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Our data, our society, our health: a vision for inclusive and transparent health data science in the United Kingdom and beyond

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Our data, our society, our health : a vision for inclusive and transparent health data science in the United Kingdom and beyond. / Ford, Elizabeth; Boyd, Andy; K. F. Bowles, Juliana; Havard, Alys; Aldridge, Robert; Curcin, Vasa; Greiver, Michelle; Harron, Katie; Katikireddi, Vittal; Rodgers, Sarah; Sperrin, Matthew.

In: Learning Health Systems, Vol. 3, No. 3, e10191, 07.2019.

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Harvard

Ford, E, Boyd, A, K. F. Bowles, J, Havard, A, Aldridge, R, Curcin, V, Greiver, M, Harron, K, Katikireddi, V, Rodgers, S & Sperrin, M 2019, 'Our data, our society, our health: a vision for inclusive and transparent health data science in the United Kingdom and beyond', Learning Health Systems, vol. 3, no. 3, e10191. https://doi.org/10.1002/lrh2.10191

APA

Ford, E., Boyd, A., K. F. Bowles, J., Havard, A., Aldridge, R., Curcin, V., ... Sperrin, M. (2019). Our data, our society, our health: a vision for inclusive and transparent health data science in the United Kingdom and beyond. Learning Health Systems, 3(3), [e10191]. https://doi.org/10.1002/lrh2.10191

Vancouver

Ford E, Boyd A, K. F. Bowles J, Havard A, Aldridge R, Curcin V et al. Our data, our society, our health: a vision for inclusive and transparent health data science in the United Kingdom and beyond. Learning Health Systems. 2019 Jul;3(3). e10191. https://doi.org/10.1002/lrh2.10191

Author

Ford, Elizabeth ; Boyd, Andy ; K. F. Bowles, Juliana ; Havard, Alys ; Aldridge, Robert ; Curcin, Vasa ; Greiver, Michelle ; Harron, Katie ; Katikireddi, Vittal ; Rodgers, Sarah ; Sperrin, Matthew. / Our data, our society, our health : a vision for inclusive and transparent health data science in the United Kingdom and beyond. In: Learning Health Systems. 2019 ; Vol. 3, No. 3.

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@article{95e4241c7efc49c990ef083f80e6fa91,
title = "Our data, our society, our health: a vision for inclusive and transparent health data science in the United Kingdom and beyond",
abstract = "The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well‐being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team‐based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.",
keywords = "Citizen-driven science, Data flows, Health data science, Health systems, Stakeholder involvement, Transparency",
author = "Elizabeth Ford and Andy Boyd and {K. F. Bowles}, Juliana and Alys Havard and Robert Aldridge and Vasa Curcin and Michelle Greiver and Katie Harron and Vittal Katikireddi and Sarah Rodgers and Matthew Sperrin",
note = "This paper is the work of the first cohort of the Farr Institute's “Future Leaders” scheme. The Future Leaders programme was funded by the Farr Institute and was financially supported by the authors' institutions or grants.",
year = "2019",
month = "7",
doi = "10.1002/lrh2.10191",
language = "English",
volume = "3",
journal = "Learning Health Systems",
issn = "2379-6146",
publisher = "John Wiley and Sons",
number = "3",

}

RIS (suitable for import to EndNote) - Download

TY - JOUR

T1 - Our data, our society, our health

T2 - a vision for inclusive and transparent health data science in the United Kingdom and beyond

AU - Ford, Elizabeth

AU - Boyd, Andy

AU - K. F. Bowles, Juliana

AU - Havard, Alys

AU - Aldridge, Robert

AU - Curcin, Vasa

AU - Greiver, Michelle

AU - Harron, Katie

AU - Katikireddi, Vittal

AU - Rodgers, Sarah

AU - Sperrin, Matthew

N1 - This paper is the work of the first cohort of the Farr Institute's “Future Leaders” scheme. The Future Leaders programme was funded by the Farr Institute and was financially supported by the authors' institutions or grants.

PY - 2019/7

Y1 - 2019/7

N2 - The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well‐being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team‐based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

AB - The last 6 years have seen sustained investment in health data science in the United Kingdom and beyond, which should result in a data science community that is inclusive of all stakeholders, working together to use data to benefit society through the improvement of public health and well‐being. However, opportunities made possible through the innovative use of data are still not being fully realised, resulting in research inefficiencies and avoidable health harms. In this paper, we identify the most important barriers to achieving higher productivity in health data science. We then draw on previous research, domain expertise, and theory to outline how to go about overcoming these barriers, applying our core values of inclusivity and transparency. We believe a step change can be achieved through meaningful stakeholder involvement at every stage of research planning, design, and execution and team‐based data science, as well as harnessing novel and secure data technologies. Applying these values to health data science will safeguard a social licence for health data research and ensure transparent and secure data usage for public benefit.

KW - Citizen-driven science

KW - Data flows

KW - Health data science

KW - Health systems

KW - Stakeholder involvement

KW - Transparency

U2 - 10.1002/lrh2.10191

DO - 10.1002/lrh2.10191

M3 - Article

VL - 3

JO - Learning Health Systems

JF - Learning Health Systems

SN - 2379-6146

IS - 3

M1 - e10191

ER -

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