No abstract

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 proposed framework to integrate two examples of provider behavior models, two examples of disease models, and four examples of payment models. We explore multiple combinations of these models and simulate the impact that alternative payment models may have on health and financial outcomes. These examples test the robustness of the simulation framework, and illustrate the value of such simulations to the policy makers who design incentives to improve cost and health outcomes, and to providers who wish to evaluate the financial impact of proposed incentives on their practice.

Sound marketing decisions often require understanding the cause-and-effect relationships between treatment and outcomes. Market research traditionally approaches such questions by designing randomized experiments that aim to isolate the effects of the specific treatment from other effects. We review an alternate methodology that is well suited to observational studies, where the analyst cannot control how treatment is applied. The methodology uses propensity scoring and matching to emulate the randomization of treatment. It is well established in other fields, but not widely known among marketers in spite of the fact that non-experimental data is common in marketing studies. We present two applications as case studies to illustrate the value of the methodology and to describe how we addressed some of the practical issues, in sufficient detail for readers to be able to use the methodology in similar studies.