Digital health

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Digital health is the convergence of digital and genomic technologies with health, healthcare, living, and society to enhance the efficiency of healthcare delivery and make medicines more personalized and precise.[1] The discipline involves the use of information and communication technologies to help address the health problems and challenges faced by patients.[1] These technologies include both hardware and software solutions and services, including telemedicine, web-based analysis, email, mobile phones and applications, text messages, and clinic or remote monitoring sensors.[2][3] 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 patients manage illnesses and health risks, as well as promote health and wellbeing.[1][3]

Digital health is a multi-disciplinary domain which involves many stakeholders, including clinicians, researchers and scientists with a wide range of expertise in healthcare, engineering, social sciences, public health, health economics and management.[2]


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,"[this quote needs a citation] 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.[1][3][4][page needed]

Elements of digital health.

The underlying concepts and technologies of digital health include:[1][2][3]


There are various domains that span digital health.[1][2] These include:

Assistive technologies and rehabilitation robotics
The use of rehabilitative systems and devices for patients with disabilities so as to aid in their independence to perform daily tasks.
Clinical decision support
The use of decision support systems to aid clinicians at the point of care. This includes diagnosis, analysis and interpretation of patient-related data.
Computational simulations, modeling and machine learning approaches
The use of computational and mathematical equations and algorithms to model health-related outcomes.
The combined use of electronic means to deliver health information and services so that data can be transmitted, stored and retrieved for clinical, educational and administrative purposes.
Healthcare technology assessment and monitoring
The use of any technological intervention to prevent, diagnose or treat diseases, monitoring of patients, or for rehabilitation or long-term care. Such technologies include assistive and rehabilitation technologies, unobtrusive monitoring sensors and wearable devices.
Health systems engineering
The use of engineering applications in health care systems, such as knowledge discovery, decision making, optimization, human factors engineering, quality engineering, and information technology and communication.
Human-computer-environment interactions
The study of interactions between people, computers and their environment. Human-computer interaction principles tend to be based around user-centered, experience-centered or activity-centered designs.
Information management and policy
The continual process of systematically reviewing and providing concise data summaries of high quality evidence on digital healthcare technologies based on principles of information design so as to inform decision and policy making regarding patient care.
Virtual reality, video gaming rehabilitation, and serious games
The use of 3D virtual worlds and gaming technologies to provide a social and interactive experience for healthcare student and patient education. The popular “Second Life” virtual world is an example.
Speech and hearing systems
The use of natural language processing, speech recognition techniques, and medical devices to aid in speech and hearing (e.g. cochlear implants).
Telehealth, telemedicine, telecare, telecoaching and telerehabilitation
The use of telecommunication and information technologies to provide various forms of patient care remotely at a distance.


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.[5] By 2014, 75% of Canadian physicians were using electronic medical records.[6]

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.[7]

Innovation cycle[edit]

The innovation process for digital health is an iterative cycle for technological solutions classified into five main activity processes beginning from the identification of the healthcare problem to implementation and evaluation in working clinical practices.[1][2]

Processes include:

Identifying the healthcare problem
This stage involves defining the healthcare problem, identifying and understanding users and their needs, and the clinical care pathway. User requirements and the context of use of digital technologies will then be formalized through relevant scientific, engineering and psychological theories and principles.
Doing the research
The research that informs the digital innovation is produced by scanning published literature to identify existing technologies that are appropriate and relevant to clinical practices, as well as potential technologies that can be developed.
Designing the digital solution
The prototype solution is designed and developed with the aid of various stakeholders according to principles of human-computer interaction, and/or activity-centered designs.
Evaluating the digital solution and generating evidence
The technological solution is pilot-tested in patient and user groups to ensure its effectiveness, safety and affordability. Impact evaluations are then carried out in large-scale clinical studies and/or trials, and the evidence is synthesized through published literature. This may also include clinical studies that evaluate the economic impact.
Supporting the digital innovation
The knowledge generated from the synthesized evidence is then shared among various stakeholders (e.g. patients, clinicians, industry) to promote and spread the digital innovation.


Impact Factors of scholarly journals publishing digital health (ehealth, mhealth) work
  1. ^ a b c d e f g 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. PMID 26873093. doi:10.1093/eurheartj/ehv770Freely accessible. 
  2. ^ a b c d e 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. PMC 4551455Freely accessible. PMID 25841251. doi:10.1016/j.mayocp.2014.12.026. 
  3. ^ a b c d "Digital health". Food and Drug Administration. US Department of Health and Human Services. 30 August 2016. Archived from the original on 12 November 2016. 
  4. ^ 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 Google Books. 
  5. ^ "Progress in Canada". Canada Health Infoway. 2016. Archived from the original on 12 November 2016. Retrieved 11 November 2016. 
  6. ^ Collier, Roger (6 January 2015). "National Physician Survey: EMR use at 75%". Canadian Medical Association Journal. 187 (1): E17–8. PMC 4284187Freely accessible. PMID 25487665. doi:10.1503/cmaj.109-4957Freely accessible. 
  7. ^ Källander, Karin; Tibenderana, James K.; Akpogheneta, Onome J.; Strachan, Daniel L.; Hill, Zelee; ten Asbroek, Augustinus H.A.; Conteh, Lesong; Kirkwood, Betty R.; Meek, Sylvia R. (25 January 2013). "Mobile health (mHealth) approaches and lessons for increased performance and retention of community health workers in low- and middle-income countries: A review". Journal of Medical Internet Research. 15 (1): e17. PMC 3636306Freely accessible. PMID 23353680. doi:10.2196/jmir.2130Freely accessible.  open access publication – free to read

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