HCC Audit Strategies For Improving V28 Coding Accuracy

Last Updated on September 28, 2026

With the 2024 CMS-HCC model, commonly referred to as V28, fully phased in for non-PACE Medicare Advantage organizations in 2026, HCC coding accuracy requires closer attention to documentation, diagnosis specificity, current mappings, and coding validation.

The transition is more than a change in HCC numbers. Diagnosis-to-HCC mappings, condition categories, coefficients, and model logic have changed. Audit processes built around historical V24 assumptions may therefore overlook coding and documentation issues that matter under the current model.

An effective HCC audit should answer two questions:

Are clinically supported diagnoses being captured accurately, and are reported diagnoses adequately supported by the medical record?

That balance is critical. Missed supported diagnoses can affect the accuracy of risk adjustment data, while unsupported coding can create compliance and audit exposure.

Why HCC Audits Matter More Under V28

For CY 2026, CMS calculates 100% of applicable non-PACE Medicare Advantage risk scores using the 2024 CMS-HCC model. Organizations are no longer operating within the transition blend of the previous and updated models.

This makes current-model alignment an important audit priority.

An HCC audit based primarily on historical category numbers or V24 mapping logic can produce an incomplete picture of current coding performance. Audit criteria should reflect the model, ICD-10-CM mappings, and coding requirements applicable to the period being reviewed.

CMS publishes both initial and midyear/final 2026 model software and ICD-10-CM mappings, giving organizations a current reference point for model-specific validation.

Organizations needing broader context on the model transition can also review Understanding HCC Coding and Risk Adjustment in 2026.

The objective of an audit is not to identify the largest possible number of HCCs. It is to determine whether coded risk adjustment data accurately represents supported clinical information.

What an HCC Coding Audit Should Evaluate

A strong HCC coding audit should examine more than whether a diagnosis maps to a payment HCC. The review should evaluate the complete path between the clinical record and submitted coded data.

Key audit areas include:

• Whether reported diagnoses are supported by clinical documentation
• Whether ICD-10-CM codes reflect the specificity available in the record
• Whether current diagnosis-to-HCC mappings are being applied
• Whether clinically supported diagnoses were missed
• Whether unsupported diagnoses were reported
• Whether documentation reflects the patient’s current clinical status
• Whether coding patterns vary significantly across providers or teams
• Whether recurring errors indicate a workflow or education issue
• Whether records can support subsequent validation or audit activity

A structured medical coding audit can provide an independent view of documentation-to-code accuracy and help identify recurring patterns that may not be visible through routine coding quality checks.

Five HCC Audit Strategies for Improving V28 Coding Accuracy

1. Audit Against Current V28 Mappings

Start by confirming that the audit uses the correct CMS-HCC model and current ICD-10-CM mappings. 

CMS publishes model-specific ICD-10-CM mappings and software for 2026. Audit tools, encoder logic, internal reports, suspecting algorithms, and educational materials should be reviewed for alignment with the current model.

This matters because a diagnosis that mapped or behaved a certain way under V24 should not automatically be assumed to have the same treatment under V28.

Auditors should verify:

• Current ICD-10-CM-to-HCC mapping
• Applicable HCC category
• Model version used by internal tools
• Outdated V24 references in reports or workflows
• Consistency between coding systems and audit methodology

Model alignment should happen before conclusions are drawn about HCC capture or coding performance.

2. Validate Documentation Before Evaluating HCC Capture

An HCC mapping does not establish that a diagnosis is appropriate for reporting.

Auditors should first determine whether the clinical record supports the coded diagnosis. Documentation should provide sufficient clinical context and specificity for the code assigned.

Common issues may include diagnoses carried forward without adequate current context, problem-list conditions treated as encounter diagnoses without sufficient support, inconsistent documentation, or codes that are more specific than the underlying record.

MEAT, meaning Monitor, Evaluate, Assess or Address, and Treat, can be a useful framework for reviewing clinical attention to a condition. However, it should be used alongside the complete medical record, official coding guidance, and applicable CMS requirements rather than treated as a standalone CMS rule.

AnnexMed’s resource on HCC coding and documentation provides additional context on documentation practices that support accurate diagnosis coding.

3. Audit Both Missed and Unsupported Diagnoses

HCC audits should work in both directions.

