Background In the wake of mandates for biomedical research to increase participation by members of historically underrepresented populations, community engagement (CE) has emerged as a key intervention to help achieve this goal.Methods Using interviews, observations, and document analysis, we examine how stakeholders in precision medicine research understand and seek to put into practice ideas about who to engage, how engagement should be conducted, and what engagement is for.Results We find that ad hoc, opportunistic, and instrumental approaches to CE exacted significant (...) consequences for the time and resources devoted to engagement and the ultimate impacts it has on research. Critical differences emerged when engagement and research decisionmaking were integrated with each other versus occurring in parallel, separate parts of the study organization, and whether community members had the ability to determine which issues would be brought to them for consideration or to revise or even veto proposals made upstream based on criteria that mattered to them. CE was understood to have a range of purposes, from instrumentally facilitating recruitment and data collection, to advancing community priorities and concerns, to furthering long-term investments in relationships with and changes in communities. These choices about who to engage, what engagement activities to support, how to solicit and integrate community input into the workflow of the study, and what CE was for were often conditioned upon preexisting perceptions and upstream decisions about study goals, competing priorities, and resource availability.Conclusions Upstream choices about CE and constraints of time and resources cascade into tradeoffs that often culminated in “pantomime community engagement.” This approach can create downstream costs when engagement is experienced as improvised and sporadic. Transformations are needed for CE to be seen as a necessary scientific investment and part of the scientific process. (shrink)

Governments are investing in precision medicine with the aim of improving healthcare through the use of genomic analyses and data analytics to develop tailored treatment approaches for individual patients. The success of PM is contingent upon clear public communications that engender trust and secure the social licence to collect and share large population-wide data sets because specific consent for each data re-use is impractical. Variation in the terminology used by different programmes used to describe PM may hinder clear communication and (...) threaten trust. Language is used to create common understanding and expectations regarding precision medicine between researchers, clinicians and the volunteers. There is a need to better understand public interpretations of PM-related terminology. This paper reports on a qualitative study involving 24 focus group participants in the multi-lingual context of Singapore. The study explored how Singaporeans interpret and understand the terms ‘precision medicine’ and ‘personalised medicine’, and which term they felt more aptly communicates the concept and goals of PM. Results suggest that participants were unable to readily link the terms with this area of medicine and initially displayed preferences for the more familiar term of ‘personalised’. The use of visual aids to convey key concepts resonated with participants, some of whom then indicated preferences for the term ‘precision’ as being a more accurate description of PM research. These aids helped to facilitate dialogue around the ethical and social value, as well as the risks, of PM. Implications for programme developers and policy makers are discussed. (shrink)

Through the use of culturally appropriate videos on precision medicine research (PMR) that were developed and tailored to five racial and ethnic groups of patients, and subsequent focus-group discu...

The United States’ ambitious Precision Medicine Initiative proposes to accelerate exponentially the adoption of precision medicine, an approach to health care that tailors disease diagnosis, treatment, and prevention to individual variability in genes, environment, and lifestyle. It aims to achieve this by creating a cohort of volunteers for precision medicine research, accelerating biomedical research innovation, and adopting policies geared toward patients’ empowerment. As strategies to implement the PMI are formulated, critical consideration of the initiative's ethical and sociopolitical dimensions is needed. (...) Drawing on scholarship of nationalism and democracy, we discuss the PMI's construction of what we term “genomic citizenship”; the possible normative obligations arising therefrom; and the ethical, legal, and social challenges that will ensue. Although the PMI is a work in progress, discussion of the existing and emerging issues can facilitate the development of policies, structures, and procedures that can maximize the initiative's ability to produce equitable and socially sensitive outcomes. Our analysis can also be applied to other population-based, precision medicine research programs. (shrink)

