基于大型语言模型的聊天机器人与外科医生生成的通用程序知情同意文档
Hannah Decker1, Karen Trang1, Joel Ramirez1
1Department of Surgery, University of California, San Francisco.
JAMA network open
|October 9, 2023
概括
大型语言模型 (LLM) 聊天机器人通过生成比外科医生生成的文件更完整,更准确的信息来提高外科知情同意的潜力. 虽然不完美,但LLM聊天机器人可以提高患者的理解,并减轻医生的文档负担.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 手术患者教育 在手术患者教育.
背景情况:
- 在侵袭性手术前的患者护理中,知情同意至关重要,但往往不足.
- 电子同意书的目的是通过可读,准确和完整的信息来提高患者的理解.
- 基于大型语言模型 (LLM) 的聊天机器人在增强知情同意文件的有效性仍然未被探索.
研究的目的:
- 将基于LLM的聊天机器人与外科医生生成的关于手术风险,益处和替代方案 (RBA) 的信息的可读性,准确性和完整性进行比较.
主要方法:
- 一项横截面研究将来自电子同意表格的外科医生生成的RBA与基于LLM的聊天机器人生成的RBA (ChatGPT-3.5) 对六种常见的外科手术程序进行了比较.
- 使用经过验证的尺度 (Flesch-Kincaid,枪支雾,SMOG,科尔曼-利亚乌) 评估可读性.
- 准确性和完整性是根据领先的医疗保健组织的建议进行评估的.
主要成果:
- 与外科医生生成的RBA (1.6) 相比,LLM聊天机器人生成的RBA显示了完整性和准确性 (2.2) 的复合得分明显更高 (P < .001).
- 聊天机器人在描述外科手术的好处 (2.3对1.4) 和替代方案 (2.7对1.4) 方面表现优于外科医生 (P < .001).
- 在报告手术风险方面没有发现显著差异 (两者均为1.7). 在LLM聊天机器人 (12.9) 和外科医生 (15.7) (P = .10) 之间,可读性得分相似.
结论:
- 基于LLM的聊天机器人有望提高知情同意文件的质量,特别是在完整性和准确性方面.
- 将LLM集成到电子健康记录中可以提供个性化的风险信息,并减少医生的工作负担,等待HIPAA合规.
- 需要进一步的研究来优化LLM的性能,并确保在临床应用中患者的安全和理解.
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