ArticleJournal of the American Medical Informatics Association : JAMIA2024
Leveraging explainable artificial intelligence to optimize clinical decision support.
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 17 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
17 citing papers in PubMed.
- Advanced systems for intraoperative cartilage evaluation and treatment demonstrate early feasibility and a shift towards integrating artificial intelligence: A scoping review.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026Article
- An Electronic Health Record-Integrated, Large Language Model-Powered Tool to Triage Surgical Patients.JAMA network open · 2026Article
- Rare-Disease Diagnosis on the ZebraMap Multimodal Case Report Dataset: A Hybrid Pipeline with Grounded Explainability.Sensors (Basel, Switzerland) · 2026Article
- Integrating Generative AI Into Patient-Centered Clinical Decision Support: Viewpoint on Research and Practice Considerations.Journal of medical Internet research · 2026Article
- Use of artificial intelligence for health science in low- and middle-income countries: NIH portfolio landscape, gaps and opportunities.BMJ open · 2026Article
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- TIC-XNet: a structured evidence translation framework for interpretable multimodal pediatric tic event detection with improved temporal alignment and fidelity.Frontiers in psychiatry · 2026Article
- Integrating rule-based NLP and large language models for statin information extraction from clinical notes.International journal of medical informatics · 2026Article
- Pseudo datasets estimate feature attribution in artificial neural networks.Scientific reports · 2025Article
- A standard-based taxonomy of features that affect user response to clinical decision support alerts.BMC medical informatics and decision making · 2025Article
- When Nudges Don't Budge: A Mixed Methods Study of Why EHR-Based Deprescribing Nudges Failed to Change Provider Behavior.Research square · 2025Article
- Prompt Engineering in Clinical Practice: Tutorial for Clinicians.Journal of medical Internet research · 2025Article
- Evaluating Equity in Usage and Effectiveness of the CONCERN Early Warning System.Applied clinical informatics · 2025Article
- Reimagining clinical AI: from clickstreams to clinical insights with EHR use metadata.npj health systems · 2025Review
- Toward a responsible future: recommendations for AI-enabled clinical decision support.Journal of the American Medical Informatics Association : JAMIA · 2024Article
- Pearls and Pitfalls for LLMs 2.0.Radiology · 2024Article
- Evaluating the Prevalence of Burnout Among Health Care Professionals Related to Electronic Health Record Use: Systematic Review and Meta-Analysis.JMIR medical informatics · 2024Review
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
objectiveTo develop and evaluate a data-driven process to generate suggestions for improving alert criteria using explainable artificial intelligence (XAI) approaches.
methodsWe extracted data on alerts generated from January 1, 2019 to December 31, 2020, at Vanderbilt University Medical Center. We developed machine learning models to predict user responses to alerts. We applied XAI techniques to generate global explanations and local explanations. We evaluated the generated suggestions by comparing with alert's historical change logs and stakeholder interviews. Suggestions that either matched (or partially matched) changes already made to the alert or were considered clinically correct were classified as helpful.
resultsThe final dataset included 2 991 823 firings with 2689 features. Among the 5 machine learning models, the LightGBM model achieved the highest Area under the ROC Curve: 0.919 [0.918, 0.920]. We identified 96 helpful suggestions. A total of 278 807 firings (9.3%) could have been eliminated. Some of the suggestions also revealed workflow and education issues.
conclusionWe developed a data-driven process to generate suggestions for improving alert criteria using XAI techniques. Our approach could identify improvements regarding clinical decision support (CDS) that might be overlooked or delayed in manual reviews. It also unveils a secondary purpose for the XAI: to improve quality by discovering scenarios where CDS alerts are not accepted due to workflow, education, or staffing issues.
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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.