Experimenting European healthcare forward: Do institutional differences condition networked governance?

Dorte Sindbjerg Martinsen*, Reini Schrama, Ellen Mastenbroek

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

11 Citations (Scopus)
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Abstract

Despite increasing interdependencies, national decision-makers have been reluctant to delegate healthcare competences to the supranational level in the European Union (EU). To overcome this impasse, EU institutions and member states have agreed on middle ground compromises by means of experimentalist governance. In this paper, we examine a tool of experimentalist governance in the making, i.e., the network formed by the cross-border healthcare expert group (CBHC) in the Patient Rights Directive. We ask whether interaction by means of transitive relations carrying trust, takes place and the extent to which domestic institutions, i.e., healthcare models, condition such interaction and thus learning. To examine network interactions, we use social network analysis on the basis of collected survey data on the exchange of information, advice and best practices within the CBHC network. We develop an Exponential Random Graph Model of the network to test the extent to which domestic institutions condition such interactions. For this, we conduct a cluster analysis and build a healthcare typology of EU27 plus the UK, Norway and Iceland, identifying five distinct healthcare types. We find that this type of networked governance brings EU healthcare cooperation forward, while domestic institutions greatly condition who interacts with and learns from whom.

Original languageEnglish
JournalJournal of European Public Policy
Volume28
Issue number11
Pages (from-to)1849-70
Number of pages22
ISSN1350-1763
DOIs
Publication statusPublished - 2021

Bibliographical note

Publisher Copyright:
© 2020 Informa UK Limited, trading as Taylor & Francis Group.

Keywords

  • Crossborder healthcare
  • European Union
  • Experimentalist governance
  • Healthcare typology
  • Interaction
  • Learning
  • Social network analysis
  • Trust

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