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J Res Med Sci 2021,  26:11

Challenges and opportunities of digital health in a post-COVID19 world


1 Student Research Committee, School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
2 Applied Physiology Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Science, Isfahan, Iran

Date of Submission30-Oct-2020
Date of Decision01-Dec-2020
Date of Acceptance25-Dec-2020
Date of Web Publication16-Feb-2021

Correspondence Address:
Prof. Shaghayegh Haghjooy Javanmard
Applied Physiology Research Center, Isfahan Cardiovascular Research Institute, Isfahan University of Medical Science, Isfahan
Iran
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/jrms.JRMS_1255_20

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  Abstract 


Digital health as a rapidly growing medical field relies comprehensively on human health data. Conventionally, the collection of health data is mediated by officially diagnostic instruments, operated by health professionals in clinical environments and under strict regulatory conditions. Mobile health, telemedicine, and other smart devices with Internet connections are becoming the future choices for collecting patient information. Progress of technologies has facilitated smartphones, wearable devices, and miniaturized health-care devices. These devices allow the gathering of an individual's health-care information at the patient's home. The data from these devices will be huge, and by integrating such enormous data using Artificial Intelligence, more detailed phenotyping of disease and more personalized medicine will be realistic. The future of medicine will be progressively more digital, and recognizing the importance of digital technology in this field and pandemic preparedness planning has become urgent.

Keywords: COVID19, digital health, mobile health, telemedicine


How to cite this article:
Manteghinejad A, Javanmard SH. Challenges and opportunities of digital health in a post-COVID19 world. J Res Med Sci 2021;26:11

How to cite this URL:
Manteghinejad A, Javanmard SH. Challenges and opportunities of digital health in a post-COVID19 world. J Res Med Sci [serial online] 2021 [cited 2021 Jun 23];26:11. Available from: https://www.jmsjournal.net/text.asp?2021/26/1/11/309599




  Introduction Top


Digital revolution has changed the product and service development in almost all aspects of human life, including human health.[1] Digital health is a rapidly expanding medical field with a significant impact on improving the quality of health care, its effectiveness, lowering the cost of the health-care system and patients, and clinical research.[2] The quick propagation of digital innovations for data gathering and communication technologies has transformed the way that physicians collect, share, and analyze health information for better clinical decision-making and health-care delivery. Digital health technology has produced a flow of data from patients vital signs,[3] lifestyles, and past medical histories[4] to health-care professionals that could support the development of a personalized medicine model.[5]

The availability of real-world health data instead of the momentary snapshots seen in hospitals and clinics will reform disease management, in both developed and underdeveloped countries.

Despite the rapid growth of digital technologies, the involvement of the various stakeholders, including patients, clinicians, the insurance industry, and regulators in medicine, remains relatively low.[6] This article aims to provide an overview of the current status of digital health, describe the future perspectives in this field, and point out some of the challenges that need to be addressed.


  The History of Digital Health Top


Digital health is a multidisciplinary domain that aims to enhance the efficiency of monitoring of the patients, diagnosis, management, prevention, rehabilitation, and long-term care delivery.[7] Digital health is not an instant overnight phenomenon. The history of digital health returned to the 1970s when health telematics came into existence.[8] Telecommunications give the health-care systems a great opportunity to improve health, health education, and follow-ups using health telematics. Health telematics at that time aims to focus on diseases and improvement in diagnosing and treatment of diseases.[9] Health telematics, which is now known as telemedicine, is one of the most famous domains of digital health nowadays.[10]

With the beginning of the 21st century and extensive use of desktop personal computers and the Internet, the health-care systems found that the Internet is a great infrastructure for health promotion. At this time, eHealth came into existence. In contrast with health telematics, eHealth focuses on health instead of diseases.[9]

In the 2010s, another shift in technology and emerging mobile phones makes an opportunity that a new domain emerges, mobile health (mHealth). One of the main differences between eHealth and mHealth is adherence. Having a device that always is with people gives the community an ability to use health care services every time and everywhere.[11]

Finally, in 2015 with the widespread use of smartphones, tablets, and improvement in other technologies like robotics, a broader term than eHealth and mHealth was emerged. A term that we called it digital health.[9],[12]


  Digital Health Domains Top


Digital health broad scope encompasses telemedicine, mHealth, wearable devices and biosensors, electronic health records (EHRs) and big data, artificial intelligence (AI), and machine learning. Augmented reality (AR) and virtual reality (VR) are other domains of digital health.[13]

