Key Takeaways
- A medical claim scrubber reviews CPT codes, modifiers, diagnosis codes, and claim structure before submission to flag errors that lead to denials
- Different specialties produce different types of claim errors — the same pre-submission review process surfaces different issues depending on the claim context
- AI-powered claim scrubbers identify relationships between coding elements that rules-based tools often miss
- Catching errors before submission reduces rework, shortens reimbursement timelines, and improves clean-claim rates over time
- The PGM Medical Claim Scrubber allows billing teams to test claim scenarios in real time — without entering any patient information
Knowing that a claim scrubber exists is one thing. Understanding how it actually works — and what it catches — is where the real value becomes clear.
A medical claim scrubber reviews claim data before it is transmitted to the payer. It evaluates CPT codes, HCPCS codes, modifiers, diagnosis codes, and the relationships between those elements to identify issues that could trigger a denial, rejection, or delay. When a problem is flagged early, the billing team has the opportunity to correct it before the claim leaves the system.
The types of errors that surface depend on the claim. A physician office visit presents different coding risks than a colonoscopy or a laboratory panel. Walking through a few realistic scenarios illustrates how claim scrubbing works in practice and why it catches problems that might otherwise slip through.
What a Medical Claim Scrubber Actually Reviews
At the claim level, a scrubber reviews the rendering provider and place of service. At the line level, it evaluates each combination of:
- CPT, HCPCS, or J-codes identifying the service performed
- Modifiers that change how the code is interpreted by the payer
- ICD-10 diagnosis codes that establish medical necessity
- NDC entries for drug-related claims
- Units billed
What matters is not just whether each field is valid on its own, but how these elements interact with one another. That relationship between coding components is where most preventable denials originate, and it is what an AI-powered claim scrubber is built to evaluate.
Example 1: A Physician Office Visit With a Modifier Issue
A practice submits a claim for an established patient office visit (CPT 99214) performed on the same day as a procedure. The claim includes the E/M code and a minor surgical procedure, but modifier 25 — which indicates the evaluation and management service was significant and separately identifiable — has been omitted from the E/M code.
Without modifier 25, the payer is likely to bundle the E/M visit into the procedure and reimburse only the procedure. The result is a partial denial or a reduced payment that the practice may not catch until it reviews the explanation of benefits.
What the claim scrubber flags
An AI-powered claim scrubber recognizes the combination of an E/M code and a same-day procedure and identifies the missing modifier. The billing team sees the flag before submission, adds modifier 25, and the claim goes out clean.
Modifier errors are one of the most common denial drivers in physician billing and one of the most preventable. They are often invisible without a structured pre-submission review because each code may appear valid on its own — the conflict only becomes apparent when the codes are evaluated together.
Example 2: A Colonoscopy Claim With a Screening vs. Diagnostic Distinction
A gastroenterology practice submits a claim for a colonoscopy with biopsy. The procedure code is CPT 45380, which is correct for a diagnostic colonoscopy with biopsy. The ICD-10 code on the claim, however, reflects a routine screening indication.
This is a coding mismatch. A screening colonoscopy and a diagnostic colonoscopy are billed differently, and payers treat them differently — particularly under Medicare, where deductible and cost-sharing rules change based on how the procedure began. Submitting a diagnostic procedure code against a screening diagnosis creates a conflict that many payers will reject outright or flag for additional review. Note: For a closer look at how this distinction plays out in practice, see our post on colonoscopy billing.
What the claim scrubber flags
The claim scrubber identifies the mismatch between the procedure code and the diagnosis code. The billing team reviews the documentation, confirms whether the procedure began as a screening or was initiated diagnostically, and corrects either the procedure code or the diagnosis code to reflect the actual clinical picture.
In high-volume GI practices, this type of error can occur repeatedly across dozens of claims before anyone notices a pattern in the denial data. Pre-submission scrubbing catches it at the source.
Example 3: A Laboratory Claim With an NCCI Bundling Conflict
A laboratory submits a claim for a comprehensive metabolic panel (CPT 80053) alongside several individual chemistry tests that are already components of the panel. The individual codes — such as CPT 82565 for creatinine and CPT 84132 for potassium — are included in the panel by definition under the National Correct Coding Initiative (NCCI) bundling rules.
Billing the panel and its component codes together results in a bundling conflict. Payers will deny the component codes as included in the panel, creating rework and delaying payment on the entire claim.
