Evidence map›Paper›PMID 21857867›Full record

ArticlePersonalized medicine2010

Capacity building for assessing new technologies: approaches to examining personalized medicine in practice.

Stephanie L Van Bebber, Julia R Trosman, Su-Ying Liang, Grace Wang, Deborah A Marshall, Sara Knight, Kathryn A Phillips

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. A Review of Challenges and Opportunities in Machine Learning for Health.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Stephanie L Van BebberUCSF Center for Translational & Policy Research on Personalized Medicine (TRANSPERS), School of Pharmacy, University of California San Francisco, 3333 California Street Suite 420, San Francisco, CA 94118, USA.
Julia R Trosman
Su-Ying Liang
Grace Wang
Deborah A Marshall
Sara Knight
Kathryn A Phillips

Funding

UTILIZATION OF PERSONALIZED MEDICINE USING GENOMICSP01CA130818 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI PHILLIPS, KATHRYN A · 2008 to 2010
$5.1M
NCI NIH HHS P01 CA130818
6 · The paper itself

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

PMID21857867
PMCPMC3157083

What OpenQuestion holds

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Registered trials

None linked

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.