Predicting individual patient and hospital-level discharge using machine learning

Abstract

Accurately predicting hospital discharge events could help improve patient flow and the efficiency of healthcare delivery. However, using machine learning and diverse electronic health record (EHR) data for this task remains incompletely explored. We used EHR data from February-2017 to January-2020 from Oxfordshire, UK to predict hospital discharges in the next 24 h. We fitted separate extreme gradient boosting models for elective and emergency admissions, trained on the first two years of data and tested on the final year of data. We examined individual-level and hospital-level model performance and evaluated the impact of training data size and recency, prediction time, and performance in subgroups. Our models achieve AUROCs of 0.87 and 0.86, AUPRCs of 0.66 and 0.64, and F1 scores of 0.61 and 0.59 for elective and emergency admissions, respectively. These models outperform a logistic regression model using the same features and are substantially better than a baseline logistic regression model with more limited features. Notably, the relative performance increase from adding additional features is greater than the increase from using a sophisticated model. Aggregating individual probabilities, daily total discharge estimates are accurate with mean absolute errors of 8.9% (elective) and 4.9% (emergency). The most informative predictors include antibiotic prescriptions, medications, and hospital capacity factors. Performance remains robust across patient subgroups and different training strategies, but is lower in patients with longer admissions and those who died in hospital. Our findings highlight the potential of machine learning in optimising hospital patient flow and facilitating patient care and recovery. Wei and colleagues use electronic health records to predict individual hospital discharge events and hospital-wide discharge numbers. Detailed data and an extreme gradient boosting model predict hospital discharge better than simple logistic regression models, highlighting the potential of machine learning approaches to help optimise patient flow. Predicting when hospital patients are ready to be discharged could help hospitals run more smoothly and improve patient care. In this study, we used three years of patient records from Oxfordshire, UK, to build a machine learning model that predicts discharges within the next 24 h. Our model includes both planned and emergency admissions. The model performs well at accurately predicting the probability, or chance, that an individual patient will be discharged and also estimating the total number of discharges each day. Important information for making the predictions includes whether patients are taking antibiotics and other medications, and whether the hospital is crowded. Overall, we show that machine learning could help hospitals manage patient flow and improve patient care.