Leveraging a Large Language Model to Assess Quality-of-Care: Monitoring ADHD Medication Side Effects

ObjectiveTo 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). MethodsRetrospective population-based cohort study of electronic health records. Cohort included children aged 6-11 years with ADHD diagnosis and >2 ADHD medication encounters (stimulants or non-stimulants prescribed) between 2015-2022 in a community-based primary healthcare network (n=1247). 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-clinic/telehealth and telephone encounters (n=15,593 notes). Model performance was assessed using holdout and deployment test sets, compared to manual chart review. ResultsThe LLaMA model achieved excellent performance in classifying notes that contain side effects inquiry (sensitivity= 87.2%, specificity=86.3/90.3%, area under curve (AUC)=0.93/0.92 on holdout/deployment test sets). Analyses revealed no model bias in relation to patient age, sex, or insurance. Mean age (SD) at first prescription was 8.8 (1.6) years; patient characteristics were similar across patients with and without documented side effects inquiry. Rates of documented side effects inquiry were lower in telephone encounters than in-clinic/telehealth encounters (51.9% vs. 73.0%, p<0.01). Side effects inquiry was documented in 61% of encounters following stimulant prescriptions and 48% of encounters following non-stimulant prescriptions (p<0.01). ConclusionsDeploying an LLM on a variable set of clinical notes, including telephone notes, offered scalable measurement of quality-of-care and uncovered opportunities to improve psychopharmacological medication management in primary care.