GPT-4产生的出院通知书对患者医学理解的影响:前性交叉研究
Friederike Holderried1, Alessandra Sonanini1, Christian Stegemann-Philipps1
1Tübingen Institute for Medical Education (TIME), University of Tübingen, Tübingen, Germany.
Journal of medical Internet research
|February 26, 2026
概括
与标准信件相比,GPT-4产生的以患者为中心的出院信显著改善了对安全相关医疗信息的理解. 这些由人工智能驱动的工具增强了理解力,特别是在药物和组织细节方面,尽管更高层次的理解需要进一步发展.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
- 患者教育 患者教育
背景情况:
- 标准的医院出院信对患者来说很难理解,增加了药物错误和理解问题的风险.
- 认知负载理论 (CLT) 表明复杂的文本会过载工作记忆;人工智能生成的以患者为中心的版本可能会减少这种认知负载.
- 人工智能在改善患者对出院信息的理解方面有效性的证据有限.
研究的目的:
- 评估GPT-4产生的以患者为中心的信件是否与标准出院信件相比,提高了标准化患者对安全相关医疗信息的保留和理解.
- 探索AI生成的字母对认知负载的潜在影响,正如认知负载理论 (CLT) 所描述的那样.
主要方法:
- 一项前性,随机化,交叉研究,涉及48名受过训练的标准化患者.
- 参与者收到了传统的出院信和匹配的GPT-4产生的以患者为中心的信,用于指定的疾病.
- 根据预先定义的与安全相关的学习目标报告的比例来评估理解,按内容领域和Bloom分类层分层.
主要成果:
- 与标准出院信相比,GPT-4生成的患者信件显著改善了整体理解能力 (OR 1.74,P<.001).
- 患者的信件导致了更高的完全或部分陈述的学习目标率和更少的遗漏.
- "药物"和"组织"内容以及"记住"级别目标的改善最为明显,但"预防并发症"和"生活方式/疾病管理"的改善较小.
结论:
- GPT-4产生的以患者为中心的信件增强了对安全相关的出院信息的理解,特别是药物和组织细节.
- 这些人工智能工具在降低认知负载和在CLT框架内改善患者理解方面表现有前途.
- 对多式联络,代人工智能支持的进一步研究是有必要的,以改善更高层次的理解,并解决剩余的信息差距.
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