Oral Presentation Australasian Association of Bioethics & Health Law and New Zealand Bioethics Conference

An Ethics Framework for Big Data in Health and Research (1069)

Vicki Xafis 1 , G.Owen Schaefer 1 , Markus Labude 1 , Wendy Lipworth 2 , Angela Ballantyne 3 , Stewart Cameron 4 , Tamra Lysaght 1
  1. Centre for Biomedical Ethics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
  2. Sydney Health Ethics, School of Public Health, The University of Sydney, Sydney, NSW, Australia
  3. Bioethics Centre (Dunedin), Department of Primary Health Care and General Practice, University of Otago, Wellington
  4. Sydney Law School, The University of Sydney, Sydney, Australia

on behalf of the SHAPES Big Data Ethics Working Group

The topic of Big Data has been explored extensively in academic, technical, government, legal and private sector literature for many decades. The potential arising from the use of Big Data in health and research is widely recognised, as are the challenges posed in this fast-paced dynamic field. Despite the wealth of literature, there is a lack of practical guidance in the form of a framework that considers ethical issues that arise from the use of Big Data in a variety of health and research contexts.

The Ethics Framework for Big Data in Health and Research (the Framework) addresses this gap and is intended for a wide-ranging professional audience: biomedical researchers; clinician-researchers; data scientists; policymakers; those involved in the governance of Big Data activities in health and research, including ethics committees and data access committees; and data controllers. Beyond the professionals who may find this resource helpful, the Framework may also be useful to lay people with an interest in Big Data, patients, and research participants.

The Framework itself comprises two main components: the articulation of relevant values and a decision-making process. This is then applied to six Domains employing big data: Openness in big data and data repositories; Precision medicine; Real-world data to generate evidence about healthcare interventions; AI-assisted decision making in healthcare; Public-private partnerships; and Cross-sectorial big data.

This paper presents details of the Framework structure and provides examples of how it can be applied to the various Domains.