Detecting Stigmatizing Language in Clinical Notes with Large Language Models for Addiction Care.

ABSTRACT:

Rationale

Recent studies have found that stigmatizing terms can incline physicians to pursue punitive approaches to patient care. The intensive care unit (ICU) contains large volumes of progress notes that may contain stigmatizing language, which could perpetuate negative biases against patients and affect healthcare delivery. Patients with substance use disorders (alcohol, opioid, and non-opioid drugs) are particularly vulnerable to stigma. This study aimed to examine the performance of Large Language Models (LLMs) in the identification of stigmatizing language from ICU progress notes of patients with substance use disorders (SUD).

Methods

Clinical notes were sampled from the Medical Information Mart for Intensive Care (MIMIC)-III, which contains 2,083,180 ICU notes. These

SUBMITTER: Sethi R 

PROVIDER: S-EPMC12363688 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature