Buy or build: challenges developing consumer digital health interventions
Open accessAbstract
BACKGROUND: Digital health interventions offer opportunities to improve collaborative care between clinicians and patients. Designing and implementing digital health interventions requires decisions about buying or building each technology-related component, all of which can lead to unanticipated issues. OBJECTIVES: This study aimed to describe issues encountered from our "buy or build" decisions developing two digital health interventions over different timeframes, designed to use patient-generated health data to: (1) improve hypertension control and (2) measure and improve adherence to HIV-related medications. METHODS: CONDUIT-HID (CONtrolling Disease Using Information Technology-Hypertension In Diabetes) was developed during 2010 to 2015 to allow patients receiving care from a multispecialty group practice to easily upload home blood pressure readings into their electronic health record and trigger clinician action if mean blood pressure values indicated inadequate control. USE-MI (Unobtrusive SEnsing of Medication Intake) was developed from 2016 to 2022 to allow entry of patients' HIV-related medication regimens, send reminders if patients had not taken their medications by the scheduled time(s), attempt to detect medication ingestion through machine learning analysis of smartwatch motion data, and present graphical adherence summaries to patients and clinicians. RESULTS: Both projects required multiple "buy or build" decisions across all system components, including data collection, transfer, analysis, and display. We used commercial, off-the-shelf technology where possible, but virtually all of these components still required substantial custom development. We found that, even though our projects spanned years, issues related to our "buy or build" decisions stemmed from several common themes, including mismatches between existing and new technologies, our use case being new or unanticipated, technology stability, technology longevity, and resource limitations. CONCLUSION: Those designing and implementing digital health interventions need to make numerous "buy or build" decisions as they create the technologies that underpin their intervention. These "buy or build" decisions, and the ensuing issues that will arise because of them, require careful planning, particularly if they represent an "edge case" use of existing commercial systems.
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Automated oscillometric blood pressure cuffs faced the highest absolute operational friction, combining significant workflow routine disruptions with digital literacy gaps.
“Automated oscillometric blood pressure cuffs faced the highest absolute operational friction, combining significant workflow routine disruptions with digital literacy gaps [6].”
From Clinical Efficacy and Patient Adherence Hurdles of Remote Patient Monitoring (RPM) Frameworks in Managing Hypertension and Type 2 Diabetes: A Scoping Review (2026) · cites this paper as [6]
The Structured Analysis of Our “Buy or Build” Decisions (Table 1; page not specified) describes operational difficulties with the home BP cuff system, including clinic staff taking over data uploads and technical fixes when connections broke. It does not rank cuffs as having the highest absolute friction or identify digital literacy gaps as a cause of those difficulties. (LLM, conf=0.85)
Experimental — accuracy not yet measuredContemporary deployment failures of RPM are increasingly attributed to operational and human factors.
“contemporary deployment failures are increasingly attributed to operational and human factors [5,6].”
From Clinical Efficacy and Patient Adherence Hurdles of Remote Patient Monitoring (RPM) Frameworks in Managing Hypertension and Type 2 Diabetes: A Scoping Review (2026) · cites this paper as [6]
The Discussion describes deployment barriers involving clinician workflow and EHR integration, while the structured analysis describes staff workload and vendor disruptions. These support operational challenges, but the paper does not establish that RPM deployment failures are increasingly attributed to operational and human factors; its focus is on technology and integration problems. (LLM, conf=0.78)
Experimental — accuracy not yet measuredLarge-scale implementation of RPM across healthcare delivery organizations remains fragmented and frequently plagued by high attrition rates.
“large-scale implementation across healthcare delivery organizations remains fragmented and frequently plagued by high attrition rates [5,6].”
From Clinical Efficacy and Patient Adherence Hurdles of Remote Patient Monitoring (RPM) Frameworks in Managing Hypertension and Type 2 Diabetes: A Scoping Review (2026) · cites this paper as [6]
The Discussion reports that few patient-generated health data interventions successfully integrated with electronic health records, supporting the claim of implementation barriers. It does not establish that large-scale RPM implementation is fragmented across healthcare organizations or report high attrition rates. (LLM, conf=0.84)
Experimental — accuracy not yet measuredThe introduction of an RPM pathway introduces structural shifts in routine clinical data workflows, creating significant friction points for both patients and healthcare providers.
