Building a learning community of Australian clinical genomics: a social network study of the Australian Genomic Health Alliance

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Background: Adopting clinical genomics represents a major systems-level intervention requiring diverse expertise and collective learning. The Australian Genomic Health Alliance (Australian Genomics) is strategically linking members and partner organisations to lead the integration of genomic medicine into healthcare across Australia. This study aimed to map and analyse interconnections between members-a key feature of complexity-to capture the collaborations among the genomic community, document learning, assess Australian Genomics' influence and identify key players. Methods: An online, whole network study collected relational data from members asking them about two time points: baseline, before Australian Genomics started operation in 2016 and current in 2018. Likert style questions assessed the influence of various sources of knowledge on the respondents' genomic practice. A secure link to the online questionnaire was distributed to all members of Australian Genomics during May 2018. Social network data was analysed and visually constructed using Gephi 0.9.2 software, and Likert questions were analysed using chi-squared computations in SPSS. The project was given ethical approval. Results: Response rate was 57.81% (222/384). The genomic learning community within Australian Genomics was constructed from the responses of participants. There was a growth in ties from pre-2016 (2925 ties) to 2018 (6381 ties) and an increase in density (0.020 to 0.043) suggesting the strong influence of Australian Genomics in creating this community. Respondents nominated 355 collaborative partners from 24 different countries outside of Australia and 328 partners from within Australia but outside the alliance. Key players were the Australian Genomics Manager, two clinical geneticists and four Operational staff members. Most influential sources of learning were hands on learning, shared decision making, journal articles and conference presentations in contrast to formal courses. Conclusions: The successful implementation of clinical genomics requires the engagement of multidisciplinary teams across a range of conditions and expertise. Australian Genomics is shown to be facilitating this collaborative process by strategically building a genomic learning community. We demonstrate the importance of social processes in building complex networks as respondents name "hands on learning" and "making group decisions" the most potent influences of their genomic practice. This has implications for genomic implementation, education and work force strategies.

LanguageEnglish
Article number44
Pages1-13
Number of pages13
JournalBMC Medicine
Volume17
Issue number1
DOIs
Publication statusPublished - 22 Feb 2019

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Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • Complexity science
  • Dissemination
  • Genomics
  • Implementation
  • Learning community
  • Social network analysis
  • Systems change

Cite this

@article{269baa60595b4f0d80917ff49b845be8,
title = "Building a learning community of Australian clinical genomics: a social network study of the Australian Genomic Health Alliance",
abstract = "Background: Adopting clinical genomics represents a major systems-level intervention requiring diverse expertise and collective learning. The Australian Genomic Health Alliance (Australian Genomics) is strategically linking members and partner organisations to lead the integration of genomic medicine into healthcare across Australia. This study aimed to map and analyse interconnections between members-a key feature of complexity-to capture the collaborations among the genomic community, document learning, assess Australian Genomics' influence and identify key players. Methods: An online, whole network study collected relational data from members asking them about two time points: baseline, before Australian Genomics started operation in 2016 and current in 2018. Likert style questions assessed the influence of various sources of knowledge on the respondents' genomic practice. A secure link to the online questionnaire was distributed to all members of Australian Genomics during May 2018. Social network data was analysed and visually constructed using Gephi 0.9.2 software, and Likert questions were analysed using chi-squared computations in SPSS. The project was given ethical approval. Results: Response rate was 57.81{\%} (222/384). The genomic learning community within Australian Genomics was constructed from the responses of participants. There was a growth in ties from pre-2016 (2925 ties) to 2018 (6381 ties) and an increase in density (0.020 to 0.043) suggesting the strong influence of Australian Genomics in creating this community. Respondents nominated 355 collaborative partners from 24 different countries outside of Australia and 328 partners from within Australia but outside the alliance. Key players were the Australian Genomics Manager, two clinical geneticists and four Operational staff members. Most influential sources of learning were hands on learning, shared decision making, journal articles and conference presentations in contrast to formal courses. Conclusions: The successful implementation of clinical genomics requires the engagement of multidisciplinary teams across a range of conditions and expertise. Australian Genomics is shown to be facilitating this collaborative process by strategically building a genomic learning community. We demonstrate the importance of social processes in building complex networks as respondents name {"}hands on learning{"} and {"}making group decisions{"} the most potent influences of their genomic practice. This has implications for genomic implementation, education and work force strategies.",
keywords = "Complexity science, Dissemination, Genomics, Implementation, Learning community, Social network analysis, Systems change",
author = "Long, {Janet C.} and Chiara Pomare and Stephanie Best and Tiffany Boughtwood and Kathryn North and Ellis, {Louise A.} and Kate Churruca and Jeffrey Braithwaite",
note = "Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.",
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Building a learning community of Australian clinical genomics : a social network study of the Australian Genomic Health Alliance. / Long, Janet C.; Pomare, Chiara; Best, Stephanie; Boughtwood, Tiffany; North, Kathryn; Ellis, Louise A.; Churruca, Kate; Braithwaite, Jeffrey.

