Turning Dialogues Into Event Data: Lessons From GPT-Based Recognition of Nursing Actions

Objective: To assess the feasibility of using a large language model (LLM) to generate structured event logs from conversational data in home-based nursing care, with the goal of reducing the documentation burden and enabling process analysis. Methods: We conducted an exploratory study involving 27 audio-recorded home care visits between district nurses and patients. These recordings were transcribed and used as input for a Generative Pre-Trained Transformer (GPT) to identify nursing interventions and construct event logs, using the standardised Nursing Interventions Classification (NIC) system. We applied and evaluated different prompts through an iterative, interdisciplinary process involving computer scientists and nurse researchers. Results: GPT demonstrated reasonable ability to extract nursing interventions from conversational transcripts, especially when activities were discussed explicitly and temporally aligned. Challenges emerged when information was implicit, ambiguous, or not captured in the dialogue. We propose five guidelines for using LLMs in this context, addressing data source limitations, activity label selection, confidence calibration, hallucination handling, and stakeholder-specific output needs. These guidelines provide lessons that extend beyond home care to other domains where conversational data must be translated into structured process insights. Conclusion: LLMs show promise for transforming informal clinical dialogue into structured representations of care. While expert oversight and tailored prompts remain essential, future model improvements may enhance reliability. Still, applications in real-world healthcare contexts must be handled with care to ensure accuracy, transparency, and stakeholder trust.