IDEAS home Printed from https://ideas.repec.org/a/eee/soceps/v50y2015icp18-31.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0038012115000166
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.seps.2015.02.002?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zied Jemai & L. Aboueljinane & E. Sahin, 2013. "A review on simulation models applied to emergency medical service operations," Post-Print hal-01672393, HAL.
    2. 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.
    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.
    4. Y Huang & P Zuniga, 2014. "Effective cancellation policy to reduce the negative impact of patient no-show," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(5), pages 605-615, May.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. Acuna, Jorge A. & Zayas-Castro, José L. & Charkhgard, Hadi, 2020. "Ambulance allocation optimization model for the overcrowding problem in US emergency departments: A case study in Florida," Socio-Economic Planning Sciences, Elsevier, vol. 71(C).
    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. 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).
    7. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Duma, Davide & Aringhieri, Roberto, 2023. "Real-time resource allocation in the emergency department: A case study," Omega, Elsevier, vol. 117(C).
    3. 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.
    4. Nelas, José & Dias, Joana, 2020. "Optimal Emergency Vehicles Location: An approach considering the hierarchy and substitutability of resources," European Journal of Operational Research, Elsevier, vol. 287(2), pages 583-599.
    5. Su, Qiang & Luo, Qinyi & Huang, Samuel H., 2015. "Cost-effective analyses for emergency medical services deployment: A case study in Shanghai," International Journal of Production Economics, Elsevier, vol. 163(C), pages 112-123.
    6. Willoughby, Keith A. & Chan, Benjamin T.B. & Marques, Shauna, 2016. "Using simulation to test ideas for improving speech language pathology services," European Journal of Operational Research, Elsevier, vol. 252(2), pages 657-664.
    7. Li, Zhong-Ping & Wang, Jian-Jun, 2021. "Effects of healthcare quality and reimbursement rate in a hospital association," Socio-Economic Planning Sciences, Elsevier, vol. 76(C).
    8. 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".
    9. Mengyu Li & Peter Vanberkel & Alix J. E. Carter, 2019. "A review on ambulance offload delay literature," Health Care Management Science, Springer, vol. 22(4), pages 658-675, December.
    10. Ľudmila Jánošíková & Marek Kvet & Peter Jankovič & Lýdia Gábrišová, 2019. "An optimization and simulation approach to emergency stations relocation," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 27(3), pages 737-758, September.
    11. Bélanger, V. & Lanzarone, E. & Nicoletta, V. & Ruiz, A. & Soriano, P., 2020. "A recursive simulation-optimization framework for the ambulance location and dispatching problem," European Journal of Operational Research, Elsevier, vol. 286(2), pages 713-725.
    12. De Matteis, Domenico & Ishizaka, Alessio & Resce, Giuliano, 2019. "The ‘postcode lottery’ of the Italian public health bill analysed with the hierarchy Stochastic Multiobjective Acceptability Analysis," Socio-Economic Planning Sciences, Elsevier, vol. 68(C).
    13. Inkyung Sung & Taesik Lee, 2018. "Scenario-based approach for the ambulance location problem with stochastic call arrivals under a dispatching policy," Flexible Services and Manufacturing Journal, Springer, vol. 30(1), pages 153-170, June.
    14. 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.
    15. Bélanger, V. & Ruiz, A. & Soriano, P., 2019. "Recent optimization models and trends in location, relocation, and dispatching of emergency medical vehicles," European Journal of Operational Research, Elsevier, vol. 272(1), pages 1-23.
    16. Masoumeh Vali & Khodakaram Salimifard & Amir H. Gandomi & Thierry J. Chaussalet, 2022. "Care process optimization in a cardiovascular hospital: an integration of simulation–optimization and data mining," Annals of Operations Research, Springer, vol. 318(1), pages 685-712, November.
    17. Li, Mengyu & Carter, Alix & Goldstein, Judah & Hawco, Terence & Jensen, Jan & Vanberkel, Peter, 2021. "Determining ambulance destinations when facing offload delays using a Markov decision process," Omega, Elsevier, vol. 101(C).
    18. Chang Wook Kang & Muhammad Imran & Muhammad Omair & Waqas Ahmed & Misbah Ullah & Biswajit Sarkar, 2019. "Stochastic-Petri Net Modeling and Optimization for Outdoor Patients in Building Sustainable Healthcare System Considering Staff Absenteeism," Mathematics, MDPI, vol. 7(6), pages 1-26, June.
    19. 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.
    20. Colla, Marcos & Santos, Gilson Ditzel & Oliveira, Gilson Adamczuk & de Vasconcelos, Renata Braga Berenguer, 2023. "Ambulance response time in a Brazilian emergency medical service," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:soceps:v:50:y:2015:i:c:p:18-31. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/seps .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.