ArticleRisk management and healthcare policy2025
Impact of Artificial Intelligence-Assisted Closed-Loop Mobile Nursing Information Management on Nursing Quality Indicators and Work Efficiency.
Article in Risk management and healthcare policy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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1 citing paper in PubMed.
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Authors and funding
4 authors.
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No grant is acknowledged in the PubMed record.
Abstract
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.
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