主管:国家卫生健康委员会
主办:国家卫生健康委医院管理研究所
中国科技核心期刊(中国科技论文统计源期刊)
中国科学引文数据库(CSCD)核心库期刊
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中国护理管理 ›› 2025, Vol. 25 ›› Issue (4): 625-629.doi: 10.3969/j.issn.1672-1756.2025.04.027

• 护理质量 • 上一篇    下一篇

基于卷积神经网络视觉识别的智能输液系统的研发与初步应用

徐海利 潘红英 黄晨 徐虹霞 陈玉萍 张文娟 乔凯   

  1. 浙江大学医学院附属邵逸夫医院普外科,310016 杭州市(徐海利,徐虹霞,陈玉萍);护理部(潘红英,黄晨);信息中心(张文娟,乔凯)
  • 出版日期:2025-04-15 发布日期:2025-04-15
  • 通讯作者: 潘红英,硕士,主任护师,博士生导师,护理部副主任,E-mail:3191016@zju.edu.cn
  • 作者简介:徐海利,硕士,副主任护师
  • 基金资助:
    浙江省重大社会公益计划项目(2023C03191);浙江省医药卫生科技计划一般项目(2023KY780)

Research and peliminary application of the intelligent infusion system based on Convolutional Neural Network for visual recognition

XU Haili, PAN Hongying, HUANG Chen, XU Hongxia, CHEN Yuping, ZHANG Wenjuan, QIAO Kai   

  1. General Surgery, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China
  • Online:2025-04-15 Published:2025-04-15

摘要: 目的:探讨基于卷积神经网络视觉识别的智能输液系统在临床应用中的有效性和安全性,以期为智能输液系统在普通病房的应用提供参考借鉴。方法:便利选取2022年10月及2023年3月于浙江省某三级甲等医院普通外科病房的280例患者及16名护士为研究对象,其中患者包括智能输液系统建立前的140例和建立后的140例。比较智能输液系统建立前后护士的工作负担、患者的满意度以及病区的噪声水平。结果:智能输液系统建立后护士往返护士站的次数少于之前(P<0.05);患者对智能输液系统的满意度高于之前(P<0.05);护士应铃时长短于之前(P<0.05);噪声水平低于之前(P<0.05)。结论:基于卷积网络视觉识别的智能输液系统可以减轻护士的工作负担,提升患者满意度,对改善输液护理质量及病区环境具有重要价值。

关键词: 卷积神经网络;视觉识别;智能输液系统;护理质量

Abstract: Objective: To explore the effectiveness and safety of an intelligent infusion system based on Convolutional Neural Network (CNN) for visual recognition in clinical practice, in order to provide references and insights for the intelligent management of infusion systems in general wards. Methods: The study selected 280 patients (140 cases each before and after the application of the intelligent infusion system) and 16 nursing staff from a general surgery ward of a tertiary grade A hospital in Hangzhou by convenience sampling method at October 2022 and March 2023 as the subjects of the study. Then the workload of nursing staff, patient satisfaction, and noise levels in the ward before and after the intervention were compared. Results: Due to the usage of the intelligent infusion system, nurses spent less time going back and forth between the nurse station and the wards (P<0.05); patient satisfaction with the intelligent infusion system increased (P<0.05); the frequency of nursing staff responding to calls decreased (P<0.05); noise levels in the ward except at nighttime were lower than before (P<0.05). Conclusion: The intelligent infusion system based on CNN for visual recognition can reduce the workload of nursing staff, improve patient satisfaction, and is of significant value in improving the quality of infusion nursing and the ward environment.

Key words: Convolutional Neural Network; visual recognition; intelligent infusion system; nursing quality

中图分类号:  R47;R197