Near real-time prediction of urgent care hospital performance metrics using scalable random forest algorithm: A multi-site development

Abstract

While previous studies have shown the potential value of predictive modelling for emergency care, few models have been implemented for producing near real-time predictions across various demand, utilisation and performance metrics. In this study, 33 independent Random Forest (RF) algorithms were developed to forecast 11 urgent care metrics over a 24-hour period across three hospital sites in an Integrated Care System (ICS) in South West England. Metrics included: ambulance handover delay; emergency department occupancy; and patients awaiting admission. Mean Absolute Error (MAE), Root Mean Squared Error (RMSE) and Symmetric Mean Absolute Percentage Error (SMAPE) were used to assess the performance of RF and compare it to two alternative models: naïve baseline (NB) and Auto-Regressive Integrated Moving Average (ARIMA). Using these measures, RF outperformed NB and ARIMA in 76% (N = 25/33) of urgent care metrics according to SMAPE, 88% (N = 29/33) according to MAE and 91% (N = 30/33) according to RMSE. The RFs developed in this study have been implemented within the local ICS, providing predictions on an hourly basis that can be accessed by local healthcare planners and managers.

Keywords

• Machine learning
• Random forest
• Forecasting
• Urgent care
• Predictive analytics
• Time series