Sustainable Smart Healthcare: Lessons Learned from the COVID-19 Pandemic (SpringerBriefs in Applied Sciences and Technology) 3031371453, 9783031371455

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SpringerBriefs in Applied Sciences and Technology Tin-Chih Toly Chen

Sustainable Smart Healthcare Lessons Learned from the COVID-19 Pandemic

SpringerBriefs in Applied Sciences and Technology

SpringerBriefs present concise summaries of cutting-edge research and practical applications across a wide spectrum of fields. Featuring compact volumes of 50 to 125 pages, the series covers a range of content from professional to academic. Typical publications can be: • A timely report of state-of-the art methods • An introduction to or a manual for the application of mathematical or computer techniques • A bridge between new research results, as published in journal articles • A snapshot of a hot or emerging topic • An in-depth case study • A presentation of core concepts that students must understand in order to make independent contributions SpringerBriefs are characterized by fast, global electronic dissemination, standard publishing contracts, standardized manuscript preparation and formatting guidelines, and expedited production schedules. On the one hand, SpringerBriefs in Applied Sciences and Technology are devoted to the publication of fundamentals and applications within the different classical engineering disciplines as well as in interdisciplinary fields that recently emerged between these areas. On the other hand, as the boundary separating fundamental research and applied technology is more and more dissolving, this series is particularly open to trans-disciplinary topics between fundamental science and engineering. Indexed by EI-Compendex, SCOPUS and Springerlink.

Tin-Chih Toly Chen

Sustainable Smart Healthcare Lessons Learned from the COVID-19 Pandemic

Tin-Chih Toly Chen Department of Industrial Engineering and Management National Yang Ming Chiao Tung University Hsinchu, Taiwan

ISSN 2191-530X ISSN 2191-5318 (electronic) SpringerBriefs in Applied Sciences and Technology ISBN 978-3-031-37145-5 ISBN 978-3-031-37146-2 (eBook) https://doi.org/10.1007/978-3-031-37146-2 © The Author(s) 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Contents

1 Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.1 Smart Healthcare . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.2 Smart Healthcare Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.3 Smart Healthcare During the COVID-19 Pandemic . . . . . . . . . . . . . . 1.4 Smart Healthcare Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.1 Smartphone Applications (Apps) . . . . . . . . . . . . . . . . . . . . . . . 1.4.2 Smart Watch Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.3 LAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.4 Telemedicine and Telecare . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.5 IoT Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.6 Machine Learning (ML) and Deep Learning (DL) Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1.4.7 Explainable Artificial Intelligence (XAI) . . . . . . . . . . . . . . . . 1.5 Organization of This Book . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Smart Technology Applications in Healthcare Before, During, and After the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1 Smart Technology Applications in Healthcare Before the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.1 Direct or Indirect Smart Healthcare Applications . . . . . . . . . 2.1.2 Features of Smart Healthcare Applications at This Stage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1.3 Data Analysis Techniques Applied at This Stage . . . . . . . . . . 2.2 Smart Healthcare Applications Amid the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2.1 Challenges Posed by the COVID-19 Pandemic . . . . . . . . . . . 2.2.2 New Smart Healthcare Applications at This Stage . . . . . . . . 2.2.3 Features of New Smart Technology Applications . . . . . . . . . 2.2.4 Selected Cases and Discussion . . . . . . . . . . . . . . . . . . . . . . . . .

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2.2.5 Data Analysis Techniques Applied During the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3 Smart Technology Applications in Healthcare After the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.1 Smart Healthcare Technology Applications for Restoring Normal Life . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.3.2 Features of Potential Smart Technology Applications . . . . . . 2.3.3 Selected Cases with Discussion . . . . . . . . . . . . . . . . . . . . . . . . 2.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Evaluating the Sustainability of a Smart Healthcare Application . . . . 3.1 Sustainability of a Smart Healthcare Application . . . . . . . . . . . . . . . . 3.2 Evaluating the Sustainability of a Smart Healthcare Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.3 Qualitative Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4 MCDM Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.4.1 Determining the Priorities of Criteria . . . . . . . . . . . . . . . . . . . 3.4.2 Evaluating the Sustainability of a Smart Healthcare Technology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.5 Time-Series Approach . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Sustainable Smart Healthcare Applications: Lessons Learned from the COVID-19 Pandemic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1 Changes in Motivations and Needs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2 Changes in Factors Influencing the Sustainability of Smart Healthcare Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3 Conflicting Assessment Results and Gaps Between Theory and Practice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4 Impact of Global Events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5 Reassessing the Sustainability Based on Evidences . . . . . . . . . . . . . . 4.5.1 Procedure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.2 Steps 1 to 3: Comparing the Relative Priorities of Criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.3 Step 4: Aggregation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.5.4 Steps 5 to 7: Comparing Objectively and Subjectively Evaluated Sustainability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Enhancing the Sustainability of Smart Healthcare Applications with XAI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.1 Three Ways to Enhance the Sustainability of a Smart Healthcare Application . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.2 Basics in XAI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5.3 XAI Applications in Medicine and Healthcare . . . . . . . . . . . . . . . . . .

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5.4 XAI Applications for Enhancing the Sustainability of Ubiquitous Clinic Recommendation Systems . . . . . . . . . . . . . . . . . 98 5.5 XAI Applications for Enhancing the Sustainability of ANN-Based Diabetes Diagnosis Systems . . . . . . . . . . . . . . . . . . . . 103 References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107

Chapter 1

Smart Healthcare

Abstract This chapter first defines smart healthcare and smart healthcare technologies. After observing changes in the number of smart healthcare applications explored before, during, and after the COVID-19 pandemic, we have to question the sustainability of smart healthcare applications. Answering this question requires a reassessment of the sustainability of each smart healthcare application. To this end, some common smart healthcare applications, including smartphone applications (apps), smartwatch applications, location-aware services (LASs), telemedicine and telecare, Internet of Things (IoT) applications, machine learning (ML) and deep learning (DL) applications, are first introduced by reviewing examples from the literature. The sustainabilities of these smart healthcare applications are then subjectively estimated. However, the actual sustainability of each smart healthcare application will be determined on a case-by-case basis and should be compared with those of other applications. Keywords Smart healthcare · Smart healthcare technology · COVID-19 pandemic · Sustainability · Machine learning · Deep learning · Internet of things · Explainable artificial intelligence

1.1 Smart Healthcare Smart technologies are technologies that use electronic devices or systems that can be connected to the Internet, used interactively, and have a certain degree of intelligence [1–3]. Smart technologies already have many applications to improve people’s quality of life. Most existing research on this topic focuses on health-related rather than overall quality of life [4, 5]. The application of smart technologies in medicine or healthcare is called smart healthcare [6–8]. In addition, the applications of location-aware services (LASs) in healthcare have been referred to as smart health or s-health by Solanas et al. [9], pointing to their functionality as a context-aware complement to mobile health in smart cities.

© The Author(s) 2023 T. T. Chen, Sustainable Smart Healthcare, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-37146-2_1

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1.2 Smart Healthcare Technologies Smart technologies that underpin smart healthcare, so-called smart healthcare technologies, can be divided into four categories: • Wirelessly connected smart healthcare technologies: Smart healthcare technologies in this category include the Internet of Things (IoT) (including ubiquitous healthcare) [10–12] (see Fig. 1.1), cloud computing [13], edge computing [14], smart baby monitors, smartphones, smart watches, smart motion sensors, smart smoke alarms, smart glasses/spectacles/contact lenses, smart body analyzers, and smart thermostats [3]. • Interactive smart healthcare technologies: This type of smart healthcare technologies includes automatic personal protective equipment (such as face masks) vending machines (see Fig. 1.2), automatic alcohol sanitizer (see Fig. 1.3) [3]. Many of these smart healthcare technologies emerged during the COVID-19 pandemic to avoid direct transactions between people to prevent cross-infection. • Predictive smart healthcare technologies: Smart healthcare technologies in this category include big data analytics [15], smart toilets, smart wigs, etc. [3, 16]. • Intelligent smart healthcare technologies: Such smart healthcare technologies include artificial intelligence (AI) [17], big data analytics, smart mobility services, smart pajamas, smart wheelchairs, and sleep monitoring systems [3, 16]. These smart healthcare technologies include software, hardware, and systems, as illustrated in Fig. 1.4. Fig. 1.1 IoT-based smart healthcare system for making dentures using 3D printing facilities

1.2 Smart Healthcare Technologies Fig. 1.2 Automatic face mask vending machine

Fig. 1.3 Automatic alcohol sanitizer with smart thermostat

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Fig. 1.4 Smart healthcare technology categories

Wirelessly connected smart technologies Smart healthcare technologies

Interactive smart technologies Predictive smart technologies Intelligent smart technologies

1.3 Smart Healthcare During the COVID-19 Pandemic Some smart healthcare applications emerged during the COVID-19 pandemic [7, 18–21]. For example, • A robot (or drone) is a smart interactive healthcare technology used to help establish contact with or deliver medicines to quarantined patients to reduce the burden on medical professionals and control infectious diseases [22]. Robots (or drones) are also being used to patrol public places, observe and broadcast information to crowds, and monitor traffic more efficiently [23]. • Smart surveillance cameras combined with image and motion recognition modules is another smart interactive healthcare application used in factories, allowing workers to use voice commands or gestures to interact with machines, thereby avoiding touching machines to spread COVID-19 [21, 24, 25]. The same purpose can be achieved by remotely controlling the machine using a smartphone [26]. • Staff wear smart wristbands or watches to check body temperature, which is a wireless connected smart healthcare application [27]. • In hotels, autonomous robots emit concentrated ultraviolet light (UV-C) to disinfect room keys, guest rooms, lobbies, gyms, and other public areas [28]. In this example, robots are regarded as an intelligent smart healthcare technology. • In museums, wearable sensors are used to measure the distance of visitors to ensure physical distance [29], which is also an application of wireless-connected smart healthcare technologies. • Another application of wireless connected smart healthcare technologies is appbased location-based services to disperse users to avoid aggregation [30]. Some smart healthcare technologies were improved during the COVID-19 pandemic. For example, automatic alcohol sanitizers are often combined with smart thermostats to form a multifunctional smart healthcare technology. Figure 1.5 provides statistics on the popularity of smart healthcare applications. Clearly, smart healthcare applications have become even more popular during the

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Number of references

140000 120000 100000 80000 60000 40000 20000 0 2017

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Fig. 1.5 Number of references about smart healthcare applications from 2017 to 2023 (Data source Google Scholar)

COVID-19 pandemic. However, after the end of the pandemic, the discussion on smart healthcare applications seems to have cooled down, which has led to doubts about whether smart healthcare applications will be sustainable. In other words, what is the sustainability of smart healthcare applications? Some market survey reports show us that the market size of related smart healthcare applications continues to grow significantly. However, in fact, many people have lost interest in related smart healthcare applications after the COVID-19 pandemic. For this reason, this book intends to discuss • How to evaluate the sustainability of a smart healthcare application? • How has the COVID-19 pandemic affected the evaluation result? Before discussing how to address the two issues, some common smart healthcare applications are first reviewed as follows.

