Pilot Application of a Large Language Model to Identify Hospitalisation from Unstructured Electronic Health Records in Residential Aged Care Facilities.

Older people in residential aged care facilities (RACFs) visit hospitals and utilise healthcare services more often than others in the community. Trends in hospitalization rates are essential for designing targeted aged care interventions to reduce preventable hospitalizations and optimize resource use. Unstructured free-text nursing notes in Australian RACFs provide updated health information but require significant manual annotation due to their lack of standardization for supervised learning applications. This manual process presents a significant barrier to efficient data mining and insight generation. To address these challenges, we developed a classification model leveraging a large language model (LLM) with few-shot prompting to identify hospitalisation records within unstructured nursing notes. We analysed six weeks of data for 970 residents; the balanced dataset includes 75 hospitalizations. The model achieved an accuracy of 92.4%, a recall of 92.0%, a precision of 79.3%, and an F1 score of 85.2%. Future research will focus on enhancing the model's performance and expanding its capability to extract additional insights, such as the reasons, times, and dates of hospitalisation. These advancements aim to provide a more comprehensive understanding of hospitalisation patterns and support the development of more effective aged care services.