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Basic interaction loop in simulated health care scenarios.

Basic interaction loop in simulated health care scenarios.

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Figure 1: Basic interaction loop in simulated health care scenarios.
Figure 3: Simplistic model of retinopathy pathway for diabetes
Figure 4: Results for FFS+Shared Risk vs. reward rate, using myopic...
Figure 5: Results for FFS+Shared Risk vs. penalty rate.
+2 Figure 6: Results for FFS+Shared Risk vs. provider cost.
Applying a framework for healthcare incentives simulation
Conference Paper
Full-text available
  • Dec 2012
At WinterSim 2011, we originally proposed an agent-based framework for healthcare simulations, enabling flexible integration of multiple simulation models, including models of disease progression, effects of provider interventions, and provider behavior models that are responsive to contractual incentives. In this paper, we report results using our...
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... depiction of the basic interaction cycle between components in our simulator can be seen in Figure 1. A population of Patients interacts with a health care Provider over a sequence of several decision cycles, typically of one year duration. ...

Citations

... A generalization beyond the patient-provider interaction has diverse additional components as described in Figure 1. The figure, itself a more detailed elaboration of the workflow defined in [2], shows behaviors, influences, and other elements of a healthcare "knowledge core" which can be called out as modules and parameters in an individual simulation model. Each interaction between agents has its own characteristics, and modeled actions to be taken. ...
Emulating a System Dynamics Model with Agent-Based Models: A Methodological Case Study in Simulation of Diabetes Progression
Article
Full-text available
  • Jan 2015
An agent-based simulation model hierarchy emulating disease states and behaviors critical to progression of diabetes type 2 was designed and implemented in the DEVS framework. This model was built to approximately reproduce some essential findings that were previously reported for a rather complex model of diabetes progression. Our models are translations of basicelements of this previously reported system dynamics model of diabetes. The system dynamics model, which mimics diabetes progression over an aggregated US population, was disaggregated and reconstructed bottom-up at the individual (agent) level. Four levels of model complexity were defined in order to systematically evaluate which parameters are needed to mimic outputs of the system dynamics model. The four estimated models attempted to replicate stock counts representing disease states in the system dynamics model while estimating impacts of an elderliness factor, obesity factor and health-related behavioral parameters. Health-related behavior was modeled as a simple realization of the Theory of Planned Behavior, a joint function of individual attitude and diffusion of social norms that spread over each agent’s social network. Although the most complex agent-based simulation model contained 31 adjustable parameters, all models were considerably less complex than the system dynamics model which required numerous time series inputs to make its predictions. All three elaborations of the baseline model provided significantly improved fits to the output of the system dynamics model, although behavioral factors appeared to contribute more than the elderliness factor. The results illustrate a promising approach to translate complex system dynamics models into agent-based model alternatives that are both conceptually simpler and capable of capturing main effects of complex local agent-agent interactions.
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