Patient-related Workload Prediction in the Emergency Department: A Big Data Approach. Issue 1 (September 2019)

• 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: Proceedings of the International Symposium of Human Factors and Ergonomics in Healthcare. Volume 8:Issue 1(2019)
• Page Start: 33
• Page End: 36
• Publication Date: 2019-09
• Subjects: Human engineering -- Periodicals; Medical care -- Periodicals; 620.8
• DOI: 10.1177/2327857919081008
• Languages: English
• ISSNs: 2327-8579

Abstract

This research is a first stage in developing a method for modeling the clinician workload associated with an emergency medicine patient in order to display workload for purposes of managing clinician workload and emergency department (ED) flow. We proposed a multi-stage approach of predicting patient-related drivers of clinician's workload in the emergency department. We trained the model from one month of electronic health record data (EHR) records of an ED. The model predicts the amount of work that individual patients contribute to the workload of clinicians. It can potentially help to manage clinician workload by supporting the decision of assigning new patients.