today integrate numerous connectivity subsystems and surprisinglly relational databases, thus, there is the embedded capability to the handheld allowing its use both as server and client. Especially, in developing countries, and during disasters, the mobile devices have proved their

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usefulness, mainlly because the tablets have a form factor and size similar to printed books. In 2004, another group of researchers (Ng et al., 2004), published a paper on using Internet search engines to find high-quality reference material for oral and maxillofacial pathology, and then caching this material on a PDA, nowadays caching is not a necessity as the mobile device is always connected. Teaching applications on the mobile device, are not only for reasearch purposes. Since 2011, there are several commercial applications being placed online, the including handbooks (Elsevier, 2015), atlases, see for example the Johns Hopkins Atlas of Pancreatic Cytopathology (Meszaros, 2014) as well as the majority of cytopathology related scientific journals. Despite all these applications were designed taking to account the special characteristics of the mobile device, the modern mobile phone browsers, have turned all those pioneering efforts almost obsolete, this is because any web page can today be viewed by the mobile phone browser and the high resolution of the screen facilitates this to some degree.

Mobile Applications for Reporting the Results of the Cytopathology Laboratory Cytopathologists and pathologists have to learn not only to formulate their diagnosis based on cellular/ tissue features, but also how to communicate the results in a report being clinically useful. Indeed, even the choice of the appropriate reporting format may have consequences on patient treatment (Crowe et al., 2011; Hirsch et al., 2015). It is frequent, that medical reports produced in laboratories, do not contain all information required for clinicians, thus they are not guided towards a good choice. Moreover, these reports may be formulated inappropriately. A group of researchers (Skeate et al., 2007) in 2007, created a software environment, and subsequently reported the usage of knowledge bases on PDAs. Their main outcome was that PDAs can enhance resident learning and pathology report completion as well. Despite their application falls within the pathology discipline, similar outcomes are expected for cytopathology, because reporting for both disciplines has similar complexity. Their experimental setting included ReportSupport, a software system operational on PDAs. ReportSupport was a navigable document, developed in the Hyper Text Mark-Up Language (HTML) using Macromedia’s Dreamweaver 4, and viewed using the device browser. Users log into the knowledge base, an introduction follows and subsequentlly instructions, then they can search for the diagnosis of interest (alphabetically or via first choosing the relevant organ systems), after this users receive a list of diagnoses in that system. The user chooses the appropriate diagnosis, and the device presents the elements required for a report to be considered complete. If the user clicks on an individual component, a description of how to determine, the value of that element is presented. As it is based on HTML, ReportSupport could be provided in desktops as well. Indeed, since 2007 ReportSupport is available on the network as well. However, the investigators preferred storage of the application within the PDA because desktop computers were not always available in slide examination places and PDAs mobile connectivity was not always feasible that time. Nowadays this is not anymore an issue. The evaluation of the system, was based on an experiment involving two groups or residents; one group used the ReportSupport system, while the second did not. The performance of the first group on results reporting was evaluated three times: T0: before the availability of ReportSupport, T1: when the users had access to ReportSupport, T2: after T1 but the users did not had access to ReportSupport. The results indicated that use of the knowledgebase does not ensure report completeness, however, it was associated with complete reports and more accurate judgments of report completeness. Additionally, this performance persisted in the absence of the knowledgebase. Concluding the use of the mobile devices prooved to be a usefull tool for user training and guided towards better reporting.

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As the massive volume of cytopathology reports is related to cervical cancer prevention (via Pap Test), it is worth looking at the use of the mobile device from a gynecologist. A very recent study (S. J. Martin et al., 2015), had the purpose to assess the baseline knowledge held by training and practicing gynecologists and obstetricians on mobile applications in their field of practice. The researchers developed an electronic questionnaire to be filled by residents at selected academic institutions. According to the responses analysis, more than 80% of practitioners were using mobile phone applications in their practice. The primary use was “physician and student reference.” The predominant applications used were those for cervical cytology guidelines and obstetric wheels. According to the responses, more than 80% of health care providers would like to have more applications available for use. This study proved that the use of mobile applications for gynaecologists is an expanding technology. Health care providers may choose to implement these applications into clinical practice, however it is more important to promote these applications in such a way that physicians have increased awareness. Finally the authors reported that these new technologies may be a benefit and advancement in gynecology and obstetrics both for health services provisioning as well as for medical education.

Diagnosis / Telepathology / Telecytology on the Mobile Device Using Static Images Digital imaging in cytopathology has undergone a period of exponential growth and expansion; this was catalyzed by changes in imaging hardware and gains in computational processing. Telecytology (Della Mea et al., 2000; Markidou et al., 1999; Williams et al., 2001) is the most obvious application having the potential to be transferred directly into the mobile device. In the past, telecytology has been used for reproducibility assessment (Archondakis, 2013, 2014; Archondakis et al., 2009; Tsilalis et al., 2012), in the field of remote diagnosis there is a plethora of applications as well (Breslauer et al., 2009; Briscoe et al., 2000; Jialdasani et al., 2006; Pantanowitz et al., 2009; Pinco et al., 2009; Raab et al., 1996; Ribu et al., 2013; Spat et al., 2013; Yamashiro et al., 2004), however in all cases are used fixed computers. Nowadays there are new advances in mobile devices: first on transmission speed and second on display resolution, only these two advancements seem to allow mobile cytopathology. Applications can be extremely simple and sometimes not even requiring development of special applications, for example images may be transmitted via e-mail or uploaded on a secure web place, additionally the image viewers are already embedded into the mobile devices. Definitely there are benefits for the health sector as mobile telecytology can be a valuable tool for medical doctors, in order to manage and promote interlaboratory collaboration mainly because it allows better cytological via instant data assessment and sharing. In the similar field of histopathology, in 2009, two independent groups (Bellina et al., 2009; McLean et al., 2009) reported on the usage of mobile phone cameras, these were used to take static digital microscopy images through the objective lens of a microscope, subsequently those images were used for telepathology. In a more specialized field, namely teledermatopathology there are numerous applications (Borve et al., 2013; Chao et al., 2013; Janda et al., 2014; Manahan et al., 2015; Massone et al., 2009; Massone et al., 2010; Wu et al., 2015), these are based on the usage of mobile phones as tools to take static dermatoscopic images and to send them for teledermatopathologic consultation. In the field of cytopathology, the image capture using the mobile phone camera, directly from the microscope is obviously a very important application.

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A telemedical application is a valuable tool for cytopathologists to manage and promote interlaboratory collaboration. The result is better cytological data management and sharing, this process, in general, can be both user-friendly and secure. Users should be able to separate easily shared and private images, and revoke access at any time. An mHealth based application for cytopathology will be able to invite collaborators and share digital slides via email or Instant Messaging (IM). With just a few clicks, they can even embed an image in a web page for viewing. Just as easily, live users can put limitations on shared files and collaborators. Several users can review and annotate images simultaneously with digital markers, making notes immediately visible to all collaborators, and all these can be available on the mobile device.

Whole Slide Imaging using the Mobile Device Today, digitization of entire glass slides at near the optical resolution limits of light can occur in a few minutes; additionally whole slides can be scanned in fluorescence or by multispectral imaging systems. Whole Slide Imaging (WSI) or Virtual Slides have been successfully used in surgical pathology, but its usefulness and clinical application have been limited in cytology for several reasons, mainly the lack of availability of z-axis depth focusing in cytological samples in contrast to histological have a 3D structure. However, nowadays there are available systems capable for whole slide imaging with z-axis control. This has boosted the application of digital slides for cytology (Al-Janabi et al., 2012; Fung et al., 2012; Ghaznavi et al., 2013; Gutman et al., 2013; Hipp et al., 2011; Krishnamurthy et al., 2013; Rudnisky et al., 2007; Taylor, 2011; Wright et al., 2013). A PubMed search for whole slide imaging or digital slides reveals that there are about 200 references. However, there is no single publication that is relevant directly to cytopathology along with the use of mobile devices. The technology seems ready to support such applications and perhaps it is only a matter of timeand recruitment of appropriate resources to implement such applications. Nowadays, there are map/navigation applications for the mobile device. For example, Google Maps, and Microsoft Bing Maps. Those applications, functionally behave extremely similar to WSI. For instance, there are used pyramidal images broken up into tiles that are served to the viewer in real-time. There are efforts using the publically available map Application Programming Interface (API) from Google and Microsoft to create WSI viewers for the desktop (Triola et al., 2011). It is obvious that this can be transferred to the mobile device as it has already happened for map/navigation. The second aspect of WSI images is depth or z-axis. This feature is required because in cytopathology doctors focus the microscope to different depths, and as mentioned earlier modern WSI systems produce multilayer scans. The capability of APIs, to present different information layers, seems compatible with this requirement. Finally, the multi-touch user interface of the mobile device screen is an additional advantage capable to provide the reach user experience. However, WSI can be considered as just a concept. The real applications are related to the purpose that clinical doctors use WSI. Reasons for WSI include: just for archiving, for diagnosis and consultation, for providing the examination media to the patient, or just for training. Actually, there is available an application for the iPad device, this is related to the online distribution of WSIs, as teaching sets especially for countries with low resources (Fontelo et al., 2012). The study was performed via a setting that involved iPad tablet devices and fifty 3rd and 4th year medical students. There were used two web servers, providing digital pathology virtual slides via a web interface. One server was remote while the second mirrored the content on the local network. The results indicated that the speed of serving the

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WSIs via the local server was much faster, thus, it was preferred by the students. This study revealed the critical role of infrastructures in the acceptance of mHealth applications.

Laboratory Management Using Mobile Devices Laboratory management operations require accurate and within time-frame transmission of critical laboratory results to the caregiver; in order to timely provide intervention and to prevent an adverse outcome. These operations are strongly linked to the provisioning of quality health services (Pouliakis et al., 2014b; Pouliakis et al., 2014c; Shen et al., 2001). Part of this process is risk management (Sciacovelli et al., 2007), either during the pre analytical phase (Vacata et al., 2007; Westbrook et al., 2008) or during sample analysis or during the post analytical phase. For this purpose, laboratories install computerized systems (Laboratory Information Systems-LIS) (Pearlman et al., 2001; Pouliakis et al., 2014b; Pouliakis et al., 2014c) to facilitate work flow and automate required actions. Mobile devices, are ideal for such applications as they are always connected and near the user. In a published study (Saw et al., 2011), the research team, reported on their experience with a setting that used SMS in reporting critical lab values. They used SMSs to notify critical laboratory results in a large teaching hospital; this application was not only to provide appropriate services; additionally it was employed to meet the documentation and audit requirements of critical result reporting, as such requirements are posed by regulatory agencies and/or ISO 15189 requirements. There was used the text messaging system (Critical Reportable Result Health care Messaging System [CRR-HMS]) that allowed a receiver to acknowledge or reject a key result by replying to a short message. When there was no response of a confirmatory receipt within 10 minutes, there were activated procedures of escalation to an alternative physician, according to a constructed roster. The benefit of this approach was that the required time for physician response was decreased from 7.3 minutes to 2 minutes. The CRR-HMS proved to be a useful tool to communicate rapidly critical results from the laboratory to targeted physicians. Eventually this enabled rapid and timely information transmission and therapeutic or patient management interventions.

Patient Doctor Interaction via the Mobile Phone or Tablet Another aspect of mobile cytopathology is related with patient communication. Today, it is impossible to imagine people without at least one mobile device; actually in most cases this is always connected (especially in the western world as well as in developed and developing countries with high speed networks either 3G/4G or local installations of Wi-Fi). This device serves as the terminal point that patients can be informed for the availability of the cytological result (especially for the popular Papanicolaou test) or receive a reminder for a forthcoming appointment related to cytological sampling and subsequent examination. Currently, IT systems can be relatively easily interfaced and transmit automatically SMSs, e-mails or any other type notification towards the patients. For example, immediately after the cytological examination result is released from the laboratory. Other types of interactions, between doctor and patient, are related to consulting; quite often patients have questions about their test results, especially when they are abnormal. Despite, cytopathology is a specialty that usually provides services to other medical specialties; on the other hand, in clinical cytopathology, there are patients treated directly by cytopathologists. As the examination outcomes eventually arrive at the patients’ hands, they have contact information of the laboratory, if they wish to communicate with cytopathologists to be informed of the meaning of their examination outcomes from

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the source, rather than consulting their clinical doctor (for example the gynecologist). The mobile device is always near the patient and the physician as well. Thus it provides the media and the communication channel to interact (speak, view or message) either in real time or asynchronously.

Doctor to Doctor Interaction via the Mobile Device In a similar manner to patient-doctor interaction; the clinical doctors collaborating with a cytopathology laboratory may receive informative messages on their mobile devices. These messages are related to the availability of a cytological examination result, requests of the laboratory for additional data that may be related to a patient under examination. Thus, mobile technology is now an open channel between cytopathology laboratories and clinical doctors. The typical cycle of the cytopathology laboratory, starts with the sample entrance to the laboratory, along with a referral form; and ends with the production and release of the cytological report (Pouliakis et al., 2014b). Relatively old fashioned cytopathology laboratories, produce reports printed on paper, and more advanced laboratories produce electronic reports that are distributed either via sending them using secure e-mail applications or by inviting the requesting physician to login into their account and retrieve them from a safe place (usually web based). The smartphone, embeds all the capabilities to receive SMSs or emails, so that the clinical doctor is immediately informed, in order to retrieve report information. Similarly to the patient – doctor interactions mentioned in the previous section, clinicians have numerous options to consult cytopathologists for explanatory details on the results. The most popular applications used by gynecologists are related to the interpretation of Papapnicolaou test results; thus those interactions between gynecologists and cytopathologists seem to be of the most interest. Of course, it is not only gynecologists that request services from the modern cytopathology laboratory, the vast majority of available medical specialties requests examinations from the cytopathology laboratory as well, therefore the groups of users are very large.

Use of the Mobile Device for Interaction of Personnel within the Laboratory Environment The third aspect related to mobile communications; is the exchange of information among the cytopathology laboratory personnel. Such communication involves the doctors’ requests for ancillary examinations, urgent examinations or requests to repeat an examination. Nowadays those requests are either verbal/ written or in the case that the laboratory is equipped by a LIS, they are available via fixed workstations. Mobile cytopathology seems to be an attractive technology having the potential to streamline everyday routine. Laboratory personnel can be independent from computers located in fixed physical locations. In contrast, a single mobile device adapted to their preferences, a personal mobile phone, or a mobile phone provided by the laboratory, can serve this purpose. The main reason is that users do not favor to carry on and take care of more than one devices. Additionally modern devices capable to have more than one communication channels (i.e. SIM cards) can serve both private and job related tasks. The mobile device is now configurable, thus can be optimized for user personal needs/tasks, and preferences and is available on every different work location. mHealth technology, when integrated into the daily workflow, can provide exceptional consultation opportunities to distant laboratories. mHealth technology innovations can improve the professional skills of the participating medical staff and make them feel more confident in their daily work.

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The Mobile Device for Quality Control and Assurance ISO 15189:2012 for medical laboratories requires successful participation in proficiency testing programs. According to the requirements of this standard, one of the greatest challenges facing cytopathology laboratories today is the design and implementation of a board certified external quality assessment (EQA) program (proficiency testing). The purpose of the adopted program aims to ensure that microscopic (cytological) findings are correctly identified and interpreted by laboratory personnel, additionally they are stored and communicated properly (Archondakis, 2013, 2014; Friedman et al., 2006; Lee et al., 2003; Nagy et al., 2006; Vooijs et al., 1998). Nowadays EQA schemes are available as telecytological applications, for example virtual slides are stored on web sites, cytopathologists of the laboratory under certification, use their computers to remotely diagnose these virtual slides. To our opinion those applications can be easily transferred on mobile devices, thus facilitating immediate quality control and collaboration. More details on the use of mobile devices for quality control in cytopathology laboratories can be found in chapter entitled: “The Use of Mobile Health Applications for Quality Control and Accreditation Purposes in a Cytopathology Laboratory.” within this book.

ISSUES, CONTROVERSIES AND PROBLEMS As in all cases of emerging technologies, the early adopters encounter not foreseen problems. In the field of mobile cytopathology there may be numerous issues and problems. When considering the use of WSI on a smartphone (Park et al., 2012); a major issue is that smartphone screens are small, which means physically small, because nowadays resolution of the mobile phone competes and sometimes is higher than desktop screens. The small physical size, is a requirement of the role of the device as a telephone, and thus it is not likely that future smartphones will have large enough displays (if they do then they will be no more telephones but rather tablets). A physically large display, however, is valuable and probably a requirement for viewing and interpreting a whole slide image. Screen size is not the sole issue, a second problem related to WSI and smartphones is the vendor specificity file/data formats, and these impede universal viewing in handheld devices and desktops as well. The slide scanner manufacturers use different and proprietary file formats for their whole slide images, mainly because the software that can interpret this proprietary format is the line of business for each manufacturer and a common standard is not agreed. Like in other business types cooperation among vendors is required for the benefit of the customer. Additionally, the fragmentation induced by all these proprietary formats, complicates any effort to create universal software for remote viewing of whole-slide images, especially from smartphones. Today, there are various efforts to create pathologycentric image viewers for handhelds, however, these are vendor specific (Leica Microsystems, 2011). The third issue related to WSI is related to the virtual slide size, this is enormous, ranging from more than tenths and thousands of mega bytes and up to Giga bytes, depending on the magnification, the scanned slide area and z-axis information. The large file size of WSIs seems to pose requirements for fast communication channels and device memory. When dealing with applications on the mobile device there are numerous issues that may arise: protected patient data is stored remotely and is sent and received through the air. This makes the data vulnerable to security issues such as computer virus threats and hacker attacks. Additionally mHealth is expected to enhance usage of services related to cytopathology laboratories, thus more and more data

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are expected to be produced. This large volume of data, especially when considering WSI, requires large storage, secured, expandable and safeguarded from failures. Similarly servers and networks require procedures to handle not only increased the load but failures as well.

SOLUTIONS AND RECOMMENDATIONS Considering WSI on the mobile device, three issues have been identified: screen size (not resolution), proprietary formats and large file sizes. One solution to screen size could be the creation of smartphones that the screen can be expanded if needed, i.e. flexible screens. Actually there is already available a prototype by Sony announced in 2010 (SONY, 2010). This screen is so thin and flexible that can be rolled around a pencil. If smartphones with such types of screens may become widely available or the screen can be interfaced as a gadget, then, they would solve the problem of small screen size for WSI. A second solution could be the use of projectors with very small size, and thus the digital slide may be presented on the wall. Alternatively, a solution may be provided by the embedded connectors available on modern smart phones, specifically miniature HDMI connectors, that can be used to leverage a nearby large screen. As large screens are nowadays a consumer item, and available in large volumes in relatively low prices, they are available in many places, it seems that is a matter of development of the workflow and software that allows use of nearby displays from smartphones, even wirelessly. The second issue related to WSI and smartphones; is the multitude of incompatible WSI image formats. Nowdays there is available a vendor neutral open source software library for WSI called OpenSlide (Goode A, 2008); this software library is available for desktop computers, but, porting to the mobile platform does not seem to be very difficult. Because OpenSlide is designed and implemented as a device driver, which can be found in all operating systems; it exploits vendor specific code and device resources of any type (at least this is the way expected to operate). OpenSlide supports remote viewing over the Internet as well and because the implementation is based on bindings with the OpenSeaDragon viewer (OpenSlide, 2015b) it is expected to be operational on the mobile platform as well, note that nowadays, whole slide images can be viewed over a 3G network on the smartphone browser at the OpenSlide demo site (OpenSlide, 2015a). The third issue of WSI and the mobile device was related to the enormous image sizes. In terms of storage space, the cloud is the solution, as can provide an affordable storage space, easily expandable and moreover offers data storage assurance. Additionally no local maintenance by laboratory technicians is required. Transfer of such data volumes from/to the server towards the mobile device is an issue. However, the revolution in telecommunication networks may provide the required bandwidth. Alternatively, it is possible to transfer to the mobile device only the virtual slide parts related to the viewing fields, and the required resolution, the concept of tiles and pyramidal image formats along with multiple layers, as is the solution to reduce the volume of data required to be transmitted, ultimately viewing WSI on the mobile device may be similar to navigation application using satellite photographs, as it is already available nowadays. In relation to the data and security issues raised by exposing data in the wireless networks as well as by exposing databases and servers in the edge of the networks, solutions may be proposed. Cloud computing, definitely is the choice (Pouliakis et al., 2014a; Pouliakis et al., 2014d) . Solutions are summarized as follows:

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•

• •

Threats of data security and privacy: may be anticipated by data encryption during storage and transfer and connecting with the server via encryption protocols, or via more enhanced methods proposed for the cloud environment, such as multi-cloud approach with a key sharing mechanism (Mouli et al., 2013) or patient identification cross reference numbers (Kondoh et al., 2013). Unauthorized access: may be anticipated by passwords and password control mechanisms and via mandatory biometric checks; note than nowadays mobile devices have the capacity for biometric security such as fingerprint recognition. Database safety and long-term archival: can be secured by passing this responsibility to Cloud computing service providers, in more detail: ◦◦ Server failures may be avoided by maintaining mirror servers and/or load/ balncing ◦◦ Efficiency of service, related to broadband speed may be ensured by multiple communication lines connected with multiple Internet service providers, thus preventing disruption of service can be assured. ◦◦ Increased load can be easily minimized by adding more processing power to existing virtual servers and/or by splitting the application to run on multiple servers, either via load balancing or by the exploitation of multi-tier architectures and splitting to one or more separate application servers and database servers.

CONCLUSION We have reached a point where both the software and the hardware of the mobile device platforms have matured and are powerful to be leveraged in cytopathology. The main advantage of implementing mHealth technology is that it gives all patients equal access to medical services, despite the fact that certain regions are more remote than others. This is especially important when it comes to specialists and other services usually inaccessible outside of large urban centers. mHealth technology also allows immediate collaboration among medical professionals. Perhaps the most elegant and technically challenging application is mobile WSI. However, this does not seem to be the most used application, more demand appears to be on simple services, such as notifications, result transmission, education and perhaps laboratory management and the interactions. One of the major benefits of the mobile platform is that provides capabilities irrelevant of time and place. The first (freedom from time) is delivered via the use of asynchronous communication channels: e-mail, SMSs, voice and even video mail. Simultaneously it is always possible to have a synchronous communication. The second (freedom from place) is related to the fact that the mobile device is personal, always on, always connected and available in every place. Therefore, there is no need for people involved in the cytopathology ecosystem to be at specific places to perform any related task. Despite there are some security and privacy risks, that modern technology seems to have already resolved via the help of cloud computing, mHealth technology has some major benefits that the public sector and government IT organizations are would like to take advantage of. In a very brief summary, they are as follows: • •

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Reduced cost (mainly for the devices) Flexibility: mHealth technology offers much more flexibility than past computing methods

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•

Mobility, employees can access information wherever they are, rather than having to remain at their desks

Work on handheld and mobile computing in cytopathology is scarce today. However, there is a great potential for new applications. Mobile hardware has nowadays become increasingly ubiquitous, reliable and fast via new devices with extremely high resolution, multicore processors, gigabytes of CPU memory and storage and multiple high-speed connectivity alternatives. Software support on such devices is comparable to that of desktop computing. However, handheld technology creates opportunities as well as challenges. Nowadays there are clear niches as well as several experimental successes with handhelds in cytopathology (especially for education purposes, telecytology, and care delivery via various communication interactions); however more study and development of standards for practicing and guidelines for validation are required. In addition, there are still questions related to security. Even so, it is likely that mobile computing will play a large role in cytopathology in the forthcoming digital decade. mHealth seems to have the potential to change the picture of future cytopathology and pathology.

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KEY TERMS AND DEFINITIONS Cytopathology: A specialty of medicine related to the study and diagnosis of diseases by the examination of cells. E-Health: Is the healthcare supported by electronics, informatics and tele-communications. E-Learning: Is a broad concept referring to the application of information and communication technologies (ICT) for learning purposes. Laboratory Information System (LIS): Or Laboratory Information Management System (LIMS) is a software-based system for the support of operations in a modern laboratory, such as workflow, sample tracking, data exchange interfaces. LIS systems are often capable to be connected with medical analyzers for automated extraction and storage of measurements. Mobile Health (mHealth): The practice of medicine and public health supported by mobile devices. Quality Control: The set of processes by which entities review the quality of all factors involved in product, service or activity. During QC processes, the products, services and activities are tested or validated in order to reveal defects and problems, before their release. Telecytology: The application of cytopathology from distance. Telediagnosis: Is the determination of a disease at a site remote from the patient based on transmitted data. Virtual Slide: or Whole Slide Imaging, a virtual slide is created when a glass slide is entirely scanned digitally.

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The Use of Mobile Health Applications for Quality Control and Accreditational Purposes in a Cytopathology Laboratory Archondakis Stavros 401 General Military Hospital of Athens, Greece Eleftherios Vavoulidis Medical School Aristotle University of Thessaloniki, Greece & Hippokration General Hospital of Thessaloniki, Greece Maria Nasioutziki Medical School Aristotle University of Thessaloniki, Greece & Hippokration General Hospital of Thessaloniki, Greece

ABSTRACT Over the last decade, the practice of clinical cytopathology was dramatically influenced by the wide implementation of informatics and computer sciences into the laboratory workflow. New applications, especially in the field of Mobile Health technology, will enhance the opportunities for improvement in the field of cytological data management and sharing. In this chapter, the authors present a thorough research of mobile applications related to cytopathology and try to foresee applications that, if available on mobile devices, may benefit the modern cytopathology laboratory and its clients. Also, the feasibility of adopting mobile applications for inter-laboratory comparisons, proficiency testing and diagnostic accuracy validation is examined. Finally, the role of mobile applications for providing or/and enhancing the existing laboratory capabilities through educational training and other research activities is investigated. Economic or medicolegal aspects of the expected wide adoption and implementation of mobile applications in the field of Cytopathology will also be covered.

DOI: 10.4018/978-1-4666-9861-1.ch013

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 The Use of Mobile Health Applications for Quality Control

INTRODUCTION During the last decades, medical data deriving from the analysis of patient samples was stored in medical laboratories and was provided to physicians manually (Brender & McNair, 1996). The absence of an integrated laboratory information system was making medical data transfer slow and possibly ineffective while results inquiry/control and quality control (QC) was a rather expensive and time-consuming process (Kubono, 2004). Over the last decade, the wide implementation of laboratory information systems became a necessity dictated by the need for real-time results and the increasing role of laboratory medicine in therapeutic decisions (Georgiou & Westbrook, 2006). Laboratory information systems have been implemented in many medical laboratories wishing to improve their quality standards. A laboratory information system (LIS) is a valuable tool for medical professionals in order to manage complex processes, ensure regulatory compliance, promote collaboration between departments of the same or different laboratories, deliver detailed reports, and enhance the laboratory networking capabilities. This results in better data management and sharing between the laboratory and its clients (laboratories, clinicians or examinees) (Brender & McNair, 1996). Cytopathology laboratory services are essential for patient care and include arrangements for examination requests, patient preparation and identification, collection, transportation, storage, processing and evaluation of clinical samples, together with subsequent interpretation, reporting and advice. The main cytological examination, the well-known Papanicolaou test consists a widely applied, costeffective screening method for the early detection of cervical dysplasia and cancer. A well-written and well-implemented LIS software can improve the diagnostic accuracy of this method by introducing new emerging technologies. Pap smears screening, and cytological diagnosis provision for the vast majority of the female population requires a large number of skilled cytotechnologists and cytopathologists. Since the number of these professionals is still inadequate, the development of automated laboratory instruments and screening systems may provide a practical and satisfactory solution. Laboratory informatics are regarded nowadays as an essential tool for laboratory’s quality assurance (QA) and improvement due to its key role in the pre-analytical, analytical and post-analytical diagnostic phases. A well-written and well-implemented LIS software can use medical data for the documentation of QC measures and the improvement of the laboratory’s performance. Mobile Health technology is changing the way enterprises, institutions and people understand and use current software systems. It allows imaging flexibility and may be used for creating a virtual mobile workplace. Security and privacy issues have to be addressed in order to ensure the wide implementation of Mobile Health technology in the near future. The purpose of this chapter is to present our experience on the application of Mobile Health technology to Cytopathology Laboratories, and on the possible ways Mobile Health technology can encourage or facilitate the wide implementation of ISO 15189:2012 specific requirements concerning every laboratory aspect and process. Furthermore, we examine the feasibility of applying Mobile Health technology for laboratory information systems data sharing and handling, for medical inter-laboratory comparisons, proficiency testing and for validating the accuracy of cytological diagnoses. In addition, we examine the role of Mobile Health applications to provide or/and enhance the existing laboratory capabilities for educational training and other research activities.

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Finally, we give clear and comprehensive guidance concerning various financial, legal, professional, and ethical problems in this field.

BACKGROUND Laboratory automation has been propelled during the last decade by the advantages of greater productivity, cost efficacy and the capacity of integration with modern instrumental equipment that has Internet Connection (Vacata, Jahns-Streubel, Baldus, & Wood, 2007; Westbrook, Georgiou, & Rob, 2008). Laboratory information systems provide better functionality through automation in parts of the inspection procedures, permitting the lab to achieve maximum efficiency (Vacata et al., 2007; Westbrook et al., 2008). Such systems also improve service to physicians and other stakeholders and ultimately reduce the probability of human errors. It is widely accepted that error-prone activities can be substantially reduced, but not eliminated. However, information technology systems can provide reasonable, accurate and reliable standardized procedures of QC for the assessment procedure as well as sophisticated quality indices for all the control system of the medical laboratory (Vacata et al., 2007; Westbrook et al., 2008). Medical laboratory computers may be used in various ways. They may be used for preparing and administering the management handbook and standard operation procedures, for personnel training, for providing and archiving documents via a network. They may also be used for creating customer databases, for evaluating test results, for online connecting with external sources of information, for contacting with customers, or as a typewriter as well. A computing system contains at least one computer unit, some peripheral devices and some software packages. Computing systems operating parameters require custom verification and validation. Verification is the confirmation that specified requirements have been fulfilled. Computing system monitoring, user acceptance testing and code reviews are some verification tools. Validation is the confirmation that the requirements for the specific use are fulfilled. Electronic records contain any combination of digital data that is created, modified, archived, or distributed by a computer system. Electronic records must be protected from exposure to accidental or malicious alteration or destruction (record security). Computing systems may be open or closed. In closed computing systems, individuals responsible for the content of the electronic records control access to medical archives. On the contrary, in open computing systems, people not responsible for the content of the electronic records (Vacata et al., 2007) control access to medical archives. The computing systems software may be used for testing, calibration and sampling purposes (testing software) or for managing document control (document software) (Vacata et al., 2007; Westbrook et al., 2008). The integrity of electronic records must be checked periodically (file integrity check) while the computing system must be tested periodically to determine if it meets specific requirements (acceptance test). Finally, according to the European Federation of National Associations of Measurement (2006), the software of the laboratory computing system must be periodically tested. The ISO 15189:2012 requirements cover all aspects of the laboratory activities, including the laboratory information system (LIS) (Pouliakis et al., 2014a, 2014b; Vacata et al., 2007; Westbrook et al., 2008). ISO 15189:2012 suggests specific measures for the protection of laboratory electronic records (Pouliakis et al., 2014a, 2014b; Vacata et al., 2007; Westbrook et al., 2008). The proposed measures comprise a valuable tool for quality improvement in the field of electronic documentation of medical records. It is worthwhile mentioning that the ISO 15189:2012 requirements do not apply to desktop 264

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calculators, small programmable technical computers, computers used only for office applications by one single user, or microprocessors integrated in assessment instruments (Kubono, 2004; Pouliakis et al., 2014a, 2014b; Westbrook et al., 2008). The laboratory information system (LIS) functions include workflow management, specimen tracking, data entry and reporting, assistance with regulatory compliance, code capture, interfacing with other systems, archiving, inventory control, security, and providing billing information. The LIS components include hardware (e.g. servers), peripherals (e.g. instruments, printers, cameras, monitors and scanners), a network, interfaces (e.g. links to other information systems), database(s) and software (e.g. database management system). In the field of Cytopathology, laboratory information systems have enabled cytotechnologists and cytolopathologists to achieve efficient, streamlined workflows, regulatory compliance, and superior reporting capabilities. A well-written and well-implemented cytopathology LIS, when integrated to Electronic Medical Records (EMRs) can provide full lab automation through connections to instrumentation and clinicians’ offices, minimize human errors and achieve detailed test order entry and efficient results retrieval. Early software programs in the field of Cytopathology included reporting, data storage, and elementary data mining. During the past ten years, laboratory information systems capabilities have been dramatically increased by automated enhancements, such as specimen tracking, barcode labeling, reflex testing, automated and customized report delivery and billing system interfaces. Laboratory information systems in the field of Cytopathology may be autonomous, or may consist a part of an integrated anatomic pathology system, or a part of a larger hospital information system. National or international regulatory agencies are nowadays specifying the minimum period of time cytopathology laboratories should record and retrieve specimen information and patient reports. The information system applied, should permit easy access to all cytology reports and, if possible, to related surgical pathology reports, in order to make possible cytologic/histologic correlation. Older records should be archived and stored offsite as long as retrieval does not hinder patient care or delay regulatory inspections. Laboratory information systems should be able to correlate or merge records when there is an alteration in patient identifiers without altering the data in the original records. It is advisable for laboratory information systems in cytopathology departments to use unique identifiers, such as the patient’s record number, in order to achieve more accurate matching. QC defines service’s quality, imparting to it the credibility needed for its intended purpose, while QA activities measure the degree to which desired outcomes are successful (Archondakis, 2015). QC may be internal or external. QC in the field of Cytology is mainly achieved by slide rescreening or by clinical-histological correlation of cytological diagnoses (Archondakis, 2015). Many slide rescreening procedures have been proposed for QA purposes, such as rapid reviewing of smears initially reported as negative or inadequate, rapid preview/prescreening of all smears, random rescreening, targeted rescreening of specific patient groups, seeding abnormal cases into the screening pools, retrospective rescreening of negative cytology specimens from patients with a current high grade abnormality and automated rescreening of smears initially reported as negative (Archondakis, 2015). The laboratory managers are responsible for the selection of the most appropriate method for QA purposes, according to the specific needs of their own laboratories (Archondakis, 2015). The practice of diagnostic cytopathology performed on digital images is a novel process that can be used for obtaining expert opinions on difficult cases from remote laboratories (telecytology) (Archondakis et al., 2009). 265

