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Risk adjustment of Medicare capitation payments using the CMS-HCC model.

Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Lezzoni LI, Ingber MJ, Levy JM, Robst J - Health Care Financ Rev (2004)

Bottom Line: This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees.We explain the model's principles, elements, organization, calibration, and performance.Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed.

View Article: PubMed Central - PubMed

Affiliation: RTI International, Waltham, MA 02452, USA. gpope@rti.org

ABSTRACT
This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees. We explain the model's principles, elements, organization, calibration, and performance. Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed.

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Model Explanatory Power as a Function of Number of Hierarchical Condition Categories (HCC)
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f4-hcfr-25-4-119: Model Explanatory Power as a Function of Number of Hierarchical Condition Categories (HCC)

Mentions: We investigated the relationship between number of diagnostic categories used in the DCG/HCC model and its predictive power (Pope et al., 2001). Figure 4 plots the relationship between number of diagnostic categories and model explanatory power measured by R2. Diagnostic categories (HCCs) were entered into the model in descending order of their incremental explanatory power using stepwise regression. The base model (with zero HCCs) includes 26 demographic variables, the 24 age/sex cells, and Medicaid and originally disabled status. Its R2 is 1.69 percent.


Risk adjustment of Medicare capitation payments using the CMS-HCC model.

Pope GC, Kautter J, Ellis RP, Ash AS, Ayanian JZ, Lezzoni LI, Ingber MJ, Levy JM, Robst J - Health Care Financ Rev (2004)

Model Explanatory Power as a Function of Number of Hierarchical Condition Categories (HCC)
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC4194896&req=5

f4-hcfr-25-4-119: Model Explanatory Power as a Function of Number of Hierarchical Condition Categories (HCC)
Mentions: We investigated the relationship between number of diagnostic categories used in the DCG/HCC model and its predictive power (Pope et al., 2001). Figure 4 plots the relationship between number of diagnostic categories and model explanatory power measured by R2. Diagnostic categories (HCCs) were entered into the model in descending order of their incremental explanatory power using stepwise regression. The base model (with zero HCCs) includes 26 demographic variables, the 24 age/sex cells, and Medicaid and originally disabled status. Its R2 is 1.69 percent.

Bottom Line: This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees.We explain the model's principles, elements, organization, calibration, and performance.Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed.

View Article: PubMed Central - PubMed

Affiliation: RTI International, Waltham, MA 02452, USA. gpope@rti.org

ABSTRACT
This article describes the CMS hierarchical condition categories (HCC) model implemented in 2004 to adjust Medicare capitation payments to private health care plans for the health expenditure risk of their enrollees. We explain the model's principles, elements, organization, calibration, and performance. Modifications to reduce plan data reporting burden and adaptations for disabled, institutionalized, newly enrolled, and secondary payer subpopulations are discussed.

Show MeSH