Patient Outcome Predictions Improve Operations at Hartford HealthCare

ABSTRACT Access to accurate predictions of patients’ outcomes can enhance decision making within healthcare institutions. Hartford HealthCare has been collaborating with academics and consultants to predict short- and medium-term outcomes for all inpatients across their seven hospitals. We develop machine learning models that predict the probabilities of next 24-hour/48-hour discharge and intensive care unit transfers, end-of-stay mortality, and discharge dispositions. All models achieve high out-of-sample area under the receiver operating curve (75.7%–92.5%) and are well calibrated. In addition, combining 48-hour discharge predictions with doctors’ predictions simultaneously enables more patient discharges (10%–28.7%) and fewer 7-day/30-day readmissions (p < 0.001). We implement an automated pipeline that extracts data and updates predictions every morning, as well as user-friendly software and a color-coded alert system to communicate these patient-level predictions to clinical teams. Since its deployment, more than 200 doctors, nurses, and case managers across seven hospitals have been using the tool in their daily patient review process. With our tool, we find that doctors start the administrative discharge process earlier, leading to a significant reduction in the average length of stay (0.63 days per patient). We anticipate substantial financial benefits (between $52 and $67 million annually) for the healthcare system.

Contributors: Liangyuan Na, Kimberly Villalobos Carballo, Jean Pauphilet, Ali Haddad-Sisakht, Daniel Kombert, Melissa Boisjoli-Langlois, Andrew Castiglione, Maram Khalifa, Pooja Hebbal, Barry Stein, Dimitris Bertsimas