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A framework for analysing learning health systems: Are we removing the most impactful barriers?

INTRODUCTION: Learning health systems (LHS) are one of the major computing advances in health care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into the barriers, benefits, and facilitating factors for LHS in order to...

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Detalles Bibliográficos
Autores principales: McLachlan, Scott, Dube, Kudakwashe, Johnson, Owen, Buchanan, Derek, Potts, Henry W.W., Gallagher, Thomas, Fenton, Norman
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6802533/
https://www.ncbi.nlm.nih.gov/pubmed/31641685
http://dx.doi.org/10.1002/lrh2.10189
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author McLachlan, Scott
Dube, Kudakwashe
Johnson, Owen
Buchanan, Derek
Potts, Henry W.W.
Gallagher, Thomas
Fenton, Norman
author_facet McLachlan, Scott
Dube, Kudakwashe
Johnson, Owen
Buchanan, Derek
Potts, Henry W.W.
Gallagher, Thomas
Fenton, Norman
author_sort McLachlan, Scott
collection PubMed
description INTRODUCTION: Learning health systems (LHS) are one of the major computing advances in health care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into the barriers, benefits, and facilitating factors for LHS in order to create a basis for their successful implementation and adoption. METHODS: First, the ITPOSMO‐BBF framework was developed based on the established ITPOSMO (information, technology, processes, objectives, staffing, management, and other factors) framework, extending it for analysing barriers, benefits, and facilitators. Second, the new framework was applied to LHS. RESULTS: We found that LHS shares similar barriers and facilitators with electronic health records (EHR); in particular, most facilitator effort in implementing EHR and LHS goes towards barriers categorised as human factors, even though they were seen to carry fewer benefits. Barriers whose resolution would bring significant benefits in safety, quality, and health outcomes remain. LHS envisage constant generation of new clinical knowledge and practice based on the central role of collections of EHR. Once LHS are constructed and operational, they trigger new data streams into the EHR. So LHS and EHR have a symbiotic relationship. The implementation and adoption of EHRs have proved and continues to prove challenging, and there are many lessons for LHS arising from these challenges. CONCLUSIONS: Successful adoption of LHS should take account of the framework proposed in this paper, especially with respect to its focus on removing barriers that have the most impact.
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spelling pubmed-68025332019-10-22 A framework for analysing learning health systems: Are we removing the most impactful barriers? McLachlan, Scott Dube, Kudakwashe Johnson, Owen Buchanan, Derek Potts, Henry W.W. Gallagher, Thomas Fenton, Norman Learn Health Syst Research Reports INTRODUCTION: Learning health systems (LHS) are one of the major computing advances in health care. However, no prior research has systematically analysed barriers and facilitators for LHS. This paper presents an investigation into the barriers, benefits, and facilitating factors for LHS in order to create a basis for their successful implementation and adoption. METHODS: First, the ITPOSMO‐BBF framework was developed based on the established ITPOSMO (information, technology, processes, objectives, staffing, management, and other factors) framework, extending it for analysing barriers, benefits, and facilitators. Second, the new framework was applied to LHS. RESULTS: We found that LHS shares similar barriers and facilitators with electronic health records (EHR); in particular, most facilitator effort in implementing EHR and LHS goes towards barriers categorised as human factors, even though they were seen to carry fewer benefits. Barriers whose resolution would bring significant benefits in safety, quality, and health outcomes remain. LHS envisage constant generation of new clinical knowledge and practice based on the central role of collections of EHR. Once LHS are constructed and operational, they trigger new data streams into the EHR. So LHS and EHR have a symbiotic relationship. The implementation and adoption of EHRs have proved and continues to prove challenging, and there are many lessons for LHS arising from these challenges. CONCLUSIONS: Successful adoption of LHS should take account of the framework proposed in this paper, especially with respect to its focus on removing barriers that have the most impact. John Wiley and Sons Inc. 2019-03-21 /pmc/articles/PMC6802533/ /pubmed/31641685 http://dx.doi.org/10.1002/lrh2.10189 Text en © 2019 The Authors. Learning Health Systems published by Wiley Periodicals, Inc. on behalf of the University of Michigan This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Reports
McLachlan, Scott
Dube, Kudakwashe
Johnson, Owen
Buchanan, Derek
Potts, Henry W.W.
Gallagher, Thomas
Fenton, Norman
A framework for analysing learning health systems: Are we removing the most impactful barriers?
title A framework for analysing learning health systems: Are we removing the most impactful barriers?
title_full A framework for analysing learning health systems: Are we removing the most impactful barriers?
title_fullStr A framework for analysing learning health systems: Are we removing the most impactful barriers?
title_full_unstemmed A framework for analysing learning health systems: Are we removing the most impactful barriers?
title_short A framework for analysing learning health systems: Are we removing the most impactful barriers?
title_sort framework for analysing learning health systems: are we removing the most impactful barriers?
topic Research Reports
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6802533/
https://www.ncbi.nlm.nih.gov/pubmed/31641685
http://dx.doi.org/10.1002/lrh2.10189
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