文档
护理文件
闭环
质量(理念)
控制(管理)
护理记录
计算机科学
可靠性工程
护理部
医学
梅德林
工程类
操作系统
人工智能
控制工程
化学
哲学
认识论
生物化学
作者
Jingwei Lv,Yangyang Xu,Mengzhu Jiang,Yi Lv,Jialu Sun,Jing Lu,Lina Wang,Hongru Wang
出处
期刊:PubMed
日期:2025-07-16
标识
DOI:10.1093/jamia/ocaf109
摘要
To develop a locally deployed DeepSeek-powered closed-loop system for electronic nursing documentation quality control (QC) and evaluate its clinical efficacy through a multidimensional validation framework. We implemented a three-dimensional (3D) QC framework (real-time, final, and vertical QC). A retrospective analysis of 556 electronic nursing records was conducted to evaluate pre- and postimplementation outcomes, with documentation accuracy and audit efficiency assessed via blinded nurse evaluations. After implementation, omission rates decreased from 7.19% to 1.79%, the prevalence of logical inconsistencies decreased from 9.35% to 0.72%, and the prevalence of timeliness errors decreased from 8.63% to 0%. The QC time per record decreased by 3.2-fold. Nurse satisfaction was evaluated using the Clinical Nursing Information System Effectiveness Evaluation Scale (Zhao Y, Gu Y, Zhang X, et al. Developed the clinical nursing information system effectiveness evaluation scale based on the new D&M model and conducted reliability and validity evaluation. Chin J Prae Nurs. 2020;36:544-550. https://doi.org/10.3760/cma.j.issn.1672-7088.2020.07.013), yielding a total score of 102.73 ± 3.25 out of a maximum 115 points. This study demonstrates that the Artificial Intelligence (AI)-powered closed-loop QC system significantly enhances documentation accuracy and workflow efficiency while ensuring data security. The 3D framework (real-time, final, and vertical QC) represents a paradigm shift from reactive to proactive quality governance in nursing practice. High nurse satisfaction (102.73/115) confirms clinical viability, offering a scalable model for intelligent health-care quality ecosystems. Future work should explore federated learning for multicenter deployment and regulatory frameworks for clinical AI. DeepSeek demonstrated robust efficacy in enhancing QC accuracy and workflow efficiency, with localized deployment ensuring data security. This system redefines nursing documentation management, heralding an era of "intelligent negative feedback" in health-care quality ecosystems.
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