ArticleJournal of the American Medical Informatics Association : JAMIA2024
The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review.
Article in Journal of the American Medical Informatics Association : JAMIA, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed, 40 citations in OpenAlex.
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- Can Artificial Intelligence Improve Deprescribing in Older Adults?Geriatrics & gerontology international · 2026Article
- Beyond Traditional Inventory Matrices: A Multidimensional Inventory and Safety Model for an Outpatient Pharmacy.Cureus · 2026Article
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- AI and clinicians growing together: A cross-sectional survey of clinicians' attitudes toward AI-CDSS with comparison to 2020 data.Clinical medicine (London, England) · 2026Article
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- Article
- Development of a High-Risk Medication List for Australian Residential Aged Care: A Modified Delphi Study.Australasian journal on ageing · 2026Article
- Beyond Human Error: Building Intelligent Resilience for Medication Safety in the ICU.Healthcare (Basel, Switzerland) · 2026Review
- Machine learning for medication error detection: a scoping review.Research square · 2026Article
- Experiences of Alert Fatigue and Its Contributing Factors in Hospitals: Qualitative Study.Journal of medical Internet research · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 6 institutions in 1 country.
Funding
Abstract
objectiveCurrent Clinical Decision Support Systems (CDSSs) generate medication alerts that are of limited clinical value, causing alert fatigue. Artificial Intelligence (AI)-based methods may help in optimizing medication alerts. Therefore, we conducted a scoping review on the current state of the use of AI to optimize medication alerts in a hospital setting. Specifically, we aimed to identify the applied AI methods used together with their performance measures and main outcome measures. MATERIALS AND
methodsWe searched Medline, Embase, and Cochrane Library database on May 25, 2023 for studies of any quantitative design, in which the use of AI-based methods was investigated to optimize medication alerts generated by CDSSs in a hospital setting. The screening process was supported by ASReview software.
resultsOut of 5625 citations screened for eligibility, 10 studies were included. Three studies (30%) reported on both statistical performance and clinical outcomes. The most often reported performance measure was positive predictive value ranging from 9% to 100%. Regarding main outcome measures, alerts optimized using AI-based methods resulted in a decreased alert burden, increased identification of inappropriate or atypical prescriptions, and enabled prediction of user responses. In only 2 studies the AI-based alerts were implemented in hospital practice, and none of the studies conducted external validation. DISCUSSION AND
conclusionAI-based methods can be used to optimize medication alerts in a hospital setting. However, reporting on models' development and validation should be improved, and external validation and implementation in hospital practice should be encouraged.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.