With the growth of precision medicine research on health data and biospecimens, research institutions will need to build and maintain long-term, trusting relationships with patient-participants. While trust is important for all research relationships, the longitudinal nature of precision medicine research raises particular challenges for facilitating trust when the specifics of future studies are unknown. Based on focus groups with racially and ethnically diverse patients, we describe several factors that influence patient trust and potential institutional approaches to building trustworthiness. Drawing on (...) these findings, we suggest several considerations for research institutions seeking to cultivate long-term, trusting relationships with patients: Address the role of history and experience on trust, engage concerns about potential group harm, address cultural values and communication barriers, and integrate patient values and expectations into oversight and governance structures. (shrink)

Rare genetic diseases collectively impact millions of individuals in the United States. These patients and their families share many challenges including delayed diagnosis, lack of knowledgeable providers, and limited economic incentives to develop new therapies for small patient groups. As such, rare disease patients and families often must rely on advocacy, including both self-advocacy to access clinical care and public advocacy to advance research. However, these demands raise serious concerns for equity, as both care and research for a given disease (...) can depend on the education, financial resources, and social capital available to the patients in a given community. In this article, we utilize three case examples to illustrate ethical challenges at the intersection of rare diseases, advocacy and justice, including how reliance on advocacy in rare disease may drive unintended consequences for equity. We conclude with a discussion of opportunities for diverse stakeholders to begin to address these challenges. (shrink)

In this issue, Lynch and colleagues discuss lessons learned from the “Operation Warp Speed” response to the COVID-19 pandemic in the United States—both about what to do and what not to do fo...

The work of Sankary et al. (2022) provides valuable insights into the experiences of participants exiting brain device research. Empirical bioethics research such as this is critical to understandi...

Background Machine learning (ML) is utilized increasingly in health care, and can pose harms to patients, clinicians, health systems, and the public. In response, regulators have proposed an approach that would shift more responsibility to ML developers for mitigating potential harms. To be effective, this approach requires ML developers to recognize, accept, and act on responsibility for mitigating harms. However, little is known regarding the perspectives of developers themselves regarding their obligations to mitigate harms.Methods We conducted 40 semi-structured interviews with (...) developers of ML predictive analytics applications for health care in the United States.Results Participants varied widely in their perspectives on personal responsibility and included examples of both moral engagement and disengagement, albeit in a variety of forms. While most (70%) of participants made a statement indicative of moral engagement, most of these statements reflected an awareness of moral issues, while only a subset of these included additional elements of engagement such as recognizing responsibility, alignment with personal values, addressing conflicts of interests, and opportunities for action. Further, we identified eight distinct categories of moral disengagement reflecting efforts to minimize potential harms or deflect personal responsibility for preventing or mitigating harms.Conclusions These findings suggest possible facilitators and barriers to the development of ethical ML that could act by encouraging moral engagement or discouraging moral disengagement. Regulatory approaches that depend on the ability of ML developers to recognize, accept, and act on responsibility for mitigating harms might have limited success without education and guidance for ML developers about the extent of their responsibilities and how to implement them. (shrink)

In their article, Nelson et al. (2023) raise concerns regarding the “paradox of experience” as it relates to the practice of bioethics. They argue that while experience provides individuals with in...

In this issue, Morain and Largent (2023) raise a pressing issue arising in the context of embedded research—the nature and extent of investigator duties to patient-participants when the line betwee...