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Media type:
Report;
E-Book
Title:
Risk-Adjusted Capitation Payments: How Well Do Principal Inpatient Diagnosis-Based Models Work in the German Situation? Results From a Large Data Set
Contributor:
Behrend, Corinne
[Author];
Buchner, Florian
[Author];
Happich, Michael
[Author];
Holle, Rolf
[Author];
Reitmeir, Peter
[Author];
Wasem, Jürgen
[Author]
imprint:
Essen: Universität Duisburg-Essen, Fachbereich Wirtschaftswissenschaften, 2004
Footnote:
Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
Description:
The Risk Adjustment Reform Act of 2001 mandates that a health-status-based risk adjustment mechanism has to be implemented in Germany's Statutory Health Insurance system by January 1, 2007. German parliament decided this as with the existing demographic risk adjustment model, that means there is cream skimming and sickness funds hesitate to engage in managing care for the chronical ill. Four approaches were used to test the feasibility of incorporating use of diagnosis as a proxy measure for health status in a German risk adjustment formula. The first two models used standard demographic and socio-demographic variables. The other two models are separately incorporating a simple binary indicator for hospitilization and Hierarchical Coexisting Conditions (HCCs: DxCG® Risk Adjustment Software Release 6.1) using inpatient diagnosis. Age and gender grouping accounted for 3.2% of the variation in total expenditures for concurrent as well as prospective models. The current German risk adjusters age, sex, and invalidity status account for 5.1% and 4.5% of the variance in the concurrent and prospective models respectively. There are substantial increases in explanatory power, however, when HCCs are added. Age, gender, invalidity status and HCC covariates explain about 37% of the variations of the total expenditures in a concurrent model and roughly 12% of the variations of total expenditures in a prospective model. For high-risk (cost) groups, substantial underprediction remains; conversely, for the low-risk group, represented by enrolees who did not show any health care expense in the base year, all of the models over-predict expenditure.