Digital health

Digital health, which includes digital care programs, is the convergence of digital technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery[1][2] and make medicine more personalized and precise.[3] The discipline involves the use of information and communication technologies to help address the health problems and challenges faced by people under treatment.[3] These technologies include both hardware and software solutions and services, including telemedicine, web-based analysis, email, mobile phones and applications, text messages, wearable devices, and clinic or remote monitoring sensors.[4][5] Generally, digital health is concerned about the development of interconnected health systems to improve the use of computational technologies, smart devices, computational analysis techniques, and communication media to aid healthcare professionals and their clients manage illnesses and health risks, as well as promote health and wellbeing.[3][5]

For the "health" of computers, see malware.

Digital health is a multi-disciplinary domain involving many stakeholders, including clinicians, researchers and scientists with a wide range of expertise in healthcare, engineering, social sciences, public health, health economics and data management.[6]

Elements

As an outgrowth of the Digital Revolution characterized by "the mass production and widespread use of digital logic circuits, and its derived technologies, including the computer, digital cellular phone, and the Internet,"[7] key elements of digital health include wireless devices, hardware sensors and software sensing technologies, microprocessors and integrated circuits, the Internet, social networking, mobile/cellular networks and body area networks, health information technology, genomics, and personal genetic information.[3][5][6][8] [9]

Elements of digital health.

Domains

Various domains span digital health.[3][4] These include Healthcare technology assessment and monitoring to prevent, diagnose or treat diseases, monitoring of patients, or for rehabilitation or long-term care. Such technologies include Assistive technologies and rehabilitation robotics for people with disabilities so as to aid in their independence to perform daily tasks, unobtrusive monitoring sensors and wearable devices. Clinical decision support aids clinicians at the point of care, including diagnosis, analysis and interpretation of patient-related data. Computational simulations, modeling and machine learning approaches can model health-related outcomes. E-health delivers health information and services to enable data transmission, storage and retrieval for clinical, educational and administrative purposes. Mobile health (or mhealth) is the practice of medicine and public health supported by mobile devices.[10]

Health systems engineering applications in health care systems includes knowledge discovery, decision making, optimization, human factors engineering, quality engineering, and information technology and communication. Human-computer-environment interactions Human-computer interaction principles tend to be based around user-centered, experience-centered or activity-centered designs. Virtual reality, video gaming rehabilitation, and serious games to provide a social and interactive experience for healthcare student and patient education. Speech and hearing systems for natural language processing, speech recognition techniques, and medical devices can aid in speech and hearing (e.g. cochlear implants). Telehealth, telemedicine, telecare, telecoaching and telerehabilitation provide various forms of patient care remotely at a distance.

Implementation

National digital programs exist to support healthcare, such as those of Canada Health Infoway built on core systems of patient and provider registries, clinical and diagnostic imaging systems, clinical reports and immunizations.[11] By 2014, 75% of Canadian physicians were using electronic medical records.[12]

In Uganda and Mozambique, partnerships between patients with cell phones, local and regional governments, technologists, non-governmental organizations, academia, and industry have enabled mHealth solutions.[13]

In the United Kingdom, the National Health Service (NHS) has commissioned a report on how to integrate digital healthcare technologies into the next generation of medicine.[14] The "Topol Review" recommended an expansion of education for both patients and providers of next-generation technologies such as Whole Genome Sequencing, and has also created Digital Fellowships for health professionals.[15]

On the other hand, the implementation of these innovations has also brought to light societal risks and regulatory needs, which are certainly challenging the current governance structures in the health sector.

Innovation cycle

The innovation process for digital health is an iterative cycle for technological solutions that can be classified into five main activity processes from the identification of the healthcare problem, research, digital solution, and evaluating the solution, to implementation in working clinical practices.[3][4] Digital health may incorporate methods and tools adopted by software engineering, such as design thinking and agile software development.[16][17] These commonly follow a user-centered approach to design, which are evaluated by subject-matter experts in their daily life using real-world data.[17]

U.S. Food and Drug Administration

In 2019, the FDA published a Digital Health Innovation Action Plan that would reduce inefficiencies for physicians in an effort to cut overhead costs, improve access, increase quality of service, and make medicine more easily adapted for each person.[5] Topics within the innovation plan are wireless devices, telemedicine, software, and cybersecurity, among others.[5] According to FDA guidelines, if you release an app designed to help someone with a medical condition then that is considered a medical device. The FDA can not regulate all healthcare apps, so they use “enforcement discretion,” and up until 2020, have chosen not to regulate all digital care programs and apps. However, programs that use the word treatment, seek to diagnose or treat a condition, or are deemed unsafe, are and will be regulated by the FDA[18].

