What is claims denial management?

In 2024, the initial claim denial rate across U.S. healthcare reached 11.8%, up from 10.2% in prior years. By 2025, 41% of providers reported that at least one in every ten claims was denied, compared with 30% in 2022. These figures come from Experian Health’s State of Claims 2025 report, based on a survey of 250 revenue cycle leaders. For healthcare executives, the numbers translate directly into delayed cash flow, increased administrative burden, and, in many cases, revenue that is written off without ever being challenged.

Claims denial management is the process organizations use to address this problem. It sits at the intersection of clinical operations, billing, and healthcare revenue cycle management, and its effectiveness determines a significant share of an organization’s financial health. This article explains what denial management is, how it works today, what causes most denials, and where agentic AI for healthcare billing is beginning to change the equation.

What claims denial management actually means

At its most basic, claims denial management is the end-to-end process of identifying, resolving, and preventing insurance claim denials. The goal is not simply to fix individual denied claims, but to build a process that reduces the volume of denials over time.

Before going further, one distinction matters: a rejected claim is returned to the provider before it is processed, typically because of formatting errors such as a typo in the patient’s name. A denied claim has been processed by the payer and deemed unpayable. This distinction is operationally important because a denial, unlike a rejection, triggers a formal appeals process with its own rules and deadlines.

Denial management operates in two modes. The reactive mode addresses denials after they occur: identifying the cause, determining whether an appeal is viable, drafting and submitting the appeal, and tracking the outcome. The proactive mode works upstream, catching errors before submission and preventing denials from occurring in the first place. Mature denial management programs operate in both modes simultaneously, using the data from resolved denials to improve front-end processes.

Denial management sits inside the broader healthcare revenue cycle management framework. It connects directly to patient registration, clinical documentation, coding accuracy, and prior authorization workflows, meaning a failure in any of those upstream areas will show up as a downstream denial.

How claims denial management works today

For most healthcare organizations, denial management is a largely manual process. This is how it typically runs.

After a payment poster receives and posts an Explanation of Benefits (EOB), denied claims become visible in the practice management system. A billing team member pulls a denial report and begins working through the list. Each denial carries a Claim Adjustment Reason Code (CARC), issued by the payer, which indicates the stated reason for non-payment. Interpreting these codes requires specialist knowledge; some payers still use legacy, non-standard codes that are not straightforward to read.

Once the reason is identified, the case is routed to the appropriate team: coding-related denials go to coders, clinical necessity disputes involve the treating clinician, registration errors go back to front-desk staff. An appeal is drafted, supporting documentation is gathered, and the corrected claim or formal appeal is submitted within the payer’s filing window. Each step is tracked manually, often in spreadsheets or basic workflow tools, against deadlines that vary by payer.

The structural problem with this workflow is volume.

“Payors appear to be using initial denials to slow payments, even though they ultimately pay approximately 90% of claims, a trend we have been tracking. Even if the claims are ultimately paid, initial denials still cost hospitals, health systems and medical providers a lot of resources to overturn, and they also slow cash flow.”

Matt Szaflarski, VP of Revenue Cycle Intelligence at Kodiak Solutions, May 2025

The data bears this out. According to SCALE Healthcare Consulting, the average health system sees approximately 20% of submitted claims denied on first submission, and up to 60% of those are abandoned without appeal, not because they are unwinnable, but because the AR team does not have the bandwidth to work every case. The rework cost alone is substantial: Experian Health puts the average cost of denial rework at $25 per provider claim and $181 per hospital claim.

The most common reasons claims are denied

Most denials trace back to a small number of recurring causes. Understanding them is the prerequisite for prevention.

Registration errors are a larger contributor than many organizations realize. Experian Health survey data shows that 26% of providers report at least one in ten denied claims traces back to intake errors at the point of registration. Because these errors occur before any clinical interaction, they are also among the most preventable: they require accurate data capture at the front end, not clinical expertise at the back end.

According to a widely cited industry estimate by Becker’s Hospital Review, approximately 90% of denials are preventable. The gap between that figure and the current reality, where denial rates continue to climb, reflects how difficult prevention is when the upstream processes generating errors remain manual, fragmented, and high-volume.

How agentic AI changes the denial management equation

Rising denial volumes are creating a gap that manual workflows cannot close. Medicare Advantage-related denials spiked 4.8% from 2023 to 2024. Commercial plan denials rose a further 1.5% over the same period. At the same time, Experian Health’s 2025 survey found that 67% of providers believe AI can improve the claims process, while only 14% currently use it to reduce denials. The opportunity, and the urgency, are both clear.

Agentic AI differs from the rules-based automation that revenue cycle teams have used for years. Standard robotic process automation (RPA) executes defined tasks in sequence: it can check eligibility or scrub a claim against a fixed rule set. An agentic system reasons across multiple data sources simultaneously, learns from payer behavior over time, and takes autonomous action across the full denial lifecycle. It does not wait to be triggered; it monitors, evaluates, and acts.

In the context of claims denial management, this translates into four practical capabilities.

Pre-submission validation: agents scan claims against payer-specific rules, clinical documentation, and coding requirements before the claim leaves the system. Errors that would generate denials are caught and corrected at the source, before they enter the payer’s adjudication process.

Denial triage: when denials do occur, agents decode CARCs, cross-reference billing and clinical records, and route each case to the correct team based on denial type and revenue priority. High-value, high-probability appeals are surfaced first; cases that are unlikely to be overturned are flagged accordingly.

Appeal drafting: agents generate payer-specific appeal letters based on denial reason, supporting documentation, and historical success patterns for that payer and denial category. This replaces a process that currently requires a billing specialist to handle one case at a time.

Pattern recognition: agents surface systemic issues across the denial dataset — for example, a specific payer consistently rejecting a specific CPT code, or a registration workflow producing a recurring eligibility error. These insights feed directly into upstream process improvements, reducing the volume of future denials rather than simply resolving current ones.

This shift from reactive rework to proactive prevention is the central value of applying agentic AI to healthcare revenue cycle management. For a closer look at how agentic systems operate in healthcare contexts, you can take a closer look at Vstorm’s healthcare AI capabilities.

Conclusion

Claims denial management is not a billing department problem. It is a revenue strategy problem that reaches from patient registration through clinical documentation, coding, and payer relations. The manual workflows that most organizations still rely on were not designed to handle denial rates of 11.8% across a high-volume claims environment — and the gap is widening as payers deploy their own AI-driven review systems to process claims faster and more stringently than before.

Organizations that address denial management as a systemic process challenge, rather than a staffing and rework problem, are the ones producing measurably different results: lower denial rates, faster appeal resolution, and upstream process improvements that compound over time. Agentic AI provides the mechanism to move in that direction at scale.