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Handbook of Healthcare Operations Management pp 45–74Cite as

Applications of Agent-Based Modeling and Simulation to Healthcare Operations Management

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 184))

Abstract

Agent-based modeling (ABM) is rapidly gaining momentum in many fields, and it has added to the insights previously contributed by other modeling and simulation methods such as system dynamics and discrete event simulation. Healthcare operations management is one field that is particularly well-suited for ABM because it involves many individuals that interact in different ways. ABM is capable of explicitly modeling these individuals and the interactions among them, which facilitates the discovery of system behavior that cannot be observed using other methods. ABM has been applied successfully to several focus areas within the field of healthcare operations management, including, but not limited to: healthcare delivery, epidemiology, economics, and policy. In this chapter, we review and evaluate a selected body of research in which agent-based modeling and simulation techniques are applied to problems in healthcare. We also highlight specific areas where agent-based modeling and simulation filled a significant gap that was not addressed previously by other methods. Finally, we propose some new questions in the field which may be of interest moving forward.

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Authors and Affiliations

  1. Robert H. Smith School of Business, University of Maryland, College Park, MD, USA

    Sean Barnes, Bruce Golden & Stuart Price

Authors
  1. Sean Barnes
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  2. Bruce Golden
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  3. Stuart Price
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Corresponding author

Correspondence to Sean Barnes .

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Editors and Affiliations

  1. , IOE Department, University of Michigan, Beal Ave 1205, Ann Arbor, 48109, Michigan, USA

    Brian T. Denton

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Barnes, S., Golden, B., Price, S. (2013). Applications of Agent-Based Modeling and Simulation to Healthcare Operations Management. In: Denton, B. (eds) Handbook of Healthcare Operations Management. International Series in Operations Research & Management Science, vol 184. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-5885-2_3

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