PRODUCTION-READY RETRIEVAL-AUGMENTED GENERATION FOR AUTOMATED HEALTHCARE CLAIMS INTELLIGENCE

Abstract

Healthcare payer organizations process millions of medical claims daily, requiring accurate coding validation, policy compliance verification, fraud detection, prior authorization support, and regulatory adherence. Conventional claims processing systems often depend on rule-based engines and manual review, resulting in delayed adjudication, inconsistent decisions, and increased operational costs. Recent advancements in Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), vector databases, and enterprise knowledge retrieval enable intelligent claims automation while maintaining factual accuracy and explainability. This paper proposes a production-ready Retrieval-Augmented Generation framework for automated healthcare claims intelligence by integrating enterprise knowledge repositories, vector search, machine learning, cloud-native deployment, and intelligent document retrieval. The proposed framework continuously retrieves relevant payer policies, coding guidelines, clinical documentation, and regulatory knowledge before generating explainable recommendations for claims processing. Experimental analysis demonstrates significant improvements in claims accuracy, retrieval precision, processing efficiency, decision consistency, and operational scalability compared with conventional rule-based claims processing systems. Keywords: Retrieval-Augmented Generation, Healthcare Claims, Large Language Models, Vector Database, Claims Intelligence, Machine Learning, Enterprise AI, Healthcare Automation, Cloud-Native Computing, Prior Authorization.