Solving Downcoding at Scale
Downcoding is easy to miss: the claim gets paid, the revenue posts, but many systems don’t have a way to flag that the remit paid a different code than what was originally billed.
Downcoding a single claim may result in a small dollar difference, but across a high-volume practice, these differences compound among all the claims into significant revenue leakage.
Many billing systems aren’t able to automatically reconcile a difference between what was billed out on a claim under a specific CPT code, and what the payer changed on that claim, including downcoding, which leads to a lower remittance. The underlying issue is a lack of visibility – RCM teams can’t appeal or write off what they can’t see is happening.
Visibility means downcoded claims can be reviewed, sized, and acted on. When claims where the paid code doesn't match the billed code route into a remit balancing queue, the mismatch becomes visible. Without that visibility, the revenue loss accumulates.
The downcoding trend is only increasing, as payers are relying more and more on artificial intelligence to process claims.
Providers need an RCM system that can handle the challenges of these AI-led payer decisions, like downcoding, with the visibility to quickly see changes and how they’re impacting revenue.
Here’s how Candid Health does that.
Downcoding at Scale Adds Up
Claims with evaluation and management codes are often the highest-volume claim type in most ambulatory practices, and they carry significant potential for lost revenue when downcoded. E/M codes have more room for interpretation since they’re based on complexity and medical decision making, as opposed to, say, a concrete treatment.
Say the provider creates a claim for a level four evaluation and management visit, which is a visit of “moderate complexity” according to the AMA. But the payer knocks that down to a level three – low complexity. The difference is typically about $40.
One large multi-specialty oncology practice that uses Candid Health receives roughly 200 downcoded E/M payments per week from a single payer. Without Candid Health, that could easily add up to $35,000 in missing revenue a month.
How Downcoding Disrupts Reconciliation
Billing platforms often match payments to charges by CPT code. When codes don’t match, there’s usually one of three outcomes:
- The payment posts to the wrong line
- The payment lands in a general exceptions bucket alongside unrelated payment issues
- The payment goes unallocated
All three outcomes create the same problem at month-close.
Why Downcoding is Hard to Track
Standard financial reporting doesn't separate claims paid at a lower level of service from claims paid in full. Payers can leverage artificial intelligence, for example, to assess claims and suggest opportunities to downcode, but that leaves providers struggling to keep pace with the change and the volume.
So the difference shows up as a shortfall in net collections. Confirming the downcoding of a specific claim requires pulling data from multiple sources — remittance files and charge data in the billing system. Tracing downcoding back to the correct service line is typically a very manual process.
Managing hundreds of downcoded claims across an entire payer mix requires dedicated staff hours that compete with every other billing priority.
How Providers Manage Downcoding Today
If revenue teams can identify a trend, they can more easily address root cause issues to decrease downcoding. For example, they might identify which CPT codes a certain payer tends to downcode, and then decide how to handle those types of appointments or claims internally, such as ensuring providers are correctly documenting the visit.
But without automated detection, organizations tend to rely on a handful of manual approaches.
The most common is periodic reconciliation. A billing manager pulls a payment mismatch report, filters for underpayments, and reviews claims where the paid amount doesn't match the expected reimbursement.
Those reports group downcoded claims alongside other payment mismatches:
- Underpayments
- Zero-pays
- Fee schedule discrepancies
The billing team sorts through the results claim by claim, reading the Claim Adjustment Reason Codes (CARCs) and Remittance Advice Remark Codes (RARCs) on each remittance to determine which mismatches represent downcoding versus fee schedule discrepancies, bundling edits, or other adjustment types.
Some practices assign billers to specific payers. A biller who handles one payer's remittances consistently may see patterns, such as claims with lower reimbursement levels. But they don't have a holistic view of the entire practice, and everything that's downcoded. And if they leave or switch roles, the pattern recognition moves with them.
The per-claim economics discourage appeals. Appealing a downcoded claim requires pulling documentation, sometimes scouring spreadsheets for information, and submitting medical records, for just $40. They can’t make changes at scale. They can’t identify trends in order to prevent future downcoding. And they don’t have visibility into how much total money is being lost.
Because of that, billing teams tend to write off the difference on a downcoded claim. But when the variance repeats hundreds of times per month, that’s a significant revenue loss.
How Candid Health Addresses Downcoding
Revenue teams often have no reliable way to identify which claims were downcoded, how much revenue was lost, or where it's getting worse.
In Candid, remit balancing compares the billed CPT code against the paid CPT code on every remittance. When the codes don't match, the claim routes into a dedicated queue — not a general exceptions bucket. Revenue teams see downcoded claims as they arrive, not weeks later during reconciliation.
The platform also aggregates downcoded claims across payers, CPT codes, and time periods into queues, so revenue teams can quantify the financial impact by payer and identify patterns around specific codes.
One customer used Candid to identify 1,050 downcoded payer payments over a two week span, and then post each one. That meant identifying the downcode, finding the payment, and tying that to the original billed claim, all in seconds instead of minutes.
In other systems, downcoded payments can fall into an exception report or never get identified at all.
In Candid, the claims automatically routed into a downcoding queue, where the appeals team could prep the claims for appeal, and then appeal them. The customer also used data insights from the process to optimize future billing workflows.
That type of visibility is the first step toward fixing the problem.
Book a demo to see how.