(2013). An Unbiased Response to the Open Peer Commentaries on “Does Consent Bias Research?”. The American Journal of Bioethics: Vol. 13, No. 4, pp. W1-W4. doi: 10.1080/15265161.2013.769824.

BackgroundHealth data holds great potential for improved treatments. Big data research and machine learning models have been shown to hold great promise for improved diagnostics and treatment planning. The potential is tied, however, to the availability of personal health data. In recent years, it has been argued that data from health records should be available for health research, and that individuals have a duty to make the data available for such research. A central point of debate is whether such secondary (...) use of health data requires informed consent.Main bodyIn response to recent writings this paper argues that a requirement of informed consent for health record research must be upheld. It does so by exploring different contrasting notions of the duty of easy rescue and arguing that none of them entail aperfectduty to participate in health record research. In part because the costs of participation cannot be limited to 1) the threat of privacy breaches, but includes 2) the risk of reduced trust and 3) suboptimal treatment, 4) stigmatization and 5) medicalisation, 6) further stratification of solidarity and 7) increased inequality in access to treatment and medicine. And finally, it defends the requirement of informed consent by arguing that the mere possibility of consent bias provides a rather weak reason for making research participation mandatory, and that there are strong, independent reasons for making.ConclusionArguments from the duty of easy rescue in combination with claims about little risk of harm and potential consent bias fail to establish not only aperfectduty to participate in health record research, but also that participation in such research should be mandatory. On the contrary, an analysis of these arguments indicates that the duty to participate in research is most adequately construed as animperfectduty, and reveals a number of strong reasons for insisting that participation in health records research is based on informed consent. (shrink)

The increased use of information technology in every day health care creates vast amounts of stored health data that can be used for research. The secondary research use of routinely collected data raises questions about appropriate consent mechanisms for such use. One option is meta consent where individuals state their own consent preferences in relation to future use of their data, e.g. whether they want the data to be accessible to researchers under conditions of specific consent, broad consent, blanket consent (...) or not at all. This study investigates whether meta consent preferences can be successfully elicited by a smartphone application in the adult Danish population. A smartphone app was developed for the elicitation of meta consent preferences. An invitation to use the app was distributed to a stratified, representative sample of the Danish adult population. The meta consent choices, the use of the app, user experience data, and demographic data were logged and analysed statistically using IBM SPSS version 20. Of 1000 potential respondents 221 used the app. One hundred eighty-eight of the respondents were female and 103 male. The age range was 19 to 79 years with an average of 51 years. Most users indicate 1) that they find the choices they are asked to make easy to understand, 2) that the application is easy to use, and 3) that this kind of choice should be offered to people. It is possible to collect meta consent preferences in the general, adult population using a smartphone app. (shrink)

This paper argues that specific individual informed consent and other forms of consent predicated on a right to autonomy may not in all circumstances be appropriate for the establishment and use of large data sets of health information. We suggest that there are inherent failings in such an approach, shortcomings that we analyse below. We argue that individuals share an obligation to contribute their data, as doing so is cost-free and benefits accrue to the population as a whole. Large health (...) data sets can be considered public goods – goods that are non-rival in consumption and in some cases non-exclusive in use – and contributing to these goods may be an obligation, the meeting of which allows citizens to invest in knowledge infrastructure. The approach argued for here is a variation of a communitarian ethic in which people have an obligation to contribute their data but have no correlative right to expect or receive an individual benefit. (shrink)

(2013). Can Informed Consent Go Too Far? Balancing Consent and Public Benefit in Research. The American Journal of Bioethics: Vol. 13, No. 4, pp. 1-2. doi: 10.1080/15265161.2013.778645.

(2013). Informed Consent, Big Data, and the Oxymoron of Research That Is Not Research. The American Journal of Bioethics: Vol. 13, No. 4, pp. 40-42. doi: 10.1080/15265161.2013.768864.

Given that biases can distort bioethics work, it has received surprisingly little and fragmented attention compared to in other fields of research. This article provides an overview of potentially relevant biases in bioethics, such as cognitive biases, affective biases, imperatives, and moral biases. Special attention is given to moral biases, which are discussed in terms of (1) Framings, (2) Moral theory bias, (3) Analysis bias, (4) Argumentation bias, and (5) Decision bias. While the overview is not exhaustive and the taxonomy (...) by no means is absolute, it provides initial guidance with respect to assessing the relevance of various biases for specific kinds of bioethics work. One reason why we should identify and address biases in bioethics is that it can help us assess and improve the quality of bioethics work. (shrink)

(2013). Electronic Fences Make Good Neighbors: The Importance of Medical Records Managers to Protecting Autonomy. The American Journal of Bioethics: Vol. 13, No. 4, pp. 50-52. doi: 10.1080/15265161.2013.767965.

(2013). A Review of Evidence on Consent Bias in Research. The American Journal of Bioethics: Vol. 13, No. 4, pp. 42-44. doi: 10.1080/15265161.2013.767958.

The clinical introduction of medical devices often occurs with relatively little oversight, regulation and (long-term) follow-up. Some recent controversies underscore the weaknesses of the current regime, such as the complications surrounding the metal-on-metal hip implants and the scandal surrounding the global breast implant scare of silicone implants made by France's Poly Implant Prothese (PIP) Company. The absence of national registries hampered the collection of reliable information on the risks and harms of the PIP breast implants. To warrant long-term safety, a (...) case can be made for mandatory post-marketing surveillance by means of the establishment of compulsory registries. In this edition of Public Health Ethics, Schofield calls for debate on how such a registry system should be initiated and maintained and how it would relate to the ethical requirement of consent. Here we use breast implant registries as a case to discuss whether and when a so-called ‘thick opt-out’ would be an appropriate method to include people in medical device registries. We conclude that a thick opt-out procedure for medical device registries is only justifiable in cases where inclusion does not involve burdens (or very low), when it does not involve a sensitive subject and when the data are stored anonymously (or at least not directly linked to the medical record). Otherwise, inclusion should be sought by means of an opt-in. (shrink)

The burden of research ethics review falls not just on researchers, but on those who serve on research ethics committees (RECs). With the advent of automated text analysis and generative artificial intelligence, it has recently become possible to teach models to support human judgment, for example by highlighting relevant parts of a text and suggesting actionable precedents and explanations. It is time to consider how such tools might be used to support ethics review and oversight. This commentary argues that with (...) a suitable strategy of engagement, artificial intelligence can be used in a variety of ways that can genuinely support RECs and manage their workload and improve the quality of review. It would be wiser to take an active role in the development of AI tools for ethics review, rather than to adopt ad hoc tools after the fact. (shrink)