A digital health ecosystem ontology from the perspective of Australian consumers: a mixed-method literature analysis. (2nd January 2023)

• Record Type: Journal Article
• Authors: Oshni Alvandi, Abraham; Burstein, Frada; Bain, Chris
• Is Part Of: Informatics for health & social care. Volume 48:Number 1(2023)
• Page Start: 13
• Page End: 29
• Publication Date: 2023-01-02
• Subjects: Digital health -- ecosystem -- healthcare systems -- ontology -- conceptual modeling -- consumer -- Australia; Medicine -- Information services -- Periodicals; Medical informatics -- Periodicals; Medicine -- Data processing -- Periodicals; 025.0661
• DOI: 10.1080/17538157.2022.2049273
• Languages: English
• ISSNs: 1753-8157

Abstract

ABSTRACT: This study presents an ontology that scopes the digital health ecosystem from a consumer–centered perspective. We used a mixed-method analysis on a set of papers collected for a comprehensive review to identify common themes, components, and patterns that repeatedly emerge within Australian-based digital health studies. Three major and four child themes were identified as the foundational aspects of the proposed ontology. The child themes have more precise concept definitions, inherited and distinguishing attributes. Out of 179 recognized concepts, 33 were related to the Healthcare theme; 23 concepts formed a cluster of employed devices under the Technology theme; 40 concepts were associated with Use and Usability factors. 60 other concepts formed the cluster of the consumer–user theme. The theme of Digital Health was seen as being connected to 2 independent clusters. The main cluster embodied 21 extracted concepts, semantically related to "data, information, and knowledge, " whilst the second cluster embodied concepts related to "healthcare." Different stakeholders can utilize this ontology to define their landscape of digitally enabled healthcare. The novelty of this work resides in capturing a consumer–centered perspective and the method we used in deriving the ontology – formalizing the results of a systematic review based on data-driven analysis methods.