Skip to main content

Denial Reduction: From 10%+ to Under 0.5%

By Sami Quazi, Founder & CEO, ClaimsRevenue™

Downward denial trend from above ten percent to below one percent with claim validation and ERA analysis workflow

TL;DR / Key takeaways

  • MoodRx internally reduced its denial rate from more than 10% to below 0.5% over roughly one year; this is an internal operating result, not an industry benchmark or independently audited study.
  • The improvement came from process discipline: basic claim validation, NUCC/CMS-1500 requirements, cross-field logic, CARC/RARC review, payer and credentialing checks, and eligibility verification.
  • The most important shift was treating each denial as a source of a future prevention rule rather than a one-time billing task.
  • Denial reduction required attention to both claim data and upstream workflows such as eligibility, payer configuration, and provider relationships.
  • The operating lessons from MoodRx became part of the product logic behind ClaimsRevenue’s ERA Analyzer and Claims Validator.

At MoodRx, our internal claim denial rate was once above 10%. Over roughly one year, we reduced it to below 0.5%. That result is an internal operating result from our own practice, not an independently audited benchmark and not a promise that another practice will achieve the same result.

What matters is how the improvement happened. It was not one magic billing rule. We progressively treated denials as structured feedback: identify the cause, correct the immediate claim, determine whether the cause could have been known earlier, and then add a process or validation control so the same problem was less likely to recur.

That operating experience is one of the reasons ClaimsRevenue exists.

What was wrong with the traditional denial workflow?

The traditional workflow is reactive:

  1. submit claim.
  2. wait.
  3. receive denial.
  4. research denial.
  5. correct or appeal.
  6. resubmit.
  7. move to the next denial.

That process can recover revenue, but it does not necessarily improve the next claim. If the cause is never converted into a rule or workflow change, the practice can spend months solving the same problem repeatedly.

We changed the question from “How do we fix this denial?” to “Why did we create this denial, and what can stop us from creating it again?”

Step 1: Validate the obvious claim fields first The first layer was basic claim hygiene. Before trying to build sophisticated denial intelligence, the practice needed consistent checks around the information required to submit professional claims.

That included items such as:

  • patient and subscriber data.
  • payer information.
  • billing and rendering provider identifiers.
  • dates of service.
  • diagnosis and procedure coding fields.
  • modifiers.
  • place of service.
  • units.
  • authorization/referral information when applicable.

The NUCC 1500 Claim Form Reference Instruction Manual is an important baseline for professional claim data, and the NUCC also publishes a map between the 1500 form and the X12 837P transaction.

Basic completeness did not eliminate every denial, but it removed noise and made the remaining denial patterns easier to interpret.

Step 2: Move from field validation to relationship validation A populated field can still be wrong.

Examples:

  • a valid NPI may not be enrolled under the billed TIN.
  • a valid CPT code may conflict with another code under an NCCI edit.
  • a valid authorization number may not apply to the billed service.
  • a valid member ID may belong to inactive coverage on the date of service.
  • a valid place of service may not fit the actual service or payer rule.

This was a major conceptual shift. The problem was not only missing data. It was the relationship among fields.

That logic is now central to the Claims Validator.

Step 3: Read CARCs and RARCs as operating data The 835 ERA became more than a payment-posting file. We reviewed the adjustment reason information to understand why claims were not paid as expected.

X12 CARCs tell you why a claim or service line was paid differently than billed. RARCs add detail. Group codes indicate adjustment categories such as contractual obligation or patient responsibility.

This matters because not every adjustment is a denial. For example, CARC 45 can represent a contractual reduction from a billed charge to an allowed amount. Counting every adjustment as a denial would produce the wrong picture.

We wanted to know the actual denial causes, not just the total adjustment dollars.

That operating concept became the basis for the ERA Analyzer.

Step 4: Classify denials by root cause Instead of maintaining one generic denial bucket, we looked at categories such as:

  • missing/invalid claim information.
  • eligibility.
  • authorization/referral.
  • provider enrollment/credentialing.
  • coding/edit conflicts.
  • duplicate claims.
  • timely filing.
  • payer configuration.
  • patient responsibility or contractual adjustments that should not be treated as denials.

The goal was to identify repeatable defects.

If three denials share the same root cause, they may be three claims but only one process problem.

Step 5: Connect the denial to the original claim A denial code by itself is not enough. We needed to compare the payer response back to the claim that produced it.

That meant looking at dimensions such as:

  • payer.
  • provider.
  • CPT/HCPCS code.
  • modifier.
  • diagnosis.
  • place of service.
  • date.
  • authorization information.
  • other claim fields.

This is where patterns emerge. A denial that appears random across the whole practice may be highly concentrated in one payer, one provider, one code pair, or one workflow.

