Healthcare organizations don’t struggle with denials because they lack effort. They struggle because denials are treated like a queue to clear instead of a system to understand. When teams work only at the surface level-reacting to payer responses and denial codes they can reduce backlog temporarily while the same operational breakdowns continue driving new denials.
The solution is a different kind of workflow: one that connects financial impact, operational root cause, and cross-functional accountability into a single, closed-loop process.
As one RCM leader put it, “Denial codes alone rarely identify the root cause.” That single idea—moving from symptom to driver—is the backbone of a modern “Denials Architecture” approach.
In this 60-minute working session, Joyful Health's Head of RCM, Becky Carlson, joined by Director of Partnerships Josh Koch, walk healthcare finance and revenue cycle leaders through the framework our team uses to turn denial data into financial clarity and operational action. Below is a look at what the session covers. The full replay is available at the bottom.
Why Denials Feel Unmanageable (Even When You Have Dashboards)
Most organizations can see that denials exist. The more difficult question is why the same denial categories keep repeating and what operational failures are actually causing them.
It’s common to have:
- Denials dashboards (“we have visibility!”)
- AR reporting (“we can see what’s outstanding”)
- Payer trend analysis (“UnitedHealthcare is worse this month”)
- Operational metrics like throughput and net collection rate
But many leaders experience what one participant described as “dashboard fatigue” - lots of information, little clarity on what to do next.
The underlying issue is fragmentation. Finance often focuses on: revenue variance, cash flow disruption, and margin pressure. RCM teams focus on tactical throughput: denial volume, aging AR, appeals backlog, and work queues. Operations focuses on: workflow inefficiencies, documentation gaps, staffing constraints, and consistency.
These teams see different symptoms of the same revenue leakage problem, but rarely connect them. So denial management becomes reactive: each function points to the next, ownership stays unclear, and root causes remain unsolved.
The result? Denials become an “appeals factory” problem instead of an operational design problem.
Define Revenue Leakage (And Stop Treating Every Denial The Same)
To manage denials effectively, you need a shared definition of the problem. A useful starting point is revenue leakage, which includes multiple failure modes, not just denied claims.
Here are four practical types:
1) Out-and-out lost revenue
This is revenue you’re unlikely to recover. Examples include:
- Timely filing denials
- Patients without active coverage
- Situations where the decision to bill the patient is often owned by the practice
2) Delayed revenue
This revenue is recoverable, but it impacts cash flow and aging AR. Examples include:
- Denials that are recoverable via appeals
- Backlog-induced slowdowns (work queues)
3) Preventable leakage
Here, revenue is lost because of an operational breakdown that could have been prevented earlier:
- Eligibility mismatches
- Scheduling a patient with a provider who isn’t in-network
- Front-end verification failures
4) Administrative waste
This is effort spent on low-value or repetitive denial rework. A key KPI is cost to collect: how much labor you spend to recover money that should not have required that much time.
One of the most important implications: “All denials” is not a strategy. Revenue leakage types require different actions.
Featured Snippet: How to Prioritize Denial Work (Beyond Denial Rate)
Denial rate is helpful, but incomplete. Two organizations can have similar denial rates while experiencing dramatically different financial outcomes.
Instead, prioritize using a layered model:
- Dollar value at risk
What is the expected allowed amount hanging in the denials bucket?
- Aging/timely filing risk
How long do you have to act before the revenue becomes unrecoverable?
- Recoverability likelihood
Be honest: is it worth the time to appeal this?
- Volume and recurrence
How frequently does this issue happen (e.g., payer/provider/system patterns)?
- Operational effort (cost to resolve)
How many labor hours will it take, and what is your cost to collect?
This model prevents a common failure mode: over-investing in high-volume denials that are low dollar and low recoverability while under-investing in fewer issues with far larger financial impact.
Why Denial Rates Mislead Leadership (A Data-Driven Example)
Denial rate alone doesn’t tell you how much cash you’re losing, how long it’s tied up, or whether appeals will work.
Consider two organizations:
- Organization A: 8% denial rate with $450,000 at risk, 42 days aging AR, and a high likelihood of recovery.
- Organization B: 5% denial rate (better on paper), but $2.5M outstanding, 118 days AR, and low recoverability.
Move From Symptoms to Root Causes: The “Denials Architecture” Model
The core shift is from working denial queues to diagnosing denial systems. The framework uses three layers:
Layer 1: Surface layer (payer symptom)
Surface-level reporting tells you what the payer rejected, but not why it entered your claim process that way.
This is what your systems show first:
- Denial codes (e.g., CO16 missing information)
- Payer response messages
- CARC/RARC reasons
- Appeal activity
Layer 2: Structural layer (operational breakdown)
A denial may appear at the payer level, but the contributing failures often originate earlier in the revenue cycle.
