Most revenue cycle teams measure denials management the same way: overturn rate and dollars recovered. Those numbers matter, and a team that moves them is doing real work. But both are lagging indicators. They tell you what you won back—not why the denial happened, or whether the same one will land again next quarter.
That is the quiet ceiling of an appeals-only approach. You can overturn nearly every claim you work and still find yourself re-solving the same denial—same diagnosis, same payer—month after month. And the volume keeps climbing. According to Kodiak Solutions’ March 2026 revenue cycle analysis, revenue leakage across more than 2,300 hospitals rose roughly 25% in 2025, to $48.4 billion, driven in part by rising clinical denials tied to medical necessity.1 In one 2026 industry survey, 76% of revenue cycle leaders said they expect denial rates to keep increasing.2 You cannot out-appeal a trend like that. You must understand it.
Recovery Is a Lagging Indicator
Worked one at a time, clinical denials never add up to an explanation. Each overturned claim is revenue you can bank, but on its own it is a single data point, disconnected from the hundreds of similar claims moving through the system during the same time period. The recurring issues simply return—and each one requires the cost and effort of the appeal process, on revenue you already earned.
There is a second cost, too. Because clinical appeals are labor-intensive, teams are forced to triage, and the “long tail” of smaller denials often gets written off—not because the claim is indefensible, but because working it by hand costs more than it is worth. To drive toward higher overturn rates, teams need to answer, “What do these denials have in common?” rather than, “How many denials did we overturn?”
Payer Problem or Documentation Problem?
Almost every recurring clinical denial traces back to one of two very different root causes, and each calls for a different response. Sometimes the clinical support is strong and the bill is denied or upheld anyway; when well-documented claims come back with low overturn rates, that points to payer behavior, and the answer is to return with the evidence. Alternatively, the denial reflects a genuine process gap—a missing detail, an unmet criterion—on the provider side, before the claim ever reached the payer. That is not a case to re-argue; it is a signal for clinical documentation improvement (CDI) and utilization management (UM) teams to address.
Telling those two apart, across an entire book of denials, is the point of a clinical denials intelligence tool.
“ClinIQ, our AI-enabled clinical denials insights platform, gets at the ‘So what?’ question,” said Sindoori Pai, a Product Manager, AI/ML Product Management at Aspirion. “If the root cause of a low overturn rate is payer behavior, you can access the evidence immediately to push back. If it’s poor documentation, we identify the missing evidence and gaps at scale, so you can improve documentation or drive education to resolve the issue.”
Reading the Whole Population, Not One Claim
Once denials are read as a population instead of isolated data points, the patterns surface quickly. Grouped by payer, by diagnosis, and by facility, a diagnosis grouping such as sepsis that is repeatedly downgraded across multiple payers stands out as one fixable pattern rather than a hundred unrelated accounts. The same data cut by facility shows which sites consistently bill claims with a weaker strength of case based on clinical documentation.
That insight is only useful if it reaches the people who can act on it. In Aspirion’s model, ClinIQ3 turns denials data into clear clinical insights, and our Client Success partners bring it directly to a health system’s CDI, utilization management, and managed care teams—along with specific ways to strengthen documentation and improve overturn rates going forward.
What Changes the Economics
Analyzing the entire population of denials—thousands of records, across every payer and diagnosis—for trends is a nearly impossible task for a team also tasked with resolving those denials successfully through appeals. That is what makes AI more than a speed play: it lowers the cost of pursuing each account, expanding what can be appealed, and it makes the patterns beneath the entire backlog visible for the first time.
“The old model forced a trade-off—work the denials you can afford to, and write off the rest,” said Jim Bohnsack, Chief Strategy and Client Officer at Aspirion. “When technology takes the cost out of building each case, that trade-off goes away, and you can finally ask the more valuable question: not just how do we win this one, but why do we keep seeing it?”
The results of working this way show up in the numbers. Aspirion’s client data shows appeals filed 2.2 times faster, closures 1.4 times faster, and a 64% successful resolution rate on clinical denials.4
Denial volume is not decreasing, and neither is its complexity. The health systems that keep pace will not be the ones with the most staff to work appeals. They will be the ones that treat their denials as a pattern to act on, not just a worklist to clear.
Sources
1. Fierce Healthcare (reporting Kodiak Solutions’ March 2026 benchmarking analysis). “Despite better cash flow, providers missed out on more revenue in 2025 due to increased payer denials.” https://www.fiercehealthcare.com/finance/despite-better-cash-flow-providers-missed-out-more-revenue-2025-due-increased-payer-denials
2. Healthcare 150. “Revenue Cycle Management: Healthcare’s Next Margin Defense.” https://www.healthcare150.com/p/revenue-cycle-management-healthcare-s-next-margin-defense
3. Aspirion. “AI Clinical Denials Intelligence (ClinIQ).” https://www.aspirion.com/ai-clinical-denials-intelligence/
4. Aspirion. “AI-Powered Denials Management” (client performance data). https://www.aspirion.com/services/olympic-level-recovery-ai-denials-management
