  Recently the patient-centered medical home (PCMH) model has become a popular
team-based approach focused on delivering more streamlined care to patients. In
current practices of medical homes, a clinical-based prediction frame is
recommended because it can help match the portfolio capacity of PCMH teams with
the actual load generated by a set of patients. Without such balances in
clinical supply and demand, issues such as excessive under and over utilization
of physicians, long waiting time for receiving the appropriate treatment, and
non-continuity of care will eliminate many advantages of the medical home
strategy. In this paper, by extending the hierarchical generalized linear model
to include multivariate responses, we develop a clinical workload prediction
model for care portfolio demands in a Bayesian framework. The model allows for
heterogeneous variances and unstructured covariance matrices for nested random
effects that arise through complex hierarchical care systems. We show that
using a multivariate approach substantially enhances the precision of workload
predictions at both primary and non-primary care levels. We also demonstrate
that care demands depend not only on patient demographics but also on other
utilization factors, such as length of stay. Our analyses of a recent data from
Veteran Health Administration further indicate that risk adjustment for patient
health conditions can considerably improve the prediction power of the model.
