Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/143519
Title: Precision medicine and big data : the application of an ethics framework for big data in health and research
Authors: Schaefer, G. Owen
Tai, E. Shyong
Sun, Shirley
Keywords: Science::Medicine
Issue Date: 2019
Source: Schaefer, G. O., Tai, E. S., & Sun, S. (2019). Precision medicine and big data : the application of an ethics framework for big data in health and research. Asian Bioethics Review, 11(3), 275–288. doi:10.1007/s41649-019-00094-2
Journal: Asian Bioethics Review
Abstract: As opposed to a ‘one size fits all’ approach, precision medicine uses relevant biological (including genetic), 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 the Ethical Framework for Big Data in Health and Research (Xafis et al. 2019) 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.
URI: https://hdl.handle.net/10356/143519
ISSN: 1793-8759
DOI: 10.1007/s41649-019-00094-2
Rights: © 2019 The Author(s). This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SSS Journal Articles

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