初级保健提供者接受生成人工智能对患者门户消息的响应
Amarpreet Kaur1, Alex Budko2, Katrina Liu3
1Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, United States.
Applied clinical informatics
|March 25, 2025
概括
大型语言模型 (LLM) 在生成患者门户消息方面表现有前途,比人类医生具有更高的同情心和可读性. 虽然相关性和准确性是可比的,但需要进一步研究伦理含义和患者接受度.
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:
- 患者门户网站改善了沟通,但也导致了临床医生的疲劳.
- 大型语言模型 (LLM) 显示出产生有利的医疗保健信息响应的潜力.
- 之前的研究缺乏多样化的消息类型和先进的提示工程.
研究的目的:
- 为了比较GPT生成的质量和精度与患者门户中的真实医生响应.
- 通过使用高级提示函数工程来评估各种消息类型的响应.
主要方法:
- 利用快速工程来为初级保健患者创建合成的GPT-3.5-turbo反应.
- 对49名医疗保健提供者进行了横截面研究,评估了响应同理心,相关性,准确性和可读性 (0-5级).
- 评估提供商生成的与GPT生成的响应,以确定质量指标的差异.
主要成果:
- 与医生反应相比,GPT产生的反应在同理心 (p<0.05) 和可读性 (p<0.05) 中得分明显高.
- 在相关性和准确性方面没有发现显著差异 (p > 0.05).
- 观察到可读性的统计学上显著差异,尽管由于绝对差异很小,临床意义仍然不确定.
结论:
- 由GPT生成的响应显示出与医疗保健提供者的同理心,相关性和可读性相匹配的潜力.
- 建议在临床工作流程中进行进一步的研究,考虑患者对AI道德和质量的态度.
- 在患者门户沟通中探索LLM集成需要仔细评估患者和提供者的观点.
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