Whole exome/genome sequencing (WES/WGS) is increasingly offered to ostensibly healthy individuals. Understanding the motivations and concerns of research participants seeking out personal WGS and their preferences regarding return-of-results and data sharing will help optimize protocols for WES/WGS. Baseline interviews including both qualitative and quantitative components were conducted with research participants (n=35) in the HealthSeq project, a longitudinal cohort study of individuals receiving personal WGS results. Data sharing preferences were recorded during informed consent. In the qualitative interview component, the dominant motivations that emerged were obtaining personal disease risk information, satisfying curiosity, contributing to research, self-exploration and interest in ancestry, and the dominant concern was the potential psychological impact of the results. In the quantitative component, 57% endorsed concerns about privacy. Most wanted to receive all personal WGS results (94%) and their raw data (89%); a third (37%) consented to having their data shared to the Database of Genotypes and Phenotypes (dbGaP). Early adopters of personal WGS in the HealthSeq project express a variety of health- and non-health-related motivations. Almost all want all available findings, while also expressing concerns about the psychological impact and privacy of their results.

The use of machine learning (ML) in healthcare raises numerous ethical concerns, especially as models can amplify existing health inequities. Here, we outline ethical considerations for equitable ML in the advancement of healthcare. Specifically, we frame ethics of ML in healthcare through the lens of social justice. We describe ongoing efforts and outline challenges in a proposed pipeline of ethical ML in health, ranging from problem selection to postdeployment considerations. We close by summarizing recommendations to address these challenges. Expected final online publication date for the Annual Review of Biomedical Data Science, Volume 4 is July 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Integrating genomic information into clinical care and the electronic health record can facilitate personalized medicine through genetically guided clinical decision support. Stakeholder involvement is critical to the success of these implementation efforts. Prior work on implementation of clinical information systems provides broad guidance to inform effective engagement strategies. We add to this evidence-based recommendations that are specific to issues at the intersection of genomics and the electronic health record. We describe stakeholder engagement strategies employed by the Electronic Medical Records and Genomics Network, a national consortium of US research institutions funded by the National Human Genome Research Institute to develop, disseminate, and apply approaches that combine genomic and electronic health record data. Through select examples drawn from sites of the Electronic Medical Records and Genomics Network, we illustrate a continuum of engagement strategies to inform genomic integration into commercial and homegrown electronic health records across a range of health-care settings. We frame engagement as activities to consult, involve, and partner with key stakeholder groups throughout specific phases of health information technology implementation. Our aim is to provide insights into engagement strategies to guide genomic integration based on our unique network experiences and lessons learned within the broader context of implementation research in biomedical informatics. On the basis of our collective experience, we describe key stakeholder practices, challenges, and considerations for successful genomic integration to support personalized medicine.

Background Variants of the APOL1 gene increase risk for kidney failure 10- fold, and are nearly exclusively found in people with African ancestry. To translate genomic discoveries into practice, we gathered information about effects and challenges incorporating genetic risk in clinical care. Methods An academic- community- clinical team tested 26 adults with self- reported African ancestry for APOL1 variants, conducting in- depth interviews about patients' beliefs and attitudes toward genetic testing- before, immediately, and 30 days after receiving test results. We used constant comparative analysis of interview transcripts to identify themes. Results Themes included: Knowledge of genetic risk for kidney failure may motivate providers and patients to take hypertension more seriously, rather than inspiring fatalism or anxiety. Having genetic risk for a disease may counter stereotypes of Blacks as non- adherent or low- literate, rather than exacerbate stereotypes. Conclusion Populations most likely to benefit from genomic research can inform strategies for genetic testing and future research.

There has been limited community engagement in the burgeoning field of genomics research. In the wake of a new discovery of genetic variants that increase the risk of kidney failure and are almost unique to people of African ancestry, community and clinical leaders in Harlem, New York, formed a community board to inform the direction of related research. The board advised all aspects of a study to assess the impact of testing for these genetic variants at primary care sites that serve diverse populations, including explaining genetic risk to participants. By reflecting on the board’s experiences, we found that community voices can have tangible impact on research that navigates the controversial intersection of race, ancestry, and genomics by heightening vigilance, fostering clear communication between researchers and the community, and encouraging researchers to cede some control. Our reflections and work provide a strong justification for longitudinal community partnerships in genomics research.

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The exponential growth of health data from devices, health applications, and electronic health records coupled with the development of data analysis tools such as machine learning offer opportunities to leverage these data to mitigate health disparities. However, these tools have also been shown to exacerbate inequities faced by marginalized groups. Focusing on health disparities should be part of good machine learning practice and regulatory oversight of software as medical devices. Using the Food and Drug Administration (FDA)'s proposed framework for regulating machine learning tools in medicine, I show that addressing health disparities during the premarket and postmarket stages of review can help anticipate and mitigate group harms.