1.4 Smart Healthcare Applications 1.4.1 Smartphone Applications (Apps) Smartphone applications are undoubtedly the most widely used smart healthcare applications [31]. The widespread adoption of smartphones has created many opportunities to more effectively prevent and manage diseases through ubiquitous health interventions, an approach named mHealth by Estrin and Sim [32]. Smartphones are the most commonly used tool in ubiquitous clinic recommendation and other mobile healthcare applications. For example, an app on smartphones allows physicians to monitor patients with chronic heart failure [33]. Patients with severe symptoms are then assisted by a ubiquitous clinic recommendation system (i.e., another app), which works in tandem with mobile navigation services such

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as Google Maps to direct patients to recommended clinics by the shortest route. However, using mobile navigation services directly for ubiquitous clinic recommendations is risky because the recommendation results may be incomplete when using foreign languages [34]. Klasnja and Pratt [35] developed five strategies to support mHealth using smartphones: tracking healthcare information, engaging medical teams, leveraging social influence, increasing the accessibility of health information, and enriching user experience with entertainment. Chen [36] created a fuzzy just-in-time (JIT) ubiquitous service, which was an innovative application of mobile commerce, ubiquitous computing, and ambient intelligence [37]. A user is directed to the best service location along the planned route. The system arranges the service when the user needs it, and when the user arrives at the service location, the required service is ready, thereby reducing the user’s anxiety caused by unnecessary waiting. Chen [38] built a ubiquitous multicriteria clinic recommendation system. In this system, patients can send requests to the system server through their smartphones for clinic recommendations. Once the patient sends this information to the system, the system server estimates the patient’s velocity based on detections from the global positioning system (GPS) receiver on the patient’s smartphone. Then a fuzzy mixed integer-nonlinear programming (FMINLP) and ordered weighted average (OWA) approach is applied to evaluate and compare nearby clinics. Moreover, improving the comfort of mobile people by joining online social networks has received extensive attention from researchers [39]. For example, Pisula and Kossakowska [40] fitted the relationship between the sense of coherence (including comprehensibility, manageability, and meaning) and the quality of life for parents of children with autism. In contrast, previous studies mainly considered fear, stress, independence, communication, and social support [41]. To improve the quality of life of these patients, Gao et al. [42] suggested integrating temporal information by joining online social networks. Smartphones (with apps) are expected to be sustainable because people will always have their smartphones with them. The data analysis and communication capabilities of smartphones are also superior to other alternatives. However, this may not necessarily be reflected in the continued growth of the smartphone market, which is affected by many factors.

1.4.2 Smart Watch Applications Smartphones can be paired with other devices, such as smart watches or smart bracelets, through Bluetooth wireless technology to achieve synergy. Smart watches can track health information, including calories burned, steps taken, altitude and distance walked, and heart rate, and perform inference processes [43]. Traditional healthcare functions of smartwatches include activity, behavior, heart rate, and seizure monitoring. Many studies have attempted to validate the monitoring

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accuracy of such functions [44]. Smartwatches have improved in terms of battery power, storage capacity, and photo resolution over time. According to the investigation by Lu et al. [45], most smartwatch applications in healthcare focus on the health monitoring of the elderly. In this regard, smart bracelets are an alternative to smart watches. However, smartwatches are larger in size and more similar to traditional watches with decorative functions. Lu et al. [46] designed an app for high-quality cardiopulmonary resuscitation (CPR) training and installed it on smart watches of healthcare professionals. According to their experimental results, this smartwatch application did improve CPR training performance. Compressions were not as fast and deep, which meant higher-quality CPR. In a regional experiment, Uzir et al. [47] used a smart watch to detect a patient’s diabetes status, blood pressure, heart rate, and other general health conditions during the COVID-19 pandemic. The findings showed that elderly patients (users) had a positive experience with using smart watches. They also expressed trust and satisfaction with smart watch applications. Nevertheless, the most significant advantage was avoiding doctor visits. As mentioned earlier, smartwatches can be an alternative to traditional watches, and thus are potentially sustainable. However, just as electronic newspapers cannot completely replace physical newspapers, some people will still stick to traditional watches. In addition, the healthcare functions of smart watches can be replaced by smart phones and smart bracelets, which is not conducive to the sustainability of smart watches.

1.4.3 LAS A LAS or location-based service (LBS) is a special context-aware service that recommends suitable services to users based on their location and other contextual information [48]. LASs have been widely used to facilitate healthcare. A LAS is typically delivered to users via their smartphones. However, more and more smart wearable devices, such as smart watches, smart bracelets, smart glasses, etc., are equipped with GPS receivers and can also support LASs. Boulos et al. [49] conducted a complete ambient-assisted living experiment (CAALYX), in which elderly people were provided with wearable devices to measure their vital signs, detect falls [50], and send medical requests in case of emergency. A healthcare provider close to them will then respond to the request. In addition to location, profile and needs should also be considered when responding to such requests. Furthermore, according to Sun et al. [51], patients’ willingness to use healthrelated LASs is influenced by five factors: performance expectations, effort expectations, social influence, facilitation conditions, and threat assessment. Thus, a patient’s acceptance of healthcare services is also influenced by other patients’ experiences.

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Ubiquitous clinic recommendation has been extensively investigated by recent studies on LASs. For example, Chen [36] identified acceptability, efficiency, and immediacy as three key criteria for patients moving and finding a suitable clinic. To optimize these criteria holistically, OWA was applied to assess a clinic’s overall performance. The core of a ubiquitous clinic recommendation system is obviously the recommendation mechanism. Commonly adopted recommendation mechanisms in existing ubiquitous clinic recommendation systems can be classified into collaborative filtering (CF) mechanisms, content-based (CB) mechanisms, and knowledge-based (KB) mechanisms [52]. However, existing mechanisms suffer from issues such as sparsity, scalability, cold-start, and over-specialized recommendations [48]. More advanced mechanisms are needed to solve these problems. Furthermore, it is sometimes difficult to accurately identify user preferences for different service locations. The first reason is that the user is unwilling or unable to express his/her preferences conveniently. Additionally, the number and format of options a mobile app can offer are limited. Without such information, the effectiveness of a ubiquitous recommendation system is low and cannot be improved. In addition, popular collaborative filtering (CF) techniques become inapplicable when the required information is insufficient, and ubiquitous clinic recommendation systems can only assume unknown preferences of patients. This problem can be addressed by mining previous patients’ preferences from historical data. The obtained results represent a major trend and can be used to estimate the unknown preferences of new patients. Based on this belief, Chen [53] modified the existing fuzzy weighted average (FWA)-nonlinear programming (NLP) [54] method by incorporating a mechanism of predicting a patient’s unknown preferences for different nearby clinics to improve the performance of ubiquitous clinic recommendation. Rabbi et al. [55] built a ubiquitous clinic recommendation system that allowed patients to change the order of recommendations. These changes can be analyzed to uncover unknown patient preferences. Other studies have attempted to divide patients into groups. For example, Lee et al. [56] constructed an artificial neural network (ANN) for classifying patients based on their behavior in choosing a hospital. However, they used questionnaires to collect the data needed to train the ANN, which was time-consuming and not suitable for online applications. LASs in healthcare will be sustainable as clinics and hospitals remain a closed environment and communicating with them is still difficult.

1.4.4 Telemedicine and Telecare A patient can visit a clinic or make an appointment over the phone. However, patients may have to travel long distances to reach a particular clinic and then wait a long time for treatment. Appointments made by phone are generally assigned low priority, and patients who made appointments by phone are treated later than other

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patients. Advances in information and communication technology present various opportunities to address these issues. The application of ubiquitous computing technologies, such as telemedicine or telecare, is gaining more and more attention from physicians, information systems scientists, and practitioners [57, 58]. Chau and Hu [59] examined factors influencing physicians’ acceptance of telemedicine technologies. Factors discussed in similar studies include perceived usefulness, perceived ease of use, attitude, and intention to use. Bradram [60] mentioned that an important function of ubiquitous computing in medical applications is to bring clinical computer support closer to the clinical work setting. Web-based systems are a potential solution to this problem. However, such systems are usually only available in large hospitals, and such hospitals do not accept appointments on the day a patient calls. Cho et al. [61] established a web-based system that enables patients to enter their glucose levels for ubiquitous monitoring. Vidyarthi and Jayaswal [62] considered the clinic location assignment problem with random demand and crowding and proposed an efficient solution to the problem. Simulation data were used to test the effectiveness of the proposed methodology. Providing telemedicine and telecare services can overcome the difficulties of clinic location assignment. Telemedicine and telecare are governed by local government laws and regulations. Once the government policy changes, the related activities will be greatly affected. Additionally, telemedicine and telecare are more prone to malpractice. Therefore, the sustainability of telemedicine and telecare is uncertain.

1.4.5 IoT Applications Several studies on smart healthcare have attempted to group patients with similar mHealth needs [49, 52]. In addition, data related to personal health and medical records are often heterogeneous and large. To address this issue, Xu et al. [63] built an IoT-based system to flexibly collect, integrate, and interoperate these data. Smart devices such as sensors that make up an IoT can be deployed to prevent and detect falls, assist with vision or hearing impairments, improve mobility, reduce isolation, administer medications, and monitor physiological parameters [50]. Such smart devices can also be equipped with software agents (or virtual experts) to facilitate on-site data analysis and decision-making [64, 65]. Hameed et al. [66] constructed a fuzzy neural network (FNN) that predicts the severity of a patient’s symptoms based on signals detected by sensors attached to the patient in an IoT-based healthcare system. A common application of IoT is smart home, where visual and tactile signaling devices, speech synthesizers, braille displays, and motion analyzers (such as Kinect) are installed and operated in the homes of elderly people to improve their quality of life and monitor their living health status [67, 68].

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Detection results using IoT-based healthcare systems are often used as the basis for telemedicine or telecare applications. However, after being tested by the COVID19 pandemic, IoT-based applications in healthcare seem to be less practical, because connecting various sensors to many people’s bodies is a technically challenging task. Their willingness is another question. The sustainability of IoT applications in healthcare is a matter of debate. From a theoretical point of view, researchers have shown continued interest in the application of IoT in healthcare. However, in practice, there is little interest in IoT applications in healthcare due to the obtrusiveness of smart devices and the risk of possible invasion of personal privacy.

1.4.6 Machine Learning (ML) and Deep Learning (DL) Applications Among ML techniques, bio-inspired (or biomimetic) algorithms have been widely used to help solve scheduling problems in medical or healthcare systems [69]. Deep learning generally refers to ANNs with multiple hidden layers or recurrent hidden layers [70, 71], i.e., deep neural networks (DNNs). An FNN [72, 73] is also an efficient deep learning tool especially suitable for analyzing IoT data involving uncertainty and subjectivity. For the same reason, fuzzy logic has been widely used in decision-making problems in healthcare [74]. Shen et al. [75] compared the performances of various ML techniques, including support vector machines (SVM), random forests, adaptive boosting (AdaBoost), knearest neighbors (kNN), naive Bayes (NB), extreme gradient boosting (XGBoost), and gradient boosted decision trees (GBDT) for diabetes diagnosis. Most of the compared ML techniques are based on decision trees that are easy to understand and communicate. For the same purpose, an ANN or DNN has been built in some past studies to predict the probability of a user having diabetes. The inputs to the ANN (or DNN) are the user’s physical condition or demographic data, including his/her age, physical activities (yes or no), weeks pregnant, diabetes in the family (yes or no), body mass index, skin fold thickness, cholesterol, diastolic blood pressure, 2-h serum insulin, the pedigree of diabetes, plasma glucose concentration, etc. These inputs are passed through the hidden layers. Finally, the output of the network is generated. The output is in the range [0, 1] and can be used to predict the probability of having (or not having) diabetes. But whether a network output greater than 0.5 can be considered diabetic is debatable. To solve this problem, the ANN constructed by Karan et al. [76] has two outputs. One predicts the likelihood of having diabetes, and the other predicts the likelihood of not having diabetes. Once the former is (significantly) greater than the latter, the user is considered to have diabetes. ML and DL applications have been at the core of most data analysis mechanisms in smart healthcare applications. If such ML and DL applications can improve their

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understandability and communicability through explainable artificial intelligence (XAI), they will be sustainable.

1.4.7 Explainable Artificial Intelligence (XAI) So far, many XAI techniques or tools have been proposed to better explain the application of AI in healthcare [77, 78]. Monteath and Sheh [79] constructed a decision tree (i.e., classification and regression tree) to estimate the probability that a patient has chronic kidney disease based on the results of various tests including hemoglobin test, red blood cell count test, blood test, blood urea nitrogen test, packed cell volume (PCV) test, and creatinine test. Likewise, Panigutti et al. [80] used a classification tree to classify patients based on their clinical history [81]. However, the prediction or classification accuracy using these simple AI (or XAI) techniques is not satisfactory. Roessner et al. [82] and many others use saliency maps (or heatmaps) to visualize the relative weights (or importance) of features in data before feeding them into DL mechanisms for automatic diagnosis. This ensures that the part emphasized by the deep learning-based diagnostic mechanism is consistent with the part emphasized by the medical staff. Wang et al. [77] applied some simple cross-domain tools and techniques, including common expressions (using linguistic terms), color management, traceable aggregation, and segmented distance diagrams, etc., to improve the interpretability of AI applications in healthcare. Four applications of AI technologies in hospitals were considered, and proposals were studied to illustrate the applicability of simple crossdomain tools. The interpretability of each AI application was evaluated before and after improvement for comparison. According to the experimental results, these AI applications were better explained by modifying their explanations with simple cross-domain tools. The application of XAI in smart healthcare encounters the following difficulties in pursuing sustainability [77]: • Developers of smart healthcare applications tend to apply more complex AI techniques regardless of the ease of explainability [83, 84]. • In addition, the (life or knowledge) backgrounds of users of smart healthcare applications vary greatly. • The applied AI technology may not necessarily be explained to all users. • Furthermore, the application of AI in smart healthcare is easy to explain for some users but not for others.

