Technology Diffusion and New Delivery Models

项目综述：控制医疗支出增速成为压倒一切的关注点 政策制定者中。现有的研究已经确定了新医疗技术(和阵列)的传播 补充服务)是增加支出的主要原因。控制支出的新战略 增长侧重于交付系统改革，包括新的护理付费模式，如基于风险的 向负责任的护理组织(ACO)支付费用。在这种安排下，服务提供者组织有 更明智地采用和使用新技术和现有技术的激励措施，而不是提供商支付的费用- 服务(FFS)基础。如果这些模型影响到技术的类型，那么它们可能会减缓支出增长 扩散和扩散的速度。如果不采用高价值技术或 慢慢地被采纳。这些战略的潜在影响可能会因组织而异。使用丰富的 从IMS Health和IMS收集2005-2015年商业和医疗保险人口的数据 医疗保险，我们建议首先考察组织特征和技术传播之间的关系， 然后探索这些新的组织和融资模式对医疗保健支出的影响 技术。在目标1中，我们将研究选定的新技术的传播，包括我们 指定在四种疾病类别中具有较高和较低的价值-癌症、抑郁症、心脏和髋部 退化--作为组织特征的函数。我们的统计方法将扩散的特性联系起来 曲线到有意义的经济概念，并提供新的方法来表征技术的路径 领养。这些方法可以确定使用新技术的决定是否 不同技术类型之间的关联，或疾病状况内部和之间的关联。在目标2中，使用质量 由专业协会颁布的措施、技术特点和FDA的警报，我们将 区分高价值服务和低价值服务，确定预测其使用的组织因素，以及 确定采用高价值服务与采用低价值服务的决策是否相关。在目标3中，我们将使用一个不同的- 评估以下授权的Medicare ACO演示计划影响的无差异方法 《平价医疗法案》关于新技术的支出以及高价值和低价值服务的支出， 将传统FFS计划中分配到Medicare ACO的受益人与FFS进行比较 未分配给ACO的受益人。大多数关于技术采用和推广的研究是由案例组成的 对单一技术的研究。我们的研究将比较不同类型技术的采用率和使用率 (药物、设备和生物制品)在疾病领域内，跨越低价值和高价值技术，以及跨 组织形式。这项研究将首次检验基于风险的新ACO模型之间的联系 融资和提供医疗服务，以及关于最终采用技术的基本决策 确定这些模型可能在多大程度上减缓医疗保健支出的增长速度。

Project Summary: Controlling the rate of health care spending growth has become an overriding concern among policymakers. Existing research has identified the diffusion of new medical technology (and the array of complementary services) as the primary reason for increased spending. New strategies to contain spending growth focus on delivery system reforms, including new models of paying for care such as risk-based payments to accountable care organizations (ACOs). Under such arrangements, provider organizations have incentives to adopt and use new and existing technologies more judiciously than providers paid on a fee-for- service (FFS) basis. These models can slow spending growth if they affect the types of technologies that diffuse and the rate of diffusion. They could be detrimental if high value technologies are not adopted or adopted slowly. The potential impact of these strategies will likely vary across organizations. Using a rich collection of data on both commercial and Medicare populations from 2005-2015 from IMS Health and Medicare, we propose to first examine the relationship between organization traits and diffusion of technology, and then to explore the impact of these new models of organizing and financing care on spending for new technologies. In Aim 1 we will study the diffusion of selected new technologies, including technologies we designate to be of higher and lower value, in 4 disease categories - cancer, depression, cardiac, and hip degeneration - as a function of organization characteristics. Our statistical methods link properties of diffusion curves to meaningful economic concepts and provide new approaches to characterize the path of technology adoption. These approaches enable determination of whether decisions to use new technologies are correlated among different technology types, or within and between disease conditions. In Aim 2, using quality measures promulgated by professional societies, technology characteristics, and FDA alerts, we will distinguish higher from lower value services, identify organizational factors predictive of their use, and determine if decisions to adopt higher vs. lower value services are correlated. In Aim 3 we will use a difference- in-differences approach to assess the impact of the Medicare ACO demonstration programs authorized under the Affordable Care Act on spending on new technologies and spending on higher and lower value services, comparing beneficiaries in the traditional FFS program who were assigned to a Medicare ACO with FFS beneficiaries not assigned to an ACO. Most research on technological adoption and diffusion consists of case studies of single technologies. Our study will compare rates of adoption and use across types of technologies (drugs, devices, and biologics) within disease areas, across lower and higher value technologies, and across organizational forms. This study will be the first to examine the link between new risk-based ACO models of financing and delivering care, and the fundamental decisions about technology adoption that ultimately determine the extent to which these models may slow the rate of growth in health care spending.