Payer-specific intelligence that equips hospitals to prevent adverse payment outcomes (denials, underpayments, takebacks, downgrades) and protect net revenue.
Milwaukee, WI
Joined August 2018
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1) Most of what health systsems and hospitals call denial management is really denial maintenance;
2) denial management and EHR tools were built for a payer environment that no longer exists, and the vendors selling them are priced to keep the problem manageable rather than fully fix it;
3) for health systems, #AI changes the question from "how do we work denials faster" to "how do we stop generating them"
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That payer-reported payer report (eye roll) on how hospital AI "coding intensity" cost Blue plans $942M over two years is also a map of upcoming DRG downgrades.
Most hospital revenue cycle teams already have *some* AI tools. Copilot likely came bundled with your Microsoft 365 agreement, and for many teams, it's been sitting there, mostly idle.
We wrote a prompt guide to get your team started. 20 prompts you can run in Copilot (Excel, Word, PPT and chat) against the denial exports and AR files your team already pulls: denial concentration by payer, appeal letters, CFO briefings, payer negotiation prep.
The guide also helps identify where general AI tools stop reading your denial data, and where deeper Payments Intelligence (predictions, scoring, prevention, actionability) begins. As payers lean harder on AI to deny, downgrade, and delay, this Payments Intelligence layer is turning from nice-to-have into necessary. But, start with the prompt guide first.
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It's absolutely crazy that payer policy changes show up as lost revenue before you know about them. Or that new downgrade patterns emerge after months of missed reimbursement. Or takebacks come 1+ years after reimbursement.
Health systems are losing $$ month after month without real payments intelligence. The data is available, but it does take real work (joining clinical inputs to financial outcomes) and real modeling to get an actionable view.
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Plenty of hospital revenue cycle teams feel they've got denials analytics handled. The dashboards are there, sorted by payer and reason code, updated daily. But... Knowing a claim denied isn't knowing *why* it denied. And knowing why isn't *fixing* it in time. Reports don't recover revenue.
The work that actually improves net revenue is getting to root cause, turning it into a specific action, and executing before a claim is submitted.
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Payers are using AI to downgrade claims at scale. Hospital teams are using best-practice PDFs to stop them...
When you stress-test AI models across 700+ DRGs and ask it for denial-prevention recommendations, sometimes the results get pretty imaginative, think hallucinated ICD-10 descriptions and invented length-of-stay numbers.
That's the problem with generic denial intelligence. It treats every DRG like the average DRG. Payers don't do that. They downgrade specific codes in specific ways. We did the painstaking work of building DRG-specific playbooks that cover the real denial pathways, clinical indicators, documentation flags, and recommendation criteria. We have 320+ MS-DRG playbooks covering 770+ MS-DRGs inside RevProtect's Actionability Engine.
Payers already built this depth into the AI that flags and downgrades at scale, but providers are mostly working off "best-practice" advice. These playbooks are part of how Sift closes that gap to drive real denials prevention for health systems.
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Payer takebacks hit well after claims adjudicate, months later. The median is ~170 days out, with a tail past 500. This is after revenue cycle teams have reported the period as collected.
A recoupment that surfaces 200+ days after service changes a net collection rate nd can re-age a resolved account back into AR>90.
Takebacks don't show up like traditional denials. These claims were paid, then partially unpaid.
Sift's Denials Insights Team put together a Deep Dive on post-payment takebacks, covering how the lag distorts metrics and how to surface, recover, and forecast them (including the out-of-window recoupments).
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"Not having an AI strategy is still a form of AI strategy."
In the #revenuecycle this sounds like, "it's on the EHR's roadmap." Most revenue cycle leaders acknowledge AI belongs in their workflows. The harder question is what counts, and what actually delivers results. Because the easy, EHR has it covered version is everywhere. It feels like the answer or progress, but a plan to have a plan isn't adoption, and the gap it leaves is growing.
While providers are planning, payers are running AI at full speed, and denials (+ downgrades, takebacks and underpayments) keep accelerating. Claims are coming back in patterns that shift faster than manual processes or even rules-based systems can manage. Every quarter spent on AI planning (or roadmap waiting), instead of real capability, is $$$$ lost, not saved.
Doing nothing, or doing the performative version of something, is the genuinely expensive path.
Health systems making progress with AI have clarity and urgency. They can tell you exactly which Adverse Payment Outcomes they're targeting, which payers, and which claim types. Then they back it with real AI solutions that hold up against payers who are getting more sophisticated all the time.
The gap doesn't close by having a plan to make a plan. "On the EHR roadmap" doesn't recover a dollar. Knowing what you're solving, and putting something real against it, does.
(Cartoon and opening quote by the always on-point Tom Fishburne the @marketoonist)
"The payers are winning."
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Every hospital has a (painful) number it never sees, the gap between what it could (should) collect on the care it delivered and what it actually keeps after the payers are "done".
This is the capture gap. It matters even more than "denials" because a denial is one event you can see. The capture gap is the full range of adverse payment outcomes (DRG downgrades, post-payment takebacks, and underpayments).
And the capture gap never closes on its own...
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A deep dive on Aetna Medicare underpayments. Inpatient claims are getting paid at observation-level rates and signaled with a remit combination that misses standard denial workflows.
