Composite Scores for Transplant Center Evaluation: A New Individualized Empirical Null Method

TL;DR

This paper introduces a new individualized empirical null method for evaluating transplant centers, which accounts for unobserved risk factors and overdispersion, leading to more accurate and fair quality assessments.

Contribution

The paper develops a novel composite score using an empirical null approach that improves transplant center evaluation by addressing incomplete risk adjustment and overdispersion.

Findings

The new score significantly alters center rankings compared to traditional methods.

Simulations demonstrate improved classification accuracy of center quality.

The method only requires publicly available center-level data.

Abstract

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.