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Reviewing clinical guideline development tools: features and characteristics

Overview of attention for article published in BMC Medical Informatics and Decision Making, September 2017
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (72nd percentile)
  • Good Attention Score compared to outputs of the same age and source (73rd percentile)

Mentioned by

policy
2 policy sources

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
50 Mendeley
citeulike
1 CiteULike
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Title
Reviewing clinical guideline development tools: features and characteristics
Published in
BMC Medical Informatics and Decision Making, September 2017
DOI 10.1186/s12911-017-0530-5
Pubmed ID
Authors

Soudabeh Khodambashi, Øystein Nytrø

Abstract

To improve consistency and streamline development and publication of clinical guidelines (GL), there is a need for appropriate software support. We have found few specific tools for the actual authoring and maintaining of GLs, and correspondingly few analyses or reviews of GL development tool functionality. In order to assist GL developers in selecting and evaluating tools, this study tries to address the perceived gap by pursuing four goals: 1) identifying available tools, 2) reviewing a representative group of tools and their supported functionalities, 3) uncovering themes of features that the studied tools support, and 4) compare the selected tools with respect to the themes. We conducted a literature search using PubMed and Google Scholar in order to find GL development tools (GDT). We also explored tools and Content Management Systems (CMS) used in representative organisations and international communities that develop and maintain GLs. By reading a selected representative group of five GL tool manuals, exploring tools hands-on, we uncovered 8 themes of features. All found tools were compared according to these themes in order to identify the level of functionality they offer to support the GL development and publishing process. In order to limit the scope, tools for designing computer-interpretable/executable GL are excluded. After finding 1552 published papers, contacting 7 organizations and international communities, we identified a total of 19 unique tools, of which 5 tools were selected as representative in this paper. We uncovered a total of 8 themes of features according to the identified functionalities that each tool provides. Four features were common among tools: Collaborative authoring process support, user access control, GL repository management, electronic publishing. We found that the GRADE methodology was supported by three of the reviewed tools, while only two tools support annotating GL with MeSH terms. We also identified that monitoring progress, reference management, Managing versions (version control), and Change control (tracking) were often the missing features. The results can promote sector discussion and eventual agreement on important tool functionality. It may aid tool and GL developers towards more efficient, and effective, GL authoring.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 50 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 50 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 10 20%
Student > Ph. D. Student 6 12%
Other 5 10%
Researcher 4 8%
Student > Postgraduate 4 8%
Other 15 30%
Unknown 6 12%
Readers by discipline Count As %
Medicine and Dentistry 10 20%
Nursing and Health Professions 8 16%
Computer Science 6 12%
Unspecified 3 6%
Psychology 2 4%
Other 10 20%
Unknown 11 22%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 6. 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 09 August 2023.
All research outputs
#5,189,622
of 24,484,013 outputs
Outputs from BMC Medical Informatics and Decision Making
#477
of 2,084 outputs
Outputs of similar age
#84,321
of 319,934 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#8
of 26 outputs
Altmetric has tracked 24,484,013 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,084 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 done well, scoring higher than 76% 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 319,934 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 72% of its contemporaries.
We're also able to compare this research output to 26 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 73% of its contemporaries.