Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation

Document Type : Original Article


1 Adelaide Nursing School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia

2 Green Templeton College, University of Oxford, Oxford, UK

3 Adelaide Dental School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia

4 Institute of Dentistry, Queen Mary University of London, London, UK

5 Alliance Manchester Business School, University of Manchester, Manchester, UK

6 Faculty of Health and Medical Sciences, The Joanna Briggs Institute, University of Adelaide, Adelaide, SA, Australia

7 Adelaide Medical School, Faculty of Health and Medical Sciences, University of Adelaide, Adelaide, SA, Australia


Many representations of the movement of healthcare knowledge through society exist, and multiple models for the translation of evidence into policy and practice have been articulated. Most are linear or cyclical and very few come close to reflecting the dense and intricate relationships, systems and politics of organizations and the processes required to enact sustainable improvements. We illustrate how using complexity and network concepts can better inform knowledge translation (KT) and argue that changing the way we think and talk about KT could enhance the creation and movement of knowledge throughout those systems needing to develop and utilise it. From our theoretical refinement, we propose that KT is a complex network composed of five interdependent sub-networks, or clusters, of key processes (problem identification [PI], knowledge creation [KC], knowledge synthesis [KS], implementation [I], and evaluation [E]) that interact dynamically in different ways at different times across one or more sectors (community; health; government; education; research for example). We call this the KT Complexity Network, defined as a network that optimises the effective, appropriate and timely creation and movement of knowledge to those who need it in order to improve what they do. Activation within and throughout any one of these processes and systems depends upon the agents promoting the change, successfully working across and between multiple systems and clusters. The case is presented for moving to a way of thinking about KT using complexity and network concepts. This extends the thinking that is developing around integrated KT approaches. There are a number of policy and practice implications that need to be considered in light of this shift in thinking.


Commentaries Published on this Paper

  • Knowledge Translation in Healthcare – Towards Understanding its True Complexities; Comment on “Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation”

          Abstract | PDF

  • Connections, Communication and Collaboration in Healthcare’s Complex Adaptive Systems; Comment on “Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation”

          Abstract | PDF

  • Applying KT Network Complexity to a Highly-Partnered Knowledge Transfer Effort; Comment on “Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation”

          Abstract | PDF

  • Using Complexity to Simplify Knowledge Translation; Comment on “Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation”

          Abstract | PDF

  • From Linear to Complicated to Complex; Comment on “Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation”

          Abstract | PDF

  • The Paradox of Intervening in Complex Adaptive Systems; Comment on “Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation”

          Abstract | PDF


Authors' Response to the Commentaries

  • The Knowledge Translation Complexity Network (KTCN) Model: The Whole Is Greater Than the Sum of the Parts - A Response to Recent Commentaries

          Abstract | PDF


Main Subjects

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