Measuring physician practice, preparedness and preferences for genomic medicine: a national survey

Abstract

Objective: Even as genomic medicine is implemented globally, there remains a lack of rigorous, national assessments of physicians' current genomic practice and continuing genomics education needs. The aim of this study was to address this gap.

Design: A cross-sectional survey, informed by qualitative data and behaviour change theory, to assess the current landscape of Australian physicians' genomic medicine practice, perceptions of proximity and individual preparedness, and preferred models of practice and continuing education. The survey was advertised nationally through 10 medical colleges, 24 societies, 62 hospitals, social media, professional networks and snowballing.

Results: 409 medical specialists across Australia responded, representing 30 specialties (majority paediatricians, 20%), from mainly public hospitals (70%) in metropolitan areas (75%). Half (53%) had contacted their local genetics services and half (54%) had ordered or referred for a gene panel or exome/genome sequencing test in the last year. Two-thirds (67%) think genomics will soon impact their practice, with a significant preference for models that involved genetics services (p<0.0001). Currently, respondents mainly perform tasks associated with pretest family history taking and counselling, but more respondents expect to perform tasks at all stages of testing in the future, including tasks related to the test itself, and reporting results. While one-third (34%) recently completed education in genomics, only a quarter (25%) felt prepared to practise. Specialists would like (more) education, particularly on genomic technologies and clinical utility, and prefer this to be through varied educational strategies.

Conclusions: This survey provides data from a breadth of physician specialties that can inform models of genetic service delivery and genomics education. The findings support education providers designing and delivering education that best meet learner needs to build a competent, genomic-literate workforce. Further analyses are underway to characterise early adopters of genomic medicine to inform strategies to increase engagement.

Keywords: Genetics; Health services administration & management; Medical education & training.

Figure 1

Number of survey attempts shown with recruitment strategies and timelines after pilot data were complete (n=41). Recruitment start dates are shown and overlapped from March to October 2019 (as described in the methods). Snowball recruitment may have continued beyond these periods (eg, forwarding a newsletter or retweeting) but this could not be monitored.

Figure 2

Proportion of each reported primary specialty in the sample (n=409) grouped by primary medical college affiliation. Grey bars signify specialties where proportions were representative of the medical specialist population when compared with reference data. The black bar signifies a specialty which was over-represented (physicians; p<0.0001). White bars signify specialties which were under-represented: anaesthesiology (p=0.002), psychiatry (p<0.0001) and surgery (p<0.0001). The reference data did not include a classification for ‘rural and remote medicine’, so representativeness could not be determined for this specialty (pale grey bar).

Figure 3

Average confidence about genomic concepts and skills on a scale of 1 ‘not at all confident’, 5 ‘neutral’ to 10 ‘very confident’ (n=273). Boxes represent the interquartile ranges with minimum and maximum value; medians are shown as white bars.

Figure 4

Steps in genomic testing that respondents (n=314) currently perform (white bars) compared with steps they expect to perform in the future, if they had adequate support, education and training (black bars). Non-clinical steps are indicated by ^a. Differences between proportions for ‘currently perform’ and ‘expect to perform’ are indicated by *p=0.004, **p=0.001, ***p=0.0006, ****p<0.0001. The difference for the first step—elicit genetic information through family history—was not significant (p=0.3). The full wording of each step is provided in online supplemental table S5.