Who is coming in? Evaluation of physician performance within multi-physician emergency departments.

Abstract

Background: This study aimed to examine how physician performance metrics are affected by the speed of other attendings (co-attendings) concurrently staffing the ED.

Methods: A retrospective study was conducted using patient data from two EDs between January-2018 and February-2020. Machine learning was used to predict patient length of stay (LOS) conditional on being assigned a physician of average speed, using patient- and departmental-level variables. A physician's patients' actual LOSs were compared to the model's predictions to calculate a measurement of that physician's speed. Linear regression models were employed to assess how physician performance changed based on the measured speed of the concurrent ED co-attendings, on outcomes including patient LOS, patients treated per hour, imaging utilization, admission rates, and 72-h ED revisits.

Results: Eighty physicians and 212,902 ED visits were included. Overall, patients assigned to the fastest physicians have a 17.8 % [13.5 %, 22.0 %] shorter LOS compared to average-speed attendings. When the fastest physicians work alongside the fastest co-attendings, their LOS benefit is reduced to 14.9 %, representing a 2.9 % [0.2 %, 5.6 %] longer LOS than when working without the fastest co-attendings. Similarly, the fastest physicians see 0.21 [0.13, 0.28] more patients per hour compared to average attendings, but this benefit decreases to 0.13 [0.09, 0.17] more patients per hour when the fastest co-attendings are present, reflecting a reduction of 0.08 [0.04, 0.11] patients per hour. The fastest physicians order 0.18 [0.13, 0.23] fewer imaging tests per patient than average-speed attendings; however, this reduction diminishes by 0.05 [0.04, 0.07] imaging tests per patient when the fastest co-attendings are present. Our model found effects of similar magnitudes but in the opposite direction when the slowest co-attendings are present. The speed of co-attendings had no significant association on the attending admission rate or 72-h revisit rate. Additionally, compared to the average attending team speed, slower attending teams, over an 8 h shift, experienced increased waiting room volume by 6.4 % [4.5 %, 8.4 %] while there was no difference when staffed by the fastest attending teams (-1.2 % [-3.2 %,0.7 %]).

Conclusion: In this exploratory analysis, physicians have slower throughput and order more imaging when faster co-attendings are present, and faster throughput with less imaging ordered when slower co-attendings are present. Administrators might consider these relationships and balancing attending speeds, particularly at the extremes (slowest and fastest), when designing staffing models as a potential strategy to enhance ED operational efficiency. What is already known on this topic: ED throughput is known to be dependent on multiple factors however physician behavior is commonly modeled as single attendings working in the ED.

What this study adds: This study examines the association between attending and co-attending speed on physician performance and finds that physicians become faster when a slow co-attending is present and slow down when a fast co-attending is present. How this study might affect research, practice or policy: Physician behavior does not exist in isolation and how an entire ED is staffed may have implications for throughput.