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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Pain Management

Background:

  • Opioid use disorder (OUD) is a significant risk for individuals with chronic pain managed by opioids.
  • Electronic health records (EHR) offer potential for large-scale studies on problematic opioid use.
  • Diagnostic codes for OUD are often unreliable and underutilized, necessitating improved identification methods.

Purpose of the Study:

  • To evaluate the efficacy of regular expressions, an interpretable natural language processing (NLP) technique, in automating the identification of problematic opioid use.
  • To compare the performance of this automated method against a validated clinical tool (Addiction Behaviors Checklist) and traditional diagnostic codes.

Main Methods:

  • A retrospective cohort study analyzed deidentified EHR data from 8063 individuals with chronic pain from 2021-2023.
  • Free-text clinical notes, demographics, and diagnostic codes were extracted.
  • The automated approach was validated against a manually reviewed holdout set and an independent external dataset of 100 patients.

Main Results:

  • The automated regular expression method demonstrated superior performance compared to diagnostic codes.
  • At the primary site, F1 scores were 0.73 vs 0.08 and AUCs were 0.82 vs 0.52.
  • At the validation site, F1 scores were 0.70 vs 0.29 and AUCs were 0.86 vs 0.59.

Conclusions:

  • Automated data extraction using regular expressions can effectively identify patients with problematic opioid use.
  • This technique facilitates earlier identification of at-risk individuals and enables new research avenues.
  • It offers a more reliable alternative to diagnostic codes for studying opioid use in chronic pain populations.