Abstract
New models and technological advances are driving the digital transformation of healthcare systems. Ontologies and Semantic Web have been recognized among the most valuable solutions to manage the massive, various, and complex healthcare data deriving from different sources, thus acting as backbones for ontology-based Decision Support Systems (DSSs). Several contributions in the literature propose Ontology engineering methodologies (OEMs) to assist the formalization and development of ontologies, by providing guidelines on tasks, activities, and stakeholders’ participation. Nevertheless, existing OEMs differ widely according to their approach, and often lack of sufficient details to support ontology engineers. This paper performs a meta-review of the main criteria adopted for assessing OEMs, and major issues and shortcomings identified in existing methodologies. The key issues requiring specific attention (i.e., the delivery of a feasibility study, the introduction of project management processes, the support for reuse, and the involvement of stakeholders) are then explored into three use cases of semantic-based DSS in health-related fields. Results contribute to the literature on OEMs by providing insights on specific tools and approaches to be used when tackling these issues in the development of collaborative OEMs supporting DSS.
Highlights
Healthcare sectors and services such as assisted care are gathering more attention from both scholars and practitioners, with the call for research and development of new models, new technological advances and more demand-oriented healthcare systems [1].The digital transformation of healthcare services is leading to a further need in eliciting the knowledge of both users and stakeholders, and to manage and use the massive, various and complex healthcare data [2]
Results contribute to the literature on Ontology engineering methodologies (OEMs) by providing insights on specific tools and approaches to be used when tackling these issues in the development of collaborative OEMs supporting Decision Support Systems (DSSs)
This study aims to contribute to the growing debate on new methods and technological developments to be considered in the digital transformation of healthcare systems
Summary
The digital transformation of healthcare services is leading to a further need in eliciting the knowledge of both users and stakeholders, and to manage and use the massive, various and complex healthcare data [2]. These are dispersedly obtained from distributed devices (including tablet computers and personal digital assistants) and cannot be used for further analyzing and decision supporting if they are collected and organized in a weak-semantic manner [3]. With the emergence of Internet of Things (IoT) and the development of IoT applications in many fields, the ontology has been used in those contexts where experts’ knowledge covers a pivotal importance: this include areas in which the digital transformation is triggering relevant societal challenges
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