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.
Highlights
The use, sharing, and re-use of big data is a defining feature of the current health and research landscape
This paper proposes an Ethics Framework for Big Data in Health and Research and provides insight into the values that are often central to decisions made in a number of contexts where big data is used
The domain papers achieve this by outlining the issues and values in the context of the particular domain and using specific cases and examples to work through the identified issues and values in a more concrete way. This project and the formation of the Working Group was initiated by the Science, Health and Policy-relevant Ethics in Singapore (SHAPES) Initiative, Centre for Biomedical Ethics (CBmE), National University of Singapore (NUS)
Summary
The use, sharing, and re-use of big data is a defining feature of the current health and research landscape. While the potential arising from big data in all fields, including health and research, is widely recognised, so too are the numerous challenges that big data poses These challenges have been identified as relating to the characteristics of big data (data challenges); issues around capturing, integrating, transforming, analysing, and interpreting big data (process challenges); and around addressing privacy concerns, data security, governance, and data sharing, as well as operational and ownership issues (management challenges) (Sivarajah et al 2017). Even though we provide discrete definitions, we acknowledge the blurring of boundaries between research and treatment (Kass et al 2013), which impacts on our definition of ‘health’ This Framework is a tool for deliberating about issues related to big data by bringing to the fore relevant values which guide or ‘frame’ decision-making (Dawson 2010). This Framework takes into account these and other relevant and emerging issues
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