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Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology

Overview of attention for article published in BMC Medical Informatics and Decision Making, July 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 (83rd percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

twitter
16 tweeters

Citations

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1 Dimensions

Readers on

mendeley
9 Mendeley
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Title
Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology
Published in
BMC Medical Informatics and Decision Making, July 2018
DOI 10.1186/s12911-018-0642-6
Pubmed ID
Authors

Alex J. Walker, Seb Bacon, Richard Croker, Ben Goldacre

Abstract

The widely used OpenPrescribing.net service provides standard measures which compare prescribing of Clinical Commissioning Groups (CCGs) and English General Practices against that of their peers. Detecting changes in prescribing behaviour compared with peers can help identify missed opportunities for medicines optimisation. Automating the process of detecting these changes is necessary due to the volume of data, but challenging due to variation in prescribing volume for different measures and locations. We set out to develop and implement a method of detecting change on all individual prescribing measures, in order to notify CCGs and practices of such changes in a timely manner. We used the statistical process control method CUSUM to detect prescribing behaviour changes in relation to population trends for the individual standard measures on OpenPrescribing. Increases and decreases in percentile were detected separately, using a multiple of standard deviation as the threshold for detecting change. The algorithm was modified to continue re-triggering when trajectory persists. It was deployed, user-tested, and summary statistics generated on the number of alerts by CCG and practice. The algorithm detected changes in prescribing for 32 prespecified measures, across a wide range of CCG and practice sizes. Across the 209 English CCGs, a mean of 2.5 increase and 2.4 decrease alerts were triggered per CCG, per month. For the 7578 practices, a mean of 1.3 increase and 1.4 decrease alerts were triggered per practice, per month. The CUSUM method appears to effectively discriminate between random noise and sustained change in prescribing behaviour. This method aims to allow practices and CCGs to be informed of important changes quickly, with a view to improve their prescribing behaviour. The number of alerts triggered for CCGs and practices appears to be appropriate. Prescribing behaviour after users are alerted to changes will be monitored in order to assess the impact of these alerts.

Twitter Demographics

The data shown below were collected from the profiles of 16 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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 33%
Researcher 2 22%
Student > Bachelor 1 11%
Student > Postgraduate 1 11%
Other 1 11%
Other 0 0%
Unknown 1 11%
Readers by discipline Count As %
Medicine and Dentistry 5 56%
Agricultural and Biological Sciences 1 11%
Pharmacology, Toxicology and Pharmaceutical Science 1 11%
Engineering 1 11%
Unknown 1 11%

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 11 October 2019.
All research outputs
#1,343,566
of 13,757,863 outputs
Outputs from BMC Medical Informatics and Decision Making
#115
of 1,237 outputs
Outputs of similar age
#43,409
of 266,744 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#1
of 9 outputs
Altmetric has tracked 13,757,863 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,237 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 particularly well, scoring higher than 90% 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 266,744 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 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them