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Sharing precision medicine data with private industry: Outcomes of a citizens��� jury in Singapore

Journal Article


Abstract


  • Precision medicine is an emerging approach to treatment and disease prevention that relies on linkages between very large datasets of health information that is shared amongst researchers and health professionals. While studies suggest broad support for sharing precision medicine data with researchers at publicly funded institutions, there is reluctance to share health information with private industry for research and development. As the private sector is likely to play an important role in generating public benefits from precision medicine initiatives, it is important to understand what the concerns are and how they might be mitigated. This study reports outcomes of a deliberative method of citizen engagement in Singapore that asked whether sharing precision medicine data with private industry would be permissible, and if so, under what circumstances. Findings from this citizens��� jury suggest sharing with industry would be permissible under certain conditions that are set out in nine recommendations. Implications of the recommendations and their underlying assumptions for policy decision makers are discussed. This study aligns with prior international studies which found conditional acceptance for data sharing with private industry, a public benefit requirement, specific reluctance to share with insurance companies and an emphasis on accountability and transparency to demonstrate trustworthiness. However, our results differ from prior studies in that opt-in consent did not dominate the deliberations as jurors were able to set it aside as an assumed prerequisite for participation in a precision medicine programme.

Publication Date


  • 2022

Citation


  • Ballantyne, A., Lysaght, T., Toh, H. J., Ong, S., Lau, A., Owen Schaefer, G., . . . Braunack-Mayer, A. (2022). Sharing precision medicine data with private industry: Outcomes of a citizens��� jury in Singapore. Big Data and Society, 9(1). doi:10.1177/20539517221108988

Scopus Eid


  • 2-s2.0-85133154324

Volume


  • 9

Issue


  • 1

Place Of Publication


Abstract


  • Precision medicine is an emerging approach to treatment and disease prevention that relies on linkages between very large datasets of health information that is shared amongst researchers and health professionals. While studies suggest broad support for sharing precision medicine data with researchers at publicly funded institutions, there is reluctance to share health information with private industry for research and development. As the private sector is likely to play an important role in generating public benefits from precision medicine initiatives, it is important to understand what the concerns are and how they might be mitigated. This study reports outcomes of a deliberative method of citizen engagement in Singapore that asked whether sharing precision medicine data with private industry would be permissible, and if so, under what circumstances. Findings from this citizens��� jury suggest sharing with industry would be permissible under certain conditions that are set out in nine recommendations. Implications of the recommendations and their underlying assumptions for policy decision makers are discussed. This study aligns with prior international studies which found conditional acceptance for data sharing with private industry, a public benefit requirement, specific reluctance to share with insurance companies and an emphasis on accountability and transparency to demonstrate trustworthiness. However, our results differ from prior studies in that opt-in consent did not dominate the deliberations as jurors were able to set it aside as an assumed prerequisite for participation in a precision medicine programme.

Publication Date


  • 2022

Citation


  • Ballantyne, A., Lysaght, T., Toh, H. J., Ong, S., Lau, A., Owen Schaefer, G., . . . Braunack-Mayer, A. (2022). Sharing precision medicine data with private industry: Outcomes of a citizens��� jury in Singapore. Big Data and Society, 9(1). doi:10.1177/20539517221108988

Scopus Eid


  • 2-s2.0-85133154324

Volume


  • 9

Issue


  • 1

Place Of Publication