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Do you see the problem? Visualising a generalised ‘complex local system’ of antibiotic prescribing across the United Kingdom using qualitative interview data

Antimicrobial resistance (AMR) is often referred to as a complex problem embedded in a complex system. Despite this insight, interventions in AMR, and in particular in antibiotic prescribing, tend to be narrowly focused on the behaviour of individual prescribers using the tools of performance monito...

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Autores principales: Glover, Rebecca E., Mays, Nicholas B., Fraser, Alec
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10388844/
https://www.ncbi.nlm.nih.gov/pubmed/38013783
http://dx.doi.org/10.1080/09581596.2023.2210743
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author Glover, Rebecca E.
Mays, Nicholas B.
Fraser, Alec
author_facet Glover, Rebecca E.
Mays, Nicholas B.
Fraser, Alec
author_sort Glover, Rebecca E.
collection PubMed
description Antimicrobial resistance (AMR) is often referred to as a complex problem embedded in a complex system. Despite this insight, interventions in AMR, and in particular in antibiotic prescribing, tend to be narrowly focused on the behaviour of individual prescribers using the tools of performance monitoring and management rather than attempting to bring about more systemic change. In this paper, we aim to elucidate the nature of the local antibiotic prescribing ‘system’ based on 71 semi-structured interviews undertaken in six local areas across the United Kingdom (UK). We applied complex systems theory and systems mapping methods to our qualitative data to deepen our understanding of the interactions among antibiotic prescribing interventions and the wider health system. We found that a complex and interacting set of proximal and distal factors can have unpredictable effects in different local systems in the UK. Ultimately, enacting performance management-based interventions in the absence of in-depth contextual understandings about other pressures prescribers face is a recipe for temporary solutions, waning intervention effectiveness, and unintended consequences. We hope our insights will enable policy makers and academics to devise and evaluate interventions in future in a manner that better reflects and responds to the dynamics of complex local prescribing systems.
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spelling pubmed-103888442023-08-01 Do you see the problem? Visualising a generalised ‘complex local system’ of antibiotic prescribing across the United Kingdom using qualitative interview data Glover, Rebecca E. Mays, Nicholas B. Fraser, Alec Crit Public Health Research Papers Antimicrobial resistance (AMR) is often referred to as a complex problem embedded in a complex system. Despite this insight, interventions in AMR, and in particular in antibiotic prescribing, tend to be narrowly focused on the behaviour of individual prescribers using the tools of performance monitoring and management rather than attempting to bring about more systemic change. In this paper, we aim to elucidate the nature of the local antibiotic prescribing ‘system’ based on 71 semi-structured interviews undertaken in six local areas across the United Kingdom (UK). We applied complex systems theory and systems mapping methods to our qualitative data to deepen our understanding of the interactions among antibiotic prescribing interventions and the wider health system. We found that a complex and interacting set of proximal and distal factors can have unpredictable effects in different local systems in the UK. Ultimately, enacting performance management-based interventions in the absence of in-depth contextual understandings about other pressures prescribers face is a recipe for temporary solutions, waning intervention effectiveness, and unintended consequences. We hope our insights will enable policy makers and academics to devise and evaluate interventions in future in a manner that better reflects and responds to the dynamics of complex local prescribing systems. Taylor & Francis 2023-06-13 /pmc/articles/PMC10388844/ /pubmed/38013783 http://dx.doi.org/10.1080/09581596.2023.2210743 Text en © 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. https://creativecommons.org/licenses/by/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.
spellingShingle Research Papers
Glover, Rebecca E.
Mays, Nicholas B.
Fraser, Alec
Do you see the problem? Visualising a generalised ‘complex local system’ of antibiotic prescribing across the United Kingdom using qualitative interview data
title Do you see the problem? Visualising a generalised ‘complex local system’ of antibiotic prescribing across the United Kingdom using qualitative interview data
title_full Do you see the problem? Visualising a generalised ‘complex local system’ of antibiotic prescribing across the United Kingdom using qualitative interview data
title_fullStr Do you see the problem? Visualising a generalised ‘complex local system’ of antibiotic prescribing across the United Kingdom using qualitative interview data
title_full_unstemmed Do you see the problem? Visualising a generalised ‘complex local system’ of antibiotic prescribing across the United Kingdom using qualitative interview data
title_short Do you see the problem? Visualising a generalised ‘complex local system’ of antibiotic prescribing across the United Kingdom using qualitative interview data
title_sort do you see the problem? visualising a generalised ‘complex local system’ of antibiotic prescribing across the united kingdom using qualitative interview data
topic Research Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10388844/
https://www.ncbi.nlm.nih.gov/pubmed/38013783
http://dx.doi.org/10.1080/09581596.2023.2210743
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