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Implementation and use of computerised clinical decision support (CCDS) in emergency pre-hospital care: a qualitative study of paramedic views and experience using Strong Structuration Theory

Overview of attention for article published in Implementation Science, July 2018
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (83rd percentile)
  • Above-average Attention Score compared to outputs of the same age and source (60th percentile)

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19 X users

Citations

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31 Dimensions

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Title
Implementation and use of computerised clinical decision support (CCDS) in emergency pre-hospital care: a qualitative study of paramedic views and experience using Strong Structuration Theory
Published in
Implementation Science, July 2018
DOI 10.1186/s13012-018-0786-x
Pubmed ID
Authors

Alison Porter, Jeremy Dale, Theresa Foster, Pip Logan, Bridget Wells, Helen Snooks

Abstract

Computerised clinical decision support (CCDS) has been shown to improve processes of care in some healthcare settings, but there is little evidence related to its use or effects in pre-hospital emergency care. CCDS in this setting aligns with policies to increase IT use in ambulance care, enhance paramedic decision-making skills, reduce avoidable emergency department attendances and improve quality of care and patient experience. This qualitative study was conducted alongside a cluster randomised trial in two ambulance services of the costs and effects of web-based CCDS system designed to support paramedic decision-making in the care of older people following a fall. Paramedics were trained to enter observations and history for relevant patients on a tablet, and the CCDS then generated a recommended course of action which could be logged. Our aim was to describe paramedics' experience of the CCDS intervention and to identify factors affecting its implementation and use. We invited all paramedics who had been randomly allocated to the intervention arm of the trial to participate in interviews or focus groups. The study was underpinned by Strong Structuration Theory, a theoretical model for studying innovation based on the relationship between what people do and their context. We used the Framework approach to data analysis. Twenty out of 22 paramedics agreed to participate. We developed a model of paramedic experience of CCDS with three domains: context, adoption and use, and outcomes. Aspects of context which had an impact included organisational culture and perceived support for non-conveyance decisions. Experience of adoption and use of the CCDS varied between individual paramedics, with some using it with all eligible patients, some only with patients they thought were 'suitable' and some never using it. A range of outcomes were reported, some of which were different from the intended role of the technology in decision support. Implementation of new technology such as CCDS is not a one-off event, but an ongoing process, which requires support at the organisational level to be effective. ISRCTN Registry 10538608 . Registered 1 May 2007. Retrospectively registered.

X Demographics

X Demographics

The data shown below were collected from the profiles of 19 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 113 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 113 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 15 13%
Researcher 11 10%
Student > Ph. D. Student 10 9%
Other 6 5%
Student > Doctoral Student 6 5%
Other 21 19%
Unknown 44 39%
Readers by discipline Count As %
Nursing and Health Professions 20 18%
Medicine and Dentistry 15 13%
Business, Management and Accounting 8 7%
Engineering 5 4%
Psychology 4 4%
Other 13 12%
Unknown 48 42%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. 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 July 2018.
All research outputs
#3,031,815
of 25,813,008 outputs
Outputs from Implementation Science
#599
of 1,822 outputs
Outputs of similar age
#57,360
of 342,590 outputs
Outputs of similar age from Implementation Science
#17
of 43 outputs
Altmetric has tracked 25,813,008 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,822 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.9. 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 342,590 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 83% of its contemporaries.
We're also able to compare this research output to 43 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 60% of its contemporaries.