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Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

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dc.contributor.authorAizat Hilmi Zamzamen_US
dc.contributor.authorAyman Khallel Ibrahim Al-Anien_US
dc.contributor.authorAhmad Khairi Abdul Wahaben_US
dc.contributor.authorKhin Wee Laien_US
dc.contributor.authorSuresh Chandra Satapathyen_US
dc.contributor.authorAzira Khalilen_US
dc.contributor.authorMuhammad Mokhzaini Azizanen_US
dc.contributor.authorKhairunnisa Hasikinen_US
dc.date.accessioned2024-05-27T14:57:09Z
dc.date.available2024-05-27T14:57:09Z
dc.date.issued2021
dc.date.submitted2021-11-18
dc.descriptionFront. Public Health 9:782203en_US
dc.description.abstractThe advancement of technology in medical equipment has significantly improved healthcare services. However, failures in upkeeping reliability, availability, and safety affect the healthcare services quality and significant impact can be observed in operations’ expenses. The effective and comprehensive medical equipment assessment and monitoring throughout the maintenance phase of the asset life cycle can enhance the equipment reliability, availability, and safety. The study aims to develop the prioritisation assessment and predictive systems that measure the priority of medical equipment’s preventive maintenance, corrective maintenance, and replacement programmes. The proposed predictive model is constructed by analysing features of 13,352 medical equipment used in public healthcare clinics in Malaysia. The proposed system comprises three stages: prioritisation analysis, model training, and predictive model development. In this study, we proposed 16 combinations of novel features to be used for prioritisation assessment and prediction of preventive maintenance, corrective maintenance, and replacement programme. The modified k-Means algorithm is proposed during the prioritisation analysis to automatically distinguish raw data into three main clusters of prioritisation assessment. Subsequently, these clusters are fed into and tested with six machine learning algorithms for the predictive prioritisation system. The best predictive models for medical equipment’s preventive maintenance, corrective maintenance, and replacement programmes are selected among the tested machine learning algorithms. Findings indicate that the Support Vector Machine performs the best in preventive maintenance and replacement programme prioritisation predictive systems with the highest accuracy of 99.42 and 99.80%, respectively. Meanwhile, K-Nearest Neighbour yielded the highest accuracy in corrective maintenance prioritisation predictive systems with 98.93%. Based on the promising results, clinical engineers and healthcare providers can widely adopt the proposed prioritisation assessment and predictive systems in managing expenses, reporting, scheduling, materials, and workforce.en_US
dc.identifier.citationZamzam AH, Al-Ani AKI, Wahab AKA, Lai KW, Satapathy SC, Khalil A, Azizan MM and Hasikin K (2021) Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management. Front. Public Health 9:782203. doi: 10.3389/fpubh.2021.782203en_US
dc.identifier.doi10.3389/fpubh.2021.782203
dc.identifier.epage19
dc.identifier.issn2296-2565
dc.identifier.other2494-9
dc.identifier.spage1
dc.identifier.urihttps://www.frontiersin.org/articles/10.3389/fpubh.2021.782203/full
dc.identifier.urihttps://oarep.usim.edu.my/handle/123456789/3984
dc.identifier.volume9
dc.language.isoenen_US
dc.publisherFrontiers Media SAen_US
dc.relation.ispartofFrontiers in Public Healthen_US
dc.subjectmedical devices, biomedical equipment, machine learning, prioritisation, predictionen_US
dc.titlePrioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Managementen_US
dc.typeArticleen_US
dspace.entity.typePublication

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