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Interconnected DES models of emergency, outpatient, and inpatient departments of a hospital
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Source Winter Simulation Conference archive
Proceedings of the 39th conference on Winter simulation: 40 years! The best is yet to come table of contents
Washington D.C.
SESSION: Health care: applying simulation to health care table of contents
Pages 1461-1466  
Year of Publication: 2007
ISBN:1-4244-1306-0
Authors
Murat M. Gunal  Lancaster University, Lancaster, United Kingdom
Michael Pidd  Lancaster University, Lancaster, United Kingdom
Sponsors
INFORMS-SIM : Institute for Operations Research and the Management Sciences: Simulation Society
NIST : National Institute of Standards and Technology
(SCS) : The Society for Modeling and Simulation International
ACM/SIGSIM : Association for Computing Machinery: Special Interest Group on Simulation
IIE : Institute of Industrial Engineers
ASA : American Statistical Association
IEEE/SMC : Institute of Electrical and Electronics Engineers: Systems, Man, and Cybernetics Society
Publisher
IEEE Press  Piscataway, NJ, USA
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Downloads (6 Weeks): 1,   Downloads (12 Months): 33,   Citation Count: 1
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ABSTRACT

National Health Service (NHS) performance targets in England have put pressure on hospital management to reduce waiting times. The stochastic nature of emergency patient arrivals creates problems for capacity planning for elective patients. We present a whole hospital model which can be used at policy level to investigate cause and effect relations, such as effects of increased emergency arrival volumes on elective waiting times. A typical general hospital can be abstracted in three main units; Accident and Emergency (A&E) department, outpatient clinics, and inpatient units. In real life these units are coupled and share hospital resources. We developed three discrete event simulation (DES) models for each unit to form a whole hospital DES model. We present our models conceptually and our main discussion is on the level of detail in these three models.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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Brooks, R. J. and A. M. Tobias. 1996. Choosing the Best Model: Level of Detail, Complexity and Model Performance. Mathematical and Computer Modeling, 24(4), 1--14.
 
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Fone, D., S. Hollinghurst, M. Temple, A. Round, N. Lester, A. Weightman, R. Roberts, E. Coyle, G. Bevan, S. Palmer. 2003. Systematic Review of the use and value of computer simulation modelling in population health and health care delivery. Journal of Public Health Medicine. 25 (4), 325--35.
 
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Healthcare Commission. 2005. Acute Hospital Portfolio Review: Accident and Emergency.
 
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Henriksen, J. O. 2006. Taming The Complexity Dragon, 2006 Winter Simulation Conference, Titans of Simulation.
 
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Jun, J. B., S. H. Jacobson, J. R. Swisher. 1999. Applications of discrete event simulation in health care clinics: a survey. Jnl Opl Res Soc. 50, 109--23.
 
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Micro Saint Sharp. 2007. Alion Micro Analysis and Design. available via <http://www.maad.com/index.pl/micro_saint> {as accessed May 2007}.
 
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Collaborative Colleagues:
Murat M. Gunal: colleagues
Michael Pidd: colleagues