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

The 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 compre...

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Autores principales:	Aizat Hilmi Zamzam, Ayman Khallel Ibrahim Al-Ani, Ahmad Khairi Abdul Wahab, Khin Wee Lai, Suresh Chandra Satapathy, Azira Khalil, Muhammad Mokhzaini Azizan, Khairunnisa Hasikin
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	medical devices biomedical equipment machine learning prioritisation prediction Public aspects of medicine RA1-1270
Acceso en línea:	https://doaj.org/article/9d819d825d5b45d48b43e5bfcd1991bf
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spelling	oai:doaj.org-article:9d819d825d5b45d48b43e5bfcd1991bf2021-11-17T04:32:28ZPrioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management2296-256510.3389/fpubh.2021.782203https://doaj.org/article/9d819d825d5b45d48b43e5bfcd1991bf2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fpubh.2021.782203/fullhttps://doaj.org/toc/2296-2565The 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.Aizat Hilmi ZamzamAizat Hilmi ZamzamAyman Khallel Ibrahim Al-AniAhmad Khairi Abdul WahabKhin Wee LaiSuresh Chandra SatapathyAzira KhalilMuhammad Mokhzaini AzizanKhairunnisa HasikinFrontiers Media S.A.articlemedical devicesbiomedical equipmentmachine learningprioritisationpredictionPublic aspects of medicineRA1-1270ENFrontiers in Public Health, Vol 9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	medical devices biomedical equipment machine learning prioritisation prediction Public aspects of medicine RA1-1270
spellingShingle	medical devices biomedical equipment machine learning prioritisation prediction Public aspects of medicine RA1-1270 Aizat Hilmi Zamzam Aizat Hilmi Zamzam Ayman Khallel Ibrahim Al-Ani Ahmad Khairi Abdul Wahab Khin Wee Lai Suresh Chandra Satapathy Azira Khalil Muhammad Mokhzaini Azizan Khairunnisa Hasikin Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management
description	The 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.
format	article
author	Aizat Hilmi Zamzam Aizat Hilmi Zamzam Ayman Khallel Ibrahim Al-Ani Ahmad Khairi Abdul Wahab Khin Wee Lai Suresh Chandra Satapathy Azira Khalil Muhammad Mokhzaini Azizan Khairunnisa Hasikin
author_facet	Aizat Hilmi Zamzam Aizat Hilmi Zamzam Ayman Khallel Ibrahim Al-Ani Ahmad Khairi Abdul Wahab Khin Wee Lai Suresh Chandra Satapathy Azira Khalil Muhammad Mokhzaini Azizan Khairunnisa Hasikin
author_sort	Aizat Hilmi Zamzam
title	Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management
title_short	Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management
title_full	Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management
title_fullStr	Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management
title_full_unstemmed	Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management
title_sort	prioritisation assessment and robust predictive system for medical equipment: a comprehensive strategic maintenance management
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/9d819d825d5b45d48b43e5bfcd1991bf
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Prioritisation Assessment and Robust Predictive System for Medical Equipment: A Comprehensive Strategic Maintenance Management

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