Intelligent Medicine · Published 2025-10-15 · DOI 10.1016/j.imed.2025.04.004
Objective: Emergency medical services (EMSs) management requires maintaining a delicate balance between time, resources, and quality of care. Rapid and effective decision-making is crucial for patient outcomes. Our goal is to integrate advanced large language models (LLMs) into EMS systems to assist in triage decisions and test their practicality and benefits. Methods: This method is designed for emergency triage scenarios. By designing specific prompts to introduce heuristic emergency strategies, it makes full use of the multi-turn dialogue capability and contextual understanding characteristics of LLMs to achieve a comprehensive assessment of the dynamic changes in the condition of the injured and emergency resources. Thus, it forms dynamic triage decisions for a large number of injured people, and can also provide detailed explanations of the decision reasons. This method was evaluated and verified using 4 different LLMs (GPT-4, GLM-4, Qwen-max-0428, and Baichuan2-7b-chat-v1) in various scenarios, including different numbers of injured individuals and various types of large-scale casualty events on our self-built emergency medical dispatch simulation platform, and was compared with the nearest transport method. Additionally, the differences between doctors and LLMs in terms of triage decisions were compared, and emergency experts were invited to evaluate the triage decision results and processes. Results: We conducted experiments on EMSs under 6 different resource environment conditions. With comprehensive patient information and hospital treatment capacity information, GLM-4, GPT-4, and Qwen-max-0428 demonstrated decision-making capabilities far surpassing traditional evacuation methods. GLM-4 and Qwen-max-0428 improved survival rates by an average of 15% after prompt optimization, whereas GPT-4 performed even better, with an average improvement in survival rates reaching 23% after prompt optimization. The consistency level of manual controlled trials (as high as 0.67) reveals that LLMs have guiding and training significance for inexperienced triage personnel in making triage decisions. However, in clinicians’ evaluations, it was revealed that LLMs possess good decision-making abilities, but there is still scope for improvement compared to the level of emergency experts. Conclusion: This study highlights the potential of LLMs in EMS diversion decision-making and suggests that more comprehensive emergency information can further enhance their decision-making abilities.
Abstract from DOAJ. Public domain (CC0 1.0).
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