The Heimdall Framework for Supporting Characterisation of Learning Health Systems

Area of Science:

• Health Informatics
• Population Medicine
• Evidence-Based Practice

Background:

• Learning Health Systems (LHS) integrate population medicine and evidence-based practice.
• Smart technology advances healthcare through Precision Medicine.
• Researchers struggle to identify work within the LHS domain.

Purpose of the Study:

• Review LHS literature to develop a domain-describing framework.
• Create a tool to analyze literature and identify health informatics investigations within LHS.
• Support researchers in recognizing their work's relevance to LHS.

Main Methods:

• A scoping review identified relevant literature.
• Ontology and framework development through literature analysis.
• Application of ontology to classify LHS solutions and framework to characterize works.

Main Results:

• The developed ontology and framework effectively classify LHS literature.
• More proposed/potential LHS solutions exist than implemented ones.
• Indicates domain immaturity and barriers to LHS realization.

Conclusions:

• Lack of a domain ontology and framework may hinder research classification.
• The new ontology and framework enable easy classification of LHS research works.
• Demonstrates a comprehensive approach compared to previous efforts.