Payment Integrity: Preventing Wrong Payments Before They Happen

Transcript

Y’all Healthcare is complicated. Not because we don’t know what we want to accomplish. We want better outcomes. Better experiences. Lower costs. Smarter operations. The complication is everything that has to happen behind the scenes to make those things possible.

This is Healthcare Power Ups—a podcast about how healthcare actually works. In each episode, we’re going to take one part of the healthcare system, pull it apart, and figure out what really happens underneath. Not the PowerPoint version. The real version. The people. The processes. The technology. The economics. And most importantly, why it matters. Today, we’re talking about something called payment integrity. And here’s the question: What if the biggest opportunity in payment integrity isn’t finding the wrong payment? What if it’s figuring out why the wrong payment happened in the first place? Let’s start with a claim. A healthcare provider delivers a service. The provider submits a claim. The health plan processes it. The claim goes through a complicated series of rules involving eligibility, benefits, contracts, coding, clinical policy and payment logic. Eventually, money changes hands.

And sometimes, the amount is wrong. Maybe the wrong organization was responsible for paying the claim. Maybe a contract term was interpreted incorrectly. Maybe the service was billed incorrectly. Maybe a policy wasn’t applied properly. Maybe information was missing somewhere upstream. One mistake. One payment. It doesn’t sound like much. But multiply those opportunities across millions of claims moving through an extraordinarily complicated healthcare system, and suddenly you have an entire discipline devoted to answering a deceptively simple question: Did the right amount of money go to the right place? That’s payment integrity. Sagility’s payment integrity leader, Christian Bass, describes the job in almost exactly those terms. Healthcare has an enormous amount of money moving through an extraordinarily complicated system. Payment integrity is there to help make sure that money changes hands correctly. Preferably the first time. That last part matters. Because historically, payment integrity has often been very good at finding problems after they happen. A claim gets processed. The payment goes out.

Later, someone reviews it and realizes: We paid too much. Then begins another process. Research the claim. Validate the finding. Notify the provider. Potentially review medical records. Manage an appeal. Attempt recovery. Reconcile the payment. That’s a lot of work to correct something that already happened. Which raises a pretty obvious question: Why did we let the wrong payment happen at all? To understand why, you have to understand where payment integrity traditionally sits in the claims lifecycle. Think of a claim moving from left to right. On the far left are all the things that happen before the claim exists. A member enrolls. A provider joins a network. Credentials are verified. Contracts are loaded. Benefits are configured. Coverage information is collected. Then care happens.

A claim is created. The claim enters adjudication. Rules are applied. A payment decision is made. And somewhere toward the right side of that process, payment integrity traditionally begins looking for what everyone else missed. Christian used a great metaphor for it. Payment integrity often comes behind the rest of the healthcare operation and mops up the errors. And because it’s sitting at the end, it has an unusual vantage point. It can see the consequences of everything that happened before it. A member enrollment problem can eventually become a payment problem. A provider contracting problem can become a payment problem. A credentialing issue can become a payment problem. A claim-processing error can become a payment problem. Every step upstream is another place where something can go wrong. And that means a payment error isn’t just a payment error. It’s evidence. Evidence that something somewhere in the system didn’t work as intended. Consider coordination of benefits. A person may have coverage from more than one source. Determining which insurer pays first can depend on information collected long before a claim arrives. You could simply ask the member whether they have other coverage. Except members may not know exactly how their coverage works.

They may misunderstand the question.

They may not know which plan is primary.

Information changes. Employer circumstances change.

And certain rules used to determine payment responsibility can be surprisingly complex. If that information is wrong upstream, the claim can still move through the system. It can adjudicate. It can pay. And only later does payment integrity discover that another payer should have been responsible.

At that point, the PI program can recover the money. Success. Except… Was it? Because the system is still capable of making the same mistake tomorrow.

That’s the bigger payment integrity question.

Do you want to become better at finding errors?

Or do you want to become better at not creating them?

This is where the concept of shift-left comes in.

The phrase comes from the idea of moving intervention earlier in a process.

Post-payment recovery sits far to the right.

Prepayment review moves left.

Instead of sending the money and trying to recover it later, identify the problem after adjudication but before the payment is released.

That’s already a major improvement.

You avoid recovery.

You avoid moving money unnecessarily.

You potentially reduce administrative work.

But even that isn’t the end of the story.

Because the next step is moving further left again.

If payment integrity repeatedly finds the same kind of error, can that intelligence change something upstream?

Can it improve enrollment?

Contract configuration?

Provider information?

Claims processing? Policy? Education? Workflow?

In other words:

Can payment integrity stop being the cleanup crew and start becoming part of the prevention system?

The industry has been talking about that idea for years.

Actually doing it is much harder.

Part of the problem is technical.

Prepayment intervention introduces timing.

Health plans don’t have unlimited time to review a claim before paying it. Payment requirements and operational constraints matter.

If you stop too many claims for manual review, you’ve created a different problem.

So the challenge isn’t simply: find more things.

It’s:

Find the right things, with enough confidence, early enough to intervene without disrupting the entire payment system.

And that brings us to one of the strangest dynamics in payment integrity.

Finding more potential savings doesn’t necessarily mean you have a better program.

Imagine two payment integrity systems.

System A identifies $100 million in potential errors.

System B identifies $80 million.

