{"doi":"10.1016/j.jacr.2024.12.010","title":"Risk-Stratified Screening: A Simulation Study of Scheduling Templates on Daily Mammography Recalls","abstract":"INTRODUCTION: 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. METHODS: 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. RESULTS: 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). CONCLUSION: 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.","journal":"Journal of the American College of Radiology","year":2025,"id":517136,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":8,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9504,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":767368,"name":"Anne Hoyt","orcid":null,"position":1,"is_corresponding":false},{"id":308072,"name":"Vladimir Manuel","orcid":"0000-0001-8101-5885","position":2,"is_corresponding":false},{"id":341927,"name":"Moira Inkelas","orcid":"0000-0003-1963-2430","position":3,"is_corresponding":false},{"id":971998,"name":"Mehmet Ayvaci","orcid":"0000-0001-6997-1639","position":4,"is_corresponding":false},{"id":12554,"name":"Mehmet Eren Ahsen","orcid":"0000-0002-4907-0427","position":5,"is_corresponding":false},{"id":422504,"name":"William Hsu","orcid":"0000-0002-5168-070X","position":6,"is_corresponding":false},{"id":563800,"name":"Yannan Lin","orcid":"0000-0003-3514-6475","position":0,"is_corresponding":true}],"reference_count":17,"raw_metadata":null,"created_at":"2026-07-19T02:48:54.768083Z","pmid":"40044308","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}