Machine Learning Methods for Predicting Patient-Level Emergency Department Workload

Abstract

BackgroundWork Relative Value Units (wRVUs) are a component of many compensation models, and a proxy for the effort required to care for a patient. Accurate prediction of wRVUs generated per patient at triage could facilitate real-time load balancing between physicians and provide many practical operational and clinical benefits. ObjectiveWe examined whether deep-learning approaches could predict the wRVUs generated by a patient's visit using data commonly available at triage. MethodsAdult patients presenting to an urban, academic emergency department from July 1, 2016–March 1, 2020 were included. Deidentified triage information included structured data (age, sex, vital signs, Emergency Severity Index score, language, race, standardized chief complaint) and unstructured data (free-text chief complaint) with wRVUs as outcome. Five models were examined: average wRVUs per chief complaint, linear regression, neural network and gradient-boosted tree on structured data, and neural network on unstructured textual data. Models were evaluated using mean absolute error. ResultsWe analyzed 204,064 visits between July 1, 2016 and March 1, 2020. The median wRVUs were 3.80 (interquartile range 2.56–4.21), with significant effects of age, gender, and race. Models demonstrated lower error as complexity increased. Predictions using averages from chief complaints alone demonstrated a mean error of 2.17 predicted wRVUs per visit (95% confidence interval [CI] 2.07–2.27), the linear regression model: 1.00 wRVUs (95% CI 0.97–1.04), gradient-boosted tree: 0.85 wRVUs (95% CI 0.84–0.86), neural network with structured data: 0.86 wRVUs (95% CI 0.85–0.87), and neural network with unstructured data: 0.78 wRVUs (95% CI 0.76–0.80). ConclusionsChief complaints are a poor predictor of the effort needed to evaluate a patient; however, deep-learning techniques show promise. These algorithms have the potential to provide many practical applications, including balancing workloads and compensation between emergency physicians, quantify crowding and mobilizing resources, and reducing bias in the triage process.