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AI-DRAFTED — PHYSICIAN REVIEW IN PROGRESSThe citation for this entry was checked against Crossref and PubMed, but its summary and interpretation were drafted by an AI model from the published abstract and have not yet been individually read by a physician. Read the linked source before relying on this operationally.
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Modeling emergency radiology demand for FIFA 2026 and the Los Angeles 2028 Olympic Games using discrete-event simulation[1]

Summary

Monte Carlo discrete-event simulation (LA General / USC group) of an 8-hour emergency radiology shift with a 2-hour event-related CT surge, built for FIFA 2026 and LA 2028 planning. With ~133 CTs per shift, local-only coverage produced mean turnaround 48.7 min (90th percentile 89.6) and 47.4 cases delayed >60 min; adding a teleradiology team triggered at a 10-case unread backlog cut mean turnaround to 18.0 min, the 90th percentile to 37.3, and >60-min delays to 2.0. Sensitivity analysis: saturation risk rises sharply when surge arrivals grow against fixed local staffing. Concludes threshold-triggered teleradiology is a surge buffer, not a substitute for adequate local staffing and predefined escalation.

So what

The first published capacity model for the US mega-event decade aimed at a downstream chokepoint nobody's event medical plan owns: CT interpretation. Its design pattern — a numeric backlog threshold that auto-triggers surge support — is the hospital-side twin of the show-stop trigger doctrine, and its punchline (buffers help, baseline staffing decides saturation) generalizes to every event-adjacent hospital resource. Direct planning input for FIFA 2026 / LA28 host hospitals and a methods template MGMI can point US planners at.

Key findings

  • Discrete-event simulation of event CT surge for FIFA 2026 / LA 2028
  • Teleradiology at 10-case backlog threshold: turnaround 48.7→18.0 min; >60-min delays 47.4→2.0
  • Saturation risk rises sharply with surge volume against fixed local staffing
  • Threshold-triggered surge support = buffer, not substitute for baseline capacity
  • Transferable pattern: numeric auto-escalation thresholds for hospital-side event resources

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