Grand Challenges in Human Factors and Digital Health

We live in a world where the potential of technology continues to expand. Every year computers become more powerful, smaller, and cheaper, and the amount of data created and stored increases. Technology is quickly entering and transforming various areas of our life, and health and healthcare are no exceptions. However, many of the promises offered by digital health—personalized care, scalable interventions, cost-effective approaches, reduced disparities (1)—are unmet. Although it has been noted that digital health can contribute to the so-called triple aim of health care reform (2)—improved patient experience, population health, and cost—healthcare often remains disjointed, significant health problems and health disparities exist, and healthcare costs continue to increase. Thus, digital health remains high on promise and potential, but low on impact and benefit, and yet research on digital health continues to expand. Much of this research, however, is problematic. A review of clinical trials of digital health found rapid growth but most studies were small with few reporting findings even among those completed or terminated (3). Thus, we have an urgent need to revamp digital health research to ensure the promise and potential of this field can be realized. Increased consideration of human factors offers the potential to improve and advance digital health research and maximize its impact in the various fields that contribute to this work, and on people’s lives. Human factors reference human emotions, behaviors, and cognitions related to the design, adoption, usage, and implementation of health technologies. As such, human factors and digital health lie at the intersection of several areas of scholarship including clinical science, human-computer interaction, implementation science, public health, and healthcare communication. Although many researchers and providers are approaching the same problems from different fields, we have an increased need to recognize ways to better work together to reach mutual goals. For example, the appreciation that the study of digital health requires addressing issues beyond the technology itself and addressing socio-technical considerations (4) aligns strongly with the socio-ecological model emphasized by the consolidated framework for implementation research (5). As such, I suggest that one grand challenge facing research related to human factors and digital health is that we have to re-think this area of study by adopting three critical reframes.

[1]  B. Waterman,et al.  Measuring Subcounty Differences in Population Health Using Hospital and Census-Derived Data Sets: The Missouri ZIP Health Rankings Project , 2017, Journal of public health management and practice : JPHMP.

[2]  Heleen Riper,et al.  A Solution-Focused Research Approach to Achieve an Implementable Revolution in Digital Mental Health , 2017, JAMA psychiatry.

[3]  M. Turakhia,et al.  Characteristics of Digital Health Studies Registered in ClinicalTrials.gov. , 2019, JAMA internal medicine.

[4]  S. Wooding,et al.  The answer is 17 years, what is the question: understanding time lags in translational research , 2011, Journal of the Royal Society of Medicine.

[5]  Alessandro Blasimme,et al.  Elements of Trust in Digital Health Systems: Scoping Review , 2018, Journal of medical Internet research.

[6]  David W. Bates,et al.  Leveraging health information technology to achieve the "triple aim" of healthcare reform , 2015, J. Am. Medical Informatics Assoc..

[7]  Gillian R. Hayes,et al.  Integrating Patient-Generated Health Data Into Clinical Care Settings or Clinical Decision-Making: Lessons Learned From Project HealthDesign , 2016, JMIR human factors.

[8]  Madhu Reddy,et al.  Three Problems With Current Digital Mental Health Research . . . and Three Things We Can Do About Them. , 2017, Psychiatric services.

[9]  Aaron R Lyon,et al.  Accelerating Digital Mental Health Research From Early Design and Creation to Successful Implementation and Sustainment , 2017, Journal of medical Internet research.

[10]  Stephen H. Bell,et al.  A ?scoping review. , 2018, Sexual health.

[11]  Sean A. Munson,et al.  A lived informatics model of personal informatics , 2015, UbiComp.

[12]  Bernard C. K. Choi,et al.  Multidisciplinarity, interdisciplinarity, and transdisciplinarity in health research, services, education and policy: 2. Promotors, barriers, and strategies of enhancement. , 2007, Clinical and investigative medicine. Medecine clinique et experimentale.

[13]  Anita W. P. Pak,et al.  Multidisciplinarity, interdisciplinarity, and transdisciplinarity in health research, services, education and policy: 3. Discipline, inter-discipline distance, and selection of discipline. , 2008, Clinical and investigative medicine. Medecine clinique et experimentale.

[14]  David C. Mohr,et al.  Realizing the Potential of Behavioral Intervention Technologies , 2013 .

[15]  野村栄一,et al.  2 , 1900, The Hatak Witches.

[16]  Dean F Sittig,et al.  A new sociotechnical model for studying health information technology in complex adaptive healthcare systems , 2010, Quality and Safety in Health Care.

[17]  J. Lowery,et al.  Fostering implementation of health services research findings into practice: a consolidated framework for advancing implementation science , 2009, Implementation science : IS.

[18]  David C. Atkins,et al.  Use of Human-Centered Design to Improve Implementation of Evidence-Based Psychotherapies in Low-Resource Communities: Protocol for Studies Applying a Framework to Assess Usability
 , 2019, JMIR research protocols.

[19]  John Torous,et al.  Assessment of the Data Sharing and Privacy Practices of Smartphone Apps for Depression and Smoking Cessation , 2019, JAMA network open.