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A Ash

Publications and source records attributed to A Ash.

39 records · Page 3Linked to original sources

Risk adjustment for measuring health outcomes: an application in VA long-term care.

An empirically derived risk adjustment model is useful in distinguishing among facilities in their quality of care. We used Veterans Affairs (VA) administrative databases to develop and validate a risk adjustment model to predict decline in functional status, an important outcome measure in long-term care, among patients residing in VA long-term care facilities. This model was used to compare facilities on adjusted and unadjusted rates of decline. Predictors of decline included age, time between assessments, baseline functional status, terminal illness, pressure ulcers, pulmonary disease, cancer, arthritis, congestive heart failure, substance-related disorders, and various neurologic disorders. The model performed well in the development and validation databases (c statistics, 0.70 and 0.68, respectively). Risk-adjusted rates and rankings of facilities differed from unadjusted ratings. We conclude that judgments of facility performance depend on whether risk-adjusted or unadjusted decline rates are used. Valid risk adjustment models are therefore necessary when comparing facilities on outcomes.

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Refinements to the Diagnostic Cost Group (DCG) model.

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.

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