  Inpatient care is a large share of total health care spending, making
analysis of inpatient utilization patterns an important part of understanding
what drives health care spending growth. Common features of inpatient
utilization measures include zero inflation, over-dispersion, and skewness, all
of which complicate statistical modeling. Mixture modeling is a popular
approach that can accommodate these features of health care utilization data.
In this work, we add a nonparametric clustering component to such models. Our
fully Bayesian model framework allows for an unknown number of mixing
components, so that the data determine the number of mixture components. When
we apply the modeling framework to data on hospital lengths of stay for
patients with lung cancer, we find distinct subgroups of patients with
differences in means and variances of hospital days, health and treatment
covariates, and relationships between covariates and length of stay.
