MEASURING PERFORMANCE FOR END-OF-LIFE CARE

Abstract

Although not without controversy, readmission is entrenched as a hospital quality metric with statistical analyses generally based on fitting a logistic-Normal generalized linear mixed model. Such analyses, however, ignore death as a competing risk, although doing so for clinical conditions with high mortality can have profound effects; a hospital's seemingly good performance for readmission may be an artifact of it having poor performance for mortality. in this paper we propose novel multivariate hospital-level performance measures for readmission and mortality that derive from framing the analysis as one of cluster-correlated semi-competing risks data. We also consider a number of profiling-related goals, including the identification of extreme performers and a bivariate classification of whether the hospital has higher-/lower-than-expected readmission and mortality rates via a Bayesian decision-theoretic approach that characterizes hospitals on the basis of minimizing the posterior expected loss for an appropriate loss function. in some settings, particularly if the number of hospitals is large, the computational burden may be prohibitive. To resolve this, we propose a series of analysis strategies that will be useful in practice. Throughout, the methods are illustrated with data from CMS on N = 17,685 patients diagnosed with pancreatic cancer between 2000-2012 at one of J = 264 hospitals in California.

Keywords: Bayesian decision theory; hierarchical modeling; provider profiling; quality of care; semicompeting risks.

Fig. 1.

Marginal 90-day readmission and 90-day mortality rates across J = 264 hospitals in California with at least 10 patients aged 65 years or older and diagnosed with pancreatic cancer between 2000–2012. Note, the rates are marginal in the sense that they are not covariate-adjusted.

Fig. 2.

Excess 90-day readmission and 90-day mortality ratios across J = 264 hospitals in California with, at least, 10 patients aged 65 years or older and diagnosed with pancreatic cancer between 2000–2012. Shown in panels (a) and (b) are posterior medians, color coded by whether the value are less than or greater than 1.0: in (a) the results are based on a Bayesian fit of the logistic-Normal GLMM (see Section 3); in (b) the results are based on a PEM-MVN semi-competing risk model (see Sections 4 and 5). In panels (c) and (d), hospitals indicated with green dots were reclassified as having lower-than-expected readmission or mortality by the semicompeting risks analysis (i.e., benefitted), while those indicated with a red dot were reclassified as having higher-than-expected readmission or mortality (i.e. lost).

Fig. 3.

Excess readmission and mortality ratios, evaluated at multiple time windows following discharge, across J = 264 hospitals in California with at least 10 patients aged 65 years or older, and diagnosed with pancreatic cancer between 2000–2012.