Artificial intelligence (AI) is set to transform healthcare. Key ethical issues to emerge with this transformation encompass the accountability and transparency of the decisions made by AI-based systems, the potential for group harms arising from algorithmic bias and the professional roles and integrity of clinicians. These concerns must be balanced against the imperatives of generating public benefit with more efficient healthcare systems from the vastly higher and accurate computational power of AI. In weighing up these issues, this paper applies the deliberative balancing approach of the Ethics Framework for Big Data in Health and Research (Xafis et al. 2019). The analysis applies relevant values identified from the framework to demonstrate how decision-makers can draw on them to develop and implement AI-assisted support systems into healthcare and clinical practice ethically and responsibly. Please refer to Xafis et al. (2019) in this special issue of the Asian Bioethics Review 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 of this paper.

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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.

BackgroundThe Vaccine Assessment using Linked Data (VALiD) trial compared opt-in and opt-out parental consent for a population-based childhood vaccine safety surveillance program using data linkage. A subsequent telephone interview of all households enrolled in the trial elicited parental intent regarding the return or non-return of reply forms for opt-in and opt-out consent. This paper describes the rationale for the trial and provides an overview of the design and methods.Methods/DesignSingle-centre, single-blind, randomised controlled trial (RCT) stratified by firstborn status. Mothers who gave birth at one tertiary South Australian hospital were randomised at six weeks post-partum to receive an opt-in or opt-out reply form, along with information explaining data linkage. The primary outcome at 10 weeks post-partum was parental participation in each arm, as indicated by the respective return or non-return of a reply form (or via telephone or email response). A subsequent telephone interview at 10 weeks post-partum elicited parental intent regarding the return or non-return of the reply form, and attitudes and knowledge about data linkage, vaccine safety, consent preferences and vaccination practices. Enrolment began in July 2009 and 1,129 households were recruited in a three-month period. Analysis has not yet been undertaken. The participation rate and selection bias for each method of consent will be compared when the data are analysed.DiscussionThe VALiD RCT represents the first trial of opt-in versus opt-out consent for a data linkage study that assesses consent preferences and intent compared with actual opting in or opting out behaviour, and socioeconomic factors. The limitations to generalisability are discussed.Trial registrationAustralian New Zealand Clinical Trials Registry ACTRN12610000332022

BackgroundA key ethical issue arising in data linkage research relates to consent requirements. Patients’ consent preferences in the context of health research have been explored but their consent preferences regarding data linkage specifically have been under-explored. In addition, the views on data linkage are often those of patient groups. As a result, little is known about lay people’s views and their preferences about consent requirements in the context of data linkage. This study explores lay people’s views and justifications regarding the acceptability of conducting data linkage research without obtaining consent.MethodsA qualitative study explored lay people’s views regarding consent requirements in data linkage via four hypothetical data linkage scenarios of increasing complexity. Prior to considering the scenarios, participants were provided with information regarding best practice data linkage processes via discussion and a diagrammatic representation of the process.ResultsLay people were able to understand the intricate processes involved in data linkage and the key protections afforded within a short amount of time. They were supportive of data linkage research and, on the whole, believed it should be conducted without consent provided a data linkage organization de-identifies the data used so that researchers do not handle identifiable data. Many thought that de-identified data holds a different status to identifiable data and should be used without specific consent in research that aims to benefit society. In weighing up conflicting values and interests, participants shifted consent preferences before arriving at their final consent preference for each scenario and provided justifications for their choices. They considered the protection of people’s information, societal benefits, and the nature and constraints of research and recognized that these need to be balanced.ConclusionsWith some exposure to the features of data linkage, lay people have the capacity to understand the processes sufficiently in order to consider ethical issues associated with consent preferences. Shifts in views reveal the complexity of such decisions. While privacy protection remained an important consideration for most participants, adequate protection measures adopted in best practice data linkage were viewed by most as protection enough for data linkage to proceed without specific individual consent.