Agentic AI in Revenue Cycle: From Hype to Outcomes

Placeholder Text: The promise of Artificial Intelligence in healthcare is vast, but often abstract. For revenue cycle leaders, the critical question is how to translate advanced concepts like agentic AI into tangible, measurable outcomes. This article provides a practical roadmap.

The Challenge with Traditional RCM Automation

Traditional automation, often based on Robotic Process Automation (RPA), is brittle. It follows rigid scripts and breaks when payer portals change or new denial reason codes appear. This requires constant maintenance and fails to address the dynamic, conversational nature of tasks like AR follow-up.

Agentic AI represents a paradigm shift from 'doing' to 'reasoning'. Instead of following a script, an AI agent understands a goal—like resolving a denied claim—and autonomously takes the necessary steps to achieve it.

An AI agent can navigate complex decision trees, interact with systems via both APIs and user interfaces (like a human), and even use voice to call payers. This adaptability is the key to automating the long tail of complex, high-value RCM tasks that have resisted automation until now.

Key Areas for Agentic Automation

• Prior Authorization: Agents can gather clinical data, submit requests through complex portals, and follow up on status, reducing the 2-3 week average turnaround time.
• Denial Management: Instead of just flagging a denial, an agent can read the reason code, access the patient record, correct the error (e.g., a missing modifier), and resubmit the claim without human intervention.
• Patient Follow-up: Agents can handle patient billing questions, set up payment plans, and provide support 24/7, improving patient satisfaction and accelerating collections.