Date: 15 Feb 2007

Assessing surrogacy of data sources for institutional comparisons

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Abstract

Can administrative claims data, Z, serve as a surrogate for better clinical data, X, when assessing institutional performance? We consider an analysis of I hospitals, each of which involves an adjusted outcome. In the i th hospital, we denote the true association between the outcome and the risk factors using one data source by θ i (X), the true association between the outcome and the risk factors using the other data source by γ i (Z), and assume we have estimates of each available. Within hospital i, the estimated association parameters are jointly normally distributed such that conditional on γ i (Z), a simple linear relationship exists between θ i (X) and γ i (Z). Methods are illustrated using mortality rates for 181,032 elderly US heart attack patients treated at 4322 hospitals. We find a strong linear relationship between the hospital standardized mortality rates adjusted by risk factors found in administrative claims data and rates adjusted by risk factors found in medical charts (posterior mean [95% interval] for slope: 0.997 [0.965,1.028]). However, the absolute and relative differences between the two sets of rates increase as hospital volume increases. For typically-sized standard deviations of claims-based rates, there is reasonable certainty of quality problems when the hospital’s claims-based rate is 0.72 times or smaller than the national mean or 1.45 times or greater than the national mean. Fewer hospitals are classified as either low-mortality or high-mortality hospitals when using claims-based estimates compared to chart-based estimates.