Applying Large Language Models to Assess Quality of Care: Monitoring ADHD Medication Side Effects.

ABSTRACT:

Objective

To assess the accuracy of a large language model (LLM) in measuring clinician adherence to practice guidelines for monitoring side effects after prescribing medications for children with attention-deficit/hyperactivity disorder (ADHD).

Methods

Retrospective population-based cohort study of electronic health records. Cohort included children aged 6 to 11 years with ADHD diagnosis and 2 or more ADHD medication encounters (stimulants or nonstimulants prescribed) between 2015 and 2022 in a community-based primary health care network (n = 1201). To identify documentation of side effects inquiry, we trained, tested, and deployed an open-source LLM (LLaMA) on all clinical notes from ADHD-related encounters (ADHD diagnosis or ADHD medication prescription), including in-cli

SUBMITTER: Bannett Y 

PROVIDER: S-EPMC11978496 | biostudies-literature | 2025 Jan

REPOSITORIES: biostudies-literature