Impact of Emergency Department Workload on Triage Behavior

33 Pages Posted: 8 Apr 2022 Last revised: 22 Mar 2023

See all articles by Arshya Feizi

Arshya Feizi

Boston University; Boston University - Questrom School of Business

Anita Tucker

Boston University - Questrom School of Business

William Baker

University of Vermont, Department of Emergency Medicine

Date Written: March 31, 2022

Abstract

Triage is the first step of emergency care, where patients are classified based on their urgency and their anticipated resource usage. Although triage nurses follow a standard classification algorithm, worker judgement plays a significant part in how urgent patients are perceived. In this paper, our goal is twofold: First, we evaluate whether downstream congestion affects prioritization. Specifically, we test whether workload in the treatment area of the emergency department (ED) affects under-triage, defined as perceiving a patient as being less urgent than they truly are. Second, we test the impact of under-triage on patient flow and quality of care. To determine a patient’s true urgency level, and subsequently, define under-triage, we develop a deep-learning model that is trained using information collected during triage. We find that under-triage has a U-shaped relationship with ED workload; it decreases up to the 84th percentile of workload, but increases thereafter. We also find that a one standard deviation increase in under-triage increases patients’ disposition time, room-to-departure times and risk of 30-day readmission by 11.2%, 13.4% and 27%, respectively. From an academic perspective, our work contributes to the healthcare and behavioral operations literature by demonstrating how workload affects customer classification, and quantifying the effects of under-triage. From a practical perspective, our predictive model achieves state-of-the-art performance, and can be employed in hospitals and EDs to assist in patient triage. Also, our results assist managers in making staffing decisions to balance the costs of under-triage.

Note:
Funding Information: This research is funded by Boston University, Questrom School of Business research grant.

Declaration of Interests: None.

Keywords: Emergency Department, Triage, Behavior, Workload, Machine Learning, Empirical

Suggested Citation

Feizi, Arshya and Tucker, Anita and Baker, William, Impact of Emergency Department Workload on Triage Behavior (March 31, 2022). Available at SSRN: https://ssrn.com/abstract=4071155 or http://dx.doi.org/10.2139/ssrn.4071155

Arshya Feizi (Contact Author)

Boston University ( email )

595 Commonwealth Avenue
Boston, MA 02215
United States

Boston University - Questrom School of Business ( email )

595 Commonwealth Avenue
Boston, MA MA 02215
United States

Anita Tucker

Boston University - Questrom School of Business ( email )

595 Commonwealth Avenue
Boston, MA MA 02215
United States

William Baker

University of Vermont, Department of Emergency Medicine ( email )

111 Colchester Avenue
Emergency Medicine, WP1-106
Burlington, VT 05401
United States

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