Meta Consent – A Flexible Solution to the Problem of Secondary Use of Health Data

Ploug, Thomas; Holm, Søren

doi:10.1111/bioe.12286

Cited by 92 publications

(83 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In the latter case, a range of research activities is concerned—eg, all observational studies in cardiology that will be performed in a given hospital or all research activities that are approved by some governance body such as a Research Ethics Board (REB). Blanket consent is a very broad consent that is “open‐ended and unspecified” …”

Section: Resultsmentioning

confidence: 99%

“…Ploug and Holm propose a model named “meta‐consent,” in which people are asked how and when they would like to be presented with requests for consent to the use of their personal health data and biological material. Their proposal stems from the risk of routinization in providing or refusing consent: given the frequency of consent requests, and the abundant information material, there is a risk that the provision or refusal of consent would become a matter of nondeliberative habit.…”

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Informed consent within a learning health system: A scoping review

Cumyn

Barton

Dault

et al. 2019

Learning Health Systems

View full text Add to dashboard Cite

Introduction: A major consideration for the implementation of a learning health system (LHS) is consent from participants to the use of their data for research purposes.The main objective of this paper was to identify in the literature which types of consent have been proposed for participation in research observational activities in a LHS. We were particularly interested in understanding which approaches were seen as most feasible and acceptable and in which context, in order to inform the development of a Quebec-based LHS.Methods: Using a scoping review methodology, we searched scientific and legal databases as well as the gray literature using specific terms. Full-text articles were reviewed independently by two authors on the basis of the following concepts:(a) LHS and (b) approach to consent. The selected papers were imported in NVivo software for analysis in the light of a conceptual framework that distinguishes various, largely independent dimensions of consent.Results: A total of 93 publications were analysed for this review. Several studies reach opposing conclusions concerning the best approach to consent within a LHS.However, in the light of the conceptual framework we developed, we found that many of these results are distorted by the conflation between various characteristics of consent. Thus, when these characteristics are distinguished, the results mainly suggest the prime importance of the communication process, by contrast to the scope of consent or the kind of action required by participants (opt-in/opt-out). We identified two models of consent that were especially relevant for our purpose: metaconsent and dynamic consent.Conclusions: Our review shows the importance of distinguishing carefully the various features of the consent process. It also suggests that the metaconsent model is a valuable model within a LHS, as it addresses many of the issues raised with regards

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Informed consent within a learning health system: A scoping review

Cumyn

Barton

Dault

et al. 2019

Learning Health Systems

View full text Add to dashboard Cite

show abstract

“…Key supporting literature Relevant aspects (ascending LoA6) The professional skills required of the healthcare workforce, including information governance skills (Kluge et al, 2018) Epistemic ( How traditional and non-traditional sources of health data can be incorporated into AI-Health decision making, how it can be appropriately protected and how it can be harmonised (e.g. Maher et al, 2019;Ploug & Holm, 2016;Richardson, Milam, & Chrysler, 2015;Townend, 2018) Epistemic (A, C, D, E, F) Normative (A, C, D, E, F) Overarching (A, C, D, E) How bioethical concepts (beneficence, nonmaleficence, autonomy and justice (Beauchamp & Childress, 2013) are challenged by AI-Health (Mittelstadt, 2019) Epistemic (B, F) Normative (A, C, D, F) Overarching (A, F)…”

Section: Considerationmentioning

confidence: 99%

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Morley¹

2019

Preprint

View full text Add to dashboard Cite

Healthcare systems across the globe are struggling with increasing costs and worsening outcomes. This presents those responsible for overseeing healthcare with a challenge. Increasingly, policymakers, politicians, clinical entrepreneurs and computer and data scientists argue that a key part of the solution will be ‘Artificial Intelligence’ (AI) – particularly Machine Learning (ML). This argument stems not from the belief that all healthcare needs will soon be taken care of by “robot doctors.” Instead, it is an argument that rests on the classic counterfactual definition of AI as an umbrella term for a range of techniques that can be used to make machines complete tasks in a way that would be considered intelligent were they to be completed by a human. Automation of this nature could offer great opportunities for the improvement of healthcare services and ultimately patients’ health by significantly improving human clinical capabilities in diagnosis, drug discovery, epidemiology, personalised medicine, and operational efficiency. However, if these AI solutions are to be embedded in clinical practice, then at least three issues need to be considered: the technical possibilities and limitations; the ethical, regulatory and legal framework; and the governance framework. In this article, we report on the results of a systematic analysis designed to provide a clear overview of the second of these elements: the ethical, regulatory and legal framework. We find that ethical issues arise at six levels of abstraction (individual, interpersonal, group, institutional, sectoral, and societal) and can be categorised as epistemic, normative, or overarching. We conclude by stressing how important it is that the ethical challenges raised by implementing AI in healthcare settings are tackled proactively rather than reactively and map the key considerations for policymakers to each of the ethical concerns highlighted.

show abstract

“…Indeed, consent procedures are often highly questionablefor example, due to obscurities or omissions in terms and conditions documents (Stoeklé et al, 2016). For these reasons, newly proposed models of informed consent attempt to articulate a continuous, open-ended agreement to studies for which subjects' data were not intended and which were likely unknown at the time of initial data-gathering (e.g., Hansson, 2006;Steinsbekk, 2013;Ploug & Holm, 2016). Undoubtedly, precision medicine, with its reliance upon future uses of enormous quantities of data, aptly raises such concerns for informed consent.…”

Section: Ethical Tensionsmentioning

confidence: 99%

Changing the mindset for precision medicine: from incentivized biobanking models to genomic data

Tigard

2019

Genet. Res.

View full text Add to dashboard Cite

The emerging paradigm in contemporary healthcare, precision medicine, is widely seen as a revolutionary approach to both clinical treatment and overall health promotion. Precision models are making use of the most up-to-date technological advancements – such as genomics and ‘big data’ processing – in an effort to tailor healthcare to each individual. Yet the list of hurdles to successful implementation of precision medicine is no secret. Among the challenges, it was recently suggested in this journal that we must change the ‘mindset’ of patients, practitioners and the wider public (McGonigle, 2016). And while precision medicine indeed demands a significant shift, we must not understate the extent of the overhaul required. In particular, I argue, against McGonigle's suggestion, that the ethical challenges regarding participant contributions cannot be tackled by relying upon existing models of incentivized blood banking or organ donation. Instead, the success of precision medicine requires a wholescale change in mindset.

show abstract

Meta Consent – A Flexible Solution to the Problem of Secondary Use of Health Data

Cited by 92 publications

References 12 publications

Informed consent within a learning health system: A scoping review

Informed consent within a learning health system: A scoping review

The Debate on the Ethics of AI in Health Care: a Reconstruction and Critical Review

Changing the mindset for precision medicine: from incentivized biobanking models to genomic data

Contact Info

Product

Resources

About