All medical information is subject to uncertainty, but the practice of medical genetics is characterized by unique magnitudes and forms of uncertainty. In fact, for many patients, a “positive” screening result still only represents a risk estimate, which is itself irreducibly uncertain. As exome- and genome-scale tests begin to replace smaller panels, and as interpretation driven by clinical indication gives way to the return of “incidental” findings and truly healthy genomes, patients will face an even greater chances of receiving uncertain results.
Project Narrative Currently, there are marked differences in variant classification among clinical laboratories, and genetic variant classification is currently in flux with improvements in the access to ethnically diverse reference data and new algorithms to predict the pathogenicity of variants. As variants are re-classified over time, there is currently no definitive guidance from professional organizations about how to handle this variant reclassification and who has the duty to re-classify variants and what those obligations are.
The overall goal of this project is to understand how to encourage and enable people who are developing artificial intelligence for personalized health care to be aware of values in their daily practice. We will examine actual practices and contexts in which design decisions are made for precision medicine applications, and use this information to design group-based workshop exercises to increase awareness of values.
Project Narrative This study would be the first to develop an initial bioethics framework to meet a critical gap in biomedical data modeling activities, where the downstream consequences of developing data models without careful and comprehensive review of ethical issues can be severe?not least because poorly developed data models have the potential to impact adversely the health of individuals, groups, and communities.
Obesity rates in the United States have escalated in recent decades and present a growing challenge in public health prevention efforts. Advances in genomics have begun to shed light on the genetic contributions to obesity. At present, it is unknown whether information about one's personal genetic predisposition to obesity will add value to traditional risk communication efforts and increase the likelihood that individuals will engage in health behaviors to reduce obesity risk.
Recent progress in genomic science has been accompanied by great expectations that we are on the verge of a medical revolution where genetic knowledge of the complex interaction between multiple genes and the environmental/behavioral factors impacting their expression, will redefine illness and health, guiding risk prediction, disease diagnosis and treatment strategies. As yet, with a few notable exceptions, the promise of genetically driven diagnoses and treatment remains largely theoretical.
Genomics-based technologies are increasingly used in clinical care and are highly relevant to public health because of their potential use in assessing risk, diagnosing, and developing treatment plans. Access to genomic tests often depends on cost and coverage of services by the health plan. No studies, to our knowledge, identify whether access and reimbursement issues relating to guideline-recommended pharmacogenomic tests exist, and what the potential implications of barriers to access and/or differential access for patients, providers, and society are.
As genomic sequence data are being produced faster and at lower cost, the most significant challenge in clinical genetic testing today is variant classification. Currently, there are marked differences in variant classification among clinical laboratories, with clinically significant discrepancies in 29% of variants interpreted. Variants that were previously categorized as pathogenic are now known to be benign with the increasing availability of more ethnically diverse reference data, and this is issue is more common for individuals of non-European ancestry.