How a Mid-Sized Health System Could Use Agentic AI to Tackle Rising Denials

Health systems are caught in a vicious cycle. Payer denials are climbing, administrative costs are spiraling, and staff burnout is so high that physician burnout alone costs the US healthcare system $4.6 billion annually, and 46% of nurses report a shortage. Meanwhile, margins continue to shrink. The impact of this cycle: financial strain. 46% of healthcare organizations report significant revenue impacts specifically due to slow administrative turnaround times. For mid-sized health systems, especially, this pressure is relentless. You don’t have the deep pockets of large academic medical centers, but you’re still dealing with the same regulatory complexity, including tens of thousands of CMS billing codes and over 1,000 Joint Commission elements of performance. And the reality is, every denied claim that sits in limbo is revenue you can’t afford to lose.

You’ve Outgrown Traditional Automation

Sure, traditional automation has helped with some of the data entry and routing tasks. But in the current environment, basic RPA is like a puppy fresh out of obedience school: it can sit when told, but it gets confused the moment a process requires a new, unfamiliar command. It doesn’t solve the root problems:

• The endless back-and-forth with payers
• The need to analyze paragraphs of complex, non-uniform information from payers to find a root cause
• The requirement to make judgment calls when documentation doesn’t quite line up with payer rules

That’s where agentic AI comes in. Unlike basic automation that follows a fixed script, agentic AI has the cognitive architecture to handle variance and complexity at scale. It can reason through ambiguous situations, learn from patterns, and even self-correct minor errors based on historical approval data rather than flagging a human coder for every exception.

Why Denials Are Getting Worse

There’s no doubt that denials are getting worse year by year. The industry reached a breaking point in the post-pandemic era when initial denial rates surged to 12%. At the time, many hoped this would be a temporary spike, but time has proven otherwise, with systems that still haven’t been modernized bleeding an average of $10 million annually in unresolved denials. For mid-sized health systems operating on thin margins, that’s a serious financial hit, making this problem an urgent priority in 2026. To tackle it, we first have to consider several factors that are driving this trend:

• Payer policy changes are constant and hard to track. What was covered last quarter might not be covered today. Keeping up with these shifts requires dedicated staff time, and even then, errors slip through.
• Prior authorization requirements are ever-expanding, and it’s tough to keep up, with more procedures now requiring pre-approval, and the criteria for approval becoming more detailed and harder to navigate.
• Medical necessity denials are increasing. Payers are scrutinizing clinical documentation more closely. If your documentation doesn’t clearly demonstrate why a service was medically necessary, the claim gets denied.
• Billing errors still happen despite automation, as coding mistakes, missing information, or failure to meet specific payer formatting requirements can all trigger denials. And sure, traditional automation helps catch some of these issues, but it’s rigid, unable to adapt when the rules change or when a claim falls into a gray area.

With all of these forces combined, revenue cycle teams are overwhelmed. They’re manually reviewing hundreds of denied claims, researching payer policies, drafting appeal letters, and resubmitting documentation, and it’s time-consuming, expensive, and frustrating.

What Makes Agentic AI Different

Traditional robotic process automation (RPA) follows predefined rules. If a claim meets X criteria, the system does Y. It’s helpful for repetitive, straightforward tasks. But healthcare revenue cycle work is rarely straightforward. Agentic AI goes further. It can interpret unstructured data, make contextual decisions, and take multi-step actions to resolve issues. Here’s what sets it apart:

• It reasons through complexity. Agentic AI doesn’t just match keywords or follow a checklist. It can analyze a denied claim, review the clinical documentation, compare it against payer policies, and determine the best course of action. Whether that’s drafting an appeal, requesting additional documentation, or escalating to a human reviewer.
• It learns from patterns. Over time, agentic AI identifies which denial reasons are most common, which payers are most likely to overturn appeals, and what documentation language tends to succeed. It uses these insights to improve its own performance and to surface recommendations for your team.
• It handles variability. Every payer has different rules. Every claim has unique circumstances. Agentic AI can adapt to this variability without needing to be reprogrammed every time a policy changes or a new edge case appears.
• It acts autonomously within defined guardrails. You set the boundaries. For example, you might allow the AI to auto-submit appeals for low-complexity denials but require human review for high-dollar claims. Within those guardrails, the AI takes action, logs everything it does, and escalates when it encounters something outside its scope.

