Optimizing Maintenance Policies of Computed Tomography Scanners with Stochastic Failures

Ortegón, Andrés Felipe Cardona; Guerrero, William J.

doi:10.1007/978-3-030-80906-5_24

Cited by 2 publications

(1 citation statement)

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“…(2020) through an early failure prediction long short-term memory (LSTM) neural network model for mechanical hospital equipment. On the other hand, Cardona Ortegón and Guerrero (2021) and González-Domínguez et al. (2021) proposed models for optimizing the frequency and policy of tomography equipment maintenance, using Markov chains, while Kamal et al.…”

Section: Introductionmentioning

confidence: 99%

An ontology-driven model for hospital equipment maintenance management: a case study

Titah,

Bouchaala

2024

JQME

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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.

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Section: Introductionmentioning

confidence: 99%