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A tutorial on discrete-event simulation for health policy design and decision making: Optimizing pediatric ultrasound screening for hip dysplasia as an illustration

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  • Ramwadhdoebe, Sabrina
  • Buskens, Erik
  • Sakkers, Ralph J.B.
  • Stahl, James E.

Abstract

Background It is increasingly recognized that healthcare is a complex system with limited resources and many interacting sources of both positive and negative feedback. Discrete-event simulation (DES) is a tool that readily accommodates questions of capacity planning, throughput management and interacting resources. As a result the use of DES in informing healthcare decision making is increasing. However, understanding when and how to build a DES model and use it for policy making is not yet a common knowledge.Methods The steps in building a DES model will be demonstrated using a real-world example, i.e., pediatric ultrasound screening for hip dysplasia. The main components of a DES model such as entities, resources and queues will be introduced and we will examine questions such as referral schedule, number of ultrasound machines and type of screeners and how these entities interact. Finally a review of the statistical techniques appropriate to DES will be provided.Conclusion Discrete-event simulation is a valuable tool in the policymakers armentarium. It can be used effectively to analyze and understand complex healthcare systems and policy problems such as population screening.

Suggested Citation

  • Ramwadhdoebe, Sabrina & Buskens, Erik & Sakkers, Ralph J.B. & Stahl, James E., 2009. "A tutorial on discrete-event simulation for health policy design and decision making: Optimizing pediatric ultrasound screening for hip dysplasia as an illustration," Health Policy, Elsevier, vol. 93(2-3), pages 143-150, December.
    • Handle: RePEc:eee:hepoli:v:93:y:2009:i:2-3:p:143-150
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    References listed on IDEAS

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    1. James E. Stahl & David Rattner & Richard Wiklund & Jessica Lester & Molly Beinfeld & G. Scott Gazelle, 2004. "Reorganizing the System of Care Surrounding Laparoscopic Surgery: A Cost-Effectiveness Analysis Using Discrete-Event Simulation," Medical Decision Making, , vol. 24(5), pages 461-471, October.
    2. Nathaniel Hupert & Alvin I. Mushlin & Mark A. Callahan, 2002. "Modeling the Public Health Response to Bioterrorism: Using Discrete Event Simulation to Design Antibiotic Distribution Centers," Medical Decision Making, , vol. 22(1_suppl), pages 17-25, September.
    3. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
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    Cited by:

    1. Fournier, Derrick L. & Zaric, Gregory S., 2013. "Simulating neonatal intensive care capacity in British Columbia," Socio-Economic Planning Sciences, Elsevier, vol. 47(2), pages 131-141.
    2. Jesús Isaac Vázquez-Serrano & Rodrigo E. Peimbert-García & Leopoldo Eduardo Cárdenas-Barrón, 2021. "Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review," IJERPH, MDPI, vol. 18(22), pages 1-20, November.
    3. Mark Roberts & Louise B. Russell & A. David Paltiel & Michael Chambers & Phil McEwan & Murray Krahn, 2012. "Conceptualizing a Model," Medical Decision Making, , vol. 32(5), pages 678-689, September.

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