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Sustainable

Smartphone Applications

ML and DL Applications

LASs XAI Applications

Telemedicine and Telecare

Smart Watch Applications

IoT Applications

Not sustainable Fig. 1.6 Subjectively estimated sustainability of smart healthcare applications

• Existing XAI technologies or tools are mostly from fields such as statistics, systems engineering, or computer science. These techniques or tools are not well explained to users who are not familiar with these fields [85]. Figure 1.6 summarizes the subjective expected sustainability of each smart healthcare technology. However, the actual sustainability of each smart healthcare application will vary on a case-by-case basis and should be compared with those of other applications.

1.5 Organization of This Book This book aims to provide technical details on evaluating and enhancing the sustainability of smart healthcare applications, including methodologies, tools, system architectures, software and hardware, examples, and applications. Smart healthcare applications were already widely used before the outbreak of the COVID-19 pandemic. However, suitable smart healthcare applications have changed after the outbreak of the COVID-19 pandemic. Several recent studies have shown greater acceptance of smart healthcare applications during the COVID-19 pandemic, including smart robots, electronic medical records (evolving into vaccine passports), smart bracelets, and sociometric badges. In contrast, some smart healthcare applications, such as wireless medical sensor networks, have exposed their weaknesses when dealing with large amounts of COVID-19 medical data. In other words, we have learned from

1.5 Organization of This Book

13

the COVID-19 pandemic that not all smart healthcare applications are sustainable, making it necessary to assess the sustainability of each smart healthcare application. From a practical point of view, many smart healthcare service providers are optimistic that the market size of smart healthcare applications will increase significantly, especially in the front end (client) [86]. However, without distinguishing the changes in the acceptance of different smart healthcare applications during the COVID-19 pandemic, their investment will be blind and not necessarily return [87]. For these smart healthcare service providers, the findings in this book will be informative. In specific, the outline of the present book is structured as follows. This chapter first defines smart healthcare and smart healthcare technologies. After observing changes in the number of smart healthcare applications explored before, during, and after the COVID-19 pandemic, we have to question the sustainability of smart healthcare applications. To answer this question, the sustainability of each smart healthcare application needs to be reassessed. To this end, some prevalent smart healthcare applications, including smartphone applications (apps), smart watch applications, LASs, telemedicine and telecare, IoT applications, ML and DL applications, are first introduced by reviewing cases in the literature. The sustainabilities of these smart healthcare applications are then subjectively estimated. However, the actual sustainability of each smart healthcare application will vary on a case-bycase basis and should be compared with those of other applications, which refers to Chap. 3. Chapter 2, Smart Technology Applications in Healthcare before, during, and after the COVID-19 Pandemic, compares smart healthcare applications before, during, and after the COVID-19 pandemic. A smart healthcare application can be judged as sustainable if it was already widely used before the COVID-19 pandemic and is also prevalent after the pandemic. In contrast, if a smart healthcare application does not survive after the COVID-19 pandemic, it is not sustainable. To analyze this, smart technology applications in healthcare before the COVID-19 pandemic are divided into direct and indirect smart healthcare applications. Then the features of smart technology applications at this stage are summarized. Data analysis techniques applicable at this stage are also introduced. Subsequently, the challenges posed by the COVID-19 pandemic to the applications of smart technologies in healthcare are listed. To address these challenges, new smart healthcare technology applications have been proposed at this stage. The features of such novel smart technology applications are discussed with the support of selected cases from the literature. Data analysis techniques applicable at this stage are also introduced. Finally, smart technology applications in healthcare post-COVID-19 pandemic are discussed, which are mainly for returning to normal life. Other novel smart healthcare applications are yet to be proposed. For this purpose, the characteristics of such novel smart technology applications are discussed. Chapter 3, Evaluating the Sustainability of a Smart Healthcare Application, first defines the sustainability of a smart healthcare application and then proposes three methods for evaluating the sustainability of a smart healthcare application: qualitative methods, multicriteria decision-making (MCDM) methods [88, 89], and time series methods by considering the inherent subjectivity and uncertainty [90, 91].

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Each method is explained through simple examples, supplemented by MATLAB or Lingo codes to facilitate calculations, allowing readers to learn quickly. Then, the advantages and/or disadvantages of each approach are also discussed. In Chap. 4, Sustainable Smart Healthcare Applications: Lessons Learned from the COVID-19 Pandemic, the first lesson we have learned from the COVID-19 pandemic is that user motivation and acceptance of smart healthcare applications have varied. This explains why some smart healthcare applications can be sustained while others cannot. Various considerations leading to this discrepancy are summarized. Additionally, global events during the COVID-19 pandemic, including the China trade war, severe delays and congestion at ports and terminals due to COVID-19, global semiconductor chip shortages, inflation, and the Ukraine–Russia war, have also impacted the sustainability of smart healthcare applications. To address this issue, the impact of these global events on the sustainability of smart healthcare applications is discussed. As a result, the sustainability of smart healthcare applications needs to be reassessed from objective and subjective perspectives based on the evidence gathered during the COVID-19 pandemic. Chapter 5, Enhancing the Sustainability of Smart Healthcare Applications with XAI, first mentions three ways to enhance the sustainability of smart healthcare applications. Then, our experience from the COVID-19 pandemic tells us that these three goals can actually be achieved by applying XAI. After introducing some basics of XAI [92], some representative cases of XAI applications in medicine and healthcare in the literature are discussed. Subsequently, two well-known applications of XAI in healthcare are detailed: an XAI application for enhancing the sustainability of a ubiquitous clinic recommendation system and another XAI application for enhancing the sustainability of an ANN-based diabetes diagnosis system. The two XAI applications enhanced both the understanding and trust of users, making them willing to use the system again and contributing to the sustainability of the smart healthcare applications.

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Chapter 2

Smart Technology Applications in Healthcare Before, During, and After the COVID-19 Pandemic

Abstract This chapter compares smart healthcare applications before, during, and after the COVID-19 pandemic. A smart healthcare application can be judged as sustainable if it was already widely used before the COVID-19 pandemic and is also prevalent after the pandemic. In contrast, if a smart healthcare application does not survive after the COVID-19 pandemic, it is not sustainable. To analyze this, smart technology applications in healthcare before the COVID-19 pandemic are divided into direct and indirect smart healthcare applications. Then the features of smart healthcare applications at this stage are summarized. Data analysis techniques applicable at this stage are also introduced. Subsequently, the challenges posed by the COVID-19 pandemic to the applications of smart technologies in healthcare are listed. To address these challenges, new smart healthcare technology applications have been proposed at this stage. The characteristics of such novel smart healthcare applications are discussed with the support of selected cases from the literature. Data analysis techniques applicable at this stage are also introduced. Finally, smart technology applications in healthcare post-COVID-19 pandemic are discussed, which are mainly for returning to normal life. Other novel smart healthcare applications are yet to be proposed. For this purpose, the features of such novel smart technology applications are discussed. Keywords Smart healthcare · Smart healthcare technology · COVID-19 pandemic · Sustainability · Data analysis · Machine learning · Deep learning · Residual neural network

2.1 Smart Technology Applications in Healthcare Before the COVID-19 Pandemic 2.1.1 Direct or Indirect Smart Healthcare Applications Existing healthcare approaches often rely on post-hoc analysis to fit causal relationships between treatments and users’ health conditions, with the goal of assessing and improving the effectiveness of treatments. However, such methods cannot be applied © The Author(s) 2023 T. T. Chen, Sustainable Smart Healthcare, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-37146-2_2

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to users who need immediate help to enhance their health. Furthermore, for users in mobile environments, location and other ambient data are crucial, but can only be detected using smart technologies [1]. Before the outbreak of the COVID-19 pandemic, the application of smart technologies had permeated every aspect of our daily life. For example, in healthcare, smart watches are equipped with many sensors, such as thermometers and heart rate monitors, to monitor and help maintain a user’s physical condition. Some smart technologies can even overcome the limitations of existing medical technologies. Smart glasses, for example, use cameras and arrays of light-emitting diodes (LEDs) to help people with extremely poor vision. Some smart glasses can talk so that blind people can “hear” the visual space around them [2]. Self-driving cars are expected to help drivers who are unable to drive safely due to sudden health problems [3]. Clearly, the application of smart technologies improves healthcare for users in terms of increased efficiency, immediacy, comfort, responsiveness, awareness, and preparedness. In addition to the direct applications of smart technologies in healthcare, there are also some indirect applications aimed at improving the overall quality of life and also benefiting users’ healthcare (see Fig. 2.1). For example, mobile guides, such as Google Maps, guide the way of travelers after detecting their locations using global positioning system (GPS), so that they can reach their destination as soon as possible, thereby reducing (enhancing) their fatigue (health) and anxiety (comfort) [1]. Moreover, joining online social networks can also indirectly improve the comfort of mobile people by considering spatial, temporal, and social information [4–7]. In fact, most past studies on quality of life have focused on health-related rather than overall quality of life [8]. In addition, some aspects of quality of life are only applicable to specific groups with healthcare problems, such as “independence” is a quality of life that is particularly important for older populations or groups with limited mobility [9].

2.1.2 Features of Smart Healthcare Applications at This Stage Before the outbreak of the COVID-19 pandemic, the focus of applying smart technologies to healthcare was primarily to improve people’s health rather than cure their diseases. While most of the smart healthcare technologies in use (including smart watches, smart glasses, smart phones (apps), location-aware services, smart bracelets [10], etc.) were also applicable during or after the COVID-19 pandemic, some were considered a fad rather than a necessity. Representative examples of such smart healthcare technologies include smart clothing or vests [11], smart backpacks [12– 14], etc. Therefore, the cost-effectiveness of smart medical technology applications was not emphasized, and many smart healthcare technologies were only applied in an experimental (controlled) environment (i.e., a laboratory or hospital) to illustrate their applicability [15], such as smart home [16], 3D body scanners [17], human/

2.1 Smart Technology Applications in Healthcare Before the COVID-19 …

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Efficiency Immediacy Comfort Responsiveness Awareness

Smart Healthcare Applications

Direct Applications

Preparedness Health

Indirect Applications

Reliability Usability Acceptability Mobility Satisfaction Affordability

Fig. 2.1 Effectiveness of smart healthcare applications before the COVID-19 pandemic

body area sensor networks [18], Internet of Things (IoT) [19], wireless medical sensor networks [20], self-driving cars, etc. All in all, smart technologies applied to healthcare prior to the COVID-19 pandemic were extensive. Among them, some are still experimental and immature.

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2.1.3 Data Analysis Techniques Applied at This Stage Before the COVID-19 pandemic, another application focus of smart technologies in healthcare was to design complex data analysis technologies, which may take considerable time for analysis after the huge amount of data collected is transmitted to the backend server, because there is no rush getting the diagnosis results for a lot of people, unlike during the COVID-19 pandemic. Hsu et al. [21] conducted a multiple regression analysis to identify the factors of mastication that were most suitable for predicting oral health. Välimäki et al. [22] applied a similar approach to assess how Alzheimer disease severity affected mobility, depression, or overall health-related quality of life. Orru et al. [23] also used regression analysis to attribute life satisfaction to the annual concentration of PM10 around the living environment. However, such regression models oversimplify causality by assuming independence of causes. To address this issue, Moonie et al. [24] applied structural equation modeling (SEM) to find factors affecting the quality of life in children with asthma. Among the seven influencing factors, chest tightness in a child has the greatest impact on his/her quality of life. Optimization is another research and application mainstream in this field. Chen [25] built a ubiquitous multicriteria clinic recommendation system, in which patients send requests to the system server via their mobile phones for clinic recommendations. After receiving a request, the system server first estimates the patient’s travel speed based on the detection result of the global positioning system. Then, a fuzzy mixed integer-nonlinear programming (FMINLP)-ordered weighted average (OWA) method is applied, which considers four criteria and finally recommends the clinic with the maximum utility to the patient. The problem with Chen’s approach is that sometimes a patient is reluctant to share all of his or her information or preferences. To address this problem, Chen [26] improved the existing fuzzy weighted average (FWA)-nonlinear programming (NLP) method by predicting patients’ unknown preferences for different nearby clinics to improve the performance of ubiquitous clinic recommendation.

