Most hospital leaders understand the financial risk of prior authorization breakdowns. But another source of revenue leakage is gaining attention: claim denials tied to medical necessity. These denials are harder to predict, tougher to appeal, and more expensive to resolve.

Even when a procedure is authorized, payment can still be denied if the diagnosis codes, clinical rationale, or documentation fail to support it. This is where agentic AI applied to medical necessity is beginning to make an impact. It identifies risks before claims are submitted and helps hospitals get it right the first time.

What this technology does, and how it differs from prior authorization

Prior authorization determines whether a service is covered and appropriate before it is delivered. But authorization does not guarantee reimbursement.

Denials based on medical necessity occur after care has been provided, when payers review documentation to assess whether the service was clinically supported. These reviews often apply a different set of criteria than those used in the authorization process.

AI that helps determine medical necessity works by:

  • Reviewing structured and unstructured clinical documentation
  • Evaluating whether diagnosis codes support the CPTs being billed
  • Flagging mismatches or missing documentation before claims are finalized
  • Applying payer-specific rules and coverage policies at the point of coding

The goal is to prevent avoidable denials by bringing clinical appropriateness checks into the pre-claim workflow.

Why hospitals are turning their focus here

Medical necessity denials often require peer-level review and are difficult to resolve. They are less predictable than technical rejections and can surface weeks after services are rendered. By then, rework is time-consuming and costly.

AI-driven validation of medical necessity provides a proactive alternative. It gives revenue cycle teams earlier visibility into documentation gaps and reduces the need for appeals or downstream corrections.

As payer rules shift and staffing constraints persist, organizations need tools that prevent problems before they happen.

A real-world example

When a national diagnostics provider previewed this approach, they saw immediate value.

The AI agent reviewed referrals and order documentation in advance. It checked CPT and ICD combinations against payer criteria and flagged cases where clinical support was insufficient. This helped the team strengthen claims before submission.

What stood out was not just automation. It was assurance. The ability to anticipate denials, clarify what was missing, and act early gave their staff more control and confidence in how claims would be received.

Where it fits in the revenue cycle

This capability operates upstream of billing. It works between documentation and coding, where claims are still flexible and improvements can be made.

When agentic AI is applied to medical necessity, it can:

  • Integrate into EHR and RCM platforms
  • Flag risk scenarios before submission
  • Improve first-pass yield on high-risk services
  • Reduce hours spent on appeals and rework
  • Provide insight for coding, CDI, and revenue integrity teams

The result is fewer denials, faster reimbursements, and stronger alignment between clinical documentation and payer expectations.

Raising the bar on documentation and reimbursement accuracy

Hospitals are under increasing pressure to deliver clean claims. Payer criteria are evolving, audits are more frequent, and denials are becoming more complex. Addressing these issues after submission is costly and inefficient.

Agentic AI that supports medical necessity review offers a better approach. It brings clinical and financial logic together in the pre-claim window, where teams can act before a denial occurs.

To explore how AI agents can be used to optimize revenue cycle workflows, visit www.infinx.com/revenue-cycle-ai-agents.