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Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management

This study was aimed at exploring the new management mode of medical information processing and emergency first aid nursing management under the new artificial intelligence technology. This study will use the artificial intelligence algorithm to optimize medical information processing and emergency...

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Detalles Bibliográficos
Autores principales: Liu, Qing, Yang, Liping, Peng, Qingrong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837421/
https://www.ncbi.nlm.nih.gov/pubmed/35154360
http://dx.doi.org/10.1155/2022/8677118
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author Liu, Qing
Yang, Liping
Peng, Qingrong
author_facet Liu, Qing
Yang, Liping
Peng, Qingrong
author_sort Liu, Qing
collection PubMed
description This study was aimed at exploring the new management mode of medical information processing and emergency first aid nursing management under the new artificial intelligence technology. This study will use the artificial intelligence algorithm to optimize medical information processing and emergency first aid nursing management process, in order to improve the efficiency of emergency department and first aid efficiency. The successful rescue rates of hemorrhagic shock, coma, dyspnea, and more than three organs injury were 96.7%, 92.5%, 93.7%, and 87.2%, respectively, after the emergency first aid nursing mode was used in the hospital emergency center. The success rates of first aid within three years were compared, which were 91.8%, 93.4%, and 94.2%, respectively, showing an increasing trend year by year. 255 emergency patients in five batches in June and five batches in July were selected as the research objects by convenience sampling method. Among them, 116 cases in June were taken as the experimental group, and 139 cases in July were taken as the control group, which was used to verify the efficiency of the design model in this study. The results showed that the triage time of the two groups was 8.16 ± 2.07 min and 19.21 ± 6.36 min, respectively, and the difference was statistically significant (P < 0.01). The triage coincidence rates were 96.35% and 90.04%, respectively, and the difference was statistically significant (P < 0.05). The research proved that the design of intelligent medical information processing and emergency first aid nursing management research model can effectively improve the triage efficiency of the wounded, assist the efficiency of emergency nursing of medical staff, and improve the survival rate of emergency patients, which is worthy of clinical promotion.
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spelling pubmed-88374212022-02-12 Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management Liu, Qing Yang, Liping Peng, Qingrong Comput Math Methods Med Research Article This study was aimed at exploring the new management mode of medical information processing and emergency first aid nursing management under the new artificial intelligence technology. This study will use the artificial intelligence algorithm to optimize medical information processing and emergency first aid nursing management process, in order to improve the efficiency of emergency department and first aid efficiency. The successful rescue rates of hemorrhagic shock, coma, dyspnea, and more than three organs injury were 96.7%, 92.5%, 93.7%, and 87.2%, respectively, after the emergency first aid nursing mode was used in the hospital emergency center. The success rates of first aid within three years were compared, which were 91.8%, 93.4%, and 94.2%, respectively, showing an increasing trend year by year. 255 emergency patients in five batches in June and five batches in July were selected as the research objects by convenience sampling method. Among them, 116 cases in June were taken as the experimental group, and 139 cases in July were taken as the control group, which was used to verify the efficiency of the design model in this study. The results showed that the triage time of the two groups was 8.16 ± 2.07 min and 19.21 ± 6.36 min, respectively, and the difference was statistically significant (P < 0.01). The triage coincidence rates were 96.35% and 90.04%, respectively, and the difference was statistically significant (P < 0.05). The research proved that the design of intelligent medical information processing and emergency first aid nursing management research model can effectively improve the triage efficiency of the wounded, assist the efficiency of emergency nursing of medical staff, and improve the survival rate of emergency patients, which is worthy of clinical promotion. Hindawi 2022-02-04 /pmc/articles/PMC8837421/ /pubmed/35154360 http://dx.doi.org/10.1155/2022/8677118 Text en Copyright © 2022 Qing Liu 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
Liu, Qing
Yang, Liping
Peng, Qingrong
Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management
title Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management
title_full Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management
title_fullStr Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management
title_full_unstemmed Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management
title_short Artificial Intelligence Technology-Based Medical Information Processing and Emergency First Aid Nursing Management
title_sort artificial intelligence technology-based medical information processing and emergency first aid nursing management
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8837421/
https://www.ncbi.nlm.nih.gov/pubmed/35154360
http://dx.doi.org/10.1155/2022/8677118
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