Critical Device Reliability Assessment in Healthcare Services

Abstract

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.

Systematic review of predictive maintenance and digital twin technologies challenges, opportunities, and best practices

“…In addition, the primary goals of maintenance are dependability, maintainability, availability, and safety. Hence, medical equipment should not fail frequently and should be repaired as soon as a flaw is discovered ( Abd Rahman et al, 2023 ).…”

Continuous quality improvement project to reduce the downtime of medical linear accelerators: A case study at Zhejiang Cancer Hospital

“…We also established a quality index evaluation system for equipment downtime events, utilising the QCC. This system included nine quality control indicators and a weighted formula [ [26] , [27] , [28] , [29] , [30] ]. The quality control indicators included the following: 1) downtime, 2) spare parts cost, 3) failure recurrence, 4) operational errors, 5) uncontrollable factors, 6) weight allocation, 7) event frequency, 8) event risk weight, and 9) MTTR/MTBF ratio.…”