“The introduction of an RPM pathway introduces structural shifts in routine clinical data workflows, creating significant friction points for both patients and healthcare providers [6].”
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Cites
James, P. A., Oparil, S., Carter, B. L., Cushman, W. C., Dennison-Himmelfarb, C., Handler, J., Lackland, D. T., LeFevre, M. L., MacKenzie, T. D., Ogedegbe, O., et al. (2014) · JAMA · doi:10.1001/jama.2013.284427 · Cited by 7,950 · see citing works
Whelton, P. K., Carey, R. M., Aronow, W. S., Casey, D. E., Collins, K. J., Dennison Himmelfarb, C., DePalma, S. M., Gidding, S., Jamerson, K. A., Jones, D. W., et al. (2018) · Hypertension · doi:10.1161/hyp.0000000000000065 · Cited by 7,391 · see citing works
Whelton, P. K., Carey, R. M., Aronow, W. S., Casey, D. E., Collins, K. J., Dennison Himmelfarb, C., DePalma, S. M., Gidding, S., Jamerson, K. A., Jones, D. W., et al. (2018) · Hypertension · doi:10.1161/hyp.0000000000000066 · Cited by 4,347 · see citing works
Barriers and facilitators to the implementation of eHealth services: systematic literature analysis
Schreiweis, B., Pobiruchin, M., Strotbaum, V., Suleder, J., Wiesner, M., & Bergh, B. (2019) · Journal of Medical Internet Research · doi:10.2196/14197 · Cited by 354 · see citing works
Screening for Hypertension in Adults
US Preventive Services Task Force, Krist, A. H., Davidson, K. W., Mangione, C. M., Cabana, M. D., Caughey, A. B., Davis, E. M., Donahue, K. E., Doubeni, C. A., Kubik, M., et al. (2021) · JAMA · doi:10.1001/jama.2021.4987 · Cited by 252 · see citing works
Implications of an emerging EHR monoculture for hospitals and healthcare systems
Koppel, R., & Lehmann, C. U. (2015) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2014-003023 · Cited by 140 · cited 1× · see citing works
“20 Our experiences are certainly not unique.”
Patient-generated health data management and quality challenges in remote patient monitoring
Abdolkhani, R., Gray, K., Borda, A., & DeSouza, R. (2019) · JAMIA Open · doi:10.1093/jamiaopen/ooz036 · Cited by 127 · see citing works
Patient generated health data use in clinical practice: A systematic review
Demiris, G., Iribarren, S., Sward, K. A., Lee, S., & Yang, R. (2019) · Nursing Outlook · doi:10.1016/j.outlook.2019.04.005 · Cited by 116 · see citing works
Challenges With Developing Secure Mobile Health Applications: Systematic Review
Aljedaani, B., & Babar, M. A. (2021) · JMIR mhealth and uhealth · doi:10.2196/15654 · Cited by 114 · see citing works
Patient-generated health data and electronic health record integration: a scoping review
Tiase, V. L., Hull, W., McFarland, M., Sward, K. A., Fiol, G. D., Staes, C. J., Weir, C. R., & Cummins, M. R. (2020) · JAMIA Open · doi:10.1093/jamiaopen/ooaa052 · Cited by 102 · see citing works
Present and Future Trends in Consumer Health Informatics and Patient-Generated Health Data
Lai, A. M., Hsueh, P.-Y. S., Choi, Y. K., & Austin, R. R. (2017) · Yearbook of Medical Informatics · doi:10.15265/iy-2017-016 · Cited by 84 · see citing works
Early experiences with patient generated health data: health system and patient perspectives
Adler‐Milstein, J., & Nong, P. (2019) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocz045 · Cited by 43 · see citing works
Lobach, D. F., Boxwala, A. A., Kashyap, N., Heaney-Huls, K., Chiao, A. B., Rafter, T., Lomotan, E. A., Harrison, M. I., Dymek, C., Swiger, J., et al. (2022) · Applied Clinical Informatics · doi:10.1055/s-0042-1758736 · Cited by 24 · see citing works
