Lin Yannan, Hoyt Anne C, Manuel Vladimir G, Inkelas Moira, Ayvaci Mehmet Ulvi Saygi, Ahsen Mehmet Eren, Hsu William
Medical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at the University of California, Los Angeles (UCLA), Los Angeles, California.
Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California; Medical Director, Barbara Kort Women's Imaging Center, Santa Monica, California; Medical Director, Santa Monica-UCLA Integrated Breast Care Clinic, Santa Monica, California; Co-Medical Director of Breast Imaging, UCLA Health. Electronic address: https://twitter.com/justAnneMD.
J Am Coll Radiol. 2025 Mar;22(3):297-306. doi: 10.1016/j.jacr.2024.12.010.
Risk-stratified screening (RSS) scheduling may facilitate more effective use of same-day diagnostic testing for potentially abnormal mammograms, thereby reducing the need for follow-up appointments ("recall"). Our simulation study assessed the potential impact of RSS scheduling on patients recommended for same-day diagnostics.
We used a discrete event simulation to model workflow at a high-volume breast imaging center, incorporating artificial intelligence (AI)-triaged same-day diagnostic workups after screening mammograms. The RSS design sequences patients in the daily screening schedule using cancer risk categories developed from Tyrer-Cuzick and deep learning model scores. We compared recall variance, required hours of operation to accommodate all patients, and patient wait times using traditional (random) and RSS schedules.
The baseline simulation included 60 daily patients, with an average of 42% receiving screening mammograms and 11% (about three patients) being recommended for diagnostic workups. Compared with traditional scheduling, RSS scheduling reduces recall variance by up to 30% (1.98 versus 2.82, P < .05). With same-day diagnostics, RSS scheduling had a modest impact, increasing the number of patients served within normal operating hours by up to 1.3% (55.4 versus 54.7, P < .05), decreasing necessary operational hours by 12 min (10.3 versus 10.5 hours, P < .05), and increasing patient waiting times by an average of 2.4 min (0.24 versus 0.20 hours, P < .05).
Our simulation study suggests that RSS scheduling could reduce recall variance. This approach might enable same-day diagnostics using AI triage by accommodating patients within normal operating hours.
风险分层筛查（RSS）计划可能有助于更有效地利用当日诊断检测来处理可能异常的乳房X光检查结果，从而减少后续预约（“召回”）的需求。我们的模拟研究评估了RSS计划对被推荐进行当日诊断的患者的潜在影响。
我们使用离散事件模拟来模拟一家高流量乳腺影像中心的工作流程，纳入了乳房X光筛查后由人工智能（AI）分类的当日诊断检查。RSS设计根据从泰勒-库齐克模型和深度学习模型分数得出的癌症风险类别，在每日筛查计划中对患者进行排序。我们比较了传统（随机）计划和RSS计划下的召回差异、容纳所有患者所需的运营小时数以及患者等待时间。
基线模拟包括每天60名患者，平均42%的患者接受乳房X光筛查，11%（约三名患者）被推荐进行诊断检查。与传统计划相比，RSS计划将召回差异降低了30%（分别为1.98和2.82，P < 0.05）。在当日诊断的情况下，RSS计划有一定影响，将正常工作时间内服务的患者数量最多增加了1.3%（分别为55.4和54.7，P < 0.05），将必要的运营小时数减少了12分钟（分别为10.3和10.5小时，P < 0.05），并将患者等待时间平均增加了2.4分钟（分别为0.24和0.20小时，P < 0.05）。
我们的模拟研究表明，RSS计划可以降低召回差异。这种方法可能通过在正常工作时间内接纳患者，实现使用AI分类进行当日诊断。
