Improving diagnosis-based quality measures: an application of machine learning to the prediction of substance use disorder among outpatients.

Journal: BMJ open quality
PMID: 40121006

Abstract

OBJECTIVE: Substance use disorder (SUD) is clinically under-detected and under-documented. We built and validated machine learning (ML) models to estimate SUD prevalence from electronic health record (EHR) data and to assess variation in facility-level SUD identification using clinically documented diagnoses vs model-based estimated prevalence.

Authors

  • Katherine J Hoggatt
    Center for Data to Discovery and Delivery Innovation (3DI), San Francisco VA Health Care System, San Francisco, California, USA katherine.hoggatt@va.gov.
  • Alex H S Harris
    Department of Orthopedic Surgery, Stanford University and VA Palo Alto Health Care System, Palo Alto, California.
  • Corey J Hayes
    Department of Biomedical Informatics, College of Medicine, University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA.
  • Donna Washington
    VA Greater Los Angeles Healthcare System, Los Angeles, California, USA.
  • Emily C Williams
    VA Puget Sound Health Care System Seattle Division, Seattle, Washington, USA.

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