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Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory

Overview of attention for article published in Journal of the American Medical Informatics Association, December 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (69th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

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3 X users
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1 Facebook page

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216 Mendeley
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3 CiteULike
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Article details
Title
Electronic health records based phenotyping in next-generation clinical trials: a perspective from the NIH Health Care Systems Collaboratory
Published in
Journal of the American Medical Informatics Association, December 2013
DOI 10.1136/amiajnl-2013-001926
Pubmed ID
Authors
Abstract

Widespread sharing of data from electronic health records and patient-reported outcomes can strengthen the national capacity for conducting cost-effective clinical trials and allow research to be embedded within routine care delivery. While pragmatic clinical trials (PCTs) have been performed for decades, they now can draw on rich sources of clinical and operational data that are continuously fed back to inform research and practice. The Health Care Systems Collaboratory program, initiated by the NIH Common Fund in 2012, engages healthcare systems as partners in discussing and promoting activities, tools, and strategies for supporting active participation in PCTs. The NIH Collaboratory consists of seven demonstration projects, and seven problem-specific working group 'Cores', aimed at leveraging the data captured in heterogeneous 'real-world' environments for research, thereby improving the efficiency, relevance, and generalizability of trials. Here, we introduce the Collaboratory, focusing on its Phenotype, Data Standards, and Data Quality Core, and present early observations from researchers implementing PCTs within large healthcare systems. We also identify gaps in knowledge and present an informatics research agenda that includes identifying methods for the definition and appropriate application of phenotypes in diverse healthcare settings, and methods for validating both the definition and execution of electronic health records based phenotypes.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 216 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 3 1%
United Kingdom 3 1%
Netherlands 1 <1%
Israel 1 <1%
Spain 1 <1%
Unknown 207 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Student > Ph. D. Student 44 20%
Researcher 37 17%
Student > Master 22 10%
Student > Postgraduate 12 6%
Other 10 5%
Other 38 18%
Unknown 53 25%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 63 29%
Computer Science 24 11%
Nursing and Health Professions 12 6%
Agricultural and Biological Sciences 7 3%
Social Sciences 7 3%
Other 33 15%
Unknown 70 32%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 17 December 2013.
All research outputs
#7,696,936
of 23,577,761 outputs
Outputs from Journal of the American Medical Informatics Association
#1,889
of 3,135 outputs
Outputs of similar age
#93,630
of 311,294 outputs
Outputs of similar age from Journal of the American Medical Informatics Association
#32
of 70 outputs
Altmetric has tracked 23,577,761 research outputs across all sources so far. This one has received more attention than most of these and is in the 67th percentile.
So far Altmetric has tracked 3,135 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.5. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
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 311,294 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.
We're also able to compare this research output to 70 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 54% of its contemporaries.