Translating evidence in complex systems: a comparative review of implementation and improvement frameworks

Area of Science:

• Healthcare implementation science
• Systems thinking in healthcare
• Evidence-based practice translation

Background:

• Numerous frameworks exist to guide healthcare change and improvement.
• Understanding how these frameworks address complexity is crucial for effective evidence translation.

Purpose of the Study:

• To explore how existing implementation and improvement frameworks conceptualize complexity in healthcare.
• To compare the comprehensiveness of these frameworks in addressing complexity.

Main Methods:

• A systematic search identified publications on implementation and improvement frameworks.
• Ten popular frameworks were selected for comparative analysis.
• An analytical framework derived from SHIFT-Evidence was used to assess complexity conceptualization.

Main Results:

• Frameworks collectively acknowledged complexity aspects like setting uniqueness, interdependencies, and emergent learning.
• Significant heterogeneity was observed in how frameworks addressed problem definition, endpoint focus, and intervention scope.
• No single framework fully incorporated all complexity implications as defined by SHIFT-Evidence.

Conclusions:

• Existing frameworks differ in their consideration of complexity in evidence translation.
• The SHIFT-Evidence framework provides a more comprehensive overview of complexity.
• Growing consensus exists across frameworks, with SHIFT-Evidence bridging implementation and improvement fields.