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The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials

Overview of attention for article published in BMC Medical Informatics and Decision Making, January 2016
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  • Above-average Attention Score compared to outputs of the same age (55th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (54th percentile)

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Title
The value of structured data elements from electronic health records for identifying subjects for primary care clinical trials
Published in
BMC Medical Informatics and Decision Making, January 2016
DOI 10.1186/s12911-016-0239-x
Pubmed ID
Authors

Mohammad B. Ateya, Brendan C. Delaney, Stuart M. Speedie

Abstract

An increasing number of clinical trials are conducted in primary care settings. Making better use of existing data in the electronic health records to identify eligible subjects can improve efficiency of such studies. Our study aims to quantify the proportion of eligibility criteria that can be addressed with data in electronic health records and to compare the content of eligibility criteria in primary care with previous work. Eligibility criteria were extracted from primary care studies downloaded from the UK Clinical Research Network Study Portfolio. Criteria were broken into elemental statements. Two expert independent raters classified each statement based on whether or not structured data items in the electronic health record can be used to determine if the statement was true for a specific patient. Disagreements in classification were discussed until 100 % agreement was reached. Statements were also classified based on content and the percentages of each category were compared to two similar studies reported in the literature. Eligibility criteria were retrieved from 228 studies and decomposed into 2619 criteria elemental statements. 74 % of the criteria elemental statements were considered likely associated with structured data in an electronic health record. 79 % of the studies had at least 60 % of their criteria statements addressable with structured data likely to be present in an electronic health record. Based on clinical content, most frequent categories were: "disease, symptom, and sign", "therapy or surgery", and "medication" (36 %, 13 %, and 10 % of total criteria statements respectively). We also identified new criteria categories related to provider and caregiver attributes (2.6 % and 1 % of total criteria statements respectively). Electronic health records readily contain much of the data needed to assess patients' eligibility for clinical trials enrollment. Eligibility criteria content categories identified by our study can be incorporated as data elements in electronic health records to facilitate their integration with clinical trial management systems.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 69 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
United Kingdom 1 1%
Unknown 66 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 11 16%
Student > Ph. D. Student 8 12%
Student > Master 8 12%
Other 6 9%
Student > Bachelor 5 7%
Other 18 26%
Unknown 13 19%
Readers by discipline Count As %
Medicine and Dentistry 20 29%
Computer Science 11 16%
Nursing and Health Professions 8 12%
Psychology 3 4%
Neuroscience 2 3%
Other 7 10%
Unknown 18 26%
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 25 March 2018.
All research outputs
#7,470,187
of 22,837,982 outputs
Outputs from BMC Medical Informatics and Decision Making
#764
of 1,990 outputs
Outputs of similar age
#124,216
of 394,936 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#16
of 37 outputs
Altmetric has tracked 22,837,982 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,990 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 58% 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 394,936 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 55% of its contemporaries.
We're also able to compare this research output to 37 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.