Health telematics or telemedicine is the oldest domain of digital health that is put into more consideration more than other domains. Telemedicine makes it possible that health-care personnel visits the patient remotely. Telemedicine is now used for screening, diagnosing, and treating patients. Moreover, follow-up visits and consultations are also available by using telemedicine.[14]

One of the most knowns screening in telemedicine refers to ophthalmology.[15] Glaucoma, diabetic retinopathies, and retinopathy of prematurity are the diseases that have a successful screening by telemedicine.[16],[17],[18] In this approach also, a teleconsultation disk photograph is sent to ophthalmologists for screening retinopathies and glaucoma. Using telemedicine for diagnosing the diseases is much more common than screening. From psychiatry disorders like autism[19] to emergencies like Myocardial infarctions are handled by telemedicine.[20] Telemedicine is a good choice for follow-ups because it can reduce the cost of accommodation of patients in hospitals, and they can have access to the doctor even in rural areas. Joint arthroplasty and traumas are two the examples of using telemedicine.[21],[22]

mHealth has been defined as medical and public health practice supported by mobile devices.[23] Using applications and web applications is now very common among people. There is a great penetration of using smartphones worldwide, even in the lifestyle of people with low socioeconomic status. Hence, health-related mobile applications might deliver the chance to overcome inequity to health-care access. Each person has tens of applications installed on their smartphones. Like telemedicine, this domain also has a variety of uses. The mHealth apps could be categorized based on their use into patient education apps,[24] clinical decision support apps;[25] therapeutics, and treatment support apps.[26],[27]

Wearable devices, gadgets, and biosensors facilitate real-time ambulatory monitoring of human vital signs throughout daily life with the least discomfort and interference with normal human activities.[28] Currently, various wearable systems including microsensors integrated into textiles and clothes, computerized watches, belt-worn sensors, glasses, gloves, and everything that are worn contacting some parts of the body are designed for relevant health data gathering.[29]

Besides, many innovative wearable biosensors have been developed to detect a wide range of multianalyte/metabolites (such as lactate or glucose), electrolytes (for example, sodium, potassium, or calcium), and other biomarkers in fluids such as sweat, saliva, or tears and skin interstitial fluid.[30],[31]

New wearable sensors like wearable glucose monitors are one of the new wearable devices that the US Food and Drug Administration approved. These sensors continuously monitor the blood glucose so the patient can control the blood glucose much more strict and prevent the complications of low or high blood glucose.[32]

Wearable biosensors must have the ability to work in uncontrolled environments, so calibration for variations in temperature, pH, and humidity is necessary for them.[31]

VR, AR, and mixed reality (MR) are being increasingly used in medical applications such as medical education, procedure simulation, rehabilitation, and psychotherapy.[33]

VR is a pure virtual digital picture, while AR is the result of the integration of information or graphical elements to the user's environment in real time.[34] Thus, AR is usually preferred to VR since the AR is focused on the real world rather than a totally artificial environment.[35]

It has been shown that VR, AR, and MR can improve the effectiveness of medical education, led to better performance of the physician.[36]

One of the main uses of AR and VR is in rehabilitation.[37] Poststroke and posttraumatic stress disorder rehabilitations are the most use of AR and VR. Creating new worlds only in small places with specific pieces of training aims to improve the disabilities is one the main reasons that VRs and ARs are used in this field.[38],[39]

Computers can act as a human because they have the ability of processing and memorizing data, so computers can tackle complex learning tasks. AI – coined by John McCarthy in 1956 - is a broad term that describes any computational programs that simulate and mimic human intelligence, such as problem solving and learning. In machine learning, the machine learns from the data and performs tasks based on the learned model.[40] An applied AI and machine learning have been usually used interchangeably.

AI has been revolutionized medicine. The popularization of big data production and computing machine power has changed the fundamentals of health-care practice and research. Traditional statistics remain effective only in simple data sets, and many areas in clinical practice and research have been transformed by robust prediction and exploration of big data using AI.

