Technology Diffusion and New Delivery Models

项目概述：控制医疗保健支出增长速度已成为压倒一切的问题 政策制定者之间。现有的研究已经确定了新的医疗技术（和阵列）的传播 补充服务）是增加支出的主要原因。控制开支的新策略 增长的重点是交付系统改革，包括支付护理费用的新模式，如基于风险的 向负责任的护理组织（ACO）付款。在这种安排下，提供者组织 鼓励采用和使用新的和现有的技术，而不是提供者支付费用， 服务（FFS）基础。如果这些模式影响到技术类型， 扩散和扩散速率。如果不采用高价值技术， 慢慢地采纳。这些战略的潜在影响可能因组织而异。使用丰富 从IMS Health收集2005-2015年商业和医疗保险人群的数据， 医疗保险，我们建议首先检查组织特征和技术扩散之间的关系， 然后探索这些组织和资助护理的新模式对新医疗支出的影响， 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 在4种疾病类别中指定较高和较低的值-癌症、抑郁症、心脏病和髋关节 退化-作为组织特征的函数。我们的统计方法将扩散的性质 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 领养这些方法有助于确定使用新技术的决定是否 在不同的技术类型之间，或者在疾病条件之内和之间。在目标2中，使用质量 专业协会颁布的措施、技术特征和FDA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， 确定采用高价值服务与低价值服务的决策是否相关。在目标3中，我们将使用一个差异- 差异方法，以评估根据授权的Medicare ACO示范计划的影响 《平价医疗法案》关于新技术支出和高价值和低价值服务支出的规定， 将传统FFS计划中被分配到Medicare ACO的受益人与FFS计划中的受益人进行比较， 受益人未分配给ACO。大多数关于技术采用和扩散的研究都是由案例组成的 单一技术的研究。我们的研究将比较各种技术的采用率和使用率 （药物，设备和生物制剂）在疾病领域，在较低和较高价值的技术， 组织形式。这项研究将是第一个研究新的基于风险的ACO模型之间的联系， 融资和提供护理，以及关于技术采用的基本决定， 确定这些模式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.