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Telecytological diagnosis can be achieved either with the use of cytological pictures viewed in real time with the microscope (dynamic telecytological systems), or with the use of cytological pictures that are first captured in a digital format and then transmitted to distant observers (static telecytological systems) (Archondakis et al., 2009; Stamataki et al., 2008). The static telecytology systems have the advantage of considerably lower cost, but they only allow the capture of a selected subset of microscopic fields (Pantanowitz, Hornish, & Goulart, 2009).The dynamic telecytology systems permit evaluation of the cytological material present on a slide (Yamashiro et al., 2004).These systems may be hampered by high network traffic and their high cost of purchase and maintenance that makes them unaffordable for small laboratories wishing to participate in proficiency testing programs. The use of digital images in quality control/assurance programs eliminates the need for glass slides retrieval from the laboratory’s registry (at least at the point of examination), allows annotations to be added to images, enhances the ability to rapidly transmit and remotely share images electronically for several purposes (telecytology, conferences, education, quality assurance, peer review) and protects more efficiently patient anonymity (Archondakis, 2013, 2014). Moreover, the use of digital images for QA programs is more practical and time-efficient, although the conversion process of glass slides into digital image files may require some additional time. Static telecytology systems are preferred due to their low cost by laboratories that cannot afford the high cost of purchasing and maintaining dynamic systems (Archondakis, 2013, 2014). The limitations and diagnostic errors related to telecytology that are already mentioned by some authors may cause misinterpretation of digital images by less experienced participants (Briscoe et al., 2000). Appropriate field selection, sufficient image quality and especially diagnostic expertise are the most crucial parameters ensuring the proper function of a static telecytological system (Archondakis, 2013, 2014). The most common manifestations of inter-observer discrepancy is upgrading of the telecytological diagnosis to a definitive carcinoma diagnosis or downgrading of a suspicious telecytological diagnosis to a rather benign lesion because of image deficiencies (Archondakis et al., 2009). Proficiency testing (PT) is a well-recognized method for evaluating actual laboratory performance usually by means of inter-laboratory comparisons (Archondakis, 2013, 2014). Proficiency testing results can be used as an independent indication of a laboratory’s diagnostic competence and can be integrated in the ordinary process of the laboratory’s assessment and accreditation (Archondakis, 2013, 2014). According to ISO 15189:2007, all accredited laboratories must conduct proficiency tests in accordance with their normal patient testing and reporting procedures (Pantanowitz et al., 2009, Archondakis et al., 2011). A recent revision of ISO 15189, published in 2012 proposes the additional use of alternative methods for the evaluation of laboratories technical competence, where accredited PT schemes are not available. The practice of diagnostic cytopathology performed on digital images is a process by which remote laboratories can obtain expert opinions on difficult cases (telecytology) (Archondakis et al., 2009, Pouliakis et al., 2014a, 2014b). Telecytological diagnosis can be achieved either with the use of cytological pictures viewed in real time from the microscope (dynamic telecytological systems), or with the use of cytological pictures that are first captured in a digital format and then transmitted to distant observers (static telecytological systems) (Archondakis, 2013, 2014; Stamataki et al., 2008). Hybrid systems have also been developed. The majority of relevant articles have focused on the possible use of telecytology for diagnostic and consultational purposes (Archondakis, 2013, 2014). Diagnostic agreement and reproducibility between the same (intra-observer) and different (interobserver) observers are the main parameters monitored and recorded during telecytology programs 266

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(Stamataki et al., 2008; Pantanowitz et al., 2009). Agreement is the total or proportional number of cases in which the same diagnosis was issued between or within observers, including the part of the agreement that may be attributed to chance (Landis & Koch, 1977). Reproducibility, which is part of the agreement that cannot be explained purely by chance (Landis & Koch, 1977) is measured by the kappa statistic. Within the positive kappa values and in accordance with the study by Landis and Koch (1977), the agreement was interpreted as follows: a range of 0.00– 0.20 indicates slight agreement, a range of 0.21–0.40 indicates fair agreement, a range of 0.41–0.60 indicates moderate agreement, a range of 0.61–0.80 indicates very good agreement, while a range of 0.81–1.00 indicates excellent or almost perfect agreement (Landis & Koch, 1977). QA in the field of Cytopathology is achieved mainly by monitoring diagnostic discrepancies, through cytological and histological correlation. The improvement of information systems and LIS in most pathology laboratories allows such correlations to be performed just immediately after the release of the examination results. Additionally, data mining either online or in a later stage may produce important knowledge for trends and problematic areas in the processes and, therefore, to initiate corrective actions for quality improvement. Also, image-based systems to evaluate the quality of classification systems such as the Bethesda System 2001 for reporting the results of cervical cytology (Solomon et al., 2002) have already been reported in the literature (Sherman, Dasgupta, Schiffman, Nayar, & Solomon, 2007). Mobile Health technology obviously can be of help for QC&QA, as the related application may be developed for such an environment, storage and application load will not be an issue, and additional benefits may be obtained as the QC&QA application can be shared among numerous laboratories and rare cytological cases can be used by all participating labs. Until now, Mobile Health technology is not widely used for the various tasks related to Cytopathology. However, there are numerous fields in which it could be applied. The envisioned advantages for the everyday practice of laboratories workflow and eventually for the patients are significant. Mobile Health technology, in the field of cytopathology, may provide a precious web-based service, which may be incorporated in the LIS and become part of a web-based Electronic Health Record (EHR). A cytopathology laboratory wishing to store a large number of images and patient information files is nowadays obliged to install one or more servers and accompanying disk arrays, having poor or no earlier experience in its upkeep. A possible server crash may result in severe data loss. Furthermore, the server may require constant upgrades. These two reasons are good enough for a modern cytopathology laboratory to shift its data to the Mobile Health. By doing so, the laboratory reduces dramatically all costs related to server software and hardware as well as the costs for maintenance and licenses. In such a scenario, the Mobile Health acts as LIS, telecytology software, and billing unit at the same time. A patient may perform a cytological examination at a hospital or a private laboratory. Representative images of the case may be stored on a hybrid Mobile Health. If the patients perform another cytological examination after some time, in another laboratory, the cytopathologists can retrieve and merge images that were stored previously in the Mobile Health directly from their offices. This way, the final diagnosis is made after having reviewed the images of the first cytological examination. Mobile Health technology, when applied in the field of Cytopathology, has some main benefits, such as innovative dashboard for easy use, report formatted custom for each participant’s lab with images, automatic versioning history, secure access to records that removes backup concerns, faster cytopathologist approval process, easy to use browser-based system, automatic spell-checking, images integration into reports, faster “voice-to-file” process, HL7 interface availability, safe storage, safe records transmission, low cost of record retrieval, labor, copying, filling and storage, easy PDF documents production 267

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for easy integration to EMR systems and use of digital signatures for document control and compliance with standards.

MOBILE HEALTH APPLICATIONS FOR QUALITY ASSURANCE PURPOSES IN A CYTOPATHOLOGY LABORATORY: BENEFITS AND CURRENT POTENTIAL Mobile Health applications provide many advantages in terms of scalability, maintainability, and massive data processing. They can provide exceptional consultation opportunities to distant cytopathology laboratories. Also, they can improve the professional skills and boost the confidence of the participating medical staff. Using Mobile Health applications, cytopathology laboratories can avoid huge spending on maintenance of costly applications and image storage and sharing. Mobile Health technology allows imaging flexibility and may be used for creating a virtual mobile workplace. Security and privacy issues have to be addressed in order to ensure Mobile Health applications wide implementation in the near future. The main components of Mobile Health technology in the field of Cytopathology are the following: •

• • • •

•

Applications: Mobile Health applications may run as Software as a Service (SaaS), Software plus Service or Data as a Service. In Mobile Health applications used by cytopathologists, the end users take advantage of some SaaS for image reviewing, creating diagnostic reports or patient billing. Client: A Mobile Health client is the electronic device that cytopathologists use to access the Mobile Health via the Internet including smartphones, tablets or computers. Platform: In the field of Cytopathology, a well-designed platform is an essential parameter for the efficient application of laboratory’s Mobile Health applications. Service: A Mobile Health service for cytopathologists can be, without having to be limited to, either a web-based image archiving system or image gallery. Storage: Mobile Health computing, in the field of Cytopathology, enables for example the storage of large medical laboratory databases in the form of documents and image libraries, instead of physical storage at site (hospital or imaging center) which is much more expensive and difficult to maintain. Processing power: Mobile Health computing can provide the infinite processing power to all cytopathology laboratories at a very low cost.

The power of Mobile Health technology combined with the convenience of web applications can improve significantly the collaboration among cytology labs participating in image or data sharing QA programs. Storing, archiving, sharing and accessing images in the Mobile Health allows a modern cytopathology laboratory to manage data more efficiently and cost-effectively while overcoming many of the legal, regulatory and technical challenges that data requirements pose. This technology enables a modern cytopathology laboratory to efficiently handle large bandwidth images, to use non-proprietary, standards-based, vendor-neutral architecture, to expand or contract storage

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capacity easily as needed, to manage authentication, encryption and security protocols, to conduct efficient system-wide application upgrades and finally to extend the life of existing infrastructure/investments. As there is pressure to reduce costs, many laboratories nowadays are exploiting virtualization capabilities that are basic characteristics of Mobile Health technology. The servers applied for image or data sharing QA programs need to support most digital slide formats and to be easily integrated with various scanners for single-click slide upload for on-premises deployment. A Mobile Health-based infrastructure for digital cytology can analyze whole slides on special image servers from each professional’s web browser with just a few clicks. Each cytopathologist simply has to select analysis, template settings and parameters with interactive visual preview, and let the server run complete slide analysis using the scalable power of the Mobile Health. All professionals, although possessing different imaging equipment, can use Mobile Health-based software to integrate seamlessly with imaging hardware. A software like that permits the digital slide to be uploaded to the user workspace via the Mobile Health server automatically. A modern cytopathology laboratory and the network of cooperating cytopathology laboratories have no obligation to conform to any single unified corporate IT policy. A modern cytopathology laboratory and the network of cooperating cytopathology laboratories can use a variety of computers, operating systems, and browsers. The special web interface can work with all the commercially available operating systems like Windows, iOS, Android and Linux that are used in modern tablets, smartphones, and personal computers without exceptions. Each professional (whether cytotechnologist or cytopathologist) can view digital slides online without having to download, install or update anything. A simple log-in to the browser is enough for establishing a connection with the Mobile Health platform. Integrated Flexible Database module can create powerful knowledge database from each cytopathologist’s digital slide collection. A Mobile Health-based software can use flexible attributes, quick tags, comments and attachments for cases and slides to organize images and keep track of all available medical data. Cytopathology professionals should be able to separate shared and private images. A modern cytopathology laboratory and the network of cooperating cytopathology laboratories can invite collaborators and share digital slides via email, by using a Mobile Health-based software. No need to mention once more that digital cytology images storage and transmission must follow strict regulations to avoid any unauthorized alteration or improper use. Current standards of electronic medical data handling are still informative, yet the need for a secure electronic environment, continues to grow. A modern cytopathology laboratory has already established a system where images and virtual slides are available online and all cytopathologists can report remotely. By using the laboratory’s server, the users experienced many difficulties because of frequent local network disruptions. By shifting the data to the Mobile Health, the cytopathologists are now able to access the web-based cytopathology software. Here, the Mobile Health enables the implementation of tele-consultation applications. Mobile Health technology can be used for making the entire medical record of a patient accessible for review by a remote cytopathologist. All cytopathologists can review admission notes for all cases, seek prior data from other laboratories that are mentioned in these notes and review all available imaging material to make all applicable correlations. Further evaluation and development of intelligent Mobile Health based search engines exploiting artificial intelligence appears worthwhile. Another aspect of primary diagnosis is related to homeworker cytopathologists. Virtual slide technology enables the digitization of complete slides. Thus, the diagnostic data can be now stored and 269

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transmitted, the barrier, in this case, is the large amount of data. Eventually, Mobile Health technology provides endless storage and fast communication channels, in this aspect; the remote primary diagnosis could be a reality. A modern cytopathology laboratory is electronically tracking QC/QA indicators can be done either within the LIS and/or by exporting data from the LIS (via spreadsheet software). Mobile Health applications can be used for monitoring laboratory requisition completeness, problems documented by the users, occurrences and trends with any particular physicians’ office sending specimens to the laboratory. Identification of such problems could prompt redesign of requisition forms. Specimen rejection incidents and labeling errors are also electronically documented, and specimen rejection frequency is regularly reported to all physicians’ offices. Comments entered within available QA fields are included in the final cytological report. Electronic monitoring of lost specimens is accomplished with a simple spreadsheet log. A cytopathology laboratory information system is monitoring electronic data integrity and is taking all available security measures, which may include regular back-up of data, password protection, data encryption, antivirus software, firewalls, and audit trails. Mobile Health applications can be used for assigning users different levels of privileges so that only certain individuals can finalize and sign out abnormal cytological diagnoses ensuring the integrity of finalized reports. Mobile Health applications can be used for recording workloads for each cytopathologist. This is achieved by evaluating the diagnostic accuracy in comparison with productivity. A modern cytopathology laboratory information system prohibits screening of more than 200 conventional slides over an 8-hour period. Non-gynecologic and gynecologic liquid-based preparation slides also count as half-slides in such daily counts. Mobile Health applications can be used for designating cases as high risk, based on database searches for particular entries within certain fields. In a modern cytopathology laboratory, computer-assisted designation of high-risk cases greatly increases the likelihood that high-risk cases are correctly identified to be a part of a detailed review process prior to signing out. These applications can be used for creating pivot tables, which are useful tools to cross-reference diagnoses by cytopathologists and for analyzing diagnostic correlations and discrepancies. In a cytopathology laboratory, software systems allow for individual cases to be easily retrieved for continuing education purposes.

THE USE OF MOBILE HEALTH APPLICATIONS FOR ACCREDITATIONAL PURPOSES IN A PRIVATE CYTOPATHOLOGY LABORATORY IN THE FUTURE Safety and Input Optimization of Clinical Information A modern cytopathology laboratory’s information system is secured from unauthorized internal and external access and preserves the confidentiality of health records, according to national law and regulations. Different levels of security are available. Mobile Health applications will be used for remote log-in access to ordering and reporting systems via a secure web browser and will also allow reliable electronic signatures for data authentication.

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Regarding test ordering, Mobile Health applications will be used for providing immediate feedback to all users. They will be used for guiding clinicians on how to order the appropriate cytological examinations while the clinicians are allowed and encouraged to enter directly the order in the system. In the future, Mobile Health applications will be used for receiving inputs from clinicians that will have to include the following information: • •

Ordering physician information (name, specialty, address, contact media for routine notification, contact information for critical result notification) Patient information (patient demographics, including date of birth/age, sex, location, results of laboratory and non-laboratory tests, medications, medical procedures applied to the patient, gynecologic and obstetric information, other pertinent clinical information)

Management of Biological Specimens & Laboratory Orders In the future, Mobile Health applications will possess a user-friendly display of the test catalog with available alternative groupings. They will be used for relaying orders to different interfaced systems without manual intervention so that tests ordered in one facility can enable specimens to be collected and accessioned at another location. Also, they will be used for keeping a list of the reference laboratories available to the ordering provider and for generating a report with sender, receiver and shipping information. Furthermore, they will be able to split clinicians’ orders, track the progress and report the status of each component separately under one order. In addition, new aspects for optimizing specimen collection and processing will be gradually introduced into the Mobile Health applications, such as: • • • • •

Specimen collection lists as appropriate to laboratory operation Printed and electronic guidelines to the specimen collectors with comprehensive display of proper instructions List of pending laboratory orders Automatic generation of unique barcoded labels Automatic recording of patient information, location, date and time of collection, and collector identity

In the future, Mobile Health applications will be used for interfacing with laboratory automation management software to ensure that all the pre-analytic requirements stipulated in the ordering process are transmitting to the specimen-processing system. These applications will be used for tracking the specimen location throughout the pre-analytic, analytic, and post-analytic phases, including transportation to various sections of the laboratory or external sites, and management of specimen storage. In terms of optimizing the analytic phase, future Mobile Health applications will support: • • •

Tracking of all the components necessary for testing and association with individual testing records Generation of printed and electronic versions of appropriate standardized operating procedures for each test upon request by the cytopathologist Generation of laboratory-specific workload lists

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•

Generation of “Incomplete Lists” of tests that have been accessioned but not completed, highlighting those that have exceeded the stated time for the category of the request Generation of lists of incomplete or unfulfilled orders

•

Management of Testing Results & Diagnostic Reports Mobile Health applications will be gradually involved in optimizing the result entry and validation through integration of: • •

Ability to record results in various data formats Automated and manual entry and correction of results of tests, with appropriate security levels applied Application of different levels of result certification Reception of results in a variety of formats from other laboratories, including external reference laboratories, through electronic interfaces Utilization of advanced expert decision support for auto-validation of results. Inputs used to arrive at an auto-validation decision include comparison with results of previous tests in the patient record, comparison with results of other related tests in the same or closely related specimens, statistical data on result distribution)

• • •

In terms of optimizing result reporting, future Mobile Health applications will support: •

Generation of reports with information on both laboratory staff and recipients (clinicians, patients) of the test Generation of reports available by user-configurable automated secure faxing and e-mailing Sophisticated graphing of laboratory results Ability to incorporate in result comments hyperlinks to pages containing further test information Ability to append appropriate interpretative comments on test results

• • • •

These applications will be used for optimizing notification management though addition of new services such as: •

Possession of a sophisticated “significant result” notification system that includes multiple tiers of urgency for significant result notification Utilization of dynamic rules to determine whether a result is critical Utilization of a rule-based notification of appropriate third parties Rapid update and notification about any changes or corrections to laboratory results

• • •

Also, these will efficiently optimize data mining and cross-sectional reports through: • • •

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Ability to perform queries into the laboratory and clinical databases Search functions for combinations of laboratory results and clinical information Production of laboratory testing turn-around time reports with the ability to consolidate or split the various components

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•

Online reporting of surveillance data to public health agencies in their required format, using the appropriate standards

Optimization of Laboratory Quality Management & Other Functionalities In the future, Mobile Health applications will be used for optimizing Quality management, by using a module that supports accreditation requirements, including International Organization for Standardization (ISO) 15189:2012 standards and includes the following functionalities: • • • • • • • • •

QC protocols and alerting mechanisms that use thresholds for acceptability Linkage of each patient test result to the relevant QC results in an easily retrievable record Automated alerts to appropriate staff to perform QC tasks Active QC rules and reports customizable by test Ability to document corrective actions resulting from QC failure in real time Ability to remove outliers and erroneous results from QC calculations Ability to provide user-definable QC summary reports for review by supervisory and management staff Ability to manage proficiency-testing (PT) programs, from inventory control of PT materials to documenting PT results and investigation of PT failures Ability to manage accreditation requirements online, including preparation of appropriate documents

In terms of incorporating advanced administrative and financial functionalities, future Mobile Health applications will support: • • • • • • •

Ability to generate and transmit the necessary forms and notifications for reimbursement of tests Intelligent generation of online and printed regulatory forms associated with laboratory testing, billing, compliance, and accreditation Tracking of costs of laboratory operation Analysis of profitability, pricing and outreach client management capabilities Ability to produce periodic reports of laboratory productivity and management efficiency, by using aggregate numbers and individual cost and productivity analysis per test Automated ordering from selected suppliers and real-time tracking of budget Tracking of human resource databases, labor-cost accounting, and credentials, competency, continuing education training, and performance appraisals

ISSUES, CONTROVERSIES, PROBLEMS The implementation of ISO 15189:2012 guidelines in medical laboratories information systems presents some problems that need further analysis. Medical data that is stored and retrieved from a laboratory information system contains valuable information that needs to remain confidential. The widespread use of computers makes access to classified documents easier than ever. The use of electronic signature in medical reports may diminish bureaucratic problems but, on the other hand, makes the laboratory

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information system more vulnerable. The laboratory management has to implement specific policies that will protect medical data from unauthorized access but will not endanger cooperation between medical and laboratory information systems (Shen & Yang, 2001; Vacata et al., 2007). Laboratory personnel training in informatics is necessary for ensuring the efficient function of the laboratory information system. The laboratory management has to plan personnel training in such a way that the laboratory main function will not be put in danger (Shen & Yang, 2001; Vacata et al., 2007). Poor hardware maintenance or improper use by inadequately trained personnel may cause a laboratory information system failure. Laboratory reports may be lost or deteriorated due to malignant software (virus programs), while LIS hardware may be damaged by adverse environmental conditions, such as heat, humidity or a possible fire, due to the vulnerability of wires and cables to unfavorable environmental conditions (Vacata et al., 2007; Westbrook et al., 2008). Finally, medical data stored only in electronic mediums may be easily lost due to a system’s unexpected failure (Vacata et al., 2007). All these possible threats of a laboratory information system require the implementation of specific measures and policies that may have a considerable economic impact, or may even prove non-affordable. The laboratory management handles the development of an economic plan, after taking into account the specific laboratory resources and needs (Shen & Yang, 2001; Westbrook et al., 2008). The implementation of Mobile Health technology in medical laboratories information systems causes some problems that need further evaluation. The main threats Mobile Health technology is anticipating in the field of Medical Informatics are: • • • • • •

New threats of data security and privacy, these threats may be anticipated by data encryption during storage and transfer and connecting with the server using encryption protocols Unauthorized access may be anticipated by passwords and password control mechanisms and via mandatory biometric checks Database safety and long-term archival process in case of emergencies and natural disasters have to be discussed with the Mobile Health technology service provider in detail Server failures may be avoided by maintaining mirror servers Efficiency of service, related to broadband speed may be ensured by the hospital cooperation with multiple Internet service providers to prevent disruption of service Increased load can be easily handled by adding more processing power to existing virtual servers and/or by splitting the application to run on multiple servers

The implementation of telemedical applications for proficiency testing purposes in medical laboratories wishing accreditation presents some problems that need further analysis. In conventional cytology, specific diagnostic criteria and pitfalls are already described. During static telecytological diagnosis, the cytopathologist has to use the same diagnostic criteria and to avoid the same pitfalls. What makes the telecytological diagnosis more demanding is the uncertainty about the real specimen’s adequacy or the representativity of the selected images (Archondakis et al., 2009). Static digital images suffer from representing only limited portions of the specimen and hence there is a potential bias of the image acquirer relative to the image observer. The inability of image focusing and image manipulating (contrast, brightness, and color) may cause additional problems. Cytology slides used for digital images capture must be validated in order to make sure that the initial cytological diagnosis was correct and did not differ significantly from the final histological diagnosis.

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Reporting terminology is well-established for some categories of cytological specimens such as thyroid fine needle aspiration specimens and cervicovaginal smears. Still cytological diagnosis for the majority of specimens remains descriptive, and no specific diagnostic categories have been established and implemented in the everyday laboratory practice. The absence of a universally accepted and adopted reporting terminology is a serious problem for the correct statistical elaboration of cytological diagnoses provided by participants in a proficiency testing scheme. A widely accepted reporting terminology would make easier the implementation of scoring systems, validating participants’ performance and improvement. Participation in proficiency testing programs is still poor. Many large Cytological Departments are reluctant to implement such practices as a measure of continuous improvement and quality assurance. Even when a proficiency testing program is ordered, only one or two certified cytopathologists are participating. Last but not least, digital images storage and transmission must follow strict regulations to avoid any unauthorized alteration or improper use (Mun, Elsayed, Tohme, & Wu, 1995). Current standards of electronic medical data handling are still informative, yet the need for a secure electronic environment, especially in the field of static telecytology, continues to grow.

SOLUTIONS AND RECOMMENDATIONS A modern cytopathology laboratory may need to cooperate in real time with a cytopathology laboratory in a remote location. In order to send or receive images in real time, a modern cytopathology laboratory can push the images into a Mobile Health application, enabling the cytopathologists to review images at nodal centers, without having to buy additional software and hardware. Here, the Mobile Health application works as a gateway to the peripheral center where all its information is available on the Mobile Health. Mobile Health applications are affordable by all cytopathology departments and give the opportunity to all scientific personnel to participate in proficiency testing programs, even when there is a significant time difference among participating laboratories. Moreover, laboratory management should encourage personal participation of all scientific personnel in such proficiency testing programs (Pinco, Goulart, Otis, Garb, & Pantanowitz, 2009).

Proper Education and Training of Laboratory Personnel Before new software or hardware is introduced in a laboratory, the risk connected with such an introduction should be assessed (Vacata et al., 2007). A risk assessment should include identification of possible events, which may result in non-compliance, estimation of their likelihood, identification of their consequences, and ways of avoiding them, costs, drawbacks, and benefits (Vacata et al., 2007). Good knowledge of computer software and hardware details is also essential for maintenance, troubleshooting, and update. Medical laboratory personnel have to be periodically trained to the use of new computer facilities and new software products. Their training may be extremely difficult. Therefore, the laboratory director has to encourage these training sessions and continuously motivate its personnel (Pearlman, Wolfert, Miele, Bilello, & Stauffer, 2001; Westbrook, Georgiou, & Rob, 2008).

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Moreover, we have to take into account that computer facilities maintenance is of paramount importance in the workflow of a medical laboratory. Therefore, the laboratory personnel should take specific measures for protecting the hardware (Markin & Whalen, 2000; Sciacovelli, Secchiero, Zardo, D’Osualdo, & Plebani, 2007). These measures should be documented in specific procedures so as to include aspects of: • • • • •

How to operate a computer How to maintain and clean the computer and its hardware components and peripherals What programs are required for specific computer labor and how to install and use them How to manage specific files and data-sets How to handle internet security programs

Hardware-Related Implemented Laboratory Procedures The hardware should also be fully protected from any actual damages, and especially fire (Markin & Whalen, 2000; Sciacovelli et al., 2007; Vacata et al., 2007). The measures should be documented in specific procedures, and might include: • • •

Installation and periodical testing of smoke alarms and fire extinguishers Storage and use of heating sources and flammable items Control of electrical wires and cables in terms of connection networking and proper maintenance

The provision of an uninterruptible power supply will protect the computer from crashing during power outages, or from low and high voltage occurrences (Markin & Whalen, 2000; Sciacovelli et al., 2007). A UPS is much better than a surge protector and can save the laboratory computer facilities from virtually any type of power failure. Medical records and computer facilities should also be well protected from unauthorized access (Markin & Whalen, 2000; Sciacovelli et al., 2007). The laboratory should establish guidelines for the protection of medical data from unauthorized access, which should: • • • • •

Protect all laboratory data and information Analyze access methods based on standards Use the standard application methods wherever possible Designate personnel responsible for the integrity of specific data-sets Restrict access to data and information and relative editing privileges to authorized individuals only

The laboratory should also obtain a complete record of all preventive actions concerning computer maintenance (Markin & Whalen, 2000; Sciacovelli et al., 2007). Hardware preventive maintenance is the best way to dramatically reduce all factors threatening or shortening computer’s life. The laboratory should implement specific procedures referring to many issues, such as: • • •

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• •

Power surges, incorrect line voltage, and power outages handling Water and corrosive agents prevention

Software-Related Implemented Laboratory Procedures Software preventive maintenance is achieved by using anti-virus applications, defragmentation software and testing utility programs (Markin & Whalen, 2000; Sciacovelli et al., 2007). The laboratory should/ must implement specific procedures referring to many issues, such as: • • •

Proper activation/deactivation and utilization of computer and installed programs Systematic back-up recovery and proper management of files and data-sets Scheduled hardware cleaning and maintenance and periodical software security and integrity verification by authorized personnel. System’s maintenance should be scheduled in such a way that it will not interrupt patient-care service

Every modification of the system hardware and software should be documented and verified while all computer problems and malfunctions should be analytically reported. A corrective action must be taken in order to avoid these problems in the future.

Specific Scoring Systems and Reporting Terminologies for Cytological Specimens In the field of telecytology, applied for accreditational purposes, the role of the person appointed to image capture and transmission is of paramount importance for the success of a static telecytology system. In our study, the person who captures and transmits the digital images is an already certified cytopathologist with adequate experience in conventional cytological diagnosis. Less specialized personnel, such as inexperienced screeners, may endanger the acquisition of representative images from each cytological slide (Archondakis et al., 2009). Besides the histological examination, other measures for cytology slides validation must be adopted in order to avoid possible indiscriminate failure of qualified, competent personnel (Nagy & Newton, 2006). Such measures may be verification of cytological diagnosis by board certified, well-trained, scientific personnel, establishment of specific scoring system and reporting terminology for all kinds of cytological specimens and finally capturing of a significant number of representative images by certified well-trained personnel (Nagy & Newton, 2006). The scoring system and reporting terminology may be simplified and possibly inappropriate and unfair for certain cytological specimens. Specific scoring systems and reporting terminologies should be established for each kind of cytological specimens in order to ensure that the cytological diagnoses reflect the clinical implications associated with this terminology in modern practice, particularly regarding recommended follow-up (Williams, Mullick, Butler, Herring, & O’Leary T, 2001).

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Participation of Laboratory Staff in Proficiency Testing Schemes Cytology scientific societies should focus on cytology proficiency testing particularities and define special technical aspects such as images size and analysis, suggested testing intervals, diagnostic categories and methodology used for statistical evaluation of the proficiency testing results (Nagy & Newton, 2006). Static telecytological systems are affordable by all cytopathology departments and give the opportunity to all scientific personnel to participate in proficiency testing programs, even when there is a significant time difference among participating laboratories (Raab et al., 1996). Laboratory management should encourage personal participation of all scientific personnel in such proficiency testing programs (Pinco et al., 2009). Laboratory management must have in mind that proficiency testing programs proffered on the basis of static digital images can improve the professional skills of the participating medical staff and make them feel more confident in their daily work. Proficiency testing providers should ensure that the personnel appointed to image capture and transmission has adequate experience in both conventional and image-based diagnosis. Previous experience in that field should be well-documented and recorded (Nagy & Newton, 2006).

FUTURE RESEARCH DIRECTIONS Telecytology, when integrated into the daily workflow, can provide exceptional consultation and professional testing opportunities to distant laboratories. Static telecytology systems are preferred by laboratories that cannot afford the high cost of buying and maintaining dynamic systems. In any case, the cost of participation in a running telecytology program is inexpensive for small cytopathology departments (Archondakis et al., 2009; Nagy & Newton, 2006). Provincial hospitals, where immediate scientific collaboration and support is necessary, can take advantage of this great opportunity to improve their cytology services (Archondakis et al., 2009). Future research must focus on the details of the implementation of a low-cost Mobile Health based application for proficiency testing purposes, that is determining the required testing interval, elucidating the validation criteria applied to electronic material used for proficiency testing purposes and possibly changing the focus of the test from individuals to laboratory level testing. The diagnostic reliability of telecytology provides the potential for the further amelioration of the laboratory services, by producing digital educational material for use in web-based training systems (Stergiou et al., 2009).Those programs can improve the professional skills of the participating medical staff and make them feel more confident in their daily work (Stergiou et al., 2009). The use of ISO 15189:2012 requirements in electronic data storage and retrieval are expanding, and many informative clauses of the standard are expected to become normative in the near future. A net of accredited laboratories is globally expanding (Kubono, 2007; Pearlman et al., 2001). Many more countries will incorporate ISO 15189 requirements in their national or local regulations. Medical laboratories that will develop the most innovative and up to date procedures for electronic medical reporting and storage will become referral laboratories for their countries or regions (Kubono, 2007; Murai, 2002; Okada, 2002, Pouliakis et al., 2014a, 2014b). All laboratories notices concerning the implementation of the ISO 15189:2012 are collected by an international working group, which handles the revision of the standards when necessary. Problems that might be reported to this international working group are examined and suitable solutions will be

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incorporated in the standard’s future editions or specific guidelines (Kubono, 2007; Murai, 2002; Okada, 2002, Pouliakis et al., 2014a, 2014b). Future research must focus on the details of the implementation of a static telecytological application for proficiency testing purposes, that is, determining the required testing interval, elucidating the validation criteria applied to electronic material used for proficiency testing purposes and possibly changing the focus of the test from individuals to laboratory level testing. Considering the future of Mobile Health technology in Cytopathology, ιt is expected that Mobile Health technology will be further exploited by cytopathology laboratories wishing to improve their quality standards. One of the major challenges of Mobile Health technology will be to resolve possible problems regarding data safety and security. A possible solution could be a multi-Mobile Health approach with a key sharing mechanism (Mouli & Sesadri, 2013) and patient identification cross reference numbers (Kondoh, Teramoto, Kawai, Mochida, & Nishimura, 2013). In the distant future, it is expected that the cytopathology departments will witness a large-scale migration to Mobile Health-based LIS due to ease of availability and low cost of ownership and maintenance. In relation to the research activities, as sequencing costs are continuously becoming lower, nowadays it is not only possible but extremely easier for genomics researchers to have large amounts of data. The increasing availability of computational power according to Moore’s Law and the falling overhead for data storage, give to the scientists the opportunity not only to create and store large genetic data-sets over the course of their research but it is possible to process them. However, the majority of the laboratories, have already become, or will soon become, oversubscribed and underpowered about the exploitation of data. Unavailable software and lack of computational power to run exhaustive search algorithms, eventually, will lead many researchers towards Mobile Health technology to conduct their research, alternatively much of the minable information may remain untouched, underutilized and not properly explored. As a bonus, Mobile Health technology provides the means for data sharing among research teams. Thus, larger data-sets can be exploited, more robust results and conclusions related to rear diseases can be obtained. In the near future, cytopathology laboratory information systems will be able to record large data-sets and interface with legacy systems to capture historical laboratory data, with the goal of storing life-long results on each patient. Capabilities for handling large genomic data-sets will be increasingly necessary for future LIS. In the near future cytopathology laboratory information systems will have to capture industry standards for coding, billing, document generation, and interface formats, such as CDC, HL7 CDA1/2, XML, ASC X12, LOINC, SNOMED-CT, ICD-9, or ICD-10, as appropriate for each data type. The user interface and navigation will become more user-friendly. The LIS will minimize the number of keystrokes required for all activities and will be uniform for similar tasks within the software. In addition, all screens and reports will be printed and exported in the appropriate document text, spreadsheet, or graphic formats, while fully functional text editors will be used in text entry fields. Moreover, cytopathology laboratory information systems will use interfaces that will be flexible in data formats and fully functional with appropriate routines available for testing the functionality of the interfaces before use to meet end-user expectations. The cytopathology laboratory information systems will be able to integrate instant messaging, forum, online meeting, and social-networking capabilities while they will become powerful enough to perform multiple functions simultaneously with an imperceptible impact on their speed.

