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.