Fixing Denial Codes | Agentic AI in Revenue Cycle Management

Before You Read | Useful Definitions

Denial codes are standardized alphanumeric codes used by insurance companies to explain why a reimbursement claim was rejected or denied.

They act as short “reason statements” that show healthcare providers what went wrong and what needs fixing before resubmission.

In short, denial codes explain why the insurer didn’t pay for the provided healthcare service.

Before we move forward, it’s crucial to explain one thing:

The higher the denial rate, the higher the cost of running healthcare services ->
That’s why fixing denial codes is so important in RCM field.

Logging into payer portals, uploading medical records, checking claim statuses, resubmitting…

Billing teams spend hours on repetitive corrections. On a larger scale, U.S. healthcare providers face:

• Rising claim volumes
• Stricter and constantly evolving payer requirements
• Labor shortages in billing teams

Recent industry data shows that around 11–12% of all healthcare claims in the U.S. are denied on first submission, with some payers reporting rates as high as 17–19%.

The cost to rework a single denied claim typically ranges from $25 to $180, depending on claim complexity and payer rules — most of it driven by avoidable administrative or documentation errors.

Why Are Denial Codes Such a Challenge?

Denial codes are part of the ANSI/Cobol standard used by payers like Blue Cross Blue Shield, UnitedHealthcare (UHC), and Aetna.

There are more than 300 active denial codes across U.S. payers – and their interpretations often vary slightly by insurer or region.

Multiply that by hundreds (or thousands) of monthly rejections, and it’s clear why denial management is one of the biggest pain points for RCM teams.

This complexity makes denial management challenging in terms of both training and processing speed.

Examples:

Let’s face it:

if your RCM team still handles denials manually, you’re losing money every month.

Even if your team resolves 95% of denials, the remaining 5% can still cost millions annually.

Example of Cost Savings:

That’s over 300 hours saved per month, or roughly 1.5–2 FTEs worth of productivity – and those are conservative estimates.

Operational costs are a bitter pill most RCM teams have learned to swallow.

Adding new people, expanding departments, to handle endless rework have long been treated as “just part of the process.”
But let’s be real:

Accepting inefficient reality isn’t sustainable.

We all know the formula: the higher your Cost to Collect (CTC), the lower your profitability.

Every new full-time hire adds salary, benefits, onboarding, and training costs.
Yet, despite all that investment, productivity doesn’t always grow proportionally.

This is where AI automation changes everything.

According to Experian’s State of Claims 2025 survey, among providers using AI, 69% report that it has reduced denials or increased resubmission success.

For the cost of a small implementation, one automation can do the work of multiple employees. Without fatigue, overtime pay, or human error.
It runs around the clock, processes hundreds of claims a day, and maintains perfect consistency even when volumes spike.

Instead of hiring more people to handle repetitive workflows, you extend the capacity of your existing team – scaling without recruiting.

The ROI speaks for itself: 
Each digital worker amplifies human efficiency rather than replacing it, freeing staff to focus on high-value analysis, appeals, and process optimization.

Simply put,

AI automation is the most scalable way to reduce CTC – a smarter, more sustainable path to growth.

How Does a Fully Automated Denial Management Process Works?

Here is a short explanation – step by step:

1. Identify – Ai agent scans your EHR or billing platform (e.g., Xifin, Epic, or PrognoCIS) for newly denied claims.
   AI models classify denials by reason code, payer, and priority.
2. Retrieve – AI agents fetch supporting documentation – medical records, lab reports, or eligibility data – from internal systems or cloud storage.
3. Fix – If the denial matches a known pattern (e.g., CO 96 – missing documentation), the AI agent fills in the missing data automatically.
4. Resubmit – The AI agent logs into payer portals (UHC, BCBS, Aetna) or APIs, uploads documentation, and resubmits claims for reconsideration.
5. Update – Finally, it updates EHR/CRM systems with claim statuses, notes, and ticket numbers while sending real-time alerts via Slack or email.

The entire cycle happens in seconds, not minutes – and 100% of the activity is logged for audit and compliance.

Interested in how such automation works in real life?

Here’s a recording of a real world example of claims processing automation:

Stil not convinced?

Here’s one of our case studies on AI automation:

PathGroup Case Study:

Challenge:
PathGroup, one of the largest pathology providers in the U.S., faced thousands of denials and coding errors monthly.
90% of denials were due to small, fixable issues like missing data or incorrect CPT codes.

Solution:
We’ve built an automation suite that:

• Identified repetitive denial codes (CO 50, PI 252)
• Cross-checked each denial with internal rules
• Corrected claims automatically

Here’s the ROI of this project:

Monthly volume: ~3000 errors/month corrected automatically

Time savings: ¼ FTE/monthly

Dart CHART Systems:

Challenge:
Manual reporting and data extraction were slowing operations and driving overtime.
Employees spent up to 36 hours downloading hundreds of reports — time-consuming, error-prone, and costly.

Solution:
We’ve deployed UiPath automations to handle report generation and data processing.
The automations completed tasks 3× faster, at one-third the cost, with full data security and zero downtime.

ROI:

• 66% faster processing
• 500+ reports automated
• 1-day implementation
• 6 automations built

AI automation offers a scalable, cost-effective alternative to traditional team expansion.

Each digital worker amplifies human efficiency rather than replacing it, freeing staff to focus on high-value analysis, appeals, and process optimization.

Simply put,

AI automation is the most scalable way to more sustainable growth.

Every automation we build is designed to deliver measurable returns – saving time, cutting costs, and improving cash flow.

Ready to automate denials and boost ROI? Let’s talk!

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