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

BACKGROUND: 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 oppo...

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Autores principales: Walker, Alex J., Bacon, Seb, Croker, Richard, Goldacre, Ben
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6038291/
https://www.ncbi.nlm.nih.gov/pubmed/29986693
http://dx.doi.org/10.1186/s12911-018-0642-6
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author Walker, Alex J.
Bacon, Seb
Croker, Richard
Goldacre, Ben
author_facet Walker, Alex J.
Bacon, Seb
Croker, Richard
Goldacre, Ben
author_sort Walker, Alex J.
collection PubMed
description BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-018-0642-6) contains supplementary material, which is available to authorized users.
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spelling pubmed-60382912018-07-12 Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology Walker, Alex J. Bacon, Seb Croker, Richard Goldacre, Ben BMC Med Inform Decis Mak Research Article BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-018-0642-6) contains supplementary material, which is available to authorized users. BioMed Central 2018-07-09 /pmc/articles/PMC6038291/ /pubmed/29986693 http://dx.doi.org/10.1186/s12911-018-0642-6 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Walker, Alex J.
Bacon, Seb
Croker, Richard
Goldacre, Ben
Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology
title Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology
title_full Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology
title_fullStr Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology
title_full_unstemmed Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology
title_short Detecting change in comparison to peers in NHS prescribing data: a novel application of cumulative sum methodology
title_sort detecting change in comparison to peers in nhs prescribing data: a novel application of cumulative sum methodology
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6038291/
https://www.ncbi.nlm.nih.gov/pubmed/29986693
http://dx.doi.org/10.1186/s12911-018-0642-6
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