Maintenance and Quality Control of Medical Equipment Based on Information Fusion Technology

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Abstract

In the medical field, to ensure the use of large medical equipment, it is necessary to carry out regular maintenance on large medical equipment. In the process of maintenance and maintenance of large-scale medical equipment, most medical personnel have not established a corresponding quality management system, neglecting daily maintenance and maintenance, resulting in many hidden dangers of medical accidents. To this end, the quality control of large medical devices should be strengthened, the control before, during, and after the event should be done well, and a comprehensive analysis of the operation methods of the equipment should be carried out to achieve reasonable maintenance of the equipment. Therefore, this paper discusses the maintenance, management, maintenance, and quality management of large medical equipment under the function of information fusion technology. This paper summarizes the problems encountered in the maintenance of medical equipment in the past and creates a medical quality control system to manage the maintenance and quality control of medical equipment. In the maintenance system of medical equipment, scientific management theories and methods are used to predict, adjust, inspect, and account for the quality of the entire production process of the equipment, and establish a complete quality monitoring and management system. To achieve optimal maintenance and economic benefits, the overall quality of medical equipment can be comprehensively improved. The data shows that the actual number of monitors for quality control testing in 2020 is 502 units, 496 units have passed the initial inspection, and 502 units have passed the maintenance, which shows that the maintenance and quality control of medical equipment based on information fusion technology is effective.

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APA

Li, J., Mao, Y., & Zhang, J. (2022). Maintenance and Quality Control of Medical Equipment Based on Information Fusion Technology. Computational Intelligence and Neuroscience, 2022. https://doi.org/10.1155/2022/9333328

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