DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services

Xiang Li; Huizi Yu; Wenkong Wang; Yiran Wu; Jiayan Zhou; Wenyue Hua; Xinxin Lin; Wenjia Tan; Lexuan Zhu; Bingyi Chen; Guang Chen; Ming-Li Chen; Yang Zhou; Zhao Li; Themistocles L. Assimes; Yongfeng Zhang; Qingyun Wu; Xin Ma; Lingyao Li; Lizhou Fan

arXiv · 2510.21228

DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services

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Abstract

Objective: Emergency medical dispatch (EMD) is a high-stakes process challenged by caller distress, ambiguity, and cognitive load. Large Language Models (LLMs) and Multi-Agent Systems (MAS) offer opportunities to augment dispatchers. This study aimed to develop and evaluate a taxonomy-grounded, LLM-powered multi-agent system for simulating realistic EMD scenarios. Methods: We constructed a clinical taxonomy (32 chief complaints, 6 caller identities from MIMIC-III) and a six-phase call protocol. Using this framework, we developed an AutoGen-based MAS with Caller and Dispatcher Agents. The system grounds interactions in a fact commons to ensure clinical plausibility and mitigate misinformation. We used a hybrid evaluation framework: four physicians assessed 100 simulated cases for "Guidance Efficacy" and "Dispatch Effectiveness," supplemented by automated linguistic analysis (sentiment, readability, politeness). Results: Human evaluation, with substantial inter-rater agreement (Gwe's AC1 > 0.70), confirmed the system's high performance. It demonstrated excellent Dispatch Effectiveness (e.g., 94 % contacting the correct potential other agents) and Guidance Efficacy (advice provided in 91 % of cases), both rated highly by physicians. Algorithmic metrics corroborated these findings, indicating a predominantly neutral affective profile (73.7 % neutral sentiment; 90.4 % neutral emotion), high readability (Flesch 80.9), and a consistently polite style (60.0 % polite; 0 % impolite). Conclusion: Our taxonomy-grounded MAS simulates diverse, clinically plausible dispatch scenarios with high fidelity. Findings support its use for dispatcher training, protocol evaluation, and as a foundation for real-time decision support. This work outlines a pathway for safely integrating advanced AI agents into emergency response workflows.

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Xiang Li, Huizi Yu, Wenkong Wang, Yiran Wu, Jiayan Zhou, Wenyue Hua, Xinxin Lin, Wenjia Tan, Lexuan Zhu, Bingyi Chen, Guang Chen, Ming-Li Chen, Yang Zhou, Zhao Li, Themistocles L. Assimes, Yongfeng Zhang, Qingyun Wu, Xin Ma, Lingyao Li, Lizhou Fan. 2025-10-24. DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services. https://arxiv.org/abs/2510.21228

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