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Actionable Feedback: Improving Model Capabilities from User Edit Data on Clinical Documentation
Not provided in the abstract
fine-tuningclinical documentationuser feedback
2611.12904
Builder Relevance
8h ago80%
Abstract
The paper discusses a method to improve large language models in clinical documentation by filtering clinician edits to identify actionable feedback.
Reality Card
Core Claim
The proposed method of filtering clinician edits significantly improves model alignment with clinician expectations while maintaining input context fidelity.
Method / Result
Naive fine-tuning on raw edited output increases ungrounded content by over 45%, while the filtering approach enhances clinician-alignment metrics.
Limitations
Real clinician edits are not publicly available, which may limit the generalizability of the findings.
Paper to code
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