Harms and risks to groups and third-parties can be significant in the context of research, particularly in data-centric studies involving genomic, artificial intelligence, and/or machine learning technologies. This article explores whether and how United States federal regulations should be adapted to better align with current ethical thinking and protect group interests. Three aspects of the Common Rule deserve attention and reconsideration with respect to group interests: institutional review board (IRB) assessment of the risks/benefits of research; disclosure requirements in the informed (...) consent process; and criteria for waivers of informed consent. In accordance with respect for persons and communities, investigators and IRBs should systematically consider potential group harm when designing and reviewing protocols, respectively. Research participants should be informed about any potential group harm in the consent process. We call for additional public discussion, empirical research, and normative analysis on these issues to determine the right regulatory and policy path forward. (shrink)

Many countries currently invest in technologies and data infrastructures to foster precision medicine (PM), which is hoped to better tailor disease treatment and prevention to individual patients. But who can expect to benefit from PM? The answer depends not only on scientific developments but also on the willingness to address the problem of structural injustice. One important step is to confront the problem of underrepresentation of certain populations in PM cohorts via improved research inclusivity. Yet, we argue that the perspective (...) needs to be broadened because the (in)equitable effects of PM are also strongly contingent on wider structural factors and prioritization of healthcare strategies and resources. When (and before) implementing PM, it is crucial to attend to how the organisation of healthcare systems influences who will benefit, as well as whether PM may present challenges for a solidaristic sharing of costs and risks. We discuss these issues through a comparative lens of healthcare models and PM-initiatives in the United States, Austria, and Denmark. The analysis draws attention to how PM hinges on—and simultaneously affects—access to healthcare services, public trust in data handling, and prioritization of healthcare resources. Finally, we provide suggestions for how to mitigate foreseeable negative effects. (shrink)

Biomedical research using data from participants’ mobile devices borrows heavily from the ethos of the “citizen science” movement, by delegating data collection and transmission to its volunteer subjects. This engagement gives volunteers the opportunity to feel like partners in the research and retain a reassuring sense of control over their participation. These virtues, in turn, give both grass-roots citizen science initiatives and institutionally sponsored mHealth studies appealing features to flag in recruiting participants from the public. But while grass-roots citizen science (...) projects are often community-based, mHealth research ultimately depends on the individuals who own and use mobile devices. This inflects the ethos of mHealth research towards a celebration of individual autonomy and empowerment, at the expense of its implications for the communities or groups to which its individual participants belong. But the prospects of group harms — and benefits — from mHealth research are as vivid as they are in other forms of data-intensive “precision health” research, and will be important to consider in the design of any studies using this approach. (shrink)

In this issue of the Hastings Center Report, Maya Sabatello and Paul Appelbaum explore the assumptions about community embedded in the U.S. Precision Medicine Initiative, which aims to recruit donor‐partners who reflect the United States’ racial and ethnic diversity. As Sabatello and Appelbaum discuss, the initiative is like other national biobanking efforts in bringing to life an imagined genetic community in need of critical attention, and given the public‐private forms of partnership at the heart of the PMI, such efforts could (...) become avenues to deepen existing inequalities rather than to alleviate them. The notion of justice has underwritten debates about genomic medicine, informed consent, citizenship, benefit sharing, and profit making since the first national biobanking project emerged at the dawn of the twenty‐first century. In a paradigmatic case, the creation, by an Icelandic company, of the deCODE genomic biobank opened up fierce debates about the proper relationship between public good and private gain and became the first global example of the economic and political implications that imagined genetic communities could have in our shared future. In Mexico, in 2001, the Icelandic case fueled a policy agenda to deal with global health justice and the prospects of a future market‐based colonialism predicated on the intimate knowledge of DNA. (shrink)

Proponents of precision medicine envision that digital phenotyping can enable more individualized strategies to manage current and future health conditions. We problematize the interpretation of digital phenotypes as straightforward representations of individuals through examples of what we call data inheritance. Rather than being a digital copy of a presumed original, digital phenotypes are shaped by larger data collectives that precede and continuously change how the individual is represented. We contend that looking beyond the individual is crucial for understanding the factors (...) that can ‘bend’ digital mirrors in specific directions. Since algorithms used for digital profiling are based on historical data, their predictions often inherit and increase the values and perspectives of past data practices. Moreover, the data legacies we leave behind today may return as so-called ‘data phantoms’ that conflict with the interests of the individual and contest who and what the ‘original’ is. (shrink)