2.2 Smart Healthcare Applications Amid the COVID-19 Pandemic 2.2.1 Challenges Posed by the COVID-19 Pandemic The COVID-19 pandemic poses the following challenges to the application of smart technologies in healthcare (see Fig. 2.2): • Supply and demand imbalance: Obviously, the supply of smart devices and equipment required for healthcare cannot meet the surge in healthcare demand during the COVID-19 pandemic. Therefore, the limited budget and resources of

2.2 Smart Healthcare Applications Amid the COVID-19 Pandemic

23

Decreased willingness to try smart technologies

Smart Technology Applications in Healthcare

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Supply and demand imbalance

Big data problem

Fig. 2.2 Challenges to the application of smart technologies in healthcare

• • • •

relevant departments must be invested in traditional medical care rather than smart medical and healthcare. Lack of economies of scale: Most smart technologies are not cheap, and it is risky to mass-produce the required smart devices in pursuit of economies of scale because of financial and time pressures. Intensified regional differences: For the same reason, the universal application of smart healthcare is unaffordable for low-income regions, and only possible for countries with more developed economies. Big data problem: The vast amount of data collected in smart technology applications is difficult to analyze. Decreased willingness to try smart technologies: Compared with the government trying various smart technologies to deal with the impact of the COVID-19 pandemic, individuals, especially patients, are less willing to try novel smart technologies. Faced with the uncertainty of the pandemic, it is understandable that patients are unwilling to try smart technologies that are uncertain and may not be easy to use.

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2.2.2 New Smart Healthcare Applications at This Stage To address these issues, during the COVID-19 pandemic, some new smart healthcare applications have emerged [27]. For example, • Robots (or drones) reduce the burden on medical professionals to establish contact with quarantined patients or send them medicines, thereby curbing infectious diseases [28]. Robots (or drones) are also applied to patrol public places, observe and broadcast information to crowds, and monitor traffic more efficiently [29]. • Remote temperature scanners have been installed in all public places to measure the temperature of every passenger (customer), which was rare before the COVID pandemic. Only airports are equipped with similar facilities. • In factories, workers interact with machines using voice commands, gestures, or smartphones to avoid spreading COVID-19 by touching machines [30–32]. They also wear smart wristbands or watches to check body temperature [33]. • A medical mask vending machine sells medical masks anytime and anywhere, reducing the need for manpower and the risk of cross-infection. • Similarly, hotels use autonomous robots that emit concentrated ultraviolet light (UV-C) to disinfect facilities such as room keys, guest rooms, lobbies, dining tables, and gyms [34]. • In museums, instead of separating visitors manually to maintain social distance, wearable sensors are used to measure the distance of visitors to achieve the same purpose [35]. • Similarly, app-based location-based services can disperse users to avoid gatherings [36].

2.2.3 Features of New Smart Technology Applications Common features of new smart technology applications during the COVID-19 pandemic include • Cheap one for many: Some smart healthcare applications can serve many people through a single facility at an affordable cost. For example, a 2-in-1 auto-sensing infrared thermometer and sanitizer dispenser costs about NT $1000 (or US $33) and can serve hundreds of shoppers, while a similarly priced smart bracelet can only check the temperature of a single user. • Eliminating the need for manual work: For example, hotels used robots to provide services, such as greeting, delivery, cleaning, etc., to tourists during the COVID-19 pandemic [37]. • Reducing the data collected: Furthermore, little or no data is collected, so there is little need to analyze the collected data. Clearly, smart technology applications in healthcare during COVID-19 are different than those used before the pandemic. Some smart technologies (such as robots or drones) are more commonly used,

2.2 Smart Healthcare Applications Amid the COVID-19 Pandemic

25

while others (such as apps that recommend restaurants for healthy eating) have lost the public’s attention. • Avoiding contagious contact: For example, according to the survey by Kim et al. [37], prior to the COVID-19 pandemic, hotel visitors were more satisfied with services provided by humans than robots, but felt more comfortable if these services were provided by robots during the pandemic. • New ways of applying existing smart technologies: For example, apps are designed to help find where masks can be bought, remind users to wear masks, or detect whether a user is wearing a mask [38]. According to statistics by ColladoBorrell et al. [39], most of the apps developed at this stage are used to provide information or news about COVID-19, record symptoms, and contact tracing. Before the pandemic, this kind of application was relatively rare for smart watches [40]. In addition, smart watches are also being used to track people’s health (including heart rate, body temperature, blood sugar level, blood pressure, and sleep time) and physical activity (including gestures, movements, steps, and movements). In the literature, there are some reviews on the application of smart technologies (especially machine learning (ML) or deep learning (DL), IoT, telemedicine, and sensor networks) in healthcare during the COVID-19 pandemic [41]. However, these reviews are too biased toward the (potential) benefits of smart technology applications, while ignoring their cost-effectiveness and limitations. Figure 2.3 provides a summary showing the number of references since 2020 related to the applications of specific smart technologies in healthcare. Some smart technology applications, such as blockchains and the Internet of Things (IoT), are relatively hot to research but not yet fully pervasive. Some selected cases of smart technology applications in healthcare during COVID are discussed below.

2.2.4 Selected Cases and Discussion Bhardwaj et al. [42] established an IoT-based smart health monitoring system that can monitor the blood pressure, heart rate, oxygen level, and body temperature of a COVID patient. To do this, four types of sensors need to be attached to the COVID-19 patient, and the signals from these sensors are transmitted wirelessly to the receiver, which then relays the signals to the backend (a computer in the hospital). Taiwo and Ezugwu [43] and Khan et al. [44] also established similar systems. However, existing wearable devices, such as smart bracelets and smart watches, combined with general household blood pressure monitors, can already achieve similar purposes. The new system may not be practical or acceptable to users. In addition, when the pandemic was at its worst, it was impossible to effectively process the surveillance data of a large number of COVID patients. Sageena et al. [45] reviewed the status, challenges, and needs to facilitate the development of telemedicine in India, especially during the COVID-19 pandemic.

2 Smart Technology Applications in Healthcare Before, During, and After …

Others

Smart bracelet

Remote temperature scanner

Blockchain

Smart watch

Wireless medical sensor network

Robot

Social distance monitor

Internet of things

70,000 60,000 50,000 40,000 30,000 20,000 10,000 0

App (smart phone)

No. of references

26

Smart healthcare applications Fig. 2.3 Number of references related to specific smart technology applications in healthcare since 2020 (Data source Google Scholar)

Telemedicine has been widely used to remotely diagnose possible COVID-19 patients before they come to a doctor. However, as mentioned earlier, such systems are not easily scalable because each patient still needs to be diagnosed by a doctor, and the number of doctors is much smaller than that of patients. One possible way to solve the scalability problem is to target only certain groups of people. For example, people with diabetes are at higher risk of contracting COVID19. Approximately 20–50% of COVID-19 cases worldwide are diabetic patients [46]. For this reason, Joshi et al. [46] attached the sensors of a noninvasive blood glucose meter to the fingertips of a diabetic patient, which wirelessly transmitted the monitoring results to an app on his or her smartphone and could also be used for telemedicine if necessary. Based on the diagnosis result, the smartphone also controlled the action of an insulin pump. There are already many applications of ML or DL to help diagnose whether a patient is infected with COVID-19. For example, Nasser et al. [47] constructed a residual neural network with 50 layers (ResNet-50). A ResNet-50 has 48 convolutional layers, one MaxPool layer, and one average pool layer. Inputs to the ResNet-50 were the chest X-ray and computed tomography (CT) scan images of a patient, which are converted into matrixes, as shown (Fig. 2.4). The matrix is processed by a feature detector (filter) using matrix operations to get a feature map. Many filters are used to derive various features of the image, resulting in the corresponding feature maps (Fig. 2.5). These feature maps are weighted and aggregated, removed negative values using a rectifier, such as the rectified linear unit (ReLU) function:

2.2 Smart Healthcare Applications Amid the COVID-19 Pandemic

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pooled (by retaining the maximum) (Fig. 2.6), and flattened (by projecting into a column vector) (Fig. 2.7), and finally inputted into a feedforward neural network (FNN) [48]. The output from the FNN is the probability of the patient being infected with COVID-19, i.e., a real value. The output is compared to the actual value, which is 0 (the patient is not infected with COVID-19) or 1 (the patient is infected with COVID-19), so as to adjust the values of FNN parameters. In their study, the accuracy was calculated as Accuracy =

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TP (True Positives) is the number of correctly identified COVID-19 patients, and TN (True Negatives) is the number of correctly identified non-COVID-19 patients. The accuracy achieved using the ResNet-50 was 98.6%. The proportion of the global population infected with COVID-19 is 10.36% [49]. While the results look

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promising, the practical challenge is that using a commercially available rapid test, the accuracy evaluated according to Eq. (2.2) is already up to 95.5%, and patients do not need to go to the hospital (see Table 2.1) [50]. Ennafiri and Mazri [51] developed a novel smart bracelet as an application of IoT. Before the COVID-19 pandemic, smart bracelets had been widely used to monitor users’ body temperature and blood oxygen levels. The novel smart bracelets of different users communicate with each other via their GPS trackers to fulfill users’ commitment to quarantine and social distancing, just like the function of sociometric Table 2.1 Accuracy evaluation of existing rapid tests Brand

Percent of positive COVID-19 cases correctly identified* (%)

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Accuracy** (%)

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100

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* Data

source [49] **Calculated based on the assumption of 10% infection rate

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29

badges. These smart technologies are typically used to facilitate human-to-human communication, but have been used to monitor and limit human-to-human interaction during the COVID-19 pandemic. Such IoT-based applications can also facilitate the establishment of a wireless medical sensor network that helps reduce the contact between doctors (or nurses) and patients.

2.2.5 Data Analysis Techniques Applied During the COVID-19 Pandemic Many attempts have been made to estimate the progress of the COVID-19 pandemic by predicting the number of confirmed cases per day. For example, Alanazi et al. [52] applied the susceptible-infected-recovered (SIR) model proposed by Hethcote [53] (i.e., a model of how susceptible, infected, and recovered cases transfer) to predict the spread of the COVID-19 pandemic. Numerical simulations were also performed using the case of the Kingdom of Saudi Arabia to assess the impact of various control strategies adopted by the local government on the pandemic. However, since it was practically impossible to conduct a field experiment to verify the simulation results, the validity of this method cannot be proven. In the view of Chen et al. [54], the factors influencing hotel selection during the COVID-19 pandemic were different from those commonly considered. Therefore, they proposed fuzzy analytic hierarchy process (FAHP)-enhanced fuzzy geometric mean (EFGM)-fuzzy technique for order preference by similarity to ideal solution approach (FTOPSIS) to recommend hotels nearby, where the EFGM method derived the near-exact values of fuzzy priorities efficiently by fitting their membership functions, and then FTOPSIS was applied to evaluate and compare possible hotels. Patients considered both subjective preferences and objective information (such as expected wait time at a COVID-19 vaccination site and crowding and reliability of the vaccination site) when choosing a COVID-19 vaccination site. However, it is not convenient for them to collect and compare such information [55]. To address this problem, Lin and Chen [56] applied web content mining (WCM) to extract the conditions of COVID-19 vaccination sites, and proposed a new asymmetric calibrated fuzzy inverse of column sum (FICSM) and fuzzy Vise Kriterijumska Optimizacija I Kompromisno Resenje (fuzzy VIKOR) approach [57, 58] to recommend suitable vaccination sites to patients. To solve the problem that the detection accuracy of a smart bracelet is not high enough, Brakenhoff et al. [59] and other researchers suggested that any symptoms of patients must be continuously recorded along with the detection data of a smart bracelet for comprehensive judgment, and a special algorithm is also required for this. Similarly, multiple studies [60] have also suggested combining smartwatch detection results with self-reported symptoms to determine whether an individual has COVID-19 after onset of symptoms.

