Predicting preventable hospital readmissions with causal machine learning.

Journal: Health services research
PMID:

Abstract

OBJECTIVE: To assess both the feasibility and potential impact of predicting preventable hospital readmissions using causal machine learning applied to data from the implementation of a readmissions prevention intervention (the Transitions Program).

Authors

  • Ben J Marafino
    Philip R. Lee Institute for Health Policy Studies, School of Medicine, University of California, San Francisco.
  • Alejandro Schuler
    Systems Research Initiative, Kaiser Permanente Division of Research, Oakland, California, USA.
  • Vincent X Liu
    3 Division of Research, Kaiser Permanente, Oakland, California.
  • Gabriel J Escobar
  • Mike Baiocchi
    Departments of Epidemiology & Population Health, Department of Medicine, Stanford University, Stanford, California, USA.