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, AIassisted decision-making in healthcare, public-private partnerships in healthcare and research, and cross-sectoral big data.

In this essay, we suggest practical ways to shift the framing of crisis standards of care toward disability justice. We elaborate on the vision statement provided in the 2010 Institute of Medicine (National Academy of Medicine) “Summary of Guidance for Establishing Crisis Standards of Care for Use in Disaster Situations,” which emphasizes fairness; equitable processes; community and provider engagement, education, and communication; and the rule of law. We argue that interpreting these elements through disability justice entails a commitment to both distributive and recognitive justice. The disability rights movement's demand “Nothing about us, without us” requires substantive inclusion of disabled people in decision‐making related to their interests, including in crisis planning before, during, and after a pandemic like Covid‐19.

Public-private partnerships (PPPs) are established to specifically harness the potential of Big Data in healthcare and can include partners working across the data chain—producing health data, analysing data, using research results or creating value from data. This domain paper will illustrate the challenges that arise when partners from the public and private sector collaborate to share, analyse and use biomedical Big Data. We discuss three specific challenges for PPPs: working within the social licence, public antipathy to the commercialisation of public sector health data, and questions of ownership, both of the data and any resulting intellectual property or products. As a specific example we consider the case of the UK National Health Service (NHS) providing patient data to Google’s DeepMind AI program to develop a diagnostic app for kidney disease. This article is an application of the framework presented in this issue of ABR (Xafis et al. 2019). Please refer to that article for more information on 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. We use four specific values to help analysis these issues: public benefit, stewardship, transparency and engagement. We demonstrate how the Deliberative Framework can support ethical governance of PPPs involving biomedical big data.

There is growing interest in contact tracing apps (CT apps) for pandemic management. It is crucial to consider ethical requirements before, while, and after implementing such apps. In this paper, we illustrate the complexity and multiplicity of the ethical considerations by presenting an ethical framework for a responsible design and implementation of CT apps. Using this framework as a starting point, we briefly highlight the interconnection of social and political contexts, available measures of pandemic management, and a multi-layer assessment of CT apps. We will discuss some trade-offs that arise from this perspective. We then suggest that public trust is of major importance for population uptake of contact tracing apps. Hasty, ill-prepared or badly communicated implementations of CT apps will likely undermine public trust, and as such, risk impeding general effectiveness.

There is increasing global pressure to ensure that pregnant women are responsibly and safely included in clinical research in order to improve the evidence base that underpins healthcare delivery during pregnancy. One supposed barrier to inclusion is the assumption that pregnant women will be reluctant to participate in research. There is however very little empirical research investigating the views of pregnant women. Their perspective on the benefits, burdens and risks of research is a crucial component to ensuring effective recruitment. The Research In Pregnancy Ethics (RIPE) study set out to ascertain the views of pregnant women about research participation using an inductive thematic analysis. We conducted semi-structured interviews with 20 women who had participated in a double-blind randomised placebo controlled trial in Wellington (New Zealand) while pregnant. Our results show that at least some pregnant women recognise the value and importance of research during pregnancy. The women we interviewed were deeply invested in the research process and outcomes. Key motivations for participating were altruism, playing a valuable civic role and the importance of research. The main perceived burdens related to inconvenience and time commitment. For some women, possible randomization to the placebo arm was regarded as a burden or disadvantage.