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CONCLUSION The standard ISO 15189:2012 includes direct and indirect references to the requirements concerning the implementation of laboratory information systems in medical laboratories. These requirements constitute a powerful tool for medical laboratories wishing to improve the quality of their laboratory information system because they diminish dramatically the possibility of human error or unexpected hardware or software failure. Laboratories should only specify the procedures to be followed according to their needs. Laboratory informatics is critical to meet current and future challenges. Many of these challenges can be met by advancing technologies, such as improving the integration of disparate information systems, automation, specimen tracking, electronic document management systems, and streamlining procedures. Telecytology can be used as an economic method for the implementation of QA programs in the everyday laboratory practice, provided that representative images be taken, standard diagnostic criteria are applied and the participants have already acquired sufficient experience in the evaluation of digital images. The use of static telecytology systems for proficiency testing provision is possible, and the first steps towards this direction are already made in Greece. Mobile Health technology promotes the concept of bedside, point-of-care, and instant cytopathology. It gives the cytopathologists, pathologists, physicians, and even patients the possibility to review images and medical data via any display device with Internet connection. Furthermore, Mobile Health technology will play a significant role in the patient-centered healthcare since each patient can be associated with a unique individual EMR, that once created, could be easily accessible by numerous physicians and doctors (if needed or required) so as to promote efficient professional collaboration in the effort to define and apply the most ideal clinical healthcare and management plan suitable to the unique medical profile of each patient making a way towards the concept of “personalized medicine”. Mobile Health technology is expected to provide flexibility to all cytopathology services and has the potential to revolutionize the way cytopathology data will be stored, accessed, and processed.

REFERENCES Archondakis, S. (2013). The Use of Static Telemedical Applications of Cytopathology for Proficiency Testing. International Journal of Reliable and Quality E-Healthcare, 2(2), 47–53. doi:10.4018/ ijrqeh.2013040104 Archondakis, S. (2014). Static Telecytological Applications for Proficiency Testing. In Nanotechnology: Concepts, Methodologies, Tools, and Applications (pp. 556–568). Hershey, PA: Information Science Reference; doi:10.4018/978-1-4666-5125-8.ch023 Archondakis, S. (2015). The Use of Information Systems in a Modern Cytopathology Laboratory. In A. Moumtzoglou, A. Kastania, & S. Archondakis (Eds.), Laboratory Management Information Systems: Current Requirements and Future Perspectives (pp. 208–236). Hershey, PA: Medical Information Science Reference; doi:10.4018/978-1-4666-6320-6.ch011

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Okada, M. (2002). Future of laboratory informatics. Rinsho Byori, 50(7), 691–693. PMID:12187706 Pantanowitz, L., Hornish, M., & Goulart, R. A. (2009). The impact of digital imaging in the field of cytopathology. CytoJournal, 6(1), 6. doi:10.4103/1742-6413.48606 PMID:19495408 Pearlman, E. S., Wolfert, M. S., Miele, R., Bilello, L., & Stauffer, J. (2001). Utilization management and information technology: Adapting to the new era. Clinical Leadership & Management Review, 15(2), 85–88. PMID:11299910 Pinco, J., Goulart, R. A., Otis, C. N., Garb, J., & Pantanowitz, L. (2009). Impact of digital image manipulation in cytology. Archives of Pathology & Laboratory Medicine, 133(1), 57–61. doi:10.1043/15432165-133.1.57 PMID:19123737 Pouliakis, A., Athanasiadi, E., Karakitsou, E., Archondakis, S., Mourtzikou, A., Stamouli, M., & Karakitsos, P. et al. (2014b). ISO 15189:2012 Management Requirements for Cytopathology Laboratory Information Systems. International Journal of Reliable and Quality E-Healthcare, 3(3), 37–57. doi:10.4018/ijrqeh.2014070103 Pouliakis, A., Margari, N., Spathis, A., Kottaridi, C., Stamouli, M., Mourtzikou, A., & Karakitsos, P. et al. (2014a). ISO 15189:2012 Technical Requirements for Cytopathology Laboratory Information Systems. International Journal of Reliable and Quality E-Healthcare, 3(3), 58–80. doi:10.4018/ijrqeh.2014070104 Raab, S. S., Zaleski, M. S., Thomas, P. A., Niemann, T. H., Isacson, C., & Jensen, C. S. (1996). Telecytology: Diagnostic accuracy in cervical-vaginal smears. American Journal of Clinical Pathology, 105(5), 599–603. PMID:8623769 Sciacovelli, L., Secchiero, S., Zardo, L., D’Osualdo, A., & Plebani, M. (2007). Risk management in laboratory medicine: Quality assurance programs and professional competence. Clinical Chemistry and Laboratory Medicine, 45(6), 756–765. doi:10.1515/CCLM.2007.165 PMID:17579529 Shen, Z., & Yang, Z. (2001). The problems and strategy relevant to the quality management of clinical laboratories. Clinical Chemistry and Laboratory Medicine, 39(12), 1216–1218. doi:10.1515/ CCLM.2001.194 PMID:11798079 Sherman, M. E., Dasgupta, A., Schiffman, M., Nayar, R., & Solomon, D. (2007). The Bethesda Interobserver Reproducibility Study (BIRST): A web-based assessment of the Bethesda 2001 System for classifying cervical cytology. Cancer, 111(1), 15–25. doi:10.1002/cncr.22423 PMID:17186503 Solomon, D., Davey, D., Kurman, R., Moriarty, A., O’Connor, D., Prey, M., & Young, N. et al. (2002). The 2001 Bethesda System: Terminology for reporting results of cervical cytology. Journal of the American Medical Association, 287(16), 2114–2119. doi:10.1001/jama.287.16.2114 PMID:11966386 Stamataki, M., Anninos, D., Brountzos, E., Georgoulakis, J., Panayiotides, J., Christoni, Z., & Karakitsos, P. et al. (2008). The role of liquid-based cytology in the investigation of thyroid lesions. Cytopathology, 19(1), 11–18. doi:10.1111/j.1365-2303.2007.00512.x PMID:17986263 Stergiou, N., Georgoulakis, G., Margari, N., Aninos, D., Stamataki, M., Stergiou, E., & Karakitsos, P. et al. (2009). Using a web-based system for the continuous distance education in cytopathology. International Journal of Medical Informatics, 78(12), 827–838. doi:10.1016/j.ijmedinf.2009.08.007 PMID:19775933

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Vacata, V., Jahns-Streubel, G., Baldus, M., & Wood, W. G. (2007). Practical solution for control of the pre-analytical phase in decentralized clinical laboratories for meeting the requirements of the medical laboratory accreditation standard DIN EN ISO 15189. Clinical Laboratory, 53(3-4), 211–215. PMID:17447659 Westbrook, J. I., Georgiou, A., & Rob, M. I. (2008). Computerised order entry systems: Sustained impact on laboratory efficiency and mortality rates? Studies in Health Technology and Informatics, 136, 345–350. PMID:18487755 Williams, B. H., Mullick, F. G., Butler, D. R., Herring, R. F., & O’Leary, T. J. (2001). Clinical evaluation of an international static image-based telepathology service. Human Pathology, 32(12), 1309–1317. doi:10.1053/hupa.2001.29649 PMID:11774162 Yamashiro, K., Kawamura, N., Matsubayashi, S., Dota, K., Suzuki, H., Mizushima, H., & Azumi, N. et al. (2004). Telecytology in Hokkaido Island, Japan: Results of primary telecytodiagnosis of routine cases. Cytopathology, 15(4), 221–227. doi:10.1111/j.1365-2303.2004.00147.x PMID:15324451

KEY TERMS AND DEFINITIONS Cloud Computing: An online data-storage platform where the users can access and process the uploaded records though an Internet connection without any limitations posed by the hardware and/or software they may use. Electronic Medical Record (EMR): The data set of demographics, medical history, clinical information and diagnostic results and reports for each patient that is gathered and stored in digital format. Laboratory Information System (LIS): A group of digital software and applications that consist a computing system with capability to provide better management, monitoring and automation for laboratory procedures and activities. Mobile Health Applications: Computer programs and digital applications that used for the practice of medicine and provision of healthcare services through the utilization of mobile devices such as tablets and smartphones. Proficiency Testing (PT): The inter-laboratory testing scheme though which the performance of an individual laboratory to carry out specific tests and measurements is evaluated in comparison with the performance of other similar laboratories. Telecytology: A concept of diagnostic evaluation and assessment of cytological smear-slides that have been converted into digital images and uploaded online so as to be accessible remotely by cytopathologists. Quality Assurance (QA): The entire system of planning procedures and systematic activities that a laboratory has implemented into its Quality Management System and carries out so as to assure that the determined quality requirements for its provided services will be fulfilled. Quality Control (QC): The testing techniques, analytical measurements and observation methods that a laboratory uses in order to ensure that the services it provides will fulfill the quality requirements as defined through the earlier QA process.

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Computer Virus Models and Analysis in M-Health IT Systems: Computer Virus Models Stelios Zimeras University of the Aegean, Greece

ABSTRACT Computer viruses have been studied for a long time both by the research and by the application communities. As computer networks and the Internet became more popular from the late 1980s on, viruses quickly evolved to be able to spread through the Internet by various means such as file downloading, email, exploiting security holes in software, etc. Epidemiological models have traditionally been used to understand and predict the outcome of virus outbreaks in human or animal populations. However, the same models were recently applied to the analysis of computer virus epidemics. In this work we present various computer virus spread models combined with applications to e-health systems.

INTRODUCTION Today, the most sophisticated types of threats to networks are presented by programs that exploit vulnerabilities in computing systems. Such threats is a malicious software that is a software intentionally included or inserted in a system for a harmful purpose. The threats to network security can be classified as hacking, inside attack, computer virus, the leak of the secret message and modification of key data in the network. All these attacks and invasions aim at wrecking information that is stored in a server by different ways. The term “computer virus”, coined by Adleman in the early 1990’s (Adleman, 1990), is suggestive of Btrong analogies between computer viruses and their biological namesakes. Both attach themselves to a small functional unit (cell or program) of the host individual (organism or computer), and co-opt the resources of that unit for the purpose of creating more copies of the virus. By using up materials DOI: 10.4018/978-1-4666-9861-1.ch014

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 Computer Virus Models and Analysis in M-Health IT Systems

(memory) and energy (CPU), viruses can cause a wide spectrum of malfunctions in their hosts. Even worse, viruses can be toxic. Computer viruses are self-replicating software entities that attach themselves parasitically to existing programs. When a user executes an infected program (an executable file or boot sector), the viral portion of the code typically executes first. The virus looks for one or more victim programs to which it has write access (typically the same set of programs to which the user has access) and attaches a copy of itself (perhaps a deliberately modified copy) to each victim. Under some circumstances, it may then execute a payload, such as printing a weird message, playing music, destroying data, etc. Eventually, a typical virus returns control to the original program, which executes normally. Unless the virus executes an obvious payload, the user is unlikely to notice that anything is amiss, and will be completely unaware of having helped a virus to replicate. Viruses often enhance their ability to spread by establishing themselves as resident processes in memory, persisting long after the infected host finishes its execution (terminating only when the machine is shut down). As resident processes, they can monitor system activity continually, and identify and infect executables and boot sectors as they become available. Over a period, this scenario is repeated, and the infection may spread to several programs on the user’s system. Eventually, an infected program may be copied and transported to another system electronically or via diskette. If this program is executed on the new system, the cycle of infection will begin anew. In this manner, computer viruses spread from program to program, and (more slowly) from machine to machine. Lately, computer worms have become a major problem for large computer networks, causing considerable amounts of resources and time to be spent recovering from large-scale attacks. It is believed that understanding the factors influencing worm propagation in technological networks (such as the Internet, the World Wide Web, phone networks, IP networks, etc.) will suggest useful ways to control them. So far, a few studies have employed simple epidemiological models to understand general characteristics of virus1 spreading. They become one of the most important factors for the security of any system. Epidemiological models have traditionally been used to understand and predict the outcome of virus outbreaks in human or animal populations. However, the same models were recently applied to the analysis of computer virus epidemics. For example, using a simple model it has been shown that networks that have a topology similar to the Internet are highly vulnerable to viral attacks. In general, epidemic models assume that individuals go through a series of states at a particular constant set of rates. Therefore, the elaboration of a model requires the definition of a set of possible states and a set of transition rates. The simplest model referred to as the SIS model (for Susceptible-InfectedSusceptible). Other more complex models include the Susceptible-Infected-Removed (SIR) model and the Susceptible-Exposed-Infected-Removed (SEIR) model. The topology also plays a role in determining the outcome of an outbreak. Technological networks appear to be best approximated using scale free graphs or homogeneous graphs network in some cases. Markov models may be introduced to analyze the network topology of the spreading into the systems. In this work, we present various computer virus spread models combined with applications to ehealth systems. Computer epidemiology techniques would be analysed, considering aspects like: Direct immunization (Whenever a user installs antivirus software (or updates it) on a machine, this machine is automatically immunized to a certain group of viruses), Antivirus availability and Curing process. Final we would introduce the meaning of cost for the virus threats during the attacks in a network system

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NETWORK CHARACTERISTICS A network is a group of computers that are interconnected by electronic circuits or wireless transmissions of various designs and technologies for the purpose of exchanging data or communicating information between them or their users. One way to categorize the different types of computer network designs is by their scope or scale. In this category included: • • • •

Local Area Network (LAN): LAN victuals networking capability to a group of computers in proximity to each other. Moreover, a LAN is practical for sharing resources like files, printers, etc. A LAN can connect to another LAN, or WAN or Internet. Metropolitan Area Network (MAN): Is a computer network which usually spanning a physical area (larger than LAN but smaller than a WAN) and connect a LAN with using high-speed backbone technologies. Wide Area Network (WAN): In this type, a WAN is connecting a number of decentralized of a wide geographical area as a community, or a province, or a country, or the world, as it through with the internet. Furthermore, the WAN usually connects smaller networks, like LANs and MANs. Wireless Local Area Network (WLAN): Is a LAN network based on wireless network technology. This network is built by attaching a device called the access point (AP) to the edge of the wired network. Moreover, clients communicate with the AP using a wireless network adapter similar in function to a traditional Ethernet adapter.

The network topology describes the structure that had been used for the physical wiring of the network. Network topologies can be categorized into five basic types 1. Bus Topology: Bus networks use a backbone, to connect all the devices on it. An interface connector has used to achieve the connection with all the devices. For the communication, a message is sent to the system but only the legal receiver can open the message (Figure 1). 2. Ring Topology: In this case, a neighborhood connection is taking place. Every device can communicate with only two neighbors. All messages travel through a ring in the same direction either clockwise or counter-clockwise. In case of failure to receive the network a message that means probably the loop maybe is breaking taking down all the system (Figure 2). 3. Star Topology: This topology is designed with each node, like workstations and file servers connected directly to a central network. On a star, the data passes through a hub, switch, or rooter before continuing to its destination (Figure 3). 4. Tree Topology: Tree topologies involve multiple star topologies together onto a bus. In its simplest form, only hub devices connect directly to the Tree Bus and each hub functions as the “root” of a tree of devices (Figure 4). 5. Mesh Topology: Each node act as an independent router, independent of the network connections. In addition, it allows continuous connections around blocked paths by jumping from one node to another node until the destination is reached (Figure 5).

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Figure 1. BUS Network topology

Figure 2. Ring Network topology

Figure 3. Star Network topology

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Figure 4. Tree Network topology

Figure 5. Mesh Network topology

FACTORS THAT AFFECT THE SPREAD OF THREATS The main threats that the most of the times we deal are the malicious virus (code). The spread of malicious software depends of the defenseless of the network system. These include security weakness in the operating system of attached computers (e.g. Windows) as well as a weakness of the internet rooters and other network devices. In addition, weakness can cause by other errors that are analyzed below. •

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Designing Protocols: The protocols define the rules and methods to enable computers to communicate one to each other. If the protocol has a designing error, then it is defenseless from malicious attacks and computer virus;

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•

• • • •

Protocol Implementation: A protocol can have weak sections during the implementation. Also, many times the weakness of an implementation can be found only when the final testing is taking place. The attackers can search and find these weak sections and using special tools can access the system spreading the virus via malicious software; Weakness in Network Configuration: In this case, the weakness originates from the way the components are installed and used. Sometimes the passwords can very easily obtain from the network, so the system is entirely unsheltered from system intruders and malicious software; Removable Disk Storage: This category includes Compact Disk (CD), hard disk, USB flash memory disk and Digital Versatile Disk (DVD). These disks can be exploited by a virus, so it becomes obvious the risk which arising from installing disk storage on a workstation; Web Browsing: The web browsing is a permanent source of dangerous connection for a workstation or a network. Also, many users visit websites that may contain malicious code, like a virus and in that way contribute in virus spreading; Mail Services: Email is one of the widely used Internet services. It is the main communication service between users. On the other hand, it is the most famous service for virus and worm propagation. Specifically a user opens a received file without scanning it and, as a result, helps the virus to execute its code.

Virus Definition Computer virus is malicious software that infects other programs by modifying them. This technique includes a copy of the virus program that can then use another uninfected program for his spreading. A computer virus carries in its initial main code to other programs by making copies of itself or by copying parts of the infected program to the network. Each time an infected host is in contact with an uninfected piece of software, a new copy of the virus infects the new program. The infection can be spread from host to host via flash disk, DVD, e-mail message and others programs over a network (Stallings, 2004). During its lifetime, a virus goes through four phases (Stallings, 2004): • • • •

Dormant Phase: In this phase a virus is waiting for initiating a particular event (e.g. date, another program or file, disc capacity). Propagation Phase: The virus places a copy of itself into other program or in specific directories on the disk. Now each infected program includes an exact copy of the virus, which itself enter a propagation phase. Triggering Phase: The virus is activated and performs for what was intended by virus programmer. The triggering phase can be activated by a variety events, including how many times a copy of virus has made copies of itself. Execution Phase: The function is executed, and sometimes are harmless (e.g. message on the screen) and other times destructive (e.g. damage data files).

According to the Figure 6, there are four levels that describe a virus anatomy. Three out of four levels are optional because a virus may not find the appropriate condition to infect a system. The four levels are listing below (Heidari, 2004). •

Mark: Can prevent re-infection attempt. 289

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Figure 6. Virus anatomy (Heidari, 2004)

Infection mechanism: Searches for weakly protected computers and when it done, the virus spread to other susceptible files. Trigger: When the conditions for delivering are appropriate to initiate the virus. Payload: Might drop a Trojan horse or parasitically infect files.

• • •

Types of Virus • •

Parasitic Virus: In this category, a virus attaches itself to executable files and replicates when the infected program is executed. Then, it tries to find other susceptible programs to infect them. Moreover, a parasitic virus uses four basic approaches for infect a host (Szor, 2005): Companion Infection: Is the simplest method by which a virus is adhered with an executable file and denominate itself with the same name as the original program. So the operating system is running the virus when a user requests to load the original program.

The code of the infected executable file remains the same. In Windows system, a way to implement the above method is in EXE. files to denominate the virus with the same name as the original program and use the suffix .COM instead .EXE. Trying to hide their existence, viruses that use this technique usually assign a hidden feature in COM file, thus reducing the possibility to discover them in the directory. Also to ensure that the victim will not understand anything from this, usually, running the original program and continuously run their codes. •

• •

290

Overwriting Infection: Virus that use this technique, replace a part of the code from the original program. One method to succeed it is by opening the target-entry program and places a copy of itself in this file. This means that when the user tries to run the program, the operating system will run the virus code. Prepending: In this case, a virus inserts the code at the beginning of a susceptible program (Figure 7). When an infected program executed, then the operating system runs firstly the virus code and then the host file. In addition, this method does not destroy the host program. Appending Technique: Such virus includes its code to the end of the target-program. In addition, these viruses are necessary to change the beginning of the infected program for looping into the

 Computer Virus Models and Analysis in M-Health IT Systems

• • • • •

code which point out the part of virus code. When the virus executed, bring the control back to the host file. This method does not destroy the host program (Figure 8). Memory-Resident Virus: Various that stays in main memory as part of a resident system program. Then the virus infects any program that execute and its host program is terminated. Boot Sector Virus: Is a virus that places its codes and commands into a computers DOS boot sector or master boot record. When the system infected, the master boot record is corrupted, and the boot sequence is changed. Stealth Virus: Is a file virus that uses various special techniques to hide its presence from users and antivirus scanners. This can be achieved by intercepting the read request to the file and returning the content of the original read request to the uninfected file. Metamorphic Virus: A metamorphic virus rewrites itself completely each time it is to infect new executables, increasing with that way the difficulty of detection. Macro-Viruses: A macro is an executable program incorporated in a word document or other type of file. Moreover, a macro code provides enough functionality to write a virus. In addition, when a document that contains macro-codes is run by the application, then these macros can be run automatically and thus allow a macro-virus to take control of that system (Aycock, 2006) (Figure 9).

Figure 7. Prepending technique (Szor, 2005)

Figure 8. Appending technique (Szor, 2005)

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Figure 9. Virus infects other Word documents by using macros (Aycock, 2006)

COMPUTER VIRUS MODELS AND ANALYSIS IN M-HEALTH IT SYSTEMS During the time, requirements of the data analysis, especially in cases of medical information, lead us to the implication of mobile devices, where the transition of those data is quicker. Mobile Health (mHealth) is a new and growing domain within public health where the advantages of the technology in medicine have improved during the time. Mobile phones have become necessary these days in communities not only for communication but for transferring medical information from a region to another. Mobile phones can collect data from communities with greater accuracy and efficiency and make the compilation of data easier; thus permitting speedier data analysis. mHealth is a new field of electronic health, and a related field of health informatics that began to develop in 1999 (Curioso & Kurth, 2007). Many developing countries have skipped the use of landline telephones and instead directly proceeded to mobile phone networks (Huang, 2009). Figure 10 illustrates this growth with the year being displayed on the y-axis and mobile phone subscriptions per 100 inhabitants on the x-axis. mHealth takes advantage of these advances in access and adapts the technology of phones, computers, and the Internet into a more efficient network to provide healthcare and conduct disease surveillance in developing countries (Meyer, 2014) Finally, the growth of Internet access through mobile phones has increased the number of devices that are potentially vulnerable to virus attacks. The consequences may be severe, especially for mHealth applications. Therefore, the study of virus models becomes nowadays crucial.

EPIDEMIOLOGICAL MODELS Spreading of the virus in any way leads us to analysis of epidemiological structures of the process and modelling of the procedures, so the researchers could apply the techniques to predict the effects of the computer virus (Mollison, 1995).

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Figure 10. Mobile Phone Subscriptions per 100 inhabitants from 1997-2007 (International Telecommunications Union, 2008; Meyer, 2014)

Susceptible Infectious (SI) Model In SI model, each host is susceptible to an infection or already infected. The only transition between hosts is from susceptible to infected. As Susceptible hosts (S) could be defined these hosts that do not currently have the virus, and they are capable of contracting the virus. As Infected hosts (I) could be defined the hosts that are currently infected with the virus, and they are capable of transmitting the virus to others (Figure 11) (Williamson, 2002; Keeling and Ronari, 2008; Mollison, 1995). Also in SI model each infected host assumed that has this infection forever. In SI model four parameters have been analysed: S(t): the number of uninfected hosts at time t; I(t): the number of infected hosts at time t; N: the size of population (N=S+I) and β: the medium rate of population. The differential equitation for the SI model is given by: dI ( t ) dt

= β I (t ) S (t )

dS ( t ) dt

= −β I (t ) S (t )

Denoting r the probability of transmission of infection per contact then the above equations becomes:

Figure 11. SI model

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dI ( t ) dt

= r β IS

1 N

dS ( t ) dt

= −r β S

1 N

Susceptible Infectious Recovered (SIR) Model The SIR model is the classical epidemiological model, including another situation (factor) in relation to the SI model. The situation is called removed and represents the hosts which have recovered from the infection and cannot be re-infected again, or which have been in quarantine and those who have died during the infection (Figure 12) (Hethcote, 2000; Diekmann and Heesterbeek, 2000). Furthermore in SIR model there are two more parameters in relation to SI model. The R(t) which represents the number of removed hosts at time t and the γ: which represents the average rate of removal and N: the size of population (N=S+I+R). The differential equitation for the SIR model is given by (Anderson et. al., 1992): dI (t ) dt

= β I (t ) S (t ) − γ I (t )

dS (t ) dt

= −β I (t ) S (t )

dR (t ) dt

= γ I (t )

In the model if we include the rate of removal the first equation becomes: dI (t ) dt

= β  S (t ) − ρ  I (t )  

Because the population is fixed, and a host can be infected only one time, the spread of the virus would be stop. In this case, we have two main cases for the hosts: either the hosts would be weak either the host would have been removed. In this case, if I(t)>0 and β>0 then dI ( t ) dt

> 0 iff S(t)>ρ

meaning that there were not be any infection to the system, except the case the number of susceptible hosts is bigger than the critical value ρ. From mathematical point of view, the second equation becomes: dI ( t ) dt

= ( IR − RR )

Figure 12. SIR model

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dR ( t ) dt

= − RR

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Figure 13. Mathematical analysis for SIR model (Hazem et. al. 2006)

where IR and RR are defined as the infection rate and the recovery rate given by: IR =

ciS N

RR =

I di

with c is the contact rate per unit of computer per time; i is the Infectivity: probability that a susceptible host gets infected when connected to an infectious host or the network and di is the Average Infectivity Period: after a machine is scanned for virus and then cleaned. Including all the above equations, the rate of infection is given by (Figure 13) (Hazem et. al., 2006):

dI ( t ) ciI ( N − I − R ) I = − dt N di

CONCLUSION The main idea of this work was to present an overview of the security problems that may appear during a network process. The main characteristics of the networks have been presented, and problematic issues based on the computer virus spreading process have been introduced. The second part attempted to analyze information about the features of the computer virus, how they can exploit the network and explain why security is important for the dealing of the network when weakness happens. Standard epidemiological models have been presented, and analysis based on them is illustrated via mathematical equations.

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REFERENCES Abdelazim, H. Y., & Wahba, K. (2006). System Dynamic Model for Computer Virus Prevalence. College of Computer Science, Cairo University. Adleman, L. (1990). An Abstract theory of computer viruses Advances in Cryptology, CRYPTO’88, LNCS: 403, (pp. 354-374). 8th Annual International Cryptology Conference, Santa Barbara, California, USA. Anderson, R. M., May, R. M., & Anderson, B. (1992). Infectious Diseases of Humans Dynamics and Control (Oxford Science Publications). New York: Oxford UP. Aycock, J. (2006). Computer Viruses and Malware. Calgary: Springer. Curioso, W., & Kurth, A. (2007). Access, use and perceptions regarding Internet, cell phones and PDAs as a means for health promotion for people living with HIV in Peru. BMC Medical Informatics and Decision Making, 7(1), 24. doi:10.1186/1472-6947-7-24 PMID:17850656 Diekmann, O., & Heesterbeek, J. A. (2000). Mathematical Epidemiology of Infectious Diseases: Model Building, Analysis and Interpretation. New York: John Wiley & Sons, Incorporated. Heidari, M. (2004). Malicious Codes in Depth. Retrieved May 20, 2015, from http://www.megasecurity. org/papers/mal_codes_in_depth.pdf Hethcote, H. W. (2000). The mathematics of infectious diseases. SIAM Review, 42(4), 599–653. doi:10.1137/S0036144500371907 Huang, C.-Y. (2011). Rethinking leapfrogging in the end-user telecom market. Technological Forecasting and Social Change, 78(4), 703–712. doi:10.1016/j.techfore.2010.10.009 International Telecommunications Union. (2008). ICT Data and Statistics. Retrieved May 20, 2015, from http://www.itu.int/ITU-D/ict/statistics/ict/ Keeling, M., & Rohani, P. (2008). Modeling Infectious Diseases in Humans and Animals. Princeton University Press. Meyer, D. (2014). Closing the Data Feedback Loop: Using Mobile Health Technology as an Informatics System Intervention. (MSc. Thesis). Tufts University School of Medicine, Boston, MA. Mollison, D. (1995). The structure of Epidemic Models. Cambridge University Press. Stallings, W. (2011). Cryptography and network Security. Pearson Education Inc. Szor, P. (2005). The Art of Computer Virus Research and Defense. Pearson Education Inc. Williamson, M.M. (2002). Biologically Inspired Approaches to Computer Security. Information Infrastructure Laboratory, HP Laboratories Bristol.

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KEY TERMS AND DEFINITIONS Downloaders: Malicious software that installs a set of harmful tools on a target machine. Keyloggers: Captures keystrokes on a compromised system and collecting sensitive information(e.g names, passwords)for the attacker. Trojan Horse: A destructive program that masquerades as a benign application. Virus: Malicious software that infects other programs by modifying them. Worm: Program that can replicate itself and send copies from computer to computer across network connections. Zombie Programs: Program that secretly activated on an infected machine for launching attacks on other machine.

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Chapter 15

Information Security Threats in Patient-Centred Healthcare Shada Alsalamah King Saud University, Saudi Arabia Hessah Alsalamah King Saud University, Saudi Arabia Alex W. Gray Cardiff University, UK Jeremy Hilton Cranfield University, UK

ABSTRACT Healthcare is taking an evolutionary approach towards the adoption of Patient-Centred (PC) delivery approach, which requires the flow of information between different healthcare providers to support a patient’s treatment plan, so the Care Team (CT) can seamlessly and securely access relevant information held in the different discrete Legacy Information Systems (LIS). Each of these LIS deploys an organisational-driven information security policy that meets its local information sharing context needs. Nevertheless, incorporating these LIS in collaborative PC care brings multiple inconsistent policies together, which raises a number of information security threats that can block the CT access to critical information across a patient’s treatment journey. Using an empirical study, this chapter identifies information security threats that can cause the issue, and defines a common collaboration-driven information security design. Finally, it identifies requirements in LIS to address the inconsistent policies in modern PC collaborative environments that would help improve the quality of care.

DOI: 10.4018/978-1-4666-9861-1.ch015

Copyright © 2016, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited.

 Information Security Threats in Patient-Centred Healthcare

INTRODUCTION Population ageing is a demographic revolution affecting the entire world (United Nations Population Fund (UNFPA), 2014) due to medical advances, increased child survival, and improved health care. This is evidenced by figures published by the UNFPA (UNFPA, 2014); see Figure 1, which shows the increasing number of people aged 60 or over between the years 1950-2050 in the world’s developed and developing countries (UNFPA, 2014). However, this does not mean that older persons should be a burden (UNFPA, 2014). Older people’s health conditions require more holistic care as comorbidity is more prevalent in older patients than in younger ones (McGarrigle, H., Personal Communication, November 2013). Patients with comorbidity suffer from more than one condition at a time, and so they follow multiple treatment pathways. It is clear that healthcare delivery systems need to cope with this emerging need, and be ready for the ageing population, with modern integrated healthcare services that can cope holistically with a patient with more than one health condition. Therefore, the delivery of healthcare in many countries has been shifting towards an integrated PC care using an evolutionary approach that incorporates Legacy Information Systems (LIS). PC healthcare is where care provision is tailored to meet an individual patient’s needs holistically. It is the basis of modern healthcare collaborative environments today, and many countries are using an evolutionary approach to shift towards PC care by building integrated systems based on the sound foundations of the current LIS to support it. The movement towards PC using LIS creates a new information sharing context that is collaboration-driven and is different from local organisation-driven contexts of LIS. This new context, however, requires medical information to flow with the patient between different healthcare providers as they follow the patient’s treatment plans and share information across healthcare organisations. This Figure 1. Number of people aged 60 or over: World, developed and developing countries, 1950-2050 (UNFPA, 2014)

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allows the CT to seamlessly access relevant information held in different discrete information systems so that a complete picture is available if required. Nevertheless, meeting this collaboration-driven information sharing context demands an information security context that can carefully balance between enabling seamless access to CT without invading the patient’s privacy. This can be addressed using an information security design that ensures the confidentiality, integrity, and availability of patient information is preserved in this collaborative environment (Calder & Watkins, 2008; Mense et al., 2013; Pfleeger and Pfleeger, 2003; Pipkin, 2000; Posthumus and von Solms, 2004; SANS Institute, 2001). Therefore, collaboration-driven information security should meet the overall care goal while retaining local information security for shared medical information among the CT. However, LIS were not designed to support a holistic view of a patient record needed in comorbidity, as they were developed to meet the needs of the disease centred approach at a time when information sharing was not common. LIS are unable to support seamless access to information because they are unable to comply with the information security of the shared information that is coming together in this collaborative environment supporting PC care, whether this information is related to a patient following one treatment pathway or one who has comorbidities. This is because the LIS incorporated in PC collaborative environments as part of the evolutionary approach are autonomous discrete information systems, where each of these systems protects its information using an information security context that is suitable for its local information sharing context. Consequently, a LIS may compromise on the availability of information by blocking a CT from accessing the information they need to care for the patient, and so interrupt care continuity. Thus, LIS require additional security features to cope with this emerging need if they are to participate in collaborative PC. This chapter aims to identify the range of information security threats that LIS present to PC care thus limiting its implementation, and derive a set of information security requirements in LIS to mitigate these threats, while being incorporated into collaborative PC care.

