Personalised Medical Content Delivery to Support Patient Empowerment based on Knowledge Graphs

Abstract

Health literacy empowers people to access, understand and apply health information to effectively manage their own health and to be an active participant in healthcare decisions. In this paper we propose a conceptual model for cognitive factors affecting health literacy and related socioeconomic aspects. Then we develop the HEALIE Knowledge Graph to represent the model, drawing from various medical ontologies, resources, and insights from domain experts. Finally, we combine the Knowledge Graph with a Natural Language Generation tool to generate personalised medical content.

Keywords

• eol>Knowledge Graph
• Natural Language Generation
• Healthcare
• Patient Empowerment
• Health Literacy 1

1. Introduction

2. The HEALIE model: A Mapping Approach to Integrate Cognitive Factors and Socio-Economic Determinants of Health Literacy We build on the integrated conceptual model established by Sørensen and colleagues [ 4 ], which consolidates four comprehensive evidence-based dimensions of health literacy, i.e. Accessing, Understanding, Appraising, and Applying information. In this work, we focus only on mapping the second (Understand) and third dimension (Appraise) and we add specific cognitive factors to Sørensen’s model. Cognitive factors are characteristics of an individual that affect performance and learning, i.e. memory-related cognitive skills can affect a patient’s ability to recall a medical text and require modifications, like repeating information multiple times or offering a bullet-point summary at the end. Or a low Knowledge factor implies the need for layman terms (e.g. replacing “hematological cancer” with “blood cancer”). Our model also includes some social determinants of health that impact well-being and health literacy. Each factor’s importance has been ranked and incorporated into the model in the form of weights.

3. The HEALIE Knowledge Graph

The HEALIE KG’s development started with an extensive literature review and identification of available data resources. Then the conceptual model was created, defining five major node clusters and diverse data types, e.g. textual descriptions of medical concepts, coupled with visual aids (diagrams, infographics, images) to accommodate to different cognitive profiles and to aid in conveying complex health information. Finally, the KG was instantiated with data points collected from the literature; ontologies and expert insights were organized and structured as nodes, properties, and relationships within the graph, following data harmonisation. Patient nodes with full profiles (clinical and demographic data, social determinants of health, cognitive factor scores etc.), corresponding to indicative use cases were also incorporated (see Figure 1). Neo4j and its Cypher query language was used to store the KG, execute path navigation queries, and extract the necessary information. For the automatic content creation, we used RosaeNLG, a template-based NLG software. RosaeNLG dynamically ingests the KG queries’ results, uses graph processing algorithms and bespoke rules curated by the domain experts to gather information for the text generation components and to identify the appropriate template type. The dynamic content spans from simple synonyms to entire text blocks of cognitive-related information, multimedia, and even customised appearance (font, font size, colour) for the generated content.

References

[1] E. Hickmann , P. Richter , and H. Schlieter , ' All together now - patient engagement, patient empowerment, and associated terms in personal healthcare' , BMC Health Services Research , ( 2022 ) doi: 10.1186/s12913-022-08501-5.

[2] I. Cutica , G. M. Vie , and G. Pravettoni, ' Personalised medicine: The cognitive side of patients' , European Journal of Internal Medicine ( 2014 ) doi: 10.1016/j.ejim. 2014 . 07 .002.

[3] W.-C. Su , ' A Preliminary Survey of Knowledge Discovery on Smartphone Applications (apps): Principles, Techniques and Research Directions for E-health' . arXiv ( 2014 ) doi: 10.48550/arXiv.1407.7314.

[4] K. Sørensen et al., ' Health literacy and public health: A systematic review and integration of definitions and models' , BMC Public Health ( 2012 ) doi: 10.1186/ 1471 -2458-12-80.