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Where the rubber meets the road: using FRAM to align work-as-imagined with work-as-done when implementing clinical guidelines

Overview of attention for article published in Implementation Science, August 2015
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (68th percentile)

Mentioned by

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16 X users

Citations

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178 Dimensions

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249 Mendeley
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Title
Where the rubber meets the road: using FRAM to align work-as-imagined with work-as-done when implementing clinical guidelines
Published in
Implementation Science, August 2015
DOI 10.1186/s13012-015-0317-y
Pubmed ID
Authors

Robyn Clay-Williams, Jeanette Hounsgaard, Erik Hollnagel

Abstract

Uptake of guidelines in healthcare can be variable. A focus on behaviour change and other strategies to improve compliance, however, has not increased implementation success. The contribution of other factors such as clinical setting and practitioner workflow to guideline utilisation has recently been recognised. In particular, differences between work-as-imagined by those who write procedures, and work-as-done-or actually enacted-in the clinical environment, can render a guideline difficult or impossible for clinicians to follow. The Functional Resonance Analysis Method (FRAM) can be used to model workflow in the clinical setting. The aim of this study was to investigate whether FRAM can be used to identify process elements in a draft guideline that are likely to impede implementation by conflicting with current ways of working. Draft guidelines in two intensive care units (ICU), one in Australia and one in Denmark, were modelled and analysed using FRAM. The FRAM was used to guide collaborative discussion with healthcare professionals involved in writing and implementing the guidelines and to ensure that the final instructions were compatible with other processes used in the workplace. Processes that would have impeded implementation were discovered early, and the guidelines were modified to maintain compatibility with current work processes. Missing process elements were also identified, thereby, avoiding the confusion that would have ensued had the guideline been introduced as originally written. Using FRAM to reconcile differences between work-as-imagined and work-as-done when implementing a guideline can reduce the need for clinicians to adjust performance and create workarounds, which may be detrimental to both safety and quality, once the guideline is introduced.

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 249 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United Kingdom 1 <1%
Spain 1 <1%
United States 1 <1%
Norway 1 <1%
Unknown 245 98%

Demographic breakdown

Readers by professional status Count As %
Student > Master 44 18%
Student > Ph. D. Student 29 12%
Researcher 27 11%
Other 18 7%
Student > Doctoral Student 12 5%
Other 58 23%
Unknown 61 24%
Readers by discipline Count As %
Engineering 47 19%
Medicine and Dentistry 44 18%
Nursing and Health Professions 19 8%
Social Sciences 17 7%
Business, Management and Accounting 16 6%
Other 38 15%
Unknown 68 27%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 9. 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 30 May 2023.
All research outputs
#3,760,361
of 23,881,329 outputs
Outputs from Implementation Science
#734
of 1,741 outputs
Outputs of similar age
#47,449
of 269,256 outputs
Outputs of similar age from Implementation Science
#17
of 51 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 84th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,741 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one has gotten more attention than average, scoring higher than 57% 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 269,256 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 82% of its contemporaries.
We're also able to compare this research output to 51 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 68% of its contemporaries.