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MedNotes is a multi-agent synthetic data generation pipeline that converts longitudinal structured EHR into source-grounded clinical note representations under explicit quality control and improves downstream CPT prediction and paragraph-level section prediction when combined with limited real data.
Under a softmax retrieval abstraction, a finite-sample upper bound is derived showing that maintaining a fixed accuracy target above base rate requires the evidence margin to scale as $\Omega(\sqrt{\log N})$, where N denotes the effective distractor count rather than necessarily the raw context length.
The results are consistent with structured, feedback-derived error memory being useful for adapting clinical coding behavior across cases without weight updates or changes to the underlying workflow.
This work proposes structured evidence routing, a router-predictor-reviewer workflow that separates full-record access from disease-specific assessment, and suggests that routing, laboratory evidence, task guidance, and review each contribute to performance.
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