ArticleJournal of the American Medical Informatics Association : JAMIA2022
The potential for leveraging machine learning to filter medication alerts.
Article in Journal of the American Medical Informatics Association : JAMIA, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Bias and Fairness Across the Healthcare AI Lifecycle: A Clinician-Oriented Review.Balkan medical journal · 2026Review
- Identifying false-positive drug allergy alerts based on drug tolerance assertions: a retrospective study.JAMIA open · 2026Article
- Effectiveness of computerised alerts to reduce drug-drug interactions (DDIs) and DDI-related harm in hospitalised patients: a quasi-experimental controlled pre-post study.BMJ quality & safety · 2025Article
- A standard-based taxonomy of features that affect user response to clinical decision support alerts.BMC medical informatics and decision making · 2025Article
- Enabling Drug-Induced Liver Injury Surveillance Through Automated Medication Extraction From Clinical Notes: A Medical Information Mart for Intensive Care IV Real-World Large Language Models Validation Study.Gastroenterology research · 2025Article
- Assessing Medication CDS Usability: Pilot Results from 10 Outpatient Clinics.Applied clinical informatics · 2025Article
- Article
- Pharmacogenomic augmented machine learning in electronic health record alerts: A health system-wide usability survey of clinicians.Clinical and translational science · 2024Article
- Balancing act: the complex role of artificial intelligence in addressing burnout and healthcare workforce dynamics.BMJ health & care informatics · 2024Article
- Why do users override alerts? Utilizing large language model to summarize comments and optimize clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- The use of artificial intelligence to optimize medication alerts generated by clinical decision support systems: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Leveraging explainable artificial intelligence to optimize clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- An Electronic Health Record Alert for Inpatient Coronavirus Disease 2019 Vaccinations Increases Vaccination Ordering and Uncovers Workflow Inefficiencies.Applied clinical informatics · 2024Article
- How can artificial intelligence decrease cognitive and work burden for front line practitioners?JAMIA open · 2023Review
- Using AI-generated suggestions from ChatGPT to optimize clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2023Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
objectiveTo evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view. MATERIALS AND
methodsWe identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate <0.01, a threshold predefined through discussion with physicians and pharmacists. We developed models while maintaining a sensitivity of 0.99. Two null hypotheses were developed: H1-there is no difference in precision among prediction models; and H2-the removal of any feature category does not change precision.
resultsA total of 3,481,634 medication alerts with 751 features were evaluated. With sensitivity fixed at 0.99, LightGBM achieved the highest precision of 0.192 and less than 0.01 for the pre-defined maximal false-negative rate by subject-matter experts (H1) (P < 0.001). This model could reduce alert volume by 54.1%. We removed different combinations of features (H2) and found that not all features significantly contributed to precision. Removing medication order features (eg, dosage) most significantly decreased precision (-0.147, P = 0.001).
conclusionsMachine learning potentially enables the intelligent filtering of medication alerts.
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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.