Physician performance scores used to predict emergency department admission numbers and excessive admissions burden

Overcrowding in hospitals is associated with a panoply of adverse events. Inappropriate decisions in the emergency department (ED) contribute to overcrowding. The performance of individual physicians as part of the admitting team is a critical factor in determining the overall rate of admissions. While previous attempts to model admission numbers have been based on a range of variables, none have included measures of individual staff performance. We construct reliable objective measures of staff performance and use these, among other factors, to predict the number of daily admissions. Such modelling will enable enhanced workforce planning and timely intervention to reduce inappropriate admissions and overcrowding.

A database was created of 232 245 ED attendances at Meir Medical Center in central Israel, spanning the years 2016-2021. We use several measures of physician performance together with historic caseload data and other variables to derive statistical models for the prediction of ED arrival and admission numbers.

Our models predict arrival numbers with a mean absolute percentage error (MAPE) of 6.85%, and admission numbers with a MAPE of 10.6%, and provide a same-day alert for heavy admissions burden with 75% sensitivity for a false-positive rate of 20%. The inclusion of physician performance measures provides an essential boost to model performance.

Arrival number and admission numbers can be predicted with sufficient fidelity to enable interventions to reduce excess admissions and smooth patient flow. Individual staff performance has a strong effect on admission rates and is a critical variable for the effective modelling of admission numbers.