Clean-claim rate is the front-end quality metric every RCM firm lives by — the industry benchmark sits above 90%, and every percentage point below it is rework, delay, and denial risk. But the data lives in two places: submission outcomes in the clearinghouse (Availity, Waystar, Office Ally) and claim origin in the client's PM system. This dashboard joins them so you can see how clean your claims are before they leave, and where the errors come from.
What this dashboard answers
Front-end quality comes down to two questions: what share of claims go out clean, and what share get paid on the first pass without anyone touching them again? The overview answers both, plus rejected-at-clearinghouse rate and average edits per claim. Cuts by clearinghouse and by PM system show whether one platform or one submission path is generating more scrubber errors than the rest.
The metrics that matter
Clean-claim rate and first-pass resolution are related but not the same — a claim can pass the scrubber clean and still be denied by the payer. First-pass resolution is the truer efficiency measure because it counts claims that needed zero rework to get paid. Avg edits per claim quantifies how much front-end scrubbing each claim demands; rising edits is an early signal of a coding or registration problem at a specific client.
Why the data is trapped across the systems
The clearinghouse knows which claims it rejected and why, but it doesn't know your client mix or your fee structure. The PM systems — athenahealth, AdvancedMD, and the rest — know the claim's origin but not its clearinghouse outcome. Neither side has the joined picture, so 'what's my clean-claim rate by client and payer' is a question that only gets answered by exporting both and merging them by hand.
How to read it
Read clean-claim rate against the 90%+ benchmark first, then drop into the client-and-payer matrix to find who's pulling it down. The bar of first-pass rate by payer surfaces payer-specific edit traps — a payer that consistently kicks back claims points to a rule your scrubber should be catching upstream. The clean-versus-first-pass scatter separates clients with clean submissions from clients whose claims still need rework to get paid. The sample uses entirely synthetic data, so there is no PHI.
Metrics it tracks
| Metric | What it means |
|---|---|
| Clean-Claim Rate | Share of claims submitted error-free on the first try — SUM(clean) ÷ total claims. |
| First-Pass Resolution Rate | Share of claims paid on first submission with no rework — SUM(first-pass paid) ÷ total claims. |
| Claims Submitted | Count of submitted claim rows in the period. |
| Rejected-at-Clearinghouse % | Share kicked back before reaching the payer — front-end scrubber catches. |
| Avg Edits per Claim | Total scrubber edits ÷ claims — front-end error density. |
| Total Billed Charges $ | Sum of billed charges on submitted claims. |
Used by: RCM operations directors and team leads
Frequently asked questions
What is a good clean-claim rate for a billing company?
The common benchmark is above 90%, with strong operations reaching the mid-to-high 90s. Clean-claim rate measures the share of claims that pass the front-end scrubber and clearinghouse without errors on the first submission — every point below the benchmark is rework that slows cash and risks denial.
What's the difference between clean-claim rate and first-pass resolution?
Clean-claim rate is whether a claim left the door error-free; first-pass resolution is whether it got paid on the first submission with no rework. A claim can be clean and still be denied by the payer, so first-pass resolution is the better measure of true efficiency and is usually a few points lower.
Does this combine clearinghouse and PM-system data?
Yes. The template joins submission outcomes from Availity, Waystar or Office Ally with claim origin from the client's PM system, so clean-claim rate can be sliced by clearinghouse, PM system, client, and payer at once. The sample uses synthetic data so you can see the joined view first.
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