A review focused only on missed diagnoses can overlook unsupported coding. A review focused only on compliance errors may fail to identify clinically supported conditions that were not accurately represented in coded data.

Audit methodology should therefore distinguish among:

• Supported and accurately coded diagnoses: Documentation and coding are aligned.
• Supported but missed or inaccurately coded diagnoses: The record contains relevant clinical information, but the coded data does not accurately represent it.
• Reported but insufficiently supported diagnoses: The coded diagnosis cannot be adequately substantiated by the available documentation.

This approach gives compliance, coding, and risk adjustment teams a more balanced picture of data integrity.

4. Segment Audit Findings Instead of Relying on an Overall Error Rate

An overall coding accuracy percentage can hide the operational cause of an issue. Audit findings become more useful when segmented by factors such as:

• Provider
• Specialty
• HCC or disease category
• Diagnosis type
• Facility or location
• Documentation issue
• Coder or coding team
• Prospective versus retrospective workflow
• Error type

For example, repeated specificity issues within one disease category may require different corrective action from unsupported diagnoses concentrated among a particular workflow or provider group.

This is where coding and documentation analytics can extend the value of chart-level auditing by showing whether individual findings represent isolated errors or broader patterns.

5. Turn Audit Findings Into Corrective Action

An audit that ends with an accuracy report has limited operational value. Findings should feed directly into corrective actions such as:

• Targeted provider education
• Coder feedback and training
• Updated coding references
• Documentation improvement initiatives
• Changes to prospective review logic
• QA rule modifications
• Workflow corrections
• Focused re-audits

Corrective action should also be specific to the underlying problem. A documentation gap should not automatically be treated as a coder error, and a mapping problem should not be addressed solely through provider education.

Organizations should track whether the corrective action actually reduces recurrence in subsequent reviews.

Using HCC Audits to Strengthen RADV Readiness

HCC coding audits and RADV readiness are closely connected because both depend on the ability to support submitted risk adjustment diagnoses with appropriate documentation and data.

RADV activity remains particularly relevant in 2026. CMS has published updated RADV guidance and audit schedules and initiated audits for multiple payment years during the year, including Payment Year 2024 audits announced in August 2026.

Internal HCC audits can help organizations identify vulnerabilities before records are requested for external validation.

A RADV-focused audit approach should evaluate:

• Documentation supporting submitted diagnoses
• Consistency between coded data and the medical record
• Record completeness and accessibility
• Recurring unsupported diagnosis patterns
• Internal validation and escalation processes
• Corrective action documentation

Audit readiness should be an ongoing control rather than a process that begins only after an organization receives an audit notice.

For organizations managing Medicare Advantage populations, healthcare payer services can also connect risk adjustment review with broader payer operations, data quality, and compliance workflows.

HCC Audit Checklist for V28

Before closing an HCC audit cycle, confirm that the review has addressed:

• Current V28 model logic and mappings
• ICD-10-CM coding accuracy and specificity
• Documentation support for reported diagnoses
• Potential missed supported diagnoses
• Potential unsupported diagnoses
• Provider-level documentation patterns
• Coding and workflow trends
• Quality assurance controls
• Corrective actions for identified issues
• Follow-up auditing
• RADV readiness

The checklist should not be treated as a one-time exercise. Findings from one audit cycle should inform the scope and priorities of the next.

Turning HCC Audit Findings Into Better V28 Coding Accuracy 

V28 changes the model environment, but improving coding accuracy depends on what organizations do with the issues their audits uncover.

An effective HCC audit program connects documentation review, coding validation, analytics, provider feedback, corrective action, and follow-up auditing. This creates a continuous process for identifying both missed supported diagnoses and unsupported coding while improving the reliability of risk adjustment data.

AnnexMed supports health plans, provider organizations, ACOs, and value-based care programs with prospective and retrospective HCC coding, chart review, documentation assessment, quality assurance, analytics, and audit readiness.

Through Risk Adjustment and HEDIS Programs, organizations can connect audit findings with broader risk adjustment workflows and build stronger controls around documentation quality and coding accuracy.

The goal is not simply to pass an audit or capture additional HCCs. It is to build a repeatable process that produces accurate, supported, and defensible risk adjustment data.

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