ACHDM

American College of Health Data Management

How to prepare for a surge in claim denials in 2026

There’s increasing likelihood that payers will increase scrutiny of claims, so providers need to become proactive to anticipate new payer logic.

Jan 20 266 min read

Moses Landon

Claim denials have been historically treated as an unfortunate yet somewhat predictable part of the revenue cycle — a back-office problem to be worked through by billing teams and denial specialists.

That era is over. In 2026, denials are poised to become one of the most powerful cost-containment levers for payers, and the volume, speed and complexity of denials will rise accordingly.

Healthcare organizations that continue to treat denials as a downstream operational issue will fall behind. Those that treat denial prevention as a strategic, data-driven discipline will protect revenue, reduce administrative waste, and strengthen financial resilience.

Here’s why denials are accelerating — and how healthcare leaders can use healthcare management data to engineer them out of the workflow.

The 2026 payer playbook

Payers are rapidly deploying AI and machine learning to automate claim adjudication. What once took days now happens in seconds. Claims can be auto-denied for missing data, subtle coding nuances or documentation gaps, often without any human review. This shift dramatically increases both the volume and speed of denials.

Commercial and Medicare Advantage plans continue to broaden prior authorization requirements across imaging, outpatient surgery, infusion services and specialty drugs. Even routine services are increasingly subject to pre-service scrutiny, creating more opportunities for administrative missteps and downstream denials.

In addition, payers are enforcing hyper-specific documentation requirements tied to proprietary medical policies. It’s no longer enough to meet CPT or ICD guidelines; clinicians must use precise language, phrases and clinical indicators that align with payer-specific rules.

Small errors that once triggered a request for correction — such as missing modifiers, coordination-of-benefits discrepancies or timely filing issues — now increasingly result in full denials. Payers are using administrative precision as a cost containment strategy.

Even when claims are initially paid, they are increasingly subject to retrospective audits and recoupments. Payment integrity programs are expanding, and providers are seeing more revenue clawed back months after services are rendered.

What healthcare organizations must change

The old playbook of working on denials after they occur is no longer sustainable. The organizations that succeed in 2026 will shift left, engineer payer logic into workflows and use data to prevent denials before they happen.

Below is a practical framework for using healthcare management data to stay ahead of payer behavior.

Turn data into pre-service intelligence. Data intelligence is the most effective way to reduce denials, and the most underutilized.

Scheduling data.

Historical authorization outcomes.

Payer policy change logs.

Service-line denial rates.

How to use it

Embed real-time authorization likelihood scores at scheduling.

Flag high-risk CPT-payer combinations before the patient arrives.

Route complex or high-risk cases to senior authorization staff or peer-to-peer specialists.

Impact

Prevents denials before documentation is even created.

Reduces rework, delays and downstream appeals costs.

Build payer-specific intelligence models. Generic workflows no longer work. Precision is the new competitive advantage.

Denial reason codes by payer.

Payer-specific policy language.

Appeal overturn rates by service line and physician.

Timely filing and COB error patterns.

How to use it

Create payer heat maps showing denial types, frequency and financial impact.

Build payer-specific playbooks outlining required documentation, common pitfalls and escalation paths.

Prioritize appeals with the highest historical probability of overturn.

Impact

Reduces low-yield appeals that drain resources.

Accelerates cash recovery with lower cost-to-collect.

Detect “false clean claims” using pattern recognition. These are claims that pass internal edits but fail payer logic, becoming the fastest-growing denial category. This is where AI and pattern recognition can deliver outsized value.

Claims that passed internal edits but were denied.

Documentation metadata (note length, keywords. templates).

Modifier and diagnosis combinations.

How to use it

Compare "clean" claims against payer denial outcomes to identify hidden rules.

Detect missing phrases, sequencing issues or documentation gaps.

Feed insights back into frontend edits and CDI prompts.

Impact

Eliminates denials your system currently cannot see.

Delivers high ROI because these claims often involve higher dollar amounts.

Apply predictive denial scoring before submission. Predictive scoring transforms denial management from reactive to proactive.

Prior denial history.

Provider-level variation.

Site-of-service trends.

Authorization outcomes vs. final determinations.

How to use it

Score claims for denial probability pre-bill.

Hold or reroute only the riskiest claims, without slowing down the entire workflow.

Enable clinicians to review documentation edits based on payer-specific rules.

Impact

Improves first-pass yield without sacrificing throughput.

Scales denial prevention without adding staff.

Use post-payment data to protect future revenue. Post-payment data is often the most underused yet most revealing dataset in the revenue cycle.

RAC and payment integrity audit findings.

Recoupment timing and rationale.

Service lines most frequently targeted.

How to use it

Identify patterns that trigger retrospective audits.

Adjust documentation and coding for similar future cases.

Quantify "recoupment risk" as part of margin forecasting.

Impact

Converts audit pain into future prevention.

Reduces revenue volatility and financial surprises.

Elevate denials to an executive-level metric. Denials are no longer a back-office problem; they are a strategic financial metric.

Denials as a percentage of net revenue.

Avoidable vs. unavoidable denials.

Cost to appeal vs. dollars recovered.

Prevention savings (avoided write-offs).

How to use it

Treat denial prevention as a margin protection initiative.

Tie leadership KPIs to denial avoidance, not just AR days.

Fund analytics and automation based on avoided loss, not headcount.

Impact

Sustainable ROI.

Alignment across clinical, operational, and financial teams.

Healthcare management data becomes transformative only when it moves upstream, becomes payer-specific and drives real-time action. The organizations that win in 2026 will predict denials rather than react to them; embed payer logic directly into workflows; and use data to eliminate denials rather than staff them.

With the right data strategy, healthcare leaders can turn payer behavior into a competitive advantage and protect revenue in an increasingly complex reimbursement landscape.

Moses Landon, MBA, EHRC, SA, FACHDM, is a senior executive advisor and works in advisory services for financial transformation for Premier Inc.

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