Clinical Note
Clinical notes, the cornerstone of patient care documentation, are increasingly the focus of research aimed at automating their creation and analysis to alleviate physician workload and improve care quality. Current research utilizes large language models (LLMs), often incorporating techniques like retrieval-augmented generation and fine-tuning strategies, to generate notes from various data sources, including doctor-patient conversations and existing records, and to extract key information for tasks such as phenotyping and diagnostic reasoning. This work is significant because it addresses the substantial administrative burden on healthcare professionals, potentially leading to improved efficiency, reduced burnout, and enhanced accuracy in diagnosis and treatment planning.
Papers
Development and validation of a natural language processing algorithm to pseudonymize documents in the context of a clinical data warehouse
Xavier Tannier, Perceval Wajsbürt, Alice Calliger, Basile Dura, Alexandre Mouchet, Martin Hilka, Romain Bey
SPeC: A Soft Prompt-Based Calibration on Performance Variability of Large Language Model in Clinical Notes Summarization
Yu-Neng Chuang, Ruixiang Tang, Xiaoqian Jiang, Xia Hu