Dependability-based maintenance optimization in healthcare domain

Mahfoud, Hassana; Barkany, Abdellah El; Biyaali, Ahmed El

doi:10.1108/jqme-07-2016-0029

Cited by 16 publications

(10 citation statements)

References 88 publications

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“…Medical Devices Management System is another topic to ensure medical device reliability. Bahreini et al [ 4 , 8 ] distinguished the factors affecting medical equipment maintenance and management and are divided to resources, human resources, financial, physical documentation, education, service, quality control, information bank, inspection and preventive maintenance, training and education, management, services, design, and implementation. Medical Devices Management System in this review highlights marketing strategies [ 34 ], service quality [ 35 , 36 ], replacement plan [ 37 ], utilization and human resource [ 10 ], quality assurance program [ 38 ], and quality control system [ 39 ], where the replacement plan, marketing strategies, and establishment of Standard Operating Procedure (SOP) are essential in management.…”

Section: Resultsmentioning

confidence: 99%

“…The World Health Organization (WHO) estimates 50–80% of equipment remains nonfunctional due to lack of maintenance culture, lack of competency, and the tendency to emphasis more on corrective maintenance than preventive maintenance [ 3 ]. Deficiency of adequate maintenance of medical devices leads to equipment downtime and reduces device performance, waste cost, and resources [ 4 ]. The data recorded in the Malaysian Government Hospitals database proved that hospital operates with massive number of aging medical devices [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

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Critical Device Reliability Assessment in Healthcare Services

Rahman

Ibrahim

Hasikin‬

et al. 2023

Journal of Healthcare Engineering

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Medical device reliability is the ability of medical devices to endure functioning and is indispensable to ensure service delivery to patients. Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) technique was employed in May 2021 to evaluate existing reporting guidelines on medical device reliability. The systematic searching is conducted in eight different databases, including Web of Science, Science Direct, Scopus, IEEE Explorer, Emerald, MEDLINE Complete, Dimensions, and Springer Link, with 36 articles shortlisted from the year 2010 to May 2021. This study aims to epitomize existing literature on medical device reliability, scrutinize existing literature outcomes, investigate parameters affecting medical device reliability, and determine the scientific research gaps. The result of the systematic review listed three main topics on medical device reliability: risk management, performance prediction using Artificial Intelligence or machine learning, and management system. The medical device reliability assessment challenges are inadequate maintenance cost data, determining significant input parameter selection, difficulties accessing healthcare facilities, and limited age in service. Medical device systems are interconnected and interoperating, which increases complexity in assessing their reliability. To the best of our knowledge, although machine learning has become popular in predicting medical device performance, the existing models are only applicable to selected devices such as infant incubators, syringe pumps, and defibrillators. Despite the importance of medical device reliability assessment, there is no explicit protocol and predictive model to anticipate the situation. The problem worsens with the unavailability of a comprehensive assessment strategy for critical medical devices. Therefore, this study reviews the current state of critical device reliability in healthcare facilities. The present knowledge can be improved by adding new scientific data emphasis on critical medical devices used in healthcare services.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Critical Device Reliability Assessment in Healthcare Services

Rahman

Ibrahim

Hasikin‬

et al. 2023

Journal of Healthcare Engineering

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“…Failures of the medical devices lead to delayed or absent treatment, and effective operational and maintenance management is the main approach to shorten equipment shutdown time and increase healthcare system availability. [135][136][137] Identical equipment used in different circumstances may need different maintenance strategies. Bahreini et al identify seven main influence factors on the effectiveness of maintenances in healthcare, including resources, quality control, information bank, education, service, inspection and preventive maintenance and design and implementation.…”

Section: Risk Reduction In Operation Of Smart Systemsmentioning

confidence: 99%

“…148 More studies about maintenance optimization in the healthcare domain can be found in the review by Mahfoud and co-authors. 135…”

Section: Risk Reduction In Operation Of Smart Systemsmentioning

confidence: 99%

Risk management of smart healthcare systems: Delimitation, state-of-arts, process, and perspectives

