用大型语言模型在急诊室进行患者分组和指导:多度测量研究
Chenxu Wang1,2, Fei Wang3, Shuhan Li2
1West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
Journal of medical Internet research
|May 15, 2025
概括
聊天GPT在急诊室分拣和指导方面表现有前途. 在快速工程后,GPT-4-Turbo在分拣准确度方面表现出色,而GPT-4o则表现出强大的门诊指导能力,特别是在内科医学中.
科学领域:
- 医疗保健中的人工智能
- 临床决策支持系统 临床决策支持系统
- 紧急医疗 技术 技术 技术
背景情况:
- 紧急部门 (ED) 面临重大挑战,包括过度拥挤和人员短缺.
- 有效的患者分组和部门指导对于管理ED压力至关重要.
- 像ChatGPT这样的大型语言模型 (LLM) 为改善紧急护理流程提供了潜在的解决方案.
研究的目的:
- 评估基于GPT-4的ChatGPT模型 (GPT-4o,GPT-4-Turbo) 的准确性和一致性,用于修改早期预警分数 (MEWS) 选.
- 用模拟患者场景评估GPT-4o在门诊病房选择中的准确性.
- 确定LLMs在增强紧急部门工作流程中的可行性.
主要方法:
- 一个两阶段的实验研究,利用模拟的患者场景.
- 阶段1:评估GPT-4o和GPT-4-Turbo的MEWS分选精度,使用1854种场景,评估快速工程的影响.
- 第2阶段:评估GPT-4o的门诊病房指导准确度,使用来自中国医疗病例库的264种场景.
主要成果:
- 快速工程提高了ChatGPT的MEWS分选精度,GPT-4-Turbo实现了100%的精度,而GPT-4o的96.2%.
- 在门诊病房指导中,GPT-4o的准确度为92.63%,在内科中准确度最高 (93.51%).
- GPT-4o显示出卓越的情绪反应能力,这是面向患者的应用程序的关键特征.
结论:
- 聊天GPT模型显示出在ED环境中支持患者分拣和门诊指导的巨大潜力.
- GPT-4-Turbo更适应快速工程进行分类,而GPT-4o在患者交互方面表现出色.
- 需要对现实世界的实施进行进一步的研究,以优化LLM在紧急护理中的临床整合.
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