Modeling patient-related workload in the emergency department using electronic health record data.
Modeling patient-related workload in the emergency department using electronic health record data.
复制标题
使用电子健康记录数据对急诊科患者相关的工作量进行建模。
DOI：
10.1016/j.ijmedinf.2021.104451
复制
发表时间：
2021
影响因子：
4.9
通讯作者：
Bisantz,AnnM
中科院分区：
文献类型：
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作者：
Wang,Xiaomei;Blumenthal,HJoseph;Hoffman,Daniel;Benda,Natalie;Kim,Tracy;Perry,Shawna;Franklin,EllaS;Roth,EmilieM;Hettinger,AZachary;Bisantz,AnnM
前言了解和管理临床医生的工作量对临床医生(护士、内科医生和高级实践提供者)的职业健康以及患者的安全都很重要。已努力制定通过改善患者分配来管理临床医生工作量的策略。本研究的目的是使用电子健康档案(EHR)数据来预测单个患者对临床医生工作量(与患者相关的工作量)的贡献。从数据中提取了工作量指标清单和五个潜在的工作量指标。使用线性回归和四种机器学习分类算法对指标与代理之间的关系进行建模。结果线性回归证明指标解释了代理的大量方差(5个代理中有4个是用R2&>0.80建模的)。分类算法还成功地根据急诊室早期的数据将患者分类为具有高或低任务需求(例如，使用第一个小时的数据进行80%的二进制分类)。结论本研究的主要贡献在于展示了利用电子病历数据自动预测急诊室患者相关工作量的潜力。预测的工作量可以通过支持将新患者分配给提供者的决策来潜在地帮助管理临床医生的工作量。未来的工作应该集中在识别工作量代理和实际工作量之间的关系，以及提高回归和多类分类的预测性能。
IntroductionUnderstanding and managing clinician workload is important for clinician (nurses, physicians and advanced practice providers) occupational health as well as patient safety. Efforts have been made to develop strategies for managing clinician workload by improving patient assignment. The goal of the current study is to use electronic health record (EHR) data to predict the amount of work that individual patients contribute to clinician workload (patient-related workload).MethodsOne month of EHR data was retrieved from an emergency department (ED). A list of workload indicators and five potential workload proxies were extracted from the data. Linear regression and four machine learning classification algorithms were utilized to model the relationship between the indicators and the proxies.ResultsLinear regression proved that the indicators explained a substantial amount of variance of the proxies (four out of five proxies were modeled with R2> 0.80). Classification algorithms also showed success in classifying a patient as having high or low task demand based on data from early in the ED visit (e.g. 80 % accurate binary classification with data from the first hour).ConclusionThe main contribution of this study is demonstrating the potential of using EHR data to predict patient-related workload automatically in the ED. The predicted workload can potentially help in managing clinician workload by supporting decisions around the assignment of new patients to providers. Future work should focus on identifying the relationship between workload proxies and actual workload, as well as improving prediction performance of regression and multi-class classification.