Hospital Discharge Prediction Using Machine Learning

OBJECTIVEReliable hospital discharge predictions still remain an unmet need. In this study, we aimed to forecast daily hospital discharges by ward, until seven days ahead, using machine learning methods. METHODSWe analyzed all (n=67308) hospital admissions proceeding from the Emergency department in a University Hospital, from January-2018 to August-2023. Several train-test splits were defined simulating a prospective, weekly acquisition of data on new admissions. First, we trained Light Gradient Boosting Machines (LGBM) and Multilayer Perceptron (MLP) models to generate predictions on length of stay (LOS) for each admission. Based on predicted LOS, timeseries were built and predictions on daily hospital discharges, by ward, seven days into the future, were created employing diverse forecasting techniques. Mean absolute error (MAE) between predicted and observed discharges was used to measure the accuracy of predictions. Discharge predictions were also categorized as successful if they did not exceed by 10% the mean number of hospital daily discharges. RESULTSLGBM slightly outperformed MLP in 25 weekly LOS predictions (MAE 4.7{+/-}0.7 vs 4.9{+/-}0.7 days, p<0.001). The best techniques to forecast, seven days ahead, the daily number of hospital discharges were obtained using Prophet (MAE 5.0, R2=0.85), LGBM (MAE 5.2, R2=0.85), seasonal ARIMA (MAE 5.5, R2=0.81) and Temporal Fusion Transformer (TFT)(MAE 5.7, R2=0.83). After categorizing the predictions, LGBM, Prophet, seasonal ARIMA and TFT reached successful predictions in 82.3%, 81.1%, 77.7% and 77.1% of days, respectively. CONCLUSIONSSuccessful predictions of daily hospital discharges, seven days ahead, were obtained combining LOS predictions using LGBM and timeseries forecasting techniques. Lay abstractCurrently, most public hospitals in western countries have close to full occupancy for significant periods of time. Under these conditions, it is common for emergency admissions to be delayed, which causes significant patient discomfort and can negatively impact their quality of care. Predicting the daily number of hospital discharges would enable hospital administrators to implement measures to prevent hospital overcrowding. In this study, we used several artificial intelligence methods to predict, seven days in advance, the number of daily hospital discharges, obtaining successful predictions in more than 80% of the days that were analyzed. In conclusion, we have shown that available machine learning methods offer new and valuable options to predict hospital discharges, until seven days in advance, with high efficiency and reliability. HIGHLIGHTSO_LIAccurate predictions of hospital discharges could enable optimization of patient flow management within hospitals. C_LIO_LIEmerging machine learning and time-series forecasting methods present novel avenues for refining hospital discharge predictions. C_LIO_LIIn this study, we integrated length of stay predictions using Light Gradient Boosting Machines with several time-series forecasting techniques to produce daily hospital discharge forecasts. C_LIO_LIThrough the combined used of these methodologies, we were able to obtain successful predictions on more than 80% of the days. C_LI