{"ID":2850521,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.21228","arxiv_id":"2510.21228","title":"DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services","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 \u003e 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.","short_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...","url_abs":"https://arxiv.org/abs/2510.21228","url_pdf":"https://arxiv.org/pdf/2510.21228v1","authors":"[\"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\"]","published":"2025-10-24T08:01:21Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.HC\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}