Time-series Machine Learning Models to Support Emergency Department Operational Planning.

Abstract

Predicting emergency department (ED) utilization can assist in resource planning like staff scheduling. Traditional time series methods and newer machine learning methods have been used to forecast ED metrics; however, they have seldom been implemented in operational settings. We leverage a user-centered design approach that engages nursing operations managers across multiple hospital sites in an integrated health system to identify the key metrics to predict, design and select the best models and time horizons, and design a production dashboard for ED operational planning. We tested various models in terms of mean absolute error and mean absolute percentage error and determined that Prophet (a non-linear open-source method) performed the best across multiple sites. We present the implementation and monitoring design for this model, generating daily, 14-day ahead predictions for ED arrival, admission, sitter needs, and ED holds, to be used by operational leaders to guide staffing decisions.

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