Abstract
BackgroundThe Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-HCC) model to risk adjust Medicare capitation payments. This study intends to assess the performance of the CMS-HCC risk adjustment method and to compare it to the Charlson and Elixhauser comorbidity measures in predicting in-hospital and six-month mortality in Medicare beneficiaries.MethodsThe study used the 2005-2006 Chronic Condition Data Warehouse (CCW) 5% Medicare files. The primary study sample included all community-dwelling fee-for-service Medicare beneficiaries with a hospital admission between January 1st, 2006 and June 30th, 2006. Additionally, four disease-specific samples consisting of subgroups of patients with principal diagnoses of congestive heart failure (CHF), stroke, diabetes mellitus (DM), and acute myocardial infarction (AMI) were also selected. Four analytic files were generated for each sample by extracting inpatient and/or outpatient claims for each patient. Logistic regressions were used to compare the methods. Model performance was assessed using the c-statistic, the Akaike's information criterion (AIC), the Bayesian information criterion (BIC) and their 95% confidence intervals estimated using bootstrapping.ResultsThe CMS-HCC had statistically significant higher c-statistic and lower AIC and BIC values than the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality across all samples in analytic files that included claims from the index hospitalization. Exclusion of claims for the index hospitalization generally led to drops in model performance across all methods with the highest drops for the CMS-HCC method. However, the CMS-HCC still performed as well or better than the other two methods.ConclusionsThe CMS-HCC method demonstrated better performance relative to the Charlson and Elixhauser methods in predicting in-hospital and six-month mortality. The CMS-HCC model is preferred over the Charlson and Elixhauser methods if information about the patient's diagnoses prior to the index hospitalization is available and used to code the risk adjusters. However, caution should be exercised in studies evaluating inpatient processes of care and where data on pre-index admission diagnoses are unavailable.
Highlights
The Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-hierarchical condition categories (HCCs)) model to risk adjust Medicare capitation payments
The CMS-Hierarchical Condition Category (CMS-HCC) model is preferred over the Charlson and Elixhauser methods if information about the patient’s diagnoses prior to the index hospitalization is available and used to code the risk adjusters
Caution should be exercised in studies evaluating inpatient processes of care and where data on pre-index admission diagnoses are unavailable
Summary
The Centers for Medicare and Medicaid Services (CMS) has implemented the CMS-Hierarchical Condition Category (CMS-HCC) model to risk adjust Medicare capitation payments. The Centers for Medicare and Medicaid Services (CMS) has implemented a risk adjustment system, the CMS hierarchical condition categories (CMS-HCC) model to adjust capitation payments made to private plans in Medicare [3] It “uses demographics and a diagnosis-based medical profile captured during all clinician encounters–both inpatient and outpatient–to produce a health-based measure of future medical need”[3,4]. In addition to generating a series of condition categories, it generates a summary risk score for each patient Because it was originally developed for payment purposes, the CMS-HCC has been shown to be a significant predictor of health care costs, but has yet to be tested with health outcomes. Current methods of risk adjustment may be improved with the additional information retrieved by the CMS-HCC
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