生成型人工智能将住院患者出院摘要转化为患者友好的语言和格式
Jonah Zaretsky1, Jeong Min Kim1, Samuel Baskharoun2
1Division of Hospital Medicine, Department of Medicine, NYU (New York University) Langone Health, New York, New York.
JAMA network open
|March 11, 2024
概括
大型语言模型 (LLM) 可以简化患者复杂的医疗出院摘要,显著提高可读性和理解性. 然而,在广泛实施之前,准确性和安全性检查至关重要.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 患者沟通 患者沟通
背景情况:
- 患者有合法权访问他们的医疗出院记录.
- 退院摘要中的技术术语和缩写妨碍了患者的理解.
- 大型语言模型 (LLM) 为简化医疗信息提供了一个潜在的解决方案.
研究的目的:
- 评估LLM在将医院出院摘要转化为患者友好的格式方面的有效性.
- 评估LLM生成的摘要的可读性和可理解性的改进.
主要方法:
- 一项横截面研究分析了50名成年患者从纽约大学朗格尼医疗中心的出院摘要.
- 使用安全,符合HIPAA的LLM平台 (微软Azure OpenAI) 处理放电摘要.
- 在原始和LLM转换的摘要之间比较了可读性 (Flesch-Kincaid等级水平) 和可理解性 (PEMAT分数). 准确性和完整性也被评估.
主要成果:
- 通过LLM转换的摘要显示Flesch-Kincaid等级水平显著降低 (6.2比11.0) 和PEMAT可理解性得分更高 (81%比13%).
- 医生评论表明,总结的准确性为54%,完整性为56%.
- 安全问题,包括遗漏和不准确 (幻觉) 在18%的审查中被指出.
结论:
- 临床医生可以有效地将出院摘要转化为患者更易于阅读和理解的格式.
- 为了实现LLM,需要进一步提高准确性,完整性和安全性.
- 由于确定的安全问题,医生监督对于初始部署至关重要.
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