This study used social network methodology to uncover systems complexity by describing and analysing the genomic learning community of Australian Genomics members. Australian Genomics appeared to be driving a significant number of new collaborative relationships in the 2 years since it started operation. Key players were operational staff or clinical geneticists. The finding that social processes, notably hands on learning and group decision-making, are perceived as the greatest influences on members’ genomic practice, underlines the importance of Australian Genomics’ work in facilitating the emergence of a genomic learning community.
Australian Genomic learning community
The interactions of members of Australian Genomics and the genomic learning community were the focus of our study. While our research revealed the shifting nature of relationships in the changing CAS, quantitative social network analysis can only describe the presence or absence of ties not the motivations behind their formation. However new ties are likely formed to access needed expertise. Generally speaking, single practitioners cannot use clinical genomics; it is a highly interdisciplinary pursuit. This is likely to be one of the strongest drivers for participation over time.
People from traditionally insular disciplines are now cooperating and learning from one another: medical specialists were being brought together as genomic testing cuts across silos based on organ group, medical condition or stage of life, and different professions are collaborating across the normal boundaries of research laboratories, testing laboratories, genetic departments and clinical groups. With the development of this national initiative, boundary-spanning roles have emerged, such as bioinformaticians and implementation scientists, each with an area of expertise to contribute to the effective use of clinical genomics: selecting appropriate patients to test; providing an informed consent process, accurate analysis, robust interpretation of variants and the clinical management implications; and returning the results to the patients and their families. A Community Advisory Group and National Advisory Group convened by Australian Genomics also bring valuable insights from the perspective of patients, carers and community members.
Measures
This study did not control for external interventions that occurred during the same time period that may also have been driving collaborative ties, so causality cannot be definitively established. However, evidence that Australian Genomics is perturbing the extant system, and driving this learning community, is the change in the number of ties seen between the “knew before” network (i.e. pre-2016) and the current 2018 “met through Australian Genomics” network. There is a notable increase in the density and the number of ties. Moreover, the “knew before” network has low network centralisation most likely reflecting the national context of State-based Genomic Alliances. Not surprisingly, the “met through Australian Genomics” network is much more centralised, showing the strong influence of the operational staff and the network manager in linking up clusters from across the States and Territories. Geography is still a dominating factor in this network. Meaningful collaboration is driven by proximity [23], and teams delivering patient care might be expected to work closely together. Funding linked to state-based genomic projects will also be a factor in determining collaborations.
Density of the current 2018 network (0.043) is higher than the 2016 (“knew before”) ties network (0.020) indicating an increase in connectivity among these members. Density measures in a social network such as this reflect the necessary trade-off between the benefits and costs of collaboration. The benefits are new ideas and knowledge, but the cost is time to maintain that relationship [24]. Maintaining too many ties may become onerous and counterproductive. There is known value in boundary-spanning ties as a source of innovative ideas from beyond the boundary of one’s close network [25]. The mixing of professions seen here shows this is happening across professions and to a lesser extent across States but the strength of a cohesive team is perhaps more conducive to collective learning [26].
Australian Genomics’ facilitation of socio-professional linkages, while substantial, is not the only influence on clinical genomics. Other significant drivers outside Australian Genomics are the ever-growing number of research outputs both in Australia and overseas requiring cooperation between contributors and rapidly advancing genomic technology [27]. State-based Genomic Alliances also are running successful programs and facilitating knowledge exchange within their States.
Within Australian Genomics, Program work, infrastructure and funding support are likely to be significant drivers of increased collaborative ties. The availability of competitively awarded funding for Flagship projects has spurred some individuals to seek out and assemble expert teams with coherent project plans in order to access funding.
Key players
Key player analysis showed the central role of the Australian Genomics Manager and two clinical geneticists. These key players had the highest in-degree, meaning they were nominated the most as part of respondents’ genomic learning community. Clinical geneticists are active members in many of the working parties and especially the Flagship Projects, valued for their specialist knowledge of genetics, so their centrality in a learning community is not unexpected.
The Australian Genomics Manager had the highest in-degree, out-degree and brokerage score. As found in other similar networks [19, 28], the manager’s role in a number of different working groups and Flagship projects makes them highly visible and knowledgeable about the whole alliance.