Criticisms

Digital healthcare has been a major focus of American healthcare policy after the passage of the Affordable Care Act and HITECH Act.[19] This has resulted in an explosion in the number of physicians who interface with digital healthcare tools known as Electronic Medical Records (EMRs).[20] However, physicians are highly critical of the utility of EMRs for patient care, and point to their rising use as a significant component in physician burnout.[21][22][20]

The ownership of health data issue

At the global level, the implementation of digital health solutions depends on large data systems, ranging from vital statistics that record every birth and death to more sophisticated systems that track diseases, outbreaks, and chronic conditions. More specifically, data is patient records, blood test results, EKGs, MRIs, billing records, drug prescriptions, and other private medical information. These are the information medical professionals need to make decisions about patient care. In this sense, a crucial debate has arisen amongst stake-holders about one of the challenges imposed by these solutions: the ownership of health data. In most cases, governments and big data and tech companies are holding citizens’ information, leaving an imminent preoccupation with transparency and privacy.


References
  1. O’Donoghue, John; Majeed, Azeem; Carroll, Christopher; Gallagher, Joseph; Wark, Petra A.; O’Connor, Siobhan; Akinlua, James Tosin; Fadahunsi, Kayode Philip (1 March 2019). "Protocol for a systematic review and qualitative synthesis of information quality frameworks in eHealth". BMJ Open. 9 (3): e024722. doi:10.1136/bmjopen-2018-024722. ISSN 2044-6055. PMC 6429947. PMID 30842114.
  2. Chen, Connie E.; Harrington, Robert A.; Desai, Sumbul A.; Mahaffey, Kenneth W.; Turakhia, Mintu P. (1 June 2019). "Characteristics of Digital Health Studies Registered in ClinicalTrials.gov". JAMA Internal Medicine. 179 (6): 838–840. doi:10.1001/jamainternmed.2018.7235. ISSN 2168-6106. PMC 6547144. PMID 30801617.
  3. Bhavnani, Sanjeev P.; Narula, Jagat; Sengupta, Partho P. (7 May 2016). "Mobile technology and the digitization of healthcare". European Heart Journal. 37 (18): 1428–38. doi:10.1093/eurheartj/ehv770. PMC 4914890. PMID 26873093.
  4. Widmer, R. Jay; Collins, Nerissa M.; Collins, C. Scott; West, Colin P.; Lerman, Lilach O.; Lerman, Amir (April 2015). "Digital Health Interventions for the Prevention of Cardiovascular Disease: A Systematic Review and Meta-Analysis". Mayo Clinic Proceedings. 90 (4): 469–80. doi:10.1016/j.mayocp.2014.12.026. PMC 4551455. PMID 25841251.
  5. "Digital health". US Food and Drug Administration. 19 July 2019. Retrieved 23 September 2019.
  6. O’Donoghue, John; Herbert, John (1 October 2012). "Data Management within mHealth Environments: Patient Sensors, Mobile Devices, and Databases". Journal of Data and Information Quality. 4 (1): 1–20. doi:10.1145/2378016.2378021.
  7. Rafael, Perez-Uribe; Carlos, Salcedo-Perez; David, Ocampo-Guzman (13 April 2018). Handbook of Research on Intrapreneurship and Organizational Sustainability in SMEs. IGI Global. ISBN 9781522535447.
  8. Topol, Eric J. (2012). The Creative Destruction of Medicine: How the Digital Revolution Will Create Better Health Care. Basic Books. ISBN 978-0-465-02550-3. OCLC 868260493 – via Internet Archive.
  9. Iyawa, G E; Herselman, M; Botha, A (2016), "Digital Health Innovation Ecosystems: From Systematic Literature Review to Conceptual Framework", Procedia Computer Science, 100: 244–252, doi:10.1016/j.procs.2016.09.149
  10. Silva, Bruno M. C.; Rodrigues, Joel J. P. C.; de la Torre Díez, Isabel; López-Coronado, Miguel; Saleem, Kashif (1 August 2015). "Mobile-health: A review of current state in 2015". Journal of Biomedical Informatics. 56: 265–272. doi:10.1016/j.jbi.2015.06.003. ISSN 1532-0464.
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  15. "The Topol Review". The Topol Review — NHS Health Education England. Retrieved 8 March 2020.
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  18. FDA (26 September 2019). "Examples of Software Functions for Which the FDA Will Exercise Enforcement Discretion". U.S. Food and Drug Administration. Retrieved 8 June 2020.
  19. Agrawal, Raag; Prabakaran, Sudhakaran (5 March 2020). "Big data in digital healthcare: lessons learnt and recommendations for general practice". Heredity. doi:10.1038/s41437-020-0303-2. ISSN 0018-067X.
  20. Journal of Clinical Pharmacy and Therapeutics. 42 (1). February 2017. doi:10.1111/jcpt.2017.42.issue-1. ISSN 0269-4727 http://dx.doi.org/10.1111/jcpt.2017.42.issue-1. Missing or empty |title= (help)
  21. Daniel Essin, M. A. (6 February 2012). "Improve EHR Systems by Rethinking Medical Billing". Physicians Practice. Retrieved 8 March 2020.
  22. Gawande, Atul. "Why Doctors Hate Their Computers". The New Yorker. Retrieved 8 March 2020.
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