Eliminating Frequency-Based Denials with AI-Driven RCM 

Organization: QWay Healthcare 
Date: 19-11-2025 
Subtitle: An Upgraded Approach to Achieving Zero CO-119 Denials 

Executive Summary 

This case study examines how a multispecialty medical group in Northern California eliminated Medicare denials for screening colonoscopies. With increasing high-risk patients and incomplete screening histories, the organization faced repeated benefit-limit denials.  

By partnering with QWay Healthcare and implementing AI- and RPA-driven RCM improvements, the group eliminated CO-119 denials within six months, reduced RCM costs by 50%, and improved overall revenue cycle performance by 35%. 

Introduction 

Frequency-based denials are one of the most common yet preventable categories of Medicare rejections in preventive services. Nearly 48% of these denials occur due to patients reaching specific benefit limits, such as coverage caps for screenings, imaging, or other preventive procedures. This trend mirrored what the group was experiencing. 

The objective was to restore compliance, reduce avoidable denials, and eliminate surprise patient bills resulting from missed eligibility checks. 

Provider Background 

A mid-to-large-sized multispecialty group in Northern California with more than 50 providers began seeing a sharp rise in denials for high-risk screening colonoscopies.  

These CPT G0105 procedures are preventive tests for patients who have a higher chance of developing colorectal cancer. 

As more patients came in for these screenings, the group’s existing processes started to fall behind. The revenue cycle team depended on manual checks for eligibility and screening frequency, while patient records were often scattered across different health systems, making it challenging to confirm prior screenings. 

Without a reliable way to track this information, gaps and errors increased, leading to a growing number of denials. 

Problem Statement 

The increasing denials often came from Medicare, with “Benefit maximum exceeded (CO-119)” listed as the reason. Although the claims were correctly coded and documented, screenings were denied for exceeding Medicare’s limit for high-risk colonoscopies. 

The pattern became clearer when reviewing individual cases. John Henderson, a 58-year-old patient with a family history of colorectal cancer, was classified as high-risk and scheduled for a screening colonoscopy. His claim was correctly coded with CPT G0105 and ICD-10 Z80.0. 

When processed, the claim was denied because John’s previous colonoscopy, performed at an outside facility 22 months earlier, exceeded Medicare’s 24-month frequency limit for high-risk patients. This CO-119 denial highlighted inefficiencies in handling external screening records and challenges in verifying patient eligibility. 

To address this issue, the practice partnered with QWay Healthcare. 

Analysis and Findings 

Using their AI-driven RCM expertise, QWay Healthcare conducted a detailed review and identified several factors contributing to the denials: 

• Incomplete verification of prior screenings through HETS or MAC portals 

• Patients switching providers, resulting in fragmented or missing historical data 

• Screening dates missing from the EHR, often overlooked during manual review 

• Absence of automated frequency checks prior to claim submission 

These system gaps were causing repeated denials, delaying reimbursements, and impacting patient satisfaction.  

The findings made it clear that manual workflows and EHR-only tracking were insufficient and that a more automated, intelligent approach was needed to prevent future denials. 

Solution Implemented 

To address the root causes of CO-119 denials, QWay Healthcare implemented a comprehensive AI, ML, and RPA-driven RCM program. The solution focused on three core areas: 

1. Automated Prior Screening Verification (RPA) 
   RPA bots extracted prior colonoscopy data from HETS and MAC portals, filling gaps left by the EHR. This ensured that all previous screenings, including those performed outside the practice, were captured and verified before claims submission. 

2. Compliance Monitoring 
   Advanced ML models analyzed patients’ prior screening dates against Medicare’s 24-month interval for high-risk colonoscopies. Claims that fell outside the allowed timeframe were flagged before submission, preventing avoidable denials. 

3. RCM Workflow Automation 
   Automation improved eligibility checks, reduced manual errors, and prioritized claims at higher risk of denial. AI-driven recommendations guided staff on corrective actions, allowing the team to focus on resolving problematic claims efficiently. 

This approach directly addressed the causes of frequency-based denials, ensuring claims were submitted with complete patient histories and proper verification. 

Results 

Within six months of implementing QWay Healthcare’s AI-driven solution, the group achieved: 

• Automation of previously manual tasks 

• 100% elimination of frequency-based CO-119 denials 

• 35% faster overall revenue cycle 

• 50% reduction in RCM operational costs 

• Improved patient satisfaction with fewer surprise bills 

Conclusion 

By adopting QWay Healthcare’s RCM services, the multispecialty group closed long-standing lapses in eligibility verification and frequency compliance.  

Automation provided visibility into external screening data, enabling the organization to adhere to Medicare’s 24-month rules and completely prevent CO-119 denials.  

This solution not only reduced surprise billing for patients but also enhanced operational efficiency, demonstrating how intelligent automation, analytics, and machine learning can transform RCM practices in high-volume clinical environments. 

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