一个LLM聊天机器人,以促进从初级到专业护理的过渡:一个随机对照试验
Xinge Tao1, Shuya Zhou1, Kai Ding2,3,4
1School of Population Medicine and Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China.
Nature medicine
|January 19, 2026
概括
一个新的大型语言模型 (LLM) 聊天机器人,预评估 (PreA),显著减少了专家咨询时间和改善了护理协调. 与当地利益相关者共同设计LLM可以增强卫生系统和以患者为中心的护理.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 临床决策支持 临床决策支持
背景情况:
- 从初级护理过渡到专业护理往往涉及效率低下.
- 面向患者的大型语言模型 (LLM) 为简化这些流程提供了潜在的解决方案.
- 开发有效的LLM应用程序需要仔细考虑当地的医疗保健环境.
研究的目的:
- 开发和评估预评估 (PreA) 聊天机器人,这是与当地利益相关者共同设计的LLM,用于管理预专家咨询.
- 评估PreA对医生咨询时间,护理协调和患者沟通便利性的影响.
- 为了比较共同设计的LLM与微调的LLM的有效性,并评估自主操作.
主要方法:
- 进行了一项随机对照试验,对24个医学学科的2,069名患者和111名专家进行了试验.
- 患者被分配到PreA-only,PreA-human支持或没有PreA的组.
- 测量的主要结果包括医生咨询的持续时间,医生感知到的护理协调,以及患者报告的沟通方便.
主要成果:
- 与没有PreA组相比,仅PreA组的医生咨询时间减少了28.7% (P < 0.001).
- 仅在PreA组 (P < 0.001) 中,观察到医生感知到的护理协调 (113.1%的增加) 和患者报告的沟通方便 (16.0%的增加) 显著改善.
- 只有PreA和PreA的人类组之间的同等结果证实了自主操作能力;共同设计的PreA优于微调模型.
结论:
- 与PreA一样,共同设计的LLM可以有效地简化医疗保健过渡并改善患者护理.
- 与被动数据收集相比,利益相关者共同设计是LLM部署的优越策略,特别是在资源有限的环境中.
- 自主LLM操作是可行的,有利于提高卫生系统效率和以患者为中心的护理.
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