Failure mode effect and criticality analysis of ultrasound device by classification tracking

Background Medical ultrasound device has been more and more widely used in the hospital and Its safety risk is significantly increased when failures occur. However, there is a lack of quantitative risk assessments of different types of failure modes for medical ultrasound device. This study utilizes...

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Published in:BMC health services research Vol. 22; no. 1; pp. 429 - 10
Main Authors: Wang, Longchen, Li, Bin, Hu, Bing, Shen, Guofeng, Zheng, Yunxin, Zheng, Yuanyi
Format: Journal Article
Language:English
Published: London BioMed Central 01.04.2022
BioMed Central Ltd
Springer Nature B.V
BMC
Subjects:
Classification
Diagnosis, Ultrasonic
Equipment and supplies
Failure
Failure analysis
Failure mode
Health Administration
Health Informatics
Health services
Human error
Medical equipment
Medical errors
Medical technology
Medicine
Medicine & Public Health
Nursing Research
Patient safety
Preventive maintenance
Public Health
Quality control
Risk assessment
Software
Ultrasonic imaging
Ultrasound device
China
Failure mode
Failure analysis
Risk assessment
Ultrasound device
ISSN:1472-6963, 1472-6963
Online Access:Get full text
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Summary:Background Medical ultrasound device has been more and more widely used in the hospital and Its safety risk is significantly increased when failures occur. However, there is a lack of quantitative risk assessments of different types of failure modes for medical ultrasound device. This study utilizes a failure mode, effect and criticality analysis (FMECA) approach for quantitative risk evaluation of different failure modes for ultrasound devices. Methods The 4216 medical ultrasound device failure records at various hospitals were investigated. A failure mode and effect analysis method was developed for the quantitative evaluation of the risks of different failure modes. Visual correlation analysis was conducted for all categories to identify the causes of various failure modes. Based on the severity, occurrence and detectability of the failure causes determined, the risk priority number (RPN) for each failure mode was back-calculated through the obtained tracking diagram. Results The failure modes of unclear images, unable to power on and dark shadows on an image had the highest RPNs. One failure mode could be caused by various factors, and the failure location was not necessarily related to the cause of the failure. Conclusions This quantitative approach more accurately evaluated the risks of different failure modes, and the results of the corresponding analysis of failure modes and causes could support the rapid determination of the causes of failures in clinical practice.
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ISSN:1472-6963
1472-6963
DOI:10.1186/s12913-022-07843-4