Comparing computable structured phenotype- versus large language model-identification of opioid use disorder using electronic health record data.

ABSTRACT:

Introduction

Opioid use disorder (OUD) is common in emergency departments (EDs); identification via structured computable phenotypes may miss important clinical context.

Objective

Compare a computable structured OUD phenotype with a zero-shot large language model (LLM) using expert review as the reference.

Methods

We retrospectively analyzed 202 adult ED encounters. Two emergency physicians independently determined OUD status with consensus adjudication. The phenotype used ICD-10 codes, medications, toxicology, consult notes, and keyword rules. The LLM (GPT-4.1) classified OUD from concatenated ED notes. Test characteristics and McNemar's tests were computed.

Results

Experts classified 56 (28%) encounters as OUD (κ=0.77). The phenotype showed sensitivity 0.98 (

SUBMITTER: Molina M 

PROVIDER: S-EPMC12723969 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature