Critical care studies using large language models based on electronic healthcare records: A technical note

Abstract

The integration of large language models (LLMs) in clinical medicine, particularly in critical care, has introduced transformative capabilities for analyzing and managing complex medical information. This technical note explores the application of LLMs, such as generative pretrained transformer 4 (GPT-4) and Qwen-Chat, in interpreting electronic healthcare records to assist with rapid patient condition assessments, predict sepsis, and automate the generation of discharge summaries. The note emphasizes the significance of LLMs in processing unstructured data from electronic health records (EHRs), extracting meaningful insights, and supporting personalized medicine through nuanced understanding of patient histories. Despite the technical complexity of deploying LLMs in clinical settings, this document provides a comprehensive guide to facilitate the effective integration of LLMs into clinical workflows, focusing on the use of DashScope's application programming interface (API) services for judgment on patient prognosis and organ support recommendations based on natural language in EHRs. By illustrating practical steps and best practices, this work aims to lower the technical barriers for clinicians and researchers, enabling broader adoption of LLMs in clinical research and practice to enhance patient care and outcomes.