AGENTIC AI FRAMEWORK FOR AUTOMATED CLINICAL CLAIMS VALIDATION AND DECISION SUPPORT

Abstract

Healthcare organizations process millions of clinical insurance claims every day to support patient care, reimbursement, hospital operations, and regulatory compliance. The increasing complexity of healthcare services, evolving insurance policies, changing clinical guidelines, and diverse coding standards have significantly increased the challenges associated with manual claims validation. Traditional claims review processes rely heavily on human auditors who examine clinical documentation, diagnosis codes, procedure codes, insurance eligibility, policy coverage, medical necessity, and reimbursement rules before approving or rejecting claims. These manual workflows are time-consuming, expensive, inconsistent, and susceptible to human error, resulting in delayed reimbursements, claim denials, administrative burden, financial losses, and reduced operational efficiency. Recent advances in Artificial Intelligence have enabled intelligent automation of healthcare administrative processes through machine learning, natural language processing, knowledge graphs, and large language models. More recently, Agentic Artificial Intelligence has emerged as a transformative paradigm in which autonomous AI agents collaborate, reason, plan, retrieve knowledge, and execute complex tasks while interacting with multiple healthcare information systems. This paper proposes an Agentic AI framework for automated clinical claims validation and decision support that integrates autonomous reasoning agents, clinical knowledge retrieval, policy interpretation, electronic health records, insurance databases, medical coding systems, and intelligent workflow orchestration into a unified decision-support architecture. The proposed framework continuously analyzes clinical documentation, diagnoses, treatment procedures, laboratory reports, physician notes, insurance policies, reimbursement guidelines, historical claim outcomes, and regulatory compliance requirements before generating evidence-based validation decisions. Multiple specialized AI agents collaborate to perform eligibility verification, medical necessity assessment, coding validation, fraud detection, policy compliance evaluation, reimbursement estimation, and recommendation generation. Contextual reasoning and explainable decision-making enable healthcare professionals and insurance reviewers to understand the rationale behind each validation outcome. Experimental evaluation using representative healthcare claim scenarios demonstrates that the proposed Agentic AI framework significantly improves claims validation accuracy, reduces processing time, minimizes false claim denials, enhances coding consistency, supports regulatory compliance, and accelerates reimbursement workflows compared with conventional rule-based and manually supervised claims review systems. The proposed architecture provides a scalable, secure, and intelligent solution suitable for hospitals, insurance providers, healthcare administrators, government healthcare programs, and digital health ecosystems seeking to modernize clinical claims management through trustworthy autonomous artificial intelligence.