Traditional revenue cycle automation can streamline tasks but often stops short of true transformation. The next frontier is agentic AI — autonomous, goal-driven systems that make decisions, act independently, and continuously improve outcomes. This session explores how agentic AI can reshape RCM into a proactive, self-optimizing function. Through real-world examples, we will examine its role in coding, prior authorization, denial prevention, and patient engagement, highlighting measurable gains in accuracy, speed, and financial performance. Attendees will learn how to distinguish agentic AI from traditional AI, as well as how to structure governance and workflows that balance autonomy with compliance and human oversight. The session will also address how to track ROI using metrics such as gross collection rate (GCR), days in A/R, and cost-to-collect.
Learning Objectives:
Differentiate traditional AI from agentic AI in RCM via high-impact use cases across coding, prior auth, denial prevention and more
Organize governance structures and human-AI collaboration workflows that ensure agent AI systems enhance staff expertise while maintaining compliance and appropriate escalation protocols
Calculate the financial and operational ROI of agentic AI initiatives using KPIs such as improved GCR, lower cost-to-collect, and reduced denials
