Faculty Bibliography

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.

In modern medicine, health risks are often managed through the collection of health data and subsequent intervention. One of the goals of clinical genetics, for example, is to identify genetic predisposition to disease so that individuals can intervene to prevent potential harms. But recently, some clinicians have suggested that patients should undergo less testing and monitoring in an effort to reduce overdiagnosis and overtreatment. In this paper, I explore how clinicians navigate the tension between identifying real disease risks for their patients with concerns about overdiagnosis and overtreatment. I focus on clinicians ordering genetic testing for inherited cardiovascular diseases. Of the genes determined to be "clinically actionable" by the American College of Medical Genetics and Genomics (ACMG), half are related to cardiovascular diseases. But, due in part to high levels of uncertainty surrounding cardiovascular genetics, there is still disagreement within the field about how to order and interpret these tests. Based on semi-structured, in-depth interviews with 20 clinicians who order genetic testing for cardiovascular diseases, I find that there is considerable variability in the ways that clinicians determine which types of genetic tests are appropriate for their patients and how they interpret test results. Most importantly, I find that many providers do not presume that more genetic data will lead to better care. Instead, increased genetic data can lead to confusion and inappropriate treatment. This re-valuation of the utility of medical data is crucial for bioethicists to explore, especially as medical fields are sorting through increasing amounts of data.

As maternal mortality increases in the United States, birth providers and policymakers are seeking new solutions to address what scholars have called the "C-section epidemic." Hospital cesarean rates vary tremendously, from 7 to 70 percent of all births. Based on in-depth, semi-structured interviews with 47 obstetricians and family physicians in the United States, I explore one reason for this variation: differences in how physicians perceive and manage risk in American obstetrics. While the dominant model of risk management encourages high levels of intervention and monitoring, I argue that a significant portion of physicians are concerned about high intervention rates in childbirth and are working to reduce cesarean rates and/or the use of monitoring technologies like continuous fetal heart rate monitors. Unlike prior theories of biomedicalization, which suggest that health risks are managed through increased monitoring and intervention, I find that many physicians are resisting this model of risk management by ordering fewer interventions and collecting less information about their patients. These providers acknowledge that interventions designed to mitigate risks may only provide an illusion of control, rather than an actual mastery of risks. By limiting interventions, providers may lose this illusion of control but also mitigate the iatrogenic effects of intervention and continuous monitoring. This alternative approach to risk management is growing in many medical fields and deserves more attention from medical sociologists.

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.