For revenue cycle leaders, few issues create more friction—or more cost—than claim denials. They disrupt cash flow, inflate administrative overhead and delay payments that are already overdue. And while denial management strategies have improved, they remain mostly reactive.
What if you could identify denials before they ever happen?
That’s the promise of AI predictive denials—a next-generation approach powered by artificial intelligence and machine learning that enables healthcare organizations to intercept at-risk claims before submission.
The Cost of Reactive Denial Management
Claim denials don’t just slow down reimbursements—they multiply manual effort. Every denied claim represents hours of rework, coordination and appeals. According to industry estimates, the average cost to rework a denied claim ranges from $25 to $118. Multiply that across thousands of denials per month and the financial impact becomes clear.
Even high-performing organizations with strong clean claim rates face challenges:
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Clean doesn’t mean denial-proof: Many “clean”claims still get denied during adjudication.
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Line-level complexity is rising: More denials are tied to individual line items rather than whole claims.
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Manual intervention is expensive: It takes time—and staff—to correct and resubmit denials.
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Delays affect everything: Days in A/R stretch out. Patient bills are delayed. Cash flow slows.
The traditional model of denial management—fixing what’s already failed—is no longer enough.
RELATED: How to Reverse the Trend on Rising Denials and Underpayments at Your Organization
How Predictive Denials Work
Predictive denials use AI to analyze historical claim data, payer behavior and patterns in past denials to score and flag claims before they are submitted. This happens in real time and at both the claim and line level.
Here’s how it works in practice:
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Historical learning: AI models are trained on thousands of real-world denials across multiple payers and scenarios.
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Claim-level risk scoring: Claims are flagged for potential denial based on known patterns—before submission.
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Line-item detection: Denials tied to specific codes, modifiers or documentation issues can be identified proactively.
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Actionable insights: Teams are given guidance on what to correct or clarify before sending the claim to the payer.
This kind of proactive intelligence creates an opportunity to intervene earlier, reduce friction and increase the likelihood of payment on the first pass.
The Financial Upside: Metrics That Matter
For healthcare revenue cycle leaders, predictive denials align directly with the outcomes that matter most:
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Lower denial and rejection rates = fewer delays and appeals
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Higher clean claim integrity = more claims paid on first submission
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Faster days in A/R = better liquidity and cash flow
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Reduced manual work = fewer FTE hours spent chasing preventable issues
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Improved payer relationships = more consistent, compliant claims
This approach doesn’t just streamline claims—it strategically improves financial and operational performance.
What’s Next: AI Predictive Denials in FinThrive Claims Manager
Recognizing the impact of predictive technology, FinThrive is bringing AI Predictive Denials to Claims Manager in early 2026.
This new feature will empower health systems to:
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Score denial risk in real-time, before claims are submitted
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Flag issues at both the claim and line-item level
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Receive payer-specific recommendations for edits
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Reduce manual rework and accelerate reimbursement
As the claims landscape grows more complex, forward-thinking organizations will move beyond reacting to denials—and start preventing them.
