Publication | Closed Access
Simulation Applied to Health Services: Opportunities for Applying the System Dynamics Approach
66
Citations
38
References
1998
Year
Health AdministrationEngineeringSimulationHealth Care ManagementCo-simulationHealth System EngineeringHospital MedicineSimulation MethodologyPrimary CareSimulation FrameworkSystems EngineeringModeling And SimulationPublic HealthSystem SimulationHealth Services ResearchDevice SimulationSystem DynamicsHealth PolicyHealth InsuranceOutcomes ResearchSimulation ApproachSystem Dynamics ApproachMedical Decision AnalysisHealth Care DeliveryNursingHealth EconomicsHealth ServicesSimulation AppliedHealth Informatics
Traditional simulation studies focus on localized, patient‑level decisions, whereas system dynamics offers a holistic modelling perspective. This essay aims to raise awareness of system dynamics in health services and demonstrate its usefulness for designing robust policies by exploring system structure. The authors review simulation use in health services, compare system dynamics to conventional approaches using coronary heart disease waiting‑time reductions, and show how the method captures time‑varying tangible and intangible factors. System dynamics is proposed to have great potential for aiding health‑care policy formation.
The aim of this essay is to raise awareness and broaden understanding within the health services community of the system dynamics (SD) simulation approach to policy analysis. The application of simulation in health services is reviewed. A comparison is made between the SD and traditional simulation approaches and is illustrated by considering reductions in waiting times for coronary heart disease treatment. Traditionally, simulation studies have tended to focus on the analysis of localized decisions and therefore on problems orientated towards individual patients. Although these methods are extremely powerful and effective, there is scope for an alternative modelling approach which is based on a more holistic perspective; SD is one such approach. It can assist in the design of robust policies by supporting debate on how the underlying structure might influence the evolutionary behaviour of a system. Using this method we can consider the time variation both of tangibles, such as waiting times and health care costs, and intangibles, such as patient anxiety and the effects of various pressures on purchasing decisions. We propose that SD holds great potential in assisting policy formation in health care.
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