生成人工智能对电子健康记录中的患者信息的反应:早期的经验教训
Sally L Baxter1,2, Christopher A Longhurst2, Marlene Millen2,3
1Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California San Diego, La Jolla, CA 92093, United States.
JAMIA open
|April 11, 2024
概括
大型语言模型 (LLM) 显示了对负面电子健康记录 (EHR) 患者信息起草响应的潜力. 然而,LLM生成的草稿需要精细化,以确保适当的关系连接和信息内容.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 电子健康记录 (EHR) 患者信息,特别是那些负面语调的信息,可能会导致临床医生倦怠.
- 解决负面患者信息是医疗保健机构面临的重大挑战.
研究的目的:
- 评估使用大型语言模型 (LLM) 来生成对负面EHR患者信息的响应初步草稿的可行性.
- 将LLM生成的响应与实际护理团队响应进行比较.
主要方法:
- 从卫生系统的电子健康记录中提取了50个负面患者信息的回顾样本.
- 消息被取消识别并输入到一个大型语言模型 (ChatGPT).
- 进行了定性分析,以比较LLM草案的响应与人体护理团队的响应.
主要成果:
- 由LLM生成的草稿响应有时在关系连接,提供的信息和推的下一步步骤方面与人类响应不同.
- 在某些情况下,LLM草稿回复有可能升级情绪充满的患者对话.
结论:
- 使用LLM来生成负的EHR患者信息的初始草稿似乎是可行的,但需要进一步改进.
- 需要进行额外的研究,以优化LLM应用程序,以便在EHR系统中管理具有挑战性的患者通信.
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