This combination of reasoning, learning, and autonomy is what makes agentic AI suited to the judgment-heavy, high-variability work of denial management.

A Real-World Scenario: 340-Bed Health System

Let’s look at how this might work in practice. Imagine a 340-bed community health system in the Midwest. It serves a mix of insured, underinsured, and Medicare/Medicaid patients. Like many mid-sized systems, it’s dealing with rising denial rates, staff turnover in the revenue cycle department, and increasing pressure from leadership to improve cash flow. Here’s how agentic AI could fit into their existing workflow.

Step 1: Denial Intake and Initial Triage

Every day, the health system receives denial notices from multiple payers. These notices come in various formats: EDI 835 files, payer portals, PDF letters, emails, ranging from straightforward to completely vague or contradictory. An agentic AI agent monitors all these sources, ingesting the denial data, extracting the relevant information (claim number, denial reason code, payer name, dollar amount), and logging it in the denial management system. Then, the agent performs an initial triage:

• Low-complexity denials (e.g., simple coding errors, missing information) are flagged for automated resolution.
• Medium-complexity denials (e.g., medical necessity, timely filing) are flagged for human-assisted review.
• High-complexity or high-dollar denials are escalated immediately to senior staff.

And it’s worth noting that this triage happens in seconds, which is significant given that manual reworks currently cost providers an average of $181 per claim.

Step 2: Root Cause Analysis and Pattern Detection

Through it all, the AI isn’t processing denials one by one. Instead, it’s looking for patterns. For example, it notices that a particular payer has started denying claims for a specific procedure code at a much higher rate over the past two weeks. The stated reason is “lack of medical necessity,” but the documentation standards haven’t changed on the health system’s end. So, the agent flags this trend and surfaces it to the revenue cycle director, who acts as an “AI auditor,” investigates and discovers that the payer quietly updated its coverage policy. And with this insight passed along, the health system can proactively adjust its documentation practices proactively and prevent future denials. As you can probably imagine, this type of detailed pattern detection would be nearly impossible for a human team to do manually at scale simply given how many claims, payers, and variables are involved, making this capability another huge win for agents.

Step 3: Automated Appeal Drafting and Submission

For low-to-medium complexity denials, the agentic AI agent drafts appeals automatically. Let’s say a claim was denied because the payer says a prior authorization wasn’t obtained. The agent reviews the claim file, finds the prior authorization number in the system, cross-references it with the payer’s records, and confirms that the authorization was indeed on file. It then drafts an appeal letter, attaches the relevant documentation, and submits it through the payer’s preferred channel (portal, fax, email). And that’s it! In just minutes, the appeal is logged, tracked, and monitored for a response without any need for human intervention. For more complex denials, like those involving clinical judgment or large dollar amounts, the agent drafts a preliminary appeal and flags it for human review. A clinician or senior revenue cycle specialist reviews the draft, makes any necessary edits, and approves it for submission.

Step 4: Documentation Augmentation and Clarification

Sometimes a claim is denied because the clinical documentation doesn’t clearly support the billed service, like if the care was appropriate, but the notes failed to spell out the medical necessity in the way the payer required. For these cases, the agentic AI agent can draft an addendum or clarification for the provider to review and sign. Here, it pulls relevant details from the patient’s chart, highlights the clinical rationale, and formats it according to payer requirements. Then, the provider reviews the draft, makes any necessary changes, signs it, and the agent submits it as part of the appeal. This saves the provider time and ensures the documentation is thorough and payer compliant.

Step 5: Proactive Denial Prevention

Over time, the AI agent builds a knowledge base of denial patterns, successful appeal strategies, and payer-specific quirks. It uses this knowledge to help prevent denials before they happen. For example:

• Pre-claim audits: Before a claim is submitted, the agent reviews it for common denial triggers. Missing information? Flag it. Procedure code mismatch? Flag it. Prior authorization needed but not documented? Flag it.
• Real-time payer policy updates: The agent monitors payer policy changes and alerts the billing team when a new rule could impact upcoming claims.
• Training insights: The agent surfaces trends to help train billing and coding staff. For example, “We’re seeing a spike in denials for this CPT code from Payer X. Here’s why and here’s how to document it correctly going forward.”

