Intelligent Automation for Streamlining Prior Authorization Workflows Integrated with EHRs Using Agentic AI

Abstract

Prior Authorization (PA) processes represent a significant administrative burden in healthcare systems worldwide, contributing to treatment delays, clinician dissatisfaction, and increased operational costs. While traditional automation approaches have addressed discrete components of the PA workflow, they lack the adaptability and autonomy necessary for comprehensive optimization. This article introduces a framework for implementing Agentic Artificial Intelligence (AI) to transform PA workflows through seamless Electronic Health Record (EHR) integration. The evolution of authorization automation is traced through three distinct technological generations, demonstrating the progressive advancement from basic rule-based systems to sophisticated goal-oriented agents capable of autonomous decision-making. A multi-layered architectural framework is presented, detailing the specialized components that enable these systems to extract clinical data, navigate payer requirements, assemble documentation, and manage submissions with minimal human intervention. Implementation strategies are outlined, emphasizing the importance of preparatory assessment, phased deployment, and critical success factors, including vendor collaboration, stakeholder engagement, and data quality initiatives. The benefits of Agentic AI implementation are substantial, including dramatic reductions in processing time, decreased operational costs, improved approval rates, and enhanced patient experience. Despite challenges in technical integration, data standardization, regulatory compliance, and change management, healthcare organizations can achieve transformative improvements through thoughtful mitigation strategies. As interoperability standards evolve and implementation methodologies mature, Agentic AI promises to fundamentally reimagine prior authorization workflows, liberating clinical teams from administrative burdens while improving patient care outcomes.