An agent-based model to study accountable care organization

Creating Accountable Care Organizations (ACOs) has been widely discussed as a strategy to control rapidly rising healthcare costs and improve the quality of care; however, little is known about how to build an ACO that can achieve these goals. Implementation of an ACO is costly in terms of time and money and could cause safety hazards because of immature design. Therefore, there is an urgent need for analytic capacity and decision-support tools that can quickly evaluate different ACO models and predict future outcomes to facilitate the ACO design and implementation process.;Industrial and Systems Engineering (ISE) methods have been used for system modeling and optimization for years. However, traditional ISE approaches are facing challenges in modeling the important features of an ACO which is composed of multiple interacting stakeholders including payers, providers, and patients who are working to maximize their own interests.;To meet the analytic demands of an ACO and address the modeling challenges, we first developed an analytic framework to guide the analysis and modeling of an ACO. We then demonstrated the framework through constructing an Agent-based simulation model to study the ACO under the Shared Saving payment model for Congestive Heart Failure (CHF) care. The goal of the Agent-based simulation model is not to substitute an ACO pilot program but to provide analytic supports for decision makers to make informed decisions.;The Agent-based simulation model has identified the critical determinants for the payment model design that can motive provider behavior changes to achieve maximal financial and quality outcomes of an ACO. In particular, for the Shared Saving payment model, the simulation model can determine the variable optimal shared saving rate as well as the distribution of the shared savings to hospitals and primary care physicians based on an ACO's financial or quality targets. The results have shown the non-linear provider behavior change patterns responding to the changes in payment model. The model also has provided insight on how providers with different priorities respond differently to a certain payment model, which indicates the payer may need to expand its strategies to motivate all types of providers. The sensitivity analysis has shown that the model outputs are most sensitive to the cost-effectiveness of the interventions that an ACO implements to improve the outcomes.;This research contributes to healthcare service and policy research by providing an analytical method that can help decision makers better understand the complexity and risks of the design and implementation of an ACO and facilitate more informed decision making. It contributes to the ISE community by addressing the modeling challenges to better model a complex system and achieve a higher impact.