Using this strategy in digital health leads to the invention of decision support systems. These systems help the doctor and the patient to personalize the decisions according to patient characteristics. Moreover, signal and image processing leads to improvements in diagnosing diseases in the field of pathology and radiology.[41],[42]

Not only screening, diagnosing, and treating the diseases improved by new technologies but also the ability to collect and storing data digitally can lead to health promotion. EHRs make it possible that all the data related to a patient are stored in one place so anybody in the health-care system can have access to it and use the data for better decisions.[43] Moreover, EHRs lead to creating big data that can have an invaluable price for research and management of the health-care system.[44] Although some governments create EHR systems, private sectors also try to create an infrastructure for big data.[45]


  COVID-19 and Digital Health Top


Coronavirus caused by SARS-CoV2 is our newest guest. The epidemic started in China and spread all over the world, affecting millions of people. This epidemic affects people's lifestyles in all ways, from the first quarantines in countries to wearing a mask, social distancing, etc.[46] People's health care is also affected by this new virus. Most of the health-care system inevitably has to serve only COVID-19 patients and in many countries, because of the lack of enough medical resources the patients faced many problems. These problems lead the medical system to put “Digital Health” into more consideration.[47]

Digital health helps the healthcare system to fight against COVID-19 in different ways: 1 – prevention and primary care, 2 - screening, 3 – monitoring, and 4 - surveillance.

Using digital health for the screening of COVID-19 can lead to a decreased number of visits in emergency departments and help the health-care systems to stay more organized.[48] Using mhealth and ehealth and developing different mobile applications and websites for screening the patients is one of the most common uses of digital health.[49]

During the pandemics, the sudden increase of patients in peaks of the disease can lead to the inability of hospitals to admit all the patients so monitoring of the patients remotely using digital health can be beneficial. Using mhealth for developing applications that the patients can use for patient education or answering certain questions for ensuring the condition of patients is one of the main uses.[50] Moreover, telemedicine for distance consultation and using health gadgets like pulse oximetry is another use of digital health in the COVID-19 pandemic.[51],[52]

Surveillance and using contact-tracing applications is another way that digital health comes to help to fight against the pandemic. Founding the pattern of the pandemic by using contact-tracing applications and isolating the suspected people is a beneficial way of controlling the spread of disease.[53],[54]


  Key Challenges of Digital Health Top


Digital health adoption has been quickly accelerated since the onset of the COVID-19 pandemic as a “no-touch” emergency state. The need for physical distancing has turned the attention of both health-care providers and patients to digital health and reduced resistance to the use of telemedicine provided an opportunity for recognizing the advantages of digital health. The COVID-19 pandemic has revealed not only the need for data sharing but also the need for serious evaluation and ethical aspects to be developed beside the emerging field of digital healthcare. Taking informed patient consent will be a key challenge to provide transparencies regarding what data are collected and which third parties can access patient data. On the other hand, the application of health tracking reward programs by insurance companies encourages using wearable health technology.

Disturbed health-care systems and the need for physical distancing seem to necessitate an extensive experience of digital health solutions, many of them might have the potential to be extended after the pandemic passes, although the long-term use of digital health solutions largely depends on handling some of the challenges.

Key challenges affecting the development of digital health consist of a lack of evidence-based digital health standards and privacy, data governance, and ethical challenges.

Digital health and using EHRs create big data that can use for creating pieces of evidence, but all these data are acquired by convenience sampling.[55] Hence, this problem affects the quality of evidence from researches on these technologies. To resolve this issue, background variables such as age, sex, socioeconomic status, and the geographical distribution must be reported and compared between the groups.[56]

Another problem that emerged from health digitalization is privacy. All domains of digital health finally create data that need protection. Although anonymization technologies improve in recent years, finally re-identification is necessary because the new data should be merged properly with the previous data of the same person. Due to this re-identification hacking, digital health platform is a big deal.[56]

Data governance is another challenge that governments have. Although the improvement of technologies lowers its cost and because of it most of the governments take a step into digitalization, only half of them have privacy policies to protect the data. Hence, it is important that governments set up policies and standards for data governance.[57]

Ethical challenges are also important in health digitalization. User consent is one of these ethical challenges. Users should know about the collection of data. Although most applications ask users for this permission, it is often neglected by users, and almost all users only push the “I agree” button at first without reading the terms of use of the applications.[58]

There is a “no evidence, no implementation–no implementation, no evidence” paradox in digital health field.[59] Evaluation of the impact of digital health interventions entails a multidimensional analysis approach employing mixed methods to study the effects of the program on health-care workers, patients/healthy people, and the health system. The main knowledge gap about the use of digital health strategies is the lack of evidence on how such strategies may influence health outcomes, health system efficiencies, and cost-effectiveness of service delivery.[7]

Cost-effectiveness and sustainability of digital care should be mentioned by policymakers. It is necessary to build public trust and confirm a commitment to take care of their privacy.


