  We collaborate with a large teaching hospital in Shenzhen, China and build a
high-fidelity simulation model for its ultrasound center to predict key
performance metrics, including the distributions of queue length, waiting time
and sojourn time, with high accuracy. The key challenge to build an accurate
simulation model is to understanding the complicated patient routing at the
ultrasound center. To address the issue, we propose a novel two-level routing
component to the queueing network model. We apply machine learning tools to
calibrate the key components of the queueing model from data with enhanced
accuracy.
