Impact of Artificial Intelligence-Assisted Closed-Loop Mobile Nursing Information Management on Nursing Quality Indicators and Work Efficiency
Bibliographic record
Abstract
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 workCorrespondence: 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.comObjective: 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
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".