提示工程利用大型语言模型在生成对InBasket消息的响应方面
Sherry Yan1,2, Wendi Knapp2,3, Andrew Leong2,4
1Center for Health Systems Research, Sutter Health, Walnut Creek, CA 94596, United States.
概括
使用大型语言模型 (LLM) 的快速工程可以有效地为患者的医疗咨询请求 (PMAR) 生成高质量的响应草案,从而提高临床医生和患者的满意度. 这种方法提高了管理临床信息的效率.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 临床交流 临床沟通
背景情况:
- 大量的患者医疗咨询请求 (PMAR) 在医疗保健提供中构成了重大挑战.
- 大型语言模型 (LLM) 正在探索,作为简化沟通和减少临床医生的工作负担的潜在解决方案.
研究的目的:
- 调查快速工程在开发PMARsLLM生成的响应草案的有效性.
- 评估患者和临床医生对LLM产生的反应的质量和满意度.
主要方法:
- 一个新的涉及人类的代过程被用来训练和验证PMAR响应生成的LLM提示.
- 使用了GPT-4,根据临床医生和患者的反在多个代中改进了提示.
- 优化提示在独立数据集上得到验证,并与初级保健临床医生在现实世界电子健康记录生产环境中进行了测试.
主要成果:
- 医生接受草稿适用性显著增加,从62%,到84%后迅速工程.
- 患者对信息语调 (78%) 和整体质量 (80%) 的支持度有所改善.
- 76%的患者无法区分人类和LLM产生的反应,72%的临床医生认为LLM可以降低认知负载.
结论:
- 通过协同临床医生和患者反获得的代提示工程有效地产生了对PMARs的临床相关和有用的响应草案.
- 由LLM驱动的草稿响应有可能提高管理患者沟通的效率和满意度.
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