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Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department

Author

Listed:
  • Kaushal, Arjun
  • Zhao, Yuancheng
  • Peng, Qingjin
  • Strome, Trevor
  • Weldon, Erin
  • Zhang, Michael
  • Chochinov, Alecs

Abstract

Different strategies have been proposed to reduce patient waiting time in hospitals. Previous investigations indicate that up to 50% or more patients can be treated in a “fast track” process compared to the standard procedure in some emergency departments. However most studies on emergency department (ED) fast tracks were based on evidence without using an efficient decision tool to show applicability of the results. An agent-based simulation tool is proposed in this research to evaluate fast track treatment (FTT) in an ED. The tool can study the behavior change of entities and resources in a complex ED system. Static and dynamic FTT processes are evaluated. The static process uses a fixed duration in the daily ED operation. In the dynamic process, FTT is triggered based on the current patient waiting time and the state of ED operations. The simulation results provide details and information for the process of the FTT implementation at the ED to reduce patient waiting time.

Suggested Citation

  • Kaushal, Arjun & Zhao, Yuancheng & Peng, Qingjin & Strome, Trevor & Weldon, Erin & Zhang, Michael & Chochinov, Alecs, 2015. "Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department," Socio-Economic Planning Sciences, Elsevier, vol. 50(C), pages 18-31.
    • Handle: RePEc:eee:soceps:v:50:y:2015:i:c:p:18-31
    DOI: 10.1016/j.seps.2015.02.002
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    References listed on IDEAS

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    1. Abo-Hamad, Waleed & Arisha, Amr, 2013. "Simulation-based framework to improve patient experience in an emergency department," European Journal of Operational Research, Elsevier, vol. 224(1), pages 154-166.
    2. Zied Jemai & L. Aboueljinane & E. Sahin, 2013. "A review on simulation models applied to emergency medical service operations," Post-Print hal-01672393, HAL.
    3. Mahdavi, Mahdi & Malmström, Tomi & van de Klundert, Joris & Elkhuizen, Sylvia & Vissers, Jan, 2013. "Generic operational models in health service operations management: A systematic review," Socio-Economic Planning Sciences, Elsevier, vol. 47(4), pages 271-280.
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    Cited by:

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    2. 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".
    3. Wanxin Hou & Shaowen Qin & Campbell Henry Thompson, 2022. "Effective Response to Hospital Congestion Scenarios: Simulation-Based Evaluation of Decongestion Interventions," IJERPH, MDPI, vol. 19(23), pages 1-11, December.
    4. Shahverdi, Bahar & Tariverdi, Mersedeh & Miller-Hooks, Elise, 2020. "Assessing hospital system resilience to disaster events involving physical damage and Demand Surge," Socio-Economic Planning Sciences, Elsevier, vol. 70(C).
    5. Thierry Moyaux & Yinling Liu & Guillaume Bouleux & Vincent Cheutet, 2023. "An Agent-Based Architecture of the Digital Twin for an Emergency Department," Sustainability, MDPI, vol. 15(4), pages 1-13, February.
    6. 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.
    7. 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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