{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T06:46:37Z","timestamp":1782542797950,"version":"3.54.5"},"reference-count":12,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T00:00:00Z","timestamp":1782518400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/100006034","name":"University of Southern California","doi-asserted-by":"crossref","id":[{"id":"10.13039\/100006034","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Emerg Radiol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To evaluate emergency CT interpretation demand during major sporting events, characterize surge-related backlog and turnaround time, and estimate how threshold-based teleradiology support could affect queue performance during short event-related imaging boluses.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a Monte Carlo discrete-event simulation in Python to model an 8-h emergency radiology shift with baseline CT demand and a 2-h event-related surge. CT arrivals followed a Poisson process. Local and teleradiology radiologists were modeled as parallel servers with stochastic interpretation times. Two scenarios were compared across 500 Monte Carlo iterations: local radiologists only and local radiologists supplemented by a rapid-response teleradiology team activated when the unread CT backlog reached 10 cases. Primary outcomes included mean turnaround time, 90th percentile turnaround time, unread queue wait time, maximum unread backlog, number of cases delayed for more than 60\u00a0min, and time above the activation threshold.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The model generated a mean of 132.7 CT examinations per shift in both scenarios. Local-only coverage resulted in a mean turnaround time of 48.7\u00a0min and a 90th percentile turnaround time of 89.6\u00a0min. With teleradiology support, the mean turnaround time decreased to 18.0\u00a0min, and the 90th percentile to 37.3\u00a0min. Mean maximum unread backlog decreased from 27.7 to 16.2 cases, and the mean number of cases delayed longer than 60\u00a0min decreased from 47.4 to 2.0. Sensitivity analysis showed that saturation risk increased sharply when surge arrivals rose and local staffing remained limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Discrete-event simulation can help emergency radiology departments estimate when CT interpretation workflows may become saturated during major sporting events. In this model, threshold-based teleradiology reduced backlog and turnaround time but functioned as a surge buffer rather than a substitute for adequate local staffing and predefined escalation pathways.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s10140-026-02510-1","type":"journal-article","created":{"date-parts":[[2026,6,27]],"date-time":"2026-06-27T06:01:44Z","timestamp":1782540104000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Modeling emergency radiology demand for FIFA 2026 and the Los Angeles 2028 Olympic Games using discrete-event simulation"],"prefix":"10.1007","author":[{"given":"Jonathan","family":"Lee","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nathan","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric D.","family":"Cyphers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brandon K. 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Ethics approval was not required.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to publish"}},{"value":"The authors have no competing interests to declare that are relevant to the content of this article.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}