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A Simple Graphical Method for Quantification of Disaster Management Surge Capacity Using Computer Simulation and Process-control Tools

  • Jeffrey Michael Franc (a1) (a2), Pier Luigi Ingrassia (a2), Manuela Verde (a2), Davide Colombo (a2) and Francesco Della Corte (a2)...
Abstract
Introduction

Surge capacity, or the ability to manage an extraordinary volume of patients, is fundamental for hospital management of mass-casualty incidents. However, quantification of surge capacity is difficult and no universal standard for its measurement has emerged, nor has a standardized statistical method been advocated. As mass-casualty incidents are rare, simulation may represent a viable alternative to measure surge capacity.

Hypothesis/Problem

The objective of the current study was to develop a statistical method for the quantification of surge capacity using a combination of computer simulation and simple process-control statistical tools. Length-of-stay (LOS) and patient volume (PV) were used as metrics. The use of this method was then demonstrated on a subsequent computer simulation of an emergency department (ED) response to a mass-casualty incident.

Methods

In the derivation phase, 357 participants in five countries performed 62 computer simulations of an ED response to a mass-casualty incident. Benchmarks for ED response were derived from these simulations, including LOS and PV metrics for triage, bed assignment, physician assessment, and disposition. In the application phase, 13 students of the European Master in Disaster Medicine (EMDM) program completed the same simulation scenario, and the results were compared to the standards obtained in the derivation phase.

Results

Patient-volume metrics included number of patients to be triaged, assigned to rooms, assessed by a physician, and disposed. Length-of-stay metrics included median time to triage, room assignment, physician assessment, and disposition. Simple graphical methods were used to compare the application phase group to the derived benchmarks using process-control statistical tools. The group in the application phase failed to meet the indicated standard for LOS from admission to disposition decision.

Conclusions

This study demonstrates how simulation software can be used to derive values for objective benchmarks of ED surge capacity using PV and LOS metrics. These objective metrics can then be applied to other simulation groups using simple graphical process-control tools to provide a numeric measure of surge capacity. Repeated use in simulations of actual EDs may represent a potential means of objectively quantifying disaster management surge capacity. It is hoped that the described statistical method, which is simple and reusable, will be useful for investigators in this field to apply to their own research.

Franc JM , Ingrassia PL , Verde M , Colombo D , Della Corte F . A Simple Graphical Method for Quantification of Disaster Management Surge Capacity Using Computer Simulation and Process-control Tools. Prehosp Disaster Med. 2015;30(1):1-7 .

Copyright
Corresponding author
Correspondence: Jeffrey Michael Franc, MD, MSc, FCFP.EM, Dip Sport Med, EMDM University of Alberta 1G1.50 Walter Mackenzie Centre 8440 - 112 Street Edmonton, Alberta, Canada T6G 2B7 E-mail jeffrey.franc@gmail.com
Footnotes
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Conflicts of interest/funding: The simulation software used in this study (SurgeSim) is property of the primary author (JMF) and is subject to a registered trademark. No authors have received external funding for this study.

Footnotes
References
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1. Hick, JL, Hanfling, D, Burstein, JL, et al. Health care facility and community strategies for patient care surge capacity. Ann Emerg Med. 2004;44(3):253-261.
2. American College of Emergency Physicians. Health care system surge capacity recognition, preparedness, and response. Ann Emerg Med. 2005;45(2):239.
3. Bayram, JD, Sauer, LM, Catlett, C, et al. Critical resources for hospital surge capacity: an expert consensus panel. PLoS Curr. 2013;5.
4. Handler, JA, Gillam, M, Kirsch, TD, Feied, CF. Metrics in the science of surge. Acad Emerg Med. 2006;13(11):1173-1178.
5. Kaji, A, Koenig, KL, Bey, T. Surge capacity for healthcare systems: a conceptual framework. Acad Emerg Med. 2006;13(11):1157-1159.
6. Schull, MJ, Guttmann, A, Leaver, CA, et al. Prioritizing performance measurement for emergency department care: consensus on evidence-based quality of care indicators. CJEM. 2011;13(5):300-309.
7. Bogucki, S. Novel metrics for quality of hospital surge capacity. Acad Emerg Med. 2012;19(3):336-337.
8. Ott, ER, Schilling, EG, Neubauer, DV, (eds). Process Quality Control: Troubleshooting And Interpretation of Data. Milwaukee, Wisconsin USA: American Society for Quality; 2005:628.
9. Franc-Law, JM, Bullard, M, Della Corte, F. Simulation of a hospital disaster plan: a virtual, live exercise. Prehosp Disaster Med. 2008;23(4):346-353.
10. Franc-Law, JM, Ingrassia, PL, Ragazzoni, L, Della Corte, F. The effectiveness of training with an emergency department simulator on medical student performance in a simulated disaster. CJEM. 2010;12(1):27-32.
11. Gilbert, EH, Lowenstein, SR, Koziol-McLain, J, Barta, DC, Steiner, J. Chart reviews in emergency medicine research: where are the methods? Ann Emerg Med. 1996;27(3):305-308.
12. Dong, SL, Bullard, MJ, Meurer, DP, et al. Reliability of computerized emergency triage. Acad Emerg Med. 2006;13(3):269-275.
13. Dong, SL, Bullard, MJ, Meurer, DP, et al. Predictive validity of a computerized emergency triage tool. Acad Emerg Med. 2007;14(1):16-21.
14. Simple Triage and Rapid Treatment. START Triage Web site. http://www.start-triage.com. Accessed May 25, 2011.
15. de Boer, J, Debacker, M. A more rational approach to medical disaster management applied retrospectively to the Enschede fireworks disaster, 13 May 2000. Eur J Emerg Med. 2003;10(2):117-123.
16. MedStatStudio Web site. http://www.medstatstudio.com/radmac.R. Accessed October 16, 2014.
17. Peng, RD. Reproducible research in computational science. Science. 2011;334(6060):1226-1227.
18. Maran, NJ, Glavin, RJ. Low- to high-fidelity simulation–a continuum of medical education? Med Educ. 2003;37(s1):22-28.
19. Carron, PN, Trueb, L, Yersin, B. High-fidelity simulation in the nonmedical domain: practices and potential transferable competencies for the medical field. Adv Med Educ Pract. 2011;2:149-155.
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Prehospital and Disaster Medicine
  • ISSN: 1049-023X
  • EISSN: 1945-1938
  • URL: /core/journals/prehospital-and-disaster-medicine
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