Liu Pai, Wu Shinyi
Daniel J. Epstein Department of Industrial and Systems Engineering, University of Southern California, Los Angeles, CA, USA.
Palo Alto Research Center, Palo Alto, CA, USA.
Health Care Manag Sci. 2016 Mar;19(1):89-101. doi: 10.1007/s10729-014-9279-x. Epub 2014 Apr 9.
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.
创建 accountable care organizations（ACO）作为控制迅速上涨的医疗成本和提高医疗质量的一种策略已被广泛讨论；然而，建立一个有效的 ACO 是一个复杂的过程，涉及多个有着自身利益的利益相关者（支付方、医疗服务提供者、患者）。此外，ACO 的实施在时间和金钱方面成本高昂。不成熟的设计可能会导致安全隐患。因此，需要基于分析模型的决策支持工具，能够预测不同策略的结果，以促进 ACO 的设计和实施。在本研究中，开发了一个基于智能体的模拟模型来研究 ACO，该模型将支付方、医疗服务提供者和患者视为智能体，处于充血性心力衰竭（CHF）护理的共享节约支付模式下，CHF 是有时可预防的住院治疗中最昂贵的病因之一。基于智能体的模拟模型已经确定了支付模式设计的关键决定因素，这些因素可以促使医疗服务提供者行为改变，以实现 ACO 的最大财务和质量结果。结果显示了与支付模式设计变化相对应的非线性医疗服务提供者行为变化模式。结果因具有不同质量或财务优先级的医疗服务提供者而异，并且对 ACO 实施的 CHF 干预措施的成本效益最为敏感。本研究展示了一种日益重要的构建医疗系统分析模型的方法，该方法可以帮助为卫生政策和医疗管理决策提供信息。该研究还指出，ACO 可能的成功与支付模式设计、医疗服务提供者特征以及医疗干预措施的成本和效果相互依存。
