一个社区编码设计的LLM-powered聊天机器人用于初级保健:一个随机对照试验
Sairan Li1, Yanzeng Li2,3,4, Shuya Zhou1
1School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Nature health
|February 9, 2026
概括
一个新的大型语言模型 (LLM) 聊天机器人,P&P Care,在一项试验中显著提高了健康意识. 这种LLM驱动的工具为服务不足地区的初级医疗保健提供了可扩展的解决方案.
科学领域:
- 主要医疗保健是一级医疗保健.
- 人工智能在医学中的应用
- 公共卫生 公共卫生
背景情况:
- 全球缺乏初级医疗保健医生,特别是在资源有限的环境中.
- 大型语言模型 (LLM) 显示了提高患者健康意识的潜力.
- 需要创新的数字健康解决方案来支持初级保健.
研究的目的:
- 开发和评估一个由LLM驱动的初级保健聊天机器人,P&P Care.
- 评估P&P护理与电子学习相结合对客观健康意识的影响.
- 探索双轨角色扮演代码设计框架对LLM部署的实用性.
主要方法:
- 使用双轨角色扮演代码设计框架与社区利益相关者和研究人员一起开发了P&P Care.
- 将聊天机器人与电子学习模块集成.
- 进行了一项随机对照试验,在11个中国省份有2,113名参与者,将P&P Care与电子学习与仅咨询的小组进行比较.
主要成果:
- 使用P&P Care的电子学习组与仅咨询的组 (平均得分2.34 ± 1.02;P < 0.001) 相比,具有明显更高的客观健康意识 (平均得分2.95 ± 1.22).
- 该研究达到其主要终点,表明LLM驱动干预的有效性.
- 代码设计方法促进了聊天机器人的开发和测试.
结论:
- 像P&P Care这样的LLM驱动的聊天机器人可以有效地提高患者的健康意识.
- 双轨角色扮演代码设计框架是一个可扩展的解决方案,用于在资源有限的初级医疗保健机构部署LLM.
- 普普护理对改善卫生教育和服务不足人群的结果显示出希望.
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