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2.3 Smart Technology Applications in Healthcare After the COVID-19 Pandemic 2.3.1 Smart Healthcare Technology Applications for Restoring Normal Life Since 2022, with the increasing popularity of vaccination, many countries or regions have begun to gradually unblock [56, 61]. The COVID-19 pandemic appears to be entering its final stages. However, the impact caused by the COVID-19 pandemic will not disappear in a short time [62, 63]. In order to restore normal life, the application of smart technologies is still necessary [64, 65]. Suitable smart technology applications in healthcare did change during the COVID-19 pandemic. Some recent studies have shown greater acceptance of smart technologies, such as smart robots, electronic medical records (evolving into vaccine passports), smart wristbands and sociometric badges, during the COVID19 pandemic [38]. In contrast, some smart technologies, such as wireless medical sensor networks, 3D body scanners, body area sensor networks, and IoT, were found to be impractical due to the large amount of COVID-19 patient data generated by the application of these smart technologies, which was beyond their processing ability [51] (see Fig. 2.8). In other words, after the COVID-19 pandemic, smart technology applications for healthcare will be different than before and during the pandemic [66]. Many smart healthcare providers are optimistic that the market size of smart technology applications will grow substantially, especially in the front-end (client) of smart healthcare [67]. However, if they do not differentiate the changes in the acceptance of different smart technologies during the pandemic, their investment will be blind and not necessarily return [68].

Fig. 2.8 Changes in the preferences for smart technology applications in healthcare after the COVID-19 pandemic

2.3 Smart Technology Applications in Healthcare After the COVID-19 …

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2.3.2 Features of Potential Smart Technology Applications Some features of potential smart technology applications after the COVID-19 pandemic are discussed below: • Motives for smart technology applications shifted again: Unlike during the COVID-19 pandemic, people are eager to restore freedom of movement after the pandemic. Smart technology applications such as vaccine passports fulfill this motivation [69]. In the late stages of the COVID-19 pandemic, only vaccinated travelers will be able to enter some countries or regions, book hotels and restaurants, and more. This restriction will be further relaxed in the near future. • Further applications of deep learning: Applications of deep learning were widely discussed both before and during the COVID-19 pandemic. This trend will continue beyond the pandemic. However, deep learning methods are not easy to understand and/or communicate, especially for users of smart technology applications in healthcare. To solve this problem, the concept of explainable artificial intelligence (XAI) was proposed [70]. • More interest in robotics but reduced practice use: In addition, there has been interest in the application of robotics in healthcare from the time of the COVID-19 pandemic into its aftermath. However, after widespread vaccination [71], the need to avoid human contact with robots and drones is no longer as strong. In addition, the cost and expense of widely deploying robots are prohibitive [72]. For these reasons, the use of robots and drones may fade in the wake of the pandemic. In addition, there are some jobs where people are afraid of being replaced by the application of robotics, such as hospitals, hotels, and banks [73]. • Higher requirements for the effectiveness (and cost-effectiveness) of a smart technology application: Many applications of existing smart technologies during COVID-19 showed that their performances were not high enough. For example, according to the experiments of Suhartina and Abuzairi [74], the blood oxygen level detected by a smart bracelet was usually more than 5% lower than the actual value. Furthermore, the ability of existing smart technologies to detect COVID19 infections was far from adequate, as patients are contagious two days before the onset of overt symptoms, and presymptomatic or asymptomatic patients are responsible for more than half of the infections [59, 60]. After the pandemic, users may no longer be able to tolerate this issue. • Continued research and development of new smart technologies: Furthermore, to tackle the scalability problem of AI applications in healthcare, federated learning (FL) is a new learning technique [70], which is to coordinate multiple clients (e.g., hospitals) to train their AI applications for the same purpose without sharing raw data. In fact, there are already some existing learning technique that are similar in concept to FL, such as collaborative computing or collaborative intelligence [75–77]. FL especially focuses on the collaboration of clients through an IoT and the application of AI technologies.

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2.3.3 Selected Cases with Discussion Wang et al. [78] applied some simple cross-domain XAI tools and techniques, including common expression (using linguistic terms) [79], color management, trackable aggregation, and segmented distance diagrams [80], etc., to improve the interpretability of four AI applications in hospital recommendation. After applying the XAI tools and techniques, the interpretability of AI applications increased by an average of 23%. Solanki et al. [81] designed an intelligent chatbot for healthcare applications using AI and ML. Natural language processing is an AI technique that is critical to developing such robots to facilitate human–robot interaction [82]. ML, such as artificial neural networks [83, 84], helps optimize responses to user queries based on their inputs [85–89]. In the view of Chen and Wang [27], among the existing smart technologies, smart watches and remote temperature scanners will replace wireless medical sensor networks and smart bracelets to be more widely used. Wireless medical sensor networks were used to transmit massive heterogeneous data during the COVID-19 pandemic, requiring huge hardware investment and operating expenses, which will limit their applications to small-scale, experimental purposes after the pandemic [90]. In addition, smartwatches are equipped with more sensors than smart bracelets [91, 92], so they are more sophisticated in tracking people’s health and physical activities, although they are more expensive. In addition, remote temperature scanners have been successfully applied to screen people for possible COVID-19 infection, and it is efficient, well-established, noninvasive, and cost-effective. Although the accuracy is not high, this technology is fast, simple, mature, and economical [93]. Therefore, remote temperature scanners are expected to continue to be used to prevent the spread of other diseases.

2.4 Summary Table 2.2 summarizes the features of smart technology applications in healthcare before, during, and after the COVID-19 pandemic.

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Table 2.2 Features of smart technology applications in healthcare before, during, and after the COVID-19 pandemic Period

Before COVID-19

During COVID-19

After COVID-19

Motivation

Better health

Avoidance of infection

Restoring mobility

Users’ acceptance Low ~ Moderate

Very high

High

More frequently applied smart technologies

• Smart bracelets • Smart watches • (Smart phones) Apps for restaurant, healthy diet, outdoor exercise or fitness recommendation etc.

• Robots (or drones) • Wireless body temperature monitors • Smart bracelets • Smart watches • (Smart phones) Apps for vaccination, buying medical masks, avoiding crowding • Wireless medical sensor networks • Deep learning etc.

• Smart body temperature monitors • Smart bracelets • Smart watches • (Smart phones) Apps for restaurant, healthy diet, outdoor exercise or fitness recommendation, Vaccine passports • Deep learning etc.

Less frequently applied smart technologies

• Robots (or drones) • Wireless body temperature monitors • Wireless medical sensor networks etc.

• Restaurant • Robots (or drones) recommendation apps • Wireless medical (for healthy diets) sensor network • (Smart phones) Apps • (Smart phones) for restaurant, healthy Apps for diet, outdoor exercise vaccination, buying or fitness medical masks, recommendation etc. avoiding crowding etc.

Effectiveness/ Performance

Important

Very important

Cost-effectiveness Somewhat unimportant ~ Moderate ~ Important Moderate

Important Important

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53. H.W. Hethcote, The mathematics of infectious diseases. SIAM Rev. 42(4), 599–653 (2000) 54. T.C.T. Chen, H.C. Wu, K.W. Hsu, A fuzzy analytic hierarchy process-enhanced fuzzy geometric mean-fuzzy technique for order preference by similarity to ideal solution approach for suitable hotel recommendation amid the COVID-19 pandemic. Digit. Health 8, 20552076221084456 (2022) 55. T. Chen, Y.C. Lin, A fuzzy back propagation network ensemble with example classification for lot output time prediction in a wafer fab. Appl. Soft Comput. 9(2), 658–666 (2009) 56. Y.-C. Lin, T. Chen, An intelligent system for assisting personalized COVID-19 vaccination location selection: Taiwan as an example. Digit. Health 8, 20552076221109064 (2022) 57. Y.C. Lin, Y.C. Wang, T.C.T. Chen, H.F. Lin, Evaluating the suitability of a smart technology application for fall detection using a fuzzy collaborative intelligence approach. Mathematics 7(11), 1097 (2019) 58. T. Chen, Y.-C. Lin, M.-C. Chiu, Approximating alpha-cut operations approach for effective and efficient fuzzy analytic hierarchy process analysis. Appl. Soft Comput. 85, 105855 (2019) 59. T.B. Brakenhoff, B. Franks, B.M. Goodale, J. van de Wijgert, S. Montes, D. Veen, E.K. Fredslund, T. Rispens, L. Risch, A.V. Dowling et al., A prospective, randomized, single-blinded, crossover trial to investigate the effect of a wearable device in addition to a daily symptom diary for the remote early detection of SARS-CoV-2 infections (COVID-RED): a structured summary of a study protocol for a randomized controlled trial. Trials 22, 1–5 (2021) 60. T. Mishra, M. Wang, A.A. Metwally, G.K. Bogu, A.W. Brooks, A. Bahmani, A. Alavi, A. Celli, E. Higgs, O. Dagan-Rosenfeld et al., Pre-symptomatic detection of COVID-19 from smartwatch data. Nat. Biomed. Eng. 2020(4), 1208–1220 (2020) 61. Y.-C. Lin, T. Chen, Type-II fuzzy approach with explainable artificial intelligence for naturebased leisure travel destination selection amid the COVID-19 pandemic. Digit. Health 8, 20552076221106320 (2022) 62. M.F. Bashir, B. Ma, L. Shahzad, A brief review of socio-economic and environmental impact of Covid-19. Air Qual. Atmos. Health 13, 1403–1409 (2020) 63. H.-C. Wu, Y.-C. Wang, T. Chen, Assessing and comparing COVID-19 intervention strategies using a varying partial-consensus fuzzy collaborative intelligence approach. Mathematics 8, 1725 (2020) 64. D.S.W. Ting, L. Carin, V. Dzau, T.Y. Wong, Digital technology and COVID-19. Nat. Med. 26(4), 459–461 (2020) 65. T. Chen, C.-W. Lin, Smart and automation technologies for ensuring the long-term operation of a factory amid the COVID-19 pandemic: an evolving fuzzy assessment approach. Int. J. Adv. Manuf. Technol. 111, 3545–3558 (2020) 66. T. Chen, M.-C. Chiu, Evaluating the sustainability of a smart technology application in healthcare after the COVID-19 pandemic: a hybridizing subjective and objective fuzzy group decision-making approach with XAI. Digit. Health 8, 20552076221136380 (2022) 67. Research and Markets, Mobile health (mHealth) market—growth, trends, COVID-19 impact, and forecasts (2021–2026) (2021). https://www.researchandmarkets.com/reports/4520220/ mobile-health-mhealth-market-growth-trends 68. P. Heijmans, Singapore PM pushes for living with COVID, without the fear (2021). https:/ /www.bloomberg.com/news/articles/2021-10-09/singapore-premier-pushes-for-living-withcovid-without-the-fear 69. M.A. Hall, D.M. Studdert, “Vaccine passport” certification—policy and ethical considerations. New Engl. J. Med. 385(11), e32(2021) (2021) 70. D.C. Nguyen, Q.V. Pham, P.N. Pathirana, M. Ding, A. Seneviratne, Z. Lin, O. Dobre, W.J. Hwang, Federated learning for smart healthcare: a survey. ACM Comput. Surv. 55(3), 1–37 (2022) 71. Y.-C. Wang, T.-C.T. Chen, Analyzing the impact of COVID-19 vaccination requirements on travelers’ selection of hotels using a fuzzy multi-criteria decision-making approach. Healthc. Anal. 2, 100064 (2022) 72. T. Chen, Obtaining the optimal cache document replacement policy for the caching system of an EC website. Eur. J. Oper. Res. 181(2), 828–841 (2007)