BACKGROUND Modern integrated healthcare services are an essential part of e-health (Powell, 2009). They use ICT to enhance collaboration, communication and coordination in the health sector (Eysenbach, 2001; Powell, 2009). At the heart of this integration of care lies PC healthcare (Allam, 2006), defined as: A collaborative effort consisting of patients, patients’ families, friends, the doctors and other health professionals […] where patients and the health care professionals collaborate as a team, share knowledge and work toward the common goals of optimum healing and recovery (International Alliance of Patient Organizations. (IAPO), 2004) In the global adoption of PC care (Department of Health (DoH), 1997; Ellingsen & Røed, 2010; Skilton, 2011), patient treatment is shifting from a traditionally fragmented disease centred approach towards an integrated PC one (Al-Salamah et al., 2011; Allam, 2006; DoH, 1997; IAPO, 2004; Skilton, 2011). Disease-centred care is also known as traditional healthcare (Smith & Eloff, 1999), doctor-centred (IAPO, 2004), hospital-centred (IAPO, 2004), location-based, and clinic-centred healthcare (Beale, 2004). In a disease centred approach, healthcare professionals use a treatment approach reflecting the needs of the disease diagnosis. Care for the patient focuses on the needs of the professionals treating the patient (Dawson et al., 2009; Skilton, 2011). This leads to each professional using an information silo to store

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information about patients with the same disease, and this is held in their organisation and managed by an independent stand-alone system not integrated with any other disease silo (Dawson et al., 2009). Access to patient information is limited to the physical boundaries of the provider (Dawson et al., 2009). Any decision-making process is fragmented and based on limited available information, as each healthcare provider keeps patient information “hidden” from care providers in other areas (Skilton, 2011). PC care has a more holistic view that considers the patient’s condition as a whole in contrast to different healthcare professionals treating each diagnosed disease separately (Al-Salamah et al., 2011; American Cancer Society, 2008). The patient is kept at the heart of these healthcare services and care is integrated and tailored to the patient’s needs and current state (Allam, 2006; Dawson et al., 2009; DoH, 1997, 2010b). It encourages healthcare professionals to adapt to these needs (DoH, 2010b) by collaborating as a CT (Al-Salamah et al., 2011) and using shared decision-making processes in regular Multi-Disciplinary Team (MDT) reviews (Skilton, 2011), mostly on a weekly basis. Also, each CT member collects and shares relevant information with other members. This collectively forms a complete patient record about the holistic condition of the patient, covering all the patient’s multiple conditions in cases of comorbidity. This encourages using appropriate information-sharing mechanisms among CT members while still preserving information confidentiality. Thus, central to PC care is the appointment of a “Guardian” of person-based clinical information in each healthcare organisation to oversee the sharing arrangements and make decisions when it comes to the use and sharing of clinical information and patient identifiable information (DoH, 2010a). Therefore, each healthcare organisation with access to patient records is mandated to have a Caldicott Guardian (Health and Social Care Information Centre (HSCIS), 2013), who is: The senior person is responsible for protecting the confidentiality of a patient and service-user information and enabling appropriate information-sharing. (HSCIS, 2013) Each Caldicott Guardian plays a key role in ensuring the organisation satisfies the highest practical standards for handling patient identifiable information (DoH, 2010a; HSCIS, 2013). There is also an overarching lead Information Governance Caldicott Guardian whose role is to make sure all local Caldicott Guardians are consistent (Crosby 2012). Both traditional and PC approaches have different attributes: the key emphasis in disease-centred care is on record keeping (Dawson et al., 2009), while the PC approach, on the other hand, creates a “culture of open information” (DoH, 2010b) emphasising accessibility to patient information (Dawson et al., 2009), teamwork and collaboration (Al-Salamah et al., 2011), and shared decision-making (DoH, 2010b; Skilton, 2011). This led to PC treatment being referred to as “shared care” of a patient (Smith & Eloff, 1999).

TOWARDS PC CARE ADOPTION USING LIS The movement towards PC healthcare is occurring in many countries. Most countries adopting PC care, including the UK, favour an evolutionary approach over a revolutionary one that involves using LIS, so that the LIS are gradually replaced with newer systems (Allam, 2006; Bisbal et al., 1999; DoH, 2000, 2010b; Morrey, 2013; Skilton, 2011). Bisbal et al. (1999) define an LIS as “any information system that significantly resist modification and evolution” (Bisbal et al., 1999). Although LIS are often brittle, slow, none extensible, expensive to maintain, and harder to integrate with other systems (Bisbal et al.,

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Table 1. NHS Information Principles NHS Principle Number

Description

NHS Principle #1

Information is person-based

NHS Principle #2

Systems are integrated

NHS Principle #3

Management information is derived from operational systems

NHS Principle #4

Information is secure and confidential

NHS Principle #5

Information is shared across the NHS

1999), they represent the backbone of the healthcare organisation’s information, hence it must be used in this movement. Also, this evolutionary approach is less expensive and has a lower risk of failure than alternative approaches (Bisbal et al., 1999), where the LIS are totally discarded and replaced with newer ones, which can have a serious impact if the information becomes unavailable for a period or lost. Hence, it is important not to discard an LIS but evolve it (Bisbal et al., 1999; DoH, 1997; Morrey, 2013). Therefore, it is not a surprise that the transformation from LIS supporting a traditional approach to systems supporting PC care is a concrete challenge the UK National Health Service (NHS) is facing whilst modernising its health services (DoH, 2002; Skilton, 2011). Data in the old format is stored in stand-alone information silos and needs to be converted into the format required by the new integrated support systems (DoH, 2002). The evolutionary movement in the NHS is based on the principle of “keeping what works and discarding what has failed” (DoH, 1997) as it believes that what is working effectively should not be discarded. This means the new integrated systems are built on the foundations of the fragmented LIS (DoH, 1997). Thus, for the time being, the LIS will not be discarded (DoH, 1997, 2000; Skilton, 2011) but will be interfaced with the new support systems (DoH, 2002). Nevertheless, the NHS strategy towards integrated healthcare specifies that healthcare systems used by healthcare professionals working patient-centrally (whether totally new or combined with LIS) should have the five information principles in Table 1 (DoH & NHS Executive, 1998; Skilton, 2011). These principles support integrated care in which the needs of patients, not the needs of support healthcare systems, are at the heart of the health services. However, meeting the above information principles in an integrated healthcare system requires a supporting collaborative environment (Shaller, 2007) that provides holistic records of a patient’s health in which all CT members treating the patient can incorporate their contributions and the record is shared by the CT (Gaunt, 2009). This is to meet the needs of PC care information sharing and security contexts. This collaborative environment should operate effectively, ensuring accessibility and flexibility of healthcare services across the organisational boundaries of the NHS healthcare organisations providing the care, to ensure that individuals experience healthcare that is well integrated with smooth transitions between health services in different settings (Gaunt, 2009; Skilton, 2011). Nevertheless, such an environment raises key information security threats that are barriers to the implementation of integrated healthcare using LIS, and thus a more secure collaborative environment is required. This is caused by the LIS being designed to meet the needs of traditional treatment models (DoH, 1997), and not fully supporting the secure cross-organisational information sharing needed in collaborative environments generated by PC care. Therefore, LIS hinder the realisation of PC care in modern healthcare, and require enhancing to create a secure collaborative environment where CT members can seamlessly access all relevant information at the point of treatment of a patient without losing control over it.

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SOLUTIONS AND RECOMMENDATIONS The study is carried out at different research stages using a mixture of different qualitative methods, including domain analysis, conceptual modelling, observations, and interviews. 1. Domain Analysis: Initially, a domain analysis (Fernandez et al., 2007) was conducted to understand the complexity of PC healthcare in a well-defined information sharing context, namely cancer care. This complexity is best investigated through a real-life treatment pathway. Therefore, to study the various complexities due to different treatment pathways, three different types of cancers were investigated: Hepatocellular (HC), Upper Gastrointestinal (UGI), and breast cancers. Their treatment pathways, also known as Integrated Care Pathways (ICP), were analysed. The pathways used are published in the Map of Medicine (MoM) (2013) clinical guidelines. HC cancer has a fairly simple one-page ICP, and UGI cancer has a more detailed two-page ICP while breast cancer is the most complex of these three cancers as it has a six-page ICP. Due to the complexity of breast cancer ICPs, it was studied in more detail using conceptual modelling to understand each treatment point. 2. Conceptual Modelling: Breast cancer is the most complex pathway of these cancers and could not be studied through its ICPs alone, and needed enrichment details, which required further investigation. A comprehensive conceptual model of the breast cancer ICPs is created, and part of this conceptual model was published in (Alsalamah et al., 2011). In the creation of this complex diagram, each treatment point was investigated to identify the healthcare professional’s role, information collected and recorded at that point, and healthcare information system and health record used for storing this information. The development of this conceptual model provided a good understanding of how breast cancer treatment should be achieved in a PC manner. 3. Observation of Current Practice and System Usage: The investigations carried out on all three cancer ICPs highlighted that MDT reviews are essential elements in PC care and that some cancers have more than one MDT review. These reviews are the most intensive points for sharing information throughout the treatment pathways. According to a General Practitioner (GP) (Sheard 2011), the MDTs were introduced in the UK less than a decade ago as an essential step towards PC care. An MDT review consists of healthcare professionals from differing specialities who meet regularly (mostly on a weekly basis) to make shared decisions about patients’ care plans (Sheard, 2011; Skilton, 2011). They are fundamental in most treatment pathways (Sheard 2011) and represent an important information sharing point as they create and monitor the care management plans. Therefore, to best understand PC care, a total of seven different MDT review sessions in the selected cancers’ pathways were observed (see details in Table 2). Table 2. Observed MDT Sessions Cancer Type

MDT Review

Total no. of Hours

Total no. of Patients

Breast Cancer

4 Normal Breast Cancer MDT reviews

8 hours 30 minutes

Average of 35-40 patients per session reaching 50 sometimes (Patel, M., Personal Communication, November 2013)

1 Metastatic Breast Cancer MDT review

1 hour

8 patients

UGI Cancer

1 UGI MDT review

1 hour

9 patients

HC Cancer

1 Hepatobiliary MDT review

1 hour 30 minutes

20 patients

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MDTs helps understand the limitations of LIS in supporting decision-making processes at MDTs. Moreover, although LIS’s support for MDTs is observed, further understanding of the architecture of these LIS is needed to understand how they are used to record and retrieve information outside MDTs in other points of treatment. Therefore, the use of three of the main information systems currently used in cancer treatments in the Welsh Cancer Centre were studied using observations inside and outside MDTs. These systems were: •

• •

CaNISC: Short for Cancer Network Information System (Cymru), the stand-alone supporting system providing information to health professionals treating Welsh cancer patients across different NHS trusts in Wales (NHS Wales Informatics Service (NWIS), 2013). It is designated as the central repository of cancer data across Wales (Cancer National Specialist Advisory Group, 2012). Centricity: The radiology system at Velindre NHS Trust. Clinical Portal: The web-based support system is providing test results and letters to healthcare professionals at different NHS Trusts or hospitals. Each hospital has its separate implementation of the Clinical Portal to view local clinical information within the hospital’s perimeter, and although they all have a similar idea, look, and feel, they are local implementations with some differences (Morrey 2013).

These observations and studies help gain a proper understanding of these LIS’s structure, limitations and weaknesses, and their usage for information sharing to support PC cancer care. However, the outcomes do not fully cover the information security context outside the MDTs, and so, the information security context is investigated next and linked with the information sharing context using a different, more direct method of inquiry. 4. Semi-Structured Interviews and Personal Communications: Different interviews and various personal communications (including email and face-to-face communication methods) are conducted with healthcare professionals, information governance personnel, and senior employees in the NHS. They are chosen because of their knowledge of information governance and healthcare systems in the UK in general, and the treatment pathways used in cancer care. The interviewees cover the 18 different roles in Table 3. These interviews and personal communications cover how PC care was being supported by the current procedures linking to LIS from the interviewees’ perspective and what would improve this support. The interactions are based on both the role of the interviewee and the problem being investigated. Dr. Tom Crosby (2012), the Velindre Cancer Centre’s Caldicott Guardian, is interviewed. His role as a Caldicott Guardian is itself part of the broader Information Governance at Velindre (DoH, 2010a). He is also Medical Director of the South Wales Cancer Network and plays a number of other leading roles, including: Clinical Director of the Velindre Cancer Centre, Chair of the Cancer Service Management Board, and Consultant Oncologist treating UGI cancer. The interview aims to identify the right balance of information security in information systems supporting cancer treatments pursuing a PC care, and the threats present in current information systems that would breach that balance. The synthesis from the Caldicott Guardian’s interview is confirmed by a second interview with Dr. Crosby (2012), then assessed by the former head of the CIU (Morrey 2013), and an IT Lead at Velindre NHS Trust (Stockdale, 2013).

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Table 3. Interviews and Personal Communications Category Senior roles in the healthcare organisation

Role(s) Chair of the Cancer Service Management Board Clinical Director of the Velindre Cancer Centre Head of the Software Service Unit at Velindre Cancer Centre Head of Information Management & Technology (IM&T) IT Lead at Velindre Hospital Head of Clinical Information Unit (CIU) at Velindre NHS Trust

Information governance and support personnel

Cancer Centre Caldicott Guardian Information Governance and Security Specialist Information Governance Support Manager

Care team members

GP Breast Cancer Nurse Specialist Breast Cancer Consultant Clinical Oncologist UGI Cancer Consultant Clinical Oncologist

Care team support personnel

Normal Breast Cancer MDT Coordinator Metastatic Breast Cancer MDT Coordinator UGI cancer MDT Coordinator HC cancer MDT Coordinator

Also, breast cancer is selected to assess the results from the initial interview with the Caldicott Guardian. This is done by interviewing a Breast Cancer Oncologist (Borley 2013), Clinical Nurse Specialist in Breast Care (McGarrigle 2013), and the Normal Breast Cancer MDT Coordinator (Patel 2013). In the remainder of this chapter, a synthesis from all interviews regarding information security issues in LIS is presented.

The Right Balance of Information Security in Cancer Care In very broad terms, according to Dr. Crosby (2012), information security implementation in healthcare must aim to carefully balance access to clinical information and patient identifiable information by those people who need to see it to support clinical decision-making, while protecting the clinical and patient identifiable information from those who do not treat the patient, and maintaining the accuracy of this information. He emphasised the need for this balance in information security to be on both levels - an individual case record basis, and also on a more population-wide group basis (Crosby 2012). Therefore, at one extreme, his role as clinical director of the cancer centre is to ensure when clinicians and staff see patients they have the right amount of clinical information available, and at the other extreme, as the Caldicott Guardian, he needs to ensure that as much security is put in place that is reasonable and practical to ensure that is done safely (Crosby 2012). This indicates that the balance of information security that Dr. Crosby is enforcing in the Cancer Centre is on a need-to-know basis, as highlighted in the NHS Plan to modernise its healthcare system (DoH, 2003, 2010a), while complying with the Data Protection Act 1998 (DoH, 2003, 2010a)

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Table 4. Principles in the Caldicott Report. Caldicott Guardian Principle Number

Description

1

Justify the purpose(s) for using confidential information

2

Only use it when necessary

3

Use the minimum that is required

4

Access should be on a strict need-to-know basis

5

Everyone must understand his or her responsibilities

6

Understand and comply with the law

and a long list of other legislations (DoH, 2010a). This includes, but is not limited to, the Human Rights Act 1998, the Freedom of Information Act 2000, the NHS Code of Practice on Confidentiality 2003 and the inception of NHS Information Governance 2003 (DoH, 2010a). The Caldicott Guardian must ensure the implementation of this information access need by making sure the use or flow of patient-identifiable information should be regularly justified and routinely tested against the principles developed in the Caldicott Report (DoH, 2010a), see Table 4.

Information Security Issues Threatening the Balance In fact, the “need-to-know” access rule has been the norm balance of information security in healthcare for decades, even in autonomous discrete information systems supporting traditional disease-centred care, each with its own information security rules deployed. A discrete LIS’s local information security balance is already enforced within their physical perimeter on a “need-to-know” basis. However, each discrete information system has interpreted this high-level information access rule from the national guideline into their information security design and expressed it differently at the lower levels of the design to enforce it at the machine level. This situation of inconsistent interpretations of the information access rule is causing an information security issue that threatens the implementation of PC care using these systems. This was clearly highlighted by the Caldicott Guardian, when he said: on a very high level I think because of varying interpretation of the guidance around information security, clinicians are often blocked from having the right information to treat a patient and I do not think enough weight is put on that (i.e. patients’ rights of access to the best healthcare available because of variation in the interpretation of security rules). (Crosby 2012) Therefore, moving towards PC care, where the medical treatment follows a treatment pathway with care at a number of locations, requires an overarching balance of information security that implements the “need-to-know” access rule at the collaboration level, without interfering with the inconsistent local implementations of this rule. This interpretation inconsistency is threatening the stability of the local balance once the information leaves the discrete LIS by compromising one or more of the security goals in many ways, along with other threats as described in the interview and discussed fully in the next section.

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1. Threats to Information Integrity: LIS supporting cancer treatment are raising integrity issues as they fall short of preserving the accuracy of clinical information in that context. Among the causes of this situation are: a. Human Error: The integrity of patient information can be hard to preserve once the human error has occurred in the recording of the information for a patient being referred to different healthcare providers. If an oncologist at one organisation receives an incorrect code for the diagnosed cancer type (i.e. a code referring to a different cancer type), current systems do not allow him or her to change it. This is because the owner who recorded it works for a different healthcare provider, and edit access right is not granted to a consultant who works with a different organisation. Another major weakness in the current system is that it is not possible to track back to the owner of information at the point where the information was compiled. In addition, even if there is a need to write to the information originator (if known) to request an alteration, it cannot be changed remotely by the current system (Crosby 2012). b. Inconsistent Results in Different Systems: Regular MDT reviews are essential to cancer treatments as the most crucial information sharing point among CT members, as critical shared decision-making processes occur at the MDT. However, the use of different hospitals’ discrete information systems in geographically distributed collaborative care affects the accuracy of the information. This was highlighted by an MDT coordinator (Patel 2013) who expressed extreme concern about information accuracy, which she finds difficult to preserve in the context of collaborative cancer care. She mentioned an incident in one of the MDT reviews when the pathologist did not have some patients’ results, but the breast care nurse did. In such a case, the consultant who examined the patient is normally at the MDT review and she/he makes the decision as to whether the results from the nurse should be considered, or the patient’s case should be rescheduled to the next review awaiting the pathologist’s results. However, the consultant’s absence, in this case, made it worse, and because patients cannot be left hanging, the MDT Coordinator had to make the decision, but she did not know what to do in this very confusing situation. Eventually, she put the patient on the following week’s MDT list, and although this delayed the patient’s treatment, if the diagnosis is reconfirmed, this decision has less risk for the patient than considering wrong results. Although such cases are very rare, they still happen, and it is critically important to deal with them professionally. However, the systems could help in these cases if information is organised in chronological order, to show the treatment points and compare the date when the nurse and the pathologist saw the patient with the date of the test results. This would help the coordinator make an informed decision. LIS compromise on the integrity of PC information, and need the following requirements to restore the right level of information integrity to suit PC cancer care: • •

Information organisation in chronological order to help track the information to the information owner and treatment points. Remote information update after dissemination to allow information owners to update the information in the case of a human error incident.

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2. Threats to Information Availability: Information availability is critical in patient care management. According to the Caldicott Guardian, more harm is done to the patient through lack of access to relevant information than by misuse, due to the risk of information falling into the wrong hands, as it prevents informed clinical decisions using it (Crosby 2012). However, LIS supporting a PC care compromise on the availability of patient information for some reasons. a. Disconnected systems at major sharing points. MDT reviews are one of the most sensitive sharing points in a treatment journey for most conditions and diseases, not only for cancer. However, in the context of cancer care, a patient’s case is normally discussed at several MDT review points: the initial diagnosis of cancer, and at key treatment points such as chemotherapy and surgery. Normally, before an MDT review, consultants or nurses based on the patients care request the patient to be added to the MDT list with a note as to why the patient’s case requires discussion and what information needs to be ready to enable the discussion. The MDT Coordinator prepares a list of all patients to be considered, and they will come from several organisations. The relevant Cancer Service Departments are then responsible for listing reports on CaNISC in the MDT Summary ready for the meeting, and the coordinator is responsible as overall coordinator for ensuring images and other information are available (Biscoe 2012). However, some MDT reviews are not very successful in achieving this goal because some systems are not connected. According to an oncologist (Borley 2013), this results in the MDT patient list that the MDT coordinator, surgeon, and pathologist have as being totally different. Therefore, the results of patients in the list the surgeon had expected and the results that the MDT coordinator and pathologist were expecting were not there, “so it was all a bit hopeless” (Borley, 2013). Moreover, in one of the observed MDT reviews, Twelve of the patients were not discussed because the information was not there at that point in time; although the patients had been referred, the information did not flow with them, and hence, was not available on time. In such a case, all the MDT members said was: ‘we do not know why, so we are going to investigate why this is happening and come back next week.’ However, a week’s delay makes a huge difference to the patient’s treatment as “the clock is ticking and the patients wouldn’t understand that delay” (Morrey 2013). b. Inconsistent information security policies. Many LIS in the UK were designed in 1948, when the NHS was established (DoH, 1997), to meet the requirements of a disease centred approach. All NHS Trusts and hospitals working under the NHS umbrella adopted the NHS national high-level policies and practice guidelines for the implementation of information access on a “need-to-know” basis, and each system adapted the policies and guidelines to achieve an organisation driven implementation of this access need locally (National Institute for Healthcare and Clinical Excellence (NICE), 2002) and implemented locally. This was achieved by interpreting high-level policies into lower-level ones, which can result in different inconsistent information security policies and rules at this level. Once information is shared, the different healthcare providers can have varying interpretations of the guidance around information security (Crosby 2012) (See Figure 2).

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Figure 2. Inconsistent interpretation of the “need-to-know” information access need among various hospitals working under the NHS umbrella

The Caldicott Guardian at Velindre explained this in an example: The South Wales service for hepatic surgery (liver surgery) is run here for patients with secondary cancers in their liver; the surgical service is based in Cardiff and the oncology is largely based here but we take patients from all over Wales. Patients are referred from West Wales to the surgeon who considers the case, using their films and x-rays sent electronically or by disk to Cardiff. They are discussed here by the MDT that you came to, or a similar one. However, Cardiff does not have direct access to those images because West Wales are only holding on to them as [...] the statutory owner of the information is the patient and the original healthcare organisation. Darren Lloyd, who is the Head of Information Governance, has said basically that it is a wrong interpretation of the rules; clinical information should be allowed to follow a patient. On a very high level I think because of varying interpretation of the guidance around information security, clinicians are often blocked from having the right information to treat a patient and I don’t think enough weight is put on that (i.e. patient’s rights of access to the best healthcare available because of variation in the interpretation of security rules). (Crosby 2012) Also, current systems cannot override access permissions locally to allow access in such cases, so CT members must contact the originator to ask for relaxation of security rules (Crosby 2012). This interrupts treatment continuity, causes delays, and hinders effective communication of information. •

Inflexible balance of information security in emergency cases. The big challenge in information security solutions in healthcare systems is that life threatening emergency situations require resilience, most importantly when the patient is unconscious, and decisions can mean life or death. In such cases, there is a need to access any information stored about the patient at very short notice, in the hope that it will help save the patient’s life. This may require trusted CT members to access information not normally required for their regular treatment role (Crosby 2012) and, therefore, there is a need to relax already assigned access rights to enable immediate access to information when every second counts, before restoring these levels of information security. A major weakness in current LIS is the inability to deal with such cases when the CT member’s access is blocked, and writing to the original organisation requesting access (Crosby 2012) may delay or prevent the treatment happening in a timely fashion.

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•

Inconsistent user-hostile information system design. LIS supporting PC healthcare today have inconsistent information system design. Two widely used LIS in cancer care across Wales are: CaNISC and Clinical Portal. On the one hand, CaNISC is a disease-centred information system designed to hold cancer-related information for each patient to be used for all organisations and groups across Wales (Morrey 2013). Although CaNISC is an effective system for information sharing across systems, it is a disease-centred system that holds cancer-related information. Therefore, it was not designed to provide a holistic view of the patient’s condition, especially if the patient has comorbidities. Also, the patient information is partitioned to each individual hospital (Morrey 2013). The Caldicott Guardian stated that: the design of CaNISC is not intuitive; it’s on a provider level and not a patient level. So you have to find your way around it (Crosby 2013). He explained this in an example: in Cardiff and Vale, if the surgeon puts in information, even if they put it into CaNISC, they’d put it under their provider episode. Moreover, you have to have a fairly good knowledge to navigate around the case note to find that if you were, say, a Velindre person (Crosby 2013). This disease-centred design makes CaNISC a slow user-hostile system that requires intensive training to be properly used to locate relevant information (McGarrigle 2013). This was confirmed by a Clinical Nurse Specialist in Breast Care who said:

We are very simple people. You know, we are nurses and we are doctors, and none of us is stupid, but that is not our priority. This is supposed to be a tool for us. Need to be able to just help us do our job, not learn somebody else’s job and we do not have much time to learn how to make it work. (McGarrigle 2013) She summed CaNISC up as very difficult to find information, and hard work. She complained CaNISC is not easy! (McGarrigle 2013). The Clinical Portal, on the other hand, is an organisation centred information system design providing test results and letters to healthcare professionals at different NHS Trusts or hospitals. Each hospital has its separate implementation of the Clinical Portal to view local clinical information within the hospital’s perimeter, and although they all have a similar idea, look, and feel, they are local implementations (Morrey 2013). It has a similar but different structure to CaNISC (McGarrigle 2013). Users have to log into the system using the hospital number that gives access to all the test results conducted in the hospital for that patient (McGarrigle 2013). Thus, CT members may have to do several login attempts to different hospital portals to collect relevant information about a particular patient if the tests have been conducted in several hospitals. This is another issue for CT members, as explained by the Clinical Nurse Specialist: the biggest problem [with] Clinical Portal is getting into it in the first place (McGarrigle 2013). This is mainly because if you do not have the hospital number, it does not like letting you in just with a name. It will sometimes, and you have to find out the right address (McGarrigle 2013). Although the Clinical Portal is a more user-friendly system in comparison with CaNISC, according to the same Clinical Nurse Specialist, she said: Portal is OK. Portal is quite quick (McGarrigle 2013), however, its structure makes it an organisation-centred system that is unable to provide a PC view, like CaNISC. •

310

Untraceable shared information. To guarantee patient care continuity, systems supporting healthcare should reflect the patient’s care management occurring in a number of healthcare organisations, and the flow of their information following the treatment pathway (Crosby 2012). This means these systems should not reflect the needs of an organisation the patient is treated in

 Information Security Threats in Patient-Centred Healthcare

•

(Clinical Portal is an organisation-centred system), nor a disease the patient is being treated for (CaNISC is a disease-centred system). Developing systems that reflect the patient’s treatment pathway helps track patient treatment as a single business process. Currently, enhanced LIS supporting PC healthcare are designed to organise patient case note data in parallel on a healthcareprovider basis and not in sequence on a treatment-point basis (Crosby 2012; Patel 2013). Thus, information management is based on the healthcare provider and each patient’s case notes are split into parallel partitions where each provider holds relevant information for a disease or part of the treatment in their partition (Crosby 2012). Each provider owns and controls their part of the information (Crosby 2012), and they give direct access to it by listing the CT member’s names as having access (Crosby 2012). If a CT member happens not to be listed for access to the information (normally caused by the interpretation of security rules), he/she will have no access until the other provider grants permission (Crosby 2012). This not only makes it difficult to find relevant clinical information, but may also cause information duplication in the different partitions (Crosby 2012). For example, when each provider submits a stage of diagnosis with obvious differences, revealing a mistake, this can cause data inconsistency issues directly affecting the patient’s clinical care, making it harder to locate and track relevant information at a point of care (Crosby 2012). Also, these problems can lead to losing track of patients and their information at some point in the treatment pathway. For example, it may be unclear which CT member is responsible for patient follow-up after treatment (Alsalamah et al., 2011; NICE, 2002) leading to the patient not receiving this necessary health service. Additionally, care management may be interrupted when information does not flow with the patient from one provider to another on the clinical pathway (for example, when patients are referred to Cardiff from Swansea, but their scan images do not follow; this can make critical information unavailable at a treatment point and cause incorrect treatment (Crosby 2012). Manual management of referrals between healthcare providers. According to a Breast Cancer Nurse Specialist (McGarrigle 2013), current systems do not automatically refer the patient to the CT member in charge, following a treatment plan. Early referrals in the treatment pathway come from the GP and are normally faxed to the hospital. Some GPs fill a pro forma with all the required information, including the last 10-15 visits to the GP, medical history, and medication they are on. The surgeon then looks at any of this information that is relevant to cancer, mostly medical history, and ignores what is not relevant. Although the faxed referral remains in paper-format, relevant information is added to the cancer record in the surgeon’s local information system. Any referrals are happening after the initial GP referral are dictated letters that are transcribed by secretaries using a dictation system, for example from an MDT review to an oncologist. The Breast Cancer Nurse Specialist explained:

At the moment [...] the doctors see a patient, they dictate into a machine and say: ‘I have just seen this lady...’ and then the secretaries pick up the tape, and they put it into a machine and they play it back, and they type it in” (McGarrigle 2013). She complained that although the dictation systems are as accurate as typing, they are “causing the secretaries trouble when they dictate the letters and that is not the word they said at all. (McGarrigle 2013) This ineffective referral approach may cause delays in information delivery, as well as exposing the information to human error, resulting in the information being inaccurate (McGarrigle 2013). It is clear 311

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that current systems are incapable of handling referrals and neither means today for referrals is practical. Therefore this aspect needs improvement. As such, an automated referral to a CT member’s role that is picked up by the recipient with all information needed is also a key requirement in PC care. LIS compromise on the availability of PC information and the requirements below will help restore the right level of information availability to suit PC cancer care. • • • • •

Common collaboration-driven information access needs to overarch the local organisation-driven policies. Consistent information organisation needs that provide a PC holistic view that gives easy access to a patient’s clinical information. Automated referrals among different healthcare providers with the right information for the person. Gathering and filtering of relevant information to avoid overwhelming CT members with irrelevant information. This increases the chance of finding the right information at the right time. Resilience in emergency cases. This is a crucial requirement that speeds up access to information for decision making in a life or death situation.

3. Threats to Information Confidentiality: Current LIS compromise on the confidentiality of patient information. •

312

Improper disclosure of medical information. Information confidentiality is essential due to the movement towards a culture of open information, in which information access is a priority to healthcare professionals (DoH, 2010b; Skilton, 2011). A higher degree of information sharing is needed in PC care than in a traditional disease-centred approach (Crosby, 2012; Eysenbach, 2001; Skilton, 2011). Confidentiality can be breached in PC care if the information is improperly disclosed to unauthorised people (Crosby 2012). There are two factors increasing the risk of improper disclosure of information: the number of people having access to the information, and the value of this information (Anderson, 1996). PC care has a higher risk of medical information being disclosed to unauthorised people than in a traditional approach. This is due to the NHS planning to integrate separate systems run by 100 Health Authorities, around 3,500 GPs and over 400 NHS Trusts, in the modernisation of UK healthcare systems (DoH, 1997). Also, there is a direct correlation between valuable information and the risk of its disclosure (Anderson, 1996), mainly because if it is valuable to its owner, it will be valuable to someone else (Calder & Watkins, 2008). There are many reasons why systems are supporting healthcare store highly valuable information. First and foremost, clinical information has value as a basis for healthcare professionals’ decisionmaking processes (Crosby 2012), and its corruption can lead to incorrect decisions that may harm or even kill a patient (Anderson, 1996). The systems hold extensive information about a patient, which may contain personal, embarrassing, and critical medical information (Alsalamah et al., 2011). This information has a longevity characteristic meaning it is highly sensitive and confidential at all times without decay, even after the patient is dead (Beale, 2004; DoH, 2003; Crosby, 2012; Smith & Eloff, 1999). Therefore, the nature of medical information means it should only be disclosed for permitted medical purposes (DoH, 2003). This puts PC information at great risk of improper disclosure (Anderson, 1996) and stresses the need to keep information protected from

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•

those not needing it, while ensuring availability of life-critical information about the patient’s medical condition on a “need-to-know” basis at the time of care (DoH, 2003, 2010a). Hospital-wide Access Control. CaNISC has a security model that reflects its information system design. Dr. Morrey explains the security model developed in CaNISC:

The way that CaNISC operated it, was to say that once you got a referral into the organisation, then anybody in the organisation can actually see it, and the way we enforced that was there was a security log, so any time anybody reads anything or changes anything, it’s recorded in the database. Moreover, everybody knows there is that full audit trail. (Morrey 2013) This security model has a hospital-wide access control model that is causing some issues. Dr. Morrey highlighted these issues: The problem we ran into was when you implement that then in terms of the security model, medical secretaries for an example, or sometimes maybe a nurse, would actually have wider access than the consultant [...] the reason is that the consultant belongs to his firm [i.e. hospital system], and he sees patients about his firm. The nurse or the medical secretary may need to cover for another medical secretary, who works in another consultant firm. So, you end up with a situation where the medical secretary or maybe the nurse in their role that spans consultant firms... have wider access than individual consultants. (Morrey 2013) The following requirements paint the full picture of a PC collaboration-driven information security that restores the right level of information confidentiality. • •

Common collaboration-driven information access needs. This requirement not only helps with information availability, but it also preserves its confidentiality as it defines the fine line between these two conflicting information security goals. Information security policies awareness in a culture of open information. This requirement raises the awareness of CT members as to how to look at another member’s information within the collaboration to help preserve the confidentiality of shared information, especially in emergency cases. The list of threats is summarised in Table 5.

4. Requirements in LIS for a Common Collaboration-Driven Information Security The threats to PC information (shown in Table 5) highlight the fact that LIS fall short of meeting the information sharing and security contexts in PC care, due to the compromises they have to make in terms of information availability, integrity, and confidentiality. Although threats target all information security goals, there is more weight on the compromises to the availability of information in the PC information sharing context. Interviews showed that the current balance of information security in LIS used in cancer care is more concerned with information confidentiality and this may be working well locally, within its physical and logical perimeters, as this meets these systems’ information sharing and security contexts.

