Research on the Application of AI Agent Technology in Quality Defect Root Cause Analysis of Central Sterile Supply Department

ObjectiveTo explore the application effect of AI agent-assisted root cause analysis in the management of quality inspection defects in the Central Sterile Supply Department (CSSD) and to systematically compare it with traditional manual analysis methods. MethodsA retrospective case simulation comparative study was conducted. Thirty typical CSSD quality inspection defect cases were selected. Root cause analysis was performed independently by an AI agent-assisted analysis group and a traditional manual analysis group. Using the consensus results of a high-level expert panel as the "gold standard," a quantitative comparison was made across four dimensions: analysis quality, efficiency, practicality, and process experience, employing t-tests and Mann-Whitney U tests. ResultsCompared with the traditional method, the AI-assisted group demonstrated a significantly higher root cause identification accuracy rate (85.6% vs. 72.3%, P<0.001), superior analysis depth (4.4 points vs. 3.6 points, P<0.001), significantly shorter time consumption per case analysis (18.5 minutes vs. 35.2 minutes, P<0.001), and generated more innovative corrective measures (1.8 items/case vs. 0.7 items/case, P<0.001). There was no statistically significant difference between the two groups regarding the feasibility of the proposed measures (4.0 points vs. 4.2 points, P>0.05). ConclusionThe AI agent-assisted root cause analysis method significantly improves the accuracy, depth, and efficiency of analyzing quality inspection defects in the CSSD and facilitates the discovery of more innovative solutions, demonstrating high application value and promotion potential. Implications for Nursing ManagementThis study provides empirical evidence that AI agent technology can be integrated into CSSD quality management to enhance defect analysis efficiency and accuracy. Nursing managers should consider adopting AI-assisted tools to standardize root cause analysis processes, reduce reliance on senior staff experience, and enable faster, data-driven decision-making. The reduced training burden and improved novice performance suggest that AI can help address workforce skill gaps. Future implementation should focus on human-AI collaboration, with managers ensuring adequate training, maintaining human oversight, and periodically updating the knowledge base to reflect local clinical contexts.