Development of an analysis support tool for near-miss events using AI technology - Improving human factor management capabilities in the field

Near-miss analysis is essential for identifying factors contributing to human errors and developing preventive measures. However, conventional text mining methods primarily extract direct causes, such as "lack of attention" or "insufficient verification," often overlooking broader background factors embedded in work environments. This study explores the use of Large Language Models (LLMs) to enhance factor analysis by capturing a more comprehensive range of underlying causes. Using Llama3-ELYZA-JP-8B, we incorporated 87 predefined background factors based on established frameworks, including PSF lists and the m-SHEL model. The developed factor analysis support system was applied to actual near-miss reports, and its effectiveness was evaluated by comparing the number and diversity of extracted factors before and after implementation. Results showed that LLM-based analysis significantly increased factor extraction and enhanced the identification of diverse causes. Additionally, factor aggregation and visualization improved the interpretation of trends over time. Despite these advantages, challenges remain, particularly regarding biases in data, factor extraction, and decision-making. Future research should focus on managing these biases through data diversity, optimized extraction balance, and improved transparency in analysis. By addressing these issues, a more reliable and practical near-miss factor analysis support system can be developed, contributing to improved workplace safety and more effective error prevention strategies.