Scalable information extraction from free text electronic health records using large language models.

ABSTRACT:

Background

A vast amount of potentially useful information such as description of patient symptoms, family, and social history is recorded as free-text notes in electronic health records (EHRs) but is difficult to reliably extract at scale, limiting their utility in research. This study aims to assess whether an "out of the box" implementation of open-source large language models (LLMs) without any fine-tuning can accurately extract social determinants of health (SDoH) data from free-text clinical notes.

Methods

We conducted a cross-sectional study using EHR data from the Mass General Brigham (MGB) system, analyzing free-text notes for SDoH information. We selected a random sample of 200 patients and manually labeled nine SDoH aspects. Eight advanced open-source LLMs were ev

SUBMITTER: Gu B 

PROVIDER: S-EPMC11773977 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature