  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.
