使用大型语言模型 (LLM) 进行救护车调度和分拣
Aditya C Shekhar1, Joshua Kimbrell2, Aaryan Saharan3
1Icahn School of Medicine at Mount Sinai, New York City, NY, United States of America.
The American journal of emergency medicine
|December 15, 2024
概括
大型语言模型 (LLM) 在对紧急医疗服务 (EMS) 呼叫进行分拣方面表现有前途. 一个法学士准确地优先考虑患者,在大多数情况下与专家护理人员达成一致,表明了资源分配的潜力.
科学领域:
- 医疗保健中的人工智能
- 紧急医疗服务研究 医疗服务研究
- 临床决策支持系统 临床决策支持系统
背景情况:
- 大型语言模型 (LLM) 展示了先进的临床推理能力.
- 紧急医疗服务 (EMS) 的有效分类对于优先考虑危急患者至关重要.
- 本研究探讨了LLM在现实世界EMS分类中的应用.
研究的目的:
- 为了评估大型语言模型 (LLM) 在分拣救护车请求中的准确性.
- 为了比较LLM分类决策与专家重症监护护理医人员.
- 评估LLM在紧急护理资源分配方面的潜力.
主要方法:
- 一个特定的LLM (ChatGPT 4o Mini) 在没有事先的任务特定培训的情况下被使用.
- 分析了来自美国主要大都市地区的真实救护车请求.
- 在分组请求上,LLM的优先级与经验丰富的重症护理护理人员的优先级相比较.
主要成果:
- 在392个申请中,LLM同意护理医师小组的优先考虑,76.5%的申请.
- 当医护人员小组达成一致决定时,LLM在93.8%的案件中同意了这一点.
- 这表明LLM和专家护理人员判断之间存在显著的一致性.
结论:
- 初步发现表明,LLM可以成为在紧急情况下进行分拣的有价值的工具.
- 在具有专家共识的场景中,LLM的实用性特别突出.
- 需要进一步的研究,以充分定义LLMs在紧急护理资源管理中的作用.
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