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已发表论文
人工智能辅助闭环移动护理信息管理对护理质量指标及工作效率的影响
Authors Yuan X, Zhu L, Jiang K, Chen J
Received 2 July 2025
Accepted for publication 4 October 2025
Published 5 November 2025 Volume 2025:18 Pages 3581—3591
DOI https://doi.org/10.2147/RMHP.S548275
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Gulsum Kaya
Xing Yuan,* Lihong Zhu,* Kaili Jiang, Jinyan Chen
Department of Pediatric Surgery, Anqing Municipal Hospital, Anqing, Anhui Province, 246000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xing Yuan, Department of Pediatric Surgery, Anqing Municipal Hospital, NO. 87 Tianzhushan East Road, Anqing, 246000, Anhui Province, People’s Republic of China, Tel +86 0556 5836117, Fax +8605565223906, Email yuanxing_yx08@126.com
Objective: This study aimed to construct and evaluate an AI-assisted mobile nursing information closed-loop management model.
Methods: This study adopted a prospective before-after control design to compare nursing indicators before and after model implementation, conducted in the Pediatric Surgery Department of Anqing Municipal Hospital Affiliated with China Pharmaceutical University, where an information management system was implemented. A statistical analysis was conducted on the quality control data of 3891 cases (from 438 hospitalized patients) before model implementation (March to May 2024) and 3697 cases (from 417 patients) after implementation (July to September 2024) to evaluate its effectiveness. Existing quality control indicators were reviewed, and new/updated metrics generated from the implementation of new nursing closed-loop management measures were evaluated. AI-driven tools were leveraged to enhance the early warning capabilities of mobile nursing information systems through data acquisition, collection, and interpretation, and establishing a closed-loop management model for mobile nursing information.
Results: Following the model implementation, significant improvements were observed in all evaluated indicators. The bedside settlement completion rate rose from 66.16% to 83.3% (χ²=61.63, *p*< 0.001), and the critical value reception rate increased from 51.72% to 93.55% (χ²=21.78, *p*< 0.001). The nursing plan and workflow completion rates improved to 98.17% and 94.89% (both *p*< 0.001), respectively. Nursing work efficiency surged from 3.03 to 25 tasks per hour, and overall patient satisfaction increased from 83.3% to 97.65%, confirming the model’s effectiveness in enhancing nursing quality and patient experience.
Conclusion: The AI-assisted mobile nursing information closed-loop management model presented here was found to enhance nursing work efficiency, improve patient experience, and optimize workflow processes, contributing to a more effective and structured nursing management system.
Keywords: AI-assisted, mobile nursing information system, nursing closed-loop management, nursing quality, process optimization
Department of Pediatric Surgery, Anqing Municipal Hospital, Anqing, Anhui Province, 246000, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Xing Yuan, Department of Pediatric Surgery, Anqing Municipal Hospital, NO. 87 Tianzhushan East Road, Anqing, 246000, Anhui Province, People’s Republic of China, Tel +86 0556 5836117, Fax +8605565223906, Email yuanxing_yx08@126.com
Objective: This study aimed to construct and evaluate an AI-assisted mobile nursing information closed-loop management model.
Methods: This study adopted a prospective before-after control design to compare nursing indicators before and after model implementation, conducted in the Pediatric Surgery Department of Anqing Municipal Hospital Affiliated with China Pharmaceutical University, where an information management system was implemented. A statistical analysis was conducted on the quality control data of 3891 cases (from 438 hospitalized patients) before model implementation (March to May 2024) and 3697 cases (from 417 patients) after implementation (July to September 2024) to evaluate its effectiveness. Existing quality control indicators were reviewed, and new/updated metrics generated from the implementation of new nursing closed-loop management measures were evaluated. AI-driven tools were leveraged to enhance the early warning capabilities of mobile nursing information systems through data acquisition, collection, and interpretation, and establishing a closed-loop management model for mobile nursing information.
Results: Following the model implementation, significant improvements were observed in all evaluated indicators. The bedside settlement completion rate rose from 66.16% to 83.3% (χ²=61.63, *p*< 0.001), and the critical value reception rate increased from 51.72% to 93.55% (χ²=21.78, *p*< 0.001). The nursing plan and workflow completion rates improved to 98.17% and 94.89% (both *p*< 0.001), respectively. Nursing work efficiency surged from 3.03 to 25 tasks per hour, and overall patient satisfaction increased from 83.3% to 97.65%, confirming the model’s effectiveness in enhancing nursing quality and patient experience.
Conclusion: The AI-assisted mobile nursing information closed-loop management model presented here was found to enhance nursing work efficiency, improve patient experience, and optimize workflow processes, contributing to a more effective and structured nursing management system.
Keywords: AI-assisted, mobile nursing information system, nursing closed-loop management, nursing quality, process optimization