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Table 5. Information Security Threats in LIS Threat Category Information integrity threats

Threat Description Human error Inconsistent results in different systems

Information availability threats

Disconnected systems at major sharing points Inconsistent information security policies Inflexible balance of information security in emergency cases Inconsistent user-hostile information system design Untraceable shared information Manual management of referrals between healthcare providers

Information confidentiality threats

Improper disclosure of medical information Hospital-wide access control

This is because information security implementation in discrete LIS focused mainly on information confidentiality and integrity as they were the information security issues at that time (Pfleeger & Pfleeger, 2003), whereas information availability was not. Pfleeger and Pfleeger recognise this phenomenon in LIS, and they add that it is not clear that a single point-of-control can enforce availability (Pfleeger & Pfleeger, 2003). Therefore, the key reason why LIS fall short of attaining a security balance in PC care is because information availability issues were only raised by the movement towards collaboration, when the need for information sharing started to emerge, making this information security goal a challenge in collaborative environments. Therefore, when this information leaves these autonomous LIS, there is a need for these systems to rebalance the information security to address the compromises it makes on the availability of information for the collaboration without interrupting the local balances of information security. To cope with this emerging need, LIS need additional requirements to define a collaborationdriven information security policy that can attain the new balance of information security that has more weight on information availability. The requirements are summarised in the following points: 1. Consistent information organisation needs that provide a PC holistic view that provides easy access to a patient’s clinical information 2. Common collaboration-driven information access needs to overarch the local organisation-driven policies. This requirement helps with information availability, and, at the same time, preserves its confidentiality. This means it is the key requirements that define the fine line between these two conflicting information security goals. 3. Information organisation in chronological order to help track the information to the information owner and treatment points. 4. Gathering and filtering of relevant information, to avoid overwhelming CT members with irrelevant information. This increases the chance of finding the right information at the right time. 5. Automated referrals among different healthcare providers with the right information for the person. 6. Resilience in emergency cases. This is a crucial requirement that speeds up access to information for decision-making in a life or death situation. 7. Remote information update after dissemination to allow information owners to update the information in the case of a human error incident. 314

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8. Information security policies awareness. This requirement raises the awareness of CT members in terms of how to look after another member’s information within the collaboration to help preserve the confidentiality of shared information, especially in emergency cases. These eight requirements aim to reduce the impact of the threats whilst attaining a common collaboration-driven balance of information security.

FUTURE RESEARCH DIRECTIONS A proof of concept prototype has been implemented using workflow technology to proof the concept and show technically how the suggested requirements could be implemented. It tested information access needs and issues in healthcare collaborative environments through three cancer treatment pathways and included collaboration between different healthcare organisations when a patient is following a treatment pathway. This could be generalised to fit any possible treatment pathway for any health condition as long as the treatment points can be predicted. The scope of the implemented prototype excluded situations when information is related to more than one disease for patients following more than one treatment pathway, and this occurs when there is comorbidity. Comorbidity is part of the notion of PC care provision. Therefore, in the future it would be interesting to test how different information access needs are in such cases, when from a technical point of view, it comes to mapping the treatment processes together, but the decisions will be far complicated than in a single treatment process as no one knows at which point the interaction will happen. This research drew boundaries around healthcare collaborative environments as it is believed to be one of the more complex environments if not the most. This is due to the fact that it involves a large number of users coming from geographically distributed environments where the fine line between information availability and confidentiality can easily get blurry. However, there are other applicable domains for these approaches with less complications, future research can study and test how general this solution is and whether it is applicable to other domains. The experiments conducted in this research suggests a number of characteristics that can predict the applicability of this research: large geographical area, large number of users with different roles, heterogeneous information systems with inconsistent information security contexts and AC models, and a common collaborative goal. Collaborative environments sharing these characteristics are more likely to suit this approach.

CONCLUSION There is a global shift in healthcare delivery towards an integrated PC treatment approach to cope with the emerging needs of an ageing population worldwide. The adoption of PC care in many countries is achieved through an evolutionary approach using existing LIS, which were developed at a time when the sharing of information was not common. In collaboration with Velindre Cancer Centre, this research defines a common collaboration-driven balance of information security in PC care, identifies weaknesses in LIS used today in cancer care and uses them to achieve a secure collaborative environment. Results show that the threats they present compromise on information security goals. Initially, human error in shared information, and inconsistent results at different systems compromise the integrity of clinical

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information. In addition, inconsistent information security policies, the inflexible balance of information security in emergency cases, untraceable shared information, and inconsistent user-hostile information system design all contribute to compromising the availability of PC information among CT members. Finally, improper disclosure of medical information, and a hospital-wide access control compromise the confidentiality of patient information. Results also show that most of the information security issues are around the availability of clinical information. This means that information security implementation in discrete LIS focused on the confidentiality and integrity of medical information, as they were a key issue while availability was not. Thus, the key reason LIS falls short of attaining a security balance in PC care is because information availability only became an issue with collaboration. These threats led to the identification of eight requirements needed to assist LIS to reduce the impact of the threats and attain a common collaboration-driven information security to assist LIS safely implement PC care without being totally discarded. This is to improve the quality of care we all receive as patients for better health, a better nation, and a better tomorrow.

REFERENCES Al-Salamah, H., Gray, A., & Morrey, D. (2011). Velindre Healthcare Integrated Care Pathway. In L. Fischer (Ed.), Taming the Unpredictable Real World Adaptive Case Management: Case Studies and Practical Guidance (pp. 183-195). Lighthouse Point: Future Strategies Inc. Allam, O. (2006). A Holistic Analysis Approach to Facilitating Communication between General Practitioners and Cancer Care Teams. (Unpublished Doctoral Dissertation). Cardiff University, Cardiff, UK. Alsalamah, S., Gray, A., & Hilton, J. (2011). Towards Persistent Control over Shared Information in a Collaborative Environment. In L. Armistead (Ed.), Proceedings of the 6th International Conference on Information Warfare and Security (ICIW) (pp. 278–287). Washington, DC: Academic Publishing International Limited. Alsalamah, S., Gray, A., & Hilton, J. (2011). Sharing Patient Medical Information among Healthcare Team Members While Sustaining Information Security. In P. A. Bath, T. Mettler, D. Raptis, & B. A. Sen (Ed.), Proceedings of the 15th International Symposium on Health Information Management Research (ISHIMR) (pp. 553–554). Zurich, Switzerland: University of Zurich, University of St. Gallen and University of Sheffield. American Cancer Society. (2008). Holistic Medicine. Retrieved February 20, 2013, from http://www. cancer.org/Treatment/TreatmentsandSideEffects/ComplementaryandAlternativeMedicine/MindBodyandSpirit/holistic-medicine Anderson, R. J. (1996). Security in Clinical Information Systems. Cambridge, UK: British Medical Association. Beale, T. (2004). The Health Record - Why Is It so Hard? In R. Haux & C. Kulikowski (Eds.), IMIA Yearbook of Medical Informatics 2005: Ubiquitous Health Care Systems (pp. 301–304). Stuttgart, Germany. Bisbal, J., Lawless, D., & Grimson, J. (1999). Legacy Information Systems: Issues and Directions. IEEE Software, 16(5), 103–111. doi:10.1109/52.795108

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Calder, A., & Watkins, S. (2008). IT Governance : A Manager’s Guide to Data Security and ISO 27001/ ISO 27002 (4th ed.). London, UK: Kogan Page Limited. Cancer National Specialist Advisory Group. (2012). Welsh Breast Cancer Clinical Audit for Patients Diagnosed 2008. Retrieved from http://www.wales.nhs.uk/sites3/Documents/322/Cancer_NSAG_WBCCA_2008.pdf Dawson, J., Tulu, B., & Horan, T. A. (2009). Towards Patient-Centered Care: The Role of E-Health in Enabling Patient Access to Health Information. In E. V. Wilson (Ed.), Patient-Centered E-Health (pp. 1–9). London, UK: IGI Global. doi:10.4018/978-1-60566-016-5.ch001 DoH. (1997). The New NHS: Modern, Dependable. London, UK: HMSO. DoH. (2000). The NHS Plan: A Summary. London, UK: Stationary Office. DoH. (2002). Delivering 21 St Century IT Support for the NHS: National Strategic Programme. London, UK: Stationary Office. DoH. (2003). Confidentiality: NHS Code of Practice. London, UK: HMSO. DoH. (2010a). Caldicott Guardian Manual 2010. London, UK: HMSO. DoH. (2010b). Equity and Excellence: Liberating the NHS. London: HMSO. DoH & NHS Executive. (1998). Information for Health: An Information Strategy for the Modern NHS 1998-2005. London, UK: Stationary Office. Ellingsen, G., & Røed, K. (2010). The Role of Integration in Health-Based Information Infrastructures. [CSCW]. Computer Supported Cooperative Work, 19(6), 557–584. doi:10.1007/s10606-010-9122-y Eysenbach, G. (2001). What Is E-Health? Journal of Medical Internet Research, 3(2), e20. doi:10.2196/ jmir.3.2.e20 PMID:11720962 Fernandez, E. B., Yoshioka, N., Washizaki, H., & Jurjens, J. (2007). Using Security Patterns to ‘build Secure Systems. In proceeding of the 1st International Workshop on Software Patterns and Quality (SPAQu) (pp.16-31). Nagoya, Japan: IGI Global. Gaunt, N. (2009). Electronic Health Records for Patient-Centred Healthcare. In W. Currie & D. Finnegan (Eds.), Integrating Healthcare with Information and Communications Technology (pp. 113–133). Oxford, UK: Radcliffe Publishing Ltd. International Alliance of Patient’ Organizations (IAPO). (2004). What Is Patient-Centred Healthcare? A Review of Definitions and Principles. Retrieved from http://iapo.org.uk/patient-centred-healthcare Kee, C. (2001). Security Policy Roadmap - Process for Creating Security Policies. Retrieved from http:// www.sans.org/reading-room/whitepapers/policyissues/ Map of Medicine (MoM). (2013). Map of Medicine. Retrieved March 20, 2013 from http://mapofmedicine.com/ Mense, A., Hoheiser-pförtner, F., Schmid, M., & Wahl, H. (2013). Concepts for a Standard Based CrossOrganisational Information Security Management System in the Context of a Nationwide EHR. In C.U. Lehmann et al. (Ed.), 14th World Congress on Medical and Health Informatics (Medinfo) (pp. 548–552). Copenhagen, Denmark: IMIA and IOS Press. 317

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National Institute for Healthcare and Clinical Excellence (NICE). (2002). Improving Outcomes in Breast Cancer - Manual Update. Retrieved from https://www.nice.org.uk/guidance/csgbc/evidence/improvingoutcomes-in-breast-cancer-manual-update-2 NHS Wales Informatics Service (NWIS). (2013). Canisc. Retrieved from http://www.wales.nhs.uk/ nwis/page/52601 Pfleeger, C. P., & Pfleeger, S. L. (2003). Security in Computing (3rd ed.). Prentice Hall. Pipkin, D. L. (2000). Information Security Protecting the Global Enterprise. Prentice Hall. Posthumus, S., & Solms, R. V. (2004). A Framework for the Governance of Information Security. Computers & Security, 23(8), 638–646. doi:10.1016/j.cose.2004.10.006 Powell, J. (2009). Integrating Healthcare with ICT. In W. Currie & D. Finnegan (Eds.), Integrating Healthcare with Information and Communications Technology (pp. 85–94). Oxford, UK: Radcliffe Publishing Ltd. Shaller, D. (2007). Patient-Centered Care: What Does It Take? Retrieved from http://www.commonwealthfund.org/usr_doc/Shaller_patient-centeredcarewhatdoesittake_1067.pdf?section=4039 Skilton, A. (2011). Using Team Structure to Understand and Support the Needs of Distributed Healthcare Teams. (Unpublished Doctoral Dissertation). Cardiff University, Cardiff, UK. Smith, E., & Eloff, J. H. (1999). Security in Health-Care Information Systems--Current Trends. International Journal of Medical Informatics, 54(1), 39–54. doi:10.1016/S1386-5056(98)00168-3 PMID:10206428 United Nations Population Fund (UNFPA). (2014). Population Ageing: A Celebration and a Challenge. Retrieved from http://www.unfpa.org/pds/ageing.html

KEY TERMS AND DEFINITIONS Caldicott Guardian: A senior person in the UK national healthcare system responsible for the confidentiality of patients’ information. Comorbidity: Simultaneous presence of more than one condition at the same time in a patient resulting in the patient following multiple treatment pathways in parallel. Disease-Centred Healthcare Delivery Model: In this model specialists treat their patients in isolation according to their specialty, regardless of other illnesses and medications taken by their patients. Information Security Threats: Indication or warning of possible security breach. Information Governance: Action or manner of managing and controlling access to information. Legacy Systems: Information systems that has been used in a place for some time and significantly resist modification and evolution. Patient-Centred Healthcare Delivery Model: In this model specialist provide care tailored to meet an individual patient’s needs holistically rather than manage separate diseases.

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Tailored M-Health Communication in PatientCentered Care Anastasius S. Moumtzoglou P. & A. Kyriakou Children’s Hospital, Greece

ABSTRACT Emerging M-Health technologies provide fundamentally different ways of looking at tailored communication technology. As a result, tailored communications research is poised at a crossroads. It needs to both build on and break away from existing frameworks into new territory, realizing the necessary commitment to theory-driven research at basic, methodological, clinical, and applied levels. In this context, the revolution of M-Health holds great promise in both health care and public health. The chapter envisions tailored M-Health communication in the context of patient-centered care, as it remains to be seen whether the revolution in M-Health will provide the tools to engineer sufficient impact on patientcentered care and tailored communication.

INTRODUCTION The health care environment is currently changing to meet technology and societal trends which converge to bring into being new communication patterns that connect and coordinate the roles of healthcare stakeholders. At the same time, the healthcare industry is steering inexorably toward a distributed service design in which essential decision-making occurs at the point of care. One of the central engines of this shift towards decentralization and reorientation of health care services is mobile healthcare (M-Health). M-Health describes the use of a broad range of telecommunication and multimedia technologies within wireless care delivery design and can be broadly defined as the delivery of healthcare services via mobile communication devices. M-Health establishes healthcare communities in which every stakeholder can participate. However, it disrupts the traditional service model where healthcare information, security and access is centrally managed, maintained and limited, transforming the healthcare sector and destroying components that are slow to adapt. DOI: 10.4018/978-1-4666-9861-1.ch016

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M-Health interventions range from simple to complex applications and systems that remotely coordinate and actively manage patient care. In this context, it offers an elegant solution to the problem of accessing the right information within highly fluid, distributed organizations. Moreover, it removes geography and time as barriers to care by establishing connectivity with remote locations and remote workers, creates new points of contact with patients, and changes the frequency and intensity of health care delivery. It also establishes effective new treatment modalities like telehealth, remote patient monitoring, selfcare, and home health while it blurs the boundaries between professional medical advice and self-care. Overall, M-Health blends three bodies of knowledge: high technology, life sciences, and human factors. Additionally, there are four predominant theories explaining the formation of health attitudes, intentions, or behaviors (Weinstein, 1993): • • • •

The protection motivation theory. The health belief model. The theory of reasoned action. The subjective expected utility.

These theories share an underlying premise. Health intentions arise to avoid potential adverse outcomes through cognitive assessment and include a cost-benefit component. However, extant studies have largely ignored the role of various message tactics and individual characteristics, contrary to the protection motivation model (Rogers, 1985). Overall, health messages accommodate risk information in different formats (Keller, 2006): • • •

To increase perceptions of vulnerability. To include action steps. To provide comparative information to increase intentions.

By the same token, tailoring is a multi‐dimensional communication strategy that involves the development of individualized messages that are based on the pre‐assessment of key variables or characteristics that are linked to the underlying model of behavior change. Several studies have found that tailored health messages demand greater attention for the following reasons: • • •

They are processed more intently. They contain less redundant information. They are perceived more positively by health consumers.

Specifically, the Elaboration Likelihood Model suggests that personal information enhances the strength of motivation and sensitivity to the argument, forcing the individual to expound on the message. Moreover, if the argument is forceful to senses, personal pertinence increases the probability for persuasion. Thus, tailoring creates an ideal environment for persuasion and health behavior change. Studies of tailored communication are exploding in an array of disciplines. In health education, studies have shown that tailored print materials are generally more effective than non-tailored ones (Prochaska et al., 1993; Campbell et al., 1994; Skinner et al., 1994; Strecher et al., 1994; Kreuter & Strecher, 1996; Brug et al., 1996, 1998; Brennan et al., 1998; Bull & Jamrozik, 1998; Dijkstra et al., 1998a,b; Marcus et al., 1998).

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Computing technologies have considerably contributed to the sophistication of tailoring as they can facilitate theory‐based assessment of tailoring. Web‐based tailored interventions have multiple advantages over single-mode, static interventions: • • • •

Abounding access probability to expert care and feedback. Capability to switch between modalities and formats of different learning styles and literacy levels. Asynchronous and synchronous communication. A vast array of interactive components to strengthen user experiences and support skills development, behavior/goal monitoring, and progress tracking.

Finally, mobile technology may be the future of patient-centered care, as M-Health applications are progressively designed to support it. In this context, there is evidence that email and SMS prompts positively promote behavior change interventions when they are associated with the use of tailored messages (Barton, 2010). Better evidence is needed on the point of engaging patients and safeguarding patient-centered clinical decisions, attributed to a number of reasons: • •

A disconnect between care processes and system designers’ understanding of clinical work. An insufficient understanding of patients’ information needs, preferences, and values.

The objective of the chapter is to envision tailored M-Health communication in the context of patient-centered care, targeting health care academics and health policy analysts rather than health care providers. As a result, it excludes population health management consideration, as its focal point and the unit of analysis is the patient.

BACKGROUND Internet Health Coalition (2000) defines health information as ‘the information for staying well, preventing and managing disease and making other decisions related to health and health care.’ Bates (2009) states ‘information behavior is the currently preferred term used to describe the many ways in which human beings interact with information.’ It is also the concept used in information studies to refer to a sub-discipline that engages in research conducted to understand the human relationship to information. Wilson (2000) defines information behavior as ‘the totality of human behavior relating to sources and channels of information, including both active and passive information seeking and information use. Thus, information behavior involves face-to-face communication with others, as well as the passive reception of information. Pettigrew et al. (2001) point out that information behavior phenomena are part of the human communicative process. Furthermore, Savolainen (2008) has introduced information practice as a co-ordinate concept for the information behavior. He considers that both concepts refer to how individuals deal with information, but from somewhat different perspectives. Derr (1983) states that information may be needed without being desired while Chatman and Pendleton (1995) separate information need and information want. The concept of ‘desire for information’ is also used to describe the amount (and frequency) an individual would like to have information (Fourie, 2008). In other words, information could be important in an individual´s life, but yet that person may have no

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interest in gaining it. According to Fidel (2012) information needs can be unconscious, but information wants (or desire for information) can usually be put into words by the individual. Information seeking is assumed to be an important element in decision-making and health outcomes (Johnson, Andrews & Allard, 2001; Rimer et al., 2004). Human information behavior research has recognized information seeking as the central form of interaction that individuals employ to get information (Fidel, 2012). It can be active or passive. Active information seeking has been defined as ‘the purposive seeking for information as a consequence of a need to satisfy some goal’ (Wilson, 2000). Information searching is seen as a narrower and more focused concept while information use is a poorly defined concept linked to the information need (Wilson, 1999). According to Cole and Leide (2006) information use is a process in which an environmental stimulus, which includes stimuli obtained from reading, viewing, and listening activities, modifies the user´s knowledge structure. Wilson (2000) states that information use behavior ‘consists of the physical and mental acts involved in incorporating the information found in the person’s existing knowledge base.’ Information is interpreted and internalized by the individual in order to construct knowledge, and this knowledge may result in further action (Savolainen, 2008). The concept of ‘information reception’ has been used to define the first stages of the information use process. The stages include noticing, filtering, evaluating, and comparing the content of the obtained information (Nahl & Bilal, 2007). In information studies, in addition to the cognitive viewpoint, information use has been considered from the constructivist and socio-constructivist viewpoints (Talja et al., 2005). These viewpoints have a lot in common as they all present information use as processes occurring within the human mind. They share the assumptions that a human being is an information processor. Moreover, comparing and interpreting qualities of things is fundamental to the information use process and that the reception of information is mediated by an individual´s existing state of knowledge. (Savolainen, 2009; Talja et al., 2005). Health behavior has been studied by several social cognition theories and models that are widely used in health promotion. Many of these theories or models can be defined as statements about causal relationships between individual level factors (such as knowledge, attitudes, motivation, sociodemographic factors, personality) and health behavior change. For health providers, they provide conceptual frameworks for developing effective health promotion programs, campaigns, and interventions (Campbell & Quintiliani, 2006; Schwarzer, 2008). Their theoretical constructs help in analyzing behavioral health problems and are also used as a basis to tailor health information and messages (Kreuter et al., 1999). However, no single theory or model can account for all complexities of behavior change and therefore theories and models should be seen as complementary rather than competing. Social cognition models have been divided into: • • • •

Motivational models. Behavioral enaction models. Multistage models of behavior change. Health Action Process Approach (HAPA) by Schwarzer (2008).

Motivational models imply that motivation is sufficient for successful behavioral enaction. Behavioral enaction models focus on bridging the ‘gap’ between motivation, intention and behavior. Stage-based health behavior change models propose that behavior change is a non-continuous process occurring through stages. Each stage refers to differing individual barriers. In this context, the transtheoretical model of 322

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behavior change (TTM) argues that individuals move through a series of five stages of change. According to this model, when adopting a particular health behavior, an individual can be at one of five stages: • • • •

Precontemplation. Contemplation. Preparation. Action or maintenance.

People in the action or maintenance stages may also relapse and then recycle between stages. It has been stated that different kinds of information, messages and support are needed for people in different stages of change (Rimer & Kreuter, 2006). The TTM also includes other theoretical constructs. They can be used to explain what motivates an individual to progress through the stages of change toward a healthy change in behavior. Self-efficacy is one of the major incentives of whether a person will progress through the stages. Self-efficacy originates from the Social Cognitive Theory and is defined as the confidence in individual abilities to overcome barriers and adopt a particular behavior (Toscos & Connelly, 2010). It can be seen as situation specific, but it has also been conceptualized as a stable, trait-like disposition (Contrada & Goyal, 2004). According to Bandura (2006) it is not a global trait but a differentiated set of self-beliefs. Increasing awareness and enhancing intentions are significant predictors of health behavior and included in many behavioral change theories and models. Individuals are often unaware of their risk behavior, making it unlikely that they would consider a behavioral change. Awareness of the relationship between behavior and outcome may also be significant, especially in earlier stages of behavioral change (Brug et al., 1994). Behavioral intentions are defined as ‘plans individuals have about whether or not they intend to perform the recommended behavior’ (Murray-Johnson & Witte, 2003). However, a definite intention may not be enough for behavioral change (Sheeran, 2005; Webb & Sheeran, 2006), especially for complex, habitual behaviors. Such behaviors depend very much on personal abilities and environmental opportunities (Brug, Oenema & Ferreira, 2005). Likewise, health communication can be partly responsible for all aspects of disease prevention and health promotion. It has been defined as ‘the crafting and delivery of messages and strategies, based on consumer research, to promote the health of individuals and communities’ (Roper, 1993). In health communication, health information or messages can be delivered to a general audience or segmented, targeted audiences (Evans, 2006). Traditionally, health promotion materials have been generic (Kreuter et al., 1999; Johnson & Case, 2012). In generic mass media, communication materials are intended to appeal to a large group of people (Brug, Oenema & Campbell, 2003). A relatively large undifferentiated audience receives identical information content (Kreuter & Wray, 2003). In many cases as much information as possible is provided, and individuals need to find the relevant information on their own. However, it is likely that only individuals, who are already motivated, are willing to search through lengthy brochures for information that applies to their situation (Brug et al., 2003). Although health information is widely available, appropriate information suited to particular individual needs cannot often be found (Williamson, 2005). However, people do not always access or obtain information that could be beneficial to them (Chatman & Pendleton, 1995). One reason for this could be that individual´s information needs can be unconscious, and thus they are not aware of them (Fourie, 2008; Case, 2012). In addition, individuals do not always know how to express their information needs 323

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(Fourie, 2008). Moreover, other psychological and cognitive barriers can inhibit the recognition of information need (Johnson, Andrews & Allard, 2001). Overall, it has been concluded that general health communication is not sufficient to meet the information needs of individuals (Docherty et al., 2008). In this context, progress in technology has led to a tailored approach to health communication. It involves soliciting information from individuals or querying information about individuals from existing records. It is consequential because it combines the potential for delivering cost-effective health communications to reach an enormous audience combined with the benefits of interpersonal communication. The reason is that communications that are tailored to be responsive to the solicited information can be used to imitate the transactional and response-dependent qualities of interpersonal communication. An interactive cycle of tailored feedback and response can be repeated to assist in motivating health behavior change. Along the way, both source and message factors can be dynamically modified to realize the advantages inherent in interpersonal channels, advantages proven essential for persuading individuals to change their health behavior. This approach, known as tailoring, has been defined as any combination of information or change strategies intended to reach one specific person. This definition highlights the two features of a tailored approach that distinguishes it from other approaches: • •

The collection of messages is intended for a particular person. The messages are based on individual-level factors.

The rationale for a tailored approach is grounded in the theory that explains how people process information. Petty and Cacioppo’s (1986) Elaboration Likelihood Model (ELM) provides a method of understanding this process. They have proposed the central and peripheral routes to attitude formation and change. The central route involves a cognitive component and necessitates effort on the part of the individual. Studies have shown that messages processed via the central route lead to more firmly held beliefs and attitudes and result in lasting attitude change. It is, therefore, considered to be more effective in changing attitudes than general information. Subsequently, the theory suggests the following rationale for a tailored approach: • • • •

By tailoring materials, superfluous information is eliminated. The remaining information is more personally relevant to the message recipient. The message recipient will pay more attention to the information if it is personally relevant. The unshared needs of a person will be useful in enacting and sustaining the desired behavior change

Tailoring enhances cognitive conditions for information processing and acceptance. A typical aim of tailoring is simply to increase attention and comprehension. Obviously, attention to information is a prerequisite for the information to have any impact. Attention is gained by communicating to the information receiver that the information addresses his or her preferences and needs (Hawkins et al., 2008). Rimer and Kreuter (2006) argue that at least four approaches to tailoring can be used to enhance health communication. The approaches are as follows: • •

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Matching content to information needs and interests. Providing information in a meaningful context.

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• •

Using design, production and channel elements to capture attention and enhance message processing. Presenting the requested information

In conclusion, tailoring of health information is a means to overcome the problems related to the provision of general health information. It attempts to provide carefully selected information suitable for an individual and consequently may lower or remove psychological or cognitive barriers to information use and decision-making. It makes easier for the receiver to interpret, understand and trust information (Te´eni, 2001) while increasing the perceived personal relevance of health information. Consequently, it helps engage individuals and create ideal conditions for persuasion and attitude or behavior change to occur (Lustria et al., 2009). Furthermore, tailoring and targeting are not discrete categories of communication, but overlapping segments of the continua (Hawkins et al., 2008). They combine the benefits of interpersonal communication and mass media (Evans, 2006). Tailoring imitates and automates the process of person-toperson counseling by providing more customized information than the mass media. Similarly, targeted communication is intended to reach some population subgroup based on characteristics presumed to be shared by the group’s members Despite these fundamental differences between tailoring and targeting, the rationale for both approaches is quite similar. The more one knows about the intended recipients of a communication; the better able one will be to make the message relevant to them. There are, however, situations in which each approach would seem to have an advantage over the other. Tailored health messages should have an advantage over targeted messages when there is significant variability within the target audience. Finally, because tailoring is a form of data-based communication, it should only be considered as a message strategy when a mechanism exists for gathering or accessing information from the target population on the key determinants of change. That is one reason that tailoring has been applied so often in health care settings. Individual-level data are already routinely gathered there; tailoring assessments can be integrated or added to existing structures without disrupting practice norms or patient expectations.

TAILORED M-HEALTH COMMUNICATION IN PATIENT-CENTERED CARE Communication has always been a fundamental component of effective health care and health promotion. In this context, throughout the last decade, tailoring systems have been developed for a very wide variety of applications providing information for: • • •

Patients at significant risk of developing chronic conditions. Patients who already have chronic conditions such as migraines, asthma, and diabetes that require long-term continuing treatment. Patients undergoing more short-term intensive treatment such as for cancer.

The goal of these systems has also been diverse, supporting the patient’s role, health promotion advice, and behavior change interventions. However, improvement of ICT has increased the potential for tailored communication (Rimer & Kreuter 2006). Computer automation allows for the rapid processing of individual responses and matches

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individuals´ answers to individually tailored messages (Kreuter et al., 1999; Noar et al., 2011). In this kind of computer-generated tailored communication (also called computer-tailoring), the combined expertise of health promoters is translated into a computer expert system (Dijkstra & De Vries, 1999). Content knowledge is needed both to determine the correct information for different kinds of individuals and to devise the decision rules on which the computer program is based (Brug, Campbell &Assema, 1999). Moreover, tailoring can be static or dynamic. In static tailoring, one baseline assessment is provided on which all tailored information is based. In dynamic tailoring, the assessment is repeated prior to providing pieces of tailored information or feedback (Krebs, Prochaska & Rossi, 2010). Computer-generated tailored information can be delivered via various channels. Channel selection can be guided by audience preferences and campaign goals. Tailored information can be delivered via: • • • • • • •

Print. Telephone call. Face to face. Mobile phone text message. CD-ROM. Computer kiosk. The Internet.

Computer-tailored but print-delivered interventions are deemed the ‘first generation’; interventions using interactive media are deemed the ‘second generation’ of tailored health communication (Oenema, Brug & Lechner, 2001). The ‘third generation’ interventions refer to interventions delivered via mobile and remote devices such as mobile phones and handheld computers (Norman et al., 2007). Information and communication technologies (ICT) such as the Internet and mobile phones provide new opportunities for delivery of innovative interventions (Pratt et al., 2012). Health behavior change programs and campaigns delivered via the Internet have become increasingly popular for the promotion of lifestyle-related health behaviors (Kroeze, Werkman & Brug, 2006). Availability, transferability, relatively small cost, and customization are some of the benefits of selecting web-based programs and campaigns as well as a perception of anonymity, which may be appealing to reluctant or self-conscious participants. In addition, Web-based health behavior programs and campaigns appear to be cost-effective (Norman et al., 2007; Tate et al., 2009; Webb et al., 2010). The Internet is increasingly used by private and public healthcare organizations in their communications and information transfer (Eng, 2002). The concept of eHealth involves the use of ICT to improve health in general and the healthcare system in particular (Eysenbach, 2001; Chau & Hu, 2004). Furthermore, mobile health (mHealth) is thought to be the next step in computerized health interventions (Riley et al., 2011). The research on health behavior change has also led to the development of technologies supporting behavior changes (Consolvo et al., 2006; Nawyn et al., 2006). These so-called persuasive technologies (Fogg, 2003) embed motivational strategies into everyday electronic devices to encourage and sustain long-term health behavior changes. They attempt to shape, reinforce or change behaviors, attitudes, feelings or thoughts about an issue, object or action (Berkovsky et al., 2012). According to Oinas-Kukkonen (2013) behavior change support systems are the primary focus of research in the area of persuasive technologies (Lehto, 2013). A fundamental challenge in persuasion is that the target audiences are large and

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heterogeneous including users with wide-ranging goals, needs and preferences (Berkovsky et al., 2012). The solutions for this can be the targeting and tailoring of health communication. (Noar et al., 2009). In addition, tailored health behavior change programs usually refer to computer-tailored programs commonly delivered by a computer or the Internet (Suggs et al., 2006). In a tailored program, for instance, tools for building self-regulatory skills can be combined with tailored health feedback messages (Lustria et al., 2009). Goal setting and action planning (Brug et al., 2005) or observational learning, providing role models, supporting emotional coping and allowing reinforcement by virtual rewards (Toscos & Connolly, 2010), can help bridging the information behavior gap. Other projects around the world are using natural language generation techniques that allow more interactivity. Interactivity is defined as the capability of new communication systems to ‘talk back’ to the user as do individuals participating in a conversation (Rogers, 1986). Although there is interest in producing tailoring systems that enable enhanced interactivity, few studies have been able to demonstrate the effectiveness of health behavior. Cawsey, Grasso & Jones (1999) developed a nutritional tailoring system based on a dialog with the user. The users receive suggestions for improving the meal after making a number of meal choices. They can answer back to each tip in various ways, asserting objections or rejecting it outright. Another example is the Patient Education and Activation System (PEAS) project, which was designed to prepare individuals take an active role in health care decisions (McRoy & LiuPerez, 1998).The project investigated strategies for helping people to identify their health care concerns. These strategies combine a multimodal computer interface with intelligent tutoring and intelligent discourse processing. As PEAS interacts with a patient, it varies the content and pace of the interaction and suggests relevant learning activities. Because of the restraints of existing tools and techniques, several of the most experimental projects attempt to use more sophisticated techniques, taking ideas from information technology and using Natural Language Generation (NLG) methods (Reiter & Sripanda, 2003). The basic idea behind most of these systems is to: • • •

Represent explicit information about the patient. Represent general rules about communication. Generate text from a database of health-related information.

Achieving this, with only limited knowledge of how humans tailor their communications has proven to be very difficult. In practice, however, even these systems have lacked access to a knowledge base that contains accurate determinants of the selected behavior. As a result, NLG approaches that incorporate tailoring on health behavior determinants have been limited. More frequently the tailoring systems are developed using NLG. Consequently, they embrace the understanding that the same semantic information can be conveyed through text and sentence structures. A multi-argument formation, which is critical to expanding communications in health behavior change, embodies two types of knowledge acquisition (KA) techniques: • •

Working with experts in a structured fashion, think-aloud protocols, sorting and laddered grids. Learning from data sets of correct solutions. Concurrently, the literature reflects the positive relationship between:

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• •

Effective communications and the delivery of safe and quality patient care (Cooper et al., 2015). Health information technology and patient-centered care (Powell, 2009; Penfield and Anderson, 2011; Finkelstein et al., 2012).

Powell (2009) identified that effective and meaningful health information technology is the cornerstone of a patient-centered health care system. Penfield and Anderson (2011) argued that health information technology is transforming the way health-related information is gathered, stored, shared and used. Finally, Finkelstein et al. (2012) concluded that evidence confirms the positive impact of health information technology on patient-centered care. Conclusively, emerging M-Health technologies provide fundamentally different ways of looking at tailored communication technology. As a result, tailored communications research is poised at a crossroads. It needs to both build on and break away from existing frameworks into new territory, realizing the necessary commitment to theory-driven research at basic, methodological, clinical, and applied levels. In this context, the revolution of M-Health holds great promise in patient-centered care and tailored communication.

