You Have 2,400 Denials. One Biller Can Work 300. The Math Isn't Working.

For many specialty practices and physician groups, Monday morning begins the same way: with a denial queue that grew over the weekend.

The team is experienced. The billing software is in place. The dashboards show exactly where the problems are. Yet Days in AR remain stubbornly high, and write-offs continue to grow.

This is not simply a people problem or a process problem. It is a capacity problem.

Payers are generating denials faster than most revenue cycle teams can work them.

The Execution Gap in a 25-Provider Group

Consider a 25-provider specialty group generating approximately 13,000 claims a month. At an 18.7% initial denial rate, that creates more than 2,400 denials every month.

Now compare that volume with the capacity of a biller who can work approximately 300 appeals a month.

That is the real execution gap: not knowing what needs to be done, but having enough capacity to do it.

Why One Appeal Takes So Much Work

A denial is rarely resolved with a single click. Even a relatively straightforward appeal may require a biller to:

1. Access the relevant payer portal.
2. Interpret EOB, ERA and remittance codes.
3. Locate supporting documentation in the EHR.
4. Review the payer's policy and filing requirements.
5. Prepare and submit the appeal.
6. Track its status and follow up until resolution.

At 25 to 40 minutes per appeal, one full-time biller can realistically process around 300 appeals a month. Complex denials may take considerably longer.

To manually address all 2,431 denials in our example, the practice would need more than eight people working exclusively on appeals. For most mid-market physician groups, adding that much headcount is neither practical nor financially sustainable.

What Happens to the Rest?

When the denial queue exceeds the team's capacity, billers have no choice but to triage.

High-value claims receive attention first. Lower-value claims wait. Filing deadlines approach. Some claims are eventually written off, simply because nobody had time to work them.

Research has estimated that up to 65% of denied claims are never corrected and resubmitted. At the same time, more than 70% of denied claims may be overturned when they are properly worked.

That makes the cost of limited execution capacity painfully clear: recoverable revenue is being left behind.

If unworked denials reduce net revenue by 3% to 5%, a physician group collecting $20 million annually could leave between $600,000 and $1 million uncollected.

Dashboards Show the Problem. They Do Not Solve It.

Healthcare technology has spent years becoming better at identifying denials. Most practices already have software that can categorize denial reasons, flag aging claims and generate worklists.

But a dashboard does not file an appeal. A recommendation does not recover revenue. And another alert does not give an overstretched billing team more hours in the day.

When technology only identifies the next task, execution still depends on the same limited human capacity.

That is the difference between AI that analyzes and AI that executes.

From Insight to Execution

Anka is designed to complete the work, not simply point to it.

Its AI execution layer can support the denial-management workflow from intake through resolution:

• Read EOBs, ERAs and remittance files
• Identify eligible denials and supporting claim history
• Retrieve the required clinical documentation
• Prepare payer- and policy-specific appeal packages
• Submit appeals within the applicable filing window
• Track claims through resolution
• Escalate exceptions requiring clinical judgment, contractual interpretation or human intervention

The result is not another list for the billing team to work through. It is work completed on the practice's behalf.

Anka operates at Level 3.

Protecting Knowledge Without Depending on Individuals

Experienced billers carry valuable knowledge about payer rules, specialty-specific coding and the quirks of individual portals. When they leave, practices lose more than headcount. They lose operational knowledge that may have taken years to build.

An execution layer makes that knowledge repeatable. Payer policies, documentation requirements and appeal workflows can be applied consistently across the denial queue, not only to the claims an individual biller has time to reach.

This matters in specialties where denial resolution requires highly specific knowledge:

Payers already process claims at scale. Practices cannot keep responding with spreadsheets, fragmented portals and manual follow-ups alone.

Growth Should Not Require a Larger Denial Team

As a practice adds providers and patients, claim volume grows. Denial volume usually grows with it.

In a manual model, every increase in volume creates a corresponding need for more billing capacity. That turns growth into higher overhead and makes the revenue cycle harder to control.

An AI execution layer breaks that relationship. It allows practices to process more denials without expanding headcount at the same rate, protecting collections while giving skilled staff more time to focus on the exceptions that genuinely require human judgment.