In this issue of Pediatrics, Bannett and colleagues present a compelling study demonstrating the potential of large language models (LLMs) to enhance quality-of-care assessments, specifically in the management of attention-deficit/hyperactivity disorder (ADHD).1 Applying an open-source LLM to clinical notes from a community-based pediatric network provides a scalable solution for monitoring clinician adherence to guidelines on side effects management following ADHD medication prescriptions. This work advances our understanding of the role of artificial intelligence (AI) in automating and scaling clinical practice assessments, overcoming traditional limitations such as the labor-intensive nature of chart reviews. We appreciate the ample analytic details provided by the authors, which improves the transparency of their work, facilitates the application of their validated methods in new settings, and supports adaptation of their methods to new clinical problems.
The authors’ work is important: the failure of clinical teams to monitor and act on ADHD medication side effects...
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