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Exploring barriers and solutions in advancing cross-centre population data science.
Jones, K H; Heys, S M; Daniels, H; Ford, D V.
Afiliação
  • Jones KH; Population Data Science, Swansea University Medical School, Singleton Park, Swansea SA2 8PP.
  • Heys SM; Population Data Science, Swansea University Medical School, Singleton Park, Swansea SA2 8PP.
  • Daniels H; Population Data Science, Swansea University Medical School, Singleton Park, Swansea SA2 8PP.
  • Ford DV; Population Data Science, Swansea University Medical School, Singleton Park, Swansea SA2 8PP.
Int J Popul Data Sci ; 4(1): 1109, 2019 Aug 05.
Article em En | MEDLINE | ID: mdl-34095536
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.

Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: Int J Popul Data Sci Ano de publicação: 2019 Tipo de documento: Article

Texto completo: 1 Bases de dados: MEDLINE Idioma: En Revista: Int J Popul Data Sci Ano de publicação: 2019 Tipo de documento: Article