Using large language models (LLMs) to apply analytic rubrics to score post-encounter notes

LLMs can reliably apply analytic rubrics to PNs with careful prompt engineering and process refinement. This study illustrates their potential as scalable, automated scoring tools in medical education, though further research is needed to explore their use with holistic rubrics. These findings demonstrate the utility of LLMs in assessment practices.