This proactive approach both reduces the volume of denials and frees up staff to focus on higher-value work.

The Financial and Operational Impact

Let’s take a look at the impact that this could have on a mid-sized health system like the one in the example we just ran over. Let’s assume that the 340-bed system processes about 200,000 claims per year. With an average denial rate of 12%, that’s 24,000 denied claims annually. Let’s say the average denied claim is worth $1,500. That’s $36 million in initially denied revenue. Industry benchmarks suggest that health systems successfully overturn about 60% of denials when they appeal, and that means $21.6 million could potentially be recovered. But to be realistic, appeals are labor-intensive, so many organizations only appeal a fraction of their denials because they just don’t have the staff capacity to pursue them all. Now, let’s say agentic AI enables the health system to appeal 90% of denials instead of 60%, and it improves the overturn rate to 70% (by ensuring appeals are better documented and submitted more quickly). The math looks like this:

• 90% of 24,000 denials = 21,600 appeals
• 70% overturn rate = 15,120 overturned denials
• 15,120 denials x $1,500 average = $22.68 million recovered

Now, if we compare that to the baseline of $21.6 million, we’re looking at an additional $1.08 million in recovered revenue. And that’s just from improving the appeal process. Add in the reduction in future denials through proactive prevention, and the financial impact grows even more. Even a modest 10% reduction in denials (from 12% to 10.8%) would mean 2,400 fewer denials per year, or $3.6 million in revenue that doesn’t get tied up in the first place. And the operational side offers even more benefits:

• Reduced manual workload: Staff spend less time on repetitive appeal drafting and more time on strategic initiatives.
• Faster appeal turnaround: Automated appeals are submitted within days instead of weeks, improving cash flow.
• Better staff morale: Revenue cycle teams are less overwhelmed and more empowered with data-driven insights.
• Improved payer relationships: Faster, more accurate appeals reduce friction with payers and lead to quicker resolution times.

What It Takes to Implement Agentic AI

This all sounds promising, but what does it actually take to get agentic AI up and running in a mid-sized health system?

1. Data Integration

Agentic AI needs access to your data, which includes electronic health record (EHR), billing system, denial management platform, and any other tools your revenue cycle team uses. But the good news is that most modern AI platforms are built to integrate with common healthcare systems via APIs or HL7/FHIR standards, so you don’t need to rip and replace your existing infrastructure. The AI layer sits on top of what you already have and connects the dots.

2. Policy and Guardrails

Before you deploy agentic AI, you need to define the rules of engagement:

• What types of denials can the AI handle autonomously?
• What requires human review?
• What thresholds trigger escalation?

These guardrails are critical, as they ensure the AI operates within acceptable risk parameters and aligns with your organization’s compliance and quality standards.

3. Change Management

Introducing AI into your revenue cycle is a people project, not just a technical one. So, your team needs to comprehensively understand what the AI is doing, why it’s doing it, and how it makes their jobs easier (not how it replaces them). To ensure all of this, you’ll need to invest in training, communication, and ongoing support. Make it clear that the AI is a tool to augment staff capabilities, not eliminate roles. In fact, many organizations find that AI allows them to redeploy staff into higher-value work, like payer relations or strategic denial prevention. Spelling out exactly how it’ll change day-to-day life for different roles is your best bet for avoiding the fear that leads to rumors and low adoption rates.

4. Monitoring and Continuous Improvement

Agentic AI improves over time, but only if you monitor its performance and feed it new information, so to make the most of your investment, it’s important to set up regular reviews to assess accuracy, identify edge cases, and refine the AI’s decision-making logic.

The Bottom Line

Mid-sized health systems don’t have the luxury of ignoring denials. Every dollar tied up in appeals is a dollar you can’t invest in patient care, staff, or infrastructure. Agentic AI offers a way to break the cycle, taking on the repetitive, judgment-heavy work of denial management, freeing your team to focus on strategy, relationships, and continuous improvement. It learns from your data, adapts to changing payer rules, and operates within the guardrails you define. And through it all, you’ll give your revenue cycle staff a powerful tool to do their jobs better, faster, and with less frustration. The denials aren’t going away, but with the right technology, you can turn them from a constant drain into a manageable challenge, and that makes all the difference.