McLachlan, S, Potts, HWW, Dube, K et al. (7 more authors) (2018) The Heimdall framework for supporting characterisation of learning health systems. Journal of Innovation in Health Informatics, 25 (2). pp. 77-87. ISSN 2058-4555
Abstract
Background: There are many proposed benefits of using learning health systems (LHSs), including improved patient outcomes. There has been little adoption of LHS in practice due to challenges and barriers that limit adoption of new data-driven technologies in healthcare. We have identified a more fundamental explanation: the majority of developments in LHS are not identified as LHS. The absence of a unifying namespace and framework brings a lack of consistency in how LHS is identified and classified. As a result, the LHS ‘community’ is fragmented, with groups working on similar systems being unaware of each other’s work. This leads to duplication and the lack of a critical mass of researchers necessary to address barriers to adoption.
Objective: To find a way to support easy identification and classification of research works within the domain of LHS.
Method: A qualitative meta-narrative study focusing on works that self-identified as LHS was used for two purposes. First, to find existing standard definitions and frameworks using these to create a new unifying framework. Second, seeking whether it was possible to classify those LHS solutions within the new framework.
Results: The study found that with apparently limited awareness, all current LHS works fall within nine primary archetypes. These findings were used to develop a unifying framework for LHS to classify works as LHS, and reduce diversity and fragmentation within the domain.
Conclusions: Our finding brings clarification where there has been limited awareness for LHS among researchers. We believe our framework is simple and may help researchers to classify works in the LHS domain. This framework may enable realisation of the critical mass necessary to bring more substantial collaboration and funding to LHS. Ongoing research will seek to establish the framework’s effect on the LHS domain.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 The Author(s). Published by BCS, The Chartered Institute for IT under the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
Keywords: | electronic health records; learning health systems; learning healthcare systems; precision medicine |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 20 Nov 2018 14:09 |
Last Modified: | 25 Jun 2023 21:36 |
Status: | Published |
Publisher: | BCS, The Chartered Institute for IT |
Identification Number: | 10.14236/jhi.v25i2.996 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:138837 |