AGENTIC ARTIFICIAL INTELLIGENCE FRAMEWORK FOR INTELLIGENT PRIOR AUTHORIZATION WORKFLOW AUTOMATION

Abstract

Prior authorization is one of the most resourceintensive administrative processes in healthcare payer organizations, requiring continuous verification of clinical documentation, payer policies, medical necessity criteria, provider eligibility, and regulatory compliance. Conventional prior authorization workflows depend heavily on manual review, rule-based systems, and fragmented enterprise knowledge, resulting in delayed approvals, inconsistent decisions, increased operational costs, and provider dissatisfaction. Recent advances in Agentic Artificial Intelligence, RetrievalAugmented Generation (RAG), Large Language Models (LLMs), machine learning, and cloud-native computing enable autonomous healthcare workflow orchestration with explainable decision-making. This paper proposes an Agentic Artificial Intelligence framework for intelligent prior authorization workflow automation by integrating autonomous AI agents, enterprise knowledge retrieval, predictive analytics, cloud-native deployment, and healthcare policy intelligence. The proposed framework continuously analyzes authorization requests, retrieves relevant clinical evidence, evaluates medical necessity, and coordinates multi-stage approval workflows while maintaining regulatory compliance. Experimental analysis demonstrates significant improvements in authorization accuracy, workflow efficiency, operational scalability, decision consistency, and healthcare automation compared with conventional prior authorization systems. Keywords: Agentic Artificial Intelligence, Prior Authorization, Healthcare Automation, Retrieval-Augmented Generation, Large Language Models, Machine Learning, Intelligent Agents, Healthcare Claims, Enterprise AI, Cloud-Native Computing.