Using Complexity and Network Concepts to Inform Healthcare Knowledge Translation.

Area of Science:

• Health Systems Research
• Knowledge Translation Science
• Complexity Theory

Background:

• Existing knowledge translation (KT) models often fail to capture the intricate dynamics of healthcare systems.
• Linear or cyclical models do not adequately represent the complex relationships, organizational politics, and systemic processes needed for sustainable improvements.

Purpose of the Study:

• To propose a new framework for understanding knowledge translation (KT) by applying complexity and network concepts.
• To argue that a conceptual shift in how KT is perceived can enhance knowledge creation and dissemination within healthcare systems.

Main Methods:

• Theoretical refinement integrating complexity and network theories.
• Conceptualization of KT as a complex network with interdependent sub-networks.

Main Results:

• KT is proposed as a complex network (KT Complexity Network) comprising five interdependent sub-networks: problem identification, knowledge creation, knowledge synthesis, implementation, and evaluation.
• These sub-networks interact dynamically across multiple sectors (e.g., community, health, government, education, research).
• Successful activation within the KT Complexity Network relies on agents working across multiple systems and clusters.

Conclusions:

• Adopting complexity and network concepts offers a more accurate and effective way to conceptualize and operationalize knowledge translation.
• This paradigm shift extends current integrated KT approaches and has significant policy and practice implications for improving healthcare.