Liu

2022

Journal of Patient Safety and Risk Management

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Sensing, communication, computation, and control technologies are facilitating smart healthcare to improve efficiency and effectiveness of medical treatment and care. This study focuses on the risk issues relevant with the adverse events where novel technical systems do not serve as expected. We discuss the unique challenges, define the scope of risk management in healthcare and review the state-of-art research on diverse topics under the framework widely used in risk management. Then, we present a systematic approach to identify the hazards to patients and other asset of interest in the perception, cyber communication, and execution of smart technologies and their operational contexts. We also investigate different methods for scenario, likelihood, and consequence analyses for specifying the risks of adverse events, and categorize the approaches of risk reduction, as the main strategy of treating risks of smart healthcare systems, into four groups of design, operation, organization, and legislation. At the last, the article proposes some research perspectives responding to the developing trend of smart healthcare.

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“…The reliability, maintainability, availability, and safety of medical devices are the ultimate goal in maintenance strategy, where medical devices should not fail frequently and must be repaired promptly when the failures are detected. Numerous investigations have associated medical device failures with severe patient injuries and deaths ( Dhillon, 2011 ; Mahfoud, Abdellah & El Biyaali, 2018 ; Palmer, 2010 ; Verbano & Turra, 2010 ). The world health organization (WHO) estimates that 50% to 80% of equipment is non-functional due to the lack of maintenance culture, competency, and a tendency to focus on corrective maintenance rather than preventative maintenance ( Kutor, Agede & Ali, 2017 ).…”

Section: Introductionmentioning

confidence: 99%

Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management

Rahman

Zaki

Hasikin‬

et al. 2023

PeerJ Computer Science

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Background The advancement of biomedical research generates myriad healthcare-relevant data, including medical records and medical device maintenance information. The COVID-19 pandemic significantly affects the global mortality rate, creating an enormous demand for medical devices. As information technology has advanced, the concept of intelligent healthcare has steadily gained prominence. Smart healthcare utilises a new generation of information technologies, such as the Internet of Things (loT), big data, cloud computing, and artificial intelligence, to completely transform the traditional medical system. With the intention of presenting the concept of smart healthcare, a predictive model is proposed to predict medical device failure for intelligent management of healthcare services. Methods Present healthcare device management can be improved by proposing a predictive machine learning model that prognosticates the tendency of medical device failures toward smart healthcare. The predictive model is developed based on 8,294 critical medical devices from 44 different types of equipment extracted from 15 healthcare facilities in Malaysia. The model classifies the device into three classes; (i) class 1, where the device is unlikely to fail within the first 3 years of purchase, (ii) class 2, where the device is likely to fail within 3 years from purchase date, and (iii) class 3 where the device is likely to fail more than 3 years after purchase. The goal is to establish a precise maintenance schedule and reduce maintenance and resource costs based on the time to the first failure event. A machine learning and deep learning technique were compared, and the best robust model for smart healthcare was proposed. Results This study compares five algorithms in machine learning and three optimizers in deep learning techniques. The best optimized predictive model is based on ensemble classifier and SGDM optimizer, respectively. An ensemble classifier model produces 77.90%, 87.60%, and 75.39% for accuracy, specificity, and precision compared to 70.30%, 83.71%, and 67.15% for deep learning models. The ensemble classifier model improves to 79.50%, 88.36%, and 77.43% for accuracy, specificity, and precision after significant features are identified. The result concludes although machine learning has better accuracy than deep learning, more training time is required, which is 11.49 min instead of 1 min 5 s when deep learning is applied. The model accuracy shall be improved by introducing unstructured data from maintenance notes and is considered the author’s future work because dealing with text data is time-consuming. The proposed model has proven to improve the devices’ maintenance strategy with a Malaysian Ringgit (MYR) cost reduction of approximately MYR 326,330.88 per year. Therefore, the maintenance cost would drastically decrease if this smart predictive model is included in the healthcare management system.

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Dependability-based maintenance optimization in healthcare domain

Cited by 16 publications

References 88 publications

Critical Device Reliability Assessment in Healthcare Services

Critical Device Reliability Assessment in Healthcare Services

Risk management of smart healthcare systems: Delimitation, state-of-arts, process, and perspectives

Predicting medical device failure: a promise to reduce healthcare facilities cost through smart healthcare management

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