RETRIEVAL-AUGMENTED GENERATION FOR INTELLIGENT HEALTHCARE CLAIMS REVIEW AND PRIOR AUTHORIZATION

Abstract

Healthcare organizations process millions of insurance claims and prior authorization requests every year, making accurate, efficient, and timely decision-making essential for improving patient care and reducing administrative burden. Traditional claims review systems rely heavily on manual verification, predefined business rules, and isolated clinical databases, resulting in delayed approvals, inconsistent decisions, increased operational costs, and higher claim denial rates. Recent advances in RetrievalAugmented Generation (RAG) provide an opportunity to combine large language models with external healthcare knowledge sources to support intelligent clinical decision-making while maintaining factual accuracy and regulatory compliance. This paper proposes a Retrieval-Augmented Generation framework for intelligent healthcare claims review and prior authorization. The proposed architecture integrates clinical guideline retrieval, electronic health records, insurance policy documents, medical coding standards, and large language models to provide evidence-based recommendations for claim validation and authorization decisions. Experimental analysis demonstrates improvements in review accuracy, processing efficiency, decision consistency, and administrative productivity while reducing manual workload and enhancing transparency in healthcare claims management. Keywords: Retrieval-Augmented Generation, Healthcare Claims, Prior Authorization, Electronic Health Records, Clinical Decision Support, Large Language Models, Medical Coding, Artificial Intelligence.