EVIDENCE-GROUNDED RAG SYSTEMS FOR HEALTHCARE POLICY RETRIEVAL AND CLAIMS VALIDATION

Abstract

Healthcare payer organizations rely on extensive policy documents, reimbursement guidelines, clinical protocols, regulatory standards, and coding manuals to validate healthcare claims accurately. Conventional claims validation systems primarily depend on static rule engines and manual document review, resulting in inconsistent decisions, delayed claim adjudication, increased operational costs, and policy interpretation errors. Recent advances in RetrievalAugmented Generation (RAG), Large Language Models (LLMs), semantic search, vector databases, and enterprise knowledge retrieval enable evidence-grounded healthcare decision support with improved factual accuracy and explainability. This paper proposes an evidence-grounded RetrievalAugmented Generation framework for healthcare policy retrieval and automated claims validation by integrating enterprise policy repositories, vector databases, machine learning, cloud-native deployment, and intelligent document retrieval. The proposed framework continuously retrieves authoritative healthcare policies, coding guidelines, reimbursement regulations, and clinical documentation before generating evidencesupported claims validation recommendations. Experimental analysis demonstrates significant improvements in retrieval precision, claims validation accuracy, operational efficiency, regulatory compliance, and enterprise scalability compared with conventional rulebased healthcare claims systems. Keywords: Retrieval-Augmented Generation, Healthcare Policy Retrieval, Claims Validation, Large Language Models, Vector Database, Enterprise AI, Healthcare Automation, Semantic Search, Machine Learning, CloudNative Computing.