Utilization of Nursing Defect Management Evaluation and Deep Learning in Nursing Process Reengineering Optimization.

Area of Science:

• Nursing Science
• Health Informatics
• Data Science

Background:

• Nursing defect management requires robust evaluation methods.
• Optimizing nursing processes is crucial for improving patient care quality and efficiency.
• Deep learning and data mining offer advanced tools for analyzing complex healthcare data.

Purpose of the Study:

• To explore the application of nursing defect management evaluation and deep learning (DL) in optimizing nursing process reengineering.
• To analyze nursing data using Convolutional Neural Network (CNN) for feature classification and improved Apriori algorithm for data mining.
• To investigate the impact of nursing process optimization on clinical outcomes and staff performance.

Main Methods:

• Root cause analysis for nursing defect management.
• Convolutional Neural Network (CNN) for data feature classification and extraction.
• Improved Apriori algorithm for nursing data mining and analysis.
• Analysis of nursing staff's knowledge and participation in process optimization.

Main Results:

• The improved Apriori algorithm demonstrated increased efficiency in data processing compared to the standard Apriori algorithm, especially with larger datasets.
• Optimized nursing processes led to significant improvements: health education (7.57%), clinical nursing (6.55%), ward management (9.85%), and service humanization (8.97%).
• Deep learning-based data analysis and rule generation provided valuable insights for decision-making in nursing.

Conclusions:

• Reengineering nursing processes using deep learning and data mining effectively reduces defects and enhances long-term specialized nursing care.
• The study highlights the potential of DL and data mining in improving the quality and efficiency of clinical nursing services.
• The findings advocate for the clinical promotion of optimized nursing processes for better patient outcomes and operational effectiveness.