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A Framework for Understanding, Designing, Developing and Evaluating Learning Health Systems

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Date 2023 Jan 19
PMID 36654802
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Abstract

Introduction: A Learning Health System is not a technical project. It is the evolution of an existing health system into one capable of learning from every patient. This paper outlines a recently published framework intended to aid the understanding, design, development and evaluation of Learning Health Systems.

Methods: This work extended an existing repository of Learning Health System evidence, adding five more workshops. The total was subjected to thematic analysis, yielding a framework of elements important to understanding, designing, developing and evaluating Learning Health Systems. Purposeful literature reviews were conducted on each element. The findings were revised following a review by a group of international experts.

Results: The resulting framework was arranged around four questions:What is our rationale for developing a Learning Health System?There can be many reasons for developing a Learning Health System. Understanding these will guide its development.What sources of complexity exist at the system and the intervention level?An understanding of complexity is central to making Learning Health Systems work. The non-adoption, abandonment, scale-up, spread and sustainability framework was utilised to help understand and manage it.What strategic approaches to change do we need to consider?A range of strategic issues must be addressed to enable successful change in a Learning Health System. These include, strategy, organisational structure, culture, workforce, implementation science, behaviour change, co-design and evaluation.What technical building blocks will we need?A Learning Health System must capture data from practice, turn it into knowledge and apply it back into practice. There are many methods to achieve this and a range of platforms to help.

Discussion: The results form a framework for understanding, designing, developing and evaluating Learning Health Systems at any scale.

Conclusion: It is hoped that this framework will evolve with use and feedback.

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