Kim Seunghwan, Lou Sunny S, Baratta Laura R, Kannampallil Thomas
Washington University in St. Louis, 660 S Euclid Ave, Campus Box 8054, St Louis, MO 63110. Email:
Am J Manag Care. 2023 Jan 1;29(1):e24-e30. doi: 10.37765/ajmc.2023.89310.
We used electronic health record (EHR)-based raw audit logs to classify the work settings of anesthesiology physicians providing care in both surgical intensive care units (ICUs) and operating rooms.
Observational study.
Attending anesthesiologists who worked at least 1 shift in 1 of 4 surgical ICUs in calendar year 2019 were included. Time-stamped EHR-based audit log events for each week were used to create event frequencies and represented as a term frequency-inverse document frequency matrix. Primary classification outcome of interest was a physician's clinical work setting. Performance of multiple supervised machine learning classifiers were evaluated.
A total of 24 attending physicians were included; physicians performed a median (IQR) of 2545 (906-5071) EHR-based actions per week and worked a median (IQR) of 5 (3-7) weeks in a surgical ICU. A random forest classifier yielded the best discriminative performance (mean [SD] area under receiver operating characteristic curve, 0.88 [0.05]; mean [SD] area under precision-recall curve, 0.72 [0.13]). Model explanations illustrated that clinical activities related to signing of clinical notes, printing handoff data, and updating diagnosis information were associated with the positive prediction of working in a surgical ICU setting.
A random forest classifier using a frequency-based feature engineering approach successfully predicted work settings of physicians with multiple clinical responsibilities with high accuracy. These findings highlight opportunities for using audit logs for automated assessment of clinician activities and their work settings, thereby affording the ability to accurately assess context-specific work characteristics (eg, workload).
我们使用基于电子健康记录（EHR）的原始审计日志，对在外科重症监护病房（ICU）和手术室提供护理的麻醉科医生的工作环境进行分类。
观察性研究。
纳入在2019日历年在4个外科ICU中的1个至少工作1个班次的主治麻醉科医生。每周基于时间戳的EHR审计日志事件用于创建事件频率，并表示为词频-逆文档频率矩阵。感兴趣的主要分类结果是医生的临床工作环境。评估了多个监督机器学习分类器的性能。
共纳入24名主治医生；医生每周基于EHR执行的操作中位数（IQR）为2545次（906 - 5071次），在外科ICU工作的中位数（IQR）为5周（3 - 7周）。随机森林分类器产生了最佳的判别性能（受试者工作特征曲线下面积的均值[标准差]，0.88 [0.05]；精确召回率曲线下面积的均值[标准差]，0.72 [0.13]）。模型解释表明，与签署临床记录、打印交接班数据和更新诊断信息相关的临床活动与在外科ICU环境中工作的阳性预测相关。
使用基于频率的特征工程方法的随机森林分类器成功地高精度预测了具有多种临床职责的医生的工作环境。这些发现突出了利用审计日志自动评估临床医生活动及其工作环境的机会，从而能够准确评估特定背景下的工作特征（如工作量）。
