# Fixing Denial Codes | Agentic AI in Revenue Cycle Management

Canonical: https://www.flobotics.io/blog/fixing-denial-codes-rcm-automation
Author: Jędrzej Szymula
Published: 2025-10-11
Updated: 2026-09-08

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[Home](https://www.flobotics.io/)/[Articles](https://www.flobotics.io/blog)/Fixing Denial Codes | Agentic AI in Revenue Cycle Management

[AI](https://www.flobotics.io/blog?category=ai)[Denials](https://www.flobotics.io/blog?category=denials)[Eligibility](https://www.flobotics.io/blog?category=eligibility)[Healthcare](https://www.flobotics.io/blog?category=healthcare)[RCM](https://www.flobotics.io/blog?category=rcm)

# Fixing Denial Codes | Agentic AI in Revenue Cycle Management

[Jędrzej SzymulaContent Manager ](https://www.flobotics.io/our-editorial-team#jedrzej)
Published October 11, 2025Updated September 8, 2026

On this page

- [Before You Read | Useful Definitions](#before-you-read-useful-definitions)

- [Intro | Fixing Denial Codes](#intro-fixing-denial-codes)

- [Why Are Denial Codes Such a Challenge?](#why-are-denial-codes-such-a-challenge)

- [Scope | The Hidden Cost of Doing Nothing](#scope-the-hidden-cost-of-doing-nothing)

- [Advice | Automate Instead of Hiring](#advice-automate-instead-of-hiring)

- [Examples | Fixing Denial Codes](#examples-fixing-denial-codes)

- [Summary | Why AI Automation?](#summary-why-ai-automation)

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### 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.

## Intro | Fixing Denial Codes

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 [private payers are now denying 15% of all claims on the first submission](https://www.hfma.org/revenue-cycle/denials-management/navigating-the-rising-tide-of-denials/), with [ACA marketplace plans reaching a 19% in-network denial rate](https://www.hfma.org/fast-finance/aca-marketplace-plans-payment-denial/) — the highest level in nine years. (Source: HFMA, Aug 2024 & Feb 2025, citing Premier and KFF data — Tier 2)

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.

According to [KFF's analysis of HealthCare.gov issuer data](https://www.kff.org/private-insurance/healthcare-gov-insurers-denied-nearly-1-in-5-in-network-claims-in-2023-but-information-about-reasons-is-limited-in-public-data/), insurers denied nearly 1 in 5 in-network claims in 2023. (Source: KFF, 2024 — Tier 2)

### Why Are Denial Codes Such a Challenge?

Payers use X12 Claim Adjustment Reason Codes (CARCs) to explain payment adjustments, including denials.

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:

- Denial Code: | Meaning:

- CO 50 | Non-covered services

- CO 96 | Non-covered charges

- PI 252 | Missing or invalid data

- N393 | Invalid modifier combination

- OA 18 | Duplicate claim submission

## Scope | The Hidden Cost of Doing Nothing

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:

- Process | Manual Work | Automation | Monthly Volume(avg.) | Hours Saved

- Denial reconsiderations | 6 min | 0.5 min | 1,000 | 92

- CPT code corrections | 3 min | 0.3 min | 3,000 | 135

- Eligibility rechecks | 4 min | 0.2 min | 1,500 | 95

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

Let’s talk!
It’s a 30-minute call. Within the first 15, we’ll know if automation works for you.

[Book a call →](https://tidycal.com/team/flobotics/flobotics-intro-call-w)

## Advice | Automate Instead of Hiring

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

Accepting inefficient reality isn't sustainable.

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 Health survey data cited by [HFMA](https://www.hfma.org/fast-finance/aca-marketplace-plans-payment-denial/), 84% of healthcare finance leaders say reducing denials is a top priority, and 45% planned to boost investment in claims-management technology within six months to address it. (Source: HFMA, Feb 2025, citing Experian Health — Tier 2)

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](https://www.flobotics.io/blog/agentic-ai-in-healthcare) – a smarter, more sustainable path to growth.

#### How Does a Fully Automated Denial Management Process Works?

Here is a short explanation – step by step:

- 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.

- Retrieve – AI agents fetch supporting documentation – medical records, lab reports, or eligibility data – from internal systems or cloud storage.

- Fix – If the denial matches a known pattern (e.g., CO 96 – missing documentation), the AI agent fills in the missing data automatically.

- Resubmit – The AI agent logs into payer portals (UHC, BCBS, Aetna) or APIs, uploads documentation, and resubmits claims for reconsideration.

- 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:

## Examples | Fixing Denial Codes

Stil not convinced?

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

#### [PathGroup Case Study](https://www.flobotics.io/case-studies/up-to-80-higher-throughput-in-rcm---denials-management-automation):

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: ~3,000 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

## Summary | Why AI Automation?

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.

See how our [denials management automation](https://www.flobotics.io/agents/denials-management-automation) handles document gathering, supported corrections and payer follow-up.

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

[👉 Book a call](https://www.flobotics.io/contact)

## FAQ:

What are the top 5 denials in medical billing?

The most common causes are eligibility problems (inactive coverage, wrong plan code), missing or invalid claim data, missing or expired prior authorization, non-covered services, and missing supporting documentation. Eligibility, authorization, and non-covered-service denials tend to be the hardest to reverse, while missing data and documentation issues are usually correctable and resubmittable.

What are the top 20 denial codes in medical billing?

There's no single official 'top 20' ranking — the most frequent codes vary by payer, specialty, and claim type, and the full standardized code list runs into the hundreds. The codes discussed most often across the industry cluster around eligibility, authorization (CO-197), coding and documentation (CO-4, CO-252), and medical necessity (CO-50).

What does CO4 mean?

CO-4 means the procedure code billed is inconsistent with the modifier used, or that a required modifier is missing entirely. It's a coding-level issue, so it's typically a soft denial a coder can fix by correcting or adding the modifier and resubmitting.

What does denial code CO 242 mean?

CO-242 means the service wasn't provided by a network or primary care provider as the plan requires. It's a network or referral-related denial, and it tends to sit closer to a hard denial since it usually reflects a real plan-design restriction rather than a paperwork error.

What is a 252 denial code?

CO-252 means the payer needs an attachment or additional documentation before it will adjudicate the claim. It's one of the more automatable denial types in RCM, since the same missing-documentation request tends to repeat in a predictable, rule-based pattern.

Written byJędrzej Szymula
Content Manager . [More from Jędrzej](https://www.flobotics.io/our-editorial-team#jedrzej)

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