通过护士和大型语言模型的合作进行门诊接待:一种随机对照试验
Peixing Wan1,2, Zigeng Huang1, Wenjun Tang1
1State Key Laboratory of Respiratory Health and Multimorbidity, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Nature medicine
|July 15, 2024
概括
大型语言模型 (LLM) 通过提高患者沟通效率和满意度来帮助医疗接待. 与传统的护士主导的互动相比,一个新的聊天机器人显著减少了查询轮和增强了患者体验.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者通信系统 患者通信系统
背景情况:
- 医疗保健接收非常重要,但劳动密集型.
- 目前的通信在很大程度上依赖于人类工作人员.
- 大型语言模型 (LLM) 为提高效率提供了一个潜在的解决方案.
研究的目的:
- 为医疗接待开发和评估一个特定站点的快速工程聊天机器人 (SSPEC).
- 评估与传统方法相比,LLM辅助沟通的有效性.
- 调查护士和SSPEC之间的合作模式.
主要方法:
- 策划了35418个门诊患者与接待员对话的数据集.
- 开发了一个特定站点的提示工程聊天机器人 (SSPEC).
- 进行了一项随机对照试验,共有2,164名参与者,将护士-SSPEC合作与只有护士的会议进行比较.
主要成果:
- 在≤2轮中,SSPEC解决了68.0%的查询,超过了护士 (50.5%).
- 护士-SSPEC模型实现了更高的患者满意度 (P < 0.001).
- 减少了重复查询 (3.2%对14.4%),消极情绪 (2.4%对7.8%),并改善了响应质量.
结论:
- 将LLM整合到医院工作流程中是可行的.
- 护士-SSPEC合作模式增强了患者的沟通和满意度.
- 这种模式改善了患者和护士的医疗保健体验.
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