How Can AI-Powered DRG Editing Improve Claims Accuracy? 

Claims inaccuracy has long been a pain point in the healthcare industry. Processing inefficiencies lead to challenges like payment delays, retroactive changes, damaged member and provider relationships, and even potential regulatory complications for healthcare payers.

Recent advancements in artificial intelligence (AI) and machine learning are driving innovations in claims processing. AI-powered tools can increase precision throughout the claims process, improving efficiency and helping reduce payment errors.

In a recent webinar from HealthEdge Source, healthcare industry experts shared effective payment integrity strategies based on their experiences, and how AI-powered solutions focused on diagnosis-related group (DRG) editing can help bring health plans into a more digitally advanced—and compliant—future.

The Scope of Claims Accuracy Challenges

Accurate claims processing is foundational to healthcare payments, yet industry statistics around inaccuracies are cause for concern. Historically, it’s been estimated that 3% to 7% of claims contain errors, though some health plans report inaccuracies exceeding 10% (Figure 1). The process is becoming harder to manage as claims grow more complex due to new care methods, constantly changing regulations, and fragmented systems.

Figure 1

For health plans, claims processing errors create a domino effect of issues. Providers have to deal with payment disputes and time-consuming corrections. Manual processes and disconnected systems slow workflows down, adding unnecessary costs. Regulatory risks from poor claims accuracy can hurt quality scores and even impact funding. And ultimately, members lose trust when payment delays and mistakes disrupt their care.

This isn’t just an internal problem for health plans—it’s one that affects the entire healthcare ecosystem. The study featured in the webinar showed that more than 91% of health plan leaders list addressing claims inaccuracies with AI as a top priority, with many wanting to act within the next year (Figure 2).

Figure 2

How AI is Reshaping Claims Accuracy

Artificial intelligence is becoming an essential tool in tackling claims processing challenges. By automating error detection and simplifying workflows, AI-powered tools can help health plans manage complex claims more efficiently and without manual intervention.

Traditional methods of claims processing often rely on disconnected, reactive processes. AI, on the other hand, integrates into existing systems to improve decision-making as it happens. Here’s how AI makes a difference in claims management:

• Automating claims adjudication: By taking over repetitive tasks, AI eliminates manual errors and speeds up the claims process. This allows claims to move faster through the system and frees staff to focus on more complex or high-priority work.
• Validating claims against clinical standards: AI checks codes for accuracy and alignment with customizable guidelines, minimizing mistakes and making sure payments match the care provided.
• Cutting post-payment recovery time: Errors caught early in the claims process means fewer corrections are needed down the line. AI can identify issues at the start, helping avoid the costly and time-consuming “pay and chase” cycle.

Advance DRG Editing with AI-Powered Tools

One particularly compelling application of AI is its use in DRG editing, a process critical to ensuring accurate payments for inpatient care claims.

Diagnosis-related groups define payment amounts for inpatient hospital services based on diagnoses, procedures, and related factors. Errors in coding or sequencing can lead to significant overpayments, making DRG accuracy a top priority for many health plans.

An AI-driven DRG solution can address common challenges by offering a sophisticated approach to analyzing claims and improving coding accuracy. The HealthEdge Source DRG Guide, powered by Gynisus, already produces significant results, helping health plans detect errors earlier, reduce inaccuracies, and improve payment processes.

Key applications of AI in claims editing

The DRG Guide incorporates advanced policy analytics to tackle errors related to:

• Code exclusions: Identifying conflicting diagnosis codes that cannot logically apply to the same claim.
• Sequencing errors: Ensuring that primary and secondary diagnosis codes are correctly ordered based on clinical logic and regulatory guidelines.

By performing automated claims reviews at the beginning of the process, the solution helps prevent costly errors from cascading through the adjudication process. This adaptability is critical to ensure efficient and accurate claims management.

For existing HealthEdge customers, using the AI-powered DRG Guide is straightforward and hassle-free. It works just like any new feature you’d find in a release note. There’s no complicated setup or extra technical work involved. Once enabled, the solution seamlessly integrates into your current system, letting you start resolving errors and improving accuracy right away.

Success Metrics and Measurable Impact

Pilot programs using the DRG Guide have shown significant results in improving claims accuracy and reducing costs for health plans by focusing on DRG editing and coding compliance.

Key Insights from the AI-Powered DRG Guide Pilot Programs 

A recent review of 100,000 healthcare claims (from a total of 200 million) provided valuable insights across both inpatient and outpatient cases. Of the claims analyzed, we discovered 19% required adjustments. We also identified $18 million in potential savings within this small sample alone, demonstrating the system’s ability to scale effectively for large volumes of claims.

Real Claims, Real Impact  

HealthEdge Source DRG Guide helped payers identify claims errors before they escalated into bigger (and more costly) issues.

For one health plan, a claim failed CMS guidelines due to coding violations, which traditionally would have resulted in denied reimbursement and additional administrative work. The DRG Guide flagged the errors, corrected them, and aligned them with regulatory guidance. This saved the health plan $22,580 while preventing costly rework.

A second health plan submitted a claim that had diagnosis codes sequenced incorrectly, leading to potential reimbursement errors. The DRG Guide automatically reordered the codes to align with ICD-10 guidelines, eliminating inaccuracies and the need for manual corrections later in the process.

These early results show what’s possible when claims are reviewed early and accurately. Digital solutions like the DRG Guide are helping health plans catch errors before they turn into bigger problems, reducing redundant work and lowering administrative costs. Whether it’s fixing a single claim or scaling up to review higher claims volumes, the process is proving efficient and practical with the DRG Guide.

What’s Next for AI in Claims Accuracy? 

Claims accuracy is a vital factor in developing equitable provider relationships, maintaining regulatory compliance, and securing member satisfaction. AI is reshaping how the healthcare industry tackles data inaccuracies, bringing much-needed precision and efficiency to payment processes.

The early results from AI-powered DRG editing prove its potential to drive significant improvements for health plans. Beyond the cost savings and operational ease, these solutions offer a roadmap for how the industry can transition from reactive to proactive approaches in addressing claims accuracy.

Our current focus on DRG editing is just the beginning. Through the HealthEdge partnership with Gynisus, we’re expanding the scope of our solutions to take on more challenges in claims processing. Looking ahead, we’re focusing on areas like medical necessity evaluations, AI for fraud detection, and streamlining the entire claims process. Our goal is to build a smarter, more efficient digital ecosystem that simplifies workflows and improves accuracy across the board.

AI won’t just make the current system better—it will redefine what “better” means. For health plans seeking to start or expand their AI initiatives, learn more work with AI in payment integrity.