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Exploring barriers and solutions in advancing cross-centre population data science

INTRODUCTION: It is widely acknowledged that population health and administrative data, especially when linked at the individual level, hold great value for research. Cross-centre working between data centres providing access to such data has the potential to further increase this value by effective...

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Autores principales: Jones, KH, Heys, SM, Daniels, H, Ford, DV
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Swansea University 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8142621/
https://www.ncbi.nlm.nih.gov/pubmed/34095536
http://dx.doi.org/10.23889/ijpds.v4i1.1109
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author Jones, KH
Heys, SM
Daniels, H
Ford, DV
author_facet Jones, KH
Heys, SM
Daniels, H
Ford, DV
author_sort Jones, KH
collection PubMed
description INTRODUCTION: It is widely acknowledged that population health and administrative data, especially when linked at the individual level, hold great value for research. Cross-centre working between data centres providing access to such data has the potential to further increase this value by effectively expanding the data available for research. However, there is limited published information on how to address the challenges and achieve success. The aim of this paper is to explore perceived barriers and solutions to inform developments in cross-centre working across data centres. METHODS: We carried out a narrative literature review on data sharing and cross centre working. We used a mixed methods approach to assess the opinions of members of the public on cross-centre data sharing, and the views and experiences of among data centre staff connected with the UK Farr Institute for Health Informatics Research. RESULTS: The literature review uncovered a myriad of practical and cultural issues. Our engagement with a public group suggested that cross-centre working involving anonymised data being moved between established centres is considered acceptable. The main themes emerging from discussions with data centre staff were dedicated resourcing, practical issues, information governance and culture. CONCLUSION: In seeking to advance cross-centre working between data centres, we conclude that there is a need for dedicated resourcing, indicators to recognise data reuse, collaboration to solve common issues, and balancing necessary barrier removal with incentivisation. This will require on-going commitment, engagement and an academic culture change.
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spelling pubmed-81426212021-06-04 Exploring barriers and solutions in advancing cross-centre population data science Jones, KH Heys, SM Daniels, H Ford, DV Int J Popul Data Sci Population Data Science INTRODUCTION: It is widely acknowledged that population health and administrative data, especially when linked at the individual level, hold great value for research. Cross-centre working between data centres providing access to such data has the potential to further increase this value by effectively expanding the data available for research. However, there is limited published information on how to address the challenges and achieve success. The aim of this paper is to explore perceived barriers and solutions to inform developments in cross-centre working across data centres. METHODS: We carried out a narrative literature review on data sharing and cross centre working. We used a mixed methods approach to assess the opinions of members of the public on cross-centre data sharing, and the views and experiences of among data centre staff connected with the UK Farr Institute for Health Informatics Research. RESULTS: The literature review uncovered a myriad of practical and cultural issues. Our engagement with a public group suggested that cross-centre working involving anonymised data being moved between established centres is considered acceptable. The main themes emerging from discussions with data centre staff were dedicated resourcing, practical issues, information governance and culture. CONCLUSION: In seeking to advance cross-centre working between data centres, we conclude that there is a need for dedicated resourcing, indicators to recognise data reuse, collaboration to solve common issues, and balancing necessary barrier removal with incentivisation. This will require on-going commitment, engagement and an academic culture change. Swansea University 2019-08-05 /pmc/articles/PMC8142621/ /pubmed/34095536 http://dx.doi.org/10.23889/ijpds.v4i1.1109 Text en https://creativecommons.org/licenses/by/4.0/This work is licenced under a Creative Commons Attribution 4.0 International License.
spellingShingle Population Data Science
Jones, KH
Heys, SM
Daniels, H
Ford, DV
Exploring barriers and solutions in advancing cross-centre population data science
title Exploring barriers and solutions in advancing cross-centre population data science
title_full Exploring barriers and solutions in advancing cross-centre population data science
title_fullStr Exploring barriers and solutions in advancing cross-centre population data science
title_full_unstemmed Exploring barriers and solutions in advancing cross-centre population data science
title_short Exploring barriers and solutions in advancing cross-centre population data science
title_sort exploring barriers and solutions in advancing cross-centre population data science
topic Population Data Science
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8142621/
https://www.ncbi.nlm.nih.gov/pubmed/34095536
http://dx.doi.org/10.23889/ijpds.v4i1.1109
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