ArticleJMIR AI2026
Evaluating an AI Decision Support System for the Emergency Department: Retrospective Study.
Article in JMIR AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- The Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Support Decision Making in Emergency Departments: Model Explainability and Feature Importance Analysis.Emergency medicine Australasia : EMA · 2026Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOvercrowding in the emergency department (ED) is a growing challenge, associated with increased medical errors, longer patient stays, higher morbidity, and increased mortality rates. Artificial intelligence (AI) decision support tools have shown potential in addressing this problem by assisting with faster decision-making regarding patient admissions; yet many studies neglect to focus on the clinical relevance and practical applications of these AI solutions.
objectiveThis study aimed to evaluate the clinical relevance of an AI model in predicting patient admission from the ED to hospital wards and its potential impact on reducing the time needed to make an admission decision.
methodsA retrospective study was conducted using anonymized patient data from St. Antonius Hospital, the Netherlands, from January 2018 to September 2023. An Extreme Gradient Boosting AI model was developed and tested on these data of 154,347 visits to predict admission decisions. The model was evaluated using data segmented into 10-minute intervals, which reflected real-world applicability. The primary outcome measured was the reduction in the decision-making time between the AI model and the admission decision made by the clinician. Secondary outcomes analyzed the performance of the model across various subgroups, including the age of the patient, medical specialty, classification category, and time of day.
resultsThe AI model demonstrated a precision of 0.78 and a recall of 0.73, with a median time saving of 111 (IQR 59-169) minutes for true positive predicted patients. Subgroup analysis revealed that older patients and certain specialties such as pulmonology benefited the most from the AI model, with time savings of up to 90 minutes per patient.
conclusionsThe AI model shows significant potential to reduce the time to admission decisions, alleviate ED overcrowding, and improve patient care. The model offers the advantage of always providing weighted advice on admission, even when the ED is under pressure. Future prospective studies are needed to assess the impact in the real world and further enhance the performance of the model in diverse hospital settings.
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