aceUsing Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Predict Inpatient Admitting Specialty for Emergency Department Patients: Single Centre Deep Learning Analysis
Objective To investigate whether the Sydney Triage to Admission Risk Tool with Artificial Intelligence (START-AI) model could be used to predict specific inpatient admitting teams based on clinical notes. Methods This was a pre-trained language model analysis using electronic medical record data from an inner-city tertiary referral hospital emergency department (ED) in Sydney, Australia. All adult patients who were admitted to an inpatient ward between January 2023 and June 2025 were included. A fine-tuned Bidir…
A fine-tuned clinical language model predicted the correct admitting specialty team from ED case notes with high discrimination in a single Sydney hospital.
The study was conducted at a single centre, and the authors state that further exploration and validation are needed to assess clinical utility and broader feasibility.
Evidence
- Peer-reviewedEmergency Medicine Australasia2026-10-01
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Truvace Impact Record TRV-2026-1564, v1: “Using Sydney Triage to Admission Risk Tool With Artificial Intelligence (START-AI) to Predict Inpatient Admitting Specialty for Emergency Department Patients: Single Centre Deep Learning Analysis.” Truvace, 2026-10-16. /record/TRV-2026-1564 (accessed at citation time). sha256 dd6561acfbec7dcf…
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