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73. S.D. Sierra Marín, D. Gomez-Vargas, N. Céspedes, M. Múnera, F. Roberti, P. Barria, S. Ramamoorthy, M. Becker, R. Carelli, C.A. Cifuentes, Expectations and perceptions of healthcare professionals for robot deployment in hospital environments during the COVID-19 pandemic. Front. Robot. AI 8, 612746 (2021) 74. R. Suhartina, T. Abuzairi, Pulse oximeter monitoring bracelet for COVID-19 patient using seeeduino. Jurnal Ilmiah Teknik Elektro Komputer dan Informatika 7(1), 81–87 (2021) 75. T. Chen, Forecasting the yield of a semiconductor product with a collaborative intelligence approach. Appl. Soft Comput. 13, 1552–1560 (2013) 76. M.-C. Chiu, T. Chen, A ubiquitous healthcare system of 3D printing facilities for making dentures: application of type-II fuzzy logic. Digit. Health 8, 20552076221092540 (2022) 77. T.C.T. Chen, Quality control in a 3D printing-based ubiquitous manufacturing system, in 3D Printing and Ubiquitous Manufacturing (2020), pp. 83–95 78. Y.C. Wang, T. Chen, M.-C. Chiu, An improved explainable artificial intelligence tool in healthcare for hospital recommendation. Healthc. Anal. 3, 100147 (2023) 79. Y.-C. Wang, T. Chen, A partial-consensus posterior-aggregation FAHP method—supplier selection problem as an example. Mathematics 7(2), 179 (2019) 80. T. Chen, Y.C. Wang, A two-stage explainable artificial intelligence approach for classificationbased job cycle time prediction. Int. J. Adv. Manuf. Technol. 123, 2031–2042 (2022) 81. R.K. Solanki, A.S. Rajawat, A.R. Gadekar, M.E. Patil, Building a conversational chatbot using machine learning: towards a more intelligent healthcare application, in Handbook of Research on Instructional Technologies in Health Education and Allied Disciplines (2023), pp. 285–309 82. T. Chen, Y.C. Wang, An agent-based fuzzy collaborative intelligence approach for precise and accurate semiconductor yield forecasting. IEEE Trans. Fuzzy Syst. 22(1), 201–211 (2013) 83. T. Chen, A hybrid fuzzy and neural approach with virtual experts and partial consensus for DRAM price forecasting. Int. J. Inno. Comput. Inf. Control 8(1), 583–597 (2012) 84. T. Chen, Applying a fuzzy and neural approach for forecasting the foreign exchange rate, in Computer Engineering: Concepts, Methodologies, Tools and Applications (2012), pp. 412–425 85. K. Ren, W. Zhang, Y. Rong, H. Zhang, Y. Yu, J. Wang, User response learning for directly optimizing campaign performance in display advertising, in Proceedings of the 25th ACM International Conference on Information and Knowledge Management (2016), pp. 679–688 86. T. Chen, A tailored non-linear fluctuation smoothing rule for semiconductor manufacturing factory scheduling. Proc. Inst. Mech. Eng. Part I: J. Syst. Control Eng. 223(2), 149–160 (2009) 87. T. Chen, Y.C. Wang, A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory—a simulation study. Int. J. Adv. Manuf. Technol. 45, 110–121 (2009) 88. H. Firdaus, S.I. Hassan, H. Kaur, A comparative survey of machine learning and meta-heuristic optimization algorithms for sustainable and smart healthcare. Afr. J. Comput. 11(4), 1–17 (2018) 89. T.C.T. Chen, K. Honda, Introduction to fuzzy collaborative forecasting systems, in Fuzzy Collaborative Forecasting and Clustering: Methodology, System Architecture, and Applications (2020), pp. 1–8 90. T.-C.T. Chen, Type-II fuzzy collaborative intelligence for assessing cloud manufacturing technology applications. Robot. Comp. Integr. Manuf. 78, 102399 (2022) 91. P. Siirtola, Continuous stress detection using the sensors of commercial smartwatch, in Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers (2019), pp. 1198–1201 92. T. Chen, Y.C. Wang, H.C. Wu, Analyzing the impact of vaccine availability on alternative supplier selection amid the COVID-19 pandemic: a cFGM-FTOPSIS-FWI approach. Healthcare 9(1), 71 (2021) 93. T. Chen, W.A. Chaovalitwongse, M.J. O’grady, K. Honda, Editorial: smart technologies for improving the quality of mobile health care. Health Care Manage. Sci. 23, 171–172 (2020)

Chapter 3

Evaluating the Sustainability of a Smart Healthcare Application

Abstract This chapter first defines the sustainability of a smart healthcare application and then proposes three methods for evaluating the sustainability of a smart healthcare application: qualitative methods, multicriteria decision-making (MCDM) methods, and time-series methods by considering the inherent subjectivity and uncertainty. Each method is explained through simple examples, supplemented by MATLAB or Lingo codes to facilitate calculations, allowing readers to learn quickly. Then, the advantages and/or disadvantages of each approach are also discussed. Keywords Smart healthcare technology · COVID-19 pandemic · Sustainability · Evaluation · Qualitative method · Multi-criteria decision-making method · Time-series method

3.1 Sustainability of a Smart Healthcare Application Sustainability is a method of consuming resources to meet the needs of the present without compromising the ability to meet the needs of the future [1, 2]. Sustainability includes three dimensions: economic, environmental, and social [3]. The sustainability of a technology usually means that it contributes to economic growth and its use consumes little energy, does not cause any harm to the environment, and does not deprive others (including future generations) of available resources [4].

3.2 Evaluating the Sustainability of a Smart Healthcare Technology The sustainability of a smart healthcare application can be evaluated in the following ways (see Fig. 3.1): • Qualitative approach: The requirements for a sustainable smart healthcare application are listed. The more requirements that are met by a smart healthcare application, the more sustainable the smart healthcare application becomes. © The Author(s) 2023 T. T. Chen, Sustainable Smart Healthcare, SpringerBriefs in Applied Sciences and Technology, https://doi.org/10.1007/978-3-031-37146-2_3

39

40

3 Evaluating the Sustainability of a Smart Healthcare Application

Smart Healthcare Application Sustainability Evaluation

Qualitative approach

Multi-criteria decisionmaking approach

Time-series approach

Fig. 3.1 Possible ways to evaluate the sustainability of a smart healthcare application

• Multicriteria decision-making (MCDM) approach [5, 6]: Several criteria for assessing the sustainability of a smart healthcare application are established. The performance of a smart healthcare technology is evaluated against each criterion. Then, the evaluation results across all criteria are aggregated to represent the sustainability of the smart healthcare application. • Time-series approach: The time-series approach considers the growth of the market size of a smart healthcare technology as a time series, thereby predicting the market size in the coming years based on the past. A smart healthcare application can be said to be sustainable if the market for the smart healthcare technology does not shrink rapidly in the foreseeable future.

3.3 Qualitative Approach The sustainability of a smart healthcare application can be evaluated by considering the following criteria [7, 8]: • A smart healthcare application is sustainable if it can provide value-added services [7–9]. • A smart healthcare application is sustainable if it is cost-effective [7, 8, 10]. • A smart healthcare application is sustainable if it can promote healthy mobility among the public [7, 8, 11–15]. • A smart healthcare application is sustainable if it is necessary or irreplaceable [7, 8, 14, 16]. • A smart healthcare application is sustainable if it can be combined with other smart technologies to achieve synergy [7, 8, 14, 17, 18]. • A smart healthcare application is sustainable if it is easy to implement and maintain [7, 8, 14]. as illustrated in Fig. 3.2. Based on these criteria, the sustainability of some smart healthcare applications was assessed, and the results are summarized in Table 3.1. The more criteria are satisfied by a smart healthcare application, the more sustainable it is.

3.4 MCDM Approach

41

Fig. 3.2 Sustainability of a smart healthcare technology

Is easy to implement and maintain Can be combined to achieve synergy

Is necessary or irreplaceable

Sustainable Smart Healthcare Technology

Provide valueadded services

Is costeffective

Promote healthy mobility

Table 3.1 Sustainability of some smart healthcare applications Criterion

Smart phone (Apps)

Smart watch

Smart bracelet

Smart glasses

Smart clothes

Can provide value-added services

V

V

V

V

V

V

V

V

V

V

V

V

V

V

V

Is cost-effective Can promote healthy mobility

V

Is necessary or irreplaceable

V

Can be combined with other smart technologies

V

Is easy to implement and maintain

V

3.4 MCDM Approach Chen and Wang [19] proposed a calibrated fuzzy geometric mean (cFGM)-fuzzy technique for ordering preference by similarity to ideal solution (FTOPSIS) approach to evaluate assess a smart technology application for healthcare after the COVID-19 pandemic. Fuzzy methods are suitable for considering the many uncertainties people face during the COVID-19 pandemic. Without loss of generality, all fuzzy parameters

42

3 Evaluating the Sustainability of a Smart Healthcare Application

1

Fig. 3.3 TFN

(x)

0.8

~ A = (3, 5, 7)

0.6 0.4 0.2 0

1

3

5

7

9

x Fig. 3.4 Procedure for the cFGM-FTOPSIS approach

Derive the fuzzy priorities of criteria using CFGM Evaluate the performance of a smart technology application in optimizing each criterion Derive the overall performance of the smart technology application using FTOPSIS Compare the overall performances of all smart technology applications Choose the best performing smart technology applications

and variables in this method are given or approximated by triangular fuzzy numbers (TFN) [20, 21] (Fig. 3.3). The procedure for the cFGM-FTOPSIS is illustrated in Fig. 3.4.

3.4.1 Determining the Priorities of Criteria The first step is to derive the fuzzy priorities of criteria for evaluating the performance of a smart healthcare technology application. To do this, the decision maker expresses his/her opinion on the relative priority of a criterion over another in linguistic (or semantic) terms. These linguistic terms are usually mapped to TFNs in [1, 9] (see ˜ = [a˜ i j ]. a˜ i j Table 3.2). The results are summarized with a fuzzy judgment matrix A

3.4 MCDM Approach

43

is the relative priority of criterion i with respect to criterion j. a˜ ji = 1/a˜ i j ; a˜ ii = 1; i, j = 1 ~ n. The TFNs for these linguistic terms overlap to reflect the uncertainty in choosing between them. The fuzzy judgment matrix satisfies the following conditions [22]: ) ( ˜ λ˜ I = 0 det A(−)

(3.1)

) ( ˜ A(−) λ˜ I (×)˜x = 0

(3.2)

˜ x˜ is the corresponding fuzzy eigenvector. λ˜ is the fuzzy maximal eigenvalue of A; (−) and (×) denote fuzzy subtraction and multiplication [23], respectively. The fuzzy priorities of criteria, indicated with {w˜ i |i = 1~n}, can be approximated using fuzzy geometric mean (FGM) [24–26] as: wi1 ∼ =

wi2 ∼ =

wi3 ∼ =

1 1+

∑ m/=i

1 1+

∑ m/=i

1 1+

∑ m/=i

√ ∏n n j=1 am j3 √ ∏n n

(3.3)

√ ∏n n j=1 am j2 √ ∏n n

(3.4)

√ ∏n n j=1 am j1 √ ∏n n

(3.5)

j=1

j=1

j=1

ai j1

ai j2

ai j3

Example 3.1 The fuzzy judgment matrix of a decision maker is Table 3.2 TFNs for expressing linguistic terms

Linguistic term

TFN

As equal as

(1, 1, 3)

As equal as or weakly more important than

(1, 2, 4)

Weakly more important than

(1, 3, 5)

Weakly or strongly more important than

(2, 4, 6)

Strongly more important than

(3, 5, 7)

Strongly or very strongly more important than

(4, 6, 8)

Very strongly more important than

(5, 7, 9)

Very strongly or absolutely more important than

(6, 8, 9)

Absolutely more important than

(7, 9, 9)

