Modelling a Hospital as a Queueing Network: Analysis for Improving Performance
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.
[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.
[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.
@article{"International Journal of Business, Human and Social Sciences:75448", author = "Emad Alenany and M. Adel El-Baz", title = "Modelling a Hospital as a Queueing Network: Analysis for Improving Performance", 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.", volume = "11", number = "5", pages = "1181-7", }
