Agentic AI Automation with Intent: A Practical Example in Denial Management

Agentic AI Automation with Intent: A Practical Example in Denial Management

by BM Chittaranjan and Albert Porco   |   July 2025

As the excitement around AI grows, it’s easy to conflate different forms of automation under the “Agentic AI” label. However, not all intelligent or automated systems qualify as agentic.

Agentic AI refers specifically to systems that operate with autonomy, goal orientation, and adaptive decision-making. They are not just tools but intelligent actors capable of planning, initiating actions, learning from outcomes, and collaborating across workflows.

Agentic AI is capturing attention because of its potential to revolutionize revenue cycle management (RCM). However, it is often misunderstood. Agentic AI is not merely automation or predictive analytics dressed in new language. These AI agents do not simply execute rules or respond to queries; they initiate, reason, and own outcomes. Unlike traditional bots, dashboards, or static machine learning models, agentic AI is designed to function more like a proactive, goal-oriented team member than a reactive tool.

Here is an example of how Agentic AI can work in Denial Management.

Almost always, denial management in healthcare relies on human analysts and AR specialists. Very few of them are using RPA (a form of automation) to assist them. However, it is still a problem that requires trained resources. For a large multi-specialty healthcare provider group with over 500,000 annual claims, it is common to face issues of claim denials. – a significant portion due to missing documentation, simple claim errors, coding discrepancies, and payer-specific rule changes. They need between 25-30 staff to manage their denials.

How the Agentic AI Works in denial management?

1. Perceive: Ingest and Understand Denial Reasons

The agent connects EDI 835 files, EMR denial codes, and payer correspondence. It understands why a claim was denied – whether due to medical necessity, eligibility issues, documentation gaps, or coding errors.

Agentic Quality: It interprets denial context across multiple systems, without being explicitly programmed for each scenario.

2. Decide: Categorize and Prioritize

It doesn’t just list denials – it ranks them based on payer impact, appeal success probability, and filing deadlines. For example, a $4,200 denied inpatient claim close to appeal expiration is ranked higher than a $72 outpatient lab denial with a 60-day appeal window.

Agentic Quality: The agent sets sub-goals aligned with broader objectives like improving the recovery rate or reducing A/R.

3. Act: Identify the disposition and take action

One of the most intelligent and value-driving capabilities of Agentic AI is its ability to determine the disposition of a denial, that is, what steps should be taken next to resolve it.

While traditional systems flag denials or categorize them by reason codes, Agentic AI takes a step further. It assesses context, evaluates payer rules, analyzes supporting documentation, and uses past outcomes to recommend or execute the best action.

Some examples are;

Initiate an appeal with appropriate documentation

Resubmit the claim with corrected codes

Request additional documentation from the provider or EMR

Route to a specialist for clinical appeal or peer review

Flag as non-appealing and categorize as write-off or patient responsibility

Group for trend analysis to identify upstream issues

Here is a real-world example of the disposition of a denial.

A denied claim for CPT 99214 by Payer X is flagged. The Agentic AI checks:

• Denial code: CO-197 (Missing Authorization)
• Patient record: Authorization was obtained, but not linked in the submission
• Payer rules: Appeals allowed within 30 days, medical notes required

Disposition:
The AI agent initiates an appeal, attaches the correct prior authorization documentation and visit notes, and submits it within the payer’s time limit without waiting for a human analyst.

Agentic Quality: The AI acts proactively, initiating workflows toward resolution without waiting for a human trigger.

4. Learn: Track Outcomes and Adjust Strategy

The agent monitors appeal outcomes over time. If it observes that appeals for a specific combination of CPT and a payer have low success, it adjusts its strategy—either escalating earlier or flagging for coding audits.

Agentic Quality: It learns from outcomes and improves decision-making over time-a hallmark of agentic behavior.

5. Collaborate: Handoff and Notifications

For cases outside its scope (e.g., clinical appeal letters), the AI flags them, packages necessary data, and notifies the right human team. It even schedules follow-up reminders within the EMR work queue.

Agentic Quality: It collaborates with humans, knowing when to take action and when to defer intelligently.

OUTCOME

Over a period of 4-6 months, the providers can see:

• 18-20% reduction in A/R days
• 22-26% increase in first-level appeal success
• 40-60% reduction in manual denial touches

The new denial team with Agentic AI looks as below:

This is not automation as we’ve known it. This is agentic intelligence at work – not just reacting, but reasoning, acting, learning, and collaborating. And it’s setting a new standard for what AI can do in healthcare.

Agentic AI is not just automation with a brain – it’s automation with intent.
Understanding this distinction is key to making smart investments and setting the right expectations in healthcare RCM transformation.