44

3 Evaluating the Sustainability of a Smart Healthcare Application

⎡

⎤ 1 (1, 2, 4) 1/(2, 4, 6) ˜ = ⎣ 1/(1, 2, 4) A 1 1/(3, 5, 7) ⎦ (2, 4, 6) (3, 5, 7) 1 Consider the first criterion, according to Eqs. (3.3) to (3.5), 1

w11 ∼ =

1+

√ 3 1/1·1·1/3 √ 3 1·1·1/6

1+

√ 3 1/2·1·1/5 √ 3 1·2·1/4

1+

√ 3 1/4·1·1/7 √ 3 1·4·1/2

+

√ 3 6·7·1 √ 3 1·1·1/6

= 0.117

+

√ 3 4·5·1 √ 3 1·2·1/4

= 0.200

+

√ 3 2·3·1 √ 3 1·4·1/2

= 0.370

1

w12 ∼ =

1

w13 ∼ =

The fuzzy priorities of other criteria can be obtained similarly as w˜ 2 ∼ = (0.065, 0.117, 0.227) w˜ 3 ∼ = (0.482, 0.683, 0.798) To enhance the accuracy of deriving fuzzy priorities, Chen and Wang [27] proposed the cFGM approach to modify the results as wi1 ∼ =

1 1+

∑ m/=i

1 + wi(c) − √ √ ∏n ∏n n n ∑ a m j3 j=1 j=1 am j2 √ √ 1 + ∏ n m/=i n ∏n n j=1

ai j1

j=1

ai j2

(c) wi2 ∼ = wi

wi3 ∼ =

1 1+

∑ m/=i

(3.7)

1 + wi(c) − √ √ ∏n ∏n n n ∑ a j=1 m j1 j=1 am j2 √ √ 1 + ∏ n m/=i n ∏n n j=1

(3.6)

ai j3

j=1

(3.8)

ai j2

where wi(c) is the (crisp) priority of criterion i derived from the defuzzified judgment matrix using an eigenanalysis. Example 3.2 The fuzzy judgment matrix of a decision maker is ⎡

1 ⎢ 1/(3, 5, 7) ⎢ ˜ =⎢ A ⎢ 1/(5, 7, 9) ⎢ ⎣ 1/(2, 4, 6) 1/(1, 3, 5)

(3, 5, 7) 1 1/(1, 3, 5) (1, 3, 5) (4, 6, 8)

(5, 7, 9) (1, 3, 5) 1 (1, 1, 3) (3, 5, 7)

(2, 4, 6) 1/(1, 3, 5) 1/(1, 1, 3) 1 (2, 4, 6)

⎤ (1, 3, 5) 1/(4, 6, 8) ⎥ ⎥ ⎥ 1/(3, 5, 7) ⎥ ⎥ 1/(2, 4, 6) ⎦ 1

3.4 MCDM Approach

45

The fuzzy priorities of criteria derived using FGM are ∼ (0.250, 0.474, 0.650) w˜ 1 = w˜ 2 ∼ = (0.033, 0.072, 0.170) w˜ 3 ∼ = (0.025, 0.052, 0.123) w˜ 4 ∼ = (0.051, 0.101, 0.249) w˜ 5 ∼ = (0.159, 0.296, 0.513)

Subsequently, the (crisp) priorities of criteria derived from the defuzzified judgment matrix using an eigenanalysis are ( ) w1(c) , w2(c) , w3(c) , w4(c) , w5(c) = (0.465, 0.077, 0.057, 0.106, 0.295) The fuzzy priority of the first criterion is calibrated as w˜ 1 ∼ = (0.250, 0.474, 0.650) + 0.465 − 0.474 = (0.240, 0.465, 0.640) Similarly, we can obtain w˜ 2 ∼ = (0.038, 0.077, 0.176) w˜ 3 ∼ = (0.026, 0.057, 0.124) w˜ 4 ∼ = (0.055, 0.106, 0.254) w˜ 5 ∼ = (0.157, 0.295, 0.511)

(w*)

The derivation results are summarized in Fig. 3.5.

1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0.000

w1 w2 w3 w4 w5

0.200

0.400

0.600 w*

Fig. 3.5 Derivation results

0.800

1.000

46

3 Evaluating the Sustainability of a Smart Healthcare Application

Subsequently, λ˜ max (λ˜ ) can be estimated as n 1 ∑ ∑ ai j1 w j1 λmax,1 ∼ =1+ n i=1 j/=i wi3

(3.9)

n 1 ∑ ∑ ai j2 w j2 λmax,2 ∼ =1+ n i=1 j/=i wi2

(3.10)

n 1 ∑ ∑ ai j3 w j3 λmax,3 ∼ =1+ n i=1 j/=i wi1

(3.11)

˜ ≤ 0.1 ~ A) CR(

(3.12)

( ( ˜ = max 0, λmax,1 − n CR1 (A) (n − 1)RI

(3.13)

˜ is consistent if [28] A

where [29]

˜ = CR2 (A)

λmax,2 − n (n − 1)RI

(3.14)

˜ = CR3 (A)

λmax,3 − n (n − 1)RI

(3.15)

˜ is not necesRI is random consistency index [28]. It is worth noting that CR1 (A) ˜ is usually determined by λmax,3 [30]. However, sarily tied to λmax,1 , while CR3 (A) ˜ considers the least consistent case, which rarely happens. Therefore, in CR3 (A) practice, ˜ ≤0.1~0.3 and • CR2 (A) ˜ = 0. • CR1 (A) are better [31, 32]. Otherwise, the decision maker needs to revise his/her pairwise comparison results. In Example 3.2, the consistency ratio is evaluated and shown in Fig. 3.6. Obviously, the pairwise comparison results are (fuzzily) consistent.

3.4 MCDM Approach

47

1

Fig. 3.6 Consistency ratio of pairwise comparison results in Example 3.2

(CR)

0.8 0.6 0.4 0.2 0 0.11 0.00

2.00

4.00

6.00

CR

3.4.2 Evaluating the Sustainability of a Smart Healthcare Technology Let the performance of smart healthcare technology q in optimize criterion i be indicated with p˜ qi . To facilitate the aggregation, p˜ qi is normalized as [33] p˜ qi ρ˜qi = /∑ Q

φ=1

2 p˜ φi

1

=/ 1+

∑ φ/=q

(

p˜ φi p˜ qi

)2

(3.16)

where 1

ρqi1 = / 1+

∑ φ/=q

1

ρqi2 = / 1+

∑

1+

(

φ/=q

1

ρqi3 = /

(

∑

(

φ/=q

pφi3 pqi1

pφi2 pqi2

pφi1 pqi3

)2

(3.17)

)2

(3.18)

)2

(3.19)

The fuzzy priority of the criterion is multiplied by the normalized performance to get the fuzzy prioritized score as s˜qi = w˜ i (×)ρ˜qi

(3.20)

sqi1 = wi1 ρqi1

(3.21)

Equivalently,

48

3 Evaluating the Sustainability of a Smart Healthcare Application

sqi2 = wi2 ρqi2

(3.22)

sqi3 = wi3 ρqi3

(3.23)

Fuzzy ideal (zenith) point and fuzzy anti-ideal (nadir) point are then specified respectively as [34] { } ˜+ = ⌃ ˜ i+ = {maxs˜qi } ⌃

(3.24)

{ } ˜− = ⌃ ˜ i− = {mins˜qi } ⌃

(3.25)

q

q

The fuzzy distances from smart healthcare technology q to the two reference points are calculated respectively as [35] ┌ | n ( )2 |∑ ˜ i+ (−)˜sqi d˜q+ = | ⌃ i=1

┌ ┌ ⎞ ⎛┌ | n | n | n ∑ ∑ |∑ | | ) ( ) ( ) ( 2 2 2 + + + ∼ ⌃i2 ⌃i3 max 0, ⌃i1 − sqi3 , | − sqi2 , | − sqi1 ⎠ = ⎝| i=1

i=1

i=1

(3.26) ┌ | n ( )2 |∑ − ˜ ˜ i− s˜qi (−)⌃ dq = | i=1

┌ ┌ ⎞ ⎛┌ | n | n | n ∑( ∑( |∑ | | ) ) ) ( 2 2 2 ∼ sqi2 − ⌃− , | sqi3 − ⌃− ⎠ max 0, sqi1 − ⌃− , | = ⎝| i3

i2

i=1

i1

i=1

i=1

(3.27) A smart healthcare technology application is more sustainable if it is closer to the ideal solution but farther from the anti-ideal solution. To this end, the fuzzy closeness of smart healthcare technology q is obtained as [36] C˜ q =

d˜q−

(3.28)

d˜q+ (+)d˜q−

Therefore, ( Cq1 = min

− dq1

− dq3

+ − , + − dq3 + dq1 dq3 + dq3

) (3.29)

3.4 MCDM Approach

49 − dq2

Cq2 = ( Cq3 = max

(3.30)

+ − dq2 + dq2 − dq1

− dq3

)

+ − , + − dq1 + dq1 dq1 + dq3

(3.31)

0 ≤ C˜ q ≤ 1. A smart healthcare technology is more sustainable if its fuzzy closeness is higher. To get an absolute ranking, the fuzzy closeness can be defuzzified using the center-of-gravity (COG) method [37]: 1 ( ) 0 xμC˜ q (x)dx ˜ COG Cq =  1 0 μC˜ q (x)dx =

Cq1 + Cq2 + Cq3 3

(3.32)

Example 3.3 Based on the derived fuzzy priorities of criteria in Example 3.2, the sustainabilities of nine smart healthcare applications are compared. The performances of these smart healthcare applications in optimizing various criteria have been evaluated. The evaluation results are summarized in Table 3.3. First, the performance of a smart healthcare application in optimizing each criterion is normalized using fuzzy distributive normalization. The results are summarized in Table 3.4. After multiplying the derived fuzzy priorities to the normalized performances, the fuzzy prioritized scores of smart healthcare applications are obtained. The results are summarized in Table 3.5. Based on the fuzzy prioritized scores of all smart healthcare applications, the fuzzy ideal point and the fuzzy anti-ideal point are respectively defined in Table 3.6. Table 3.3 Performances of nine smart healthcare applications q

p˜ q1

p˜ q2

p˜ q3

p˜ q4

p˜ q5

1

(3, 4, 5)

(1.5, 2.5, 3.5)

(4, 5, 5)

(3, 4, 5)

(1.5, 2.5, 3.5)

2

(1.5, 2.5, 3.5)

(0, 0, 1)

(1.5, 2.5, 3.5)

(1.5, 2.5, 3.5)

(3, 4, 5)

3

(1.5, 2.5, 3.5)

(1.5, 2.5, 3.5)

(3, 4, 5)

(1.5, 2.5, 3.5)

(1.5, 2.5, 3.5)

4

(3, 4, 5)

(1.5, 2.5, 3.5)

(3, 4, 5)

(1.5, 2.5, 3.5)

(3, 4, 5)

5

(0, 1, 2)

(3, 4, 5)

(1.5, 2.5, 3.5)

(1.5, 2.5, 3.5)

(3, 4, 5)

6

(0, 1, 2)

(1.5, 2.5, 3.5)

(1.5, 2.5, 3.5)

(0, 1, 2)

(0, 1, 2)

7

(4, 5, 5)

(0, 1, 2)

(3, 4, 5)

(1.5, 2.5, 3.5)

(1.5, 2.5, 3.5)

8

(1.5, 2.5, 3.5)

(0, 0, 1)

(3, 4, 5)

(0, 1, 2)

(1.5, 2.5, 3.5)

9

(3, 4, 5)

(1.5, 2.5, 3.5)

(3, 4, 5)

(0, 1, 2)

(1.5, 2.5, 3.5)

50

3 Evaluating the Sustainability of a Smart Healthcare Application

Table 3.4 Normalized performances of smart healthcare applications q ρ˜q1

ρ˜q2

ρ˜q3

ρ˜q4

ρ˜q5

1 (0.26, 0.41, 0.62) (0.17, 0.36, 0.64) (0.3, 0.45, 0.57)

(0.33, 0.56, 0.83) (0.13, 0.28, 0.5)

2 (0.13, 0.26, 0.45) (0, 0, 0.22)

(0.16, 0.35, 0.64) (0.27, 0.45, 0.68)

(0.11, 0.22, 0.4)

3 (0.13, 0.26, 0.45) (0.17, 0.36, 0.64) (0.23, 0.36, 0.55) (0.16, 0.35, 0.64) (0.13, 0.28, 0.5) 4 (0.26, 0.41, 0.62) (0.17, 0.36, 0.64) (0.23, 0.36, 0.55) (0.16, 0.35, 0.64) (0.27, 0.45, 0.68) 5 (0, 0.1, 0.27)

