From Feedback to Checklists: Grounded Evaluation of AI-Generated Clinical Notes

Abstract

AI-generated clinical notes are increasingly used in healthcare, but evaluating their quality remains a challenge due to high subjectivity and limited scalability of expert review.Existing automated metrics often fail to align with real-world physician preferences.To address this, we propose a pipeline that systematically distills real user feedback into structured checklists for note evaluation.These checklists are designed to be interpretable, grounded in human feedback, and enforceable by LLM-based evaluators.Using deidentified data from over 21,000 clinical encounters (prepared in accordance with the HIPAA safe harbor standard) from a deployed AI medical scribe system, we show that our feedback-derived checklist outperforms a baseline approach in our offline evaluations in coverage, diversity, and predictive power for human ratings.Extensive experiments confirm the checklist's robustness to quality-degrading perturbations, significant alignment with clinician preferences, and practical value as an evaluation methodology.In offline research settings, our checklist offers a practical tool for flagging notes that may fall short of our defined quality standards.

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