Abstract
The Big Data vision for biomedicine supports compilation, long-term banking, and sharing of sensitive personal information, and potentially allows an individual’s data to be combined and utilised with other data indefinitely in innumerable research projects. But this vision does not adhere to the case-specific and jurisdiction-specific oversight models upon which current governance has been founded. Informed consent, for instance, a core principle of bioethics, no longer seems feasible as data repositories and Big Data methodologies become central to research. Policymakers have not yet found a consistent way to address ethical, legal, and social issues (ELSI) in this new data environment. Systematic ways of thinking are needed which reflect the new uses of biomedical data with a view toward upholding basic ELSI standards. The aim of this chapter is to present a view of governance where dataflow itself, not institutional or national boundaries, is taken as the de facto framework for research, and where metadata on consent play a central role in how data is governed. I identify types of consent as a place to begin to develop ELSI metadata procedures for data-enabled research contexts. Such metadata can assure data production, dissemination, and reuse is in accordance with participants’ and researchers’ expectations. Ultimately, it can assist with codification of criteria and standards that demonstrate impact of data intensive research along its ethical, legal, and social dimensions, at multiple levels of governance.
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Notes
- 1.
Here the term “metadata” is used to mean data which convey information surrounding other data.
- 2.
See: http://www.hhs.gov/ohrp/archive/nurcode.html. See also Spigner (2007).
- 3.
Respect for autonomy, beneficence, non maleficence, and justice (Beauchamp 2001).
- 4.
Though they will not be a focus of this chapter, there are cases where this dichotomy does not necessarily apply. Much depends upon where one stands on the issues of privacy and on the technological ability to preserve it in a given research context. It can be argued that protections of privacy can translate into lower risks to participants, and lower risks to participants can tip the scales toward ethical mandates that, since no true harms are incurred by individuals, emphasize the pursuit of public good over and above individual rights. Here, one needs to consider the value of Big Data analytics in cases where they have no clear public benefit (e.g. market research), in cases where analysis is carried out in a manner where risks to privacy are not a real concern, and in cases where they can answer questions in a way that is more protective of privacy than in other methods of research. Each of these scenarios requires a different balancing of individual rights with the public good.
- 5.
“The requirement for specific consent fails to take account of the fact that this research is subject to ethical approval and strict confidentiality safeguards, and the identity of individuals is often masked” (Wellcome Trust 2015).
- 6.
“In many studies that will be affected, individuals have voluntarily given broad consent for their data to be used in research to further our understanding of society, health and disease. Their valuable contributions could be wasted if the amendments become law” (Wellcome Trust 2015).
- 7.
Lack of uniformity is a major problem (Veerus et al. 2013). GA4GH are looking for ways to streamline oversight and make it more efficient for data use. See: https://genomicsandhealth.org/files/public/3Plenary2Presentation-REWG-BarthaKnoppers-KazutoKato.pdf
- 8.
Several signators are from the Wellcome Trust.
- 9.
- 10.
The GA4GH has recently completed a study that examines variation in data use conditions which are based on consent provisions for genomics datasets in both research and clinical settings. The study reviews guidance of the National Institutes of Health, data use conditions at Broad Institute (of MIT and Harvard) and the European Genome-phenome Archive of the European Bioinformatics Institute (EMBL-EBI), as well as data use conditions within the GA4GH’s own Data Working Group and the Matchmaker Exchange Project. Their proposed structure for recording categories and requirements for data and data use are in many ways compatible with metadata objectives proposed in this chapter. (Dyke et al. 2016).
- 11.
For an example see Kaye et al. (2014).
- 12.
Though agreement may sometimes be attained through “presumed consent,” an “opt out” model. See for example: http://www.nhs.uk/NHSEngland/thenhs/records/healthrecords/Pages/care-data.aspx. See also, McCartney (2014).
- 13.
“Biomedical research, however, is not primarily about our own health but rather about potential health benefit for future generations. An important reason for active engagement and participation in biomedical research is thereby lacking compared with general health care” (Steinsbekk et al. 2013).
- 14.
Informed consent is also sometimes criticised as being paternalistic in that it can prevent the giving of consent, prevent the exercise of autonomy, if requirements for what “informed” means are not met.
- 15.
In addition to “informed consent,” “broad consent,” and “dynamic consent,” we have “tiered consent,” “blanket consent,” “open consent,” “presumed consent,” “implied consent,” “precautionary consent,” and “waiver of consent” (in retrospective research).
- 16.
This has been likened to Wittgenstein’s language-game. (Karlsen et al. 2011).
- 17.
