ArticlePersonalized medicine2010
Capacity building for assessing new technologies: approaches to examining personalized medicine in practice.
Article in Personalized medicine, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- A Review of Challenges and Opportunities in Machine Learning for Health.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Payer Coverage for Hereditary Cancer Panels: Barriers, Opportunities, and Implications for the Precision Medicine Initiative.Journal of the National Comprehensive Cancer Network : JNCCN · 2017Article
- Decision Making on Medical Innovations in a Changing Health Care Environment: Insights from Accountable Care Organizations and Payers on Personalized Medicine and Other Technologies.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2017Article
- Barriers and Facilitators to Adoption of Genomic Services for Colorectal Care within the Veterans Health Administration.Journal of personalized medicine · 2016Article
- Challenges of coverage policy development for next-generation tumor sequencing panels: experts and payers weigh in.Journal of the National Comprehensive Cancer Network : JNCCN · 2015Article
- Diffusion of molecular diagnostic lung cancer tests: a survey of german oncologists.Journal of personalized medicine · 2014Article
- Implementation of personalized medicine services in community pharmacies: perceptions of independent community pharmacists.Journal of the American Pharmacists Association : JAPhAArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.