We examined the Institutional Review Board (IRB) process at 9 academic institutions in the electronic Medical Records and Genomics (eMERGE) Network, for proposed electronic health record-based genomic medicine studies, to identify common questions and concerns. Sequencing of 109 disease related genes and genotyping of 14 actionable variants is being performed in ~28,100 participants from the 9 sites. Pathogenic/likely pathogenic variants in actionable genes are being returned to study participants. We examined each site’s research protocols, informed-consent materials, and interactions with IRB staff. Research staff at each site completed questionnaires regarding their IRB interactions. The time to prepare protocols for IRB submission, number of revisions and time to approval ranged from 10–261 days, 0–11, and 11–90 days, respectively. IRB recommendations related to the readability of informed consent materials, specifying the full range of potential risks, providing options for receiving limited results or withdrawal, sharing of information with family members, and establishing the mechanisms to answer participant questions. IRBs reviewing studies that involve the return of results from genomic sequencing have a diverse array of concerns, and anticipating these concerns can help investigators to more effectively engage IRBs.
Objectives:
To identify clinically actionable genetic variants from targeted sequencing of 68 disease-related genes, estimate their penetrance, and assess the impact of disclosing results to participants and providers.
Patients and Methods:
The Return of Actionable Variants Empirical (RAVE) Study investigates outcomes following the return of pathogenic/likely pathogenic (P/LP) variants in 68 disease-related genes. The study was initiated in December 2016 and is ongoing. Targeted sequencing was performed in 2533 individuals with hyperlipidemia or colon polyps. The electronic health records (EHRs) of participants carrying P/LP variants in 36 cardiovascular disease (CVD) genes were manually reviewed to ascertain the presence of relevant traits. Clinical outcomes, health care utilization, family communication, and ethical and psychosocial implications of disclosure of genomic results are being assessed by surveys, telephone interviews, and EHR review.
Results:
Of 29,208 variants in the 68 genes, 1915 were rare (frequency <1%) and putatively functional, and 102 of these (60 in 36 CVD genes) were labeled P/LP based on the American College of Medical Genetics and Genomics framework. Manual review of the EHRs of participants (n=73 with P/LP variants in CVD genes) revealed that 33 had the expected trait(s); however, only 6 of 45 participants with non–familial hypercholesterolemia (FH) P/LP variants had the expected traits.
Conclusion:
Expected traits were present in 13% of participants with P/LP variants in non-FH CVD genes, suggesting low penetrance; this estimate may change with additional testing performed as part of the clinical evaluation. Ongoing analyses of the RAVE Study will inform best practices for genomic medicine.
Background While use of artificial intelligence (AI) in healthcare is increasing, little is known about how patients view healthcare AI. Characterizing patient attitudes and beliefs about healthcare AI and the factors that lead to these attitudes can help ensure patient values are in close alignment with the implementation of these new technologies. Methods We conducted 15 focus groups with adult patients who had a recent primary care visit at a large academic health center. Using modified grounded theory, focus-group data was analyzed for themes related to the formation of attitudes and beliefs about healthcare AI. Results When evaluating AI in healthcare, we found that patients draw on a variety of factors to contextualize these new technologies including previous experiences of illness, interactions with health systems and established health technologies, comfort with other information technology, and other personal experiences. We found that these experiences informed normative and cultural beliefs about the values and goals of healthcare technologies that patients applied when engaging with AI. The results of this study form the basis for a theoretical framework for understanding patient orientation to applications of AI in healthcare, highlighting a number of specific social, health, and technological experiences that will likely shape patient opinions about future healthcare AI applications. Conclusions Understanding the basis of patient attitudes and beliefs about healthcare AI is a crucial first step in effective patient engagement and education. The theoretical framework we present provides a foundation for future studies examining patient opinions about applications of AI in healthcare.
a Mean age of 55 years (range, 32-97 years). Mean licensed years of practice of 25 years (range, 2-61 years). b Includes respiratory therapists, estheticians, clinical laboratory technicians, athletic trainers, radiology practitioner assistants, and support staff.
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