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Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2006
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (74th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
1 tweeter
patent
2 patents

Citations

dimensions_citation
54 Dimensions

Readers on

mendeley
98 Mendeley
citeulike
1 CiteULike

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Title
Assessment of the potential impact of a reminder system on the reduction of diagnostic errors: a quasi-experimental study
Published in
BMC Medical Informatics and Decision Making, April 2006
DOI 10.1186/1472-6947-6-22
Pubmed ID
16646956
Authors

Padmanabhan Ramnarayan, Graham C Roberts, Michael Coren, Vasantha Nanduri, Amanda Tomlinson, Paul M Taylor, Jeremy C Wyatt, Joseph F Britto

Abstract

Computerized decision support systems (DSS) have mainly focused on improving clinicians' diagnostic accuracy in unusual and challenging cases. However, since diagnostic omission errors may predominantly result from incomplete workup in routine clinical practice, the provision of appropriate patient- and context-specific reminders may result in greater impact on patient safety. In this experimental study, a mix of easy and difficult simulated cases were used to assess the impact of a novel diagnostic reminder system (ISABEL) on the quality of clinical decisions made by various grades of clinicians during acute assessment.

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Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 98 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 4 4%
United States 4 4%
Ecuador 1 1%
Germany 1 1%
Spain 1 1%
Italy 1 1%
Unknown 86 88%

Demographic breakdown

Readers by professional status Count As %
Researcher 17 17%
Student > Master 14 14%
Student > Ph. D. Student 12 12%
Other 11 11%
Professor 7 7%
Other 27 28%
Unknown 10 10%
Readers by discipline Count As %
Medicine and Dentistry 44 45%
Computer Science 11 11%
Psychology 7 7%
Engineering 5 5%
Social Sciences 4 4%
Other 13 13%
Unknown 14 14%

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 29 May 2013.
All research outputs
#2,392,302
of 9,613,531 outputs
Outputs from BMC Medical Informatics and Decision Making
#374
of 1,040 outputs
Outputs of similar age
#32,461
of 129,591 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#17
of 28 outputs
Altmetric has tracked 9,613,531 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 1,040 research outputs from this source. They receive a mean Attention Score of 4.8. This one has gotten more attention than average, scoring higher than 62% 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 129,591 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 74% of its contemporaries.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.

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