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Understanding and Identifying Key Issues with the Involvement of Clinicians in the Development of Decision-Analytic Model Structures: A Qualitative Study

Overview of attention for article published in PharmacoEconomics, August 2018
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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 (90th percentile)
  • High Attention Score compared to outputs of the same age and source (85th percentile)

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1 blog
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24 X users
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1 Facebook page

Citations

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8 Dimensions

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23 Mendeley
Title
Understanding and Identifying Key Issues with the Involvement of Clinicians in the Development of Decision-Analytic Model Structures: A Qualitative Study
Published in
PharmacoEconomics, August 2018
DOI 10.1007/s40273-018-0705-7
Pubmed ID
Authors

Samantha Husbands, Susan Jowett, Pelham Barton, Joanna Coast

Abstract

Decision-analytic models play an essential role in informing healthcare resource allocation decisions; however, their value to decision makers will depend on model structures being clinically valid to determine cost-effectiveness recommendations. Clinician involvement can help modellers to develop clinically valid but straightforward structures; however, there is little guidance available on methods for clinician input to model structure. This study aims to provide an in-depth exploration of clinician involvement in structural development, highlighting key issues and generating recommendations to optimise practices. A qualitative study was undertaken with a range of modellers and clinicians working in different modelling contexts. In-depth interviews and case studies using observations were carried out to understand how clinicians are involved in model structural development and to identify problems and optimal approaches from informants' perspectives. Twenty-four interviews and two case studies were undertaken with modellers and modelling teams. Key issues included the number and diversity of clinicians contributing to structural development, potentially impacting the generalisability of structures, and problems with clinician understanding of important information to contribute to model pathways. Modellers and clinicians suggested that clinician training in modelling could enhance structural processes. Recommendations to optimise current practices include recruiting clinicians from a variety of backgrounds and using discussions between experts to develop valid and generalisable structures. Future research should focus on developing training materials for clinicians and finding ways to help modellers recruit clinicians from different settings.

X Demographics

X Demographics

The data shown below were collected from the profiles of 24 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 23 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 23 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 4 17%
Researcher 4 17%
Student > Master 2 9%
Unspecified 1 4%
Student > Bachelor 1 4%
Other 2 9%
Unknown 9 39%
Readers by discipline Count As %
Medicine and Dentistry 4 17%
Nursing and Health Professions 3 13%
Social Sciences 2 9%
Unspecified 1 4%
Pharmacology, Toxicology and Pharmaceutical Science 1 4%
Other 1 4%
Unknown 11 48%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 23. 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 17 July 2019.
All research outputs
#1,499,523
of 24,026,368 outputs
Outputs from PharmacoEconomics
#80
of 1,940 outputs
Outputs of similar age
#32,134
of 336,480 outputs
Outputs of similar age from PharmacoEconomics
#5
of 28 outputs
Altmetric has tracked 24,026,368 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 93rd percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,940 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.3. This one has done particularly well, scoring higher than 95% 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 336,480 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% 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 has done well, scoring higher than 85% of its contemporaries.