Journal article
Journal of Quality in Maintenance Engineering, 2024
University of Oran2 Mohamed ben Ahmed
APA
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Titah, M., & Bouchaala, M. A. (2024). An ontology-driven model for hospital equipment maintenance management: a case study. Journal of Quality in Maintenance Engineering.
Chicago/Turabian
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Titah, Mawloud, and Mohammed Abdelghani Bouchaala. “An Ontology-Driven Model for Hospital Equipment Maintenance Management: a Case Study.” Journal of Quality in Maintenance Engineering (2024).
MLA
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Titah, Mawloud, and Mohammed Abdelghani Bouchaala. “An Ontology-Driven Model for Hospital Equipment Maintenance Management: a Case Study.” Journal of Quality in Maintenance Engineering, 2024.
BibTeX Click to copy
@article{mawloud2024a,
title = {An ontology-driven model for hospital equipment maintenance management: a case study},
year = {2024},
journal = {Journal of Quality in Maintenance Engineering},
author = {Titah, Mawloud and Bouchaala, Mohammed Abdelghani}
}
Purpose This paper aims to establish an efficient maintenance management system tailored for healthcare facilities, recognizing the crucial role of medical equipment in providing timely and precise patient care.Design/methodology/approach The system is designed to function both as an information portal and a decision-support system. A knowledge-based approach is adopted centered on Semantic Web Technologies (SWTs), leveraging a customized ontology model for healthcare facilities’ knowledge capitalization. Semantic Web Rule Language (SWRL) is integrated to address decision-support aspects, including equipment criticality assessment, maintenance strategies selection and contracting policies assignment. Additionally, Semantic Query-enhanced Web Rule Language (SQWRL) is incorporated to streamline the retrieval of decision-support outcomes and other useful information from the system’s knowledge base. A real-life case study conducted at the University Hospital Center of Oran (Algeria) illustrates the applicability and effectiveness of the proposed approach.Findings Case study results reveal that 40% of processed equipment is highly critical, 40% is of medium criticality, and 20% is of negligible criticality. The system demonstrates significant efficacy in determining optimal maintenance strategies and contracting policies for the equipment, leveraging combined knowledge and data-driven inference. Overall, SWTs showcases substantial potential in addressing maintenance management challenges within healthcare facilities.Originality/value An innovative model for healthcare equipment maintenance management is introduced, incorporating ontology, SWRL and SQWRL, and providing efficient data integration, coordinated workflows and data-driven context-aware decisions, while maintaining optimal flexibility and cross-departmental interoperability, which gives it substantial potential for further development.