Search Broadly, Seek Evidence on Both Sides, Decide Narrowly: Evidence-Admissible GraphRAG for Longitudinal Clinical Event Verification
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The study evaluates an auditable patient‑timeline reconstruction system that tracks provenance, records revisions, and refuses to answer when evidence is missing. Using a synthetic corpus of 1,000 patients and 3,353 notes, two provenance‑aware Evidence Graph operators reduced graph size by 33–37% while preserving all answers across 6,813 query points; a fixed‑window baseline failed to answer over half of the points. The system’s evidence‑gating mechanisms (BioClinicalBERT and a zero‑shot LLM) responded appropriately to evidence‑unavailable controls, but performance varied on marker‑free controls, with BERT maintaining high accuracy but the LLM’s coverage dropping sharply. "whyItMatters":"The results demonstrate that provenance‑aware evidence graphs can significantly reduce data complexity while maintaining answer integrity, highlighting a practical approach to building auditable clinical NLP systems."
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