用可解释的机器学习和电子健康记录数据估计经风险调整的术后感染结果
Kathryn L Colborn1, Yizhou Fei2, William G Henderson3
1Department of Medicine, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO; Adult and Child Center for Outcomes Research and Delivery Science, University of Colorado Anschutz Medical Campus, Aurora, CO; Department of Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO.
American journal of infection control
|October 3, 2025
概括
使用电子健康记录 (EHR) 数据的统计模型准确地估计了医院感染结果,与手动图表审查相似. 这些自动化方法可以加强术后感染监测,以提高质量监测.
科学领域:
- 医疗信息学 医疗信息学
- 改善外科手术的质量
- 流行病学 流行病学
背景情况:
- 使用电子健康记录 (EHR) 的自动化统计模型与手动图表审查 (ACS-NSQIP) 进行手术后感染结果的比较.
- 评估风险调整的感染监测方法的准确性.
研究的目的:
- 评估使用电子健康记录数据和统计模型用于估计医院水平手术后感染率的有效性.
- 确定自动化方法是否可靠地取代或增强质量监测的传统图表审查.
主要方法:
- 在五家医院的9个外科专业中分析了441,047次手术 (2013-2019).
- 将患者记录与ACS-NSQIP数据库进行链接以进行比较.
- 使用已建立的模型计算四种感染类型的观察到预期 (O/E) 事件比率.
主要成果:
- 自动化方法显示,在各医院和感染类型中,O/E比率与ACS-NSQIP相似 (皮尔森相关性=0.77).
- O/E比率的平均绝对差异是最小的 (0.13%),有重叠的置信区间.
- 对于具有更高发病率的感染类型,准确性得到改善.
结论:
- 应用到EHR数据上的节统计模型提供了准确的医院风险调整后术后感染结果估计.
- 基于EHR的自动化模型可以有效地增加术后感染监测,用于医院质量监测.
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