McLachlan Scott, Potts Henry W W, Dube Kudakwashe, Buchanan Derek, Lean Stephen, Gallagher Thomas, Johnson Owen, Daley Bridget, Marsh William, Fenton Norman
Queen Mary University of London.
University College London.
J Innov Health Inform. 2018 Jun 15;25(2):77-87. doi: 10.14236/jhi.v25i2.996.
Learning Health Systems (LHS) can focus population medicine and Evidence Based Practice; smart technology delivering the next generation of improved healthcare described as Precision Medicine, and yet researchers in the LHS domain presently lack the ability to recognise their relevant works as falling within this domain.
To review LHS literature and develop a framework describing the domain that can be used as a tool to analyse the literature and support researchers to identify health informatics investigations as falling with the domain of LHS.
A scoping review is used to identify literature on which analysis was performed. This resolved the ontology and framework. The ontology was applied to quantify the distribution of classifications of LHS solutions. The framework was used to analyse and characterise the various works within the body of LHS literature.
The ontology and framework developed was shown to be easily applicable to the literature, consistently describing and representing the goals, intentions and solutions of each LHS investigation in the literature. More proposed or potential solutions are described in the literature than implemented LHS. This suggests immaturity in the domain and points to the existence of barriers preventing LHS realisation.
The lack of an ontology and framework may have been one of the causes for the failure to describe research works as falling within the LHS domain. Using our ontology and framework, LHS research works could be easily classified, demonstrating the comprehensiveness of our approach in contrast to earlier efforts.
学习型健康系统（LHS）可聚焦于群体医学和循证实践；智能技术带来了被称为精准医学的下一代改进型医疗保健，然而LHS领域的研究人员目前缺乏能力将其相关工作认定为属于该领域。
回顾LHS文献并开发一个描述该领域的框架，该框架可作为一种工具来分析文献，并支持研究人员将健康信息学调查认定为属于LHS领域。
采用范围综述来识别进行分析的文献。这确定了本体和框架。本体用于量化LHS解决方案分类的分布情况。该框架用于分析和描述LHS文献主体内的各种工作。
所开发的本体和框架被证明易于应用于文献，能够始终如一地描述和呈现文献中每项LHS调查的目标、意图和解决方案。文献中描述的更多是提议的或潜在的解决方案，而非已实施的LHS。这表明该领域尚不成熟，并指出存在阻碍LHS实现的障碍。
缺乏本体和框架可能是未能将研究工作描述为属于LHS领域的原因之一。使用我们的本体和框架，LHS研究工作可以很容易地进行分类，这表明我们的方法相较于早期努力具有全面性。
