Risk-adjusted quality measures are used to evaluate healthcare providers
while controlling for factors beyond their control. Existing healthcare
provider profiling approaches typically assume that the risk adjustment is
perfect and the between-provider variation in quality measures is entirely due
to the quality of care. However, in practice, even with very good models for
risk adjustment, some between-provider variation will be due to incomplete risk
adjustment, which should be recognized in assessing and monitoring providers.
Otherwise, conventional methods disproportionately identify larger providers as
outliers, even though their provider effects need not be "extreme.'' Motivated
by efforts to evaluate the quality of care provided by transplant centers, we
develop a composite evaluation score based on a novel individualized empirical
null method, which robustly accounts for overdispersion due to unobserved risk
factors, models the marginal variance of standardized scores as a function of
the effective center size, and only requires the use of publicly-available
center-level statistics. The evaluations of United States kidney transplant
centers based on the proposed composite score are substantially different from
those based on conventional methods. Simulations show that the proposed
empirical null approach more accurately classifies centers in terms of quality
of care, compared to existing methods