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Transient Modeling in Simulation of Hospital Operations for Emergency Response

Published online by Cambridge University Press:  28 June 2012

Jomon Aliyas Paul ,
Santhosh K. George ,
Pengfei Yi  and
Li Lin
Jomon Aliyas Paul
Affiliation:
Department of Industrial Engineering, University at Buffalo, Buffalo, New York, USA
Santhosh K. George
Affiliation:
Department of Industrial Engineering, University at Buffalo, Buffalo, New York, USA
Pengfei Yi
Affiliation:
Department of Industrial Engineering, University at Buffalo, Buffalo, New York, USA
Li Lin*
Affiliation:
Department of Industrial Engineering, University at Buffalo, Buffalo, New York, USA
*
Dr. Li Lin 438 Bell Hall, Department of Industrial Engineering University at Buffalo Buffalo, New York 14260, USA E-mail [email protected]
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Abstract

Rapid estimates of hospital capacity after an event that may cause a disaster can assist disaster-relief efforts. Due to the dynamics of hospitals, following such an event, it is necessary to accurately model the behavior of the system. A transient modeling approach using simulation and exponential functions is presented, along with its applications in an earthquake situation. The parameters of the exponential model are regressed using outputs from designed simulation experiments. The developed model is capable of representing transient, patient waiting times during a disaster. Most importantly, the modeling approach allows real-time capacity estimation of hospitals of various sizes and capabilities. Further, this research is an analysis of the effects of priority-based routing of patients within the hospital and the effects on patient waiting times determined using various patient mixes. The model guides the patients based on the severity of injuries and queues the patients requiring critical care depending on their remaining survivability time. The model also accounts the impact of prehospital transport time on patient waiting time.

Keywords

arrival ratemeta-modelpatient mixregressionseverity typesimulationsurvivability timetransient
Type
Original Research
Information
Prehospital and Disaster Medicine , Volume 21 , Issue 4 , August 2006 , pp. 223 - 236
Copyright
Copyright © World Association for Disaster and Emergency Medicine 2006

