Zentist Analyzed 22.4M Dental Service Lines. The Denial Patterns Are More Local Than They Look.

Dental claim denials are often discussed as a single revenue cycle metric. New benchmarking from Zentist suggests the more useful question is where those denials are happening—and why.

Zentist's 2026 Dental Claims Denial Benchmark Report, powered by Remit AI, analyzes 22.4 million dental service lines across 1,890 practice locations in the United States. The underlying dataset spans 125 active dental organizations over a 12-month period and evaluates reimbursement at the procedure-line level rather than only at the claim level.

For dental practices and DSOs, the takeaway is significant: denial risk is highly concentrated by geography, payer, procedure and denial reason.

Explore Zentist on Avized, or browse the broader Revenue Cycle category.

Geography matters more than many organizations realize

Denial rates varied dramatically by state.

At the high end, Mississippi recorded a 31.0% denial rate and Oklahoma 30.9%. At the other end of the benchmark, Idaho was 11.2% and South Carolina 11.0%. Texas came in at 22.6% and Florida at 25.1%.

That creates an important operating question for multi-state DSOs.

A standardized national billing workflow may miss meaningful differences in local reimbursement environments. Zentist's benchmark argues that organizations operating in higher-denial markets may benefit from more localized, payer-specific workflows rather than treating every market identically.

The broader lesson is that a denial rate without geographic context can be misleading. A DSO operating in a higher-denial market may need a very different operating model than one with the same procedure mix in a lower-denial market.

The same payer can behave very differently by market

The payer data is one of the report's most useful sections.

At the national level, several large commercial and dental-specific payers cluster in a relatively narrow range: Delta Dental Insurance Co. at 25.0%, Aetna at 22.0%, Cigna at 22.0%, MetLife at 21.0% and Guardian Life at 21.0%.

But the outliers are substantial.

Medicaid of Oklahoma reached 63.8%, Anthem 43.2%, and Blue Cross Blue Shield of Georgia 38.5%.

The state-level analysis makes the point even more clearly. Anthem ranged from 18.8% in California to 70.5% in Maryland in the benchmark. Medicaid of Oklahoma ranged from 41.9% in Illinois to 70.7% in Texas, while Blue Cross Blue Shield of Georgia ranged from 16.2% in Georgia to 64.8% in Maryland.

That means payer name alone is not enough.

For larger dental organizations, benchmarking increasingly needs to happen at the payer + market level. National averages can hide the local rules, benefit designs, contracting requirements and operational patterns that actually determine whether a claim gets paid cleanly.

This is also why payer intelligence becomes more valuable as an organization expands geographically. The same workflow may perform well in one state and generate significant rework in another.

Procedure-level data exposes another layer of risk

The report also identifies procedures with exceptionally high denial rates.

Among the elevated-risk CDT codes analyzed:

• D9910 — Desensitizing Medicament: 90.0%
• D9911 — Desensitizing Resin: 89.0%
• D7922 — Complex Oral/Maxillofacial Surgery: 85.0%
• D0460 — Pulp Vitality Tests: 78.5%
• D9999 — Unspecified Procedure: 76.5%

The important distinction is that the highest-volume procedures are not necessarily the highest-risk procedures.

That changes how an RCM leader should think about claim quality. A code can represent a relatively small share of total volume and still create disproportionate follow-up work if its denial probability is structurally high.

The operational response should therefore be targeted: identify the codes where documentation, benefit verification, frequency rules or payer-specific requirements create repeat problems, and build validation around those procedures before submission.

Many of the biggest denial opportunities appear operational

The denial-category analysis may be the most actionable part of the benchmark.

Several of the highest-rate categories were administrative rather than clinical, including:

• Duplicate Services / Previously Paid: 98.0%
• Waiting Period / Patient Responsibility: 97.0%
• Deferred Payment: 94.0%
• Serviced Prior to Coverage / Patient Responsibility: 89.0%
• Benefits Termed / Patient Responsibility: 88.0%
• Frequency Limit / Patient Responsibility: 80.0%

That points upstream.

If a denial is caused by a waiting period, terminated coverage, frequency limitation, coordination-of-benefits issue or duplicate submission, the highest-value intervention may happen before the claim is ever sent.

This is a different problem from improving appeal productivity after the denial occurs.

It suggests that the most effective denial-management strategy may connect front-end eligibility, claims history, coverage rules and claim validation with downstream denial analytics. The goal is not merely to work denied claims faster. It is to identify which denials should never have entered the AR queue in the first place.

Denial benchmarking should drive workflow, not just reporting

Zentist's report makes an important point: denial analytics are most valuable when organizations use them to change operating behavior.

A useful denial dashboard should answer more than "what is our denial rate?"

Revenue leaders should be able to ask:

• Which payers are creating the most denied service lines?
• Which payer-state combinations materially underperform?
• Which CDT codes have unusually high denial rates?
• Which denial reasons are preventable before submission?
• Which problems are increasing or decreasing over time?
• How much staff effort is being spent on repeatable administrative issues?
• Where would an automated validation step have the highest ROI?

That is where benchmarking becomes operational intelligence.

The benchmark itself is based on service lines rather than practice-level or claim-level reporting. That level of granularity matters because a single claim can contain multiple procedures with different adjudication outcomes. Looking only at the claim can mask where the actual reimbursement problem sits.

What this means for DSOs

For multi-location groups, the report reinforces a broader shift in dental RCM: the best operating model is increasingly exception-driven rather than transaction-driven.

Instead of treating every payer, location, code and denial with the same workflow, organizations can concentrate resources where reimbursement risk is highest.

That could mean:

• tighter eligibility checks in markets with elevated administrative denials;
• payer-specific work queues for high-denial plans;
• pre-submission rules around high-risk procedures;
• localized denial benchmarks for regional operators;
• contract discussions supported by actual denial and downcoding data;
• automation for repetitive status, remittance and denial-classification work.

The report also shows why enterprise RCM technology is moving toward more centralized data. A large DSO cannot manage this level of variation effectively if payer data, EOBs, claim status and denial reasons remain fragmented across local offices and portals.

Where Zentist fits

Zentist positions Remit AI as an RCM platform for dental groups and DSOs, with capabilities around remittance data, payment workflows, denial management and reporting.

In the report, Zentist describes Remit AI as automatically categorizing denials using payer reason codes and tracking CARC and RARC codes from Electronic Remittance Advice in a centralized view. It also describes workflow automation intended to let AR teams focus on exceptions rather than manually checking every claim.

That positioning aligns closely with what the benchmark itself suggests: the opportunity is not simply better reporting. It is using granular denial data to determine what should be automated, what should be prevented and what genuinely requires human intervention.

Avized Take

The most important finding in Zentist's benchmark is not that dental claims are denied.

It is that denials are unevenly distributed.

They cluster around specific states, payers, procedures and administrative categories. That means broad RCM improvement programs can waste effort by treating every claim path as equally risky.

The stronger model is more precise:

Benchmark locally. Identify concentrated risk. Move prevention upstream. Automate repeatable work. Keep humans focused on the exceptions that actually require judgment.

For DSOs, that is a much more actionable framework than simply trying to push a national denial percentage down.

Read more about Zentist on Avized, visit Zentist, or explore Avized's Revenue Cycle research.

Source: Zentist 2026 Dental Claims Denial Benchmark Report, powered by Remit AI.