Markov model of computed tomography equipment

González-Domínguez, Jaime; Sánchez-Barroso, Gonzalo; Aunión-Villa, Juan

doi:10.1016/j.engfailanal.2021.105506

Cited by 5 publications

(2 citation statements)

References 40 publications

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“…The improvement scenarios consider different assumptions to determine the behavior that they would have for the waiting time through the simulation model ( 42 ). The number and combination of scenarios could vary depending on the experience and training of the analyst ( 43 ).…”

Section: Discussionmentioning

confidence: 99%

Reduction of the Cycle Time in the Biopsies Diagnosis Through a Simulation Based on the Box Müller Algorithm

Badilla-Murillo

Vargas-Vargas

Víquez-Acuña

et al. 2022

Front. Public Health

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Anatomic pathology services study disease in hospitals on the basis of macroscopic and microscopic examination of organs and tissues. The focus of this research investigation was on improving clinical biopsy diagnosis times through simulation based on the Box-Muller algorithm to reduce the waiting time in the diagnosis of clinical biopsies. The data were provided by a hospital in San José (Costa Rica). They covered 5 years and showed waiting times for a pathological diagnosis that for some biopsies were close to 120 days. The correlation between the main causes identified and the cycle time in the biopsy diagnostic process was defined. A statistical analysis of the variables most representative of the process and of the waiting times was carried out. It followed the DMAIC structure (Define, Measure, Analyse, Improve, Control) for the continuous improvement of processes. Two of the activities of the process were identified as being the main bottlenecks. Their processing times had a normal distribution, for which reason a Box-Muller algorithm was used to generate the simulation model. The results showed that waiting times for a diagnosis can be reduced to 3 days, for a productive capacity of 8 000 biopsies per annum, optimizing the logistics performance of health care.

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Section: Discussionmentioning

confidence: 99%

Reduction of the Cycle Time in the Biopsies Diagnosis Through a Simulation Based on the Box Müller Algorithm

Badilla-Murillo

Vargas-Vargas

Víquez-Acuña

et al. 2022

Front. Public Health

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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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Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction

Prieto-Fernández,

Sánchez-Barroso,

González-Domínguez

et al. 2023

Expert Review of Medical Devices

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Markov model of computed tomography equipment

Cited by 5 publications

References 40 publications

Reduction of the Cycle Time in the Biopsies Diagnosis Through a Simulation Based on the Box Müller Algorithm

Reduction of the Cycle Time in the Biopsies Diagnosis Through a Simulation Based on the Box Müller Algorithm

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

Interaction between maintenance variables of medical ultrasound scanners through multifactor dimensionality reduction

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