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Computable Structured Phenotype Versus Large Language Model Identification of Opioid Use Disorder Using Electronic

Melanie F Molina1, Cynthia Fenton2, Kathy T LeSaint3

  • 1Department of Emergency Medicine, University of California, San Francisco, CA; Division of Clinical Informatics and Digital Transformation, Department of Medicine, University of California, San Francisco, CA.

Annals of Emergency Medicine
|June 13, 2026
PubMed
Summary
This summary is machine-generated.

A large language model and a rule-based phenotype showed strong performance in identifying opioid use disorder in the emergency department (ED). The large language model demonstrated higher specificity, potentially reducing false-positive alerts.

Keywords:
Clinical decision supportLarge language modelsOpioid use disorderScreeningStructured computable phenotype

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Clinical Decision Support

Background:

  • Accurate identification of opioid use disorder (OUD) in the emergency department (ED) is crucial for timely intervention.
  • Existing rule-based computable phenotypes offer a structured approach to OUD detection.
  • The emergence of large language models (LLMs) presents new opportunities for clinical data analysis.

Purpose of the Study:

  • To compare the diagnostic performance of a rule-based computable phenotype against a large language model (LLM) for identifying OUD in ED encounters.
  • To utilize expert physician review as the gold standard for OUD diagnosis.

Main Methods:

  • Retrospective analysis of adult ED encounters with random sampling.
  • Development of a rule-based phenotype incorporating diagnosis codes, medications, toxicology, and keywords.
  • Application of a large language model (ChatGPT 4.1) using zero-shot prompting on ED notes.
  • Independent chart review by two emergency physicians for OUD determination, with adjudication for discrepancies.
  • Estimation of performance metrics (sensitivity, specificity, PPV, NPV) using inverse probability weighting.

Main Results:

  • Weighted prevalence of OUD was 5.6% in the study population.
  • The rule-based phenotype achieved a sensitivity of 0.84 and specificity of 0.964.
  • The large language model demonstrated a sensitivity of 0.81 and a significantly higher specificity of 0.996 (P<.0001).
  • The LLM also showed a higher positive predictive value (0.92) compared to the phenotype (0.58).

Conclusions:

  • Both the rule-based phenotype and the large language model exhibit strong diagnostic capabilities for OUD in the ED.
  • The LLM's superior specificity and positive predictive value suggest its potential to minimize false-positive OUD identifications in clinical workflows.
  • Further prospective validation of LLMs in diverse patient populations is warranted.