Direct-to-consumer genetic testing (DTC-GT) companies are engaging health consumers in unprecedented ways and leveraging the genetic information they collect to further engage health companies. This has produced controversy about DTC-GT consumer expectations, standards, and perceptions of privacy. In this commentary, we highlight recent events involving DTC-GT companies and controversy about privacy that followed those events and discuss recent studies that have explored DTC-GT consumer concerns about privacy. We discuss DTC-GT company standards of upholding consumer privacy and the general accessibility of DTC-GT company terms of use agreements and privacy policies that are written at reading levels above that of many consumers. We conclude that broader discussions and more research are needed to identify DTC-GT consumer concerns about and expectations of privacy. We anticipate that our recommendations will advance discussions on consumer privacy expectations and protections in an era of increasing engagement in DTC-GT.

Background: Artificial intelligence (AI) and machine learning (ML) technology design and development continues to be rapid, despite major limitations in its current form as a practice and discipline to address all sociohumanitarian issues and complexities. From these limitations emerges an imperative to strengthen AI and ML literacy in underserved communities and build a more diverse AI and ML design and development workforce engaged in health research. Objective: AI and ML has the potential to account for and assess a variety of factors that contribute to health and disease and to improve prevention, diagnosis, and therapy. Here, we describe recent activities within the Artificial Intelligence/Machine Learning Consortium to Advance Health Equity and Researcher Diversity (AIM-AHEAD) Ethics and Equity Workgroup (EEWG) that led to the development of deliverables that will help put ethics and fairness at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. Methods: The AIM-AHEAD EEWG was created in 2021 with 3 cochairs and 51 members in year 1 and 2 cochairs and ~40 members in year 2. Members in both years included AIM-AHEAD principal investigators, coinvestigators, leadership fellows, and research fellows. The EEWG used a modified Delphi approach using polling, ranking, and other exercises to facilitate discussions around tangible steps, key terms, and definitions needed to ensure that ethics and fairness are at the forefront of AI and ML applications to build equity in biomedical research, education, and health care. Results: The EEWG developed a set of ethics and equity principles, a glossary, and an interview guide. The ethics and equity principles comprise 5 core principles, each with subparts, which articulate best practices for working with stakeholders from historically and presently underrepresented communities. The glossary contains 12 terms and definitions, with particular emphasis on optimal development, refinement, and implementation of AI and ML in health equity research. To accompany the glossary, the EEWG developed a concept relationship diagram that describes the logical flow of and relationship between the definitional concepts. Lastly, the interview guide provides questions that can be used or adapted to garner stakeholder and community perspectives on the principles and glossary. Conclusions: Ongoing engagement is needed around our principles and glossary to identify and predict potential limitations in their uses in AI and ML research settings, especially for institutions with limited resources. This requires time, careful consideration, and honest discussions around what classifies an engagement incentive as meaningful to support and sustain their full engagement. By slowing down to meet historically and presently underresourced institutions and communities where they are and where they are capable of engaging and competing, there is higher potential to achieve needed diversity, ethics, and equity in AI and ML implementation in health research.

Familial Hypercholesterolemia (FH) is an underdiagnosed condition in the United States (US) and globally, affecting an estimated 1/250 individuals. It is a genetic risk factor for premature cardiovascular disease and is responsible for an estimated 600,000 to 1.2 million preventable vascular events. Studies show that FH genetic testing can identify a causal gene variant in 60 to 80% of clinically suspected FH cases. However, FH genetic testing is currently underutilized in clinical settings in the US despite clinical recommendations and evidence supporting its use. Reasons for underutilization are not well understood. We conducted a literature review in the PubMed/MEDLINE database and eight peer-reviewed journals. After filtering for and reviewing 2340 articles against our inclusion criteria, we included nine commentaries or expert opinions and eight empirical studies reported between January 2014 and March 2019 in our review. After applying the Consolidated Framework for Implementation Research (CFIR), we identified a total of 26 potential barriers and 15 potential facilitators (estimated barrier to facilitator ratio of 1.73). We further estimated ratios of potential barriers to facilitators for each CFIR domain (Characteristics of Intervention, Outer Setting, Inner Setting, Characteristics of Individuals, and Process). Findings derived from our systematic approach to the literature and calculations of estimated baseline ratios of barriers and facilitators can guide future research to understand FH genetic testing implementation in diverse clinical settings. Our systematic approach to the CFIR could also be used as a model to understand or compare barriers and facilitators to other evidence-based genetic testing processes in health care settings in the US and abroad.

This Viewpoint discusses privacy and discrimination issues raised when direct-to-consumer (DTC) genetic testing companies give law enforcement agencies access t

Mental health remains an urgent global priority, alongside efforts to address underlying social determinants of health (SDoH) that contribute to the onset or exacerbate mental illness. SDoH factors can be captured in the form of International Classification of Disease, Tenth Revision, Clinical Modification (ICD-10-CM), SDoH Z codes. In this scoping review, we describe current SDoH Z-code documentation practices, with a focus on mental health care contexts. Among 2 743 061 374 health care encounters noted across 12 studies in the United States, SDoH Z-code documentation rates ranged from 0.5% to 2.4%. Documentation often involved patients under 64 years of age who are publicly insured and experience comorbidities, including depression, bipolar disorder and schizophrenia, chronic pulmonary disease, and substance abuse disorders. Documentation varied across hospital types, number of beds per facility, patient race/ethnicity, and geographic region. Variation was observed regarding patient sex/gender, although SDoH Z codes were more frequently documented for males. Documentation was most observed in government, nonfederal, and private not-for-profit hospitals. From these insights, we offer policy and practice recommendations, as well as considerations for patient data privacy, security, and confidentiality, to incentivize more routine documentation of Z codes to better assist patients with complex mental health care needs.

Cardiovascular disease (CVD) is the leading cause of death in the United States (US), with familial hypercholesterolemia (FH) being a major inherited and genetic risk factor for premature CVD and atherosclerosis. Genetic testing has helped patients and providers confirm the presence of known pathogenic and likely pathogenic variations in FH-associated genes. Key organizations, such as the Centers for Disease Control and Prevention (CDC), American Heart Association (AHA), FH Foundation, and National Lipid Association (NLA), have recognized the clinical utility of FH genetic testing. However, FH genetic testing is underutilized in clinical practice in the US for reasons that are underexplored through the lens of implementation science. In this commentary, we discuss seven key implementation challenges that must be overcome to strengthen the clinical adoption of FH genetic testing in the US. These implementation challenges center on evidence of cost-effectiveness, navigating patient and provider preferences and concerns, gender and ethnic diversity and representation in genetic testing, and establishing clinical consensus around FH genetic testing based on the latest and most relevant research findings. Overcoming these implementation challenges is imperative to the mission of reducing CVD risk in the US.