The DOI matches its Crossref record; 27 of the 28 references it cites were found in a registry or an index.
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Figures
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The check wasn’t confident either way, so it gives no verdict — never disputed.
In the citing paper
“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]
Experimental — accuracy not yet measuredIn the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
From Triple to Quadruple Aim: Care of the Patient Requires Care of the Provider
Bodenheimer, T., & Sinsky, C. (2014) · The Annals of Family Medicine · doi:10.1370/afm.1713 · Cited by 3,462
Cresswell, K., & Sheikh, A. (2013) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2012.10.007 · Cited by 690 · cited 1×
“Many frameworks exist for clinical validation of these tools 3,9 – 11 , but few strategies describe how to integrate these validated tools in health systems; the literature on digital health implementation within large organizations is limited 12 – 14 .”
BackgroundDigital health: a path to validation
Mathews, S. C., McShea, M. J., Hanley, C. L., Ravitz, A., Labrique, A. B., & Cohen, A. B. (2019) · npj Digital Medicine · doi:10.1038/s41746-019-0111-3 · Cited by 508
Reeves, J. J., Hollandsworth, H. M., Torriani, F. J., Taplitz, R., Abeles, S., Tai-Seale, M., Millen, M., Clay, B. J., & Longhurst, C. A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa037 · Cited by 429
Cresswell, K. M., Bates, D. W., & Sheikh, A. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2013-001684 · Cited by 366
Randomized controlled trial of a 12-week digital care program in improving low back pain
Shebib, R., Bailey, J. F., Smittenaar, P., Perez, D. A., Mecklenburg, G., & Hunter, S. (2019) · npj Digital Medicine · doi:10.1038/s41746-018-0076-7 · Cited by 341
Beyond validation: getting health apps into clinical practice
Gordon, W. J., Landman, A., Zhang, H., & Bates, D. W. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0212-z · Cited by 322
Self-citationBlockchain vehicles for efficient Medical Record management
Vazirani, A. A., O’Donoghue, O., Brindley, D., & Meinert, E. (2020) · npj Digital Medicine · doi:10.1038/s41746-019-0211-0 · Cited by 245
Powell, A. C., Landman, A., & Bates, D. W. (2014) · JAMA · doi:10.1001/jama.2014.2564 · Cited by 236
Self-citationDeveloping a delivery science for artificial intelligence in healthcare
Li, R., Asch, S. M., & Shah, N. H. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-00318-y · Cited by 206
Research works printed but not linked: 1
- NEJM Catalyst. How to Avoid Common Pitfalls of Health IT Implementation. https://catalyst.nejm.org/doi/full/10.1056/CAT.20.0048 (2020). · DOI
Cited by
Sorted by citations
Ethical and regulatory challenges of AI technologies in healthcare: A narrative review
Mennella, C., Maniscalco, U., De Pietro, G., & Esposito, M. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26297 · Cited by 1,229
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915
Binuya, M. A. E., Engelhardt, E. G., Schats, W., Schmidt, M. K., & Steyerberg, E. W. (2022) · BMC Medical Research Methodology · doi:10.1186/s12874-022-01801-8 · Cited by 246
Hogg, H. D. J., Al-Zubaidy, M., Technology Enhanced Macular Services Study Reference Group, Talks, J., Denniston, A. K., Kelly, C. J., Malawana, J., Papoutsi, C., Teare, M. D., Keane, P. A., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/39742 · Cited by 163
iCHECK-DH: Guidelines and Checklist for the Reporting on Digital Health Implementations
Franck, C. P., Babington‐Ashaye, A., Dietrich, D., Bediang, G., Veltsos, P., Gupta, P. P., Juech, C., Kadam, R., Collin, M., Setian, L., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/46694 · Cited by 114
Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
