使用大型语言模型指导患者创建高效和全面的临床护理信息
Siru Liu1,2, Aileen P Wright1,3, Allison B Mccoy1
1Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, United States.
概括
大型语言模型 (LLM) 可以为患者信息生成澄清后续问题,提高沟通效率. 一个微调的LLM,CLAIR,显示了与人类提供者相比的清晰度和简洁性,具有更高的实用性.
科学领域:
- 医疗保健中的人工智能
- 临床通信系统 临床通信系统
- 自然语言处理自然语言处理.
背景情况:
- 患者与医疗服务提供者之间的沟通往往因初始信息不完整而延迟.
- 确保医疗保健提供者获得足够的信息对于安全和准确的医疗建议至关重要.
研究的目的:
- 评估使用大型语言模型 (LLM) 来生成面向患者的后续问题的可行性.
- 在发送给提供者之前,提高患者信息中信息的完整性.
- 为了减少沟通摩擦和延误在医疗保健互动.
主要方法:
- 从范德比尔特大学医学中心收集的患者信息 (2022年1月 - 2023年3月).
- 开发并测试了三个LLM:CLAIR (微调),GPT4 (简单提示),GPT4 (复杂提示).
- 医生评估了LLM生成的问题与人类生成的问题之间的清晰度,完整性,简洁性和实用性.
主要成果:
- 克莱尔模型在七种场景中的五种场景中表现出卓越的性能.
- 清晰的问题表现出与人类生成的问题相似的清晰度和简洁性.
- 克莱尔比人类提供者和GPT4提供了更高的实用性,尽管其完整性低于GPT4.
结论:
- 简单的临床医疗模式 (LLM) 是为产生有效的患者信息澄清提供可行的工具.
- 由LLM生成的后续问题可以与医疗保健提供者的后续问题相比较好.
- 这项技术有可能简化患者与医疗服务提供者之间的沟通.
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