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Setting staffing requirements for time dependent queueing networks: The case of accident and emergency departments

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  • Izady, Navid
  • Worthington, Dave

Abstract

An incentive scheme aimed at reducing patients’ waiting times in accident and emergency departments was introduced by the UK government in 2000. It requires 98% of patients to be discharged, transferred, or admitted to inpatient care within 4hours of arrival. Setting the minimal hour by hour medical staffing levels for achieving the government target, in the presence of complexities like time-varying demand, multiple types of patients, and resource sharing, is the subject of this paper. Building on extensive body of research on time dependent queues, we propose an iterative scheme which uses infinite server networks, the square root staffing law, and simulation to come up with a good solution. The implementation of this algorithm in a typical A&E department suggests that significant improvement on the target can be gained, even without increase in total staff hours.

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  • Izady, Navid & Worthington, Dave, 2012. "Setting staffing requirements for time dependent queueing networks: The case of accident and emergency departments," European Journal of Operational Research, Elsevier, vol. 219(3), pages 531-540.
    • Handle: RePEc:eee:ejores:v:219:y:2012:i:3:p:531-540
    DOI: 10.1016/j.ejor.2011.10.040
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    References listed on IDEAS

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    Cited by:

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    3. Athanasopoulos, George & Hyndman, Rob J. & Kourentzes, Nikolaos & Petropoulos, Fotios, 2017. "Forecasting with temporal hierarchies," European Journal of Operational Research, Elsevier, vol. 262(1), pages 60-74.
    4. Onggo, S. & Pidd, M. & Soopramanien, D. & Worthington, D.J., 2012. "Behavioural modelling of career progression in the European Commission," European Journal of Operational Research, Elsevier, vol. 222(3), pages 632-641.
    5. Na Li & Xiaorui Li & Paul Forero, 2022. "Physician scheduling for outpatient department with nonhomogeneous patient arrival and priority queue," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 879-915, December.
    6. Niyirora, Jerome & Zhuang, Jun, 2017. "Fluid approximations and control of queues in emergency departments," European Journal of Operational Research, Elsevier, vol. 261(3), pages 1110-1124.
    7. Ran Liu & Xiaolan Xie, 2018. "Physician Staffing for Emergency Departments with Time-Varying Demand," INFORMS Journal on Computing, INFORMS, vol. 30(3), pages 588-607, August.
    8. Brandon M McConnell & Thom J Hodgson & Michael G Kay & Russell E King & Yunan Liu & Greg H Parlier & Kristin Thoney-Barletta & James R Wilson, 2021. "Assessing uncertainty and risk in an expeditionary military logistics network," The Journal of Defense Modeling and Simulation, , vol. 18(2), pages 135-156, April.
    9. Xi Chen & Dave Worthington, 2017. "Staffing of time-varying queues using a geometric discrete time modelling approach," Annals of Operations Research, Springer, vol. 252(1), pages 63-84, May.
    10. Hainan Guo & David Goldsman & Kwok-Leung Tsui & Yu Zhou & Shui-Yee Wong, 2016. "Using simulation and optimisation to characterise durations of emergency department service times with incomplete data," International Journal of Production Research, Taylor & Francis Journals, vol. 54(21), pages 6494-6511, November.
    11. Carmen, Raïsa & Van Nieuwenhuyse, Inneke & Van Houdt, Benny, 2018. "Inpatient boarding in emergency departments: Impact on patient delays and system capacity," European Journal of Operational Research, Elsevier, vol. 271(3), pages 953-967.
    12. Rostami-Tabar, Bahman & Ziel, Florian, 2022. "Anticipating special events in Emergency Department forecasting," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1197-1213.
    13. Miguel Angel Ortíz-Barrios & Juan-José Alfaro-Saíz, 2020. "Methodological Approaches to Support Process Improvement in Emergency Departments: A Systematic Review," IJERPH, MDPI, vol. 17(8), pages 1-41, April.
    14. Weiwei Chen & Siyang Gao & Wenjie Chen & Jianzhong Du, 2023. "Optimizing resource allocation in service systems via simulation: A Bayesian formulation," Production and Operations Management, Production and Operations Management Society, vol. 32(1), pages 65-81, January.

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