There's a structural imbalance sitting at the center of healthcare revenue cycle that almost nobody talks about directly. Payers -- commercial insurers and Medicare Advantage plans especially -- have spent years and significant capital building automated adjudication systems that flag and deny claims at machine speed. The logic is deliberate: a denied claim that is never appealed is effectively free revenue. The math works for them because most hospitals don't have the operational infrastructure to appeal systematically, and because the volume of denials is simply overwhelming to work manually.
What providers are doing in response is largely reactive. A claim goes out. Days or weeks later, a denial comes back through the clearinghouse. A staff member -- already working a backlog of hundreds of accounts -- picks it up, researches the reason code, determines whether it's worth appealing, documents the appeal, submits it, and waits. McKinsey estimates this rework costs between $25 and $118 per denied claim. Across a health system processing thousands of claims weekly, that's a significant operational tax on revenue that was never really lost -- just delayed or abandoned.
$25-$118
Rework cost per denied claim (McKinsey)
60%
Of denials are never appealed
Milliseconds
Time for payer AI to deny a claim vs. days for provider response
The standard RCM workflow maps against the HFMA framework across three phases -- front end (pre-service), middle (point of service), and back end (post-service). Most of the AI tools on the market today live in the back end. By the time work reaches the back end, the clinical and documentation decisions that determined whether the claim was clean or not have already been made -- and cannot be undone efficiently.
Where the intelligence layer needs to live
Front end
Pre-service
Middle
Point of service
Back end
Post-service
Upstream intelligence layer
Proactive — before claims are filed · Front end + Middle
Where most tools focus
Reactive — post-claim
Clearinghouse / remittance data
Claims, 835 ERA, denial codes (CARC/RARC) · feeds back into the intelligence layer
HFMA Revenue Cycle Framework · Digital Scientists · digitalscientists.com/healthcare/domains/revenue-cycle-ai/
Reactive vs. proactive -- the operational difference
Today — most providers
Reactive revenue cycle
Claim filed based on available documentation
No payer-specific pre-check
Payer auto-adjudication reviews and denies
Algorithmic denial — often in seconds
Denial received — added to the queue
$25–$118 rework cost per denied claim
Staff manually works the denial backlog
60% of denials are never appealed
Some revenue recovered — weeks later
Root cause repeats on the next claim
Payer advantage
Payer AI denies in milliseconds. Provider teams respond manually, days later, on a fraction of denied claims.
With upstream intelligence
Proactive revenue cycle
Payer-specific risk flagged at point of care
Before documentation is finalized
Coding and CDI guidance surfaced in-workflow
Provider-specific patterns, not generic alerts
Claim pre-checked against live payer rules
Denial prediction before submission
Clean claim submitted — paid first pass
Higher clean claim rate, lower cost to collect
Every outcome refines the model
Clearinghouse data feeds back continuously
Provider advantage
AI learns your payer's denial patterns and CMS rules — applied upstream with the same rigor payers use.
Payers have industrialized denial. Providers need to industrialize the response.
A payer's adjudication system can review a claim, apply thousands of plan-level rules, and generate a denial in milliseconds. It doesn't get tired. It doesn't have a backlog. It fires consistently, at scale, knowing that the economics of non-response favor the payer. The response has to be upstream and systematic, not downstream and manual.
In our work building revenue cycle AI systems, the pattern is consistent. The organizations that move from reactive to proactive close three gaps simultaneously: they connect clinical documentation to financial outcomes in real time; they train models on their specific payer behavior -- not industry averages; and they ingest every clearinghouse and remittance result as training data so the model gets smarter with every claim. Closing those gaps is a healthcare app development problem as much as a modeling problem: the intelligence has to be embedded in the clinical and billing systems people already use.
That last point is where clearinghouse integrations become genuinely strategic rather than just operational. The remittance files, the CARC and RARC denial codes, the claim status responses -- all of this is rich signal data that most organizations use only to work the immediate denial. A payer-aware intelligence layer treats every one of those outcomes as a learning event. A denial from a specific Medicare Advantage plan on a specific procedure code should immediately update the model's prediction for every future claim with similar characteristics. Over time, the organization knows its payers' denial behavior better than its payers expect.
The payvider shift: making clinical workflows payer-aware
There's a term becoming increasingly relevant in value-based care: payvider. It describes organizations that function as both payer and provider. But the concept applies more broadly. Any provider operating in a value-based or risk-based contract needs to understand the payer perspective from the inside -- what their patient population's risk profile looks like, where documentation gaps create HCC coding misses, and how prior authorization patterns vary by plan in ways that affect utilization and cost. That's not billing knowledge -- it's clinical workflow knowledge that has direct financial implications.
The question isn't whether to make clinical workflows payer-aware. In a value-based world, it's whether your providers have the information they need to make decisions that serve both the patient and the organization. Right now, most don't.
We've seen this play out directly in RAF and HCC coding work for Medicare Advantage populations. The issue isn't that clinicians are coding incorrectly -- it's that chronic conditions are documented in some encounters and not others, that medications appear without supporting diagnoses, that a patient who was an amputee last year isn't reflected as one this year because the relevant note wasn't pulled forward. When we built upstream intelligence for a multi-state Medicare Advantage population, the result was $12 million in recurring annual revenue with 98 percent clinician adoption -- because the tool made their job easier rather than harder.
$12M
Recurring annual revenue from upstream RAF/HCC coding intelligence
98%
Clinician adoption rate -- because it made their job easier
140+
Facilities where originating site fees were being missed silently
Through our work with NeverAlone — a virtual care command center platform serving post-acute skilled nursing facilities — we've seen this gap play out directly. When a SNF originates a Medicare telehealth encounter, CMS allows the facility to bill a separate originating site fee. It's not large per encounter, but across 140+ locations and thousands of monthly encounters, it's meaningful revenue that offsets the cost of virtual care delivery.
Most facilities don't bill it — not because they're making a deliberate choice, but because keeping up with CMS policy changes is a full-time job most lean post-acute teams can't staff. CMS updated telehealth flexibilities as recently as February 2026, with significant rule changes taking effect January 1, 2028. An intelligence layer that tracks and operationalizes those updates automatically captures revenue that would otherwise be lost silently.
Source: CMS Telehealth FAQ, updated 2/26/26 · NeverAlone virtual care platform: neveralone.com
What this means for technology partners and tool builders
If you're building RCM tools -- clearinghouse integrations, prior auth platforms, CDI software, coding assistance tools -- the upstream opportunity is where the next generation of value will be created. The back-end RCM market is increasingly commoditized. The organizations that will build durable differentiation are the ones that can connect clinical workflow data to financial outcomes in a way that learns continuously from what the clearinghouse reports back.
Building that capability requires production experience with EHR integrations that don't disrupt clinical flow, clearinghouse data pipelines that treat remittance files as training data, and models specific enough to be actionable at the plan level. The tools that win will be the ones that make providers proactive, not just more efficient at being reactive.