Step 6: Turn repeated denials into pre-submission rules This was the biggest improvement.

For each recurring denial, we asked:

  1. Could this condition have been known before submission?
  2. What data would have revealed it?
  3. Is the rule universal or payer-specific?
  4. Should the system block the claim, warn the user, or simply flag it for review?
  5. What evidence would tell us whether the rule worked?

Examples of potential rules include:

  • likely duplicate claim exists for the same patient/date/provider/service.
  • claim is approaching a payer filing deadline.
  • authorization does not match the billed service or date.
  • provider is not configured as active under the billed payer/TIN relationship.
  • CPT/HCPCS code pair triggers an applicable PTP edit.
  • units exceed a public MUE threshold and require review; or
  • a required payer-specific data element is missing.

Not every denial can or should become a hard rule. Some are too dependent on clinical facts or benefit design. In those cases, a warning may be more appropriate.

Step 7: Include eligibility and payer configuration in denial reduction A claim can be perfectly formatted and still go to the wrong payer or reflect stale coverage. We therefore treated eligibility and payer setup as part of denial prevention rather than as unrelated front-desk tasks.

That meant paying closer attention to:

  • current coverage.
  • payer identifiers and aliases.
  • primary/secondary order.
  • payer-specific requirements.
  • provider-payer relationships.

This is why denial reduction is not only the billing department’s responsibility. Registration, scheduling, credentialing, clinical documentation, and billing all influence the final claim.

Step 8: Measure recurrence, not just recovery A denial team can look productive because it recovers money. But if the same denial keeps returning, the process is still defective.

The better measures include:

  • denial rate by root cause.
  • denial dollars by root cause.
  • repeat denial frequency.
  • time to resolution.
  • recovered dollars.
  • percentage of denials classified as prospectively preventable.
  • recurrence after a new rule or process control is implemented.

The final measure is critical. It tells you whether your prevention work changed behavior.

What did we learn from reducing denials?

Three lessons shaped ClaimsRevenue.

First: denial data is valuable only if it changes the next claim

An ERA can tell you what happened. The real value comes when you use that information to improve future submissions.

Second: payer-specific history matters

Practices have unique payer mixes, specialties, providers, workflows, and coding patterns. Their denial histories therefore contain practice-specific intelligence.

Third: clinicians should not have to become billing experts

We think about ClaimsRevenue as TurboTax® for Medical Claims. TurboTax did not simplify the tax code; it made a complex tax system easier for individuals and small businesses to navigate without becoming tax experts. Medical claims have a similar problem: clinicians should be able to focus on patient care while software helps identify preventable billing errors and denial risk.

TurboTax® is a registered trademark of Intuit Inc. ClaimsRevenue is not affiliated with, endorsed by, or sponsored by Intuit.

What this case study does not prove The MoodRx result should be interpreted carefully.

  • It is one practice’s internal operating result.
  • It was achieved over roughly one year.
  • It has not been presented here as an independently audited clinical or economic study.
  • It does not establish that every practice will achieve the same denial rate.
  • Different specialties, payers, claim volumes, provider models, and workflows can produce different outcomes.

The useful evidence is the operating method: learn from denials, classify root causes, validate earlier, and measure recurrence.

How can another practice start?

Start with the last 3-6 months of ERA data and answer five questions:

  1. What are the top denial CARC/RARC combinations by count?
  2. What are the top denial categories by dollars?
  3. Which payers and providers are driving them?
  4. Which causes could have been detected before submission?
  5. Which three rules would prevent the most repeated rework?

You do not need to solve every denial at once. Start with high-frequency, high-dollar, and highly preventable categories.

ClaimsRevenue is built around that operating loop. Learn more about the ERA Analyzer, Claims Validator, and pricing.

FAQ

Did MoodRx really reduce denials below 0.5%?

ClaimsRevenue leadership reports that MoodRx’s internal denial rate declined from more than 10% to below 0.5% over roughly one year. This is presented as an internal operating result, not an independently audited benchmark.

What was the biggest change?

The biggest process change was using denials as feedback for future prevention rather than treating each denial as an isolated billing task.

Did one software rule create the improvement?

No. The process involved basic claim validation, NUCC requirements, cross-field logic, ERA analysis, payer and credentialing review, eligibility checks, and iterative rule building.

Can every denial be prevented?

No. Some denials depend on coverage, clinical facts, payer review, or information not available before submission. The goal is to eliminate repeatable and detectable causes.

What should a practice analyze first?

Start with recent ERA data, rank denials by count and dollars, identify recurring CARC/RARC patterns, and determine which causes can become pre-submission checks.

Authoritative sources