Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept

• URL: http://arxiv.org/abs/2507.01080v1
• Date: Tue, 01 Jul 2025 16:37:55 GMT
• Title: Development and Comparative Evaluation of Three Artificial Intelligence Models (NLP, LLM, JEPA) for Predicting Triage in Emergency Departments: A 7-Month Retrospective Proof-of-Concept
• Authors: Edouard Lansiaux, Ramy Azzouz, Emmanuel Chazard, Amélie Vromant, Eric Wiel,
• Abstract summary: Triage errors, including undertriage and overtriage, are persistent challenges in emergency departments (EDs)<n>This study compares the performance of three AI models in predicting triage outcomes against the FRENCH scale and clinical practice.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Triage errors, including undertriage and overtriage, are persistent challenges in emergency departments (EDs). With increasing patient influx and staff shortages, the integration of artificial intelligence (AI) into triage protocols has gained attention. This study compares the performance of three AI models [Natural Language Processing (NLP), Large Language Models (LLM), and Joint Embedding Predictive Architecture (JEPA)] in predicting triage outcomes against the FRENCH scale and clinical practice.We conducted a retrospective analysis of a prospectively recruited cohort gathering adult patient triage data over a 7-month period at the Roger Salengro Hospital ED (Lille, France). Three AI models were trained and validated : (1) TRIAGEMASTER (NLP), (2) URGENTIAPARSE (LLM), and (3) EMERGINET (JEPA). Data included demographic details, verbatim chief complaints, vital signs, and triage outcomes based on the FRENCH scale and GEMSA coding. The primary outcome was the concordance of AI-predicted triage level with the FRENCH gold-standard. It was assessed thanks to various indicators : F1-Score, Weighted Kappa, Spearman, MAE, RMSE. The LLM model (URGENTIAPARSE) showed higher accuracy (composite score: 2.514) compared to JEPA (EMERGINET, 0.438) and NLP (TRIAGEMASTER, -3.511), outperforming nurse triage (-4.343). Secondary analyses highlighted the effectiveness of URGENTIAPARSE in predicting hospitalization needs (GEMSA) and its robustness with structured data versus raw transcripts (either for GEMSA prediction or for FRENCH prediction). LLM architecture, through abstraction of patient representations, offers the most accurate triage predictions among tested models. Integrating AI into ED workflows could enhance patient safety and operational efficiency, though integration into clinical workflows requires addressing model limitations and ensuring ethical transparency.

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