Offense or Defense? How Health Systems Can Fight Back Against Payer AI

Hospital denials are increasingly being shaped by payer AI and analytics — and not simply by whether a claim is payable, but by whether a provider is likely to fight back.

In this Revecore conversation, Heather Berwager and Missy Harbert explore whether health systems are playing offense or defense against increasingly strategic payer AI tactics and what it takes to build a smarter, more resilient denial strategy.

This discussion covers how leverage has shifted toward payers, why some denials work precisely because they're wrong, why the most dangerous revenue loss is the kind that never shows up in standard reporting, and how hospitals can use data to move from reactive appeals to proactive denial prevention.

Watch the full conversation:

How Payer Denial Strategies Are Changing for Health Systems

Set the stage for us. Hospital leaders feel like something has changed with payers, but they can't always name what it is. From where you sit, what's shifted?

Missy: There's definitely been a shift in the denial market, and every one of our hospital clients is feeling it. It's less about individual claims tied to a specific case or record review, and more about payers systematically denying a large volume of low-dollar claims for what looks like no particular reason. In reality, it's strategic. Payers are targeting the areas where they expect providers won't have the resources to appeal and fight back.

How Payers Are Using AI to Target Hospital Claims

You used the word “strategic.” What does that actually mean here? How is AI being used differently than traditional payer automation?

Missy: It used to work claim by claim: a payer would request a medical record, review the case against medical policy, and issue a denial you could then fight if you disagreed. Now payers are more strategic. They're finding niche categories of lower-balance claims where they know providers may not have the resources to push back.

When providers do appeal those claims, the success rate is often far higher than a typical medical necessity denial. But payers are doing that math too. They know hospitals are weighing the cost of appealing against the size of the claim, and they're targeting the areas that are easy to overlook and slip past detection.

Why AI-Driven Payer Denials Can Succeed Even When Claims Are Payable

Some denials work precisely because they're wrong, which sounds like a strange thing to say. Can you explain that dynamic?

Missy: What's different now is that the claims aren't necessarily wrong on their face, they just point to something that looks off. For example, a claim might show a lower-than-average geometric length of stay, so the payer assumes it isn't medically necessary and denies it in bulk based on that pattern alone.

It's only once a provider appeals and fights it that the payer effectively concedes the claim was payable all along. It's no longer about reviewing each claim individually, it's systematic, and it relies on providers not having the resources to keep up.

How Hospitals Can Detect Hidden Revenue Loss From Payer Denials

If these denials are essentially designed to stay under the radar, how does a hospital catch that it's losing revenue?

Missy: It comes down to analytics. If a hospital is only tracking denial volume, the overall dollar amount denied can erode slowly while looking relatively steady. A large portion of these also get reprocessed as a contractual write-off, so they don't even register as a denial. That makes it a real challenge just to identify that something has been denied in the first place.

And because these are often lower-dollar, higher-volume claims, it can take a while for a hospital to realize this is a real problem, because a single $300 or $400 claim gets written off without much thought. Multiply that by 2,000 claims, though, and it becomes a significant issue, with cash slowly eroding in the background.

Using Analytics and AI to Build a Proactive Denial Strategy

Let's shift to offense. What does it look like if a hospital uses analytics and AI effectively against this dynamic?

Missy: Without an analytical view, it's easy to miss a $300 or $400 claim that you decide isn't worth appealing. Payers are counting on that.

Being on the offense means having strategic analytics that show the big picture, not just one $300 denial, but the same pattern across 2,000 claims. When you appeal at that scale, you might see a 70% overturn rate, which is well above what you'd typically see appealing an individual medical necessity denial.

Analytics let you understand what payers are doing and why, and they also feed directly into prevention. If you know payers are targeting claims with a shorter geometric length of stay, for example, you can proactively strengthen documentation in that area before the denial ever happens.

How Denial Analytics Give Hospitals More Leverage With Payers

What changes when a hospital can show a documented pattern to a payer, instead of disputing individual claims one at a time?

Missy: Analytics are what make that possible. A provider can show that it isn't one denial, it's 2,000 claims, often concentrated around a specific claim type a payer is targeting, like certain ED claims.

If appealing those claims produces a 70–80% overturn rate, that data becomes leverage. You can go back to the payer and ask why they're denying these claims in the first place, especially once review shows they were medically necessary or met criteria, and use that pattern to push the payer to change its behavior going forward.

Without that documentation, a hospital is stuck in a cycle of denials and appeals that may bring in cash, but at a real cost. The data is what gives hospitals leverage they'd otherwise be missing.

Denial Prevention vs. Appeals: Why Health Systems Need Both

Coming back to the question in the title: offense or defense? Where do you land?

Missy: Offense matters because it means taking what you learn on the back end and moving it upstream to fix documentation and prevent the denial before it happens. But the most resilient providers I work with do both, and do both well.

You still have to appeal to keep cash flowing, because hospitals need that revenue. At the same time, you need the analytics on overturn rates to drive front-end prevention, since a lot of these denials aren't the result of the provider doing something wrong. They're the result of payers strategically targeting claim types they expect will go unappealed or under-documented.

Both sides have to work together.

How Revenue Cycle Leaders Can Stay Ahead of Changing Payer Tactics

Before we wrap, if a revenue cycle leader takes one thing away from this conversation, what should it be?

Missy: We have to accept that we can't control payer behavior. We can fight it, and we should, bringing as much data and analytics as possible to support our position, but we can't change how payers operate.

What we can control is our own adaptability on the back end. A denial program you built one, two, or five years ago isn't guaranteed to work going forward, because payers are constantly shifting where they target. When you close off one area, they'll move to another.

The job is to stay adaptive, arm yourself with analytics, and use your data and relationships to push back on that behavior, rather than assuming today's program will hold up indefinitely.

Key Takeaways

• Payers are increasingly targeting high-volume, lower-dollar claims that hospitals may be less likely to appeal.

• Some payer denial strategies rely on providers lacking the resources to challenge otherwise payable claims.

• Contractual write-offs can conceal revenue loss that never appears in standard denial reporting.

• Analytics can reveal denial patterns and identify opportunities with unusually high overturn rates.

• Combining appeals with upstream denial prevention gives hospitals a stronger long-term strategy.