Evidence map›Paper›PMID 42364073›Full record

ArticleEmergency radiology2026

Modeling emergency radiology demand for FIFA 2026 and the Los Angeles 2028 Olympic Games using discrete-event simulation.

Jonathan Lee, Nathan Kim, Eric D Cyphers, Brandon K K Fields, Bryce D Beutler, Ali Gholamrezanezhad, John Brunner

Abstract read
PubMed Publisher
In one paragraph

Article in Emergency radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Jonathan LeeDepartment of Radiology, Los Angeles General Medical Center, Los Angeles, CA, USA.
Nathan KimKeck School of Medicine of USC, Los Angeles, CA, USA.
Eric D CyphersDepartment of Radiology, Los Angeles General Medical Center, Los Angeles, CA, USA.
Brandon K K FieldsDepartment of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, USA.
Bryce D BeutlerDepartment of Radiology, Keck Medicine of USC, Los Angeles, CA, USA.
Ali GholamrezanezhadDepartment of Radiology, Los Angeles General Medical Center, Los Angeles, CA, USA. AGholamrezanezhad@dhs.lacounty.gov.ORCID http://orcid.org/0000-0001-6930-4246
John BrunnerDepartment of Radiology, Los Angeles County Department of Health Services, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo 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.

methodsWe 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 min, and time above the activation threshold.

resultsThe 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 min and a 90th percentile turnaround time of 89.6 min. With teleradiology support, the mean turnaround time decreased to 18.0 min, and the 90th percentile to 37.3 min. Mean maximum unread backlog decreased from 27.7 to 16.2 cases, and the mean number of cases delayed longer than 60 min decreased from 47.4 to 2.0. Sensitivity analysis showed that saturation risk increased sharply when surge arrivals rose and local staffing remained limited.

conclusionDiscrete-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.

Indexed as

Computed tomographyDiscrete-event simulationEmergency radiologyMass gatheringTeleradiologyTurnaround time

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.