Login / Signup

Evaluating Reception Center Models for Radiation Response Screening Capacity and Throughput Predictions.

Lauren FinkleaRobert GoffErica Houghton
Published in: Health physics (2024)
Introduction: The current fleet of nuclear reactors in the United States is mandated to provide evidence that surrounding jurisdictions can screen their populations should an incident occur. Capacity can be measured as throughput in reception centers used for screening. Due to the significant staffing and resources required to exercise screening capacity, most jurisdictions typically perform smaller exercises and use models to estimate their overall throughput. Objective: To evaluate the applicability and realism of current throughput models and practices. Methods: Throughput capacity for radiation screening is estimated with a mathematical model derived by the Federal Emergency Management Agency (FEMA). The Centers for Disease Control and Prevention developed a discrete event simulation model as a tool, SimPLER, to evaluate capacity and make throughput predictions. Model estimates will be compared and evaluated using timing data collected at a large-scale exercise. Results: The FEMA model estimated a throughput 41.2% higher than the actual radiation screening throughput, while the SimPLER model provided identical values. The FEMA and SimPLER models' predicted throughputs were 50% and 3.8%, respectively, higher than total exercise throughput. Applying each model to the throughput projections for a 12-hour shift, the FEMA model estimates ranged from 665 to 6,646 people and the SimPLER model yielded an estimated throughput of 1,809 people with a standard deviation of 74.6. Conclusion: Discrete event simulation models, such as SimPLER, may provide more realistic and accurate predictions of radiation screening and throughput capacity of reception centers than mathematical models such as the FEMA model.
Keyphrases
  • healthcare
  • high intensity
  • primary care
  • type diabetes
  • public health
  • cardiovascular disease
  • emergency department
  • radiation therapy
  • artificial intelligence
  • genetic diversity
  • emergency medical