Measuring performance for end-of-life care

TL;DR

This paper develops new statistical methods to evaluate hospital performance in end-of-life care by jointly analyzing readmission and mortality rates, accounting for their competing risks, and applies these methods to pancreatic cancer data.

Contribution

It introduces multivariate performance measures based on semi-competing risks and Bayesian decision theory for hospital profiling, addressing limitations of traditional univariate analyses.

Findings

New joint measures for readmission and mortality performance.

Effective hospital classification of performance levels.

Practical strategies for large-scale analysis.

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 hospitals 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.