Large Language Models Outperform Traditional Natural Language Processing Methods in Extracting Patient-Reported Outcomes in Inflammatory Bowel Disease.

ABSTRACT:

Background and aims

Patient-reported outcomes (PROs) are vital in assessing disease activity and treatment outcomes in inflammatory bowel disease (IBD). However, manual extraction of these PROs from the free-text of clinical notes is burdensome. We aimed to improve data curation from free-text information in the electronic health record, making it more available for research and quality improvement. This study aimed to compare traditional natural language processing (tNLP) and large language models (LLMs) in extracting 3 IBD PROs (abdominal pain, diarrhea, fecal blood) from clinical notes across 2 institutions.

Methods

Clinic notes were annotated for each PRO using preset protocols. Models were developed and internally tested at the University of California, San Francisco, a

SUBMITTER: Patel PV 

PROVIDER: S-EPMC11772946 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature