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A DeepSeek-powered locally deployed closed-loop system for enhancing quality control in electronic nursing
Jinhong Lv1, Yangyang Xu2, Mengzhu Jiang1
1Department of Nursing, First People's Hospital of Xinxiang and The Fifth Affiliated Hospital of Henan Medical University, Xinxiang 453003, China.
Summary
A new AI system using DeepSeek improved electronic nursing documentation quality control, significantly reducing errors and saving time. This closed-loop system enhances accuracy and efficiency, ensuring data security and high nurse satisfaction.
Area of Science:
- Health Informatics
- Artificial Intelligence in Healthcare
- Nursing Quality Improvement
Background:
- Electronic nursing documentation is crucial for patient care quality and safety.
- Current quality control (QC) methods for nursing documentation are often reactive and inefficient.
- The integration of Artificial Intelligence (AI) offers potential for proactive and automated QC.
Purpose of the Study:
- To develop and evaluate a locally deployed DeepSeek-powered closed-loop system for electronic nursing documentation quality control (QC).
- To assess the clinical efficacy of this AI-driven system using a multidimensional validation framework.
- To investigate the impact on documentation accuracy, audit efficiency, and nurse satisfaction.
Main Methods:
- Implementation of a three-dimensional (3D) QC framework: real-time, final, and vertical QC.
- Retrospective analysis of 556 electronic nursing records pre- and post-system implementation.
- Blinded nurse evaluations to assess documentation accuracy and audit efficiency.
Main Results:
- Omission rates decreased from 7.19% to 1.79%.
- Logical inconsistencies reduced from 9.35% to 0.72%.
- Timeliness errors decreased from 8.63% to 0%, with a 3.2-fold reduction in QC time per record.
- High nurse satisfaction reported (102.73/115 on the evaluation scale).
Conclusions:
- The AI-powered closed-loop QC system significantly enhances nursing documentation accuracy and workflow efficiency.
- The 3D QC framework shifts quality governance from reactive to proactive.
- The system demonstrates clinical viability and offers a scalable model for intelligent healthcare quality ecosystems.
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