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Emergency department (ED) crowding is a significant threat to patient safetyand it has been repeatedly associated with increased mortality. Forecastingfuture service demand has the potential patient outcomes. Despite activeresearch on the subject, several gaps remain: 1) proposed forecasting modelshave become outdated due to quick influx of advanced machine learning models(ML), 2) amount of multivariable input data has been limited and 3) discreteperformance metrics have been rarely reported. In this study, we document theperformance of a set of advanced ML models in forecasting ED occupancy 24 hoursahead. We use electronic health record data from a large, combined ED with anextensive set of explanatory variables, including the availability of beds incatchment area hospitals, traffic data from local observation stations, weathervariables, etc. We show that N-BEATS and LightGBM outpeform benchmarks with 11% and 9 % respective improvements and that DeepAR predicts next day crowdingwith an AUC of 0.76 (95 % CI 0.69-0.84). To the best of our knowledge, this isthe first study to document the superiority of LightGBM and N-BEATS overstatistical benchmarks in the context of ED forecasting.

Forecasting Emergency Department Crowding with Advanced Machine Learning Models and Multivariable Input