Abstract
Creating accountable care organizations (ACOs) has been widely discussed as a strategy to control rapidly rising healthcare costs and improve quality of care; however, building an effective ACO is a complex process involving multiple stakeholders (payers, providers, patients) with their own interests. Also, implementation of an ACO is costly in terms of time and money. Immature design could cause safety hazards. Therefore, there is a need for analytical model-based decision-support tools that can predict the outcomes of different strategies to facilitate ACO design and implementation. In this study, an agent-based simulation model was developed to study ACOs that considers payers, healthcare providers, and patients as agents under the shared saving payment model of care for congestive heart failure (CHF), one of the most expensive causes of sometimes preventable hospitalizations. The agent-based simulation model has identified the critical determinants for the payment model design that can motivate provider behavior changes to achieve maximum financial and quality outcomes of an ACO. The results show nonlinear provider behavior change patterns corresponding to changes in payment model designs. The outcomes vary by providers with different quality or financial priorities, and are most sensitive to the cost-effectiveness of CHF interventions that an ACO implements. This study demonstrates an increasingly important method to construct a healthcare system analytics model that can help inform health policy and healthcare management decisions. The study also points out that the likely success of an ACO is interdependent with payment model design, provider characteristics, and cost and effectiveness of healthcare interventions.
Highlights
The current health system in the United States lacks mechanisms and incentives for providers to control rapidly rising healthcare costs and improve quality of care [1]
The design and implementation of accountable care organizations (ACOs) are complex and risky, and there is a need for predictive analytics tools that can predict future outcomes of different ACO models and generate recommendations based on goals set by policymakers
The complexity and risks of the ACO model design generate a need for modeling and simulation tools that are flexible, capable, and user-adjustable to facilitate the design and implementation of an ACO model for different disease conditions, payment models, and provider and patient agent characteristics
Summary
The current health system in the United States lacks mechanisms and incentives for providers to control rapidly rising healthcare costs and improve quality of care [1]. An idea was proposed in 2007 of an accountable care organization (ACO), a group of providers who accept greater accountability for total costs and quality of care [2, 3]; this has been widely discussed since it was outlined in the Patient Protection and Affordable Care Act [4] in 2010. Many private insurers and healthcare organizations have launched or will launch their ACO demonstrations in the near future [6]. Wu shared saving) [7]; it is unclear how ACOs should be structured and implemented [8] to deliver high-quality care and control spending. The design and implementation of ACOs are complex and risky, and there is a need for predictive analytics tools that can predict future outcomes of different ACO models and generate recommendations based on goals set by policymakers
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