Patient Outcome Predictions Improve Operations at Hartford HealthCare
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
Abstract
We build and deploy machine learning models that accurately predict short- and medium-term inpatient outcomes for Hartford HealthCare, including 24–48 hour discharge, ICU transfer, mortality, and discharge disposition (AUC 76%–93%). More than 200 clinicians currently use these predictions in daily rounds, leading to earlier discharge planning, shorter length of stay (0.63 days per patient), and substantial financial benefits (between $52 and $67 million annually) for the healthcare system.
Topics & Concepts
Medical emergencyPipeline (software)Process (computing)Sample (material)Health careMedicineEmergency medicineOperations managementComputer scienceEngineeringOperating systemEconomicsEconomic growthChemistryProgramming languageChromatographyMachine Learning in HealthcareHealthcare Operations and Scheduling OptimizationHospital Admissions and Outcomes