使用基于触发的策略,识别急诊部的诊断错误
Mahsa Khalili1,2, Moein Enayati3, Shrinath Patel3
1Department of Emergency Medicine, The University of British Columbia, Vancouver, British Columbia, Canada mahsa.khalili@ubc.ca.
BMJ open quality
|August 7, 2025
概括
电子健康记录 (EHR) 触发器可以识别一些诊断错误,但单独是不够的. 推使用机器学习策略,以提高诊断错误检测在急诊室的准确性.
科学领域:
- 患者安全 患者安全
- 临床信息学 临床信息学
- 医疗保健服务研究 医疗服务研究
背景情况:
- 诊断错误是一个重要的患者安全问题,影响结果.
- 基于触发器的策略用于识别诊断错误和改善护理.
- 现有的基于电子健康记录 (EHR) 的触发器需要评估其有效性.
研究的目的:
- 评估三种预先建立的基于EHR的触发器在紧急部门 (ED) 中检测诊断错误的性能.
- 评估这些触发器在识别潜在诊断错误的有效性,以便进一步审查.
主要方法:
- 在美国一所学术ED. 连续队列的回顾性观察研究.
- 利用EHR数据,根据预先定义的标准 (非计划的再访,护理升级,ED内/ED后早期死亡) 识别触发阳性和触发阴性病例.
- 通过使用SaferDx工具来确认诊断错误,对触发阳性和触发阴性病例的随机样本进行了审查.
主要成果:
- 确定了5791个触发因子阳性和118,262个触发因子阴性病例.
- 诊断错误的积极预测值 (PPV) 很低:5.4% (T1),8.9% (T2) 和6.9% (T3).
- 负预测值 (NPV) 为100%,表明在触发负的情况下没有错过的错误. 败血症是最常见的错误诊断.
结论:
- 预先建立的基于EHR的触发器可以识别一些诊断错误,但不足以进行全面的检测.
- 需要进一步开发和探索数据驱动的策略,如机器学习,以提高诊断错误检测的准确性.
- 改善诊断错误检测对于提高ED环境中的患者安全至关重要.
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