ED-triage-agent: a multi-agent framework for human-in-the-loop emergency triage
Abstract
BACKGROUND: Emergency Department (ED) triage requires rapid assessment of acuity under time pressure with incomplete information. Existing AI triage systems require complete clinical data, including vital signs, before generating a prediction, and so cannot support the sequential, information-gathering reality of triage in which patients accumulate in queues before vital signs are collected. Pre-vital queue prioritisation, deciding which patients are seen next before vital signs exist, remains unaddressed.
OBJECTIVE: To design and evaluate ED-Triage-Agent (ETA), a multi-agent large language model architecture that produces a provisional, ESI-scaled acuity signal from symptom-only data for queue prioritisation, and a criterion-linked ESI recommendation once vital signs are available.
METHODS: ETA employs five specialised agents orchestrated via LangGraph, grounded in the ESI Implementation Handbook v4 via retrieval-augmented generation. Confidence weights and decision thresholds were calibrated on 30 standardised ESI Handbook Practice Cases; performance was evaluated on the external TRIAGEAGENT benchmark using exact-match accuracy, quadratic-weighted Cohen's κw, F1, under-triage and over-triage rates, and high-priority sensitivity, with 95% bootstrap confidence intervals. Single-model and multi-agent variants, with and without RAG, were compared against the full architecture on 30 standardised ESI Handbook Competency Cases.
RESULTS: On the TRIAGEAGENT benchmark, Phase 1…
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