The Sydney Triage to Admission Risk Tool With Artificial Intelligence ( <scp>START</scp> ‐ <scp>AI</scp> ): Prediction of Inpatient Admission From Emergency Departments Using Ensemble Machine Learning

Abstract

OBJECTIVE: Use artificial intelligence (AI) to extend the Sydney triage to admission risk tool (START) and improve prediction of emergency department (ED) patient disposition. METHODS: The study was conducted at an inner-city tertiary referral hospital ED. Adult (age ≥ 16 years) presentations from 1 January 2023 to 30 June 2025 were included. Participants were excluded if dead on ED arrival or left ED prior to completing treatment. The primary outcome was admission to an inpatient ward. A sequential ensemble modelling approach was used. To predict patient disposition, the original START was combined (stacked) with vital signs, blood results and CT imaging orders using a gradient boosting decision tree algorithm (XGBoost) and a pre-trained transformer model for clinical free text. RESULTS: of 0.92 (95% CI: 0.67, 0.99) with a drop-off in correlation at the highest predicted probability ranges (> 0.80). After classifying inpatient stays < 24 h as potential discharges, a sensitivity analysis demonstrated AUROC for the final model of 0.89 (95% CI: 0.88, 0.89). CONCLUSIONS: An ensemble machine learning model was developed to accurately predict patient disposition from ED using structured and unstructured data. Prototype development and prospective evaluation of START-AI are required to assess model performance in clinical settings.

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