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A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system

Overview of attention for article published in Genetics in Medicine, August 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (89th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (64th percentile)

Mentioned by

blogs
1 blog
twitter
26 X users
facebook
1 Facebook page

Citations

dimensions_citation
21 Dimensions

Readers on

mendeley
72 Mendeley
citeulike
1 CiteULike
Title
A proposed approach to accelerate evidence generation for genomic-based technologies in the context of a learning health system
Published in
Genetics in Medicine, August 2017
DOI 10.1038/gim.2017.122
Pubmed ID
Authors

Christine Y Lu, Marc S Williams, Geoffrey S Ginsburg, Sengwee Toh, Jeff S Brown, Muin J Khoury

Abstract

Genomic technologies should demonstrate analytical and clinical validity and clinical utility prior to wider adoption in clinical practice. However, the question of clinical utility remains unanswered for many genomic technologies. In this paper, we propose three building blocks for rapid generation of evidence on clinical utility of promising genomic technologies that underpin clinical and policy decisions. We define promising genomic tests as those that have proven analytical and clinical validity. First, risk-sharing agreements could be implemented between payers and manufacturers to enable temporary coverage that would help incorporate promising technologies into routine clinical care. Second, existing data networks, such as the Sentinel Initiative and the National Patient-Centered Clinical Research Network (PCORnet) could be leveraged, augmented with genomic information to track the use of genomic technologies and monitor clinical outcomes in millions of people. Third, endorsement and engagement from key stakeholders will be needed to establish this collaborative model for rapid evidence generation; all stakeholders will benefit from better information regarding the clinical utility of these technologies. This collaborative model can create a multipurpose and reusable national resource that generates knowledge from data gathered as part of routine care to drive evidence-based clinical practice and health system changes.GENETICS in MEDICINE advance online publication, 10 August 2017; doi:10.1038/gim.2017.122.

X Demographics

X Demographics

The data shown below were collected from the profiles of 26 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 72 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 72 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 19%
Researcher 10 14%
Student > Ph. D. Student 9 13%
Other 7 10%
Student > Bachelor 6 8%
Other 10 14%
Unknown 16 22%
Readers by discipline Count As %
Medicine and Dentistry 14 19%
Biochemistry, Genetics and Molecular Biology 5 7%
Business, Management and Accounting 5 7%
Computer Science 5 7%
Social Sciences 5 7%
Other 18 25%
Unknown 20 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 22. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 June 2019.
All research outputs
#1,723,761
of 25,382,440 outputs
Outputs from Genetics in Medicine
#582
of 2,945 outputs
Outputs of similar age
#33,352
of 327,545 outputs
Outputs of similar age from Genetics in Medicine
#19
of 54 outputs
Altmetric has tracked 25,382,440 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,945 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 19.0. This one has done well, scoring higher than 80% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 327,545 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 89% of its contemporaries.
We're also able to compare this research output to 54 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 64% of its contemporaries.