Capacity building for assessing new technologies: approaches to examining personalized medicine in practice. (July 2010)

• Record Type: Journal Article
• Authors: Van Bebber, Stephanie L; Trosman, Julia R; Liang, Su-Ying; Wang, Grace; Marshall, Deborah A; Knight, Sara; Phillips, Kathryn A
• Is Part Of: Personalized medicine. Volume 7:Number 4(Year 2010)
• Page Start: 427
• Page End: 439
• Publication Date: 2010-07
• Subjects: cancer -- data development -- evidence development -- health payer -- organized delivery system -- personalized medicine -- pharmacy benefits manager; Pharmacogenomics -- Periodicals; Pharmacogenetics -- Periodicals; 615.19
• DOI: 10.2217/pme.10.36
• Languages: English
• ISSNs: 1741-0541

Abstract

This article focuses on the overarching question: how can we use existing data to develop the capacity to improve the evidence base on personalized medicine technologies and particularly regarding their utilization and clinical utility? We focus on data from health payers who are key stakeholders in capacity building, as they need data to guide decisions and they develop data as part of operations. Broadly defined, health payers include insurance carriers, third party payers, health-plan sponsors and organized delivery systems. Data from health payers have not yet been widely used to assess personalized medicine. Now, with an increasing number of personalized technologies covered and reimbursed by health payers, and an increasing number of emerging technologies that will require policy decisions, there is a great opportunity to develop the evidence base using payer data and by engaging with these stakeholders. Here, we describe data that are available from, and are being developed by, health payers and assess how these data can be further developed to increase the capacity for future research, using three examples. The examples suggest that payer data can be used to examine clinical utility and approaches can be developed that simultaneously address the characteristics of personalized medicine, real world data and organizations. These examples can now help us to elucidate how to best examine clinical utility in actual practice and build evaluation approaches that can be applied to future technologies.