Developing a decision support tool to predict delayed discharge from hospitals using machine learning.

Journal: BMC health services research
PMID: 39799370

Abstract

BACKGROUND: The growing demand for healthcare services challenges patient flow management in health systems. Alternative Level of Care (ALC) patients who no longer need acute care yet face discharge barriers contribute to prolonged stays and hospital overcrowding. Predicting these patients at admission allows for better resource planning, reducing bottlenecks, and improving flow. This study addresses three objectives: identifying likely ALC patients, key predictive features, and preparing guidelines for early ALC identification at admission.

Authors

  • Mahsa Pahlevani
    Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, B3H 4R2, NS, Canada.
  • Enayat Rajabi
    Cape Breton University, Sydney, NS, Canada.
  • Majid Taghavi
    Department of Engineering Mathematics, University of Bristol, Bristol, United Kingdom.
  • Peter Vanberkel
    Department of Industrial Engineering, Dalhousie University, 5269 Morris Street, Halifax, B3H 4R2, NS, Canada. peter.vanberkel@dal.ca.

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