  Conclusion Top


Digital health will support the future needs of medicine by analyzing the massive amounts of recorded patient's data that generate by high-tech devices from multiple sources. Digital care can transform disease-centered services toward patient-centered services. Many of the digital health solutions are still in their infancy and need to be improved. Furthermore, they need extensive and successful validation in human testing and improved clinical reliability. Medical professionals also need to be familiarized and adapt themselves with these advances for better health-care delivery to the patients. Along with digital care growth, researchers, scientists, clinicians, payers, and regulators must accompany technology developers to reach the ultimate goal, which is to help patients live longer and feel better.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
Miller EA, West DM. Where's the revolution? Digital technology and health care in the internet age. J Health Polit Policy Law 2009;34:261-84.  Back to cited text no. 1
    
2.
World Health Organization. Monitoring and Evaluating Digital Health Interventions: A Practical Guide to Conducting Research and Assessment. Geneva: World Health Organization; 2016.  Back to cited text no. 2
    
3.
Fischell D, Fischell T, Harwood J, Johnson S, Turi G. Implantable device for vital signs monitoring. Google Patents; 2007.  Back to cited text no. 3
    
4.
Serbanati LD, Ricci FL, Mercurio G, Vasilateanu A. Steps towards a digital health ecosystem. J Biomed Inform 2011;44:621-36.  Back to cited text no. 4
    
5.
Swan M. Health: The realization of personalized medicine through crowdsourcing, the quantified self, and the participatory Biocitizen. J Pers Med 2012;2:93-118.  Back to cited text no. 5
    
6.
Birnbaum F, Lewis D, Rosen RK, Ranney ML. Patient engagement and the design of digital health. Acad Emerg Med 2015;22:754-6.  Back to cited text no. 6
    
7.
Greaves F, Joshi I, Campbell M, Roberts S, Patel N, Powell J. What is an appropriate level of evidence for a digital health intervention? Lancet 2019;392:2665-7.  Back to cited text no. 7
    
8.
WHO. A Health Telematics Policy in Support of WHO's Health-For-All Strategy for Global Health Development. Report of the WHO Group Consultation on Health Telematics. Geneva: World Health Organization; 1997. p. 11-6.  Back to cited text no. 8
    
9.
Meister S, Deiters W, Becker S. Digital health and digital biomarkers–enabling value chains on health data. Curr Dir Biomed Eng 2016;2:577-81.  Back to cited text no. 9
    
10.
Stanberry B. Telemedicine: Barriers and opportunities in the 21st century. J Intern Med 2000;247:615-28.  Back to cited text no. 10
    
11.
World Health Organization. MHealth: New Horizons for Health through Mobile Technologies. MHealth: New Horizons for Health through Mobile Technologies?. Geneva: World Health Organization; 2011.  Back to cited text no. 11
    
12.
McAuley A. Digital health interventions: Widening access or widening inequalities Public Health 2014;128:1118-20.  Back to cited text no. 12
    
13.
Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J 2016;37:1428-38.  Back to cited text no. 13
    
14.
Waller M, Stotler C. Telemedicine: A primer. Curr Allergy Asthma Rep 2018;18:54.  Back to cited text no. 14
    
15.
Parikh D, Armstrong G, Liou V, Husain D. Advances in telemedicine in ophthalmology. Semin Ophthalmol 2020;35:210-5.  Back to cited text no. 15
    
16.
Vujosevic S, Midena E. Diabetic retinopathy in Italy: Epidemiology data and telemedicine screening programs. J Diabetes Res 2016;2016:3627465.  Back to cited text no. 16
    
17.
Ossandón D, Zanolli M, Stevenson R, Agurto R, Ortiz P, Dotan G. A national telemedicine network for retinopathy of prematurity screening. J AAPOS 2018;22:124-7.  Back to cited text no. 17
    
18.
Hark LA, Katz LJ, Myers JS, Waisbourd M, Johnson D, Pizzi LT, et al. Philadelphia telemedicine glaucoma detection and follow-up study: Methods and screening results. Am J Ophthalmol 2017;181:114-24.  Back to cited text no. 18
    
19.
Knutsen J, Wolfe A, Burke BL, Hepburn S, Lindgren S, Coury D. A systematic review of telemedicine in autism spectrum disorders. Rev J Autism Dev Disord 2016;3:330-44.  Back to cited text no. 19
    
