Process indicators and outcome measures in the treatment of Acute Myocardial Infarction patients

Studies of variations in healthcare utilization and outcome involve the analysis of multilevel, clustered data, considering in particular the estimation of a cluster-specific adjusted response, covariate effects and components of variance. Besides reporting on the extent of observed variations, these studies quantify the role of contributing factors including patients’ and providers’ characteristics. In addition, they may assess the relationship between healthcare process and outcomes. We consider Bayesian generalized linear mixed models to analyze MOMI2 (Month MOnitoring Myocardial Infarction in MIlan) data on patients admitted with ST-elevation myocardial infarction (STEMI) diagnosis in the hospitals belonging to the Milano Cardiological Network. Both clinical registries and administrative databanks were used to predict survival probabilities. We fit a logit model for the survival probability with one random effect (the hospital), under a semiparametric prior. We take advantage of the in-built clustering property of the Dirichlet process prior assumed for the random-effects parameters to obtain a classification of providers