NIH Research Festival
–
A taxonomy of medical uncertainties in clinical genome sequencing
– Poster Session I
3:00 – 4:30 p.m.
FAES Terrace
NHGRI
GEN-4
Authors
- KL Umstead
- PK Han
- LG Biesecker
- BA Bernhardt
- RC Green
- S Joffe
- B Koenig
- I Krantz
- BB Biesecker
Abstract
While genomic technologies afford clinicians an unprecedented capacity for understanding pathology and treating individuals, they also introduce complex and diverse medical uncertainties. These uncertainties permeate all levels of genetic medicine, having profound effects on, for example, researchers’ ability to establish representative models, the ability of laboratory personnel to correctly interpret testing results, and counselors’ ability to communicate this information to patients and families. Delineating the intricacies of these uncertainties is crucial to maximizing benefit and minimizing consequence of the incorporation of clinical genome sequencing into standard practice. The objective of this project is to promote a clear and consistent understanding of medical uncertainties in genomics through dissemination of a parsimonious, systematic taxonomy. This study built upon a three-dimensional taxonomy of medical uncertainties published previously by Han and colleagues, which was refined and expanded using thematic analysis of a series of qualitative interviews with leaders in the field of clinical genomics. Finally, an interactive online platform has been launched for the purpose of both distributing the taxonomy and encouraging its further development and eventual expansion. As exemplified through an illustrative vignette of its applicability, the taxonomy facilitates thorough consideration of the many uncertainties that may affect an individual in the laboratory or the clinic. Genomic medicine will never be entirely certain. At a minimum, isolating the uncertainties frequently experienced by various stakeholders could serve to establish reasonable expectations, guide future research, and optimize the patient experience.
Scientific Focus Area: Genetics and Genomics
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