Using interpretable survival analysis to assess hospital length of stay.

Journal: BMC health services research
Published Date: May 22, 2025

Abstract

Accurate in-hospital length of stay prediction is a vital quality metric for hospital leaders and health policy decision-makers. It assists with decision-making and informs hospital operations involving factors such as patient flow, elective cases, and human resources allocation, while also informing quality of care and risk considerations. The aim of the research reported in this paper is to use survival analysis to model General Internal Medicine (GIM) length of stay, and to use Shapley value to support interpretation of the resulting model. Survival analysis aims to predict the time until a specific event occurs. In our study, we predict the duration from patient admission to discharge to home, i.e., in-hospital length of stay. In addition to discussing the modeling results, we also talk about how survival analysis of hospital length of stay can be used to guide improvements in the efficiency of hospital operations and support the development of quality metrics.

Authors

  • Yan Li
    Interdisciplinary Research Center for Biology and Chemistry, Liaoning Normal University, Dalian, China.
  • Trevor Hall
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada.
  • Fahad Razak
    St Michael's Hospital, Unity Health Toronto, Toronto, Ontario, Canada.
  • Amol Verma
    St. Michael's Hospital, Unity Health Toronto, Toronto, Canada; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
  • Mark Chignell
    Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON, Canada. chignell@mie.utoronto.ca.
  • Lu Wang
    Department of Laboratory, Akesu Center of Disease Control and Prevention, Akesu, China.

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