What the claim scrubber flags
The claim scrubber identifies the NCCI bundling conflict between the panel code and the individual component codes. The billing team removes the redundant line items before submission, and the claim moves forward without a denial.
Bundling errors are particularly common in laboratory billing because of the overlap between panel codes and their component tests. A scrubber that evaluates code relationships — rather than just individual fields — is essential for catching these before they reach the payer.
Example 4: A Behavioral Health Claim With a Medical Necessity Mismatch
A behavioral health provider submits a claim for a 60-minute individual psychotherapy session (CPT 90837) with a diagnosis code that reflects an administrative or screening purpose rather than an active mental health condition. The ICD-10 code on the claim is a Z-code used for a routine mental health evaluation — not a diagnosis that typically supports ongoing treatment services.
Payers reviewing a claim for a 60-minute psychotherapy session will expect to see a diagnosis that reflects active, ongoing treatment necessity. An administrative or screening diagnosis paired with a treatment-level service code is a mismatch that can trigger a medical necessity denial.
What the claim scrubber flags
The claim scrubber identifies the mismatch between the procedure code and the diagnosis code, noting that the diagnosis does not support the level of service billed. The provider reviews the documentation, confirms the correct working diagnosis, and updates the claim before submission.
Diagnosis-to-procedure mismatches are among the most common — and most consequential — errors in behavioral health billing. They are also among the hardest to catch manually when staff are processing high claim volumes.
Try the PGM Medical Claim Scrubber
The examples above illustrate the kinds of issues a medical claim scrubber can identify across different specialties and claim types. Seeing it work in real time makes the value more concrete.
PGM Billing offers a live AI-powered medical claim scrubber that allows billing teams, practices, and laboratories to enter claim elements and receive immediate feedback — no patient information required. Try the medical claim scrubber to see how pre-submission validation applies to the claims your team handles every day.
If you are looking to reduce denials and improve clean-claim performance across your revenue cycle, PGM also provides full-service billing and revenue cycle management for physician practices and laboratories. Contact us to learn more!
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FAQs About Using a Medical Claim Scrubber
How does claim scrubbing work differently across specialties?
The pre-submission review process is the same, but the errors that surface vary by specialty. Physician billing tends to produce modifier conflicts and E/M bundling issues. GI billing frequently involves screening-versus-diagnostic mismatches. Laboratory billing is prone to NCCI bundling errors between panel codes and their components. Behavioral health claims often have diagnosis-to-procedure mismatches related to medical necessity. A scrubber evaluates the specific combinations present in each claim, so the output reflects the actual coding context rather than a generic checklist.
What is an NCCI bundling conflict?
The National Correct Coding Initiative (NCCI) is a CMS program that defines which procedure codes cannot be billed together because one is considered included in the other. When a claim includes both a panel code and individual component codes that are already part of that panel, the result is a bundling conflict. Payers will deny the component codes, and the claim requires correction before resubmission. An AI-powered claim scrubber identifies these conflicts automatically by evaluating code relationships rather than individual fields in isolation.
How do I know if my claim has a modifier error?
Modifier errors are not always obvious because each code may appear valid on its own — the problem only emerges when codes are evaluated in combination. A missing modifier 25, for example, will not trigger a format error; it creates a bundling issue that surfaces at the payer level. The most reliable way to identify modifier errors before submission is to run claims through a scrubber that analyzes how codes and modifiers interact, rather than checking fields individually.
Does a claim scrubber guarantee payment?
No. A claim scrubber identifies potential issues based on coding relationships and known denial patterns, but it cannot account for every payer-specific policy or eligibility situation. It significantly reduces the likelihood of avoidable denials, but it is one component of a broader revenue cycle management strategy.
Can a claim scrubber be used for laboratory billing?
Yes, and it is particularly valuable in that setting. Laboratory billing involves complex interactions between panel codes, component tests, NCCI bundling rules, and diagnosis-to-procedure pairing requirements. An AI-powered scrubber can identify bundling conflicts and medical necessity mismatches that are common in lab claim submissions and difficult to catch manually at volume.
How do I try PGM’s medical claim scrubber?
Try the PGM Medical Claim Scrubber on the PGM website. Enter CPT codes, modifiers, ICD-10 codes, and other claim elements to see pre-submission validation in action. No patient information is required, and feedback is immediate. It is designed to reflect real claim scenarios, so it works best when you test the types of claims your team actually submits.