At first glance, System A looks better

But then providers challenge the findings.

A large portion of System A’s findings are overturned.

System B’s findings largely stand.

Suddenly, gross savings doesn’t tell you very much.

The more meaningful question becomes:

What survived?

And at what cost?

Because every questionable finding has a consequence. A provider receives a notice.

Someone has to investigate.

Someone may appeal. Someone reviews the appeal. Administrative work accumulates on both sides. The provider gets frustrated. The health plan spends resources defending or overturning the finding. That is provider abrasion. And it’s why sophisticated payment integrity programs have to balance several outcomes simultaneously. Savings matter. Accuracy matters. Net savings matter. Speed matters. Cost matters. And the provider relationship matters. A program that produces enormous gross savings by generating enormous numbers of bad findings isn’t necessarily successful. It may simply be moving administrative cost somewhere else. Which points to something deeper. Payment integrity isn’t really about finding the maximum number of errors. It’s about making better payment decisions. And those decisions become increasingly important as healthcare organizations face pressure to control medical costs. Payment integrity has historically been viewed as a fairly specialized function. But the economics are changing its role.

When healthcare costs rise, avoiding unnecessary spend becomes increasingly important. And payment integrity sits in a fascinating position. It’s downstream enough to see what went wrong. But if connected to the rest of the operation, it can potentially influence what happens next. Think about that as a feedback loop. An error is detected. The system determines why it occurred. Similar patterns are identified. Analytics reveal whether the issue is isolated or systemic. The information moves upstream. The process changes. Future claims avoid the same error. Now the value isn’t simply the money recovered from the original claim. It’s the future errors that never happen. That is a very different economic model. And it also changes how we think about AI. There is a temptation in healthcare to describe AI as if it were a magical detector. Feed it enough claims and it will find more anomalies. That can be useful. But anomaly detection is only one part of the opportunity. The more interesting role for intelligence may be connecting patterns across the lifecycle. Which providers have unusual error patterns?

How does that compare with similar providers?

Which payment issues keep recurring?

Which findings survive appeal? Which don’t?

What upstream process produced them?

Where would intervention create the greatest value?

What should be automated?

What needs expert review?

And what information should go somewhere else in the healthcare organization so the error doesn’t recur?

That turns payment integrity from an audit activity into something closer to a learning system.

And there’s another reason this matters.

Not all payment integrity work looks the same.

Some issues can be identified through data.

Others require medical records.

Some require clinical judgment.

Some involve contract interpretation.

Some require investigation.

Some involve recovery.

And each has a different time-to-value.

A relatively straightforward data-driven finding can begin producing measurable value quickly once the data and workflows are operational.

Complex medical-record reviews work differently.

Records have to be requested.

Providers need time to respond. The records need to be reviewed. Findings may be communicated. Appeal periods may follow. The value can be larger, but the cycle can take months. So when someone asks: How quickly does payment integrity create value? The right answer is: What kind of payment integrity? Because the strongest programs aren’t one giant audit. They’re portfolios of interventions operating at different points in the lifecycle, with different levels of complexity, cost and return. And there’s one category Christian raised that illustrates just how deep this can go: provider contract repricing. Provider contracts can be extraordinarily complex. Rates. Terms. Exceptions. Multiple reimbursement methodologies. And when those terms are translated into payment systems, discrepancies can occur. What’s interesting is that these aren’t necessarily problems a health plan knows to look for. You can’t shop for a solution to a problem you don’t realize exists.

That’s a recurring theme in healthcare operations. Some of the largest opportunities are hiding inside processes everyone assumes are already working. And mature payment integrity programs can continue finding value precisely because the underlying system remains complex. But this brings us back to the paradox at the center of the episode. If payment integrity gets better and better at finding errors, does that mean the program is succeeding? Maybe. But the ultimate version of success might actually make parts of traditional payment integrity less necessary. Imagine an operation where every finding contributes to a learning loop. A contract issue is identified and corrected upstream. An enrollment issue becomes better enrollment logic. A recurring provider billing pattern triggers earlier intervention. An adjudication problem becomes a better rule. Analytics continuously identify emerging patterns. AI helps prioritize where human expertise should be applied. The system learns. The error rate falls. And fewer incorrect payments ever make it to recovery. That’s the evolution: Post-pay asks: How do we get the money back? Prepay asks: How do we stop the wrong payment before it leaves? The next generation asks: Why was the wrong payment possible at all?

And that may be the most important shift. Because healthcare has a habit of building entire industries around correcting downstream consequences. Denials. Appeals. Recoveries. Rework. Call centers. Manual reviews. All necessary. All valuable. But every one of them should eventually force us to ask: What happened upstream? Payment integrity is particularly well positioned to answer that question because it sits where the consequences become visible. The opportunity is to take that intelligence and send it back through the system. Not as another report. As an intervention. Fix the rule. Improve the workflow. Change the process. Educate the provider. Correct the data. Refine the model. Prevent the next error.

And then do it again. That’s how payment integrity stops being something that happens after healthcare gets payment wrong. And starts becoming part of how healthcare gets payment right. The first time. And that is today’s Healthcare Power Up. One piece of healthcare operations. Pulled apart. Put back together. And hopefully, a little easier to understand than it was twenty minutes ago. Until next time, keep asking the question that tends to make healthcare a lot more interesting: What actually happens next?