Extraction and Summarization from Visiting Nurse Transcriptions Using Improved Prompt Techniques

In this paper, we propose a modular framework that integrates fewshot and Generated Knowledge Prompting (FS-GKP) for health information extraction and summarization from nurse-elderly conversation transcripts. This tasks is essential for monitoring elderly patients and assisting nurses in completing the visiting nurse form. FS-GKP generates additional domain-specific knowledge from transcription data, which serves as the basis for more accurate extraction and summarization. FS-GKP uses a structured chain of prompts that allows each step to build on the previous step, thus improving interpretability and precision. Experiments reveal that the GKP using few-shot technique significantly enhances extraction performance with average accuracy across all health categories is 78.57%, outperforming individual methods like zero-shot (52.49%) and few-shot (45.24%). FS-GKP also provides the best results for the summarization task compared to the other five techniques (zero-shot, few-shot, Chain-of-Thouth (CoT), Self-consistency, Few-shot CoT) with ROUGE-1: 0.43, ROUGE-2: 0.22, ROUGE-L: 0.32, BLEU: 0.28, BERTScore Precision: 0.75, Recall: 0.72, F1: 0.73, and SBERT Cosine Similarity: 0.83. These results highlight the potential of FS-GKP, to improve the accuracy of health information extraction and streamline the summarization process, effectively aligning it with categories in visiting nurse forms.