Human Factors and Organizational Issues in Health Informatics: Innovations and Opportunities
Marquard, J. L. (2021) · Yearbook of Medical Informatics · doi:10.1055/s-0041-1726511 · Cited by 12 · see citing works
Continuous Measurement of Interactions with the Physical World with a Wrist-Worn Backscatter Reader
Kiaghadi, A., Hu, P., Gummeson, J., Rostaminia, S., & Ganesan, D. (2020) · ACM Transactions on Internet of Things · doi:10.1145/3375800 · Cited by 6 · see citing works
Flagged first, then by citations
Research works printed but not linked: 1
- [2]Integrating Patient-Generated Digital Health Data into Electronic Health Records in Ambulatory Care Settings: An Environmental Scan. 2021. AHRQ Publication No. 21-0031. Accessed January 25, 2023 at: https://digital.ahrq.gov/sites/default/files/docs/citation/pghd-environmental-scan.pdf 42691194
Cited by
Sorted by citations
Anand-Kumar, V., Schreier, M., Bakshi, N., Messiha, K., Longworth, G. R., An, Q., Agnello, D. M., CHASTIN, S. F. M., & Lippke, S. (2025) · Public Health · doi:10.1016/j.puhe.2025.105847 · Cited by 5 · see citing works
Consumer Involvement in the Co-Design of Diabetes Self-Management Smartphone Apps: A Scoping Review
Martin, C. L., Bakker, C., Sarkar, S., Kang, R. M., Reid, N., Chih, M., Sittig, S., Gao, G., Smith, C., Morse, B., et al. (2025) · Applied Clinical Informatics · doi:10.1055/a-2564-7682 · Cited by 1 · see citing works
Mekniran, W., & Kowatsch, T. (2026) · BMC Health Services Research · doi:10.1186/s12913-026-14075-3 · Cited by 1 · see citing works
“Out-of-pocket models face high churn and price sensitivity, making early revenues fragile [47].”
Athukorala, S. C. (2026) · Cureus · doi:10.7759/cureus.113642
“While early digital health literature focused predominantly on technological feasibility and accuracy, contemporary deployment failures are increasingly attributed to operational and human factors [5,6].”
Influential
- Authors
- Saver, B. G., Marquard, J. L., Gummeson, J., Stekler, J., & Scanlon, J. M.
- Affiliations
- Swedish Family Medicine Residency - Cherry Hill (US)
- University of Minnesota (US)
- University of Massachusetts Amherst (US)
- University of Washington (US)
- Swedish Medical Center (US)
- Corresponding author
- Saver, Barry G.
- Venue
- Applied Clinical Informatics
- Publisher
- Georg Thieme Verlag KG
- Published
- 2023-08
- Volume
- 14
- Issue
- 04
- Pages
- 803-810
- ISSN
- 1869-0327 (ISSN-L)
- PMC
- PMC10567137
- OpenAlex
- W4385566838
- Cited by
- 4
- References
- 15
- Citations by year
- 2026: 2 · 2025: 2
- FWCI
- 1.42
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Applied Clinical Informatics (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Agency for Healthcare Research and Quality (R18 HS018461)
- The National Institute of Mental Health (R01 MH109319)
- Topics
- Health Sciences › Health Professions › General Health Professions › Mobile Health and mHealth Applications
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Social Sciences › Psychology › Applied Psychology › Digital Mental Health Interventions
- Keywords
- digital health interventions, patient-generated health data, hypertension control, HIV treatment adherence, home blood pressure monitoring, electronic health records, machine learning, off-the-shelf technology
- MeSH terms
- Digital Health, Diabetes Mellitus, Humans, Hypertension, HIV Infections, Electronic Health Records
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