使用人工智能增强血管外科病历摘要的可读性和可理解性
Federico Francisco Pennetta1, Ciro Ferrer2, Rocco Giudice2
1Vascular and Endovascular Surgery Department, University of Rome Tor Vergata, Rome, Italy.
Vascular
|February 9, 2026
概括
大型语言模型 (LLM) 显著改善了血管外科的出院摘要,使患者更容易理解. 虽然准确,但需要进一步改进,以尽量减少人工智能在患者沟通中产生的错误.
科学领域:
- 医疗信息学 医疗信息学
- 卫生沟通健康沟通
- 医疗保健中的人工智能
背景情况:
- 患者理解医疗信息,特别是出院摘要,对于有效的术后护理和参与至关重要.
- 传统的出院摘要经常呈现复杂的医学术语,阻碍患者理解和遵守治疗计划.
- 简化医疗文档是改善以患者为中心的护理和健康素养的关键目标.
研究的目的:
- 评估大语言模型 (LLM) 在简化血管外科手术出院总结方面的有效性.
- 评估人工智能生成的摘要是否保持准确性和完整性,同时提高患者的可读性和参与度.
- 确定LLM对患者理解和术后护理指令可执行性的影响.
主要方法:
- 一项涉及90个血管外科手术出院总结的跨截面多中心研究,涉及三组患者病理学.
- 排放摘要由LLM (例如,ChatGPT-4) 处理,以达到6年级的阅读水平.
- 可读性 (Flesch-Kincaid),可理解性和可操作性 (PEMAT-P),准确性和完整性被量化评估.
主要成果:
- 人工智能生成的摘要显示,阅读等级水平下降了39.6%,易读性增加了106.37%.
- 在可理解性 (77.71) 和准确性 (5.21) 中获得了高分,尽管可操作性得分中等 (52.12).
- 一小部分摘要包含遗漏 (10.8%) 或幻觉 (7.5%),需要人类审查和纠正.
结论:
- LLM可以显著提高血管外科解剖摘要的可读性和可访问性,提高患者的理解和参与度.
- 人工智能生成的摘要显示出高准确度,但需要保持警,以解决和最大限度地减少遗漏和幻觉等错误.
- 法律职业管理 (LLM) 是优化医疗保健沟通的一个有希望的工具,尽管需要进一步的研究来完善错误减少和增强可操作内容.
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