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Inadequate postcare patient education contributes to preventable readmissions and adverse outcomes that disproportionately affect medically complex, high-need communities. Large language models (LLMs) show promise for generating personalized, plain-language patient education at scale. However, existing LLM evaluation frameworks prioritize technical accuracy over patient accessibility and alignment with health literacy, and few explicitly account for the attitudinal influences that clinician evaluators may introduce into the rating process. In this viewpoint, we introduce an evaluation framework that pairs the Technology Acceptance Model (TAM) with the Medical Condition Regard Scale (MCRS). We call it the TAM-MCRS LLM evaluation framework, a novel clinician-centered approach for comparing which LLMs produce the highest-quality postcare patient education across accuracy, appropriateness, clarity, and completeness. We intend for this framework to be used to evaluate LLM-generated patient education outputs through a 2-arm design that pairs an expert clinician panel with automated assessment methods, allowing for interarm comparison using clinical vignettes while accounting for measured evaluator attitudinal variance. The framework was developed through the National Institutes of Health artificial intelligence (#AI)/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Clinicians Leading Ingenuity IN AI Quality (CLINAQ) fellowship program, in partnership with Ochsner Health and Xavier University of Louisiana. The TAM-MCRS framework integrates two theoretical lenses. TAM maps perceived usefulness onto accuracy and completeness, and perceived ease of use onto clarity and appropriateness. Clinicians rate each output with a TAM-based questionnaire, and we then administer the MCRS as a postscoring attitudinal covariate to see whether their regard for the conditions represented in the vignettes influences those ratings. Together, the 2 lenses are intended to produce evidence that is objective, theoretically grounded, clinically realistic, and disparity-responsive. Implications for clinician informaticists, health system governance, and responsible AI deployment are discussed. This viewpoint reflects the authors’ position and is written for clinician informaticists, health system AI governance leaders, implementation scientists, and investigators evaluating LLM-generated patient education.

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