A High-Fidelity, Machine-Learning Enhanced Queueing Network Simulation Model For Hospital Ultrasound Operations

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 understand 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 and use machine learning tools to calibrate the routing components from data. Our empirical results show that the calibrated model is of high fidelity and yields accurate prediction results for the performance metrics.