Forecasting hospital room and ward occupancy using static and dynamic information concurrently (Preprint)

Abstract

Predicting bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling.

The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp BOR for each ward and room according to different time periods.

We trained long short-term memory (LSTM) time-series models using hourly aggregated individual bed data on a daily basis to predict BOR for each ward and room in the hospital. Wards were trained with two models based on 7- and 30-day time windows, and rooms were trained with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we also added two models trained by concatenating dynamic data with static data representing room-specific details.

The ward-level prediction model with a mean absolute error (MAE) of 0.057, a mean squared error (MSE) of 0.007, a root mean squared error (RMSE) of 0.082, and an R2 score of 0.582. Among the room-level prediction models, the model that combined static data exhibited superior performance with an MAE of 0.123, an MSE of 0.051, an RMSE of 0.226, and an R2 score of 0.320. Model results can be displayed on an electronic dashboard for easy access via the web.

We propose predictive BOR models for individual wards and rooms that demonstrate high performance. This result can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource utilization.