Deploying digital health tools within large, complex health systems: key considerations for adoption and implementation
Open accessAbstract
In recent years, the number of digital health tools with the potential to significantly improve delivery of healthcare services has grown tremendously. However, the use of these tools in large, complex health systems remains comparatively limited. The adoption and implementation of digital health tools at an enterprise level is a challenge; few strategies exist to help tools cross the chasm from clinical validation to integration within the workflows of a large health system. Many previously proposed frameworks for digital health implementation are difficult to operationalize in these dynamic organizations. In this piece, we put forth nine dimensions along which clinically validated digital health tools should be examined by health systems prior to adoption, and propose strategies for selecting digital health tools and planning for implementation in this setting. By evaluating prospective tools along these dimensions, health systems can evaluate which existing digital health solutions are worthy of adoption, ensure they have sufficient resources for deployment and long-term use, and devise a strategic plan for implementation.
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Health systems commonly require eHealth software to be approved through a governance board process before deployment into patient care settings.
“Health systems commonly require eHealth software to be approved through a governance board process before deployment into patient care settings [65].”
From Patient Engagement with Conversational Agents in Health Applications 2016–2022: A Systematic Review and Meta-Analysis (2024) · cites this paper as [65]
The sections “Are there executive sponsors?” and “What data assets are required for product functionality?” discuss executive oversight and data-governance agreements, respectively, but neither describes a governance board approving software before patient-care deployment. Searches for governance boards, approval committees, and pre-deployment approval found no passage establishing that this is a common health-system requirement. Page numbers were not available in our copy. (LLM, conf=0.73)
Experimental — accuracy not yet measuredDirectly increasing patient and caregiver awareness of risk factors for depression can help promote proactive pursuit of treatment or incentivize visits to the clinic.
“Directly increasing patient and caregiver awareness of risk factors for depression can help promote proactive pursuit of treatment or incentivize visits to the clinic.78”
From Digital health technologies and major depressive disorder (2023) · cites this paper as [78]
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- Authors
- Marwaha, J. S., Landman, A. B., Brat, G. A., Dunn, T., & Gordon, W. J.
- Affiliations
- Beth Israel Deaconess Medical Center (US)
- Brigham and Women's Hospital (US)
- Corresponding author
- Marwaha, Jayson S.
- Venue
- npj Digital Medicine
- Published
- 2022-01-27
- Volume
- 5
- Issue
- 1
- Pages
- 13-13
- ISSN
- 2398-6352 (ISSN-L)
- PMC
- PMC8795422
- OpenAlex
- W4210337422
- Cited by
- 241
- References
- 27
- Citations by year
- 2026: 43 · 2025: 63 · 2024: 59 · 2023: 42 · 2022: 34
- FWCI
- 7.32
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- npj Digital Medicine (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Harvard University (Biomedical Informatics and Data Science Research Training (BIRT) Program)
- U.S. Department of Health & Human Services | NIH | U.S. National Library of Medicine (T15LM007092)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Technology and Human Factors in Education and Health
- Keywords
- digital health tools, implementation planning, enterprise adoption, clinical validation, workflow integration, implementation strategies
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