Application the Risk-Based Maintenance for Optimizing the Overall Medical Devices Safety

Mkalaf, Khelood A.

doi:10.51173/jt.v4i4.615

Cited by 3 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Intravenous infusion machines, electrocardiographs, ventilators, centrifuges, physiotherapists, hemodialysis machines, magnetic resonance scanners, and surgical instruments are eight common types of equipment. The use of these devices not only improves medical effectiveness, but also provides doctors with more accurate diagnostic results, thereby improving the quality of life of patients and prolonging their lives [10][11].…”

Section: Data Preprocessingmentioning

confidence: 99%

Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

Tan,

Mao,

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

With the continuous updating and progress of medical equipment, overdue medical device has problems such as management difficulties, resource waste and potential security risks. Therefore, this paper used Kohonen network algorithm to quantitatively evaluate and analyze the surplus value of overdue medical devices. In this paper, Kohonen network algorithm was used to build a quantitative model of the surplus value of overdue medical device, and the self-organization characteristics and data-driven learning ability of Kohonen network were used to more accurately predict the surplus value of equipment. Support vector machine was used to quantitatively evaluate and predict the surplus value of overdue medical device, and further optimize the model performance, so as to provide more accurate and reliable decision support for medical equipment management. The Kohonen network algorithm used in this paper well evaluated the correlation between the service life and maintenance cost of eight types of overdue medical device, and quantitatively predicted the surplus value of overdue medical device with the random forest algorithm. According to the comparison of prediction bias, the Kohonen network algorithm in this paper has better prediction performance than the random forest algorithm. In the experiment of comparative analysis and verification by introducing decision tree algorithm, the average error rate of Kohonen network algorithm in this paper was only 20.57%, which was far lower than 46.34% of random forest algorithm and 65.31% of decision tree algorithm. The Kohonen network algorithm used in this paper can effectively quantitatively evaluate and predict the surplus value of overdue medical device, thus improving the efficiency of medical equipment management, reducing costs and ensuring patient safety.

show abstract

Section: Data Preprocessingmentioning

confidence: 99%

Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

Tan,

Mao,

Zhang

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…They cannot breathe enough on their own due to impaired lung system functions [4]. This ventilator device is classified as a critical one by Mkalaf (2015) study as it needs complex electronic software to set different modes of ventilation [3,4,27,29]. Ventilators device has an integrated processor system that begins with an Inlet regulation gas unit (oxygen and air supplier), blender, microcontroller, power source unit, human interface unit, and completion with a patient circuit [4].…”

Section: Ventilator Devicementioning

confidence: 99%

Application of Overall Equipment Effectiveness for Optimizing Ventilator Reliability in Intensive Care Units and Emergency Departments

Mkalaf

Al-Hadeethi

2023

View full text Add to dashboard Cite

Healthcare organizations seek to offer a comprehensive medical services system framework with safety and high quality. This needs continuously developing integrated governance systems that provide safe, reliable, and maintaining high-quality care and value-based care for patients. In addition to improving access to health services in time, increasing efficiencies in providing medical services, reducing the risk-based medical services, and selecting a suitable maintenance policy for maintaining the medical devices. This paper adopted a quantitative analysis method based on Reliability-Based Maintenance (RCM) to comprehensively evaluate the overall medical equipment's effectiveness. RCM is an integrated approach to continuous improvement of the maintenance programs because it can predict early medical equipment failure and determine the mean time between failures. It also assesses the risk level to the patient's life in case of a medical equipment breakdown while provisioning medical services. This study targeted biomedical engineering, intensive care units, and emergency departments at 24 public hospitals in a top 20 OECD country. It selected these departments due to the rise in the rate of patients needing ventilator equipment availability during COVID-19, risk based on the sudden breakdown of the ventilator machines, and increasing the annual budget percentage required to provide medical services, where 239 of the ventilator equipment were investigated. Staff Experience-based Evidence was adopted to collect data by interviewing staff and distributing the survey. The study found that the average OEE for ventilator devices in Intensive Care Units and Emergency Departments was 63%. The device's performance was rated at 65%, while its availability and quality rate were both rated at 100%. These findings suggest that the use of the OEE metric has improved ventilator device reliability and performance in selected hospitals. The OEE metric may have potential benefits for improving the performance of other medical devices as well.

show abstract

“…Moreover, medical facilities worldwide have been overwhelmed with patients who have contracted the disease. Consequently, seeking medical attention at these facilities for disease diagnosis may pose significant challenges in terms of time, expense, and severity of the condition for the individual [7,8].…”

Section: Introductionmentioning

confidence: 99%

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

Qahtan,

Yatim,

Osman

et al. 2023

View full text Add to dashboard Cite

E-health Industry 4.0 systems ranking based on Blockchain is a multi-criteria decision-making (MCDM) problem, considering the multiple evaluation properties, their significance, and data variety. The final closeness between the evaluation sample and ideal solutions also constitutes an optimisation problem. To the authors’ knowledge, no study has provided a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems based on Blockchain. Consequently, this study proposes and discusses a multi-privacy and security ranking approach solution for E-health Industry 4.0 systems, utilising the SFS-FWZIC method to address the significance of properties issue and the Grey-TOPSIS method to tackle data variation, multiple evaluation properties, and the ideal solutions optimisation problem. The methodology of the proposed approach consists of three sections. First, three decision matrices are constructed based on the intersection of E-health systems with the mentioned properties, intersecting Electronic Health Records (EHRs), Electronic Medical Records (EMRs), and Personal Health Records (PHRs) with seven properties. Second, the weights of each privacy and security property are calculated using the SFS-FWZIC method. Finally, the weights determined by the SFS-FWZIC method and the three decision matrices are input into the Grey-TOPSIS method to rank E-health systems across the three categories. The findings reveal the following: (1) The SFS-FWZIC method effectively assigned weights to privacy and security properties, with access control achieving the highest significance weight (0.1934) and Secure-search receiving the lowest weight (0.0603). (2) The Grey-TOPSIS method efficiently ranked E-health systems across the three categories based on various parameters, including α=0.1,α=0.3,α=0.5,α=0.7, and α=0.9. Sensitivity and correlation analysis were conducted to evaluate the results, revealing high correlation results based on each α value across all discussed scenarios of changing property weights. The implications of this study can lead to better decision-making, improved security and privacy, increased competition, and widespread adoption, ultimately contributing to more efficient and effective healthcare delivery.

show abstract

Application the Risk-Based Maintenance for Optimizing the Overall Medical Devices Safety

Cited by 3 publications

References 23 publications

Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

Surplus Value Quantification of Overdue Medical Devices Based on Kohonen Network Algorithm

Application of Overall Equipment Effectiveness for Optimizing Ventilator Reliability in Intensive Care Units and Emergency Departments

A Decision Cloud Ranking Approach Based on Privacy and Security in Blockchain E-Health Industry 4.0 Systems

Contact Info

Product

Resources

About