Aetna provides a clear and timely example of hidden revenue loss for hospitals. It highlights a whole category of loss that includes underpayments, DRG downgrades, and takebacks.
These don't cleanly fit into the definition of "denial" so they're incredibly easy to miss. We put the full breakdown into a one-page deep dive with the exact remit combination and five actions your team can take right now address these...
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Denial appeal letter generation an AI use case everyone is selling right now, because LLMs are good at writing appeals (when they don't make things up).
But appeal automation doesn't change what happens next month. An appeal works one claim at a time. The edit that produced the denial is still live, the policy behind it still isn't in your contract, and the fastest-growing categories don't generate a denial to appeal in the first place (downgrades, underpayments, takebacks).
Automate the appeals, but don't stop there. Your 835 and 837 history shows which payers treat you differently than their peers and differently than your contract defines, and that's what changes reimbursement risk and adverse payment outcomes.
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A hospital can improve clinical documentation and still see its DRG downgrades rate stay flat, or even increase. Denial and DRG downgrade rates reflect payer behavior as much as provider performance. When one target becomes harder to challenge, payers can move the pressure somewhere else, like from sepsis to respiratory failure, encephalopathy or level of care.
A more useful measure, though harder to calculate, is the payment difference between clinically comparable cases. When two comparable cases meet the same clinical criteria but one is paid and one is downgraded, the difference may not be documentation but payer behavior.
A documentation problem, a payer-specific payment pattern and an inefficient appeal strategy need different responses. Denial rate alone can’t tell you which problem you have.
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Revenue cycle isn't what's driving thin and negative hospital margins. But it's the most effective lever for hospital CFOs.
Margin pressure comes from payer mix, labor, and capacity. None of those move quickly. Payer contracting runs on annual or multi-year cycles. Labor structure is a multi-year problem and often a union negotiation. Service line rationalization takes 18 months.
Revenue cycle yield is the one thing a CFO can change in two quarters. And so many systems miss the low-hanging fruit:
1) Denials get worked by $$ or age, so teams spend the same time/touches on a claim with a 15% chance of overturning as on one with an 80% chance.
2) Health systems are appealing claims they'll lose and writing off claims that could have been clean. Sift's RevProtect Payments Intelligence predicts denials and overturns. With workflow-integrated alerts and recommendations to prevent/address.
None of this asks for headcount or structural changes. It puts predictions upstream of the work, so fewer claims are denied in the first place, and the ones that do get handled based on what's actually worth pursuing.
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Payers use AI to scan charts for claims to deny, so hospitals scan the same charts first. This is how you get to "bots fighting bots."
Payers say they're getting a wave of appeals with "LLM fingerprints" on them, and a director from the @Peterson_HTI said both sides keep going extra rounds because it's cheap.
Cheap for who? For a hospital, every round is unrealized revenue, plus staff time spent chasing it. Health systems don't need to win more rounds, they need fewer rounds. That means knowing which claims a payer is likely to challenge before they go out, and which denials are worth appealing once they come back.
An appeal you were going to lose (or have to spend multiple rounds/investigations/touches on), sent faster, is still an appeal you lost.
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This is the same structure that produces a DRG downgrade, just pointed at a different line of business. A reviewer applies criteria a clinical team never had access to to a chart they finished last month, and what the hospital gets paid follows from the criteria rather than from the care.
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The last time "coding intensity" rose this fast, CMS cut Medicare inpatient rates for everyone. After the move to MS-DRGs in 2008, hospitals got better at documenting and coding under the new system. CMS concluded that case mix had risen without patients getting any sicker. Congress had CMS recoup $11B through across-the-board inpatient rate cuts from 2014 to 2017, and hospitals argue they never got the full 3.9% back.
This week BCBSA called out $942M on AI-driven coding intensity. @DrOzCMS said AI will be inflationary in the short term because it makes billing systems work better. When the payers and the regulator describe the same problem in the same week, expect a policy response to follow.
The uncomfortable part -- a rate adjustment can't tell the hospital whose AI captured real, documented acuity from the one that drifted. Everyone takes the cut. The health systems that come through it well will be the ones that can show, encounter by encounter, that what they billed is what the record supports. They'll also know which payments are at risk before the payer comes back for them.
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The AI models that actually predict revenue risk and prevent adverse payment outcomes work like very disciplined analysts who never guess. ..
This exemplifies the AI back-and-forth between payers and providers. Payers have been denying and downgrading more aggressively, and providers have responded with AI coding tools. BCBSA now says those tools have pushed inpatient claims into higher-severity DRGs with no change in the care delivered.
For years, coders missed conditions that physicians documented but that never made it onto the claim. When an AI tool catches a documented diagnosis a human would have skipped, that's arguably payment the hospital was always owed.
Payers will counter this “coding intensity” with increased post-payment takebacks, that will hit months or years after reimbursement. For health systems revenue risk sits in the space between "the hospital coded what was documented" and "the payer doesn't believe the documentation."
Coding less won't close that gap. Knowing which encounters a payer is likely to challenge, while there's still time to fix the record, will.
fiercehealthcare.com/finance…
Pondering... How will payers respond to the ongoing spike in utilization rates? Will there be a downstream effect -- increasing denial rates at health systems and hospitals?
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