In: BMC Medicine, Vol. 17, No. 1, 44, 22.02.2019, p. 1-13.

Research output: Contribution to journalArticleResearchpeer-review

TY - JOUR

T1 - Building a learning community of Australian clinical genomics

T2 - BMC Medicine

AU - Long, Janet C.

AU - Pomare, Chiara

AU - Best, Stephanie

AU - Boughtwood, Tiffany

AU - North, Kathryn

AU - Ellis, Louise A.

AU - Churruca, Kate

AU - Braithwaite, Jeffrey

N1 - Copyright the Author(s) 2019. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

PY - 2019/2/22

Y1 - 2019/2/22

N2 - Background: Adopting clinical genomics represents a major systems-level intervention requiring diverse expertise and collective learning. The Australian Genomic Health Alliance (Australian Genomics) is strategically linking members and partner organisations to lead the integration of genomic medicine into healthcare across Australia. This study aimed to map and analyse interconnections between members-a key feature of complexity-to capture the collaborations among the genomic community, document learning, assess Australian Genomics' influence and identify key players. Methods: An online, whole network study collected relational data from members asking them about two time points: baseline, before Australian Genomics started operation in 2016 and current in 2018. Likert style questions assessed the influence of various sources of knowledge on the respondents' genomic practice. A secure link to the online questionnaire was distributed to all members of Australian Genomics during May 2018. Social network data was analysed and visually constructed using Gephi 0.9.2 software, and Likert questions were analysed using chi-squared computations in SPSS. The project was given ethical approval. Results: Response rate was 57.81% (222/384). The genomic learning community within Australian Genomics was constructed from the responses of participants. There was a growth in ties from pre-2016 (2925 ties) to 2018 (6381 ties) and an increase in density (0.020 to 0.043) suggesting the strong influence of Australian Genomics in creating this community. Respondents nominated 355 collaborative partners from 24 different countries outside of Australia and 328 partners from within Australia but outside the alliance. Key players were the Australian Genomics Manager, two clinical geneticists and four Operational staff members. Most influential sources of learning were hands on learning, shared decision making, journal articles and conference presentations in contrast to formal courses. Conclusions: The successful implementation of clinical genomics requires the engagement of multidisciplinary teams across a range of conditions and expertise. Australian Genomics is shown to be facilitating this collaborative process by strategically building a genomic learning community. We demonstrate the importance of social processes in building complex networks as respondents name "hands on learning" and "making group decisions" the most potent influences of their genomic practice. This has implications for genomic implementation, education and work force strategies.

AB - Background: Adopting clinical genomics represents a major systems-level intervention requiring diverse expertise and collective learning. The Australian Genomic Health Alliance (Australian Genomics) is strategically linking members and partner organisations to lead the integration of genomic medicine into healthcare across Australia. This study aimed to map and analyse interconnections between members-a key feature of complexity-to capture the collaborations among the genomic community, document learning, assess Australian Genomics' influence and identify key players. Methods: An online, whole network study collected relational data from members asking them about two time points: baseline, before Australian Genomics started operation in 2016 and current in 2018. Likert style questions assessed the influence of various sources of knowledge on the respondents' genomic practice. A secure link to the online questionnaire was distributed to all members of Australian Genomics during May 2018. Social network data was analysed and visually constructed using Gephi 0.9.2 software, and Likert questions were analysed using chi-squared computations in SPSS. The project was given ethical approval. Results: Response rate was 57.81% (222/384). The genomic learning community within Australian Genomics was constructed from the responses of participants. There was a growth in ties from pre-2016 (2925 ties) to 2018 (6381 ties) and an increase in density (0.020 to 0.043) suggesting the strong influence of Australian Genomics in creating this community. Respondents nominated 355 collaborative partners from 24 different countries outside of Australia and 328 partners from within Australia but outside the alliance. Key players were the Australian Genomics Manager, two clinical geneticists and four Operational staff members. Most influential sources of learning were hands on learning, shared decision making, journal articles and conference presentations in contrast to formal courses. Conclusions: The successful implementation of clinical genomics requires the engagement of multidisciplinary teams across a range of conditions and expertise. Australian Genomics is shown to be facilitating this collaborative process by strategically building a genomic learning community. We demonstrate the importance of social processes in building complex networks as respondents name "hands on learning" and "making group decisions" the most potent influences of their genomic practice. This has implications for genomic implementation, education and work force strategies.

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KW - Learning community

KW - Social network analysis

KW - Systems change

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