Refinements to the Diagnostic Cost Group (DCG) model.
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TL;DR
This paper extends previous work by estimating the model using 1984-85 data and by developing a more thorough method for classifying hospitalizations by degrees of discretion, and explores the loss of predictive power resulting from not using diagnoses for the most discretionary hospitalizations for calculating payments.
Abstract
The Diagnostic Cost Group (DCG) model, originally developed by Ash et al. (1986, 1989), has been proposed as an alternative to the existing payment system for reimbursing Medicare health maintenance organizations, the Adjusted Average Per Capita Cost (AAPCC). The DCG model is a linear regression model that uses both demographic and diagnostic information to predict total plan payments for health care. This paper extends previous work by estimating the model using 1984-85 data and by developing a more thorough method for classifying hospitalizations by degrees of discretion. It also explores the loss of predictive power resulting from not using diagnoses for the most discretionary hospitalizations for calculating payments. The paper examines a number of extensions and refinements to the basic DCG model.