Risk-Stratified Screening: A Simulation Study of Scheduling Templates on Daily Mammography Recalls
Published:
Our paper, “Risk-Stratified Screening: A Simulation Study of Scheduling Templates on Daily Mammography Recalls,” was published in the Journal of the American College of Radiology in March 2025. The study examines how risk-stratified screening schedules can improve the organization of mammography workflows by coordinating screening examinations with same-day diagnostic evaluation for patients who may need additional imaging.
The study uses a discrete-event simulation of a high-volume breast imaging center and incorporates AI-based triage together with risk information from the Tyrer-Cuzick model and a deep-learning risk model. We compare alternative scheduling templates and examine how they affect daily recalls, workflow congestion, and the use of same-day diagnostic capacity. The results show how scheduling decisions can be designed around patient risk rather than treating every screening appointment in the same way.
More broadly, the work highlights an important connection between predictive analytics and operations management in healthcare: improving outcomes does not depend only on developing better risk models, but also on designing workflows that use those predictions effectively. Risk-stratified scheduling can help align diagnostic resources with patient needs and reduce unnecessary follow-up visits.
The paper was published in Journal of the American College of Radiology, volume 22, issue 3, pages 297–306, in March 2025.