EVIDENCE-GROUNDED GENERATIVE AI FOR INTELLIGENT HEALTHCARE POLICY RETRIEVAL AND CLAIMS VALIDATION

Abstract

Healthcare claims validation requires accurate interpretation of payer policies, clinical documentation, coding standards, and regulatory guidelines to ensure timely reimbursement and compliance. Conventional rule-based claims validation systems often struggle to process complex medical records and rapidly evolving payer policies, leading to claim denials and administrative inefficiencies. EvidenceGrounded Generative Artificial Intelligence addresses these challenges by combining Large Language Models with enterprise knowledge retrieval mechanisms that generate responses supported by authoritative healthcare documents. This paper proposes an Evidence-Grounded Generative AI framework integrating semantic retrieval, vector databases, Retrieval-Augmented Generation, enterprise knowledge management, and automated MLOps governance for intelligent healthcare policy retrieval and claims validation. The proposed architecture retrieves relevant payer policies, coding guidelines, and historical claims before generating explainable validation recommendations supported by evidence. Experimental evaluation demonstrates improvements in retrieval precision, validation accuracy, decision consistency, regulatory compliance, operational efficiency, and administrative productivity. The proposed framework provides a scalable, trustworthy, and production-ready solution for intelligent healthcare claims validation. Keywords— Evidence-Grounded AI, Healthcare Claims, Policy Retrieval, Claims Validation, Retrieval-Augmented Generation, Large Language Models, Semantic Search, MLOps.