The manager is also the obvious “go-to” person for information concerning funding opportunities. Moreover, the high profile of the Manager in attending Program and Flagship meetings means she is accessible to many members. The central coordinating and brokerage role that this manager is enacting contributes significantly to the success of the network functionality.
The effectiveness of central actors (here, the network manager, operational staff and clinical geneticists) as knowledge brokers is usually a combination of both their expertise and their accessibility [29]. We did not explore here whether these central actors are nominated because they are available, or because they have the sought after expertise, or because they are likely to be able to direct enquiries to the appropriate person (acting as a broker). As seen in other networks [19], there are risks here for a key player to be overloaded with requests, or to leave the network, leading to the potential for network fragmentation or for a regression of actor behaviour back into silos, or for a slowing down of collaborative working [19, 28]. Mitigation strategies include sharing the particular role they are enacting (brokerage or source of expertise) across other members, or diffusing responsibilities across the network. For routine requests that the network manager might field, making information available via electronic means may also be considered.
Operational staff (Australian Genomics employed Project Officers and Managers) also figure highly on the key player list, notably as people with high out-degree, a measure of their connectedness to others. Hiring operational staff to communicate across network activities and encourage collaboration was one of Australian Genomics’ tactics to facilitate Flagships and Program activities. From inception, they were empowered to be “ambassadors” for Australian Genomics. Operational staff are known to be influential connectors within similar networks [19, 30]. Their role affords them a broad overview of members and members’ expertise and gives them awareness of specific needs individuals may have (i.e., for resources or research partners). This allows them to act as intermediaries and introduce the two parties. Personal introductions such as these are valued highly and are more likely to result in a fruitful collaborations than a more objective process, say, self-selection of a partner from a website list [31].
Figure 2b shows that Victoria, New South Wales, Western Australia, Queensland and South Australia each have respondents who are shown as outliers who are significantly more influential than the rest of the respondents in their State. This suggests pockets of strong influence spread across these States.
External collaboration
Australia is, of course, not the first country to introduce genomic medicine into health services. There are many national implementation consortiums such as Genomics England [32] and international alliances such as the Global Genomic Medicine Collaborative [33] which have been in operation longer than Australian Genomics. The imperative for global data sharing of genomic data, especially for genes implicated in rare diseases, is considered essential to meet the aims of individual countries [34]. It seemed likely that Australian researchers would be learning from, be influenced by, and be collaborating with key players from overseas and this was borne out in our results. The 93 respondents to this question nominated 355 people from outside of Australia, and this is likely to be an underestimate of true connection levels. Respondents could name up to 10 overseas collaborators. Since 17 respondents filled all 10 spaces, it is possible that key people may have been able to nominate many more than 10.
The largest number of overseas collaborators is from the USA (n = 113) followed by the UK (n = 100). Collaborative links from the UK reflects not only a common language and heritage but a similar health system based on a universal insurance model. The predominantly private health system of the USA in which insurers are key stakeholders makes many aspects of implementation different, but a large population and generous funding for genomic research also makes individuals working in this system valuable collaborative partners.
Many people named as collaborators within Australia but external to Australian Genomics were inside Australian Genomics according to our definition. This implies that Australian Genomics has unclear borders for some. Possibly, some of the people nominated have joint affiliations with a state-based genomic alliance or had only recently moved to a new position within Australian Genomics.
Influences on genomic practice
It is clear from the questions around influences on the respondents’ genomic practice that self-directed activities and social and professional processes predominate. Among the formal sources of influence, self-directed learning from journal articles or conferences and in-service education were the more popular sources. Formal courses were rated “Not at all [influential on my genomic practice]” by 31% of respondents. A formal course was not defined in the question, so we do not know if respondents were thinking of their graduate degrees or specialist training and stating that they had not been helpful, or that more recent, genomic-focussed courses had not been useful.
Among the more informal sources of influence, hands on learning was clearly the most popular with 50% of respondents reporting it influenced their genomic practice “To a large extent,” followed by “Making decisions collectively” with 46%, both socially based processes. In contrast, the social process of being influenced by groups outside one’s immediate team seems less potent. The response to most of the groups listed was lukewarm at best. We tested whether clinically based respondents (which we identified as people working in a Flagship genomics program) might have answered differently to those outside (who we identified as people in non-clinically based working groups or committees), but no significant correlation was found. This could suggest that people with direct patient contact were influenced by much the same factors as people in other positions, such as Community Advisory Group members or laboratory scientists. On the other hand, many of the members of the various governance committees have dual or triple roles: scientists or clinicians also involved in Flagships or Programs and this may suggest that clinical experience was being shared across all groups.
