Development and evaluation of a machine learning model to predict acute care for opioid use disorder among Medicaid enrollees engaged in a community-based treatment program.

Journal: Addiction (Abingdon, England)
Published Date: Apr 29, 2025

Abstract

AIMS: To develop machine-learning algorithms for predicting the risk of a hospitalization or emergency department (ED) visit for opioid use disorder (OUD) (i.e. OUD acute events) in Pennsylvania Medicaid enrollees in the Opioid Use Disorder Centers of Excellence (COE) program and to evaluate the fairness of model performance across racial groups.

Authors

  • Lingshu Xue
    Department of Health Policy and Management, University of Pittsburgh, Pittsburgh, PA, USA.
  • Ruofei Yin
    Department of Biostatistics and Health Data Science, University of Pittsburgh, Pittsburgh, PA, USA.
  • Evan S Cole
    Department of Health Policy and Management, University of Pittsburgh, Pittsburgh, PA, USA.
  • Wei-Hsuan Lo-Ciganic
    *Center for Pharmaceutical Policy and Prescribing, University of Pittsburgh, Pittsburgh, PA †Department of Pharmacy, Practice and Science, College of Pharmacy, University of Arizona, Tucson, AZ Departments of ‡Health Policy and Management, Graduate School of Public Health §Pharmacy and Therapeutics, School of Pharmacy, University of Pittsburgh ∥Center for Health Equity Research and Promotion, Veterans Affairs Pittsburgh Healthcare System Departments of ¶Biostatistics, Graduate School of Public Health #Medicine, School of Medicine, University of Pittsburgh, Pittsburgh, PA **Department of Pharmacy, School of Pharmacy, University of Washington, Seattle, WA.
  • Walid F Gellad
  • Julie Donohue
    Department of Health Policy and Management, University of Pittsburgh, Pittsburgh, PA, USA.
  • Lu Tang
    Department of Communication and Journalism, Texas A&M University.

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