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Understanding the Emergency Department Ecosystem Using Agent-Based Modeling: A Study of the Seven Oaks General Hospital Emergency Department

  • Oluwayemisi Olugboji
  • Sergio G. Camorlinga
  • Ricardo Lobato de Faria
  • Arjun Kaushal
Chapter

Abstract

The emergency department (ED) is the first point of contact in a hospital, and managing the inflow and outflow of patients using a decision support system can help the providers and other stakeholders optimally utilize the limited resources available. The limited resources observed in this research are the providers and the bed resources available in the pods (or different treatment areas in the ED). These limited resources often lead to problems like diversion and overcrowding in the ED, and predicting when these scenarios are likely to happen is very important in the optimal utilization of these resources. The decision support system described in this chapter employs the use of agent-based models to simulate a real-life system. It utilizes the Emergency Department Information System (EDIS) historical dataset from the Seven Oaks General Hospital emergency department (SOGH-ED). The agent-based NetLogo simulation assists in decision support, by helping the user determine the best combination of hospital resources that will lead to a more stable system. This agent-based model, developed using the NetLogo simulation software, was run using the same scenarios evident in the SOGH-ED, and a comparison was made with the historical dataset for validation. In this chapter, we describe how we used the causal link and the stock and flow modeling paradigms to analyze the coupling of the system between different scales and how we used the agent-based modeling concept to build the model.

References

  1. 1.
    Wilensky U. NetLogo. Evanston, IL: Center for Connected Learning and Computer-Based Modeling, Northwestern University; 1999. http://ccl.northwestern.edu/netlogo/.Google Scholar
  2. 2.
    Hiroki S. Introduction to the modeling and analysis of complex systems. Geneseo: Milne Library State University of New York; 2015.Google Scholar
  3. 3.
    Marshall DA, Burgos-Liz L, Eng I, IJzerman MJ, Osgood ND, Padula WV, Higashi MK,Wong PK, Pasupathy KS, Crown W. Applying dynamic simulation modeling methods in health care delivery research-the SIMULATE checklist: report of the ISPOR simulation modeling emerging good practices task force. Value Health 2015;18(1):5–16.CrossRefGoogle Scholar
  4. 4.
    Borshchev A, Filippov A. From system dynamics and discrete event to practical agent based modeling: reasons, techniques, tools. In: Proceedings of the 22nd International Conference of the System Dynamics Society; 2004, 22.Google Scholar
  5. 5.
    Diez Roux AV. Complex systems thinking and current impasses in health disparities research. Am J Public Health 2011;101(9):1627–34.CrossRefGoogle Scholar
  6. 6.
    Bullard MJ, Unger B, Spence J, Grafstein E. Revisions to the Canadian emergency department triage and acuity scale (CTAS) adult guidelines. CJEM 2008;10(2):136–51.CrossRefGoogle Scholar
  7. 7.
    SAS Institute. Base SAS 9. 4 procedures guide: statistical procedures. Cary, NC: SAS Institute; 2014.Google Scholar
  8. 8.
    Kaushal A, Zhao Y, Peng Q, Strome T, Weldon E, Zhang M, Chochinov A. Evaluation of fast track strategies using agent-based simulation modeling to reduce waiting time in a hospital emergency department. Socio Econ Plan Sci. 2015;50:18–31.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Oluwayemisi Olugboji
    • 1
  • Sergio G. Camorlinga
    • 1
  • Ricardo Lobato de Faria
    • 2
  • Arjun Kaushal
    • 3
  1. 1.Department of Applied Computer ScienceUniversity of WinnipegWinnipegCanada
  2. 2.Seven Oaks General HospitalUniversity of ManitobaWinnipegCanada
  3. 3.George & Fay Yee Centre for Healthcare InnovationWinnipegCanada

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