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Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 2013
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (76th percentile)

Mentioned by

twitter
9 tweeters

Citations

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92 Dimensions

Readers on

mendeley
123 Mendeley
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Title
Development and evaluation of a de-identification procedure for a case register sourced from mental health electronic records
Published in
BMC Medical Informatics and Decision Making, July 2013
DOI 10.1186/1472-6947-13-71
Pubmed ID
Authors

Andrea C Fernandes, Danielle Cloete, Matthew TM Broadbent, Richard D Hayes, Chin-Kuo Chang, Richard G Jackson, Angus Roberts, Jason Tsang, Murat Soncul, Jennifer Liebscher, Robert Stewart, Felicity Callard

Abstract

Electronic health records (EHRs) provide enormous potential for health research but also present data governance challenges. Ensuring de-identification is a pre-requisite for use of EHR data without prior consent. The South London and Maudsley NHS Trust (SLaM), one of the largest secondary mental healthcare providers in Europe, has developed, from its EHRs, a de-identified psychiatric case register, the Clinical Record Interactive Search (CRIS), for secondary research.

Twitter Demographics

The data shown below were collected from the profiles of 9 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 123 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 <1%
Unknown 122 99%

Demographic breakdown

Readers by professional status Count As %
Researcher 24 20%
Student > Master 21 17%
Student > Ph. D. Student 18 15%
Student > Postgraduate 9 7%
Student > Bachelor 8 7%
Other 23 19%
Unknown 20 16%
Readers by discipline Count As %
Medicine and Dentistry 28 23%
Computer Science 22 18%
Psychology 15 12%
Social Sciences 10 8%
Agricultural and Biological Sciences 5 4%
Other 12 10%
Unknown 31 25%

Attention Score in Context

This research output has an Altmetric Attention Score of 5. 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 21 February 2014.
All research outputs
#3,524,829
of 14,045,196 outputs
Outputs from BMC Medical Informatics and Decision Making
#368
of 1,284 outputs
Outputs of similar age
#36,081
of 154,245 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 1 outputs
Altmetric has tracked 14,045,196 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 1,284 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one has gotten more attention than average, scoring higher than 70% 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 154,245 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 76% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them