Evaluating a Large Language Model for Emergency Triage Using Synthetic Clinical Dialogues: Toward a Multimodal Clinical Safety Framework

Background/Objectives: Prioritizing patient safety through accurate emergency triage is critical during mass-gathering events such as Hajj and Umrah, where high patient volumes can significantly increase the risk of clinical errors. Although the Emergency Severity Index (ESI) provides a standardized triage protocol, it remains dependent on human judgment, while traditional artificial intelligence (AI) models often struggle to capture the nuanced clinical context embedded in patient narratives. To address this gap, this study evaluates GPT-5.2 using synthetically generated dialogues as a step toward a conceptual clinical decision-support framework that bridges structured electronic health records (EHRs) with triage interactions through clinical dialogues. By combining narrative inputs with structured physiological indicators, it explores the potential to support more informed clinical decision-making under controlled simulation conditions.