Patient Outcome Predictions Improve Operations at a Large Hospital
  Network
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
Problem definition: Access to accurate predictions of patients' outcomes can
enhance medical staff's decision-making, which ultimately benefits all
stakeholders in the hospitals. A large hospital network in the US has been
collaborating with academics and consultants to predict short-term and
long-term outcomes for all inpatients across their seven hospitals.
Methodology/results: We develop machine learning models that predict the
probabilities of next 24-hr/48-hr discharge and intensive care unit transfers,
end-of-stay mortality and discharge dispositions. All models achieve high
out-of-sample AUC (75.7%-92.5%) and are well calibrated. In addition, combining
48-hr discharge predictions with doctors' predictions simultaneously enables
more patient discharges (10%-28.7%) and fewer 7-day/30-day readmissions
($p$-value $<0.001$). 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
(alongside explanations) to clinical teams. Managerial implications: Since we
have been gradually deploying the tool, and training medical staff, over 200
doctors, nurses, and case managers across seven hospitals use it in their daily
patient review process. We observe a significant reduction in the average
length of stay (0.67 days per patient) following its adoption and anticipate
substantial financial benefits (between \$55 and \$72 million annually) for the
healthcare system.