Introduction

Understanding 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).

Methods

One 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.

Results

Linear 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).

Conclusion

The 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.

中文翻译：

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使用电子健康记录数据模拟急诊科患者相关工作量

介绍

了解和管理临床医生的工作量对于临床医生（护士、医生和高级实践提供者）的职业健康和患者安全非常重要。已经努力通过改善患者分配来制定管理临床医生工作量的策略。当前研究的目标是使用电子健康记录 (EHR) 数据来预测个体患者对临床医生工作量（患者相关工作量）贡献的工作量。

方法

从急诊科 (ED) 检索了一个月的 EHR 数据。从数据中提取了工作量指标列表和五个潜在的工作量代理。利用线性回归和四种机器学习分类算法对指标和代理之间的关系进行建模。

结果

线性回归证明，这些指标解释了代理的大量方差（五分之四的代理采用 R ² > 0.80 建模）。分类算法还显示成功地根据 ED 访问早期的数据将患者分类为具有高或低的任务需求（例如，使用第一个小时的数据进行 80% 准确的二元分类）。

结论

本研究的主要贡献是展示了使用 EHR 数据在 ED 中自动预测与患者相关的工作量的潜力。通过支持将新患者分配给提供者的决策，预测的工作量可能有助于管理临床医生的工作量。未来的工作应侧重于识别工作负载代理与实际工作负载之间的关系，以及提高回归和多类分类的预测性能。