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 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. (shrink)

As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological, medical, behavioural and environmental information about a person to further personalize their healthcare. This could mean better prediction of someone’s disease risk and more effective diagnosis and treatment if they have a condition. Big data allows for far more precision and tailoring than was ever before possible by linking together diverse datasets to reveal hitherto-unknown correlations and causal pathways. But it also raises ethical issues relating to (...) the balancing of interests, viability of anonymization, familial and group implications, as well as genetic discrimination. This article analyses these issues in light of the values of public benefit, justice, harm minimization, transparency, engagement and reflexivity and applies the deliberative balancing approach found in theEthical Framework for Big Data in Health and Research to a case study on clinical genomic data sharing. Please refer to that article for an explanation of how this framework is to be used, including a full explanation of the key values involved and the balancing approach used in the case study at the end. Our discussion is meant to be of use to those involved in the practice as well as governance and oversight of precision medicine to address ethical concerns that arise in a coherent and systematic manner. (shrink)

Background We aimed to examine the ethical concerns Singaporeans have about sharing health-data for precision medicine and identify suggestions for governance strategies. Just as Asian genomes are under-represented in PM, the views of Asian populations about the risks and benefits of data sharing are under-represented in prior attitudinal research. Methods We conducted seven focus groups with 62 participants in Singapore from May to July 2019. They were conducted in three languages and analysed with qualitative content and thematic analysis. Results Four (...) key themes emerged: nuanced understandings of data security and data sensitivity; trade-offs between data protection and research benefits; trust in the public and private sectors; and governance and control options. Participants were aware of the inherent risks associated with data sharing for research. Participants expressed conditional support for data sharing, including genomic sequence data and information contained within electronic medical records. This support included sharing data with researchers from universities and healthcare institutions, both in Singapore and overseas. Support was conditional on the perceived social value of the research and appropriate de-identification and data security processes. Participants suggested that a data sharing oversight body would help strengthen public trust and comfort in data research for PM in Singapore. Conclusion Maintenance of public trust in data security systems and governance regimes can enhance participation in PM and data sharing for research. Contrary to themes in much prior research, participants demonstrated a sophisticated understanding of the inherent risks of data sharing, analysed trade-offs between risks and potential benefits of PM, and often adopted an international perspective. (shrink)

Precision medicine is an emerging approach to treatment and disease prevention that relies on linkages between very large datasets of health information that is shared amongst researchers and health professionals. While studies suggest broad support for sharing precision medicine data with researchers at publicly funded institutions, there is reluctance to share health information with private industry for research and development. As the private sector is likely to play an important role in generating public benefits from precision medicine initiatives, it is (...) important to understand what the concerns are and how they might be mitigated. This study reports outcomes of a deliberative method of citizen engagement in Singapore that asked whether sharing precision medicine data with private industry would be permissible, and if so, under what circumstances. Findings from this citizens’ jury suggest sharing with industry would be permissible under certain conditions that are set out in nine recommendations. Implications of the recommendations and their underlying assumptions for policy decision makers are discussed. This study aligns with prior international studies which found conditional acceptance for data sharing with private industry, a public benefit requirement, specific reluctance to share with insurance companies and an emphasis on accountability and transparency to demonstrate trustworthiness. However, our results differ from prior studies in that opt-in consent did not dominate the deliberations as jurors were able to set it aside as an assumed prerequisite for participation in a precision medicine programme. (shrink)