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 - Bowles, Juliana K.F.
AU - Havard, Alys
AU - Aldridge, Robert W.
AU - Curcin, Vasa
AU - Greiver, Michelle
AU - Harron, Katie
AU - Katikireddi, Vittal
AU - Rodgers, Sarah E.
AU - Sperrin, Matthew
PY - 2019/7/1
Y1 - 2019/7/1
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
UR - http://www.scopus.com/inward/record.url?scp=85063578311&partnerID=8YFLogxK
U2 - 10.1002/lrh2.10191
DO - 10.1002/lrh2.10191
M3 - Comment/debate (Academic Journal)
C2 - 31317072
AN - SCOPUS:85063578311
VL - 3
JO - Learning Health Systems
JF - Learning Health Systems
SN - 2379-6146
IS - 3
M1 - e10191
ER -