Hostname: page-component-848d4c4894-tn8tq Total loading time: 0 Render date: 2024-07-07T10:14:51.510Z Has data issue: false hasContentIssue false

An Organizational Metamodel for Hospital Emergency Departments

Published online by Cambridge University Press:  14 November 2014

Kubilay Kaptan*
Affiliation:
Center for Disaster Resilience, International Blue Crescent Relief and Development Foundation, Istanbul, Turkey.
*
Correspondence and reprint requests to Kubilay Kaptan, PhD, Director, Center for Disaster Resilience, International Blue Crescent Relief and Development Foundation, Istanbul, Turkey (e-mail: kaptankubilay@gmail.com).

Abstract

I introduce an organizational model describing the response of the hospital emergency department. The hybrid simulation/analytical model (called a “metamodel”) can estimate a hospital’s capacity and dynamic response in real time and incorporate the influence of damage to structural and nonstructural components on the organizational ones. The waiting time is the main parameter of response and is used to evaluate the disaster resilience of health care facilities. Waiting time behavior is described by using a double exponential function and its parameters are calibrated based on simulated data. The metamodel covers a large range of hospital configurations and takes into account hospital resources in terms of staff and infrastructures, operational efficiency, and the possible existence of an emergency plan; maximum capacity; and behavior both in saturated and overcapacitated conditions. The sensitivity of the model to different arrival rates, hospital configurations, and capacities and the technical and organizational policies applied during and before a disaster were investigated. This model becomes an important tool in the decision process either for the engineering profession or for policy makers.(Disaster Med Public Health Preparedness. 2014;8:436-444)

Type
Concepts in Disaster Medicine
Copyright
Copyright © Society for Disaster Medicine and Public Health, Inc. 2014 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1. Bruneau, M, Chang, S, Eguchi, R, et al. A framework to quantitatively assess and enhance the seismic resilience of communities. Earthquake Spectra. 2003;19(4):733-752.Google Scholar
2. Cimellaro, GP, Fumo, C, Reinhorn, AM, et al. Quantification of seismic resilience of health care facilities. MCEER Technical Report-MCEER-09-0009. Buffalo, NY: Multidisciplinary Center for Earthquake Engineering Research; 2009.Google Scholar
3. Richards, ME, Crandall, CS, Hubble, MW. Influence of ambulance arrival on emergency department time to be seen. Prehosp Emerg Care. 2006;12(1–17):440-446.Google Scholar
4. Di Bartolomeo, S, Valent, F, Rosolen, V, et al. Are pre-hospital time and emergency department disposition time useful process indicators for trauma care in Italy? Injury. 2007;38(3):305-311.CrossRefGoogle ScholarPubMed
5. Maxwell, RJ. Quality assessment in health. Br Med J (Clin Res Ed). 1984; 288(6428):1470-1472.Google Scholar
6. McCarthy, K, McGee, HM, O’Boyle, CA. Outpatient clinic waiting times and non-attendance as indicators of quality. Psychol Health Med. 2000; 5:287.Google Scholar
7. Vieth, TL, Rhodes, KV. The effect of crowding on access and quality in an academic ED. Am J Emerg Med. 2006;24(7):787.Google Scholar
8. Thompson, DA, Yarnold, PR, Williams, DR, Adams, SL. Effects of actual waiting time, perceived waiting time, information delivery, and expressive quality on patient satisfaction in the emergency department. Ann Emerg Med. 1996;28(6):657.Google Scholar
9. Yi, P. 2005. Real-time Generic Hospital Capacity Estimation Under Emergency Situations [dissertation]. Buffalo, NY: State University of New York at Buffalo; 2005.Google Scholar
10. Paul, JA, George, SK, Yi, P, Lin, L. Transient modeling in simulation of hospital operations for emergency response. Prehosp Disaster Med. 2006; 21(4):223-236.CrossRefGoogle ScholarPubMed
11. Asmussen, S, Glynn, PW. Stochastic Simulation. New York: Springer; 2007.Google Scholar
12. Soong, TT. Fundamental of Probability and Statistics for Engineers. New York: Wiley; 2004.Google Scholar
13. Cimellaro, GP, Reinhorn, AM, Bruneau, M. Seismic resilience of a hospital system. Struct Infrastruct Eng. 2010;6(1-2):127-144.CrossRefGoogle Scholar
14. 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.CrossRefGoogle ScholarPubMed
15. Jain, S, McLean, CR. Modeling and Simulation for Emergency Response Workshop Report, Standards and Tools. Washington, DC: U.S. Department of Commerce, Technology Administration, National Institute of Standards and Technology; 2003.Google Scholar
16. Kuhl, ME, Sumant, S, Wilson, JR. An automated multiresolution procedure for modeling complex arrival processes. INFORMS J Comput. 2006;18(1):3-18.Google Scholar
17. Kuhl, ME, Wilson, JR. Least squares estimation of nonhomogeneous Poisson processes. J Stat Comput Simul. 2000;67:75-108.CrossRefGoogle Scholar
18. Kuhl, ME, Wilson, JR, Johnson, MA. Estimating and simulating Poisson processes having trends or multiple periodicities. IIEE Trans. 1997;29(3):201-211.Google Scholar
19. Trofimov, V, Feigin, PD, Mandelbaum, A, et al. DataMOCCA. DATA MOdel for Call Center Analysis. Volume 1: Model Description and Introduction to User Interface. Technion, Israel Institute of Technology, Technical Report. http://ie.technion.ac.il/Labs/Serveng/files/Model_Description_and_Introduction_to_User_Interface.pdf. Updated July 29, 2006. Accessed October 13, 2014.Google Scholar