Override rate of drug-drug interaction alerts in clinical decision support systems: A brief systematic review and meta-analysis
Open accessAbstract
Primary studies have demonstrated that despite being useful, most of the drug-drug interaction (DDI) alerts generated by clinical decision support systems are overridden by prescribers. To provide more information about this issue, we conducted a systematic review and meta-analysis on the prevalence of DDI alerts generated by CDSS and alert overrides by physicians. The search strategy was implemented by applying the terms and MeSH headings and conducted in the MEDLINE/PubMed, EMBASE, Web of Science, Scopus, LILACS, and Google Scholar databases. Blinded reviewers screened 1873 records and 86 full studies, and 16 articles were included for analysis. The overall prevalence of alert generated by CDSS was 13% (CI95% 5–24%, p-value <0.0001, I^2 = 100%), and the overall prevalence of alert override by physicians was 90% (CI95% 85–95%, p-value <0.0001, I^2 = 100%). This systematic review and meta-analysis presents a high rate of alert overrides, even after CDSS adjustments that significantly reduced the number of alerts. After analyzing the articles included in this review, it was clear that the CDSS alerts physicians about potential DDI should be developed with a focus on the user experience, thus increasing their confidence and satisfaction, which may increase patient clinical safety.
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A recent systematic review and meta-analysis reported a pooled drug-drug interaction alert override rate of 90% (95% CI 85%-95%) across 16 studies.
“A recent systematic review and meta-analysis reported a pooled drug-drug interaction alert override rate of 90% (95% CI 85%-95%) across 16 studies [134].”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [134]
Alert fatigue is already a major problem in clinical informatics.
“Alert fatigue is already a major problem in clinical informatics.”
From Explainability of decoder-only clinical large language models: A scoping review (2026) · cites this paper as [134]
Cites
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · BMJ · doi:10.1136/bmj.n71 · Cited by 108,283 · see citing works
Rayyan-a web and mobile app for systematic reviews.
Ouzzani, M., Hammady, H., Fedorowicz, Z., & Elmagarmid, A. (2016) · Systematic Reviews · doi:10.1186/s13643-016-0384-4 · Cited by 26,092 · see citing works
The benefits of imperfect diagnostic automation: a synthesis of the literature
Wickens, C. D., & Dixon, S. R. (2007) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220500370105 · Cited by 423 · cited 1× · see citing works
“37 In this context, Wickens and Dixon (2007) 38 reviewed the literature in search of indirect Figure 3.”
Drug interaction alert override rates in the Meaningful Use era: No evidence of progress
Bryant, A. D., Fletcher, G. S., & Payne, T. H. (2014) · Applied Clinical Informatics · doi:10.4338/aci-2013-12-ra-0103 · Cited by 172 · cited 5× · see citing works
“Most studies demonstrated high methodological quality, 21,23 – 27, 32,33,35,”
InfluentialAppropriateness of Overridden Alerts in Computerized Physician Order Entry: Systematic Review
Poly, T. N., Islam, M. M., Yang, H.-C., & Li, Y.-C. (. (2020) · JMIR Medical Informatics · doi:10.2196/15653 · Cited by 118 · see citing works
Van De Sijpe, G., Quintens, C., Walgraeve, K., Van Laer, E., Penny, J., De Vlieger, G., Schrijvers, R., De Munter, P., Foulon, V., Casteels, M., et al. (2022) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-022-01783-z · Cited by 54 · see citing works
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Explainability of decoder-only clinical large language models: A scoping review
Mishra, N., Abu-Hanna, A., & Calixto, I. (2026) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2026.103501
- Authors
- Felisberto, M., Lima, G. D. S., Celuppi, I. C., Fantonelli, M. D. S., Zanotto, W. L., Dias de Oliveira, J. M., Mohr, E. T. B., dos Santos, R. A., Scandolara, D. H., Cunha, C. L., Hammes, J. F., da Rosa, J. S., Demarchi, I. G., Wazlawick, R. S., & Dalmarco, E. M.
- Affiliations
- Universidade Federal de Santa Catarina (BR)
- Bridge Laboratory, Technological Center, Federal University of Santa Catarina, Florianópolis, Brazil
- Department of Clinical Analysis, Federal University of Santa Catarina, Florianópolis, Brazil
- Corresponding author
- Dalmarco, Eduardo Monguilhott
- ORCID
- Venue
- Health Informatics Journal
- Publisher
- SAGE Publications
- Published
- 2024-04
- Volume
- 30
- Issue
- 2
- Pages
- 14604582241263242-14604582241263242
- ISSN
- 1355-4743 (ISSN-L)
- 1460-4582
- 1741-2811
- OpenAlex
- W4399858384
- Cited by
- 28
- References
- 6
- Citations by year
- 2026: 20 · 2025: 7 · 2024: 1
- FWCI
- 2.19
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Health Informatics Journal (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Brazilian Ministry of Health (e-SUS PHC Project Stage 6)
- Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (Finantial code - 001)
- Conselho Nacional de Desenvolvimento Científico e Tecnológico (Productivity fellow in technology development and)
- Topics
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Health Sciences › Medicine › Geriatrics and Gerontology › Pharmaceutical Practices and Patient Outcomes
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- drug-drug interactions, clinical decision support systems, alert override, prescriber behavior, alarm fatigue, clinical safety, user experience
- MeSH terms
- Drug Interactions, Humans, Medication Errors, Decision Support Systems, Clinical, Medical Order Entry Systems
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