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Are family physicians comprehensively using electronic medical records such that the data can be used for secondary purposes? A Canadian perspective

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2015
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  • Good Attention Score compared to outputs of the same age (70th percentile)
  • Good Attention Score compared to outputs of the same age and source (72nd percentile)

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6 X users

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Title
Are family physicians comprehensively using electronic medical records such that the data can be used for secondary purposes? A Canadian perspective
Published in
BMC Medical Informatics and Decision Making, August 2015
DOI 10.1186/s12911-015-0195-x
Pubmed ID
Authors

Karen Tu, Jessica Widdifield, Jacqueline Young, William Oud, Noah M. Ivers, Debra A. Butt, Chad A. Leaver, Liisa Jaakkimainen

Abstract

With the introduction and implementation of a variety of government programs and policies to encourage adoption of electronic medical records (EMRs), EMRs are being increasingly adopted in North America. We sought to evaluate the completeness of a variety of EMR fields to determine if family physicians were comprehensively using their EMRs and the suitability of use of the data for secondary purposes in Ontario, Canada. We examined EMR data from a convenience sample of family physicians distributed throughout Ontario within the Electronic Medical Record Administrative data Linked Database (EMRALD) as extracted in the summer of 2012. We identified all physicians with at least one year of EMR use. Measures were developed and rates of physician documentation of clinical encounters, electronic prescriptions, laboratory tests, blood pressure and weight, referrals, consultation letters, and all fields in the cumulative patient profile were calculated as a function of physician and patient time since starting on the EMR. Of the 167 physicians with at least one year of EMR use, we identified 186,237 patients. Overall, the fields with the highest level of completeness were for visit documentations and prescriptions (>70 %). Improvements were observed with increasing trends of completeness overtime for almost all EMR fields according to increasing physician time on EMR. Assessment of the influence of patient time on EMR demonstrated an increasing likelihood of the population of EMR fields overtime, with the largest improvements occurring between the first and second years. All of the data fields examined appear to be reasonably complete within the first year of adoption with the biggest increase occurring the first to second year. Using all of the basic functions of the EMR appears to be occurring in the current environment of EMR adoption in Ontario. Thus the data appears to be suitable for secondary use.

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X Demographics

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 122 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Canada 3 2%
United Kingdom 2 2%
Unknown 117 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 22 18%
Student > Master 21 17%
Student > Ph. D. Student 11 9%
Student > Postgraduate 9 7%
Student > Doctoral Student 9 7%
Other 25 20%
Unknown 25 20%
Readers by discipline Count As %
Medicine and Dentistry 43 35%
Computer Science 9 7%
Nursing and Health Professions 8 7%
Social Sciences 4 3%
Psychology 4 3%
Other 20 16%
Unknown 34 28%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 August 2015.
All research outputs
#6,739,845
of 22,821,814 outputs
Outputs from BMC Medical Informatics and Decision Making
#640
of 1,988 outputs
Outputs of similar age
#78,027
of 264,395 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#9
of 33 outputs
Altmetric has tracked 22,821,814 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 1,988 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 67% 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 264,395 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 70% of its contemporaries.
We're also able to compare this research output to 33 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 72% of its contemporaries.