20.
Miller AC, Ward MM, Ullrich F, Merchant KA, Swanson MB, Mohr NM. Emergency department telemedicine consults are associated with faster time-to-electrocardiogram and time-to-fibrinolysis for myocardial infarction patients. Telemed J E Health 2020;26:1440-8.  Back to cited text no. 20
    
21.
Rao SS, Loeb AE, Amin RM, Golladay GJ, Levin AS, Thakkar SC. Establishing telemedicine in an academic total joint arthroplasty practice: Needs and opportunities highlighted by the COVID-19 pandemic. Arthroplast Today 2020;6:617-22.  Back to cited text no. 21
    
22.
Kim PT, Falcone RA Jr. The use of telemedicine in the care of the pediatric trauma patient. Semin Pediatr Surg 2017;26:47-53.  Back to cited text no. 22
    
23.
Ryu S. Book Review: MHealth: New horizons for health through mobile technologies: Based on the findings of the second global survey on eHealth (global observatory for eHealth series, volume 3). Healthc Inform Res 2012;18:231-3.  Back to cited text no. 23
    
24.
Wood J, Keen A, Basu N, Robertshaw S. The Development of Mobile Applications for Patient Education. Proceedings of the 2003 Conference on Designing for user Experiences; 2003.  Back to cited text no. 24
    
25.
Sarkar U, Samal L. How effective are clinical decision support systems? Br Med J 2020;370:m3499.  Back to cited text no. 25
    
26.
Gardner B, de Bruijn GJ, Lally P. A systematic review and meta-analysis of applications of the self-report habit index to nutrition and physical activity behaviours. Ann Behav Med 2011;42:174-87.  Back to cited text no. 26
    
27.
Lui JH, Marcus DK, Barry CT. Evidence-based apps? A review of mental health mobile applications in a psychotherapy context. Prof Psychol Res Pr 2017;48:199.  Back to cited text no. 27
    
28.
Dias D, Paulo Silva Cunha J. Wearable health devices-vital sign monitoring, systems and technologies. Sensors (Basel) 2018;18:2414.  Back to cited text no. 28
    
29.
Mertz L. Convergence revolution comes to wearables: Multiple advances are taking biosensor networks to the next level in health care. IEEE Pulse 2016;7:13-7.  Back to cited text no. 29
    
30.
Bollella P, Sharma S, Cass AE, Antiochia R. Microneedle-based biosensor for minimally-invasive lactate detection. Biosens Bioelectron 2019;123:152-9.  Back to cited text no. 30
    
31.
Kim J, Campbell AS, de Ávila BE, Wang J. Wearable biosensors for healthcare monitoring. Nat Biotechnol 2019;37:389-406.  Back to cited text no. 31
    
32.
Cohen AB, Dorsey ER, Mathews SC, Bates DW, Safavi K. A digital health industry cohort across the health continuum. NPJ Digit Med 2020;3:68.  Back to cited text no. 32
    
33.
Zhan T, Yin K, Xiong J, He Z, Wu ST. Augmented reality and virtual reality displays: Perspectives and challenges. Iscience 2020;23:101397.  Back to cited text no. 33
    
34.
Hu HZ, Feng XB, Shao ZW, Xie M, Xu S, Wu XH, et al. Application and prospect of mixed reality technology in medical field. Curr Med Sci 2019;39:1-6.  Back to cited text no. 34
    
35.
Barsom EZ, Graafland M, Schijven MP. Systematic review on the effectiveness of augmented reality applications in medical training. Surg Endosc 2016;30:4174-83.  Back to cited text no. 35
    
36.
Moro C, Štromberga Z, Raikos A, Stirling A. The effectiveness of virtual and augmented reality in health sciences and medical anatomy. Anat Sci Educ 2017;10:549-59.  Back to cited text no. 36
    
37.
Howard MC. A meta-analysis and systematic literature review of virtual reality rehabilitation programs. Comput Hum Behav 2017;70:317-27.  Back to cited text no. 37
    
38.
Mousavi Hondori H, Khademi M, Dodakian L, Cramer SC, Lopes CV. A spatial augmented reality rehab system for post-stroke hand rehabilitation. Stud Health Technol Inform 2013;184:279-85.  Back to cited text no. 38
    
39.
Kothgassner OD, Goreis A, Kafka JX, Van Eickels RL, Plener PL, Felnhofer A. Virtual reality exposure therapy for posttraumatic stress disorder (PTSD): A meta-analysis. Eur J Psychotraumatol 2019;10:1654782.  Back to cited text no. 39
    
