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The Geisinger MyCode community health initiative: an electronic health record–linked biobank for precision medicine research

Overview of attention for article published in Genetics in Medicine, February 2016
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (95th percentile)
  • High Attention Score compared to outputs of the same age and source (89th percentile)

Mentioned by

news
2 news outlets
policy
2 policy sources
twitter
25 X users
patent
2 patents
facebook
3 Facebook pages
wikipedia
1 Wikipedia page

Citations

dimensions_citation
350 Dimensions

Readers on

mendeley
218 Mendeley
citeulike
1 CiteULike
Title
The Geisinger MyCode community health initiative: an electronic health record–linked biobank for precision medicine research
Published in
Genetics in Medicine, February 2016
DOI 10.1038/gim.2015.187
Pubmed ID
Authors

David J. Carey, Samantha N. Fetterolf, F. Daniel Davis, William A. Faucett, H. Lester Kirchner, Uyenlinh Mirshahi, Michael F. Murray, Diane T. Smelser, Glenn S. Gerhard, David H. Ledbetter

Abstract

Geisinger Health System (GHS) provides an ideal platform for Precision Medicine. Key elements are the integrated health system, stable patient population, and electronic health record (EHR) infrastructure. In 2007, Geisinger launched MyCode, a system-wide biobanking program to link samples and EHR data for broad research use. Patient-centered input into MyCode was obtained using participant focus groups. Participation in MyCode is based on opt-in informed consent and allows recontact, which facilitates collection of data not in the EHR and, since 2013, the return of clinically actionable results to participants. MyCode leverages Geisinger's technology and clinical infrastructure for participant tracking and sample collection. MyCode has a consent rate of >85%, with more than 90,000 participants currently and with ongoing enrollment of ~4,000 per month. MyCode samples have been used to generate molecular data, including high-density genotype and exome sequence data. Genotype and EHR-derived phenotype data replicate previously reported genetic associations. The MyCode project has created resources that enable a new model for translational research that is faster, more flexible, and more cost-effective than traditional clinical research approaches. The new model is scalable and will increase in value as these resources grow and are adopted across multiple research platforms.Genet Med advance online publication 11 February 2016Genetics in Medicine (2016); doi:10.1038/gim.2015.187.

X Demographics

X Demographics

The data shown below were collected from the profiles of 25 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 218 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
United States 1 <1%
Unknown 216 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 37 17%
Student > Ph. D. Student 28 13%
Student > Master 17 8%
Other 16 7%
Student > Postgraduate 10 5%
Other 37 17%
Unknown 73 33%
Readers by discipline Count As %
Medicine and Dentistry 47 22%
Biochemistry, Genetics and Molecular Biology 26 12%
Agricultural and Biological Sciences 18 8%
Computer Science 11 5%
Nursing and Health Professions 7 3%
Other 28 13%
Unknown 81 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 42. 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 31 January 2023.
All research outputs
#981,054
of 25,394,764 outputs
Outputs from Genetics in Medicine
#285
of 2,945 outputs
Outputs of similar age
#18,060
of 410,168 outputs
Outputs of similar age from Genetics in Medicine
#6
of 56 outputs
Altmetric has tracked 25,394,764 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 96th percentile: it's in the top 5% 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 particularly well, scoring higher than 90% 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 410,168 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 95% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 89% of its contemporaries.