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Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives.

Improving postsurgical fall detection for older Americans using LLM-driven analysis of clinical narratives. medRxiv : the preprint server for health sciences Pillai, M., Blumke, T. L., Studnia, J., Wang, Y., Veigulis, Z. P., Ware, A. D., Hoover, P. J., Carroll, I. R., Humphreys, K., Osborne, T. F., Asch, S. M., Hernandez-Boussard, T., Curtin, C. M. 2024

Abstract

Postsurgical falls have significant patient and societal implications but remain challenging to identify and track. Detecting postsurgical falls is crucial to improve patient care for older adults and reduce healthcare costs. Large language models (LLMs) offer a promising solution for reliable and automated fall detection using unstructured data in clinical notes. We tested several LLM prompting approaches to postsurgical fall detection in two different healthcare systems with three open-source LLMs. The Mixtral-8*7B zero-shot had the best performance at Stanford Health Care (PPV = 0.81, recall = 0.67) and the Veterans Health Administration (PPV = 0.93, recall = 0.94). These results demonstrate that LLMs can detect falls with little to no guidance and lay groundwork for applications of LLMs in fall prediction and prevention across many different settings.

View details for DOI 10.1101/2024.06.25.24309480

View details for PubMedID 38978655