40.
Pesapane F, Tantrige P, Patella F, Biondetti P, Nicosia L, Ianniello A, et al. Myths and facts about artificial intelligence: Why machine-and deep-learning will not replace interventional radiologists. Med Oncol (Northwood, London, England) 2020;37:40.  Back to cited text no. 40
    
41.
Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med 2019;25:1301-9.  Back to cited text no. 41
    
42.
Neri E, Miele V, Coppola F, Grassi R. Use of CT and artificial intelligence in suspected or COVID-19 positive patients: Statement of the Italian society of medical and interventional radiology. Radiol Med 2020;125:505-8.  Back to cited text no. 42
    
43.
Xhafa F, Li J, Zhao G, Li J, Chen X, Wong DS. Designing cloud-based electronic health record system with attribute-based encryption. Multimed Tools Appl 2015;74:3441-58.  Back to cited text no. 43
    
44.
Ross M, Wei W, Machado OL. “Big data” and the electronic health record. Yearb Med Inform 2014;9:97.  Back to cited text no. 44
    
45.
Adler-Milstein J, Longhurst C. Assessment of patient use of a new approach to access health record data among 12 US health systems. JAMA Netw Open 2019;2:e199544.  Back to cited text no. 45
    
46.
Wen J, Kozak M, Yang S, Liu F. COVID-19: Potential effects on Chinese citizens' lifestyle and travel. Tour Rev 2020.  Back to cited text no. 46
    
47.
Fagherazzi G, Goetzinger C, Rashid MA, Aguayo GA, Huiart L. Digital health strategies to fight COVID-19 worldwide: Challenges, recommendations, and a call for papers. J Med Internet Res 2020;22:e19284.  Back to cited text no. 47
    
48.
Chou E, Hsieh YL, Wolfshohl J, Green F, Bhakta T. Onsite telemedicine strategy for coronavirus (COVID-19) screening to limit exposure in ED. Emerg Med J 2020;37:335-7.  Back to cited text no. 48
    
49.
Liu J. Deployment of IT in China's Fight against the COVID-19 Pandemic. ITN Online; 2020.  Back to cited text no. 49
    
50.
Alwashmi MF. The use of digital health in the detection and management of COVID-19. Int J Environ Res Public Health 2020;17:2906.  Back to cited text no. 50
    
51.
Seshadri DR, Davies EV, Harlow ER, Hsu JJ, Knighton SC, Walker TA, et al. Wearable sensors for COVID-19: A call to action to harness our digital infrastructure for remote patient monitoring and virtual assessments. Front Digit Health 2020;2:8.  Back to cited text no. 51
    
52.
Kriegel G, Bell S, Delbanco T, Walker J. Covid-19 as innovation accelerator: Cogenerating telemedicine visit notes with patients. Nejm Catalyst Innov Care Deliv 2020.  Back to cited text no. 52
    
53.
Park YJ, Choe YJ, Park O, Park SY, Kim YM, Kim J, et al. Contact tracing during coronavirus disease outbreak, South Korea. Emerg Infect Dis 2020;26:2465-8.  Back to cited text no. 53
    
54.
Reintjes R. Lessons in contact tracing from Germany. BMJ 2020;369:m2522.  Back to cited text no. 54
    
55.
Khoury MJ, Evans JP. A public health perspective on a national precision medicine cohort: Balancing long-term knowledge generation with early health benefit. JAMA 2015;313:2117-8.  Back to cited text no. 55
    
56.
Vayena E, Haeusermann T, Adjekum A, Blasimme A. Digital health: Meeting the ethical and policy challenges. Swiss Med Wkly 2018;148:w14571.  Back to cited text no. 56
    
57.
World Health Organization. Global Diffusion of eHealth: Making Universal Health Coverage Achievable: Report of the third Global Survey on eHealth. World Health Organization; 2017.  Back to cited text no. 57
    
58.
Vayena E, Mastroianni A, Kahn J. Caught in the web: Informed consent for online health research. Sci Transl Med 2013;5:173fs6.  Back to cited text no. 58
    
59.
Guo C, Ashrafian H, Ghafur S, Fontana G, Gardner C, Prime M. Challenges for the evaluation of digital health solutions-A call for innovative evidence generation approaches. NPJ Digit Med 2020;3:110.  Back to cited text no. 59
    




 

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Introduction
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