Issues, Controversies, Problems The most significant barriers to information use and information reception are psychological and cognitive. For instance, an individual may feel bombarded with too much information. This kind of situation is called information overload, and it may lead to information avoidance (Case, 2012). Information overload is directly related to information use because the experience of information overload affects the way in which information sources are selected or rejected. According to Savolainen (2008) there is no consensus among researchers about the definition of information overload and whether the phenomenon exists. Eysenbach (2003) states ‘individuals who are exposed to excessive information may make poor health decisions that can potentially have harmful effects on outcomes.’ Additionally, the terminology used in health information may be difficult and presented in a way that the information receiver does not understand (Docherty et al., 2008; Fourie, 2008). Coping with differences between ‘lay language’ and professional terminology can be a barrier to information use and decision-making (McKenzie, 2002; Brennan & Safran, 2005; Eriksson-Backa, 2008). Thus, one way to avoid the experience of information overload or avoidance is to become information literate. The Medical Library Association (2003) defines health information literacy as a ‘set of abilities needed to: recognize a health information need; identify information sources and use them to retrieve relevant information; assess the quality of the information and its applicability to a particular situation; and analyze, understand, and use the information to make good health decisions’. On the other hand, no studies to date have directly compared tailored and targeted approaches to health communication. One particular area of inquiry would be to test the variability on the principal determinants of the intended outcome. More variability on the key determinants of some expected outcome is associated with the tailored messages being superior to targeted messages. Such studies would provide valuable information contributing to greater evidence-based practice in health communication. However, analysis elucidated that well-suited non-tailored materials can function as well or better than tailored materials. At the same time, moderate and poor-fitting non-tailored materials were usually subordinate to both approaches. These findings suggest two important points. First, there is considerable

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variation in the effectiveness of any single communication approach. Second, current tailored print communications may be no more effective than generic materials that are well matched to a particular person. Nevertheless, the art of creating tailored health communication is still evolving. The tailored print materials tested to date probably lie in the middle of a continuum from totally generic to perfectly tailored health communication.However, enhanced tailored communication addresses not only behavioral constructs from a few selected theories of health behavior change, but also factors such as: • • • • •

Learning style. Preferred media. Cultural norms and values. Need for cognition. Use of emotional versus cognitive appeals.

As a result, research on tailored health communication should identify and test new types of tailoring variables that could improve behavior change and health messages. Moreover, studies of tailored content matching have compared a group of targeted communication with the experimental group. In effect, the designs tested if tailoring is more efficient than little or no segmentation and customization. In this context, research questions should focus on the attributes of tailoring: • •

The cognitive and behavioral determinant goals. The strategies to achieve them.

One obvious way to approach such designs is the presence versus absence of specific attributes, but many creative alternatives are also possible. Beyond this, research should also address the circumstances different tailoring tactics elicit different results. It is also necessary to compare specific tailoring strategies and tactics. One method is a dismantling design. Another approach, the parametric or ‘dosing’ model, examines the effects of the same tailoring strategy but various intensities. Third, individual tailoring strategies may be added to those with some degree of tailoring. Finally, because segmentation and customization rely on information about individuals, individualbased assessments are considered to be indispensable to tailored interventions. However, such assessments can have an independent impact on behavior.

SOLUTIONS AND RECOMMENDATIONS A conceptual framework is needed to broaden the scope and boundaries of tailored communications research. The traditional communications model consists of a source, a message, a channel of delivery, a receiver, and an effect on the receiver. Anything that impedes message transfer through the channel is considered ‘noise’. This basic model, developed prior to the informatics revolution, could not anticipate the new tools that are now available. Ideally, both sender and receiver continually adapt their presentations until both are satisfied that knowledge has been appropriately transferred. As computers become faster, multimedia, interactive communications are now also possible, using sophisticated expert systems and inference engines to reduce ‘noise’. Virtual reality can help individuals ‘prelive’ the future conse-

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quences of their decisions and explore the effect of their lifestyle choices on their biology and social and emotional well-being. Palmtop computers and personal organizers make it possible to provide ‘on demand’ information in ‘real time’. The concept of a computer ‘coach’ that is available on demand is rapidly becoming a reality. Tailored communications can come in an unlimited number of forms, including tailored telephone counseling and voice response systems. As the information superhighway evolves, the World Wide Web will bring accessibility to interactive multimedia intervention technologies. Given such rapid technological advances, tailored communications may now reduce ‘noise’ by gathering detailed personal information for input at the source and then presenting appropriate messages through the channel. Computer-based delivery options can simulate a conversation by tailoring information, in real time, based on user responses. The potential for rapid feedback in real time provides the capability to modify subsequent messages and/or deliver repeated communications. Such factors offer qualitative advances in the communication model of the 1960s. As a result, the theoretical models of the future must continuously evolve to keep pace with new technologies. Theories of behavior change must also guide tailored message algorithms. In the last 30 years, Social Cognitive Theory has served as an overarching theoretical framework; one that has identified specific mediating mechanisms that lead to behavior change. Related models emphasize different potential mediating mechanisms that are thought to be most relevant to creating tailored communications (Glanz et al., 1997). They range from the Health Belief Model, the Theory of Reasoned Action, and the Transtheoretical Stages of Change Model to more recent theories of risk perception such as the Precaution Adoption Model (Weinstein, 1993). Research can help identify what specific mediating mechanisms and processes are best targeted to enhance tailored message effects. Such variables include: • • • • • •

Intrinsic versus extrinsic motivation. Emotional blunting versus monitoring. Availability and type of coping responses. Self-efficacy expectations. Variety of proximal and distal outcome expectations. Appraisal of the decisional balance between the advantages and disadvantages of changing behavior.

Neural nets and intelligence technologies blur the boundaries between computer learning algorithms and the human brain. Intelligence research can inform tailored communications research and vice versa. Current advanced tailoring technology uses deductive and inductive inferencing systems to generate new data points based on an individual’s profile information. Artificial intelligence applications, such as heuristics and neural networks hold much promise for generating tailored content from generic content and for modifying intervention curricula and presentation based on individual case observations. Moreover, the particular neurobiological substrate determines the parameters and limits of the tailored communications. Basic mechanisms might include individual differences in speed of information processing and preferences regarding modes of message delivery. Research on risk perception and optimistic versus pessimistic bias can further contribute to improving understanding of how to present information to patients and what questions to ask.

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In the final analysis, the impact of computer and provider delivered tailored messages may also hinge on the receiver’s perceptions of self-efficacy, outcome expectations, and feelings of empowerment. Depending upon how messages are framed, receivers may feel more or less empowered and more or less self-confident. As a result, we need to understand more about how computer versus human experts influence the receiver’s perceptions of empowerment and self-efficacy. Within Anderson’s (1998) interdisciplinary biopsychosocial framework, the interface between biobehavioral and biosocial disciplines is particularly relevant for advancing theory and guiding research on the mediating mechanisms underlying tailored communications. Models of self-control or self- regulation, derived from Social Cognitive Theory, are at the heart of understanding behavior change for tailored communications. However, different biological, psychological, or social mechanisms underlie change for different target behaviors and populations. Prior research offers little specific guidance about how to bridge the gap between theory and application. The gap between theory and practice raises fundamental questions about how to define the boundaries and limits of tailoring in actual practice. That raises the issue of whether the principles of stepped care should be considered when designing future tailored interventions. A stepped-care model would first disseminate a relatively brief inexpensive and less sophisticated tailored intervention. Only the subset who failed to respond would then be stepped up. This type of model that combines stepped care with tailoring has been proposed to improve the cost-effectiveness. It provides a rational means of allocating finite resources to achieve the greatest population impact by reserving the more complicated and expensive interventions for a smaller group who have failed at all previous steps. Despite the intuitive appeal of stepped-care models to tailoring, it is also possible that a very inexpensive intervention produce little change.Very little research to date has addressed these issues. Ultimately, future research should provide information relevant to the costs of tailoring per incremental increase in outcome and population impact. It should also shed light on linking such gains to measures of quality-adjusted life years saved (39,40). Cost-effectiveness and cost-benefit information are critical for informing health policy and convincing payers to consider investing in tailored message technologies over other ideas within their budget allocations. In summary, traditional communication theories need to be expanded, or new ones developed. Bridges must be built between communication and behavior change theory. Standardization of language, models, mechanisms, and measures is needed to advance the field, producing unique additions to outcome variance. More research must focus on how to define and identify the mediators that optimize the impact of tailored communications. Variables that cut across biopsychosocial domains should be incorporated into a single model or theory of tailored communications. Future research on theory and mechanisms should conduct a more prospective process to outcome evaluations of causal pathways and examine the predictive and incremental value of new variables and mechanisms over and above existing ones. A broader theoretical model must also incorporate other disciplines and bodies of knowledge beyond those listed above. Some of the major factors in developing a credible and trusting relationship among humans is the consistency of responses over time. Finally, a fundamental issue that deserves more attention is who should receive tailored messages.

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FUTURE RESEARCH DIRECTIONS It is complex to employ persuasive argumentation theories to behavioral change communication. Moreover, research in argumentation has been concerned only with the structure of single arguments. Likewise, NLG systems do not explore the planning mechanisms that would account for the generation of text. One also needs a theory that would describe (Ajzen, 1991): • •

How arguments could be put together. Why particular multiargument structures are more persuasive.

Some of the computational tailoring systems have attempted to combine theories of argumentation with behavioral theories. They realized that if the aim of an intervention is to induce people to modify their behavior, particular theories of the advising process are necessary. These interventions have used Stages of Change and the Health Belief Model (Grasso, 1998). However, all of these systems have been difficult to move into real-world environments primarily because of the entanglement of using NLG techniques to generate multi-argument structures in domains as complex as health behavior. In addition, little is known in the reusable NLG resources while the nonlinguistic tailoring approach has other limitations. It is possible that the integration of both the nonlinguistic tailoring approach and computer science methods is essential for the development of tailored messages. To design a system whose ultimate aim is to try influencing the user’s behavior, very diverse sources of knowledge have to be integrated. Theories of argumentation and persuasive structure are perhaps what is needed to build on and extend current tailoring research. In addition, additional types of tailoring variables should be tested. Theory must adopt the most parsimonious strategies without omitting essential mechanisms. That will require the adoption of a common language and standard measures for the underlying mechanism and processes. For unification of the more sophisticated technologies, theory, and real-world applications, joint research is needed. As such, it remains to be seen whether the advances of the tailoring process will deliver the tailored health communication approaches sufficient to engineer an impact on: • • •

Improved decision-making. Patient health behavior. Chronic disease management.

CONCLUSION Robinson et al. (1998) cautioned that health communications applications hold great promise but can cause harm. They encouraged health care providers to speed the advancement of knowledge and evaluate its safety, quality, and utility. They also proposed a standardized reporting template to: • • •

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Guide developers and evaluators. Conduct evaluations. Disclose results in a uniform fashion to help clinicians, purchasers, and consumers judge their quality, efficacy, and efficiency.

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Of critical importance is the need for research on implementation to elucidate the mechanisms for cost-effective diffusion of tailored interventions to populations. Safeguards must also ensure confidentiality and ethical standards to protect consumers. Finally, theory and applications are lagging behind the rapid pace of technological advances. Standardization of language, research designs, methods, and measures are crucial along with developing broader interdisciplinary conceptual models. The gap between technology, theory, and application can be closed by: • • •

Providing opportunities for basic research into the fundamental mechanisms of tailored communications. Broadening theories of behavior change for tailored communications research. Enhancing message effectiveness and efficient impact on outcomes.

It remains to be seen whether the revolution in M-Health will provide the tools to engineer sufficient impact on patient-centered care, and tailored communication.

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Rippen, H., & Risk, A. (2000). eHealth Code of Ethics. Journal of Medical Internet Research, 2(2), e9. doi:10.2196/jmir.2.2.e9 Robinson, T. N., Patrick, K., Eng, T. R., & Gustafson, D. (1998). An evidence-based approach to interactive health communication. Journal of the American Medical Association, 280(14), 1264–1269. doi:10.1001/jama.280.14.1264 PMID:9786378 Rogers, E. M. (1986). Communication Technology: The New Media in Society. New York: The Free Press. Rogers, R. W. (1985). Attitude Change and Information on Integration in Fear Appeals. Psychological Reports, 56(1), 179–182. doi:10.2466/pr0.1985.56.1.179 Roper, W. L. (1993). Health communication takes on new dimensions at CDC. Public Health Reports, 108(2), 179–183. PMID:8385358 Savolainen, R. (2008). Everyday Information Practices. A Social Phenomenological Perspective. Lanham: Scarecrow. Savolainen, R. (2009). Information use and information processing: Comparison of conceptualizations. The Journal of Documentation, 65(2), 187–207. doi:10.1108/00220410910937570 Schwarzer, R. (2008). Modeling health behavior change: How to predict and modify the adoption and maintenance of health behaviors. Applied Psychology, 57, 1–29. Sheeran, P. (2005). Intention–Behavior Relations: A Conceptual and Empirical Review. In W. Stroebe & M. Hewstone (Eds.), European Review of Social Psychology. Chichester: John Wiley & Sons. doi:10.1002/0470013478.ch1 Skinner, C. S., Strecher, V. J., & Hospers, H. (1994). Physician recommendations for mammography: Do tailored messages make a difference? American Journal of Public Health, 84(1), 43–49. doi:10.2105/ AJPH.84.1.43 PMID:8279610 Strecher, V. J., Kreuter, M. W., Den Boer, D.-J., Kobrin, S., Hospers, H. J., & Skinner, C. S. (1994). The effects of computer-tailored smoking cessation messages in family practice settings. The Journal of Family Practice, 39, 262–270. PMID:8077905 Suggs, L. S., Cowdery, J. E., & Carroll, J. B. (2006). Tailored program evaluation: Past, present, future. Evaluation and Program Planning, 29(4), 426–432. doi:10.1016/j.evalprogplan.2006.08.003 PMID:17950872 Talja, S., Tuominen, K., & Savolainen, R. (2005). Isms in information science: Constructivism, collectivism and constructionism. The Journal of Documentation, 61, 70–101. Tate, D. F., Finkelstein, E. A., Khavjou, O., & Gustafson, A. (2009). Cost effectiveness of Internet interventions: Review and recommendations. Annals of Behavioral Medicine, 38(1), 40–45. doi:10.1007/ s12160-009-9131-6 PMID:19834778 Te’eni, D. (2001). Review: A cognitive-affective model of organizational communication for designing IT. MIS Quaterly, 25(2), 251–312. doi:10.2307/3250931

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Toscos, T., & Connelly, K. (2010). Using behavior change theory to understand and guide technological interventions. In B. M. Hayes & A. William (Eds.), Health Informatics: A patient-centered approach to diabetes (pp. 295–326). Cambridge, MA: MIT Press. doi:10.7551/mitpress/9780262014328.003.0011 Webb, T. L., Joseph, J., Yardley, L., & Michie, S. (2010). Using the Internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12(1), e4. doi:10.2196/jmir.1376 PMID:20164043 Webb, T. L., & Sheeran, P. (2006). Does change behavioral intentions engender behavior change? A meta-analysis of the experimental evidence. Psychological Bulletin, 132(2), 249–268. doi:10.1037/00332909.132.2.249 PMID:16536643 Weinstein, N. D. (1993). Testing four competing theories of health-protective behavior. Health Psychology, 12(4), 324–333. doi:10.1037/0278-6133.12.4.324 PMID:8404807 Williamson, K. (2005). Where one size does not fit all: Understanding the needs of potential users of a portal to breast cancer knowledge online. Journal of Health Communication: International Perspectives, 10(6), 567–580. doi:10.1080/10810730500228961 PMID:16203634 Wilson, T. D. (1999). Models in information behaviour research. The Journal of Documentation, 55(3), 249–270. doi:10.1108/EUM0000000007145 Wilson, T. D. (2000). Human information behavior. Information Science Research, 3(2), 49–55.

KEY TERMS AND DEFINITIONS Health Behavior: Behavior directed at promoting, protecting and maintaining health. Health Communication: Informing, influencing and motivating about important health issues. Health Information: Information for staying well, preventing and managing disease and making other decisions related to health and health care. M-Health: It describes the use of a broad range of telecommunication and multimedia technologies within wireless care delivery design and can be broadly defined as the delivery of healthcare services via mobile communication devices. Patient-Centered Care: It is more than a method of communication which focuses on patients’ preferences, experienced needs and values in decisions about care and treatment. Self-Efficacy: It is a person’s belief in his or her ability to complete a future task or solve a future problem. Tailored Health Communication: Any combination of information strategies intended to reach an individual. Targeted Health Communication: It corresponds to a process appealing to a defined population subgroup.

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The Nexus of M-Health and Self-Efficacy Anastasius S. Moumtzoglou P. & A. Kyriakou Children’s Hospital, Greece

ABSTRACT Self-care emerged from the concept of health promotion in the 1970s while from 2000 onwards the term ‘self-management’ gained popularity, with a greater focus on long-term conditions and the trend towards more holistic models of care. Although ‘self-management’ and ‘self-care’ are often used interchangeably, a distinction between the two concepts can be made. Both can be considered in terms of a continuum, with self-care at one end as ‘normal activity’ and self-management an extension of this. Self-management support is the assistance given to patients in order to encourage daily decisions that improve healthrelated behaviors and clinical outcomes. Self-efficacy, which is grounded in social cognitive theory, is defined as confidence in one’s ability to perform given tasks. The chapter envisions these concepts on a continuum with one pole representing mobile health and the other self-efficacy. It concludes that selfmanagement support is the nexus of mobile health and self-efficacy.

INTRODUCTION The health care environment is currently changing to meet technology and societal trends which converge to bring into being new communication patterns that connect and coordinate the roles of healthcare stakeholders. At the same time, the healthcare industry is steering inexorably toward a distributed service design in which essential decision-making occurs at the point of care. One of the central engines of this shift towards decentralization and reorientation of health care services is mobile healthcare (M-Health). M-Health describes the use of a broad range of telecommunication and multimedia technologies within wireless care delivery design and can be broadly defined as the delivery of healthcare services via mobile communication devices. M-Health establishes healthcare communities in which every stakeholder can participate. However, it disrupts the traditional service model where healthcare information, security and access is centrally managed, maintained and limited, transforming the healthcare sector and destroying components that are slow to adapt. DOI: 10.4018/978-1-4666-9861-1.ch017

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 The Nexus of M-Health and Self-Efficacy

M-Health interventions range from simple to complex applications and systems that remotely coordinate and actively manage patient care. In this context, it offers an elegant solution to the problem of accessing the right information where and when it is needed within highly fluid, distributed organizations. Moreover, it removes geography and time as barriers to care by establishing connectivity with remote locations and remote workers, creates new points of contact with patients, and changes the frequency and intensity of health care delivery. It also establishes effective new treatment modalities like telehealth, remote patient monitoring, self-care, and home health while it blurs the boundaries between professional medical advice and self-care. Overall, M-Health blends three bodies of knowledge: high technology, life sciences, and human factors. Self-care emerged from the concept of health promotion in the 1970s. The 1980s there was increasing recognition of ‘partnership’ with health care professionals, and the 1990s saw more emphasis on the continuity of self-care and so-called ‘growth’ models. From 2000 onwards the term ‘self-management’ gained popularity, with a greater focus on long-term conditions and the trend towards more holistic models of care. Although ‘self-management’ and ‘self-care’ are often used interchangeably, a distinction between the two concepts can be made. Both can be considered in terms of a continuum, with self-care at one end as ‘normal activity’ and self-management an extension of this. Self-management is principally justified in two different but interlinked ways. The first is that longterm conditions are most effectively managed when patients and professionals work in partnership, combining their different skills and expertise. Secondly, reference is made to the growing older population and incidence of long-term conditions and the increasing demands on health services that result from these trends. In this context, supporting people to become more efficient self-managers of their conditions is presented as an essential strategy for managing health care demand and ensuring the long-term sustainability of health services. Attempts to encourage and enable people to self-manage have focused on two primary strategies: • •

Educational, training and peer-support programs that are provided separately from clinical health care consultations and tend to have little connection to the patients’ usual clinical care. Approaches to health care meetings in which clinicians put a strong emphasis on supporting people to manage their conditions rather than rely on the clinician.

On the other hand, self-management support is the assistance given to patients in order to encourage daily decisions. It may be viewed in two ways: • •

As a portfolio of techniques and tools that help patients choose healthy behaviors. As a fundamental transformation of the patient-caregiver relationship into a collaborative partnership.

The purpose of the self-management support is to aid patients take an active role in their treatment. Education is a feature of self-management support. In self-management support interventions, this is often in the form of patients teaching more about their health condition, the circumstances that trigger and potential options for managing symptom exacerbation.

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Moreover, self-efficacy, which is grounded in social cognitive theory, is defined as confidence in one’s ability to perform given tasks. An improvement in self-efficacy has been identified as an achievable and measurable outcome for self-management support interventions. In general, the research indicates that higher self-efficacy beliefs are beneficial while weaker selfefficacy beliefs are associated with poorer outcomes and adverse behaviors. Such findings allude to the central role of self-efficacy in relation to individuals’ self-care and selfmanagement behavior during, and following, illness. Moreover, experience or the enactive attainment, facilitated by M-Health, is the most important factor determining a patient’s self-efficacy. Thus, the objective of the chapter is to argue that the different concepts of mobile health, self-management support, and self-efficacy are interconnected and reflect a continuum with one pole representing mobile health and the other self-efficacy. Analysis and interpretation go beyond summarizing and synthesizing concepts and evidence. They take concepts apart and put them back together in a new perspective. Specifically, the premise that mobile health, self-management support, and self-efficacy reflect a continuum with one pole representing mobile health and the other self-efficacy comes into existence by exploring historical and conceptual information in the background. In this context, the concepts of selfcare, self -management, self-management support, self-efficacy and M-Health are defined and examined. The rationale of the presentation is to show the evolution and conceptual relationship of the concepts of self-care, self -management and self-management support and provide relevant information about the distinct concepts of self-efficacy and M-Health. The main section of the chapter explicates the nexus of M-Health and self-efficacy by reframing self-management support within patient-centeredness (Ahmad, Ellis, Krelle, and Lawrie, 2014). The rationale and the sequence for developing the arguments is that patient-centeredness involves patients in decisions as well as in their health and health care. Involving patients in decisions relates to the concept of shared decision making while involving patients in their health and health care implies the concepts of self-management support and care and support planning. The presentation, at this point, emphasizes the presentation of existing self-management support tools that embrace the dimensions of information, patient empowerment, and behavior change (Ahmad, Ellis, Krelle, & Lawrie, 2014). Finally, the chapter concludes by providing a discussion of relevant issues, controversies, problems, and emerging trends relating to the conceptualization of the main argument.

BACKGROUND The term self-care is often associated with a lack of theoretical clarity and confusion because its scope and boundaries are difficult to define (Soderhamn, 2000; Barlow et al., 2002; Clark, 2003). It is frequently viewed as a spectrum starting from the individual responsibility people take in managing the daily choices that they make in relation to their lifestyle, maintaining their health and preventing illness (Chambers, 2006). Several definitions of self-care have been proposed over the past few decades that highlight the broad spectrum of activities that self-care seems to encompass. Levin et al. (1977), state that self-care is ‘the process whereby patients deliberately act on their behalf in health, promotion, prevention of illness, and the detection and treatment of health deviations’. Orem (1995), states that self-care is ‘the practice of activities that individuals initiate and perform on their behalf in maintaining life, health, and well-being’ and that self-care is ‘an adult’s continuous contribution to his or her continued existence, health, and well-being’. These definitions clearly acknowledge that the nature of self-care encourages individuals to

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adopt responsibility for undertaking their self-care but also highlight the spectrum of activities considered to fall within the sphere of self-care. In reflecting the earlier discussion on the medical and social ideologies associated with self-care, the Department of Health (2005) defines self-care as ‘the actions individuals and carers take for themselves, their children, their families, and others to stay fit and maintain good physical and mental health, meet social and psychological needs, prevent illness or accidents, care for minor ailments and long-term conditions, and maintain health and well-being after an acute illness or discharge from hospital.’ Although, these definitions view self-care as occurring without professional assistance, Levin and Idler (1983) have acknowledged that in carrying out self-care ‘individuals are informed by technical knowledge and skills derived from the pool of both professional and lay experience.’ Hickey et al. (1986) also proposed that self-care be interactive with healthcare professionals, rather than being independent of professional care. Moreover, Orem (1995) stated that a large part of self-care includes knowing when to seek medical advice and participating in interactions with health professionals. Notwithstanding the previous discussion has highlighted the different focus of each of the definitions presented above, all of the definitions highlight several key themes that can be considered central to the concept of self-care. The prominent idea is that of the patient being encouraged to take a more active role in, and a greater level of control over, its self-care. As Rodgers et al. (1999) acknowledged self-care transcends the idea of patients as dependent, customary recipients of health services to one where patients become a provider of a large part of their care. Ultimately, the choices for self-care are within the control of the patient, rather than, for example, the health professional (Rodgers & Hay, 1998). As a result, the second theme is the idea of self-care, occurring not in isolation from health professionals’ provision of care, but in collaboration with health professionals. Self-care is often seen as the antithesis of formal care delivered by health professionals (Dill et al., 1995). However, it should be seen as an approach that requires and promotes a greater level of collaboration between patients and health professionals (Paterson & Sloan, 1994; Rodgers & Hay, 1998; Kolbe, 2002; Redman, 2005; Chambers, 2006). It is an approach that should acknowledge the importance of actively listening to patients about why, when and how they self-care (Ryan et al., 2007). It should also appropriately guide and support patients in their self-care practices (Richardson & Ream, 1997; Rodgers & Hay, 1998; Koch et al., 2004). A number of theories and conceptual models, rooted in different disciplines, seem relevant to the concept of self-care. However, there are few which have been explicitly posited as a model designed to underpin self-care research and self-care in clinical practice and few which have been empirically tested for their utility. Fu et al. (2004) identified five theoretical or conceptual models central to the idea of self-care and symptom management. These included: • • • • •

The Self Care Model (Orem, 1991; 1995). The Conceptual Model for Symptom Management (Larson et al., 1994; Dodd et al., 2001). The Common Sense Model (Leventhal et al., 1984, 1997, 2001). The Symptom Interpretation Model (Teel et al., 1997). The Theory of Unpleasant Symptoms (Lenz et al., 1995; 1997).

The Self Care Model (Orem, 1991; 1995) focuses on determining the extent of one’s ability to carry out self-care. The Conceptual Symptom Management Model focuses on the subjective symptom experiences of individuals, the influence factors, the symptom management strategies and symptom outcomes. The Common Sense Model or Leventhal’s Self-Regulation Model (Leventhal et al, 1984, 1997, 2001) 344

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theorizes that the perception of fear and threat resulting from the experience of a symptom are determining factors in the initiation of one’s self-care (Fu et al., 2004). The Symptom Interpretation Model (Teel et al, 1997) postulates that an individual receives and recognizes a stimulus from a symptom and makes a decision about how to manage that symptom (Fu et al., 2004). Finally, the Theory of Unpleasant Symptoms (Lenz et al, 1995; 1997) argues that the same factors that influence symptom experience lead to similar alleviating interventions for more than one symptom (Fu et al., 2004). Policy support for self-care has been firmly established in a number of published reports such as: • • • • •

‘The NHS Plan’ (Department of Health, 2000). ‘The Expert Patient’ (Department of Health, 2001). ‘Supporting People with Long Term Conditions’ (Department of Health, 2005a). ‘Self-Care – a real choice’ (Department of Health, 2005b). ‘Our Health, Our Care, Our Say’ (Department of Health, 2006a).

Additionally, a focus on self-care has also been encouraged by the Scottish healthcare system with the publication of: • • •

The ‘Partnership for Care’ (Scottish Executive, 2003). The ‘National Framework for Service Change in Scotland’ (Scottish Executive, 2005a). The ‘Delivering for Health’ (Scottish Executive, 2005b).

These reports appear to have acknowledged the changing focus of chronic disease management and service provision within the NHS. More, political support for self-care may also have been influenced by: • •

The recent moves towards the greater patient and public involvement in healthcare (Hubbard et al., 2005). The growing evidence base on involving patients in decision-making related to their care (Degner et al., 1997; Beaver et al., 1999; Davison et al., 1999, 2004).

Such calls reinforce support for a move away from the existing top-down model of care to a culture where patients’ subjective experiences are considered an essential contribution to understanding the experience of illness. Although ‘self-management’ and ‘self-care’ are often used interchangeably, a distinction between the two concepts can be made. Both can be considered in terms of a continuum, with self-care at one end as ‘normal activity’, and self-management an extension of this. It is defined as managing ‘ailments’ either with or without the assistance of a health care professional. Likewise, self-management support is the help health care professionals give to patients with chronic disease in order to stimulate everyday decisions that ameliorate health-related behaviors and clinical outcomes. Self-management support may be thought in two ways: • •

A portfolio of techniques and tools that assist patients choose healthy behaviors. A radical change of the patient-caregiver interrelationship into a synergetic partnership.

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The intention of self-management support is to assist patients to become informed about their conditions and take an active role in their treatment. The concept of self-management support has evolved to encompass a wide variety of interventions with different intentions. It is largely a result of various disciplines having contributed to its evolution. There is an important distinction to make between ‘selfmanagement’ and ‘self-management support’. The former takes account of the fact that individuals are self-managing (to a greater or lesser extent) all the time in their daily lives. Self-management, therefore, refers to the behaviors that individuals engage in outside of the health care framework. Self-management support refers to individuals’ support for their self-management goals and activities by health care professionals. Self-management support emanated from a social model of health and disability. The voluntary sector and service user groups were influential in the development of lay-led programs of support. Early models of self-management support were grounded in educational approaches. However, psychological influences became more prominent, especially from the late 1990s onwards, with the realization that behavioral change is not predicted by improvements in knowledge alone. In the 1960s, a behaviorist approach pinpointing the explanation of human behavior was introduced. The new approach viewed action as the offshoot of an interaction between personal, behavioral, and environmental factors instead of an unconscious process with psychodynamic roots. Moreover, it did not consider deviant behavior a disease symptom (Bandura, 2004). It was a theory, as well as a construct of Social Cognitive Theory, which argued that people, in most cases, will attempt things they believe they can succeed in doing. The theory introduced the idea that the perception of efficacy is influenced by four factors (Bandura, 1994, 1997; Pajares, 2002): • • • •

Mastery experience. Vicarious experience. Verbal persuasion. Somatic and emotional state.

Moreover, it affirmed that self-efficacy is the belief in one’s own ability to accomplish something. It referred to the extent of an individual’s belief in his or her abilities and is based on feelings of selfconfidence and control. It is worthwhile mentioning that the research has shown: • •

Health care professionals can have an impact on self-efficacy. Changes in self-efficacy and behavior are associated.

It also asserted that we all have mastery experiences, which occur when we attempt to do something and are successful. In this context, mastery experiences are the most efficient way to boost self-efficacy. People are more likely to keep the faith they can accomplish something new if it is similar to what they have already done well (Bandura, 1994). However, mastering something new is not relatively simple in all contingencies. If the new tasks are always straightforward and similar to ones already learned, then a high sense of efficacy does not develop. A high sense of efficacy develops if difficult tasks are attempted, and obstacles worked through (Bandura, 1994).

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Overall, skills mastery refers to the technique of breaking down skills to be learned in minuscule, manageable tasks. Correspondingly, patients are more likely to adopt a health behavior if they think they will be successful. As a result, interventions should increase confidence by giving patients many little successes. Another factor influencing the perception of self-efficacy is vicarious experience or the observation of others’ accomplishments (models) that are similar to one’s self. Watching someone accomplish something increases self-efficacy. Conversely, observing someone fail detracts or threatens self-efficacy. Bandura (1994) contends that ‘the extent to which vicarious experiences affect self-efficacy is related to the model’s equivalence’. In this context, vicarious learning is at the core of coach/trainer–student/client instruction with the coach or trainer demonstrating the skill, the student/client copying. The third factor affecting self-efficacy is verbal or social persuasion. When people are verbally persuaded that they can master a task, they are more likely to do it. Moreover, if others verbally support their attainment or mastery that goes a long way in supporting a person’s belief. Coaches frequently use this tactic with their teams, as they verbally psyche them up, before a game. Conversely, when individuals are told they do not have the skill or ability to do something, they tend to quickly give up (Bandura, 1994). The physical and emotional states that occur when someone contemplates doing something provide clues as to the likelihood of success or failure. Stress, anxiety, worry, and fear all negatively affect self-efficacy and can lead to a self-fulfilling prophesy of failure (Pajares, 2002). By the same token, stressful situations create emotional arousal, which in turn affects a person’s perceived self-efficacy in coping with the situation (Bandura & Adams, 1977). Social persuasion refers to individual efforts to influence behavior. One aspect of belief that is particularly useful is to urge the patient to do slightly more. When using this strategy, goals should be short-term and realistic. Modeling is a self-efficacy technique by which the patient becomes knowledgeable by seeing someone else with a similar problem. At the heart of modeling, patients are matched with models that are as much as them. It is important to avoid using superachieving people who have overcome problems in a dramatic manner while it is helpful to emphasize the similarity of the learning task. Mobile health became functional in biomedical engineering and started with looking at wireless and sensor technologies that could be incorporated to monitor people’s health at a distance. M-Health implementation came out in developing countries out of access necessity. Mobile phones have been around for years, but it was not until 1976 that mobile phones first appeared in Japan. However, much work happened predominantly in the early millennium when M-Health started to develop mobile health applications for cell phones. The early days there were things like remote cardiac monitors that evolved to look at diabetes monitoring and other types of sensor technologies. The early programs provided support tools for supply chain management while mobile communications gave access to areas that people never had using fixed line telephones. More recent, M-Health evolvement provided access to emergency medical transportation services, facilitated patient-doctor encounter, and there was a movement to personal digital assistants use. There is no standardized definition of M-Health. In most cases, mobile health or M-Health is defined as ‘medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices’. (WHO, 2011). It involves:

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• •

The use and capitalization on a mobile phone’s core utility of voice and short messaging service. More complex functionalities and applications including general packet radio service (GPRS), third and fourth-generation mobile telecommunications (3G and 4G systems), global positioning system (GPS), and Bluetooth technology.

M-Health is increasingly being used in the healthcare field since its use is becoming a cost-effective method of identifying and monitoring health issues. It also provides health professionals with: • •

Access to patient data. Access to various information sources.

