CMS HCC risk adjustment

Principles for Medicare Risk Adjustment

The CMS-HCC risk adjustment model is prospective—it uses demographic information (age, sex, Medicaid dual eligibility, disability status) and a profile of major medical conditions in the base year to predict Medicare expenditures in the next year.  It is calibrated on the FFS population because this population, unlike the MA population, submits complete Medicare claims data, including both diagnoses and expenditures.  Determining which diagnosis codes should be included, how they should be grouped, and how the diagnostic groupings should interact for risk adjustment purposes was a critical step in the development of the model. The following 10 principles guided the creation of the CMS-HCC diagnostic classification system:

Principle 1 Diagnostic categories should be clinically meaningful.  Each diagnostic category is a set of ICD-9-CM codes (Centers for Disease Control and Prevention [CDC], 2010).  These codes should all relate to a reasonably well-specified disease or medical condition that defines the category.  Conditions must be sufficiently clinically specific to minimize opportunities for gaming or discretionary coding.  Clinical meaningfulness improves the face validity of the classification system to clinicians, its interpretability, and its utility for disease management and quality monitoring.

Principle 2 Diagnostic categories should predict medical expenditures.  Diagnoses in the same HCC should be reasonably homogeneous with respect to their effect on both current (this year’s) and future (next year’s) costs.  

Principle 3 Diagnostic categories that will affect payments should have adequate sample sizes to permit accurate and stable estimates of expenditures.  Diagnostic categories used in establishing payments should have adequate sample sizes in available data sets.  Given the extreme skewness of medical expenditure data, the data cannot reliably determine the expected cost of extremely rare diagnostic categories.

Principle 4 In creating an individual’s clinical profile, hierarchies should be used to characterize the person’s illness level within each disease process, while the effects of unrelated disease processes accumulate.  Because each new medical problem adds to an individual’s total disease burden, unrelated disease processes should increase predicted costs of care.  However, the most severe manifestation of a given disease process principally defines its impact on costs.  Therefore, related conditions should be treated hierarchically, with more severe manifestations of a condition dominating (and zeroing out the effect of) less serious ones.

Principle 5 The diagnostic classification should encourage specific coding.  Vague diagnostic codes should be grouped with less severe and lower-paying diagnostic categories to provide incentives for more specific diagnostic coding.

Principle 6 The diagnostic classification should not reward coding proliferation. The classification should not measure greater disease burden simply because more ICD-9-CM codes are present.  Hence, neither the number of times that a particular code appears, nor the presence of additional, closely related codes that indicate the same condition should increase predicted costs.

Principle 7 Providers should not be penalized for recording additional diagnoses (monotonicity).  This principle has two consequences for modeling: (1) no condition category (CC) should carry a negative payment weight, and (2) a condition that is higher-ranked in a disease hierarchy (causing lower-rank diagnoses to be ignored) should have at least as large a payment weight as lower-ranked conditions in the same hierarchy.

Principle 8 The classification system should be internally consistent (transitive). If diagnostic category A is higher-ranked than category B in a disease hierarchy, and category B is higher-ranked than category C, then category A should be higher-ranked than category C.  Transitivity improves the internal consistency of the classification system and ensures that the assignment of diagnostic categories is independent of the order in which hierarchical exclusion rules are applied.

Principle 9 The diagnostic classification should assign all ICD-9-CM codes (exhaustive classification).  Because each diagnostic code potentially contains relevant clinical information, the classification should categorize all ICD-9-CM codes. 
Principle 10 Discretionary diagnostic categories should be excluded from payment models. Diagnoses that are particularly subject to intentional or unintentional discretionary coding variation or inappropriate coding by health plans/providers, or that are not clinically or empirically credible as cost predictors, should not increase cost predictions.  Excluding these diagnoses reduces the sensitivity of the model to coding variation, coding proliferation, gaming, and upcoding.


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