DispatchMAS: Fusing taxonomy and artificial intelligence agents for emergency medical services
On the map: LLM agents and tool use → Medical and scientific domain agents
Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use. Builds on 12 of its 73 references that are in the map. First posted on arXiv 24 Oct 2025.
Builds on
12 of 73 references.
References · 12
- 2023AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversationcited by 1,906
- 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
- 2025Medical Hallucinations in Foundation Models and Their Impact on Healthcarecited by 89
- 2024LLM-Assisted Crisis Management: Building Advanced LLM Platforms for Effective Emergency Response and Public Collaborationcited by 20
- 2024On Large Language Models in National Security Applicationscited by 18
- 2022Multimodal Learning on Graphs for Disease Relation Extractioncited by 15
- 2024Development of a Large Language Model-based Multi-Agent Clinical Decision Support System for Korean Triage and Acuity Scale (KTAS)-Based Triage and Treatment Planning in Emergency Departmentscited by 10
- 2024Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysiscited by 9
- 2024Real-Time Multimodal Cognitive Assistant for Emergency Medical Servicescited by 8
- 2024Sim911: Towards Effective and Equitable 9-1-1 Dispatcher Training with an LLM-Enabled Simulationcited by 6
- 2024Multi-Agent Reinforcement Learning with Hierarchical Coordination for Emergency Responder Stationingcited by 4
- 2024A Machine Learning Approach for Emergency Detection in Medical Scenarios Using Large Language Modelscited by 1
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Citing papers per year: 2026: 2.
2 papers.
Similar, not cited
Close in wording, no citation either way
- 2026EMR: Self-Evolving Medical Multi-Agent System via Experience Mining and Reuse
- 2025AutoMedic: An Automated Evaluation Framework for Clinical Conversational Agents with Medical Dataset Grounding
- 2025Small Language Models for Emergency Departments Decision Support: A Benchmark Study
- 2024Exploring LLM Multi-Agents for ICD Coding
- 2026EMSDialog: Synthetic Multi-person Emergency Medical Service Dialogue Generation from Electronic Patient Care Reports via Multi-LLM Agents
- 2025A Survey of LLM-based Agents in Medicine: How far are we from Baymax?
- 2023Large Language Models Illuminate a Progressive Pathway to Artificial Healthcare Assistant: A Review
- 2024Towards Automatic Evaluation for LLMs' Clinical Capabilities: Metric, Data, and Algorithm