A framework for understanding, designing, developing and evaluating learning health systems.

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Title: A framework for understanding, designing, developing and evaluating learning health systems.
Authors: Foley, Tom1 (AUTHOR) thomas.foley@ncl.ac.uk, Vale, Luke2 (AUTHOR)
Source: Learning Health Systems. Jan2023, Vol. 7 Issue 1, p1-13. 13p.
Subject Terms: *Instructional systems, Thematic analysis
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. [ABSTRACT FROM AUTHOR]
Copyright of Learning Health Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Education Research Complete
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An: 161284728
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  Data: A framework for understanding, designing, developing and evaluating learning health systems.
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  Data: <searchLink fieldCode="AR" term="%22Foley%2C+Tom%22">Foley, Tom</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> thomas.foley@ncl.ac.uk</i><br /><searchLink fieldCode="AR" term="%22Vale%2C+Luke%22">Vale, Luke</searchLink><relatesTo>2</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Learning+Health+Systems%22">Learning Health Systems</searchLink>. Jan2023, Vol. 7 Issue 1, p1-13. 13p.
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  Data: *<searchLink fieldCode="DE" term="%22Instructional+systems%22">Instructional systems</searchLink><br /><searchLink fieldCode="DE" term="%22Thematic+analysis%22">Thematic analysis</searchLink>
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Learning Health Systems is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1002/lrh2.10315
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        Text: English
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              Text: Jan2023
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