This is where operational workflow failures happen upstream:
- Intake and registration workflows
- Authorization processes
- Eligibility verification timing
- Documentation workflows
- Charge capture and coding processes
- Claim submission/billing workflows
Layer 3: Root cause layer (systemic drivers)
When teams focus at the root cause level, denial recurrence decreases, time to resolution improves, and revenue recovery becomes more sustainable.
Root causes are systemic conditions that cause recurring operational breakdowns:
- Workflow design gaps
- Ownership ambiguity
- Training inconsistencies
- System configuration problems
- Policy variability
- Payer-specific requirements and nuance
A Critical Warning: CO16 Can Be “15 Denials Wearing a Trench Coat”
One of the sharpest insights from the discussion is that identical denial codes can come from entirely different operational issues. If you stop at the denial code, you group unrelated issues together, assign the wrong owner, and fail to correct the actual drivers.
For example, two claims may both show CO16 (missing/incomplete information), but the underlying causes could differ:
- Missing provider taxonomy due to enrollment errors (credentialing/billing configuration owner)
- Coding workflow omissions (coding/billing owner)
- Provider documentation timing and data mapping differences (clinical workflow owner)
Structured Denial Categorization: Domain → Failure Type → Root Cause
To translate diagnosis into action, organizations need structured categorization instead of broad buckets.
Many teams categorize using payer responses like:
- “Authorization denials”
- “Eligibility denials”
- “Coding denials”
But these buckets are often too broad to operationalize. “Authorization” doesn’t tell you whether the issue is missing authorization, expired authorization, or mismatch.
A stronger model uses a three-part hierarchy:
- Domain (operational area)
Examples: authorization, eligibility, coding, documentation
- Failure type (the nature of the breakdown)
Examples: missing, invalid, mismatch, expired
- Root cause driver (systemic why)
Examples: training gap, workflow design, ownership ambiguity, system configuration issue
This produces categories that are:
- Consistent
- Action-oriented
- Clear on ownership
- Designed for financial prioritization
It also reduces “naming convention chaos,” where different teams interpret the same label differently.
Who Owns What? (Because Accountability Is Usually The Bottleneck)
A structured framework only works if the right owners receive the right insights. The key is ownership aligned with “jobs to be done,” not with where the denial was noticed.
Common ownership patterns (across many organizations) are:
- Eligibility verification → front desk / intake team
- Authorization management → authorization team
- Documentation deficiencies → clinical operations / clinical teams
- Coding/modifier errors → coding or billing/charge entry teams
- Enrollment/taxonomy issues → credentialing and billing configuration teams
- Claim submission errors → billing team
Featured Snippet: Translate Root Cause Insights Into Operational Action
This is how denial management stops being repetitive and becomes preventive. Once you know the denial patterns and their drivers, the next step is translation: turning insight into specific operational changes.
Use a simple mapping logic:
- High eligibility denial volume → adjust verification timing and intake workflows
- Recurring modifier denial patterns → implement coding review/checklists; clarify coding responsibility
- Authorization mismatches → standardize intake and authorization escalation; confirm timing and responsibilities
- Timely filing issues → redesign claim submission/escalation workflow; address provider note turnaround expectations
- Credentialing-related denials → improve payer enrollment monitoring; confirm provider enrollment before visits
Closed-Loop Denials Management: Measure What Changed
This is where organizations shift from “working overtime” to continuous improvement. Even strong frameworks fail without measurement. Denial rate, denial volume, and AR volume are not enough.
Mature organizations track metrics such as:
- Repeat denial rate by category
How many denials come back after you “fixed” them?
- Time to resolution
Are you resolving in days, not months?
- Recoverability rate
What portion is realistically paid versus sunk-cost appeal time?
- Preventable denial rate
How many denials should not have occurred with improved front-end workflow?
- Dollars recovered vs. dollars prevented
Did actions actually improve financial outcomes, not just reporting?
What Mature Denials Programs Look Like
Mature programs focus on recurrence reduction, not just backlog reduction.
Across the discussion, a clear maturity model emerged:
- Denial trends link to financial impact and operational risk
- Root causes are identified beyond the surface symptom layer
- Operational ownership is explicitly defined
- Preventable denial patterns are monitored—not ignored
- Corrective actions are tested for effectiveness
- RCM, finance, and operations work as true partners
Final Takeaways: Your Next Denials Move
Because denials don’t have to be an endless appeals cycle. With the right architecture, they become a feedback system—one that helps you build a healthier revenue cycle over time.
If your organization wants denials to become manageable, start with three commitments:
- Categorize structurally, not just by denial codes or payer messages
- Prioritize financially, using dollar impact, aging/timely filing risk, recoverability, recurrence, and operational effort
- Close the loop, translating root cause insights into workflow changes and measuring repeat outcomes
