Risk adjustment involves billing diagnosis information that is properly supported in the medical record. It estimates the expected annual cost of care and aligns payments to the complexity of a patient’s conditions. Because payments rise with diagnosis severity, an improper diagnosis can map to a higher-value Hierarchical Condition Category (HCC), artificially inflating risk scores, increasing reimbursement, and making a patient appear sicker on paper than they are. As a result, risk adjustment accuracy is both a financial imperative and a growing enforcement focus.
What to Look For
Diagnosis validation and documentation integrity are central to appropriate billing. Severity inflation, or upcoding, occurs when a more serious condition is reported than the documentation supports—for example, major depression instead of situational anxiety. Phantom or unsupported diagnoses may also carry forward from legacy records, including acute conditions that should be reported as history of. For instance, billing acute stroke inflates risk far more than history of stroke.
Auditing for risk adjustment errors often requires a look-back approach. Focus on diagnoses that lack supporting evidence, such as related prescriptions or diagnostic testing. Also review permanent or recurrent conditions that should no longer appear; for example, a patient is unlikely to have an acute myocardial infarction every year. Deleting unsupported codes is just as important as reporting accurate ones initially. Also watch for diagnosis cloning, such as identical clinic notes or repeated diagnosis combinations across many patients.
These documentation and coding risks are drawing increased regulatory attention. In February 2026, the HHS Office of Inspector General issued Medicare Advantage compliance guidance highlighting key program risks, including risk adjustment and oversight of downstream entities[1]. CMS also recently published the CRUSH Initiative (Comprehensive Regulations to Uncover Suspicious Healthcare)[2].
HCFS Has You Covered
Prioritizing reviews for conditions that materially affect risk scores requires a layered approach. Payers should use advanced analytics to identify outlier billing patterns and unsupported diagnoses. If you’re using the HCFSPlatform™, our AI models can detect spikes and other anomalies, highlight outlier provider patterns, and compare peers to help identify inappropriate billing quickly. HCFS also offers dashboards and targeted alerts related to HCC diagnoses, including cardiorespiratory failure and shock, acute myocardial infarction, septicemia, and COPD. Examples include:
[2007-01] – MISREPRESENTATION OF SERVICES, HIERARCHICAL CONDITION CATEGORY – AGE
[2663-01] – MISREPRESENTATION OF DIAGNOSIS, RISK ADJUSTMENT, ACUTE MI, SEPTIC SHOCK, CVA
[1] https://oig.hhs.gov/compliance/ma-icpg/
[2] https://www.cms.gov/fraud
If you’re not using the HCFSPlatform™ and you’re interested in knowing more, email us at [email protected].
If you have questions or comments, you may email us at [email protected].
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