Health Apps by Design: A reference architecture
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This document outlines a reference architecture for creating the next generation of mHealth apps. It discusses challenges such as information asymmetry and low credibility of patient data that prevents adoption of mHealth apps. A solution is proposed that embeds the patient-physician relationship, uses behavior change models, and ensures bi-directional integration of apps with electronic medical records to collect and use patient experience and outcome data to improve care. The authors applied their criteria to review 201 diabetes apps but found none met all criteria, highlighting a need for higher quality, clinically-integrated apps.
Health Apps by Design: A reference architecture
- 1. CREATING THE NEXT GENERATION MHEALTH APP: A REFERENCE ARCHITECTURE Karim Keshavjee MD, MBA, CCFP, CPHIMS-CA Pannel Chindalo, PhD Arsalan Karim, MD, MBA Ronak Brahmbhatt, MD, MPH Nishita Saha Ryerson University, Feb 27, 2017
- 2. OUTLINE • Background • Challenges • Complexities, rabbit holes and confusion • Solution • Reference architecture • Conclusion
- 3. BACKGROUND: MOBILE APPS ARE TAKING OFF
- 4. DOCTORS WANT TO RX HEALTH APPS
- 5. BUT: HEALTH APP USE IS LOW
- 6. WHY?: HEALTH APPS DON’T WORK
- 7. SOME EXCEPTIONS Use a wearable fitness tracker Use a smart- phone application Do not use a device or app Plan to start using a device or app
- 8. TYPE 1 DIABETES APPS • Patients have traditionally always written down their insulin dosages and their blood sugar readings • Apps are a natural replacement of paper logs and provide some additional useful functionality • But does not apply to Type 2 diabetes, who make up 90% of patients with diabetes.
- 9. COMPLEXITIES, RABBIT HOLES AND CONFUSION What is preventing patients and health practitioners from adopting mHealth apps?
- 10. METHODS • Searched: PubMed & Google Scholar • Used gap analysis: that drew on philosophy, data science, education, life science and business analyses methods to develop a concept that would overcome the constraints and meet the goals identified • Through analysis, discussion and iteration: arrived at an evidence-informed proposed architecture • Works within the framework of clinical practice • Values the patient-physician relationship • Uses scientifically validated tools effectively
- 11. OUR PUBLICATION
- 12. 9 BARRIERS TO MHEALTH USE Conflicting Information: App provides information that conflicts with that received from health care providers (Bierbrier, Lo & Wu, 2014); Health Literacy: Language and terminology of the app may not be compatible with the patient’s health literacy (Caburnay, 2015); Data Entry: Patient has to enter the data themselves (Gruman, 2013); Meaningful Use: Patient cannot use information in a meaningful way; e.g., he or she cannot order diagnostic testing or prescribe medications to himself or herself; Lack of incentives like cost saving or social approval;
- 13. BARRIERS TO MHEALTH USE (CONT’D.) Not Habit Forming: Daily use of the app is not required and therefore the patient does not get into the habit of using it; Unknown Provenance: Providers don’t value data collected by patients in apps downloaded from an app store whose provenance and pedigree is not known or established (Terry, 2015); Lack of Tools: There is no way for providers to consume the large amounts of data that are collected in apps (Terry, 2015); i. i.e., visualize, analyze, derive meaning from; Lack of Interoperability: Providers unable to integrate app data into their own (EMR) for analysis or follow-up or share the data in their EMR with their patient’s apps (Abebe, 2013).
