Modeling patient-related workload in the emergency department using electronic health record data. (June 2021)

• Record Type: Journal Article
• Authors: Wang, Xiaomei; Blumenthal, H. Joseph; Hoffman, Daniel; Benda, Natalie; Kim, Tracy; Perry, Shawna; Franklin, Ella S.; Roth, Emilie M.; Hettinger, A. Zachary; Bisantz, Ann M.
• Is Part Of: International journal of medical informatics. Volume 150(2021)
• Publication Date: 2021-06
• Subjects: EHR electronic health record -- ED emergency department -- ESI emergency severity index; Electronic health record -- Workload -- Emergency department -- Machine learning; Medical informatics -- Periodicals; Information science -- Periodicals; Computers -- Periodicals; Medical technology -- Periodicals; Medical Informatics -- Periodicals; Technology, Medical -- Periodicals; Computers; Information science; Medical informatics; Medical technology; Electronic journals; Periodicals; Electronic journals; 610.285
• DOI: 10.1016/j.ijmedinf.2021.104451
• Languages: English
• ISSNs: 1386-5056

Abstract

Highlights: Efforts has been made on improving patient assignment at triage to improve clinician workload management. Data contained within the EHR have the potential to support automatic patient-related workload prediction. One can predict patient-related workload at the early stage of patient visit and update the prediction as the visit proceeds. The predicted workload can be used to in assigning new patients to clinicians in a way that better balanced workload, or to identify clinicians that are overloaded. Abstract: 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 R 2 > 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.