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Inpatient care is a large share of total health care spending, makinganalysis of inpatient utilization patterns an important part of understandingwhat drives health care spending growth. Common features of inpatientutilization measures include zero inflation, over-dispersion, and skewness, allof which complicate statistical modeling. Mixture modeling is a popularapproach that can accommodate these features of health care utilization data.In this work, we add a nonparametric clustering component to such models. Ourfully Bayesian model framework allows for an unknown number of mixingcomponents, so that the data determine the number of mixture components. Whenwe apply the modeling framework to data on hospital lengths of stay forpatients with lung cancer, we find distinct subgroups of patients withdifferences in means and variances of hospital days, health and treatmentcovariates, and relationships between covariates and length of stay.

Subgroup Identification and Interpretation with Bayesian Nonparametric Models in Health Care Claims Data