Individuals can use M-Health to obtain resource materials on health issues, and patients can selfmonitor and transmit information to their health care provider. While the timely emergence of M-Health will not resolve the myriad problems, it offers unique opportunities to increase efficiency and improve the quality and access to care. Moreover, with rapid consumer adoption of smartphones, physicians can now perform two-way video conferencing. Patients and physicians have access to medical records and vital signs. Wireless technology will also allow physicians to serve more patients despite geographical limitations. On current trends, M-Health embraces medical and public health practice sustained by mobile phones, patient monitoring devices, personal digital assistants (PDAs), and tablet PCs. The spread of 3G and 4G networks has boosted the use of mobile applications offering healthcare services. 4G is a mobile network, IP-based, providing connection via the best network using seamless roaming and independent radio access technologies (Kastania & Moumtzoglou, 2012). In 4G mobile systems, different access technologies are combined in the best possible way for different communication environments and service requirements. They promise much larger data rates supporting full mobility while enabling wireless connection and access to multimedia services with high-quality voice and high-definition video. In addition, the availability of satellite navigation technologies in mobile devices supports safety and autonomy of patients. Through sensors and mobile applications, M-Health permits the accumulation of extensive medical, physiological, lifestyle, daily activity and environmental data. Consequently, M-Health serves evidence-driven care practice and research activities while expediting patients’ access to health information and accommodating lifestyle and wellbeing applications, counseling systems, health information and medication reminders. However, beyond clinical connectivity, M-Health is a field that came to light holding the promise of quality improvement, cost reduction, wholesale gains in population health, access to care and better allocation of health-delivery resources. With M-Health, healthcare professionals can continuously monitor and manage health conditions. As a result, M-Health becomes embedded into a number of care delivery strategies, including the medical home, a health information exchange, the care team and patient-centric healthcare. In its fullest flowering, M-Health is expected to address the most intractable problems of healthcare quality and cost, chronic disease management, public health, wellness, and prevention (Krohn & Metcalf, 2012). However, the impact of M-Health is just beginning to be felt as it results in more personalized medication and treatment and contributes to the empowerment of patients.

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THE NEXUS OF M-HEALTH AND SELF-EFFICACY Patient-centered care is more than a method of communication. It focuses on patients’ preferences, experienced needs and values in decisions about care and treatment. It has a broadened perspective of illness, in which patients’ experiences and control are prominent. It also focuses on interactions, striving for an alliance between patients and professionals working together and having common grounds and goals (Epstein et al., 2005; Glasgow et al., 2008; Mead et al., 2000; Michie et al., 2003; Stewart et al., 2000; Swedish Agency for Health and Care Services Analysis, 2012). Howie et al. (2004) reported that patient-centeredness is based on the patients’ concerns, putting emphasis on giving the patients time to express these concerns. Patient-centeredness pertains to patient empowerment which is grounded in equality and respect. As a result, It features self-determining agents with some control over their health and health care and conceptualizes personal control and self-efficacy. For patient empowerment, it is important that patients have knowledge and skills to define and achieve their goals (Funnell et al., 2003; Nyatanga et al., 2002). It is furthermore important that care be planned and performed from this perspective (Ekman et al. 2011; Funnell & Anderson, 2003). In this context, the features of patient empowerment are mapped into agenda, goal setting and its follow up regarding self-management support. Ovearll, patient-centeredness involves patients in decisions as well as in their health and health care. Involving patients in decisions relates to the concept of shared decision making while involving patients in their health and health care implies the concepts of self-management support and care and support planning (Ahmad, Ellis, Krelle, & Lawrie, 2014). Concurrently, the concept of self-management support has evolved to encompass a wide variety of interventions with different intentions that is the result of various disciplines having contributed to its evolution. Moreover, a multitude of approaches have been tested to support self-management. These range from more passive information sharing approaches at one end of the spectrum to more active behavioral change interventions at the other. Another way to conceptualize self-management support is to divide interventions into those that focus on building knowledge and technical skills versus those that aim to develop self-efficacy. The most promising way of supporting self-management involves a person empowerment and activation. There is also strong evidence suggesting that improved self-efficacy is associated with better clinical outcomes (Hibbard & Mahoney, 2010; Remmers et al., 2009; Cunningham, Lockwood & Cunningham, 1991). These approaches include: • • • •

Motivational interviewing by telephone or in person. Group or individual education programs with an active component. Coaching with proactive goal setting and follow up. Programs based on psychological and emotional support that acknowledge people’s stage of change.

Individual and group education sessions are the most commonly evaluated interventions of this type though there is also an increasing focus on telephone coaching by nurses. In this context, the Patient Activation Measure (PAM), a tool for measuring the level of patient engagement, was designed to assess an individual’s knowledge, skill and confidence for self-management. It was developed as a 22 item scale, resulting in a 13 item short form, which asks people about their beliefs,

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knowledge and confidence for engaging in a broad range of health behaviors. It suggested four stages of activation that patients go through on their way to becoming fully activated in managing their health: • • • •

Knowledge. Skills. Confidence. Behavior.

However, it is difficult to categorize approaches in self-management support because there is a wide variation. Historically, approaches built on patient compliance have been frequently used in health care. The approach emphasizes that health care professionals define the problems and give advice about solutions (Funnell et al., 2000). On the other hand, the patients are expected to follow the advice and instructions (Lutfey et al., 1999). Patients that do not follow the advice have frequently been labeled as non-compliant (Lutfey et al., 1999). Non-compliance, though, is invalid and not useful construct for understanding the behavior of patients. The patient is viewed as the source of the problem when adapting to a compliance approach. In addition, the solution is that the patient must change and follow the recommendations for lifestyle changes (Anderson & Funnell, 2000). Because of the criticism of the concept of compliance, alternative terms have been developed. The term adherence has a larger focus on the provider–patient relationship and the patient’s involvement in care. Therefore, adherence has been seen as a more patient-centered concept than compliance (Vlasnik et al., 2005). Differently, other researchers state that adherence represents a broader interpretation and understanding of factors that affect a person’s ability to follow treatment recommendations. Anyhow, providing information about people’s condition and how to manage it is an important component of supporting self-management and patient-centered care. Information can be provided using leaflets, websites, email, text messages, electronic forums, by telephone and in person individually or in groups. A great deal has been written about different ways to provide information to people with health conditions. There is some evidence that written motivational leaflets or letters can help people feel more confident to raise their concerns and discuss their symptoms (Glasgow et al., 2003). There is sparse evidence that such methods improve self-management behavior or clinical outcomes. Other reviews suggest that printed materials can enhance knowledge (Dally et al., 2002: van Boeijen et al., 2005; Roberts et al., 2010) but may not impact behavior when used alone (Morisson, 2001: Gibson et al., 2004). On the other hand, findings are mixed. Some trials suggest that postal educational materials are as effective for improving symptoms and self-efficacy as group education sessions (Lorig et al., 2004). There is also evidence that combining written information with lectures or other educational activities can be more effective than written information alone (Forster et al., 2004; Seals & Keith, 1997). It is worth considering the characteristics of the most effective written information tools. There is some evidence that targeting and personalizing written information is more effective than standardized printed materials (Kennedy et al., 2003; Lafata et al., 2002; Sethares & Elliott, 2004; Enwald & Huotari, 2010). Self-management support can also be delivered using audiovisual technology, computers and the mass media (Oermann, Webb & Ashare, 2003; Grilli, Ramsay & Minozzi, 2004; Buchbinder, 2008; Williams et al., 2010). Moreover, there is evidence that providing structured education programs by video/DVD, audio or computer may be as effective as in person education groups (Cordina, McElnay & Hughes, 2001; Samoocha et al., 2010).

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Other novel approaches have also been tried. A number of computer-based peer to peer communities and electronic groups have been set up to support self-management. Some descriptive studies suggest that computer chat rooms, coaching, and other online forums can provide a good motivator for self-care (Hoffman-Goetz & Donelle, 2007; Barrera et al., 2002; Stinson et al., 2010). However, the effect on clinical outcomes is uncertain. Another novel approach is using text messages or pager messages as reminders and support mechanisms (Simoni et al., 2009; Faridi et al., 2008). Systematic reviews and randomized trials have found that when used alone, information provision can improve some health behaviors. However, when used as part of a broader support initiative, information provision has been found to be useful. Especially if it is targeted or personalized to account for people’s individual needs (Kennedy et al., 2003). Decision support tools support self-management and encourage service users and carers to (Protheroeet al., 2010; Maly et al., 1996; Laffel et al., 2003): • • • •

Take more responsibility for their care. Assist self-control in the long term conditions. Encourage adoption of care protocols. Have an impact on quality of life.

However, reviews about written decision aids suggest that such aids affect attitudes and knowledge rather than behavior. A number of strategies have been experimented to increase people’s involvement in healthcare processes and decision making as a way of facilitating self-management. Sometimes people are given their medical records to keep and bring to each consultation, which is known as patient held records. However, a number of reviews and trials suggest that patient held records have limited effects on self-management. There is also interest in making records available electronically for service users (Ball, Smith & Bakalar, 2007; Winkelman, Leonard & Rossos, 2005; Currell & Urquhart, 2004). A randomized trial in the US provided patient records online to people with heart failure. After one year, those who had access to their records online were more likely to adhere to treatment, but there were no differences in self-efficacy or satisfaction with care (Ross et al., 2004). That implies that the patient held records might have some impact on self-management strategies, but it is unequivocal. The evidence is excessively diversified to suggest that the patient held records were a useful enabler for self-management. Studies have also attempted to explore why action plans and agenda setting seem to work well, but few firm conclusions are possible. Plans and agenda setting appear to be better when care plans are provided and supported in primary care compared to secondary care. This approach may be better as a ‘preventive’ measure rather for those with the most severe disease, or for those who are hospitalized for the first time (Osman et al., 2002). Self-monitoring, a self-management support tool, involves service users monitoring their symptoms in order to track their progress, modify their behavior and judge when to seek help (Bradley & Blenkinsopp, 1996). Self-monitoring is often linked with electronic monitoring devices, but this term can also refer to written management plans and systems to help patient’s self-refer to health services (Coster et al., 2000). Randomized trials suggest that electronic self-monitoring may have some clinical benefits (Mailloux et al., 1996; Siebenhofer et al., 2008; Menendez-Jandula et al., 2005). However, there are some conflicting findings (Coster et al., 2000).

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Finally, schemes that use telecommunications systems such as the internet or telephone lines to transfer or record monitoring information are often referred to as ‘telemonitoring’. Telemonitoring is not always strictly ‘self-monitoring’ as it may involve interaction between service users and health professionals (Biermann, Dietrich & Standl, 2000; Kruger et al., 2003). However, it is another way to support self-management although there is mixed evidence about the value of telemonitoring (de Lusignan et al., 2001). Notwithstanding, it is well received by patients and providers (De Clercq, Hasman & Wolffenbuttel, 2003; Cho et al., 2006). To summarize, the evidence suggests that not all mechanisms to support self-management have equal outcomes. While information provision and building technical skills are necessary, this is just one aspect of self-management support. Approaches have been found to have more sustainable impact on behavior, clinical outcomes and healthcare resource use when (Ahmad, Ellis, Krelle & Lawrie, 2014): • • •

People’s motivations and needs are recognized. The stage of change is taken into account. People are emotionally and psychologically supported.

Notwithstanding, the psychological concept of self-efficacy is a model of reciprocal causality. It emphasizes the individual’s role in its development and learning from the social environment. It also proposes that internal factors (cognition, emotion, and biology), behavioral patterns, and environmental events all influence one another. In this model, the behavior is, therefore, a function of internal and environmental factors, as well as being influenced by outcome expectations and perceived self-efficacy (Bandura, 1986). Thus, elucidating the interactions, social cognition as an underlying theory can be seen in the many self-management support interventions that focus on problem-solving skills and goal-setting. Moreover, an improvement in self-efficacy has been identified as an achievable and measurable outcome for self-management support interventions. Finally, telehealth improves self-management confidence and behavior while mobile solutions preeminently address the barriers to self-management through automation, analysis and decision support, the flip side of self-management support. Conclusively, the concepts of mobile health, self-management support, and self-efficacy could be apparently thought on a continuum with one pole representing mobile health and the other representing self-efficacy. In this context, self-management support is the nexus of mobile health and self-efficacy.

Issues, Controversies, Problems There are a number of barriers to self-management, self-management support, and self-efficacy. The Macmillan self-management study (Fenlon & Foster, 2009) identified the following number of obstacles to self-management: • • • • • • • 352

Lack of, or limited, support and help from health care professionals. Lack of information. Conflicting advice. Lack of financial information. Limited access to others’ experiences. Emotional barriers. Lacking focus.

 The Nexus of M-Health and Self-Efficacy

Moreover, it identified specific factors that affect the engagement in self-management support. Specifically, self-management programs are more available to those who are well educated and have higher self-efficacy limiting their effectiveness for all groups. Finally, information sources are also better utilized by those who are better educated. By the same token, Eastman and Marzillier (1984) sketched three main criticisms of Bandura’s SelfEfficacy Theory. The first was a lack of definition of self-efficacy while the second included methodological deficiencies that could cast doubt on the relationship between the experimental findings and self-efficacy. The third explained that Bandura’s conclusions were insufficiently evaluated, and more precise definitions and modification of assessment procedures were necessary. In regards to the conceptual problem, it was thought that efficacy expectations included the expectations of the outcome (Eastman & Marzillier, 1984). Bandura wanted to discern self-efficacy and outcomes, but other researchers found his statements misleading. Specifically, the belief that one can successfully execute the behavior to produce the outcomes was criticized severely. Moreover, Kazdin (1978) found that the outcome expectations and self-efficacy were interacting. In this context, while critics of Bandura’s self-efficacy agree that there is value in his experiments, they doubt that outcomes can be distinct. Bandura (1978) replied that the outcomes are conditional upon the behavior and that the critics were misreading the definition of efficacy. Moreover, the scale used in Bandura’s experiment studies was criticized for two main reasons. The first argued that the scale is not precise and the second criticized the ability only to select between 10 possible numbers in a 100-point probability scale. Specifically, criticism contended that there is no zero on the scale, and the scale also does not tolerate for numbers between the numbers listed on the scale. To summarize, criticism of self-efficacy revolves on the following: • • • •

High self-efficacy beliefs vary considerably and do not assure positive outcome expectations. People with high self-efficacy and high skills may lack the resources and equipment to perform. Basing one’s self-efficacy on results of previous tasks may be deceptive. Personal factors and distorted memories of past performance can distort self-efficacy.

SOLUTIONS AND RECOMMENDATIONS The use of mobile technology has the potential to improve self-management. Providing access to health information refers to a patient, caregiver, or provider being able to obtain patient-specific health information through a mobile device. This sharing of information may take place with: • •

A personal health record. A social network.

Alternatively, in the form of sending/collecting physiological and other health data. Personal Health Records refer to a group of technologies that help patients track their health care services, access health records and manage their health information. The patient controls the Personal Health Record and chooses whether to share the health information with family members, caregivers, and providers. Personal Health Records enable patients to store and share historical information about their diagnoses, medications, and hospitalizations.

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Current applications of Personal Health Records tend to target essential functions such as: • • • • •

Storage of a patient’s medical history. Access to vital health records. Support for diet changes and wellness activities. Assistance with medication management. Secure forum for patient-clinician communication.

Social networking and care coordination technologies support seniors and their care providers. These social networks utilize a variety of means to facilitate communication between patients including discussion groups, chat, messaging, email, video, and file-sharing. While currently more often web-based, these social networking and care coordination programs are becoming ever more accessible on mobile devices. Online social networking emerged as a way to connect peers independent of geography. Currently available M-Health social networking and care coordination technologies include Kaiser Permanente Texting using MobileStorm, mPro Appointment Reminders and Patient Diary Cards, Smile Reminder, Tyze, and Kinnexxus. Moreover, a high percentage of older adults are challenged by: • • • • •

Chronic illnesses. Improper medication use. Falls and injuries. Frailty. Limited access to their personal health information.

Chronic disease and medication management, safety monitoring, and improved access to personal health information provide significant opportunities for the application of M-Health technologies. The primary goals of fall detection technologies distinguish falls from daily activities and then contact authorities who can quickly assist the individual. In this context, fall detection M-Health systems can be active, passive or a combination. Active systems are devices that users must activate to obtain assistance, most commonly by pushing a button. Passive systems contain the use of sensors to monitor movement continuously and utilize specific algorithms and alert systems to inform of potential falls. Some passive systems provide a backup system where users can activate the device for assistance. A range of passive fall detection devices are based around an M-Health platform and make use of a variety of sensors, including: • • •

Motion and pressure sensors. Accelerometers. Gyroscopes.

Finally, algorithms are utilized to set thresholds for alert notification tailored to each older adult by monitoring patterns of movement and behavior. Location tracking technologies enable locating older adults. The majority of these techniques involve the older adult carrying the location tracking device or mobile enabled tracking device, like a cell phone. These technologies vary in range and accuracy of location due to selected tracking techniques, signal activation methods, and technology support systems.

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Currently, there are different types of tracking methods. GPS technology location systems utilize satellites to locate individuals, but they are limited in their coverage, as signals are often lost in areas of high-density. Some location tracking technologies require remote activation while others provide an automatic stream of the user’s location. Location information can also be available to providers and family caregivers or third party vendors and/or the authorities. Some location tracking devices require activation through the supplier, who then provides law enforcement personnel with location information of the older adult. Other tools provide algorithms with set alerts to notify providers, neighbors, family, and friends when the older adults leave a particular area. Finally, integration of multiple tracking techniques with mobile phones and other devices is becoming commonplace. As tracking techniques have resolution or accessibility limitations in particular fields, hardware platforms and network infrastructures that utilize, multiple methods may make attractive options for location tracking. The availability of such M-Health technology interventions for wellness is rapidly growing. Currently, available mobile health wellness technology interventions are categorized according to three primary wellness functions, fitness, nutrition, and quality of life. Furthermore, the number of M-Health interventions for medication adherence has been rapidly expanding in both variety and sophistication. These technologies can assist: • • • • •

Proper medication information. Patient education. Medication organization. Dispensing, dose reminders. Notification when doses are missed.

The primary functions that M-Health technologies can provide in the medication administration continuum are fill, remind, dispense, and report. M-Health technology interventions for these features can include: • • • •

SMS medication reminders. SMS, email, and phone calls to healthcare professionals when a dose is missed. Mobile pill dispensers as a connector to cell phones or standalone devices. A smartphone application providing medication instructions.

The pill phone, which was developed by Verizon in 2008, is a cell phone software system that has fill, remind, and report functionality. The software can be used on particular cell phones and operates on basic cell phone models or smartphones. Reminders can be created around what drugs to take at what times. Older adults can reply to the alert saying they took the medication, skipped it, or may ‘snooze’ the alert to receive it at a later time. In the same context, a number of diagnostic/monitoring devices can provide alert notifications to patients and caregivers via email, text, or a phone call. The systems will lock away the dispensed medication and alert up to four caregivers that a dose was missed. Dispensing and alert history is daily uploaded to a web support system allowing caregiver and clinician review.

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FUTURE RESEARCH DIRECTIONS The contemporary way of managing patients will quickly be obsolete as intelligent software steps in. Paper documentation will become uncommon, and mobile technology will become ubiquitous. Medical records will become profoundly detailed and personalized, as the acquisition of data becomes streamlined. Moreover, as medical records increase in number and reliability, datasets and analytics will start to play a role in diagnoses. Moreover, sensors will display their potential through mobile devices. They will be embedded in ubiquitous items and transform data collection and real-time analysis feedback into the new, normal industry standards. The integration of mobile devices into the lives of ordinary people through wearable technology will be a useful platform through which maximum impact can be achieved. Progressive methods of disease management will be continuously updated with the information provided by cloud data. It is obvious that mobile devices will operate on touchscreen platforms while innovations in human-computer interaction will drive the usefulness of M-Health. Overall, M-Health has the potential: • • • •

To improve the quality of care through self-management support. To reduce the strain on resources and healthcare workers. To redefine the roles of physicians, nurses, pharmacies, clinical research. To shift the point of care.

However, the continuum of mobile health, self-management support, and self-efficacy as well as the self-management support as the nexus of mobile health and self-efficacy should be thoroughly examined.

CONCLUSION Theoretically, the concepts of mobile health, self-management support, and self-efficacy could be thought on a continuum with one pole representing mobile health and the other self-efficacy. In this framework, self-management support is the nexus of mobile health and self-efficacy.

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KEY TERMS AND DEFINITIONS M-Health: It describes the use of a broad range of telecommunication and multimedia technologies within wireless care delivery design and can be broadly defined as the delivery of healthcare services via mobile communication devices. Patient Activation: It is the individual’s knowledge, skill, and confidence for managing his health and health care. Patient-Centered Care: It is more than a method of communication which focuses on patients’ preferences, experienced needs and values in decisions about care and treatment.

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Patient Empowerment: It features self-determining agents with some control over their health and health care and conceptualizes personal control and self-efficacy. Self-Care: It emerged from the concept of health promotion in the 1970s and describes what people do for themselves to establish and maintain their health, and prevent and deal with illness. Self-Efficacy: It is a person’s belief in his or her ability to complete a future task or solve a future problem. Self-Management: The taking of responsibility for one’s behavior and well-being. Self-Management Support: It is the assistance given to patients in order to encourage daily decisions that improve health-related behaviors and clinical outcomes.

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Yperzeele, L., Van Hooff, R. J., De Smedt, A., Valenzuela Espinoza, A., Van Dyck, R., Van de Casseye, R., & Brouns, R. et al. (2014). Feasibility of AmbulanCe-Based Telemedicine (FACT) Study: Safety, Feasibility and Reliability of Third Generation In-Ambulance Telemedicine. PLoS ONE, 9(10), e110043. doi:10.1371/journal.pone.0110043 PMID:25343246 Yuen, S., Yaoyuneyong, G., & Johnson, E. (2011). Augmented reality: An overview and five directions for AR in education. Journal of Educational Technology Development and Exchange, 4(1), 119–140. Yun, L., Boles, R. E., Haemer, M. A., Knierim, S., Dickinson, L. M., Mancinas, H., & Davidson, A. J. et al. (2015). A randomized, home-based, childhood obesity intervention delivered by patient navigations. BMC Public Health, 23(1), 506. doi:10.1186/s12889-015-1833-z PMID:26002612 Zapata, B. C., Fernandez-Aleman, J. L., Idri, A., & Toval, A. (2015). Empirical Studies on Usability of mHealth Apps: A Systematic Literature Review. Journal of Medical Systems, 39(2), 182. doi:10.1007/s10916-014-0182-2 PMID:25600193 Zerth, J., Besser, J., & Reichert, A. (2012). Effectiveness and efficiency for ambulatory care assisted by mobile technological devices. Biomed Tech (Berl), 57(1). Zhenwei Qiang, C., Yamamichi, M., Hausman, V., Miller, R., & Altman, D. (2012). Mobile Applications for the Health Sector. Washington, DC: ICT Sector Unit World Bank. Retrieved March 26, 2015, from http://siteresources.worldbank. org/INFORMATIONANDCOMMUNICATIONANDTECHNOLOGIES/Resources/mHealth_report_(Apr_2012).pdf Zhu, R., Cao, Z., & Que, R. (2014). Integration of Micro-sensors with Mobile Devices for Monitoring Vital Signs of Sleep Apnea Patients. In Proceedings of the 9th IEEE International Conference on Nano/Micro Engineered and Molecular Systems. Waikiki Beach, HI: IEEE Publishers. doi:10.1109/NEMS.2014.6908850 ZooBurst. (2015). About zooburst. Retrieved January 11, 2015, from http://www.healthline.com/health-slideshow/topiphone-android-apps-bipolar-disorder#1 Zurovac, D., Larson, B. A., Sudoi, R. K., & Snow, R. W. (2012). Costs and cost-effectiveness of a mobile phone textmessage reminder programmes to improve health workers’ adherence to malaria guidelines in Kenya. PLoS ONE, 7(12), e52045. doi:10.1371/journal.pone.0052045 PMID:23272206

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About the Contributors

Anastasius Moumtzoglou is a Former Executive Board Member of the European Society for Quality in Health Care, President of the Hellenic Society for Quality & Safety in Health Care, holds B.A in Economics, MA in Health Services Management, MA in Macroeconomics, Ph.D. in Economics. He teaches the module of quality at the graduate and postgraduate level. He has also written three Greek books, which are the only ones in the Greek references. The first deals with “Marketing in Health Care”, the following with “Quality in Health Care” and the third with “Quality and Patient Safety in Health Care”. He has edited the books “E-Health Systems Quality and Reliability: Models and Standards”, “Ehealth Technologies & Improving Patient Safety: Exploring Organizational Factors”, “Cloud Computing Applications for Quality Health Care Delivery”, and “Laboratory Management Information Systems: Current Requirements and Future Perspectives”. He is the Editor-in-Chief of the “Advances in Healthcare Information Systems and Administration” book series and the International Journal of Reliable & Quality in Healthcare (IJRQEH). He has also served as the scientific coordinator in research programs in Greece and participated as a researcher in European research programs. In 2004, he was declared “Person of Quality”, with respect to Greece. *** Hessah Alsalamah is an Assistant Professor at the department of Information Systems, College of Computer and Information Sciences, King Saud University, Saudi Arabia. She currently holds the position of the Vice-Dean of the College of Computer and Information Sciences and the Vice-Chair of the Information Systems Department. She obtained a PhD and an MSc degrees in Computer Science from the School of Computer Science & informatics at Cardiff University and a BSc in Computer application from the College of Computer and Information Sciences at King Saud University. Hessah’s research interest is in health informatics, business process management and workflow technology. Shada Alsalamah is an Assistant Professor at the College of Computer and Information Science (CCIS) at King Saud University (KSU), Riyadh, Saudi Arabia. In 2014, she completed a PhD degree in healthcare information systems’ security and privacy, and received an MSc degree in 2010 in Strategic Information Systems with Information Assurance from the School of Computer Science & Informatics (COMSC) at Cardiff University, Cardiff, UK. Her research interests lie primarily in the area of information security in a multi-agent collaboration such as virtual organisations and patient-centred care in e-Health.

 

About the Contributors

Hakan Altınpulluk is a Research Assistant in Distance Education at the College of Open Education of Anadolu University, Turkey. He undertook undergraduate studies in the field of Computer Education and Instructional Technologies (CEIT) between the years of 2005-2009 at Anadolu University. Also, he is currently a doctoral student in the Department of Distance Education at Anadolu University since 2010. Hakan Altınpulluk continues to work in the field of Augmented Reality, Massive Open Online Courses, Learning Management Systems, Open Educational Resources, Personal Learning Environments, Mobile Learning and E-Learning Systems. Stavros Archondakis (MD, PhD) is certified pathologist, director of Cytopathology Department of 401 Athens Army Hospital. He has graduated from Thessaloniki Medical School and National Military Medical School in 1996. Since 2007, he is appointed assessor of the Hellenic Accreditation System (ESYD) for the accreditation of medical laboratories according to ISO 15189:2012. He speaks English and French. He is member of Hellenic Society of Clinical Cytopathology, Society of Medical Studies and Society for Quality Management in the Health Sector. He is the author of 29 medical books, some of them awarded by the Greek Anticancer Society. He has participated with posters and oral presentations in more than 200 congresses and seminars, he has authored more than 25 articles in Greek and foreign medical journals. He possesses more than 800 hours of teaching experience in medical and paramedical schools. Chinmay Chakraborty received the B.Tech. degree in Electronics and Communication Engineering from West Bengal University of Technology (WBUT), India in 2006. From 2006 to 2007, he was with the Dept. of Industrial Engineering and Management, Indian Institute of Technology, Kharagpur (IIT Kharagpur), India as a Research Consultant. He received the M.S. degree in Telecommunication Engineering from G. S. Sanyal School of Telecommunications, IIT Kharagpur, India in 2010. He is worked at ICFAI University, Agartala, India as Senior Lecturer. Currently He is working at Birla Institute of Technology, Mesra, India as Assistant Professor. His research interests include Wireless Body Sensor Networks, Telemedicine, Medical Imaging and Communication Networks. Vassilia Costarides has graduated from National Technical University of Athens, from the school of Mining and Metallurgical Engineering with a specialization in Materials Engineering. She proceeded her studies in Biomedical Engineering and has worked in the medical device industry for 6 years as a technical specialist. Since 2013 she has been participating in projects regarding public health, medical device nomenclature, health technology assessment and medical device technical specifications. Among her interests is also quality in health care and she is an ISO9001:2008 and ISO13485:2003 auditor. She is currently a PhD candidate in Biomedical Engineering in the National Technical University of Athens and a member of the Biomedical Engineering Lab and Applied Informatics in m-Health research team. She is also a member of the Technical Chamber of Greece. Gulsun Eby is a professor in Distance Education at the College of Open Education of Anadolu University. Dr. Eby undertook graduate studies at Anadolu University, Turkey (MA. Educational Technology) and the University of Cincinnati, USA (Ed.D. Curriculum & Instruction), and also has worked a post-doctoral fellow at the College of Education at New Mexico State University, USA (2001-2002). Dr. Eby earned her B.S. degree in Computer Engineering from the College of Informatics Technologies and Engineering of Hoca Ahmet Yesevi International Turk-Kazakhstani University in the year 2012-2013. 425

About the Contributors

Also, she is currently a graduate student in the Department of Computer and Instructional Technologies of Anadolu University. Dr. Eby has thirty years of experience in focusing on the egalitarian and ecological aspects of distance education; finding new answers, viewpoints and explanations to online communication problems through critical pedagogy; and improving learner critical and creative thinking skills through project-based online learning, universal design principles and emerging information and communication technologies. She continues to manage and provide pedagogical support for distance learning programs. Ioannis Fezoulidis graduated from the Medical School of Vienna University in 1979; in 1988 he received specialization in Radiology and a Ph.D. degree from the same University. Now he is Professor of Radiology and Director of the Department of Radiology at the Medical School of the University of Thessalia (University Hospital). He is member of three Greek and three international Radiological Societies. He has contributed chapters to three radiological books. Principal investigator in “THALIS” research program (Smart magnetic nanoparticles probes for magnetic resonance imaging), 177 papers in peer-reviewed journals and more than 60 presentations or invited lectures in international scientific congresses. He was programme committee member in more than 25 international and Greek scientific conferences and course moderator in more than 37 international and Greek congresses. Dimitrios I. Fotiadis has received the Diploma degree in chemical engineering from the National Technical University of Athens, Athens, Greece, in 1985, and the Ph.D. degree in chemical engineering and materials science from the University of Minnesota, Minneapolis, in 1990. He is currently a Professor of Biomedical Engineering in the Department of Materials Science and Engineering, University of Ioannina, Ioannina, Greece, and the Director of the Unit of Medical Technology and Intelligent Information Systems. He is an Affiliated Member of the Foundation for Research and Technology Hellas, Institute of Molecular Biology and Biotechnology, Dept. of Biomedical Research. He has coordinated and participated in several R&D funded projects. He is the author or coauthor of more than 220 papers in scientific journals, 380 papers in peer-reviewed conference proceedings, and more than 40 chapters in books. He is the editor or coeditor of 18 books. He is a senior member of IEEE, member of IEEE Technical Committee of information Technology in Healthcare, Chairman of the IEEE EMBS Greek Chapter, Associate Editor in the journals IEEE Journal of Biomedical Health Informatics and Computers in Biology and Medicine and Receiving Editor in the Biomedical Signal Processing and Control Journal. He was the founder of the Science and Technology Park in Ioannina, Greece. His research interests include multiscale modeling of human tissues and organs, processing of heterogeneous medical and genetic data for diagnosis and prognosis, intelligent systems for patient monitoring and treatment, wearable monitoring platforms and bioinformatics. Aggelos Georgoulas holds a diploma (M.Eng.) in Electrical Engineering and Computer Science and a Ph.D. degree in Biomedical Engineering, both from the National Technical University of Athens (NTUA). He has been working as Scientific Associate and Research Fellow at the Institute of Communication and Computer Systems (ICCS) and the Research Committee of NTUA, participating in numerous European and National funded R&D programs in the field of e-health, medical informatics and Information and Communications Technologies. He is currently offering consulting services as a freelance IT Consultant/ Expert for the preparation and implementation of e-health and e-government projects. His related areas of expertise include Medical informatics, Telemedicine and e-health Applications, PKI technology in 426

About the Contributors

healthcare networks, Smart Card technology in healthcare and Electronic Healthcare Record. Since 2010 he is working as an Assistant Professor for the Technological Institute of Athens (TEI of Athens), teaching a number of undergraduate courses both in the Faculty of Health and Caring Professions and the Faculty of Technological Applications. He is a Certified Engineer by the Technical Chamber of Greece (1999) and member of the IEEE and the Greek Society of Biomedical Technology. Kostas Giokas has since 1994, had a long career in consulting in several companies in the UK and Greece. He graduated in 2000 from the University of Westminster in London, UK obtaining a BSc in Business Information Technology. He received his MBA from the UK Open University in 2002. He then joined the Biomedical Engineering Laboratory of the National Technical University of Athens where he worked as a Researcher in Biomedical Engineering focusing on large network analysis, design and deployment while consulting on National and European deployment projects. At the same time he is part of the R&D team that undertakes EU research projects in BEL. He has been involved in research proposals leading more than 35 of them. He has been team member/leader in more than 20 European and National research projects in the field of health informatics and has published 55 scientific papers and 6 chapters. He is currently the leader of the Applied Informatics in mHealth (AiM) Research Team. Alex Gray is Professor of Advanced Information Systems at Cardiff University. His Research Interests is in: - Metadata and its role in integration and interoperation of heterogeneous distributed information systems: the enrichment of metadata with quality measures and knowledge from knowledge bases and ontologies to improve the linking of data; reverse engineering of legacy systems to find conceptual models and business rules; - The design and architecture of distributed systems to support distributed concurrent working over networks in the areas of bioinformatics, concurrent engineering and health informatics - this includes the role of constraints, and architecture issues such as scalability and autonomy; -The role of metadata in constructing the information and knowledge layers of the Information GRID. Bharat Gupta received B.E and M.Tech degrees in Electronics Engineering from Govt. Engineering College of Ujjain, Madhya Pradesh, India and Birla Institute of Technology in 2000 and 2003 respectively. The Ph.D. degree he received in the area of Wireless Communication in Health care Monitoring from the Department of Microelectronics and Telecommunication at Univ. of Rome, Tor Vergata, Italy, in 2011. He has been teaching at the Birla Institute of Technology, Mesra, since August 2004 where he is presently an Associate Professor of the Electronics and Communication Engineering. His teaching and research interests include FM UWB Communication, Energy Efficient MAC for WBAN, Vital Sign Monitoring and WSN. He has published more than 20 technical papers in various journals and. He is a senior member of IEEE and member of GTTI. Jeremy Hilton lectures, conducts research and consults in systems thinking methods and tools, applied in the Defence and other industrial, government and academic sectors. The focus is on enabling effective organisations and decision-making, decision support, resilience and Cyber/Information security. Director of the Socio-Technical and Cyber Complex Systems Lab; a multi-disciplinary research facility at Shrivenham. Currently involved in a number of areas of research covering organisations, systems and information security/cyber defence including: resilient enterprises and systems; systems methods related to enterprise systems engineering, programme and project management and cyber defence; contemporary risk modelling and analysis methods for interconnected systems; and dependency modelling. 427

