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LAGOS: learning health systems and how they can integrate with patient care

Overview of attention for article published in BMJ Health & Care Informatics, October 2019
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  • Good Attention Score compared to outputs of the same age (67th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (58th percentile)

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7 X users

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34 Mendeley
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Title
LAGOS: learning health systems and how they can integrate with patient care
Published in
BMJ Health & Care Informatics, October 2019
DOI 10.1136/bmjhci-2019-100037
Pubmed ID
Authors

Scott McLachlan, Kudakwashe Dube, Evangelia Kyrimi, Norman Fenton

Abstract

Learning health systems (LHS) are an underexplored concept. How LHS will operate in clinical practice is not well understood. This paper investigates the relationships between LHS, clinical care process specifications (CCPS) and the established levels of medical practice to enable LHS integration into daily healthcare practice. Concept analysis and thematic analysis were used to develop an LHS characterisation. Pathway theory was used to create a framework by relating LHS, CCPS, health information systems and the levels of medical practice. A case study approach evaluates the framework in an established health informatics project. Five concepts were identified and used to define the LHS learning cycle. A framework was developed with five pathways, each having three levels of practice specificity spanning population to precision medicine. The framework was evaluated through application to case studies not previously understood to be LHS. Clinicians show limited understanding of LHS, increasing resistance and limiting adoption and integration into care routine. Evaluation of the presented framework demonstrates that its use enables: (1) correct analysis and characterisation of LHS; (2) alignment and integration into the healthcare conceptual setting; (3) identification of the degree and level of patient application; and (4) impact on the overall healthcare system. This paper contributes a theoretical framework for analysis, characterisation and use of LHS. The framework allows clinicians and informaticians to correctly identify, characterise and integrate LHS within their daily routine. The overall contribution improves understanding, practice and evaluation of the LHS application in healthcare.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 34 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 34 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 5 15%
Student > Ph. D. Student 3 9%
Student > Doctoral Student 3 9%
Professor 2 6%
Student > Bachelor 2 6%
Other 8 24%
Unknown 11 32%
Readers by discipline Count As %
Medicine and Dentistry 5 15%
Computer Science 4 12%
Nursing and Health Professions 4 12%
Chemistry 2 6%
Psychology 2 6%
Other 5 15%
Unknown 12 35%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 5. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 08 August 2022.
All research outputs
#7,329,716
of 26,433,695 outputs
Outputs from BMJ Health & Care Informatics
#170
of 513 outputs
Outputs of similar age
#121,614
of 371,729 outputs
Outputs of similar age from BMJ Health & Care Informatics
#7
of 17 outputs
Altmetric has tracked 26,433,695 research outputs across all sources so far. This one has received more attention than most of these and is in the 72nd percentile.
So far Altmetric has tracked 513 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.7. This one has gotten more attention than average, scoring higher than 66% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 371,729 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 67% of its contemporaries.
We're also able to compare this research output to 17 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 58% of its contemporaries.