Excludes1 Denials: A Growing Reimbursement Challenge for Provider Organizations

Excludes1 denials present the latest hidden barrier to healthcare reimbursement, and the problem isn’t going away. While the concept is as old as the ICD-10 diagnosis coding system itself, Excludes1 conflicts haven’t been a significant source of claim denials…until now. Anomaly has uncovered which payers are enforcing Excludes1 denials, and how, leading to lost revenue for provider organizations already struggling to maintain margins.

‍

What is an Excludes1 Rule?

In the ICD-10 coding system, an Excludes1 note indicates that two conditions cannot be reported together because they are mutually exclusive. For example, congenital and acquired forms of the same condition should not be coded simultaneously. These rules are designed to prevent coding errors that could result in inappropriate billing or misrepresentation of a patient’s condition. 

‍

Impact 

Anomaly reviewed over 250 million claims from our partner healthcare organizations and identified Excludes1 coding conflicts on 2.5% of claims. This translates to over $57 million in provider revenue at risk annually in our customer base alone.

‍

A Growing Problem 

The increasing enforcement of Excludes1 edits has led to a rise in claim denials. These denials pose financial and operational challenge for healthcare providers:

• Lack of Transparency: Unlike other denial types, there is no designated Claim Adjustment Reason Code or Remark Code indicating when a claim is denied for an Excludes1 violation, making it difficult for providers to quickly identify and address Excludes1 denials.
• Inconsistent Enforcement: Not all payers enforce Excludes1 denials, creating unpredictability. Our AI models show that each payer applies Excludes1 rules differently, resulting in inconsistent and unpredictable outcomes for provider organizations. For instance, our AI models identify distinctly different Excludes1 denial patterns from Blue Cross Blue Shield Texas versus Humana.
• Selective Application: Even when enforced, not all Excludes1 code combinations are consistently applied, and not all claim types are impacted.  For example, we see lab, durable medical equipment, and select specialties (e.g., anesthesiology) are disproportionately impacted by Excludes1 denials, though we are beginning to see these denials cropping up in inpatient claims as well.

These challenges complicate compliance efforts and increase the risk of revenue loss.

‍

Taking a Data-Driven Approach

Traditional rule-based approaches struggle to keep up with the complexities of payer enforcement. At Anomaly, we take a dynamic approach—leveraging AI to decode Excludes1 denial patterns, payer-specific behaviors, and enforceable rules to ensure claims are coded correctly before submission:

• Payer-Specific Enforcement Detection: Anomaly analyzes real-time denial patterns to pinpoint where and how different payers enforce Excludes1 rules—eliminating guesswork.
• Adaptive Intelligence: We build dynamic exclusion tables and refine them against actual denial trends.  When payers change their rules, so do we.
• AI-Assisted Claims Editing: We identify potential Excludes1 conflicts at the point of coding, along with other necessary conditions for payment approval, allowing providers to take corrective action before submission. 

‍

The Result: Fewer Denials, Faster Payments

With Anomaly’s AI-powered approach, providers gain real-time visibility into Excludes1 risks and payer-specific nuances, reducing denials and accelerating reimbursement. For one Anomaly customer, we reduced Excludes1 coding conflicts by 86% after deploying automated intelligence at billing.

‍

Let Anomaly show you the financial impact of Excludes1 denials and how to take back control of your reimbursement.

‍