Health Systems Simulation

Health Systems Simulation

David Lyell (University of New South Wales, Australia), Rosemarie Sadsad (University of New South Wales, Australia) and Andrew Georgiou (University of Sydney, Australia)
Copyright: © 2008 |Pages: 10
DOI: 10.4018/978-1-59904-889-5.ch082
OnDemand PDF Download:


Most problems arising from the operation of the health system are studied and addressed using conventional reductionist methods, which reduce, isolate, and freeze aspects of the system at a given time. This fails to deal with the dynamic complexity inherent in the health system and which is often the source of the problem. The result is that all too often, well intentioned interventions make the original problem worse by failing to fully understand the complexities involved in the origin of a problem (Sterman, 2000). In this article, we introduce system simulation as a means of exposing the underlying causes and systemic structures of problems within the health care system, as well as providing a tool for assessing the likely impact of new interventions. The following sections will examine the advantages of simulation, areas of application, how simulation experiments can overcome some of the limitations of randomised control trials (RCTs), and various simulation methodologies as well as the challenges of conducting simulation experiments.

Key Terms in this Chapter

Health System Simulation: The application of quantitative modelling and computer simulation methods to study the interactions between individuals and/or components of the health care system and how these interactions over time produce the observed behaviour which is produced by the health care system.

Multi-Method Simulation: Asimulation which uses more than one simulation method.

Dynamic Complexity: Occurs when systems are dynamic, tightly coupled, governed by feedback; plays out over time.

System Dynamics (SD): A method of simulation that focuses on system interactions over time, which considering feedback effects. System Dynamics models are made up of a series of differential equations and are continuous. They are highly aggregated, focus on patterns of behaviour, and can incorporate abstract concepts. It is a useful method for policy level analysis.

Discrete Event Simulation (DES): A method of simulation that treats the operation of a system as a sequence of chronological events, with changes in the state of the system being triggered by a discrete event.

Dynamical System Simulation (DSS): A method of simulation that is similar to System Dynamics (SD). However in contrast to SD, Dynamical Systems simulation are usually limited to simulations of physical or physiological systems where the relationships are expressed as mathematical equations.

System: A combination of parts which form a whole entity.

Agent Based Simulation (AB): A method of simulation which focuses on the interaction of individual agents. In Agent Base simulations, agents are encoded with attributes and rules governing how they interact with other agents and what attributes may change as a result of the interaction.

Complex System: A highly coupled system where the outcomes of the system are the result of the interactions that occur between its different components.

Complete Chapter List

Search this Book: