Using Simulation in an Acute-care Hospital: Easier Said Than Done

  • Michael W. Carter
  • John T. Blake
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 70)


Simulation, as it is typically taught, is a rather mechanical process. Students are taught to follow a recipe: analyze a system, design a model, convert the model to computer code, collect data, verify, validate, and analyze the output. In practice, many analysts find that simulation is an odd combination of art, science, and marketing. Using this technique appropriately, in any industry, involves more than simply following the text book. In our experience, health care provides some rather unique challenges for the modeler. This chapter describes four different practical examples of using simulation to analyze a problem in an acute care hospital. The specific examples are not described in detail, since the applications have appeared in other publications. The emphasis here is to present some of the obstacles that were encountered and the lessons learned.

Key words

Simulation Nursing human resources Surgical schedules Emergency department modeling Drug order entry 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. [1]
    Blake, J.T., M.W. Carter, L.L. O’Brien-Pallas, and L. McGillis-Hall (1995). A surgical process management tool. Proceedings of the 8th World Congress on Medical Informatics MEDINFO 95.Google Scholar
  2. [2]
    Carter, M.W., L.L. O’Brien-Pallas, J.T. Blake, L. McGillis, and S. Zhu (1992). Simulation, scheduling and operating rooms. Proceedings of the 1992 Simulation in Health Care and Social Services Conference, J.G. Anderson, Ed., Simulation Council Inc., San Diego, 28–30.Google Scholar
  3. [3]
    Blake, J.T., M.W. Carter, and S. Richardson (1996). An evaluation of emergency room wait time issues via computer simulation. INFOR, 34, 263–273.Google Scholar
  4. [4]
    Ash, J.S., P.N. Gorman, and W.R. Hersh (1998). Physician order entry in U.S. hospitals. Proceedings of the AMIA Annual Symposium, 235–239.Google Scholar
  5. [5]
    Bates, D.W., L.L. Leape, D.J. Cullen, N. Laird, et al. (1998). Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. Journal of the American Medical Association, 280, 1311–1316.CrossRefPubMedGoogle Scholar
  6. [7]
    Wong, C., G. Geiger, Y.D. Derman, C.R. Busby, and M.W. Carter, (2003). Redesigning the medication ordering, dispensing, and administration process in an acute care academic health science centre. Proceedings of the 2003 Winter Simulation Conference, S. Chick, P.J. Sánchez, D. Ferrin, and D.J. Morrice, Eds., New Orleans, LA, 1894–1902.Google Scholar
  7. [8]
    Derlet, R.W. and J.R. Richards (2000). Overcrowding in the nation’s emergency departments: Complex causes and disturbing effects. Annals of Emergency Medicine, 35, 63–68.CrossRefPubMedGoogle Scholar
  8. [9]
    Andrulis, D.P., A. Kellermann, E.A. Hintz, B.B. Hackman, and V.B. Weslowski (1991). Emergency departments and crowding in United States teaching hospitals. Annals of Emergency Medicine, 20, 980–986.CrossRefPubMedGoogle Scholar
  9. [10]
    Miro, O., M.T. Antonio, S. Jimenez, A. De Dios, M. Sanchez, and A. Borras (1999). Decreased health care quality associated with emergency department overcrowding. European Journal of Emergency Medicine, 6, 105–107.PubMedGoogle Scholar
  10. [11]
    Jun, J., S. Jacobson, and J. Swisher (1999). Applications of discrete event simulation in health care clinics. Journal of the Operational Research Society, 50, 109–123.Google Scholar
  11. [12]
    Siddharthan, K., W.J. Jones, and J.A. Johnson (1996). A priority queueing model to reduce waiting times in emergency care. International Journal of Health Care Quality Assurance, 9, 10–16.CrossRefPubMedGoogle Scholar
  12. [13]
    Bagust, A., M. Place, and J.W. Posnett (1999). Dynamics of bed use in accommodating emergency admissions: Stochastic simulation model. British Medical Journal, 319, 155–158.PubMedGoogle Scholar
  13. [14]
    Kumar, A.P. and R. Kapur (1989). Discrete event application-Scheduling staff for the emergency room. Proceedings of the 1989 Winter Simulation Conference, MacNair, E.A., K.J. Musselman, and P. Heidelberger, Eds., IEEE, Washington, DC, 1112–1120.Google Scholar
  14. [15]
    Rossetti, M.D., G.F. Trzcinski, and S.A. Syverud (1999). Emergency department simulation and the determination of optimal attending physician staffing schedules. Proceedings of the 1999 Winter Simulation Conference. Farrington, P.A., H.B. Nembhard, D.T. Sturrock, and G.W. Evans, Eds., Phoenix, AZ, 1532–1540.Google Scholar
  15. [16]
    Kirtland, A., J. Lockwood, K. Poisler, L. Stamp, and P. Wolfe (1995). Simulating an emergency department is as much fun as ⋯. Proceedings of the 1995 Winter Simulation Conference, Alexopoulus, C., K. Kang, W.R. Lilegdon, and D. Goldman, Eds., Arlington, VA.Google Scholar
  16. [17]
    Carter, M.W. (2002). Health care management-Diagnosis: mismanagement of resources. OR/MS Today, April, 26–32.Google Scholar

Copyright information

© Springer Science + Business Media, Inc. 2005

Authors and Affiliations

  • Michael W. Carter
    • 1
  • John T. Blake
    • 2
  1. 1.Department of Mechanical and Industrial EngineeringUniversity of TorontoTorontoCanada
  2. 2.Department of Industrial EngineeringDalhousie UniversityHalifaxCanada

Personalised recommendations