About the Contributors

Petre Iltchev received Ph.D. in Economics in Sofia, Bulgaria. In the years 1999 – 2009 hi worked in Poland as leading analyst at ACP PHARMA S.A. wholesale pharmaceutical company as responsible for the data warehouse design and implementation and reports and analyses based on a data warehouse supporting decision-making in the fields of strategic management, sales forecasting, supply chain management. From 2009 hi work as a lecturer in the Medical University of Lodz. Petros Karakitsos graduated from the Medical School of Athens University in 1982; in 1988 he received specialization in Cytopathology and a Ph.D. degree from the same University Now he is Professor of Cytopathology and Director of the Department of Cytopathology at the Medical School of the University of Athens (“Attikon” University Hospital). He is responsible for the specialized for Cytopathology eLearning platform of the Department. He is member of four Greek and four international Scientific Societies. He has contributed chapters to ten medical books and two educational CDs. Principal investigator in 22 research programs (artificial intelligence in pathology, cervical screening, molecular pathways in colon carcinogenesis, implication of molecular markers in HPV-related oncogenesis, ThinPrep cytology, e-Learning and e-Health) having yielded, up to now, 156 papers in peer-reviewed journals and more than 200 presentations or invited lectures in international scientific congresses, with 12 scientific awards. Panagiotis Katrakazas is a Ph.D. Candidate in the Biomedical Engineering Laboratory at the National Technical University of Athens, since November 2014. He holds a diploma in Electrical and Computer Engineering (National Technical University of Athens). He is also appointed IT Associate in the Department of Transportation Planning and Engineering of the School of Civil Engineering of the National Technical University of Athens. He prepares and teaches the undergraduate course “Biomedical Technology Laboratory”. His scientific specialization and interests include Acoustics, Noise Control, Medical Information Systems, Data Mining and Biomedical Technology. Yiannis Koumpouros, Lecturer in the Technological Educational Institute of Athens, Department of Informatics. Specialized in health informatics, e-health, telemedicine, and strategic management. Expert for the European Commission in the e-health sector, and evaluator of numerous research and development projects. Senior manager in business operations with wide expertise in design and implementation of strategic, conceptual and complex changes in an international framework. Significant experience in introducing organizational, process and IT solutions for business performance improvement. Top-level managerial experience in private and public healthcare sector (as chairman of hospitals). Teaching in several universities for almost 10 years. Project and R&D manager in IT fields with European experience. Chairman of several Permanent Committee of Experts (Ministry of Development, General Secretariat of Trade), for issues related to Electronic Scientific Equipments and Medical Equipments. Regular member of the Consultative Technical Council (Ministry of Internal Public Administration and Decentralisation) for subjects on new technologies and IT in the wider public administration, etc. Dimitris Koutsouris was born in Serres, Greece in 1955. He received his Diploma in Electrical Engineering in 1978 (Greece), DEA in Biomechanics in 1979 (France), Doctorat in Genie Biologie Medicale (France), Doctorat d’ Etat in Biomedical Engineering 1984 (France). Since 1986 he was research associate on the USC (Los Angeles), Renè Dèscartes (Paris) and Assoc. Professor at the Dept. of Electrical & Computers Engineering of National Technical University of Athens. He is currently Professor and 428

About the Contributors

Head of the Biomedical Engineering Laboratory. He has published over 150 research articles and book chapters and more than 350 peer reviewed conference communications. He has been the former elected president of the Hellenic Society of Biomedical Technology, HL7 Hellas and Chairman at the School of Electrical and Computer Engineering. Prof. D. Koutsouris has been principal investigator in more than 100 European and National Research programs, especially in the field of Telematics and Informatics in Healthcare. His work has received more than 1800 citations. Michal Marczak, in the period 1971 - 1976 as part of improving research techniques workshop and mathematical background, worked on mathematical modeling in mathematical engineering. In 1983 in the Institute of Fundamental Technological Research in Warsaw he defended his doctoral dissertation. Then his interest evolved into modeling system security, taking into account the human factor and human error (including socio-economic systems, social engineering and anthropo) and risk management. Part of the study involved safety and risk management in transport systems and Road Traffic, i.e. the category of socio-economic and socio-technical systems and organizations operating systems. Subsequent research has been aimed at managing the risks of sickness and loss of health in the health care system. This is done with the main emphasis on the specifics of which there is a risk of the systems with respect to the highest level of significance, the “human factor” and errors of human activities. For methodological reasons, but the utilitarian (implementation) has proved to be reasonable generalization models to the level of risk management. Currently, his research interests focus in particular on: risk management, management in organizations on the example of the health care system, mathematical modeling of functional and management processes. Approaches to risk management of adverse events (method black spots) developed by his team have been implemented in five hospitals in Poland. Niki Margari (MD, Ph.D.) is a consultant Cytopathologist at the Department of Cytopathology at the University of Athens Medical School (“Attikon” University Hospital). She was lecturer in graduate and post graduate courses related to cytopathology. She is member of the Hellenic Society of Clinical Cytology. She has participations in numerous Greek research programs having yielded 14 publications in international peer reviewed medical journals, more than 120 presentations and lectures in Greek and international scientific congresses, with one scientific award and was in the organisational committee of two Greek conferences. She has participated in the development of a specialized for Cytopathology eLearning platform and contributed to one medical book related to endometrial cytopathology. She is fluent Greek, English, French and Hungarian speaker. Bibiana Metelmann, M.D., was born 1987, graduated from medical school at Greifswald University, Germany in December 2012 and started working as a resident physician at the Department of Anesthesiology and Intensive Care Medicine at Greifswald University. Besides working at the intensive care unit and in the operating theatre, she is a researcher in the FP7-EU-funded LiveCity Project („Live Video-to-Video Supporting Interactive City Infrastructure“). Her main research area is telemedicine in emergency medicine. Camilla Metelmann, M.D., was born 1987 and attended medical school in Greifswald, Germany and joined the Department of Anesthesiology and Intensive Care Medicine, Greifswald University Medicine as a resident physician in 2013. In addition to clinical rotations in the operating theatre and the intensive care unit, she got involved in an FP7-EU-funded collaborative project dealing with telemedicine aspects 429

About the Contributors

of emergency medicine, which has evolved into a successful contribution to the LiveCity consortium. She is currently pursuing an integrated physician-scientist career pathway. Maria Nasioutziki is a certified cytopathologist director of the accredited according to ISO 15189:2012 Molecular Clinical Cytopathology Diagnostic Laboratory of 2nd Obstetrics & Gynaecology Department Medical School (AUTH) at Hippokration Hospital in Thessaloniki. She graduated from Thessaloniki Medical School in 1982. She speaks English and French. She has been Vice President, President and General Secretary of the Hellenic Society of Clinical Cytology since 2007 and a member of the Hellenic Society for Quality & Safety in Health Care. She has authored more than 23 publications in Greek and foreign medical journals and more than 3 chapters in medical books. She has also participated in more than 125 posters or oral presentations in Greek and International Congresses, 7 of them awarded. She has participated as a speaker in more than 36 and as president in more than 37 round tables in congresses. She possesses many hours of teaching experience in medical school and in training courses for the uncertified doctors. She has also participated as a member, Vice President or President of the Organizing-Scientific Committees in 23 Greek or European Congresses. Abraham Pouliakis is physicist, holds an M.Sc. in electronics and radio-communications, and a Ph.D. degree from the Medical School (University of Athens) related to the application of neural networks in cytopathology diagnosis. Since 1993 he has participated in numerous National, European and European Space Agency Research projects related to information and communication technology, artificial intelligence applied in medicine, collaborative systems, e-learning and e-health. Currently he is senior researcher and responsible for quality control and assurance in the Department of Cytopathology, University of Athens, School of Medicine. He has 34 publications in international peer reviewed scientific magazines, more than 90 presentations in international and Greek conferences and is co-author of five books chapters. His research interests are related to information and communication technology, image/ signal processing and artificial intelligence including their applications in the fields of industry and health. Assim Sagahyroon received the M.Sc. degree in Electrical Engineering from Northwestern University, Evanston, IL, USA, and the Ph.D. degree from the University of Arizona, Tucson, AZ, USA. From 1993 to 1999, he has been a member of the Department of Computer Science and Engineering at Northern Arizona University, and then joined the Department of Math and Computer Science, California State University. In 2003, he joined the Department of Computer Science and Engineering at the American University of Sharjah where he is currently a Professor and Head of the Department. He was a technical reviewer for the National Science Foundation and many conferences and journals. In industry he worked with Zhone Technologies and briefly with Lucent in California. He has many publications in international conferences and journals. His research interests include innovative applications of emerging technology in the medical field, power consumption and testing of digital systems, hardware design, FPGAs based designs, and computer architecture. Andrzej Sliwczynski received Ph. D. in the Health Sciences at the Faculty of Health Sciences, Medical University of Lodz, Poland. The subject of the Ph.D. thesis was economic efficiency in the treatment programs financed by public funds (National Health Fund (NHF)). Hi, is an economist - a graduate of the Faculty of Management at the University of Lodz. In the years 1987-2005, he held managerial positions at the state-owned pharmaceutical wholesale company “Cefarm” in Lodz. Since 2005, he is working at 430

About the Contributors

the Headquarters of the National Health Fund as Head of drug programs in the Department of the NHF Mazowiecki. Since 2009 is the Deputy Director of the Department of Drug in the National Health Fund and, since May 2015, Director of the Department of Analysis and Strategy in the Headquarters of the National Health Fund. In 2012, he completed postgraduate studies in informatics, databases. Author and co-author of approx. 40 articles published in national and international journals in the field of medicine, focusing on the financing of the health system, epidemiology, pharmacoeconomics. Piotr Syznkiewicz is a graduate of the University of Gdansk (Faculty of Earth Sciences) and MBA Program at the University of Strathclyde. Manager and entrepreneur. Since 1998 chairman of the board at Prometriq Academy of Management Ltd. - a consulting company specializing in the field of Business Process Management, Balanced Scorecard and Business Intelligence. Author of papers and publications, including the textbook “The Balanced Scorecard - a guide to implement” (2007) and „Business Process Controlling in 7 Steps” (2008). Since 2012 involved in projects in the health sector. Co-author of the concept of medical controlling and IT system supporting management decisions in hospitals based on monitoring clinical and accounting data. Guest lecturer at medical schools in the subject of risk management, accounting, controlling, strategic management, process modelling. Research interests: defining KPIs in health care for the needs of quality management, risk management, streamlining of hospital processes; use of e-learning and education simulators in the process of continuous professional development; integrated care and telemedicine. Ioannis Tamposis is software engineering, holds an B.Sc. in Computing. Since 2005 he has been working in the fields of information systems analysis and design, application development, IT Project planning and web design. He has also been responsible for the development of programs, such as payroll and pension management systems, budget and financial management systems Fiscal Audits and web portals. Last years, his focus area is the design and implementation of advanced healthcare IT solutions. He has 2 publications in international peer reviewed scientific magazines, more than 10 presentations in international and Greek conferences. His research interests include Software Engineering, Web Services, Web Engineering, Web Usage Mining, Business Systems, information and communication technology, Medical Industry Standards (HL7, DICOM and IHE), Web and Mobile Information Systems including their applications in the fields of industry and health. Eleftherios Vavoulidis is a certified molecular biologist and quality manager of the Molecular Clinical Cytopathology Diagnostic Laboratory of 2nd Obstetrics & Gynaecology Department of Aristotle University of Thessaloniki (AUTH) located at Hippokration General Hospital in Thessaloniki. He obtained his Bachelor’s Degree from the Department of Molecular Biology & Genetics of Democritus University of Thrace in 2009. In 2011, he completed his Master’s Degree in Nanosciences & Nanotechnologies at the Physics Department of AUTH. His BSc and MSc research projects were published in International Scientific Journals. During his studies, he was awarded 3 scholarships for excellent academic performance from the State Scholarship Foundation. He speaks fluently English. He is a member of the Hellenic Society for Quality & Safety in Health Care. He has participated with posters in Greek and International Conferences. He possesses more than 5 years of Laboratory Medicine experience, having worked for diagnostic laboratories in both academic and private sector.

431

About the Contributors

Elpis-Athina Vlachopapadopoulou received the MD degree from the University of Athens, Greece with “magna cum laude” in 1986. Following completion of Pediatric Residency at St. Luke’s/ Roosevelt Hospital Center of the Columbia College of Physicians and Surgeons campus (1990) she proceeded to a Pediatric Endocrinology Fellowship at New York Hospital- Cornell Medical Center in NYC (1990-94). She became Board Certified in both Pediatrics 1990 and Pediatric Endocrinolgy, 1997. She returned to Athens, Greece in 1994, and she is working in Children’s Hospital “P. & A. Kyriakou”, Dept. of Growth and Development-Endocrinology since 1997, currently as Director. She holds Greek Boards in Pediatrics and also Endocrinology. Dr Vlachopapadopoulou is a member of the Endocrine Society since 1994, of the European Society of Pediatric Endocrinology since 1997 and of the European Society of Endocrinology since 2006. She is actively participating in International meetings with more than 100 presentations, as well as, several in Greek symposia and meetings. Her main research interests focus on growth disorders, growth hormone deficiency, precocious puberty, obesity and long-term endocrine sequelae following treatment for childhood malignancies and bone marrow transplantation. She has authored 30 publications in peer-reviewed international and Greek journals, as well as book chapters. She has been a reviewer for journals, ESPE and ECOG meetings, as well as, national journals and meetings. She is the principal investigator in three Phase –II international protocols and co-investigator in four international observational studies. She is very active in the field of childhood obesity. Since 2003, has been a member of the European Childhood Obesity Group. She follows and offers treatment plan to more than 600 /year overweight –obese children. She is leading investigator for the Hellenic National ActionPlan for the Assessment Prevention and Treatment for Childhood Obesity in Greece with important research and field interventions among children and adolescents relating to obesity in Greece. C. Peter Waegemann was CEO of Medical Records Institute, a Boston-based organization involved in applied research and functioning as an educational clearinghouse, as well as Executive Director of Center for Cell-Phone Applications in HealthCare (C-PAHC). Since the 1980s, he has been a visionary and promoter of electronic medical record systems (EMRs). Twenty-five years ago, he started the annual conference Toward an Electronic Patient Record (TEPR) that drew several thousand attendees. He is internationally known as one of the top experts in healthcare informatics, has published both in the US and in Germany, and is a sought-after speaker on EHRs, eHealth, and mHealth. He has special expertise in electronic patient record systems, standards, networking, telemedicine, and the creation of the national information infrastructure. Waegemann has testified to US Congressional committees. In 2007, Waegemann was cited as one of 20 outstanding people who make healthcare better (HealthLeaders). Waegemann is currently based in Berlin, Germany Professional positions Present- Consultant, Speaker, Author Principal, Waegemann Associates LLC, Malden/Boston, USA 2009-2011 Vice President, mHealth Initiative, Boston, MA, USA 2007-2009 Executive Director, C-PAHC (Center for cell-Phone Applications in HealthCare) 1980-2009 CEO Medical Records Institute, Cambridge/Newton/Boston, MA USA 1975-1980 Vice President, Ames Color File, Inc., Somervillle, MA USA 1970-1975 Managing Director, Herbert Zippel (UK) Ltd, London, UK Author of hundreds of publications, including • Knowledge Capital in the Digital Society, published by CreateSpace (Amazon) April/May 2012 • The Future of mHealth – Chapter in the book “mHealth: From Smart Phones to Smart Systems published by the Health Information Management Systems Society (HIMSS) 2012 • mHealth, New opportunities for healthcare improvement, Asian Hospital and Healthcare Management, Issue 23, June 2011 • Internationale Entwicklungen in HIT und medizinischer Dokumentation, DVMD, Germany 2011 • Patienten- und Arztkommunikation mit der Elektronischen Patientenakte in Personalisierte Medizin & 432

About the Contributors

Informationstechnologie, (Dresden: Health Academy 14, 2011) • Communication-based Medicine and m-Health • mHealth – The Next Generation of Telemedicine? Published by Telemedicine and eHealth, 2010 • Handbook of Record Storage and Space Management, Greenwood Press, 1987 • Strategy for Information and Image Management for the 1990s, Optical Disk Institute, 1991 • Handbook of Optical Memory Systems, Medical Records Institute (4th Edition) • Editor of 18 proceedings books; 100+ published articles: • Past Editor-in-Chief, “Health IT Advisory Report” Leadership (past and present) • 2008-2012: Board Member, Global Patient Identifiers, Inc. • 2004-2005 Chair, MoHCA (Mobile Healthcare Alliance) • 2004: Board Member of SNOMED INTERNATIONAL • 2001-2005 Chair, ASTM International, Chair of Committee E31 • 2000-2001 Chair of ASTM Subcommittee E 31.26 on Personal (Consumer) Health Records • 1995-2002, Chairman, Centre for the Advancement of Electronic Health Records, Ltd. (London, England) • 1995-2001 Chair, American National Standards Institute’s Healthcare Informatics Standards Board (ANSI HISB), the predecessor of HITSP • 1997 G8 National Coordinator for the United States: GIS Theme Two (Health Care, Intelligent Transportation Systems, Education, and other services to the public) • 1995- Sept.1998 Co-chair of CORBAmed (Healthcare Domain Task Force of the world’s largest software consortium on interoperability) – • 1994-1995 Chair of Subcommittee ASTM E31.20 on Authentication of Healthcare Information (Electronic Signatures) • 1991-1995 Chair, International Patient Card Standards Council • Past Member of the Board of Advisors, Illinois Institute of Technology • Past Member of the Chairman’s Advisory Committee for Business Planning, ISO TC 215 • Former Member of the Executive Committee American National Standards Institute’s Healthcare Informatics Standards Board (ANSI HISB, after 8 years of being Chair – HISB was the predecessor of HITSP) • Past Chair, TEPR Conference • Past Chair, US Technical Advisory Group (TAG) for ISO Technical Committee TC 215 for Health Informatics • Past Chair, Task Group on Consumer Interests, ISO TC 215 on Health Informatics • Past Chair, Task Group on Web Activity Standards, ISO TC 215 on Health Informatics • Past Chair, ASTM E31 Standards Committee on Health Informatics • Past Chair, Subcommittee ASTM E31.26 on Personal (Consumer) Health Records • CoChair of Consensus Group on Information Capture and Report Generation • Past Chair, HISB Taskforce on USHIK • Past Chair, National Conference on Health Information Capture Awards and recognitions 2007 Cited by HealthLeaders as “one of the 20 outstanding people who make healthcare better” 2003 Award by International Medical Informatics Conference, Hong Kong 2001 Award by American National Standards Institute for “Appreciation of work in ANSI HISB” 2000 Distinguished Service Award by AAMT (American Association for Medical Transcription) 1999 Elmer Gabrieli Award from ASTM for “Contributions to healthcare standards work” 1995 American Society of Engineers (ASE) for “Outstanding work in standardization in health care.” 1994 Recognition Award by CPRI (Computer-based Patient Record Institute). Stelios Zimeras holds a BSc. (Hons) on Statistics and Insurance Sciences from University of Piraeus (5fth in the rank) and Ph.D. on Statistics from the University of Leeds, U.K. He had received a full scholarship (fees and maintenance) during his research studies (1993-1997) from the University of Leeds. Since 2008, he is a full time staff member (Assistant Prof) on statistics and probabilities at the Department of Statistics and Financial-Actuarial Mathematics, University of the Aegean, Samos, Greece. At the University he teaches Statistical Packages (SPSS), Categorical Data Analysis, General Statistical Models, and Data Analysis. He also teaches Categorical Data Analysis, Data Analysis, and Statistical Simulations in postgraduate courses at the University of Aegean, Crisis Management in postgraduate courses at the University of Piraeus Department of Digital Systems, and Spatial Statistics models in post433

About the Contributors

graduate courses at the University of Aegean, Environmental Department. He had worked as post-doc at University of Leeds, Department of Electrical Engineering (funded by EPSRC), at IRISA/INRIA-VISTA PROJECT institute, France (funded by EUMETSAT-ESA) and at MedCom GmbH, Γερμανία (funded by Marie-Curie grants). His published material and presentations are on a number of topics of Statistical Simulations, Image analysis, Medical Image analysis, Spatial Statistics, Telemedicine, and Statistical Modelling with applications in biology, ecology, and medicine, statistical epidemiology, and Bayesian statistics. He has been a member of a number of professional and scientific societies and associations; while he is a reviewer for a number of Journals and conferences.

434

435

Index

3-D Pop-Up Book 149, 157-164, 170

A accreditation 252, 262, 264, 268, 275, 276, 283, 285 adherence 32, 37, 38, 46, 64, 72-74, 77, 115, 131, 135-139, 142, 352, 357, 358, 362, 363, 366 application development cycle 172 Apps 1-23, 26-34, 38-46, 49, 53, 56-63, 66-68, 99, 110, 115, 117, 127, 130, 137-141, 161, 168, 169, 183, 194-198, 201, 202, 209-211, 238, 256-259 augmented reality 149, 150, 158-170

B barriers to implementation 172 Behavioral Intervention Technologies (BITs) 39, 50 behavior change 70, 135, 145, 146, 321-335, 338342, 345, 359 behavior interventions 128, 134, 141, 147, 338 behaviour 49, 53, 65, 72, 82, 143, 337, 342, 358, 359, 362 Biosensors 32, 37, 40, 50, 134, 147, 230 bipolar disorder 30, 38, 46, 48, 149, 150, 157-171 blood pressure 6, 8, 11, 17, 28, 31, 32, 36, 37, 50, 62, 63, 72-77, 81-90, 118, 130, 133, 138, 202, 216 Body Area Network (BAN) 50

C Caldicott Guardian 300, 303, 306-312, 319, 320 childhood obesity 128-132, 142, 144, 147 Chronic Care Management (CCM) 50 chronic disease 36, 45, 52-55, 66, 68, 71, 75, 79, 168, 172, 176, 180-183, 187, 192, 334, 347, 350, 356, 359, 360 chronic wound 215, 216, 219, 220, 225, 236, 238, 241

CIRS-G 58, 67, 71 clinical protocol 58, 71 cloud 13, 21, 22, 26, 33, 34, 37, 42, 47, 49, 53, 56, 68, 92-97, 112-115, 132, 133, 138, 141, 143, 147, 174, 175, 178, 179, 190, 196, 232, 243246, 253, 254, 258-260, 283, 285, 358 Cloud Computing 21, 22, 26, 33, 42, 47, 49, 92, 93, 112-115, 147, 174, 175, 179, 190, 243, 244, 253, 254, 258-260, 283, 285 collaboration 53, 54, 66, 74, 109, 141, 160, 166, 183, 248, 249, 252, 254, 265, 270, 280, 282, 300-303, 308, 315-318, 346 Comorbidity 49, 58, 67, 69, 75, 129, 130, 147, 167, 192, 300-303, 312, 317, 320 compliance 9, 40, 72-74, 86, 90, 95, 114, 131, 135, 138, 139, 146, 154, 175, 265, 267, 270, 275, 352, 358-366 computer virus 13, 252, 286, 287, 290, 291, 294, 297, 298 Conditions for Implementation 172 Costs of Asthma 172 cytopathology 106, 242-272, 277, 280-285

D Digital Imaging and Communications in Medicine (DICOM): It is a standard for handling 115 Disease-Centred Healthcare 300, 320 Disease-Centred Healthcare Delivery Model 320 Disease Management Programme 71 distributed systems 63, 91 Downloaders 299

E edutainment 150, 160, 171 E-Health 3, 20-23, 39, 44-50, 56, 69, 71, 90, 124129, 143-147, 151, 168, 174, 189, 196, 198, 212, 213, 232, 236, 240, 255, 261-264, 283, 286, 287, 302, 319, 328, 337-341, 366

Index

E-Learning 263 Electronic Health Record (EHR) 1, 9, 14, 19, 34, 46, 59, 60, 64, 65, 69, 70, 232, 269, 319 Electronic Medical Record (EMR)|Electronic Medical Record/ Electronic Medical Record (EMR) emergency doctor 199, 200, 203-208, 213 empowerment 23, 45, 59, 66, 71, 72, 90, 333, 343, 345, 350, 351, 361, 364-367 epidemiological models 286, 287, 294, 297 e-technology 128 evidence-based 39, 52, 53, 81, 131, 136, 330, 341

F financing cycle 172

G Global Asthma Network 173, 195, 198 Global Initiative for Asthma 173, 175, 196, 198 Greifswald University 199

H health behavior 132, 141-146, 322-331, 334, 338342, 349 health care 20-23, 26, 27, 36, 38, 41-44, 47, 50, 54, 60, 61, 66-68, 71, 75-78, 82, 83, 86, 92-96, 103, 104, 108, 110, 113, 114, 128-130, 166, 172-181, 184-186, 189-199, 210, 215, 222-225, 228, 232, 235, 241, 248, 250, 258, 260, 301, 302, 318, 321-323, 327-330, 334-339, 342-355, 360, 362, 365-367 healthcare ecosystem 19, 90, 93, 94, 133 health care ecosystem 22 Healthcare Information and Management Systems Society (HIMSS) 4, 18, 21, 44-47, 50, 70, 195198, 362 health communication 113, 144, 168, 321, 325-331, 334, 337-342, 361 health information 6, 12-14, 18-21, 27, 32, 48, 56, 60, 64-66, 70, 75, 76, 91, 94, 96, 101, 124, 130, 131, 175, 181, 229, 242, 318-327, 330, 337342, 350, 355, 356 health information systems 14, 91, 242 health interventions 41, 128, 141, 328, 339 Health Level-7 (HL7) 28, 47, 53, 56, 95, 96, 105, 112, 115, 116, 269, 281 Health Management Platform 76, 90 high definition 110, 199, 207, 208, 213, 245 hypertension 58, 72-90, 113, 147, 183, 258

436

I IHE 53, 95, 96, 112, 115 Information Governance 300, 303, 306, 308, 311, 320 Information Security Threats 300-304, 316, 320 Integrated Platform 72, 85 Internet of Medical Things 1, 3, 16-19 Internet of Things (IoT) 17, 19, 141, 147 interoperability 2, 16, 21, 39, 40, 45-47, 53, 56, 67, 71, 92, 95, 96, 113, 116, 175, 229 ISO 15189:2012 252, 260, 264-266, 275, 280-284

K K4Health Knowledge for Health 198 Keyloggers 299

L Laboratory Information System (LIS) 21, 251, 263269, 272, 276, 281-285, 300-318 legacy systems 281, 320 lifestyle 34, 38, 48, 56, 72-79, 82-88, 129-135, 138141, 148, 175, 332, 339, 345, 350, 352

M medical imaging 91-93, 96-100, 103, 112-116, 227, 228 medical simulation 200, 206, 208, 214 medical simulation center 200, 206, 208, 214 medical software systems 242 mental health 38, 39, 48, 50, 76, 112, 125, 126, 149, 156, 157, 160, 165-168, 256, 346 mHealth Initiative 1-6 mHealth Working Group 198 Mobile Application Development 116 mobile apps 40, 56, 59, 62, 117, 127, 194, 211 mobile computing 3, 8, 18, 91, 92, 241, 243, 255 Mobile Devices 1, 2, 7-9, 12, 15, 18, 27, 37, 117, 135, 150, 153, 163, 242, 250 Mobile Health 20, 23, 36, 39, 41, 44-49, 60, 67-69, 91, 92, 110-117, 124-132, 141, 146, 151, 165172, 183, 195, 196, 232, 242, 243, 252, 260, 263-265, 269-277, 280-282, 285, 294, 298, 328, 335, 343, 345, 349, 354, 357, 358 Mobile Health applications 36, 39, 60, 92, 110, 130, 169, 172, 183, 252, 264, 265, 270-277, 285, 349

Index

Mobile Health (mHealth) 1-23, 26-29, 34-53, 56, 59-71, 91-98, 101-104, 110-133, 136, 139-142, 146, 149-158, 162-209, 213, 231, 232, 237, 242-244, 249-265, 269-277, 280-282, 285, 286, 294, 298, 321-323, 327-330, 335, 342-345, 349-351, 354-358, 362, 366 mobile telemedicine 154, 215 multimorbidity 52-62, 67-71 myocardial infarction 73-76, 199-202, 206, 208, 212, 214

N networks 5, 17, 26, 34-36, 42, 47-51, 78, 87, 98, 102, 113, 126, 139, 151, 153, 167, 175, 179, 208, 229-245, 250, 253, 286-288, 294, 297, 332, 350, 356 nexus 343, 345, 351, 354, 358 NHS 40, 304-314, 319, 320, 337, 347, 360, 365

O OSA 117-123, 127

P paramedic 199, 204-207, 212, 214 participatory health 1, 11, 19 pathology 97, 158, 242-249, 255-262, 267, 269, 283-285 Patient Activation 343, 351, 364, 366 patient-centered 20, 22, 27, 51, 54, 282, 319-323, 327, 330, 335, 337, 340-343, 351, 352, 360, 361, 366 patient-centered care 20, 22, 27, 51, 319-323, 327, 330, 335, 337, 340-343, 351, 352, 366 Patient-Centred Healthcare 300, 319, 320 Patient-Centred Healthcare Delivery Model 320 patient empowerment 45, 59, 66, 343, 345, 351, 361, 365, 367 Pattern Analysis 72 Personal Area Network (PAN) 51 pHealth 74, 90, 240 physiological sensors 28, 117-122, 127 platform 15, 28-35, 38, 45, 48, 52, 53, 57, 61-63, 68, 72-76, 79-86, 90-94, 98-106, 114, 115, 121, 130, 132, 139-141, 183-187, 197, 212, 213, 229-233, 238, 241, 244, 253, 254, 260, 270, 271, 285, 356, 358 polysomnography 117-121, 127 prehospital emergency medicine 199-209, 214 Prevalence of Asthma 172

Proficiency Testing (PT) 252, 255, 259, 262-265, 268, 276, 277, 280-285 PSG 117-123

Q Quality Assurance (QA) 265, 285 Quality Control (QC) 242, 246, 252, 263-265, 268, 285 Quality Management 261, 264, 275, 283-285

R raising awareness 149, 155, 156 real time 31, 33, 39, 121, 122, 126, 136, 137, 158, 199-205, 208, 251, 268, 275, 277, 332 remote monitoring 31, 38, 42, 128, 154, 177, 182, 228, 231, 240, 241 remote patient’s monitoring 215, 234 requirements 3, 9, 40, 41, 57, 60, 64, 91, 95, 100, 102, 124, 172-182, 193, 202, 231, 250, 252, 259-262, 265, 266, 270, 273, 275, 280-285, 294, 300, 302, 309, 310, 314-318, 336, 350 Requirements and Functionality of Applications 172 Responsive Web Design (RWD) 116

S Security Architecture 116 self-care 68, 322, 343-347, 353, 362-367 self-efficacy 68, 77, 112, 256, 321, 325, 332-335, 342-345, 348-355, 358-362, 367 self management 51, 359-364 self-management 34, 38, 45, 46, 52-55, 58-60, 65, 67, 72, 74, 77, 85-87, 112, 128, 133, 134, 138141, 146, 152, 173-176, 179, 182-185, 193, 222, 257, 260, 343-348, 351-355, 358, 359, 365-367 self-management support 65, 133, 343-348, 351355, 358, 367 sensors 17-23, 27-37, 50, 51, 56, 58, 63, 82, 117123, 127, 132-134, 137, 138, 141, 147-152, 179, 183, 192, 229-232, 237, 350, 356, 358 serious games 75, 84, 87-90, 128, 138-141, 146 SI,SIR 286 Sleep Apnea 9, 74, 117-127, 130, 147 Smartphones 1-15, 18, 19, 27-38, 46, 47, 52, 56, 60, 62, 82, 84, 92, 103, 105, 108, 109, 116-142, 145, 148, 151-153, 159, 169, 171, 174, 182, 183, 192-201, 208, 210, 215, 225, 228-235, 238, 241, 251-253, 259, 270, 271, 285, 350, 357, 362

437

Index

software 2, 12, 14, 19, 31, 35, 40, 44, 50, 68-71, 82, 83, 91, 98, 103, 104, 114-116, 150, 159, 161, 174, 179, 180, 188, 193, 202, 205, 211, 228, 229, 232, 242-247, 252-255, 265-273, 276-282, 285-287, 290, 291, 299, 318, 319, 357, 358 spreading virus 286 Startup Health 198 stimuli 72-75, 79, 82-87, 324 stroke 72-76, 86, 199-214, 361

T tailored health communication 328, 331, 334, 339342, 361 targeted health communication 144, 338, 342 Telecytology 242, 246, 248, 255-264, 267-269, 277-285 Telediagnosis 246, 263 Telemedicine 4, 5, 19, 39, 41, 44, 98, 111, 114, 132, 137, 144, 152, 154, 171, 174, 189, 195, 196, 199-206, 209-215, 223, 224, 228-237, 240-243, 255-261, 283, 336 Tele-wound technology network 215, 234, 241 trauma 199-203, 206-220 Trojan Horse 292, 299

438

U Universal Design 149, 150, 162-171 Universal Design principles 149, 162, 164

V video communication 199-208, 211, 213 virtual slide 252, 253, 263, 271 virus 13, 252, 276, 286, 287, 290-299

W wearable devices 23, 51 worm 287, 291, 299 wound database 241 wound tissue 218, 219, 224, 225, 234, 241

Z Zombie Programs 299