“[T]he information that makes them informed is different from the specific individual consent case. In broad consent cases, the relevant information is about the person (or institution) who will make the decision for me. In biobanking, the relevant information might be about the overall goals of the research supported by the biobank and details of the decision making processes within the institution – how are decisions made about suitable research and by whom?” (Sheehan 2011a).
- 18.
- 19.
As per Steinsbekk’s argument above.
- 20.
For instance in South Africa ownership of genomic information can be tribal. Consent is thereby given by tribal leaders, not individuals. This is a consent model that challenges autonomy as it is conceived in Western law and bioethics, yet it is one that respects and is responsive to cultural needs surrounding international data sharing.
- 21.
As compared to “beneficence,” for example.
- 22.
I use the term “Boolean” for this example here. But in fact the variables can have more than two options. They can use as many as are needed.
- 23.
It could, for instance, be little different than a survey that attends the consent form, though its function is very different.
- 24.
- 25.
See for examples: UK Biobank consent information at http://www.ukbiobank.ac.uk/wp-content/uploads/2011/06/Participant_information_leaflet.pdf?phpMyAdmin=trmKQlYdjjnQIgJ%2CfAzikMhEnx6; Genomics England, 100,000 Genomes Project consent form at http://www.genomicsengland.co.uk/wp-content/uploads/2015/02/3b_CFProbandAdultPatientor-their-AdultRelsRareDisease-v2.0.pdf
- 26.
See for examples: GA4GH consent tools at http://www.p3g.org/news/consent-tools-prepared-global-alliance-genomics-and-health-p3g-ipac; International Cancer Genome Consortium (ICGC) Research Study Model Consent Brochure https://icgc.org/files/daco/ICGC_prosp_consent_290110.pdf. See also Wallace (2011).
- 27.
- 28.
This latter part would not have to be part of the matrix. Ideally it would, but it would require that the matrix code is updated every time data us used. Integrating updating procedures into the process is a greater challenge than simply capturing the initial conditions of data collection. It would require a detailed knowledge of data sharing networks. Well positioned organizations such as the GA4GH and the European Data sharing Network could be key players in undertaking efforts here.
- 29.
Take, for instance, information required by law or governing bodies, to be made available to RECs. In the initial phases of the application and review processes, the basis for deciding what information is relevant is a consequence of established governance, not normative theory. Though those governance practices may have an ethical basis, the mechanisms developed to support them are not, in themselves, normative judgments.
- 30.
Such as type of consent, whether the participant is an invested party (e.g. whether he or she has a rare disease), or something as simple as the geolocation and jurisdiction of data source.
- 31.
See: https://gold.jgi-psf.org/. At the time this was written, the database hosts information for about 22,000 studies, 67,000 Biosamples, 67,000 sequencing projects and 54,000 analysis projects. “More than just a catalog of worldwide genome projects, GOLD is a manually curated, quality-controlled metadata warehouse” (Reddy et al. 2014).
- 32.
For a report on an online consent matrix see Thiel et al. (2015).
- 33.
- 34.
- 35.
- 36.
A goal of the GA4GH, through a different strategy.
- 37.
This is not the same as the issue of anonymity of data, deidentification of data, or other methods used to mitigate risks associated with identification. Whether such precautions are effective in Big Data contexts is a matter of debate. This is more closely related to the issue of data security.
- 38.
Here, examples for managing metadata from areas whose objectives are in some way analogous to those of the ELSI community help. Metadata frameworks developed in e-governance, for instance, have been established to make information accessible to policymakers where and when it is needed. (Inigo et al. 2010; Linzer 1988) Metamodeling methods have been developed to increase coordination of data use across government agencies. (Shukair et al. 2013) Additionally, where governance of sensitive data flowing through international contexts is a primary concern, as it is in biomedicine, there may be no better model than finance. When reporting earnings from multiple countries for tax purposes, each jurisdiction has its own regulations. There is high risk of inadvertent non-compliance with local laws. The financial sector has developed intricate metamodelling procedures to address this. They use metadata from multiple jurisdictions at once to create international systems of finance which leave regulations intact at the national level. (Inmon et al. 2008).
- 39.
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Acknowledgements
The author thanks Dr. Brent Mittelstadt of the Oxford Internet Institute (OII) for stimulating discussion which led to additions and improvements to this chapter, and Dr. Harriet Teare of the Centre for Health, Law and Emerging Technologies at Oxford (HeLEX) for her helpful comments and corrections. The author also thanks HeLEX and Harris Manchester College, Oxford for making this research possible.
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Patrick Woolley, J. (2016). How Data Are Transforming the Landscape of Biomedical Ethics: The Need for ELSI Metadata on Consent. In: Mittelstadt, B., Floridi, L. (eds) The Ethics of Biomedical Big Data. Law, Governance and Technology Series, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-319-33525-4_8
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