Recommendations
The observation from our study that respondents consider learning in the genomic context to be predominantly a self-directed, social process, combined with the evidence of tie formation across medical specialty and disciplinary silos, has implications for genomic networks, implementation efforts and education strategies.
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(a)
Genomic networks should recognise the value of and actively foster the formation of interdisciplinary learning communities carrying out genomic medicine alongside their support of smaller interdisciplinary teams. Many papers have emphasised that clinical genomics requires the formation of new, small, interdisciplinary teams that combine medical scientists, genetic and medical specialists (e.g., [35]). Our study showed that participants included in their genomic learning community members from other Flagships (meaning members working within another specialty/body system or life-stage) and Programs (including, e.g., implementation scientists and ethicists) showing that links outside the traditional silos of medical specialty and field are also considered important.
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(b)
Educational resources should be tailored to support self-directed and group learning, favouring interactive material over passive information giving sessions. Paul and colleagues [36] note in their systematic review of factors affecting clinicians’ genetic testing practices that clinician education was the only strategy proposed in 24 of the 39 studies identified, and further note that there was scant evidence of its success. Our findings confirm that formal courses are ranked as weak influencers of practice. Members reported self-directed, experiential learning as more effective implying a slower process of up-skilling that needs appropriate resourcing.
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(c)
Service providers and managers should allow quarantined time for both up-skilling and implementation of genomic practice. Building of genomic knowledge and skills through hands on experience and mentoring across traditional silos will take time and needs to be seen as additional to routine service delivery. Moreover, the perceived importance of group decision-making and experiential learning reflects the evolving, emergent nature of the genomic practice field. There are not yet clear protocols to direct practice and the wealth of new research on clinically relevant gene variants presents significant challenges. The importance of varied expertise within teams to provide specialist knowledge and the usefulness of supportive technologies in uncertain situations should be recognised and supported.
Limitations and strengths
Two limitations of social network designs are their reliance on self-report and issues of missing data. Our use of a name interpreter design, that is, providing a list of Australian Genomic members, assisted recall and enabled respondents to quickly select ties. However, the pragmatic decision to format the names of members by working groups or Flagships may have introduced artificial clustering, especially if respondents did not go on to select other groups with whom they were working.
Care was taken to ensure the relationship question was not ambiguous in any way: crafted over several sessions by an expert panel and incorporating feedback from piloted versions. Missing data from non-respondents is partly mitigated by the answers of other respondents. If Person X does not take part but Person Y does and nominates Person X as a collaborator, Person X will be included. In this way, all 384 members could be represented in the sociograms. Whole network studies are most affected by missing data when key players do not respond. If we consider operational staff (n = 5), and project leads (n = 31) as being the most active in Australian Genomics, all but three of these people responded, giving us confidence the data is adequate, with sufficient fidelity to be meaningful. This was partly achieved through a targeted reminder email sent from Australian Genomics.
Operations, 
KidGen Renal Genetics, 
Acute Care Genomic Testing, 
Genetic Immunology, 
Cardiovascular Genetic Disorders, 
National Steering Committee, 
Acute Lymphoblastic Leukaemia. b Genomic learning community of respondents before Australian Genomics Health Alliance started operation in 2016. Colour of node shows professional qualification of the respondent: 
Medical scientist, 
Genetic specialist, 
Other (e.g. consumer, student, operational staff), 
Medical specialist, 
Researcher. c “Met through Australian Genomics” network: genomic learning community in 2018 after 2 years’ operation of Australian Genomics Health Alliance. Colour of node (see b) shows professional qualification of respondent. d Collaborators from outside Australian Genomics, resident in Australia. Nodes here are respondents to this question and the people they nominated. Colour of node indicated State or Territory of Australia: 
New South Wales, 
Queensland, 
Victoria, 
South Australia, 
Australian Capital Territory, 
Western Australia, 
Tasmania
