The First-In, First-Out Era of Denial Appeals Is Ending. Here's What Providers Told Us They're Doing Instead.

By Missy Harbert, MBA, Solutions Advisor, Revecore 

I recently presented a webinar to hospital revenue cycle leaders, and one exchange really stuck with me. We asked attendees how their organizations currently prioritize which denials or underpayments to pursue. Only 10% said they still work strictly first-in, first-out. Forty-eight percent are already prioritizing by likely recovery and true financial impact. That tracks with a shift we've seen accelerating across our client base over the past couple of years, as more providers move away from working denials by age or dollar amount alone. 

Two comments from the session added real context to that shift, and both deserve a closer look. 

The math providers are quietly doing on every claim 

Our webinar moderator, Phil Rooney, who leads denial prevention for Johns Hopkins Health System, introduced this tension that's all too familiar for most revenue cycle teams. Appealing a denial with a 2% chance of overturning doesn't make financial sense on its own. But choosing not to appeal it sends the payer a signal that this type of denial is safe to keep issuing, which compounds rapidly. 

That tension holds up against what we know about how payer AI works. Eighty-four percent of health insurers now report using AI or machine learning across their product lines, with prior authorization among the most common applications. Once a payer model has enough claims data to learn from, appeal history becomes an input to future decisions. A denial type that a provider consistently lets go is, mathematically, a lower-risk one for the payer to keep issuing. 

So, the decision comes down to something other than pure financial math: whether to absorb a loss on one claim to protect your position on the next thousand claims like it. There's no formula that resolves that cleanly, and no one at the webinar pretended there was one. 

High volume, low dollar, fight anyway 

The second comment came from an attendee who used ED-to-observation downgrades as her example. Each of these denials is relatively low-dollar on its own. But providers see them in high volume, and in her experience the overturn rate on this category runs well above average. Low value per claim, high volume, and better-than-typical odds. Her point: appeal all of them, even when the per-claim math looks thin. 

That's a useful complement to the effort-probability-impact framework we walked through earlier in the session. The framework depends on providers having good visibility into their own claims data and outcomes. Sometimes that means fighting a low-dollar category harder than a high-dollar one, because the data says the odds favor you there. 

The math doesn’t work claim by claim 

The average cost to work a denial start to finish is $57.23, blended across all denial types; clinical denials requiring a nurse or physician reviewer run closer to $150. Most physicians surveyed by the AMA doubt an appeal will succeed and say patients can't wait out multi-week timelines regardless of the odds. That's the trap: a high win rate doesn't help a team that can't afford to chase every claim, and it gets worse because most teams size up one claim at a time (balance, age, deadline) while payer models score an entire book of business at once. Closing that gap means tracking overturn rates by payer and denial type over time, and knowing in advance which policies, like Aetna's LLS repricing on short Medicare Advantage stays, route to arbitration instead of a real appeal path. 

Where AI helps on our side 

If payers are using AI to decide what to deny, providers need something comparable to decide what to fight. That's the same shift we've built into our own denial work: moving from a manual, volume-driven process to one that prioritizes by recovery opportunity, overturn likelihood, and payer behavior, so the highest-value claims get worked first, not just the oldest ones. 

A few questions to bring back to your team 

Do you know your overturn rate by payer and denial type, separate from your overall denial rate? For your highest-volume, lowest-dollar categories, have you checked whether the odds favor you? And when a denial goes without an appeal, do you have visibility into what that pattern tells the payer over time? 

None of this resolves into a single rule. What separates providers making real progress is the data and the judgment to know where that work pays off: tracking overturn rates, payer patterns, and true financial impact well enough to act on them before the next round of claims comes in. Building that layer, and using it every day, is exactly the work we do alongside our clients. 

Ready to Shift from Defense to Strategy? 

The questions above are just the starting point. Our whitepaper, When AI Tips the Scales: How Payers Are Redefining Denials — and What Hospitals Can Do About It, goes deeper into how payer AI is calibrated to predict which denials you'll challenge, where revenue leaks without ever generating a denial flag, and the framework hospitals are using to evaluate denials by effort, probability, and true financial impact. 

Download the Whitepaper