- Excludes1 denials are growing and costly
- Coding gaps make these errors easy to miss
- Manual rules can't keep up with payer edits
- Automation helps catch issues before claims go out
As payers intensify claims scrutiny through increasingly complex edits, Excludes1 denials – which result from violations of ICD-10-CM coding exclusions – have become a costly and growing issue for health systems. In some organizations, payers enabling Excludes1 edits has led to revenue losses exceeding $5 million per month, significantly impacting cash flow and burdening administrative teams.
Excludes1 meaning
Excludes1 is an ICD-10-CM coding directive that indicates two specific diagnoses should not be reported together because they are mutually exclusive.
For example:
F32.0 – Major depressive disorder, single episode, mild
Excludes1: F33.0-F33.9 – Major depressive disorder, recurrent
Why they exclude each other: ICD-10-CM guidelines state that a patient cannot simultaneously be diagnosed with a single and recurrent episode of major depression. Only one diagnosis should be coded based on the patient’s clinical history.
These types of exclusions help maintain clinical accuracy in coding but can easily result in denials if not identified prior to claim submission. This underscores the need for automated tools and education around Excludes1 rules.
Why are Excludes1 denials increasing?
Payers are implementing new Excludes1 edits. Professional claims are particularly at risk due to workflow and training gaps:
- Most are coded directly by providers, not professional coders
- Little to no coder review occurs prior to billing
- Providers may be unaware of ICD-10-CM guidelines
- There are few safeguards to catch these coding errors pre-bill
As a result, health systems experience:
- Prolonged denial-rework-appeal cycles
- Delayed cash flow
- Higher cost to collect
- Operational strain on revenue cycle teams
- Increased write-offs
In one large health system, an audit of 750,000 encounters revealed that 4.1% (30,750 encounters) contained diagnosis codes that excluded each other, demonstrating the widespread nature of the issue.
How to prevent Excludes1 denials and optimize reimbursement
To address this growing challenge, IMO Health has introduced a powerful new solution: Coding Intelligence, which automates the detection of Excludes1 violations before claims are submitted.
Key benefits and functionality:
- Automated alerts for revenue cycle managers and coders when diagnosis exclusions are detected
- Seamless integration into existing workflows, such as Epic’s claim edit work queues
- Enables pre-bill intervention, reducing denials and avoiding the costly rework cycle
- Helps providers by reducing documentation queries and enabling cleaner claims upfront
- Helps coders by identifying and categorizing potential Excludes1 violations for correction
Unlike traditional, manually maintained rulesets, which are difficult to scale and often incomplete, Coding Intelligence is powered by comprehensive, continuously updated Excludes1 content. One health system identified over 29 million potential diagnosis exclusion combinations, highlighting the impracticality of capturing these manually.
Why it matters now
Health systems are already facing resource constraints, and payer-driven edits like Excludes1 add further pressure. Without scalable, intelligent tools, teams are left scrambling to patch revenue leaks with labor-intensive processes that can’t keep pace.
IMO Health’s Coding Intelligence offers a better path forward; automating Excludes1 compliance, preventing denials before they happen, and restoring operational and financial stability to a strained healthcare environment.
