Meeting Banner
Abstract #4343

Using machine learning with dynamic exam block lengths to decrease patient wait time and optimize MRI schedule fill rate.

Michael C. Muelly1, Paul B. Stoddard1, and Shreyas S. Vasanwala1

1Department of Radiology, Stanford University, Stanford, CA, United States

MRI has advantages compared to other radiologic modalities in terms of tissue visualization, versatility, and lack of risks associated with ionizing radiation. However, cost of MRI is often the limiting factor favoring other modalities. Using historical scanner data and a Monte Carlo type discrete event simulation, we investigated how estimating exam length on the basis of patient demographics and dynamic block lengths affect mean patient wait times and schedule fill rate. In our simulation we are able to significantly lower mean patient wait times and optimize the schedule fill rate, which would theoretically result in lower cost per exam while enhancing patient satisfaction.

This abstract and the presentation materials are available to members only; a login is required.

Join Here