Evidence map›Paper›PMID 40044308›Full record

ArticleJournal of the American College of Radiology : JACR2025

Risk-Stratified Screening: A Simulation Study of Scheduling Templates on Daily Mammography Recalls.

Yannan Lin, Anne C Hoyt, Vladimir G Manuel, Moira Inkelas, Mehmet Ulvi Saygi Ayvaci, Mehmet Eren Ahsen, William Hsu

Abstract read
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In one paragraph

Article in Journal of the American College of Radiology : JACR, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Review
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Yannan LinMedical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at the University of California, Los Angeles (UCLA), Los Angeles, California. Electronic address: yannanlin@mednet.ucla.edu.
Anne C HoytDepartment 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.
Vladimir G ManuelDepartment of Family Medicine, David Geffen School of Medicine at UCLA, Los Angeles, California; UCLA Clinical and Translational Science Institute, Los Angeles, California.
Moira InkelasDepartment of Health Policy and Management, UCLA Fielding School of Public Health, Los Angeles, California; Co-Lead, Population Health Program, UCLA Clinical and Translational Science Institute, Los Angeles, Califonia; Co-Lead, UCLA TL1 Translational Science Training Program, Los Angeles, California.
Mehmet Ulvi Saygi AyvaciArea Coordinator, Healthcare Management, Naveen Jindal School of Management, The University of Texas at Dallas, Richardson, Texas.
Mehmet Eren AhsenDepartment of Business Administration, Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, Illinois; Department of Biomedical and Translational Sciences, Carle Illinois School of Medicine, University of Illinois at Urbana-Champaign, Urbana, Illinois.
William HsuMedical & Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, Los Angeles, California; Deputy Editor, Radiology: Artificial Intelligence; Department of Bioengineering, University of California, Los Angeles, Los Angeles, California. Electronic address: https://twitter.com/uclawillhsu.

Funding

AHRQ HHS R21 HS029257AHRQ HHS R33 HS029257
6 · The paper itself

Abstract

introductionRisk-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.

methodsWe 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.

resultsThe 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).

conclusionOur 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.

Indexed as

Appointments and SchedulesBreast NeoplasmsEarly Detection of CancerMammographyArtificial IntelligenceComputer SimulationFemaleHumansMiddle AgedRisk AssessmentTriageWorkflowappointment schedulingartificial intelligence (AI)breast cancer screeningclinical workflowrisk-stratified screening

Identifiers

PMID40044308
PMCPMC11925393

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.