Large Language Models for Generating Semantic Nursing Activity Logs: Exploiting Temporal and Contextual Information

In this paper, we try to show that by utilizing the temporal and context information in care record data in a well-guided manner, Large Language Models (LLMs) can help us correct the inaccurate and incomplete activity log. Nursing activity logs are essential for documenting patient care but often suffer from incomplete or inaccurate time data due to nurses' workload. This deficiency hinders care analysis, resource allocation, and quality control. While ma- chine learning struggles with the complexities of nursing narratives, LLMs hold promise. This research explores using LLMs to address missing and incorrect timestamps in nursing logs. We propose a temporal-context-aware prompting strategy that guides LLMs to think about the relationship between patient condition and activity occurrence and duration. In our experiment with five prominent LLMs, Gemini achieved the best accuracy (82%) with manageable hallucination rates (18%) for the Few-Shot Chain-of-Thought (CoT) prompting method. Here we try to show that by guiding the LLM in the correct direction, it is possible to improve the care record log. This approach has the potential to significantly improve the quality of nursing activity data, leading to better patient outcomes.