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Comparison of Prediction Models for Use of Medical Resources at Urban Auto-racing Events

Published online by Cambridge University Press:  26 September 2014


Jose V. Nable
Affiliation:
Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland USA Department of Emergency Health Services, University of Maryland Baltimore County, Baltimore, Maryland USA Department of Emergency Medicine, MedStar Georgetown University Hospital, Georgetown University School of Medicine, Washington D.C. USA
Asa M. Margolis
Affiliation:
Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland USA
Benjamin J. Lawner
Affiliation:
Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland USA Baltimore City Fire Department, Baltimore, Maryland USA
Jon Mark Hirshon
Affiliation:
Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland USA Charles McC. Mathias, Jr., National Study Center for Trauma and Emergency Medical Systems, University of Maryland School of Medicine, Baltimore, Maryland USA
Alexander J. Perricone
Affiliation:
Baltimore City Fire Department, Baltimore, Maryland USA
Samuel M. Galvagno
Affiliation:
Divisions of Critical Care and Trauma Anesthesiology, Department of Anesthesiology, University of Maryland School of Medicine, Baltimore, Maryland USA
Debra Lee
Affiliation:
Department of Emergency Medicine, University of Maryland School of Medicine, Baltimore, Maryland USA
Michael G. Millin
Affiliation:
Department of Emergency Medicine, The Johns Hopkins University School of Medicine, Baltimore, Maryland USA
Richard A. Bissell
Affiliation:
Department of Emergency Health Services, University of Maryland Baltimore County, Baltimore, Maryland USA
Richard L. Alcorta
Affiliation:
Maryland Institute for Emergency Medical Services Systems, Baltimore, Maryland USA
Corresponding
E-mail address:

Abstract

Introduction

Predicting the number of patient encounters and transports during mass gatherings can be challenging. The nature of these events necessitates that proper resources are available to meet the needs that arise. Several prediction models to assist event planners in forecasting medical utilization have been proposed in the literature.

Hypothesis/Problem

The objective of this study was to determine the accuracy of the Arbon and Hartman models in predicting the number of patient encounters and transportations from the Baltimore Grand Prix (BGP), held in 2011 and 2012. It was hypothesized that the Arbon method, which utilizes regression model-derived equations to estimate, would be more accurate than the Hartman model, which categorizes events into only three discreet severity types.

Methods

This retrospective analysis of the BGP utilized data collected from an electronic patient tracker system. The actual number of patients evaluated and transported at the BGP was tabulated and compared to the numbers predicted by the two studied models. Several environmental features including weather, crowd attendance, and presence of alcohol were used in the Arbon and Hartman models.

Results

Approximately 130,000 spectators attended the first event, and approximately 131,000 attended the second. The number of patient encounters per day ranged from 19 to 57 in 2011, and the number of transports from the scene ranged from two to nine. In 2012, the number of patients ranged from 19 to 44 per day, and the number of transports to emergency departments ranged from four to nine. With the exception of one day in 2011, the Arbon model overpredicted the number of encounters. For both events, the Hartman model overpredicted the number of patient encounters. In regard to hospital transports, the Arbon model underpredicted the actual numbers whereas the Hartman model both overpredicted and underpredicted the number of transports from both events, varying by day.

Conclusions

These findings call attention to the need for the development of a versatile and accurate model that can more accurately predict the number of patient encounters and transports associated with mass-gathering events so that medical needs can be anticipated and sufficient resources can be provided.

Nable JV , Margolis AM , Lawner BJ , Hirshon JM , Perricone AJ , Galvagno SM , Lee D , Millin MG , Bissell RA , Alcorta RL . Comparison of Prediction Models for Use of Medical Resources at Urban Auto-racing Events. Prehosp Disaster Med. 2014;29(6):1-6 .


Type
Original Research
Copyright
Copyright © World Association for Disaster and Emergency Medicine 2014 

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