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Background <p>Learning Health Systems (LHS) have proven efficacy in catalysing healthcare improvement, but adoption and scale-up remains challenging due to limited implementation guidance and evaluation tools. To address this gap, guide LHS implementation, and measure alignment with LHS principles, we aimed to codesign, iteratively refine, and apply an LHS Maturity Matrix (LHS-MM) based on the Monash LHS framework.</p> Methods <p>In this mixed methods study, the double diamond design and innovation model (discover, define, develop, deliver) was applied in the codesign of the LHS-MM. Insights and tools uncovered from a scoping review and the Monash LHS Framework were leveraged to develop an initial version of the LHS-MM. This was then refined through codesign with subject matter experts (<i>n</i> = 18), and potential users of the LHS-MM (<i>n</i> = 11), followed by triangulating insights from evidence-based implementation frameworks: the Consolidated Framework of Implementation Research (CFIR) and the Reach, Efficacy, Adoption, Implementation and Maintenance (RE-AIM) framework. The LHS-MM was then evaluated in a test case for stroke.</p> Results <p>Tools uncovered in the discover and define stage from the scoping review included the Cincinnati Network Maturity Grid that we then adapted to the Monash LHS framework to produce the initial draft. Codesign elevated the tool to focus on assessing complex systems behaviours related to LHS principles, with significant changes to assessment criteria, rating scale wording and scenarios for use. The LHS-MM assesses system-level, LHS behaviours across eight components on a numerical, five-point scale (1–5), which can be visualised as a radar chart. Components include stakeholder engagement, priority identification, evidence-based information, evidence synthesis and guidelines, data systems, benchmarking, implementation, and healthcare improvement. The Australian Stroke LHS test case revealed ratings from 4/5 (established) to 5/ 5 (Transformative), and informed system level opportunities for improvement.</p> Conclusion <p>Through an evidence-informed, iterative co-design process, we have created the Monash LHS-MM. Our test case example utilising the Monash LHS-MM illustrates opportunities for improving LHS fidelity and implementation, with further research needed beyond the Australian healthcare system.</p>

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The Monash learning health system maturity matrix: codesign of a tool to measure and guide improvement in complex health system behaviour

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月24日

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  • Darren Rajit,
  • Alison Johnson,
  • Sandy Reeder,
  • Dominique Cadilhac,
  • Joanne Enticott,
  • Helena Teede

摘要

Background

Learning Health Systems (LHS) have proven efficacy in catalysing healthcare improvement, but adoption and scale-up remains challenging due to limited implementation guidance and evaluation tools. To address this gap, guide LHS implementation, and measure alignment with LHS principles, we aimed to codesign, iteratively refine, and apply an LHS Maturity Matrix (LHS-MM) based on the Monash LHS framework.

Methods

In this mixed methods study, the double diamond design and innovation model (discover, define, develop, deliver) was applied in the codesign of the LHS-MM. Insights and tools uncovered from a scoping review and the Monash LHS Framework were leveraged to develop an initial version of the LHS-MM. This was then refined through codesign with subject matter experts (n = 18), and potential users of the LHS-MM (n = 11), followed by triangulating insights from evidence-based implementation frameworks: the Consolidated Framework of Implementation Research (CFIR) and the Reach, Efficacy, Adoption, Implementation and Maintenance (RE-AIM) framework. The LHS-MM was then evaluated in a test case for stroke.

Results

Tools uncovered in the discover and define stage from the scoping review included the Cincinnati Network Maturity Grid that we then adapted to the Monash LHS framework to produce the initial draft. Codesign elevated the tool to focus on assessing complex systems behaviours related to LHS principles, with significant changes to assessment criteria, rating scale wording and scenarios for use. The LHS-MM assesses system-level, LHS behaviours across eight components on a numerical, five-point scale (1–5), which can be visualised as a radar chart. Components include stakeholder engagement, priority identification, evidence-based information, evidence synthesis and guidelines, data systems, benchmarking, implementation, and healthcare improvement. The Australian Stroke LHS test case revealed ratings from 4/5 (established) to 5/ 5 (Transformative), and informed system level opportunities for improvement.

Conclusion

Through an evidence-informed, iterative co-design process, we have created the Monash LHS-MM. Our test case example utilising the Monash LHS-MM illustrates opportunities for improving LHS fidelity and implementation, with further research needed beyond the Australian healthcare system.