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References

1.Henderson, AK, Lillibridge, SR, Salinas, C, Graves, RW, Roth, PB, Noji, EK: Disaster medical assistance terms: Providing health care to a community struck by Hurricane Iniki. Ann Emerg Med 1994;23:726.CrossRefGoogle Scholar
2.Cote, MJ: Patient flow and resource utilization in an outpatient clinic. Socio-Econ Plann Sci 1999;33:231.CrossRefGoogle Scholar
3.Lowery, JC: Multi-Hospital Validation of Critical Care Simulation Model. In: 1993 Winter Simulation Conference Proceedings. IEEE, Piscataway. 1993, pp 1207–1215.CrossRefGoogle Scholar
4.Swisher, JR, Jacobson, SH, Jun, JB, Balci, O: Modeling and analyzing a physician clinic environment using discrete-event (visual) simulation. Computers and Operations Research 2001;28:105.CrossRefGoogle Scholar
5.Weng, ML, Houshmand, AA: Healthcare Simulation, A Case Study at a Local Clinic. In: Winter Simulation Conference Proceedings. IEEE, Piscataway, 1999.Google Scholar
6.Yi, P, George, SK, Paul, JA, Lin, L: Hospital capacity planning for emergency management in disaster mitigation. Socio-Econ Plann Sci (in print) 2006.Google Scholar
7.Boyd, KM: The Ethics of Resource Allocation in Health Care. Edinburgh: University Press, 1979.Google Scholar
8.Zon, A, Kommer, GJ: Patient flows and optimal health-care resource allocation at the macro-level: A dynamic linear programming approach. Health Care Manag Sci 1999;2:87.CrossRefGoogle ScholarPubMed
9.Bretthauer, KM, Shetty, B: The nonlinear knapsack problem—Algorithms and applications. Eur J Oper Res 2002;138:459.CrossRefGoogle Scholar
10.Royston, G, Dost, A, Townshend, J, Turner, H: Using system dynamics to help develop and implement policies and programmes in health care in England. System Dynamics Review 1999;15:293.3.0.CO;2-1>CrossRefGoogle Scholar
11.Jun, JB, Jacobson, SH, Swisher, JR: Application of discrete-event simulation in health care clinics: A survey. J Oper Res Soc 1999;50:109.CrossRefGoogle Scholar
12.Giraldo, BF, Martinez, P, Caminal, P: Parametric Modeling of the Hospital Activity Applied to the Simulation of Patients in Waiting-List. In: Proceedings of the 22nd Annual EMBS International Conference. Chicago 2000. pp 2334–2336.Google Scholar
13.Chang, HH: Determinants of hospital efficiency: The case of central government-owned hospitals in Taiwan. International Journal of Management Science 1998;26:307.Google Scholar
14.Groenendaal, WJ, Kleijnen, JP: Regression Metamodels and Design of Experiments. In: Proceedings of the 28th Conference on Winter Simulation. Coronado, 1996, pp 1433–1439.CrossRefGoogle Scholar
15.Law, AM, Kelton, WD: Simulation Modelling and Analysis. 2d ed.McGrawHill Companies, Inc., 1991.Google Scholar
16.Ishii, N, Talavagf, JJ: Transient-based real-time scheduling algorithm in FMS. International Journal of Production Research 1991;29:2501.CrossRefGoogle Scholar
17.Cochran, JK, Lin, L: Compound dynamic event metamodels for electronic assembly line systems. IIE Transactions 1993;(25)2:12–25.CrossRefGoogle Scholar
18.Ortega, MJ: Analysis of Production-Inventory Systems by Dual-Models of Control-Theoretic and Discrete-Event Simulations. PhD Dissertation, Department of Industrial Engineering, University at Buffalo, The State University of New York, 2001.Google Scholar
19.Pan American Health Organization (PAHO): Establishing a Mass Casualty Management System. 1995, p 58.Google Scholar
20.Wheeler, DS, Poss, WB: Masscasualty management in a changing world. Pediatr Ann 2003;(32)2:98–105.CrossRefGoogle Scholar
21.Saunders, CE, Makens, PK, Leblanc, LJ: Modeling emergency department operations using advanced computer simulation systems. Ann Emerg Med 1989;(18)2:134–140.CrossRefGoogle Scholar
22.Garcia, ML, Centeno, MA, Rivera, C, DeCario, N: Reducing time in an emergency room via a fasttrack. Proceedings of the 1995 Winter Simulation Conference. 1995, pp 1048–1053.CrossRefGoogle Scholar
23.Glance, LG, Osler, T, Shinozaki, T: Effect of varying the case mix on the standardized mortality ratio and W statistic: A simulation study. Chest 2000;117:1112.CrossRefGoogle Scholar
24.Petri, RW, Dyer, A, Lumpkin, J: The effect of prehospital transport time on the mortality from traumatic injury. Prehosp Disast Med 1995;10(1):24–29.CrossRefGoogle ScholarPubMed
25.Pepe, PE, Wyatt, CH, Bickell, WH, et al. : The relationship between total prehospital time and outcome in hypotensive victims of penetrating injuries. Ann Emerg Med 1987;16(3):293–297.CrossRefGoogle ScholarPubMed
26.Davis, DP, Poste, JC, Polk, D, et al. : Hospital bed surge capacity in the event of a mass-casualty incident. Prehosp Disast Med 2005;(20)3:169–176.CrossRefGoogle Scholar
27.Federal Emergency Management–366, HAZUS 99: Estimated annualized earthquake loss for the US, Available at http://www.fema.gov/hazus/dl_eqpub.shtm. Accessed January 2005.Google Scholar
28.AHA: Hospital Statistics. Healthcare InfoSource, Chicago, 1998.Google Scholar
29.Aroni, S, Durkin, ME: Injuries and Occupant Behavior in Earthquakes. In: Joint US-Romanian Seminar on Earthquakes and Energy. Bucharest (Earthquakes Architectural Research Centers Consortium, Washington D.C., 1987, p 3–40.Google Scholar
30.Cheu, DH: Northridge Earthquake, January 17, 1994: The Hospital Response. California Seismic Safety Commission, Sacramento, 1994.Google Scholar
31.GAMS, The General Algebraic Modeling System. Available at http://www.gams.com. Accessed June 2003.Google Scholar