Policy-Aware Documentation Reasoning for Presubmission Claim Denial Prevention in Specialty Oncology: An Iterative Development Journey from Fact Extraction to Claim Validation

Healthcare claim denials represent a significant financial and operational burden, withspecialty oncology claims facing particularly high denial rates due to complex coverage criteriarequiring precise documentation. We present CXR (Claim eXamination and Reasoning), apolicy-aware documentation reasoning system that transforms unstructured clinical notesinto structured claim admissibility assessments for presubmission denial prevention.This paper documents a comprehensive four-version iterative development process thatevolved from a simple fact extractor to a question-driven claim validation system withintegrated safety workflows. Our journey was driven by critical assessments identifyingfundamental architectural flaws and potential patient safety risks. Through systematicevaluation on 60 challenging synthetic oncology cases addressing three critical challenges—(1)fact extraction ambiguity, (2) policy interpretation granularity, and (3) negative evidencedistinction—we achieved 100% technical success rate with 96.7% appropriate human reviewtriggers.The system demonstrates appropriate uncertainty recognition (93.3% low-confidenceassignments on intentionally ambiguous cases) rather than false confidence, embodying asafety-first design philosophy. Clinical value analysis shows potential time savings of 8–12minutes per case (133–200 hours annually for 1000 cases) and 60–70% reduction in cliniciancognitive load.Our work contributes: (1) a novel question-driven architecture for policy-aware claimvalidation, (2) multi-layered uncertainty detection mechanisms preventing false-positive factextraction, (3) a three-dimensional schema for negative evidence distinction, (4) evidenceanalysis signals providing multi-dimensional confidence assessment, and (5) a documentedtransformation from treatment decision framing to claim denial prevention framing, ensuringalignment with regulatory and clinical safety requirements. This paper provides unprecedenteddetail on the rationale, challenges, and iterative refinement process that led to a production-ready system prioritizing safety through uncertainty recognition and human-in-the-loopworkflows.