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arXiv · 2609.22164

A Multi-Agent Pipeline for Source-Grounded Synthetic Note Generation from Longitudinal Structured EHR

Nina Fatehi·Reihaneh Hassanzadeh·Meysam Ghaffari·Animesh Agarwal·Carlos Morato
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Abstract

Structured EHR is abundant but sparse, coded, and difficult to use directly for note-centric clinical modeling. We present MedNotes, a multi-agent synthetic data generation pipeline that converts longitudinal structured EHR into source-grounded clinical note representations under explicit quality control. MedNotes treats structured-data-to-text synthesis as a closed-loop agentic process: a generator proposes a note, evaluator agents diagnose factual, coverage, structural, and hallucination-related failures, and a router accepts, revises, or rejects the draft. On 1,485 EHRSHOT encounters, MedNotes achieves a 91.4% pass rate, with mean factual accuracy of 0.980, completeness of 99.1%, structural fidelity of 0.761, and 0.028 critical hallucinations per encounter. Iterative refinement improves acceptance from 69.4% to 91.4%. The resulting synthetic corpus improves downstream CPT prediction and paragraph-level section prediction when combined with limited real data.

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Nina Fatehi, Reihaneh Hassanzadeh, Meysam Ghaffari, Animesh Agarwal, Carlos Morato. 2026-08-26. A Multi-Agent Pipeline for Source-Grounded Synthetic Note Generation from Longitudinal Structured EHR. https://arxiv.org/abs/2609.22164

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