1. An Ethics Framework for Big Data in Health and Research
- Author
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Graeme Laurie, Angela Ballantyne, Shirley Hsiao-Li Sun, G. Owen Schaefer, Wendy Lipworth, Tamra Lysaght, Iain Brassington, Markus K. Labude, Vicki Xafis, E. Shyong Tai, Hannah Yeefen Lim, Cameron Stewart, Nanyang Business School, and School of Social Sciences
- Subjects
medicine.medical_specialty ,Artificial intelligence ,Health (social science) ,Open sharing ,Sociology [Social sciences] ,Big data ,Medical law ,0603 philosophy, ethics and religion ,Health administration ,03 medical and health sciences ,0302 clinical medicine ,Health care ,Openness to experience ,medicine ,Precisionmedicine ,030212 general & internal medicine ,Sociology ,Real-world evidence ,Original Paper ,business.industry ,Health Policy ,Public health ,Precision medicine ,06 humanities and the arts ,Bioethics ,2201 Applied ethics ,Health and research ,Philosophy ,Public–private partnership ,Ethics framework ,1117 Public Health and Health Sciences ,Data repositories ,Engineering ethics ,060301 applied ethics ,business ,Public-private partnership ,Cross-sectorial data - Abstract
Ethical decision-making frameworks assist in identifying the issues at stake in a particular setting and thinking through, in a methodical manner, the ethical issues that require consideration as well as the values that need to be considered and promoted. Decisions made about the use, sharing, and re-use of big data are complex and laden with values. This paper sets out an Ethics Framework for Big Data in Health and Research developed by a working group convened by the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative. It presents the aim and rationale for this framework supported by the underlying ethical concerns that relate to all health and research contexts. It also describes a set of substantive and procedural values that can be weighed up in addressing these concerns, and a step-by-step process for identifying, considering, and resolving the ethical issues arising from big data uses in health and research. This Framework is subsequently applied in the papers published in this Special Issue. These papers each address one of six domains where big data is currently employed: openness in big data and data repositories, precision medicine and big data, real-world data to generate evidence about healthcare interventions, AI-assisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data. Published version The development of the Framework and its application to the six Domain papers was funded and supported by the Singapore National Medical Research Council Research, Innovation and Enterprise 2020 Grant.
- Published
- 2019