Learning accountable governance: Challenges and perspectives for data-intensive health research networks

Sam H.A. Muller*, Menno Mostert, Johannes J.M. van Delden, Thomas Schillemans, Ghislaine J.M.W. van Thiel

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Current challenges to sustaining public support for health data research have directed attention to the governance of data-intensive health research networks. Accountability is hailed as an important element of trustworthy governance frameworks for data-intensive health research networks. Yet the extent to which adequate accountability regimes in data-intensive health research networks are currently realized is questionable. Current governance of data-intensive health research networks is dominated by the limitations of a drawing board approach. As a way forward, we propose a stronger focus on accountability as learning to achieve accountable governance. As an important step in that direction, we provide two pathways: (1) developing an integrated structure for decision-making and (2) establishing a dialogue in ongoing deliberative processes. Suitable places for learning accountability to thrive are dedicated governing bodies as well as specialized committees, panels or boards which bear and guide the development of governance in data-intensive health research networks. A continuous accountability process which comprises learning and interaction accommodates the diversity of expectations, responsibilities and tasks in data-intensive health research networks to achieve responsible and effective governance.

Original languageEnglish
Pages (from-to)1-13
Number of pages13
JournalBig Data and Society
Volume9
Issue number2
DOIs
Publication statusPublished - Jul 2022

Keywords

  • accountability
  • data linkage
  • ethics
  • governance
  • health data research
  • networks

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