Knowledge, attitudes, and values among physicians working with clinical genomics: a survey of medical oncologists.

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Abstract

BACKGROUND:

It has been over a decade since the completion of the Human Genome Project (HGP), genomic sequencing technologies have yet to become parts of standard of care in Canada. This study investigates medical oncologists' (MOs) genomic literacy and their experiences based on their participation in a cancer genomics trial in British Columbia, Canada.

METHODS:

The authors conducted a survey of MOs from British Columbia, Canada (n = 31, 52.5% response rate), who are actively involved in a clinical genomics trial called Personalized Onco-Genomics (POG). The authors also measured MOs' level of genomic knowledge and attitudes about clinical genomics in cancer medicine.

RESULTS:

The findings show a low to moderate level of genomic literacy among MOs. MOs located outside the Vancouver area (the major urban center) reported less knowledge about new genetics technologies compared to those located in the major metropolitan area (26.7 vs 73.3%, P < 0.07, Fisher exact test). Forty-two percent of all MOs thought medical training programs do not offer enough genomic training. The majority of the respondents thought genomics will have major impact on drug discovery (67.7%), and treatment selection (58%) in the next 5 years. They also thought the major challenges are cost (61.3%), patient genomic literacy (48.3%), and clinical utility of genomics (42%).

CONCLUSIONS:

The data suggest a high need to increase genomic literacy among MOs and other doctors in medical school training programs and beyond, especially to physicians in regional areas who may need more educational interventions. Initiatives like POG play a critical role in the education of MOs and the integration of big data clinical genomics into cancer care.

KEYWORDS:

Applied cancer genomics; Cancer clinical trials; Cancer genomics; Genomic literacy; Genomic sciences; Genomic technologies; Health education; Health professionals; Medical big data