- 14. AT HEART – INFORMATION ASYMMETRY…
- 15. …AND INFORMATION CREDIBILITY Doctors are trained to trust only their own data Patient-entered data is not trusted
- 16. OPEN MHEALTH GROUP Lots of technology and data × No workflow or information
- 17. ENGAGING THE PATIENT Does not happen in a vacuum
- 18. GAMIFICATION IS NOT ENOUGH • Patient engagement needs to be rooted in science, not games • Need behavior change models with evidence behind them • Prochaska’s Model • Behavior Change Wheel • Commitment needs to be cemented • Accountability to health care team • Follow-up by physician and their staff • App is ‘prescribed’ from EMR which activates App • Doctors are regulated by their colleges and are expected to act according to professional rules
- 19. SOLUTION: NEEDS LOTS OF INFORMATION (DATA) A. Healthcare without data is meaningless B. Must meet evidence-based content and process requirements C. Also need to track patient experience and outcome measures
- 20. Solution:
- 21. APPLICATION TO DIABETES TYPE 2 APPS • We developed screening criteria using our reference architecture for design and development of mhealth apps, • Apple iTunes and Google Play app stores were searched for diabetes apps –found 201 • Following a calibration exercise, two individuals independently reviewed and evaluated each app against the screening criteria • Data was collated and analyzed
- 22. RESULTS: • 201 total apps were reviewed • No app met all the criteria outlined • Most apps were replacement of paper journals or diaries • Many apps were recipe apps • Majority of the apps provided education/recommendations • Most of the apps failed at integrations with devices (glucometer, BP machine) and patients medical records (EMR, primary care provider)
- 23. RESULTS
- 24. SCORES (MAX = 15)
- 25. WE INTERRUPT THIS BROADCAST….. • Bluestar is a new diabetes app that must be prescribed! • It recently obtained FDA approval • Got a score of 12 (out of 15) on our rating scale • Very promising development
- 26. REASONS FOR NOT MEETING CRITERIA • Many apps were conference apps or guideline apps for professionals • Of the highest scoring apps, major reasons for not getting a higher score • Lack of integrations with devices–relatively easy these days (but requires FDA approval) • Lack of integration with EMRs –many features are dependent on this
- 27. DISCUSSION • There is great need for high quality apps which can be prescribed by a physician and whose use can be monitored by the health care team • Apps need to focus on managing the whole patient along with their disease and not a small part of a patient’s care such as self management • Better embedding physician patient relationship into patient app interactions for provider guided management
- 28. LIMITATIONS • Due to budgetary constraints, we did not download apps from the stores • Some vendors had poorer descriptions of their product than others • A very small number of apps were in languages that are not understood by the people conducting the review • We were not able to quantitate which apps are used and which ones are not • We did not include any patients in defining the criteria nor in reviewing the apps.
- 29. RECOMMENDATIONS • Apps should be prescribed and monitored by health care providers • Requires participation of EMR vendors in developing APIs for apps • mhealth app certification by a standards organization would go a long way to ensuring higher quality apps and increasing the level of trust for apps by health providers • An Interoperability Kit for EMRs and Apps would help make it easier to deploy an app • Standard interoperability for apps with medical devices would lower the investments required to create good apps
- 30. CONCLUSION • mHealth Apps show lots of promise, but not being used • Information asymmetry, data credibility and entering own data are main causes • Engagement framework requires a commitment to communication and empowerment • Need to use scientific, proven behavior change models • Needs to be grounded in clinical practice and within the patient-provider relationship –needs to support on-going communication and interaction after the visit is over
- 31. CONCLUSION • Health behavior changes are difficult to sustain • Need external motivation and commitment –App needs to be “prescribed” • Needs consistent follow-up and reinforcement • mHealth Apps need to have bi-directional integration with EMRs • Need to collect lots of experience, process and outcome data – lab, questionnaire and system use data • Use the data to constantly improve the user engagement and user experience • Use the data to improve patient disease outcomes
- 32. REFERENCES Chindalo P, Karim A, Brahmbhatt R, Saha N, Keshavjee K. Health Apps by Design: A Reference Architecture for Mobile Engagement. International Journal of Handheld Computing Research (IJHCR). 2016 Apr 1;7(2):34-43. Balouchi S, Keshavjee K, Zbib A, Vassanji K, Toor J. Creating a Supportive Environment for Self-Management in Healthcare via Patient Electronic Tools. Social Media and Mobile Technologies for Healthcare. 2014 Jun 30;109. Brahmbhatt R, Niakan S, Saha N, Tewari A, Pirani A, Keshavjee N, Mugambi D, Alavi N, Keshavjee K. Diabetes mHealth Apps: Designing for Greater Uptake. Studies in health technology and informatics. 2017;234:49.