A High-fidelity, Machine-learning Enhanced Queueing Network Simulation Model for Hospital Ultrasound Operations

TL;DR

This paper presents a high-fidelity simulation model for hospital ultrasound operations, integrating machine learning to accurately predict queue lengths and waiting times by modeling complex patient routing.

Contribution

It introduces a novel two-level routing component and applies machine learning for precise calibration of the queueing network model.

Findings

Accurately predicts queue length and waiting time distributions.

Enhances simulation fidelity for hospital ultrasound centers.

Addresses complex patient routing with a novel modeling approach.

Abstract

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.