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Modeling the emergency cardiac in-patient flow: an application of queuing theory

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  • Arnoud Bruin
  • A. Rossum
  • M. Visser
  • G. Koole

Abstract

This study investigates the bottlenecks in the emergency care chain of cardiac in-patient flow. The primary goal is to determine the optimal bed allocation over the care chain given a maximum number of refused admissions. Another objective is to provide deeper insight in the relation between natural variation in arrivals and length of stay and occupancy rates. The strong focus on raising occupancy rates of hospital management is unrealistic and counterproductive. Economies of scale cannot be neglected. An important result is that refused admissions at the First Cardiac Aid (FCA) are primarily caused by unavailability of beds downstream the care chain. Both variability in LOS and fluctuations in arrivals result in large workload variations. Techniques from operations research were successfully used to describe the complexity and dynamics of emergency in-patient flow. Copyright Springer Science+Business Media, LLC 2007

Suggested Citation

  • Arnoud Bruin & A. Rossum & M. Visser & G. Koole, 2007. "Modeling the emergency cardiac in-patient flow: an application of queuing theory," Health Care Management Science, Springer, vol. 10(2), pages 125-137, June.
    • Handle: RePEc:kap:hcarem:v:10:y:2007:i:2:p:125-137
    DOI: 10.1007/s10729-007-9009-8
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    8. Gustavo Ramiro Rodríguez Jáuregui & Ana Karen González Pérez & Salvador Hernández González & Manuel Darío Hernández Ripalda, 2017. "Analysis of the emergency service applying the queueing theory," Contaduría y Administración, Accounting and Management, vol. 62(3), pages 733-745, Julio-Sep.
    9. Hui Zhang & Thomas J. Best & Anton Chivu & David O. Meltzer, 2020. "Simulation-based optimization to improve hospital patient assignment to physicians and clinical units," Health Care Management Science, Springer, vol. 23(1), pages 117-141, March.
    10. A. Bruin & R. Bekker & L. Zanten & G. Koole, 2010. "Dimensioning hospital wards using the Erlang loss model," Annals of Operations Research, Springer, vol. 178(1), pages 23-43, July.
    11. Jennifer Gillespie & Sally McClean & Lalit Garg & Maria Barton & Bryan Scotney & Ken Fullerton, 2016. "A multi-phase DES modelling framework for patient-centred care," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 67(10), pages 1239-1249, October.
    12. Pengyi Shi & Mabel C. Chou & J. G. Dai & Ding Ding & Joe Sim, 2016. "Models and Insights for Hospital Inpatient Operations: Time-Dependent ED Boarding Time," Management Science, INFORMS, vol. 62(1), pages 1-28, January.
    13. Carri W. Chan & Vivek F. Farias & Nicholas Bambos & Gabriel J. Escobar, 2012. "Optimizing Intensive Care Unit Discharge Decisions with Patient Readmissions," Operations Research, INFORMS, vol. 60(6), pages 1323-1341, December.
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    15. Tom van Woensel & Frederico R B Cruz, 2014. "Optimal Routing in General Finite Multi-Server Queueing Networks," PLOS ONE, Public Library of Science, vol. 9(7), pages 1-15, July.
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    18. Gregory Dobson & Hsiao-Hui Lee & Edieal Pinker, 2010. "A Model of ICU Bumping," Operations Research, INFORMS, vol. 58(6), pages 1564-1576, December.
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    21. R. Bekker & A. Bruin, 2010. "Time-dependent analysis for refused admissions in clinical wards," Annals of Operations Research, Springer, vol. 178(1), pages 45-65, July.

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