3 Ways AI Agents Are Reshaping Prior Auth — and Why a Human Touch Still Matters – Becker’s Hospital Review | Healthcare News

Everyone’s talking about AI in healthcare — but in revenue cycle management, confusion remains about what it actually does.  For RCM and finance leaders, the key question is: What makes an AI solution truly intelligent?

One way to demystify AI is to look closely at how it’s being applied in a high-friction area like prior authorization. Here’s what a real AI agent looks like — and how it helps solve one of healthcare’s most persistent administrative challenges.

What Is an AI Agent in Revenue Cycle?

Think of an AI agent as a digital teammate. Unlike simple automation scripts or macros, AI agents are designed to take in data, reason through a problem, and act on it with minimal human input. In the revenue cycle, that means processing information from multiple sources, identifying what needs to happen next, and executing tasks in a smart, standard operating procedure-driven way — without waiting for staff to step in.

The key differentiator? AI agents learn from the outcomes. They’re not just repeaters of static instructions. They adapt over time, improving accuracy and helping staff work more efficiently.

Just as importantly, they offer explainability. A mature AI agent doesn’t operate in a black box — it shows its work. It can surface why a decision was made, which payer rule triggered an action, and what data supported that path. This transparency builds trust with staff and supports clinical validation when questions arise.

A Closer Look: Prior Authorization AI in Action

Prior authorization is a textbook example of a broken process. It’s slow, error-prone, and often results in delayed care or denied claims. Here’s how AI agents are changing that:

1. Automated Insurance Verification

An AI agent begins by verifying patient insurance in real time — checking eligibility and confirming coverage for specific services that will be performed before the service is scheduled. This eliminates the need for staff to log into payer portals and reduces the risk of authorization-related denials down the line.

2. Smart Authorization Submission

The agent collects the clinical documentation required by each specific payer, pulling from referrals, medical records, and structured data fields. Then, based on payer  clinical policies and guidelines, it submits the authorization request through the correct channel — without manual rekeying. This precision minimizes back-and-forth with payers and reduces turnaround times.

3. Proactive Denial Prevention

Before submission, the AI agent scans for missing data, guideline mismatches, or incomplete documentation. If something’s off, it flags the issue immediately to a prior auth specialist — not days later when the authorization gets denied or sent to peer review.

Why the Human Role Still Matters

Despite these gains, AI doesn’t replace human expertise — it enhances it. For complex or ambiguous cases, human agents remain critical. They intervene in peer-to-peer reviews, escalate urgent authorizations, and provide patients with support that AI can’t replicate.

In fact, combining automation with expert staff tends to improve job satisfaction. One supervisor at Tennessee Orthopedic Alliance noted that her MRI team is more engaged and less frustrated since removing the need to log into multiple portals for each authorization.

Measuring Success: What to Track

 For RCM leaders, these three KPIs show if AI is working:

• Turnaround Time: Faster approvals = improved patient flow and quicker reimbursements.
• First-Pass Authorization Rate: A higher rate signals fewer payer pushbacks and cleaner submissions.
• Denial Rate: A drop in authorization-related denials means the process is becoming more reliable.

Why It Matters Now

The urgency to adopt smarter solutions isn’t just about efficiency — it’s about survival.

• CFOs are asking sharper questions about the difference between real AI and generic automation. The pressure to justify ROI is higher than ever.
• Staffing gaps are persistent. Many teams can’t just hire their way out of backlogs — they need tools that actually extend their capacity.
• Payers are raising the bar. With stricter documentation requirements and growing interest in automation on their end, provider organizations must keep pace to stay competitive.

In this environment, the question isn’t if AI agents should be part of the revenue cycle — it’s how soon they can be deployed to drive real results.

Getting It Right: Interoperability and Change Management

AI agents built for prior authorization must integrate seamlessly with existing systems. Interoperability with EHRs and practice management platforms ensures that key data — from physician orders to insurance eligibility — flows into the AI agent’s logic without adding clicks for clinicians or burdening frontline teams.

But technology alone isn’t enough. Successful deployment requires structured change management. Engaging end users early, training them on exception handling, and clearly defining roles around AI outputs are essential for long-term success and adoption.

To explore how AI agents can support your revenue cycle team, visit www.infinx.com/revenue-cycle-ai-agents.