Cargando…

Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England

BACKGROUND: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are...

Descripción completa

Detalles Bibliográficos
Autores principales: Hopcroft, Lisa EM, Massey, Jon, Curtis, Helen J, Mackenna, Brian, Croker, Richard, Brown, Andrew D, O'Dwyer, Thomas, Macdonald, Orla, Evans, David, Inglesby, Peter, Bacon, Sebastian CJ, Goldacre, Ben, Walker, Alex J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162592/
https://www.ncbi.nlm.nih.gov/pubmed/37074763
http://dx.doi.org/10.2196/44237
_version_ 1785037725461643264
author Hopcroft, Lisa EM
Massey, Jon
Curtis, Helen J
Mackenna, Brian
Croker, Richard
Brown, Andrew D
O'Dwyer, Thomas
Macdonald, Orla
Evans, David
Inglesby, Peter
Bacon, Sebastian CJ
Goldacre, Ben
Walker, Alex J
author_facet Hopcroft, Lisa EM
Massey, Jon
Curtis, Helen J
Mackenna, Brian
Croker, Richard
Brown, Andrew D
O'Dwyer, Thomas
Macdonald, Orla
Evans, David
Inglesby, Peter
Bacon, Sebastian CJ
Goldacre, Ben
Walker, Alex J
author_sort Hopcroft, Lisa EM
collection PubMed
description BACKGROUND: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are made publicly available by the National Health Service (NHS) Business Services Authority for all general practices. There is an opportunity to adopt a more data-driven approach to capture variability and identify outliers by applying hypothesis-free, data-driven algorithms to national data sets. OBJECTIVE: This study aimed to develop and apply a hypothesis-free algorithm to identify unusual prescribing behavior in primary care data at multiple administrative levels in the NHS in England and to visualize these results using organization-specific interactive dashboards, thereby demonstrating proof of concept for prioritization approaches. METHODS: Here we report a new data-driven approach to quantify how “unusual” the prescribing rates of a particular chemical within an organization are as compared to peer organizations, over a period of 6 months (June-December 2021). This is followed by a ranking to identify which chemicals are the most notable outliers in each organization. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups, and sustainability and transformation partnerships in England. Our results are presented via organization-specific interactive dashboards, the iterative development of which has been informed by user feedback. RESULTS: We developed interactive dashboards for every practice (n=6476) in England, highlighting the unusual prescribing of 2369 chemicals (dashboards are also provided for 42 sustainability and transformation partnerships, 106 clinical commissioning groups, and 1257 primary care networks). User feedback and internal review of case studies demonstrate that our methodology identifies prescribing behavior that sometimes warrants further investigation or is a known issue. CONCLUSIONS: Data-driven approaches have the potential to overcome existing biases with regard to the planning and execution of audits, interventions, and policy making within NHS organizations, potentially revealing new targets for improved health care service delivery. We present our dashboards as a proof of concept for generating candidate lists to aid expert users in their interpretation of prescribing data and prioritize further investigations and qualitative research in terms of potential targets for improved performance.
format Online
Article
Text
id pubmed-10162592
institution National Center for Biotechnology Information
language English
publishDate 2023
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-101625922023-05-06 Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England Hopcroft, Lisa EM Massey, Jon Curtis, Helen J Mackenna, Brian Croker, Richard Brown, Andrew D O'Dwyer, Thomas Macdonald, Orla Evans, David Inglesby, Peter Bacon, Sebastian CJ Goldacre, Ben Walker, Alex J JMIR Med Inform Original Paper BACKGROUND: Approaches to addressing unwarranted variation in health care service delivery have traditionally relied on the prospective identification of activities and outcomes, based on a hypothesis, with subsequent reporting against defined measures. Practice-level prescribing data in England are made publicly available by the National Health Service (NHS) Business Services Authority for all general practices. There is an opportunity to adopt a more data-driven approach to capture variability and identify outliers by applying hypothesis-free, data-driven algorithms to national data sets. OBJECTIVE: This study aimed to develop and apply a hypothesis-free algorithm to identify unusual prescribing behavior in primary care data at multiple administrative levels in the NHS in England and to visualize these results using organization-specific interactive dashboards, thereby demonstrating proof of concept for prioritization approaches. METHODS: Here we report a new data-driven approach to quantify how “unusual” the prescribing rates of a particular chemical within an organization are as compared to peer organizations, over a period of 6 months (June-December 2021). This is followed by a ranking to identify which chemicals are the most notable outliers in each organization. These outlying chemicals are calculated for all practices, primary care networks, clinical commissioning groups, and sustainability and transformation partnerships in England. Our results are presented via organization-specific interactive dashboards, the iterative development of which has been informed by user feedback. RESULTS: We developed interactive dashboards for every practice (n=6476) in England, highlighting the unusual prescribing of 2369 chemicals (dashboards are also provided for 42 sustainability and transformation partnerships, 106 clinical commissioning groups, and 1257 primary care networks). User feedback and internal review of case studies demonstrate that our methodology identifies prescribing behavior that sometimes warrants further investigation or is a known issue. CONCLUSIONS: Data-driven approaches have the potential to overcome existing biases with regard to the planning and execution of audits, interventions, and policy making within NHS organizations, potentially revealing new targets for improved health care service delivery. We present our dashboards as a proof of concept for generating candidate lists to aid expert users in their interpretation of prescribing data and prioritize further investigations and qualitative research in terms of potential targets for improved performance. JMIR Publications 2023-04-19 /pmc/articles/PMC10162592/ /pubmed/37074763 http://dx.doi.org/10.2196/44237 Text en ©Lisa EM Hopcroft, Jon Massey, Helen J Curtis, Brian Mackenna, Richard Croker, Andrew D Brown, Thomas O'Dwyer, Orla Macdonald, David Evans, Peter Inglesby, Sebastian CJ Bacon, Ben Goldacre, Alex J Walker. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 19.04.2023. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included.
spellingShingle Original Paper
Hopcroft, Lisa EM
Massey, Jon
Curtis, Helen J
Mackenna, Brian
Croker, Richard
Brown, Andrew D
O'Dwyer, Thomas
Macdonald, Orla
Evans, David
Inglesby, Peter
Bacon, Sebastian CJ
Goldacre, Ben
Walker, Alex J
Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England
title Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England
title_full Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England
title_fullStr Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England
title_full_unstemmed Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England
title_short Data-Driven Identification of Unusual Prescribing Behavior: Analysis and Use of an Interactive Data Tool Using 6 Months of Primary Care Data From 6500 Practices in England
title_sort data-driven identification of unusual prescribing behavior: analysis and use of an interactive data tool using 6 months of primary care data from 6500 practices in england
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10162592/
https://www.ncbi.nlm.nih.gov/pubmed/37074763
http://dx.doi.org/10.2196/44237
work_keys_str_mv AT hopcroftlisaem datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT masseyjon datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT curtishelenj datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT mackennabrian datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT crokerrichard datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT brownandrewd datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT odwyerthomas datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT macdonaldorla datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT evansdavid datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT inglesbypeter datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT baconsebastiancj datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT goldacreben datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland
AT walkeralexj datadrivenidentificationofunusualprescribingbehavioranalysisanduseofaninteractivedatatoolusing6monthsofprimarycaredatafrom6500practicesinengland