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使用GPT-4:观察性研究改善机构心力衰竭相关患者教育材料的可读性:观察性研究
Ryan C King1, Jamil S Samaan2, Joseph Haquang1
1Department of Medicine, Division of Cardiology, University of California, Irvine Medical Center, 101 The City Dr S, Orange, CA, 92868, United States, 1 714-456-7890.
JMIR cardio
|July 8, 2025
概括
像GPT-4这样的大型语言模型可以简化心力衰竭患者教育材料,提高可读性和理解力,以便更好地自我管理. 这种人工智能工具提高了心力衰竭患者的教育内容的准确性和全面性.
科学领域:
- 医疗信息学 医疗信息学
- 卫生识字 卫生识字
- 医疗保健中的人工智能
背景情况:
- 心力衰竭管理需要患者坚持改变生活方式,强调需要清晰的患者教育材料 (PEMs).
- 现有的心血管PEM通常会在超过推标准的阅读水平上提供信息,从而阻碍患者的理解.
- 大型语言模型 (LLM) 显示了提高健康信息可访问性的潜力.
研究的目的:
- 为了评估心力衰竭的可读性,来自主要心脏病学机构的PEMs.
- 评估GPT-4在提高PEM可读性,同时保持准确性和全面性的能力.
主要方法:
- 从美国顶级心脏病学机构收集了143个心力衰竭PEM.
- 使用GPT-4来简化PEM使用标准化提示符.
- 使用多个指标 (Flesch阅读易度,FKGL等) 评估可读性. ) 的情况.
- 由心脏病学家对经过修订的PEM进行评估,以确保其准确性和全面性.
主要成果:
- GPT-4显著降低了Flesch-Kincaid等级水平的中位数,从10.3降至7.3 (p<.001).
- 在GPT-4修订后,低于六年级阅读水平的PEM比例从9.1%增加到23.1% (p<.001).
- 没有观察到准确性或全面性下降;23.1%的修订PEM被认为更全面.
结论:
- GPT-4有效地提高了心力衰竭PEMs的可读性.
- LLM可以作为宝贵的工具来补充心力衰竭患者的医疗保健专业指导.
- 进一步的研究对于验证人工智能的安全性,有效性和影响在提高患者健康素养方面至关重要.
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