Denials are the most expensive recurring problem in a DME/HME business. Industry guides put supplier denial rates at roughly 15-20%, and on the claim volume a supplier runs, that's six figures a year walking out the door. Most of those denials are avoidable — a missing CMN, an expired authorization, an eligibility lapse, a wrong modifier — and most billing platforms surface a flat denial list without telling the owner which causes to attack first. This dashboard puts the whole denial picture on one screen: the rate, the dollars, the root causes, and what's recoverable.
What this report answers
Three questions every billing lead asks: how much of what we submit gets denied, why, and how much of that we could have prevented? The overview shows denial rate, denied claims, denied dollars, and first-pass resolve rate, then breaks denied dollars down by payer and denied counts by root cause, with a monthly denial-rate trend so you can see whether process fixes are working. The root-cause page isolates avoidable denial dollars and appeal recovery, then crosses payer against root cause so you know exactly which denial type, at which payer, is costing the most.
The metrics that matter
Denial rate (denied ÷ submitted dollars) is the headline. Avoidable denial dollars is the one that turns the report into an action list — denials from authorization, eligibility, documentation/CMN, and coding are process failures you control, unlike a medical-necessity downcode. First-pass resolve rate measures clean submission: the higher it is, the less your team is reworking claims. Appeal recovery dollars tells you how much of the denied pile you're actually clawing back versus writing off.
Why the data is trapped
Denial reasons live as remittance codes inside Brightree, NikoHealth, Bonafide, WellSky DME, or Computers Unlimited's TIMS — readable one claim at a time, but rarely rolled up into "avoidable dollars by payer and cause." Suppliers on legacy stacks export the denial log to Excel and lose the trend the moment they pull it. The mapping from raw remittance reasons to a clean root-cause bucket (auth vs. eligibility vs. documentation vs. coding) is exactly the modeling step most teams never do, so the pattern that would tell them where to fix the process stays buried.
How to read it
Start at avoidable denial dollars — that's your recoverable money. Then read the payer × root-cause table: a cluster of authorization denials at one payer points upstream to intake and auth tracking; a spike in documentation/CMN denials points to the referral and order-entry process. A low first-pass resolve rate means clean-claim submission is the real fix, not appeals. Work the biggest avoidable cell first. The sample on this page uses entirely synthetic claims — no PHI — so you can see the finished layout before connecting your own export.
Metrics it tracks
| Metric | What it means |
|---|---|
| Denial Rate | Denied claim dollars divided by submitted claim dollars — SUM(DeniedAmount) ÷ SUM(SubmittedAmount). |
| Denied Claims Count | Count of claim lines flagged as denied (DeniedFlag = 1). |
| Denied Dollars | Total denied dollars across all claims — SUM(DeniedAmount). |
| First-Pass Resolve Rate | Share of submitted claims paid without rework — SUM(FirstPassPaidFlag) ÷ count of claims. |
| Appeal Recovery Dollars | Denied dollars recovered through appeals — SUM(AppealRecoveredAmount). |
| Avoidable Denial Dollars | Denied dollars from auth, eligibility, documentation, or coding causes — SUM(DeniedAmount) where AvoidableFlag = 1. |
Used by: Billing & ops director; revenue cycle manager
Frequently asked questions
What is a typical DME claim denial rate?
Industry guidance commonly cites DME/HME denial rates around 15-20%, which at supplier claim volumes adds up to six figures a year. The point of tracking it isn't a single benchmark — it's separating avoidable denials (authorization, eligibility, documentation/CMN, coding) from unavoidable ones so you know how much is actually recoverable.
What are the most common DME denial root causes?
The recurring avoidable ones are missing or expired authorization, eligibility/coverage lapses, missing or incomplete documentation (CMN, Rx, chart notes), and coding or modifier errors. Medical-necessity denials are harder to prevent. Ranking denied dollars by root cause and payer shows whether the fix belongs in intake/auth tracking or in billing.
What is first-pass resolve rate and why track it?
It's the share of submitted claims paid the first time without rework. A high first-pass rate means clean claims and a lean billing team; a low one means staff are reworking and appealing claims that should have gone out clean — which is a submission problem, not an appeals problem.
Build this report on your own data
Clone this DME/HME template — describe it and we’ll generate sample data so you can try it free, or upload your own export. You get a fully modeled, branded Power BI project that opens in Power BI Desktop.
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