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Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things

The continuous progress in modern medicine is not only the level of medical technology, but also various high-tech medical auxiliary equipment. With the rapid development of hospital information construction, medical equipment plays a very important role in the diagnosis, treatment, and prognosis ob...

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Detalles Bibliográficos
Autores principales: Yao, Lin, Shang, Danmei, Zhao, Hui, Hu, Shuyu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7946454/
https://www.ncbi.nlm.nih.gov/pubmed/33763195
http://dx.doi.org/10.1155/2021/6685456
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author Yao, Lin
Shang, Danmei
Zhao, Hui
Hu, Shuyu
author_facet Yao, Lin
Shang, Danmei
Zhao, Hui
Hu, Shuyu
author_sort Yao, Lin
collection PubMed
description The continuous progress in modern medicine is not only the level of medical technology, but also various high-tech medical auxiliary equipment. With the rapid development of hospital information construction, medical equipment plays a very important role in the diagnosis, treatment, and prognosis observation of the disease. However, the continuous growth of the types and quantity of medical equipment has caused considerable difficulties in the management of hospital equipment. In order to improve the efficiency of medical equipment management in hospital, based on cloud computing and the Internet of Things, this paper develops a comprehensive management system of medical equipment and uses the improved particle swarm optimization algorithm and chicken swarm algorithm to help the system reasonably achieve dynamic task scheduling. The purpose of this paper is to develop a comprehensive intelligent management system to master the procurement, maintenance, and use of all medical equipment in the hospital, so as to maximize the scientific management of medical equipment in the hospital. Scientific Management. It is very necessary to develop a preventive maintenance plan for medical equipment. From the experimental data, it can be seen that when the system simultaneously accesses 100 simulated users online, the corresponding time for submitting the equipment maintenance application form is 1228 ms, and the accuracy rate is 99.8%. When there are 1000 simulated online users, the corresponding time for submitting the equipment maintenance application form is 5123 ms, and the correct rate is 99.4%. On the whole, the medical equipment management information system has excellent performance in stress testing. It not only predicts the initial performance requirements, but also provides a large amount of data support for equipment management and maintenance.
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spelling pubmed-79464542021-03-23 Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things Yao, Lin Shang, Danmei Zhao, Hui Hu, Shuyu J Healthc Eng Research Article The continuous progress in modern medicine is not only the level of medical technology, but also various high-tech medical auxiliary equipment. With the rapid development of hospital information construction, medical equipment plays a very important role in the diagnosis, treatment, and prognosis observation of the disease. However, the continuous growth of the types and quantity of medical equipment has caused considerable difficulties in the management of hospital equipment. In order to improve the efficiency of medical equipment management in hospital, based on cloud computing and the Internet of Things, this paper develops a comprehensive management system of medical equipment and uses the improved particle swarm optimization algorithm and chicken swarm algorithm to help the system reasonably achieve dynamic task scheduling. The purpose of this paper is to develop a comprehensive intelligent management system to master the procurement, maintenance, and use of all medical equipment in the hospital, so as to maximize the scientific management of medical equipment in the hospital. Scientific Management. It is very necessary to develop a preventive maintenance plan for medical equipment. From the experimental data, it can be seen that when the system simultaneously accesses 100 simulated users online, the corresponding time for submitting the equipment maintenance application form is 1228 ms, and the accuracy rate is 99.8%. When there are 1000 simulated online users, the corresponding time for submitting the equipment maintenance application form is 5123 ms, and the correct rate is 99.4%. On the whole, the medical equipment management information system has excellent performance in stress testing. It not only predicts the initial performance requirements, but also provides a large amount of data support for equipment management and maintenance. Hindawi 2021-03-03 /pmc/articles/PMC7946454/ /pubmed/33763195 http://dx.doi.org/10.1155/2021/6685456 Text en Copyright © 2021 Lin Yao et al. https://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Yao, Lin
Shang, Danmei
Zhao, Hui
Hu, Shuyu
Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things
title Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things
title_full Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things
title_fullStr Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things
title_full_unstemmed Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things
title_short Medical Equipment Comprehensive Management System Based on Cloud Computing and Internet of Things
title_sort medical equipment comprehensive management system based on cloud computing and internet of things
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7946454/
https://www.ncbi.nlm.nih.gov/pubmed/33763195
http://dx.doi.org/10.1155/2021/6685456
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