An Estimate of Patient Incidents Caused by Medical Equipment Maintenance Omissions

Wang, Binseng; Rui, Torgeir; Balar, Salil D.

doi:10.2345/0899-8205-47.1.84

Cited by 15 publications

(4 citation statements)

References 3 publications

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“…Clinical engineering papers present relevant medical studies which include (1) medical devices maintenance management (Wang et al , 2013), (2) clinical engineering systems (Ibey et al , 2015), (3) quality management (Koustenis and To, 2012) and (4) risks (Chen et al , 2018; Mahfoud et al , 2016). Such studies present analyzes and solve problems that are often supply chain related, therefore presenting real possibilities of completing the SCRM theory.…”

Section: Techniques and Approaches In Healthcare Supply Chainsmentioning

confidence: 99%

A systematic literature review on supply chain risk management: is healthcare management a forsaken research field?

Senna

Reis

Santos

et al. 2020

BIJ

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PurposeThis paper aims to present a systematic literature review (SLR) to investigate how supply chain risk management (SCRM) is applied to the healthcare supply chains and which improvement opportunities are being missed in this segment.Design/methodology/approachThis SLR used the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method to answer three research questions: (1) Which are the main gaps concerning healthcare supply chain risk management (HCSCRM)? (2) What is the definition of HCSCRM? and (3) What are the risk management techniques and approaches used in healthcare supply chains?FindingsThe authors present a complete summary of the HCSCRM body of research, investigating research strings like clinical engineering and high reliability organizations (HROs) and its relations with HCSCRM; (1) This research revealed the five pillars of HCSCRM; (2) The authors proposed a formal definition for HCSCRM considering all the literature blocks explored and (3) The authors generated a list of risks present in healthcare supply chains resulting from extensive article research.Research limitations/implicationsThe authors only reviewed international journal articles (published in the English language), excluding conference papers, dissertations and theses, textbooks, book chapters, unpublished articles and notes. In addition, the study did not thoroughly investigate specific countries' particularities concerning how the healthcare providers are organized.Originality/valueThe contribution of this article is threefold: (1) To the best of authors knowledge, there is no other SLR about HCSCRM published in the scientific literature by the time of realization of authors’ work, suggesting that is the first effort to fulfill this research gap; (2) Following the previous contribution, in this work the authors propose a first formal definition for HCSCRM and (3) The authors analyzed concepts such as clinical engineering and HROs to establish the building blocks of HCSCRM.

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Section: Techniques and Approaches In Healthcare Supply Chainsmentioning

confidence: 99%

A systematic literature review on supply chain risk management: is healthcare management a forsaken research field?

Senna

Reis

Santos

et al. 2020

BIJ

View full text Add to dashboard Cite

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“…Accidents related to medical equipment (ME) may be caused by a lack of knowledge and skills regarding the ME and related medical procedures [1][2][3][4][5]. Underutilization of ME may also result in critical accidents.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Medical Equipment Management in Relation to the Mandatory Medical Equipment Safety Manager (MESM) in Japan

Ishida

Hirose

Fujiwara

et al. 2014

Journal of Healthcare Engineering

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Half a decade has passed since the fifth revision of the medical law and mandatory appointment of a medical equipment safety manager (MESM) in hospitals in Japan. During this period, circumstances have changed regarding maintenance of medical equipment (ME). We conducted a survey to examine these changes and the current situation in ME management. Maintenance of ME and related work were found to have increased in many hospitals, but the number of clinical engineering technologists (CETs) has only slightly increased. The appointed MESM was a CET or physician in most hospitals. In hospitals where physicians were appointed as the MESM, 81% had operation managers. Many respondents commented that it was difficult for one person to cover all the tasks required by the MESM, due to a lack of knowledge, too much work, or other reasons. This suggests the importance of an operation manager for ME to work under the MESM.

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“…The Joint Commission investigated the required practice maintenance instead of executing manufacturers' recommendations on procedures and frequencies [48]. The study concludes that even though the users differ in the procedures and maintenance frequencies, there is no evidence the method could negatively impact users and patients in American hospitals.…”

Section: Cost Impactmentioning

confidence: 99%

Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records

Rahman,

Hasikin,

Razak

et al. 2023

IEEE Access

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Medical device failure and maintenance records are essential information, as some nations lack dedicated systems for capturing this valuable data. In addition to making healthcare more intelligent and individualized, machine learning has the potential to transform the conventional healthcare system. Optimizing AI models in decision-making could mitigate the effects of three research issues: malfunctioning medical devices, high maintenance costs, and the lack of a strategic maintenance framework. This study proposes a data-driven machine-learning model for predicting medical device failure. The proposed predictive model is developed using multimodal data of structured maintenance and unstructured text narrative of maintenance reports to predict the failure of 8,294 critical medical devices. In developing the model, 44 varieties of essential medical devices from 15 healthcare institutions in Malaysia are utilized. A classification problem is addressed by classifying failure into three prediction classes: (i) class 1, unlikely to fail within the first three years, (ii) class 2, likely to fail within three years; and (iii) class 3, likely to fail after three years from the date of commissioning. The topic modelling and synthesis strategy: Latent Dirichlet Allocation is applied to unstructured data in order to uncover concealed patterns in maintenance notes captured during failures. In addition, sensitivity analysis is performed to select only the most significant parameters affecting the failure performance of the medical device. Then, four machine learning algorithms and three deep learning networks are evaluated to determine the best predictive model. Based on the performance evaluation, the Ensemble Classifier is further optimized and demonstrates improved accuracy of 88.80%, specificity of 94.41%, recall of 88.82%, precision of 88.46%, and F1 Score of 88.84%. The study proves a reduction in intervention from 18 to 8 features and a reduction in training time from 1660.5 to 901.66 seconds for comprehensive model development.

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An Estimate of Patient Incidents Caused by Medical Equipment Maintenance Omissions

Cited by 15 publications

References 3 publications

A systematic literature review on supply chain risk management: is healthcare management a forsaken research field?

A systematic literature review on supply chain risk management: is healthcare management a forsaken research field?

Analysis of Medical Equipment Management in Relation to the Mandatory Medical Equipment Safety Manager (MESM) in Japan

Medical Device Failure Predictions Through AI-Driven Analysis of Multimodal Maintenance Records

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