Enhancing emergency medical service diversion decision-making through large language models integration

作者：Z. Shen, Yu Tian, Qiang Li, Dubai Li, Yinghao Zhao, Tianshu Zhou, Mao Zhang, Jingsong Li · 发表于：Intelligent Medicine · 年份：2025 · DOI：10.1016/j.imed.2025.04.004 · 被引用次数：3 · 研究领域：Artificial Intelligence in Healthcare and Education、Trauma and Emergency Care Studies、Emergency and Acute Care Studies

Managing emergency medical services requires maintaining a delicate balance between time, resources, and the quality of care. Rapid and effective decision-making is crucial for patient outcomes. Our goal is to integrate advanced large language models into emergency medical service (EMS) systems to assist in triage decisions and test their practicality and benefits. 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 large language models to achieve a comprehensive assessment of the dynamic changes in the condition of the injured and emergency resources. In this way, 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 four different large language models (as GPT-4, GLM-4, Qwen-max-0428 and Baichuan2-7b-chat-v1) in various scenarios, including different numbers of injured people and different 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 large language models in triage decisions were compared, and emergency experts were invited to evaluate the triage decision results and processes. We conducted experiments on em...