REINFORCEMENT LEARNING-BASED ADAPTIVE RETRIEVAL FOR ENTERPRISE HEALTHCARE KNOWLEDGE SYSTEMS

Abstract

Enterprise healthcare knowledge systems manage vast collections of clinical guidelines, payer policies, reimbursement regulations, Electronic Health Records (EHRs), medical literature, provider contracts, and regulatory documents that continuously evolve over time. Conventional retrieval systems typically employ static ranking algorithms and predefined retrieval strategies, limiting their ability to adapt to changing user contexts, knowledge repositories, and healthcare workflows. Recent advances in Reinforcement Learning (RL), Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), semantic search, and cloud-native computing enable adaptive knowledge retrieval through continuous interaction and learning from enterprise feedback. This paper proposes a Reinforcement Learning-based adaptive retrieval framework for enterprise healthcare knowledge systems by integrating intelligent retrieval agents, vector databases, machine learning, enterprise knowledge management, and cloud-native deployment. The proposed framework continuously optimizes retrieval strategies based on user interactions, retrieval relevance, clinical feedback, and organizational objectives to improve knowledge accessibility and healthcare decision support. Experimental analysis demonstrates significant improvements in retrieval precision, response relevance, knowledge utilization, operational efficiency, and enterprise scalability compared with conventional static retrieval systems. Keywords: Reinforcement Learning, Adaptive Retrieval, Retrieval-Augmented Generation, Healthcare Knowledge Systems, Large Language Models, Vector Database, Enterprise AI, Semantic Search, Machine Learning, Cloud-Native Computing.