Predicting Inpatient Discharge Prioritization With Electronic Health Records
arXiv:1812.00371
The paper develops machine‑learning models using eight years of Stanford Hospital electronic health records to predict which inpatients will be discharged within the next 24 hours, achieving an AUROC of 0.85 and demonstrating utility for hospital resource planning.
Abstract
Identifying patients who will be discharged within 24 hours can improve hospital resource management and quality of care. We studied this problem using eight years of Electronic Health Records (EHR) data from Stanford Hospital. We fit models to predict 24 hour discharge across the entire inpatient population. The best performing models achieved an area under the receiver-operator characteristic curve (AUROC) of 0.85 and an AUPRC of 0.53 on a held out test set. This model was also well calibrated. Finally, we analyzed the utility of this model in a decision theoretic framework to identify regions of ROC space in which using the model increases expected utility compared to the trivial always negative or always positive classifiers.