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Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions

Author

Listed:
  • Yong-Hong Kuo

    (The Chinese University of Hong Kong)

  • Omar Rado

    (PriceWaterhouseCoopers Advisory Spa)

  • Benedetta Lupia

    (Luxottica Group Spa)

  • Janny M. Y. Leung

    (The Chinese University of Hong Kong)

  • Colin A. Graham

    (The Chinese University of Hong Kong)

Abstract

This paper presents a case study which uses simulation to analyze patient flows in a hospital emergency department in Hong Kong. We first analyze the impact of the enhancements made to the system after the relocation of the Emergency Department. After that, we developed a simulation model (using ARENA) to capture all the key relevant processes of the department. When developing the simulation model, we faced the challenge that the data kept by the Emergency Department were incomplete so that the service-time distributions were not directly obtainable. We propose a simulation–optimization approach (integrating simulation with meta-heuristics) to obtain a good set of estimate of input parameters of our simulation model. Using the simulation model, we evaluated the impact of possible changes to the system by running different scenarios. This provides a tool for the operations manager in the Emergency Department to “foresee” the impact on the daily operations when making possible changes (such as, adjusting staffing levels or shift times), and consequently make much better decisions.

Suggested Citation

  • Yong-Hong Kuo & Omar Rado & Benedetta Lupia & Janny M. Y. Leung & Colin A. Graham, 2016. "Improving the efficiency of a hospital emergency department: a simulation study with indirectly imputed service-time distributions," Flexible Services and Manufacturing Journal, Springer, vol. 28(1), pages 120-147, June.
    • Handle: RePEc:spr:flsman:v:28:y:2016:i:1:d:10.1007_s10696-014-9198-7
    DOI: 10.1007/s10696-014-9198-7
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    Cited by:

    1. Hainan Guo & Haobin Gu & Yu Zhou & Jiaxuan Peng, 2022. "A data-driven multi-fidelity simulation optimization for medical staff configuration at an emergency department in Hong Kong," Flexible Services and Manufacturing Journal, Springer, vol. 34(2), pages 238-262, June.
    2. Stefanini, Alessandro & Aloini, Davide & Benevento, Elisabetta & Dulmin, Riccardo & Mininno, Valeria, 2020. "A data-driven methodology for supporting resource planning of health services," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    3. Wang, Wei & Wang, Shuaian & Zhen, Lu & Qu, Xiaobo, 2022. "EMS location-allocation problem under uncertainties," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 168(C).
    4. You-Xuan Lin & Chi-Hao Lin & Chih-Hao Lin, 2021. "A challenge for healthcare system resilience after an earthquake: The crowdedness of a first-aid hospital by non-urgent patients," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-16, April.
    5. Alberto De Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2020. "An optimal non-uniform piecewise constant approximation for the patient arrival rate for a more efficient representation of the Emergency Departments arrival process," DIAG Technical Reports 2020-01, Department of Computer, Control and Management Engineering, Universita' degli Studi di Roma "La Sapienza".
    6. Alves de Queiroz, Thiago & Iori, Manuel & Kramer, Arthur & Kuo, Yong-Hong, 2023. "Dynamic scheduling of patients in emergency departments," European Journal of Operational Research, Elsevier, vol. 310(1), pages 100-116.
    7. Alberto Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2023. "A simulation-based optimization approach for the calibration of a discrete event simulation model of an emergency department," Annals of Operations Research, Springer, vol. 320(2), pages 727-756, January.
    8. Lien Vanbrabant & Kris Braekers & Katrien Ramaekers, 2021. "Improving emergency department performance by revising the patient–physician assignment process," Flexible Services and Manufacturing Journal, Springer, vol. 33(3), pages 783-845, September.
    9. Alberto De Santis & Tommaso Giovannelli & Stefano Lucidi & Mauro Messedaglia & Massimo Roma, 2022. "Determining the optimal piecewise constant approximation for the nonhomogeneous Poisson process rate of Emergency Department patient arrivals," Flexible Services and Manufacturing Journal, Springer, vol. 34(4), pages 979-1012, December.
    10. Miguel Ortiz-Barrios & Juan-José Alfaro-Saiz, 2020. "An integrated approach for designing in-time and economically sustainable emergency care networks: A case study in the public sector," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-28, June.

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