RT Journal Article SR Electronic T1 The Heimdall framework for supporting characterisation of learning health systems JF BMJ Health & Care Informatics FD BMJ Publishing Group Ltd SP 77 OP 87 DO 10.14236/jhi.v25i2.996 VO 25 IS 2 A1 Scott McLachlan A1 Henry W. W. Potts A1 Kudakwashe Dube A1 Derek Buchanan A1 Stephen Lean A1 Thomas Gallagher A1 Owen Johnson A1 Bridget Daley A1 William Marsh A1 Norman Fenton YR 2018 UL http://informatics.bmj.com/content/25/2/77.abstract AB 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.