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A methodological protocol for selecting and quantifying low-value prescribing practices in routinely collected data: an Australian case study

Overview of attention for article published in Implementation Science, May 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (63rd percentile)

Mentioned by

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6 tweeters

Citations

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17 Dimensions

Readers on

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60 Mendeley
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Title
A methodological protocol for selecting and quantifying low-value prescribing practices in routinely collected data: an Australian case study
Published in
Implementation Science, May 2017
DOI 10.1186/s13012-017-0585-9
Pubmed ID
Authors

Jonathan Brett, Adam G. Elshaug, R. Sacha Bhatia, Kelsey Chalmers, Tim Badgery-Parker, Sallie-Anne Pearson

Abstract

Growing imperatives for safety, quality and responsible resource allocation have prompted renewed efforts to identify and quantify harmful or wasteful (low-value) medical practices such as test ordering, procedures and prescribing. Quantifying these practices at a population level using routinely collected health data allows us to understand the scale of low-value medical practices, measure practice change following specific interventions and prioritise policy decisions. To date, almost all research examining health care through the low-value lens has focused on medical services (tests and procedures) rather than on prescribing. The protocol described herein outlines a program of research funded by Australia's National Health and Medical Research Council to select and quantify low-value prescribing practices within Australian routinely collected health data. We start by describing our process for identifying and cataloguing international low-value prescribing practices. We then outline our approach to translate these prescribing practices into indicators that can be applied to Australian routinely collected health data. Next, we detail methods of using Australian health data to quantify these prescribing practices (e.g. prevalence of low-value prescribing and related costs) and their downstream health consequences. We have approval from the necessary Australian state and commonwealth human research ethics and data access committees to undertake this work. The lack of systematic and transparent approaches to quantification of low-value practices in routinely collected data has been noted in recent reviews. Here, we present a methodology applied in the Australian context with the aim of demonstrating principles that can be applied across jurisdictions in order to harmonise international efforts to measure low-value prescribing. The outcomes of this research will be submitted to international peer-reviewed journals. Results will also be presented at national and international pharmacoepidemiology and health policy forums such that other jurisdictions have guidance to adapt this methodology.

Twitter Demographics

The data shown below were collected from the profiles of 6 tweeters 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 60 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 60 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 22%
Student > Ph. D. Student 10 17%
Student > Master 8 13%
Student > Bachelor 5 8%
Other 3 5%
Other 8 13%
Unknown 13 22%
Readers by discipline Count As %
Medicine and Dentistry 12 20%
Nursing and Health Professions 6 10%
Psychology 4 7%
Biochemistry, Genetics and Molecular Biology 3 5%
Social Sciences 3 5%
Other 11 18%
Unknown 21 35%

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 05 May 2017.
All research outputs
#6,631,401
of 21,338,015 outputs
Outputs from Implementation Science
#1,149
of 1,677 outputs
Outputs of similar age
#101,914
of 282,932 outputs
Outputs of similar age from Implementation Science
#7
of 7 outputs
Altmetric has tracked 21,338,015 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 1,677 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.6. This one is in the 30th percentile – i.e., 30% of its peers scored the same or lower than it.
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 282,932 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 63% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.