Claim scrubber checklist: 12 questions to ask

A claim scrubber can mean anything from a form that checks whether a field is empty to a system that evaluates a claim against current payment-rule data. Those are both useful, but they are not the same purchase. A sensible evaluation begins by naming the denials you want to prevent and asking the vendor to show exactly how its result is sourced, dated and handled when the source has no answer. Glossy pass/fail badges are not enough when a claim line contains protected information and a payer can change its rules next quarter.

Ask what the verdict is based on

For NCCI and MUE checks, CMS publishes official resources and updates relevant files over time. A credible tool should identify the source version, effective date and scope of a result. It should also distinguish an actual pass from no data or a check outside its coverage. A clean-looking green badge without a source is not a rule explanation; it is an unsupported product assertion.

Evaluate the response, not the demo

Use a small set of realistic non-PHI test cases: a pair that should trigger an edit, a unit count that should be reviewed and a code with no available dataset match. Check whether the tool says what it knows, cites its basis and refrains from advising a modifier where the rule cannot support one. This gives a truer view of a scrubber than a guided demo built around only favourable examples.

A careful workflow

1. List the denials to prevent. Rank code-pair, unit, demographic and payer-rule problems by real operational impact.
2. Run controlled examples. Use safe test data representing a fail, a pass and an unknown result.
3. Inspect evidence and limits. Look for source links, versions, scope and clear uncertainty handling.
4. Test the workflow. Check whether staff can act on the result without copying data into another system.

Two products, different evidence

Illustrative scenario: One tool flags a code pair as risky but gives no source, date or modifier indicator. Another shows the current edit context and marks an unrelated line as unverified because its dataset does not cover the payer's rule. The second result is less dramatic, but it is operationally safer: staff know what to correct, what to investigate and what not to overstate to a patient or payer.

Review traps

• Comparing headline feature counts rather than the checks tied to your denial patterns.
• Sending real PHI through a trial before understanding its data path and retention controls.
• Treating a product's prediction as a guarantee of payer payment.

For the underlying rule, start with CMS NCCI programme page and CMS Medicare MUE page. Those sources explain the programme and code-set mechanics; the payer's current contract, remittance and written policy still decide an individual claim.

Questions billers ask

What should claim scrubbing software check?

The answer depends on workflow, but ask specifically about field validation, code-pair logic, units, payer rules, source currency and explanation quality.

Can a claim scrubber guarantee payment?

No. A scrubber can identify known risks; payer coverage, contract, documentation and adjudication still control payment.

How should a scrubber handle unknown rules?

It should label the result as unverified or outside scope rather than reporting a pass.