Patient Outcome Predictions Improve Operations at a Large Hospital Network

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...

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Bibliographic Details
Published in:arXiv.org 2023-05
Main Authors: Liangyuan Na, Kimberly Villalobos Carballo, Pauphilet, Jean, Haddad-Sisakht, Ali, Kombert, Daniel, Boisjoli-Langlois, Melissa, Castiglione, Andrew, Khalifa, Maram, Hebbal, Pooja, Stein, Barry, Bertsimas, Dimitris
Format: Article
Language:English
Subjects:
Decision making
Hospitals
Machine learning
Medical personnel
Physicians
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Summary: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 \(
ISSN:2331-8422