Faculty Bibliography

INTRODUCTION:As the utility of genomic sequencing increases, its use in healthcare will continue to expand beyond expert clinics toward nonspecialist practices such as primary care. At the same time, discordance in genetic variant identification and classification between laboratories remains a concern for the field. This research assesses how clinicians with and without genetics expertise understand and trust genetic test results, underscoring how variation in the handling of genetic test results can have real impact on patient care. METHODS:We conducted 40 interviews with genetics experts, including clinical geneticists and genetic counselors, and nonexpert clinicians including primary care providers and cardiologists. RESULTS:Clinical geneticists and genetic counselors reported spending significant time assessing the validity of results from genetic testing laboratories, conversing with laboratories about those results, and potentially reinterpreting results. Conversely, primary care providers and cardiologists without specific genetics expertise reported high levels of trust in laboratory accuracy and variant interpretation, and did not reassess results. CONCLUSION:We find significant variation in how genetics experts and nonexperts understand the trustworthiness of genetic laboratory reports. This variation could lead to differences in patient care between clinical settings and requires additional guidance for clinicians regarding the handling of genetic test results.

Research institutions often lack policies addressing the risks and benefits of enrolling "invested parties" such as investigators, research staff, and patient, caregiver, and community representatives (groups most affected by a disease or intervention) in studies where they have direct involvement. Invested parties may have both strong motivations to study the condition or intervention and to participate as study subjects. More guidance is needed to promote appropriate access to research participation and mitigate potential risks. This article addresses the gap in guidance by presenting an ethical framework and practical guidelines for the enrollment of invested parties. Drawing from experiences with the Researching COVID to Enhance Recovery (RECOVER) Initiative, a large multisite observational cohort study, we argue that invested parties should not be categorically excluded from enrollment in their own research studies if certain criteria are met and appropriate safeguards are in place. We underscore the need to balance inclusion with fairness, promote valid voluntary informed consent, ensure data privacy, protect scientific validity, and mitigate unique risks to invested parties as participants. Additionally, we recommend regular reporting and empirical assessment to evaluate the impact of enrolling invested parties on participants and study outcomes.

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.

Abstract In Tyranny of the Gene: Personalized Medicine and Its Threat to Public Health (Knopf, 2023), James Tabery traces the ascendance of personalized or precision medicine in America, arguing that America's emphasis on genetics offers more hype than transformational power. In his examination of the power struggles, social relationships, and technological advances that centered the gene in American health policy, Tabery demonstrates how an intensive focus on genetics draws attention away from both the fundamental causes of health disparities and more-effective changes that could be made to developmental, physical, and social environments. American policy-makers, health care institutions, funders, and bioethicists should not let the technological shine and attractive politics of personalized medicine continue to replace the hard but necessary work of addressing sociopolitical causes of disease and illness.

Over the last decade, the concept of actionability has become a primary framework for assessing whether genetic data is useful and appropriate to return to patients. Despite the popularity of this concept, there is little consensus about what should count as "actionable" information. This is particularly true in population genomic screening, where there is considerable disagreement about what counts as good evidence and which clinical actions are appropriate for which patients. The pathway from scientific evidence to clinical action is not straightforward-it is as much social and political as it is scientific. This research explores the social dynamics shaping the integration of "actionable" genomic data into primary care settings. Based on semi-structured interviews with 35 genetics experts and primary care providers, we find that clinicians vary in how they define and operationalize "actionable" information. There are two main sources of disagreement. First, clinicians differ on the levels and types of evidence required for a result to be actionable, such as when we can be confident that genomic data provides accurate information. Second, there are disagreements about the clinical actions that must be available so that patients can benefit from that information. By highlighting the underlying values and assumptions embedded in discussions of actionability for genomic screening, we provide an empirical basis for building more nuanced policies regarding the actionability of genomic data in terms of population screening in primary care settings.

As the era of big data transforms modern medicine, clinicians have access to more health data than ever. How do medical providers determine which data are relevant to patient care, which are irrelevant, and which may be inappropriately used to justify potentially harmful interventions? One of the most prominent medical fields to address these questions head on - clinical genomics - is actively debating how to assess the value of genomic data. In-depth interviews with clinicians and a content analysis of policy documents demonstrate that while many clinicians believe that collecting as much patient data as possible will lead to better patient care, a sizeable minority of clinicians preferred to collect less data. These clinicians worried that large genomic tests provided too much data, leading to confusion and inappropriate treatment. Clinical geneticists have also started developing the concept of "actionability" to assess which types of genomic data are worth collecting and interpreting. By classifying data as useful when it can or should lead to action, clinicians can formalize and institutionalize what types of data should be ignored. But achieving consensus about what counts as "actionable" has proven difficult and highlights the different values and risk philosophies of clinicians. At the same time, many clinicians are fighting against the ignorance arising from genomic databases predominantly filled with samples from European ancestry populations. Debates about how and when to institutionalize ignorance of health data are not unique to clinical genomics, but have spread throughout many fields of medicine. As the amount of health data available to clinicians and patients grows, social science research on the politics of knowledge and ignorance should inform debates about the value of data in medicine.