(0.34, 0.58, 0.83) (0.11, 0.22, 0.4)

(0.16, 0.35, 0.64) (0.27, 0.45, 0.68)

6 (0, 0.1, 0.27)

(0.17, 0.36, 0.64) (0.11, 0.22, 0.4)

(0, 0.14, 0.41)

(0, 0.11, 0.31)

7 (0.34, 0.52, 0.65) (0, 0.14, 0.41)

(0.23, 0.36, 0.55) (0.16, 0.35, 0.64) (0.13, 0.28, 0.5)

8 (0.13, 0.26, 0.45) (0, 0, 0.22)

(0.23, 0.36, 0.55) (0, 0.14, 0.41)

(0.13, 0.28, 0.5)

9 (0.26, 0.41, 0.62) (0.17, 0.36, 0.64) (0.23, 0.36, 0.55) (0, 0.14, 0.41)

(0.13, 0.28, 0.5)

Table 3.5 Fuzzy prioritized scores of smart healthcare applications q

s˜q1

s˜q2

s˜q3

s˜q4

s˜q5

1

(0.063, 0.192, 0.395)

(0.006, 0.028, 0.112)

(0.008, 0.026, 0.071)

(0.018, 0.06, 0.211)

(0.021, 0.082, 0.257)

2

(0.031, 0.12, 0.29)

(0, 0, 0.038)

(0.003, 0.013, 0.049)

(0.009, 0.037, 0.162)

(0.042, 0.132, 0.347)

3

(0.031, 0.12, 0.29)

(0.006, 0.028, 0.112)

(0.006, 0.02, 0.068)

(0.009, 0.037, 0.162)

(0.021, 0.082, 0.257)

4

(0.063, 0.192, 0.395)

(0.006, 0.028, 0.112)

(0.006, 0.02, 0.068)

(0.009, 0.037, 0.162)

(0.042, 0.132, 0.347)

5

(0, 0.048, 0.175) (0.013, 0.044, 0.151)

(0.003, 0.013, 0.05)

(0.009, 0.037, 0.162)

(0.042, 0.132, 0.347)

6

(0, 0.048, 0.175) (0.006, 0.028, 0.112)

(0.003, 0.013, 0.049)

(0, 0.015, 0.103) (0, 0.033, 0.157)

7

(0.082, 0.24, 0.426)

(0, 0.011, 0.071) (0.006, 0.02, 0.072)

(0.009, 0.037, 0.162)

8

(0.031, 0.12, 0.29)

(0, 0, 0.038)

(0.006, 0.02, 0.068)

(0, 0.015, 0.103) (0.021, 0.082, 0.257)

9

(0.457, 0.898, 0.985)

(0.035, 0.202, 0.791)

(0.055, 0.237, 0.729)

(0, 0.106, 0.722) (0.147, 0.609, 0.958)

(0.021, 0.082, 0.264)

Table 3.6 Fuzzy ideal point and fuzzy anti-ideal point ˜ +/− ⌃ 1

˜ +/− ⌃ 2

˜ +/− ⌃ 3

˜ +/− ⌃ 4

˜ +/− ⌃ 5

Fuzzy ideal point

(0.457, 0.898, 0.985)

(0.035, 0.202, 0.791)

(0.055, 0.237, 0.729)

(0.018, 0.106, 0.722)

(0.147, 0.609, 0.958)

Fuzzy anti-ideal point

(0, 0, 0)

(0, 0, 0)

(0, 0, 0)

(0, 0, 0)

(0, 0, 0)

Reference point

3.5 Time-Series Approach

51

Table 3.7 Distances and closenesses d˜q+ d˜q− q

C˜ q

( ) COG C˜ q

1

(0.062, 0.924, 1.833)

(0.07, 0.221, 0.533)

(0.037, 0.193, 0.896)

0.375

2

(0.167, 0.964, 1.847)

(0.053, 0.183, 0.484)

(0.028, 0.159, 0.743)

0.310

3

(0.167, 0.982, 1.854)

(0.039, 0.154, 0.44)

(0.021, 0.136, 0.725)

0.294

4

(0.062, 0.899, 1.826)

(0.077, 0.238, 0.565)

(0.041, 0.21, 0.901)

0.384

5

(0.282, 1.015, 1.857)

(0.045, 0.152, 0.45)

(0.024, 0.13, 0.614)

0.256

6

(0.282, 1.07, 1.885)

(0.007, 0.067, 0.284)

(0.004, 0.059, 0.502)

0.188

7

(0.031, 0.894, 1.83)

(0.086, 0.258, 0.536)

(0.045, 0.224, 0.945)

0.405

8

(0.167, 0.99, 1.86)

(0.038, 0.148, 0.409)

(0.02, 0.13, 0.71)

0.287

9

(0, 0, 1.576)

(0.484, 1.134, 1.889)

(0.235, 1, 1)

0.745

Subsequently, the distances from each smart healthcare application to the two reference points are measured respectively. The results are summarized in Table 3.7. Finally, the sustainability of each smart healthcare application, in terms of its fuzzy closeness, is evaluated. The results are shown in the same table. The fuzzy closeness of each smart healthcare application is defuzzified using the COG method. In this example, the most sustainable smart healthcare application is the 9th smart healthcare application. A new explainable artificial intelligence (XAI) tool, a bidirectional scatterplot, can be used to summarize the evaluation results [38] (see Fig. 3.7): • The ideal solution is at the top, while the anti-ideal solution is at the bottom. • The ideal solution is white, while the anti-ideal solution is black. • All smart healthcare applications are placed anywhere in the bidirectional scatter plot if the following requirement is met. The distances from each smart healthcare application to the two reference points are measured using the FTOPSIS method, as illustrated in this figure. • The closer a smart healthcare application is to the ideal solution, the whiter (lighter) its color is. Conversely, a smart healthcare application becomes blacker if it is closer to the anti-ideal solution.

3.5 Time-Series Approach The time-series approach considers the growth of the market size of a smart healthcare technology as a time series, thereby predicting the market size in the coming years based on the past. Furthermore, stochastic or fuzzy methods are able to deal with inherent uncertainties. Among them, fuzzy methods are particularly suitable due to their ease of understanding and calculation [39, 40]. For example, a fuzzy linear regression (FLR) equation [41, 42] can be fitted:

52

3 Evaluating the Sustainability of a Smart Healthcare Application

Fig. 3.7 Bidirectional scatterplot

Ideal Solution Smart healthcare application #1 Smart healthcare application #3 Smart healthcare application #2

Anti-ideal Solution

˜ y˜t = a(+) ˜ bt

(3.33)

where a˜ and b˜ are constant. y˜t is the predicted market size of a smart healthcare technology in period t, which is approximated by a TFN. yt is the actual value. Equation (3.33) can be fitted by solving the following linear programming (LP) problem, according to Tanaka’s method [43]: Min Z 1 =

T ∑

(yt3 − yt1 )

(3.34)

yt1 = a1 + b1 t; t = 1 ∼ T

(3.35)

yt2 = a2 + b2 t; t = 1 ∼ T

(3.36)

t=1

subject to

3.5 Time-Series Approach

53

yt3 = a3 + b3 t; t = 1 ∼ T

(3.37)

(1 − s)yt1 + syt2 ≤ yt ≤ (1 − s)yt3 + syt2 ; t = 1 T

(3.38)

a1 ≤ a2 ≤ a3

(3.39)

b1 ≤ b2 ≤ b3

(3.40)

0 ≤ yt1 ≤ yt2 ≤ yt3 ; t = 1 T

(3.41)

where s is a pre-specified satisfaction level within [0, 1]. However, the collected data should be pre-processed by removing the seasonality [44], as shown in the following example. Example 3.4 According to the statistics by O’Brien [45], the global market size of smart watches, in terms of global shipments of OLED smart watches by panel suppliers, is summarized in Table 3.8. There is seasonality in the data. The seasonal relatives are derived according to Table 3.9. The derivation results are Q1: (0.81 + 0.77 + 0.76)/3 = 0.78 Q2: 0.73. Table 3.8 Global shipments of smart watches

Period #

Period

Global shipments (Millions)*

1

2017 Q1

6

2

2017 Q2

6.8

3

2017 Q3

7.4

4

2017 Q4

9.5

5

2018 Q1

7

6

2018 Q2

7.8

7

2018 Q3

13.5

8

2018 Q4

22.8

9

2019 Q1

14

10

2019 Q2

14.4

11

2019 Q3

30.1

12

2019 Q4

33.8

13

2020 Q1

22.7

14

2020 Q2

26.5

15

2020 Q3

41.5

16

2020 Q4

44.7

* Approximate

values

54

3 Evaluating the Sustainability of a Smart Healthcare Application

Table 3.9 Calculation of seasonal relatives Period # Period

Global shipments (Millions)

1

2017 Q1 6

2

2017 Q2 6.8

4Q moving average

2Q moving average

Global shipments/2Q moving average

7.55

0.98

7.80

1.22

8.69

0.81

11.11

0.70

13.65

0.99

15.35

1.49

18.25

0.77

21.70

0.66

24.16

1.25

26.76

1.26

29.70

0.76

32.49

0.82

7.43 3

2017 Q3 7.4 7.68

4

2017 Q4 9.5

5

2018 Q1 7

7.93 9.45 6

2018 Q2 7.8 12.78

7

2018 Q3 13.5

8

2018 Q4 22.8

14.53 16.18 9

2019 Q1 14 20.33

10

2019 Q2 14.4

11

2019 Q3 30.1

12

2019 Q4 33.8

23.08 25.25 28.28 13

2020 Q1 22.7 31.13

14

2020 Q2 26.5

15

2020 Q3 41.5

16

2020 Q4 44.7

33.85

Q2: 0.73 Q3: 1.07 Q4: 1.32. The data after removing the seasonality is shown in Table 3.10. Example 3.5 A LP problem is formulated in Fig. 3.8 using Lingo based on the data after removing the seasonality in the previous example. The data of the first eight

3.5 Time-Series Approach

55

Table 3.10 Data after removing the seasonality Period #

Period

1

2017 Q1

Global shipments (Seasonality removed) (Millions) 7.70

2

2017 Q2

9.35

3

2017 Q3

6.91

4

2017 Q4

7.19

5

2018 Q1

8.99

6

2018 Q2

10.73

7

2018 Q3

12.60

8

2018 Q4

17.25

9

2019 Q1

17.97

10

2019 Q2

19.81

11

2019 Q3

28.09

12

2019 Q4

25.57

13

2020 Q1

29.14

14

2020 Q2

36.45

15

2020 Q3

38.73

16

2020 Q4

33.81

quarters are used to build the forecasting model, and the remaining data are used to evaluate the forecasting performance [46, 47]. s = 0.3. The optimal solution is a˜ = (1.924, 1.924, 8.778) b˜ = (1.315, 1.315, 1.315) The forecasting results are shown in Fig. 3.9. Subsequently, the seasonal relatives are multiplied to the corresponding fuzzy forecasts. The results are summarized in Table 3.11, which are compared with the original data in Fig. 3.10. A fuzzy market size forecast can be defuzzified using the COG method: COG( y˜t ) =

yt1 + yt2 + yt3 3

(3.42)

Then, the accuracy of predicting the market size of a smart healthcare technology can be evaluated in terms of mean absolute error (MAE), mean absolute percentage error (MAPE), or root mean squared error (RMSE) [48–50]: ∑T MAE =

t=1

|yt − COG( y˜t )| T

(3.43)

56

3 Evaluating the Sustainability of a Smart Healthcare Application

min=y83-y81+y73-y71+y63-y61+y53-y51+y43-y41+y33-y31+y23-y21+y13-y11; y11=a1+b1; y12=a2+b2; y13=a3+b3; y21=a1+2*b1; y22=a2+2*b2; y23=a3+2*b3; … y81=a1+8*b1; y82=a2+8*b2; y83=a3+8*b3; 7.70>=0.7*y11+0.3*y12; 7.70=0.7*y21+0.3*y22; 9.35=0.7*y81+0.3*y82; 17.25