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Risk-Stratified Screening: A Simulation Study of Scheduling Templates on Daily Mammography Recalls
Yannan Lin1, Anne C Hoyt2, Vladimir G Manuel3
1Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at the University of California, Los Angeles (UCLA), Los Angeles, California.
Journal of the American College of Radiology : JACR
|March 5, 2025
Summary
Risk-stratified screening (RSS) scheduling can reduce recall variance for mammograms. This approach may improve same-day diagnostic testing efficiency when combined with AI triage.
Area of Science:
- Breast imaging analytics
- Radiology workflow optimization
- Artificial intelligence in healthcare
Background:
- Mammography screening aims to detect breast cancer early.
- Abnormal mammograms often require same-day diagnostic testing.
- Reducing follow-up appointments (recalls) is a key efficiency goal.
Purpose of the Study:
- To assess the impact of risk-stratified screening (RSS) scheduling on same-day diagnostic testing.
- To evaluate how RSS affects recall variance, operational hours, and patient wait times.
- To determine the potential of RSS in conjunction with AI triage for mammography centers.
Main Methods:
- A discrete event simulation modeled a high-volume breast imaging center.
- Incorporated artificial intelligence (AI) for triaging same-day diagnostic workups.
- Compared traditional (random) scheduling with RSS using cancer risk categories.
Main Results:
- RSS scheduling reduced recall variance by up to 30%.
- RSS slightly increased patient throughput within operating hours (1.3%).
- Operational hours decreased by 12 minutes, with a 2.4-minute increase in patient wait times.
Conclusions:
- RSS scheduling shows potential to decrease recall variance in mammography.
- This strategy may facilitate same-day diagnostics when integrated with AI triage.
- RSS can help accommodate patients within standard operating hours, improving efficiency.
