Modelling a Hospital as a Queueing Network: Analysis for Improving Performance

Authors: Emad Alenany, M. Adel El-Baz

Abstract:

In this paper, the flow of different classes of patients into a hospital is modelled and analyzed by using the queueing network analyzer (QNA) algorithm and discrete event simulation. Input data for QNA are the rate and variability parameters of the arrival and service times in addition to the number of servers in each facility. Patient flows mostly match real flow for a hospital in Egypt. Based on the analysis of the waiting times, two approaches are suggested for improving performance: Separating patients into service groups, and adopting different service policies for sequencing patients through hospital units. The separation of a specific group of patients, with higher performance target, to be served separately from the rest of patients requiring lower performance target, requires the same capacity while improves performance for the selected group of patients with higher target. Besides, it is shown that adopting the shortest processing time and shortest remaining processing time service policies among other tested policies would results in, respectively, 11.47% and 13.75% reduction in average waiting time relative to first come first served policy.

Keywords: Queueing network, discrete-event simulation, health applications, SPT.

Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1130251

References:

[1] Creemers S., Lambrecht M.R., Modeling a healthcare system as a queueing network: the case of a Belgian hospital. In: FBE publications: Research Reports and Discussion papers, 2007.
[2] Jiang L., Giachetti R.E., A queueing network model to analyze the impact of parallelization of care on patient cycle time. Health Care Management Science 11(3):248- 261, 2008.
[3] Whitt W, The queuing network analyzer. Bell Syst Tech J 62:2779- 2815. 1983.
[4] Bhattacharjee P., Ray, P.K. Patient flow modelling and performance analysis of healthcare delivery processes in hospitals: A review and reflections. Computers & Industrial Engineering, 2014.
[5] Lakshmi C., Sivakumar Appa Iyer, Application of queueing theory in health care: A literature review, Oper. Res. for Health Care, 2013
[6] Zondeland M. E. "Curing the Queue." Ph.D., University of Twente, Enschede, the Netherlands, 2012.
[7] Boucherie R.J., van Dijk NM (eds) Queueing networks: a fundamental approach. Springer, New York, NY, USA, 2011.
[8] Albin L. S., Jerrey Barrett, David Ito, John E. Mueller, A queueing network analysis of a health center. Queueing Systems, 1990, Volume 7, Issue 1, pp 51-61.
[9] Zondeland M. E., Boer F., Boucherie R.J., De Roode A., Kleef J. W. van. Redesign of a University Hospital Preanesthesia Evaluation Clinic Using a Queuing Theory Approach. Anesthesia & Analgesia: November 2009 - Volume 109 - Issue 5- pp 1612-1621.
[10] Creemers S, Lambrecht M, Modeling a hospital queueing network. In: Boucherie RJ, van Dijk NM (eds) Queueing networks: a fundamental approach. Springer, New York, NY, USA, 2011.
[11] Law AM, Kelton WD: Simulation Modelling and Analysis. Third Edition. New York, NY,The McGrow-Hill, 1999.
[12] Thorwarth, Michael and Arisha, Amr, "Application of Discrete-Event Simulation in Health Care: a Review", Reports. Paper 3. http://arrow.dit.ie/buschmanrep/3, 2009. Accessed at: 13/3/2015.
[13] Saghafian S., Hopp W. J, Desmond S. Jeffry, Van Oyen, Kronick, L. Steven, Patient Streaming as a Mechanism for Improving Responsiveness in Emergency Departments, Operations Research, 1080- 1097, 2012.
[14] Gul S., Denton B., Fowler J., Huschka T., BI-Criteria Scheduling of Surgical Services for An Outpatients Procedure Center, Production and Operations Management, 2011.
[15] Yang, M., Fry, M. J., & Surlock, C. The ICU will see you now:. IIE Transactions, 2015.
[16] Yang, M., Fry, M. J., Raikhlekar J., Chin C., Anyanwu A., Brand J., Scurlock C., Efficient-equitable admission control policies for a surgical ICU with batch arrivals, ccmjournal, volume 41, number 2, Februrary 2013.
[17] Bose K. S., An introduction to queueing systems, Springer Science+Business media New York, 2002.
[18] Lawerance Leemis, Steve Park, Discrete-Event Simulation: A First Course; ISBN: 0-13-142917-5 .
[19] Whitt W., Performance of the queuing network analyzer. Bell Syst Tech J 62:2817-2843. 1983.
[20] Whitt W., Partitioning customers into service groups. Management Science, 45, 579–1592.
[21] P. Joustra, E. van der Sluis, N.M. van Dijk, To pool or not to pool in hospitals: a theoretical and practical comparison for a radiotherapy outpatient department, Ann of Oper Res, 2010.
[22] Hopp W. J., Lovejoy W. S., Hospital Operations: Principles of High Efficiency Health Care, FT Press Operations Management, November 9, 2012.