Abstract
Hospitals are critical infrastructures which are vulnerable to natural disasters, such as earthquakes, manmade disasters and mass causalities events. During an emergency, the hospital might also incur in structural and non-structural damage, have limited communication and resources, so they might not be able to treat the large number of incoming patients. For this reason, the majority of medium and large size hospitals have an emergency plan that expands their services quickly beyond normal operating conditions to meet an increased demand for medical care, but it is impossible for them to test it before an emergency occurs. In this chapter is presented a simplified model that can describe the ability of the Hospital Emergency Department to provide service to all patients after a natural disaster or any other emergency. The waiting time is the main response parameter used to measure hospital resilience to disasters. The analytical model has been built using the following steps. First, a discrete event simulation model of the Emergency Department in a hospital located in Italy is developed taking into account the hospital resources, the emergency rooms, the circulation patterns and the patient codes. The results of the Monte Carlo simulations show that the waiting time for yellow codes, when the emergency plan is applied, are reduced by 96 %, while for green codes by 75 %. Then, using the results obtained from the simulations, a general metamodel has been developed, which provides the waiting times of patients as function of the seismic input and the number of the available emergency rooms. The proposed metamodel is general and it can be applied to any type of hospital.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Cimellaro GP, Reinhorn AM, Bruneau M (2010) Seismic resilience of a hospital system. Struct Infrastruct Eng 6(1–2):127–144
Cimellaro G, Reinhorn AM, Bruneau M (2011) Performance-based metamodel for health care facilities. Earthq Eng Struct Dyn 40(11):1197–1217
Dansky KH, Miles J (1997) Patient satisfaction with ambulatory healthcare services: waiting time and filling time. Hosp Health Serv Adm 42(2):165–177
Davies R (2007) “see and treat” or “see” and “treat” in an emergency department. In: Henderson SG, Biller B, Hsieh MH, Shortle J, Tew JD, Barton RR (eds) Proceedings of the 2007 Winter Simulation Conference, Washington DC IEEE, pp 1498–1501
Duda CI (2011) Applying process analysis and discrete event simulation to improve access and customer service times at the Ronald Reagan UCLA Medical Center. Ph.D. thesis, University of California, Los Angeles (UCLA), Los Angeles
Fetter RB, Thompson JD (1965) The simulation of hospital systems. Oper Res 13(5):689–711
Günal M, Pidd M (2010) Discrete event simulation for performance modelling in health care: a review of the literature. J Simul 4:42–51
Hu X (2013) Optimization analysis of patient waiting time and resource utilization in an ob/gyn clinic – a simulation approach. Ph.D. thesis, University of Central Arkansas
Kirtland A, Lockwood J, Poisker K, Stamp L, Wolfe P (1995) Simulating an emergency department “is as much fun as…”. In: Proceedings of the 1995 winter simulation conference, Arlington, pp 1039–1042
Komashie A, Mousavi A (2005) Modeling emergency departments using discrete event simulation techniques. In: Proceedings of the winter simulation conference, Orlando. IEEE
Lau R (2008) Applying discrete-event simulation to orthopaedic clinics: case studies and perspectives. M.A.Sc., University of Toronto, Toronto
Mahapatra S, Koelling C, Patvivatsiri L, Fraticelli B, Eitel D, Grove L (2003) Pairing emergency severity index5-level triage data with computer aided system design to improve emergency department access and throughput. In: Proceedings of the 2003 winter simulation conference, New Orleans, pp 1917–1925
Martin E, Gronhaug R, Haugene K (2003) Proposals to reduce over-crowding, lengthy stays and improve patient care: study of the geriatric department in Norway’s largest hospital. In: Proceedings of the 2003 winter simulation conference, New Orleans, vol 2, pp 1876–1881
McArthur DL, Peek-Asa C, Kraus JF (2000) Injury hospitalizations before and after the 1994 Northridge, California earthquake. Am J Emerg Med 18(4):361
Medeiros DJ, Swenson E, DeFlitch C (2008) Improving patient flow in a hospital emergency department. In: Mason SJ, Hill RR, Mönch L, Rose O, Jefferson T, Fowler JW (eds) Proceedings of the 2008 Winter Simulation Conference, Miami. IEEE, pp 1526–1531
Morales G (2011) Using discrete event computer simulation to analyze the effects of proposed changes to personnel in a hospital medical laboratory. M.S., The University of Texas – Pan American, Ann Arbor
Morganti KG, Bauhoff S, Blanchard JC, Abir M, Iyer N, Smith AC, Vesely JV, Okeke EN, Kellermann AL (2013) The Evolving Role of Emergency Departments in the United States. RAND Corporation, Santa Monica
Morgareidge D, Hui CAI, Jun JIA (2014) Performance-driven design with the support of digital tools: applying discrete event simulation and space syntax on the design of the emergency department. Front Archit Res 3(3):250–264
NTC-08 (2008) Nuove norme tecniche per le costruzioni (ntc08) (in Italian)
Peek-Asa C, Kraus JF, Bourque LB, Vimalachandra D, Yu J, Abrams J (1998) Fatal and hospitalized injuries resulting from the 1994 northridge earthquake. Int J Epidemiol 27(3):459–465
ProModel C (2014) Promodel 2014 user guide
Samaha S, Armel WS, Starks DW (2003) The use of simulation to reduce the length of stay in an emergency department. In: Proceedings of the 2003 winter simulation conference, New Orleans, pp 1876–1881
Santibáñez P, Chow V, French J, Puterman M, Tyldesley S (2009) Reducing patient wait times and improving resource utilization at British Columbia cancer agency’s ambulatory care unit through simulation. Health Care Manag Sci 12(4):392–407
Šteins K (2010) Discrete-event simulation for hospital resource planning – possibilities and requirements. Ph.D. thesis, Linköping University, Norrkoping
Stratton SJ, Hastings VP, Isbell D, Celentano J, Ascarrunz M, Gunter CS, Betance J (1996) The 1994 northridge earthquake disaster response: the local emergency medical services agency experience. Prehosp Disaster Med 11(3):172–179
Takakuwa S, Shiozaki H (2004) Functional analysis for operating emergency department of a general hospital. In: Proceedings of the 2004 winter simulation conference, Washington DC, pp 2003–2010
Van der Meer RB, Rymaszewski L, Findlay H, Curran J (2005) Using or to support the development of an integrated musculo-skeletal service. J Oper Res Soc 56(2):162–172
Yerravelli S (2010) Computer simulation modeling and nurse scheduling for the emergency department at Kishwaukee community hospital. Ph.D. thesis, Northern Illinois University, Schaumburg, 60173, United States
Yi P (2005) Real-time generic hospital capacity estimation under emergency situations. Ph.D. thesis, State University of New York at Buffalo, Buffalo
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Cimellaro, G.P. (2016). A Model to Evaluate Disaster Resilience of an Emergency Department. In: Urban Resilience for Emergency Response and Recovery. Geotechnical, Geological and Earthquake Engineering, vol 41. Springer, Cham. https://doi.org/10.1007/978-3-319-30656-8_11
Download citation
DOI: https://doi.org/10.1007/978-3-319-30656-8_11
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-30655-1
Online ISBN: 978-3-319-30656-8
eBook Packages: Earth and Environmental ScienceEarth and Environmental Science (R0)