Using natural language processing to identify opioid use disorder in electronic health record data.

Journal: International journal of medical informatics
Published Date: Dec 10, 2022

Abstract

BACKGROUND: As opioid prescriptions have risen, there has also been an increase in opioid use disorder (OUD) and its adverse outcomes. Accurate and complete epidemiologic surveillance of OUD, to inform prevention strategies, presents challenges. The objective of this study was to ascertain prevalence of OUD using two methods to identify OUD in electronic health records (EHR): applying natural language processing (NLP) for text mining of unstructured clinical notes and using ICD-10-CM diagnostic codes.

Authors

  • Jade Singleton
    Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY 40536, United States; University of Kentucky Healthcare IT Department, Business Intelligence, Lexington, KY 40517, United States. Electronic address: jade.singleton@seattlechildrens.org.
  • Chengxi Li
    Department of Computer Science, College of Engineering, University of Kentucky, Lexington, KY 40526, United States.
  • Peter D Akpunonu
    Emergency Medicine & Medical Toxicology, University of Kentucky Hospital, Lexington, KY 40536, United States.
  • Erin L Abner
    c Sanders-Brown Center on Aging & Department of Epidemiology, College of Public Health , University of Kentucky , Lexington , KY , USA.
  • Anna M Kucharska-Newton
    Department of Epidemiology, College of Public Health, University of Kentucky, Lexington, KY 40536, United States; Department of Epidemiology, The Gillings School of Global Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, United States.

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