Leveraging a Large Language Model for Streamlined Medical Record Generation: Implications for Health Care Informatics

作者：Yi-Ling Chiang, Kuei-Fen Yang, Pei-Yu Su, Shang‐Feng Tsai, Kai-Li Liang · 发表于：Applied Clinical Informatics · 年份：2025 · DOI：10.1055/a-2707-2959 · 被引用次数：3 · 研究领域：Artificial Intelligence in Healthcare and Education、Machine Learning in Healthcare、Nursing Diagnosis and Documentation

Abstract This study aimed to leverage a large language model (LLM) to improve the efficiency and thoroughness of medical record documentation. This study focused on aiding clinical staff in creating structured summaries with the help of an LLM and assessing the quality of these artificial intelligence (AI)-proposed records in comparison to those produced by doctors. This strategy involved assembling a team of specialists, including data engineers, physicians, and medical information experts, to develop guidelines for medical summaries produced by an LLM (Llama 3.1), all under the direction of policymakers at the study hospital. The LLM proposes admission, weekly summaries, and discharge notes for physicians to review and edit. A validated Physician Documentation Quality Instrument (PDQI-9) was used to compare the quality of physician-authored and LLM-generated medical records. The results showed no significant difference was observed in the total PDQI-9 scores between the physician-drafted and AI-created weekly summaries and discharge notes (p = 0.129 and 0.873, respectively). However, there was a significant difference in the total PDQI-9 scores between the physician and AI admission notes (p = 0.004). Furthermore, there were significant differences in item levels between physicians' and AI notes. After deploying the note-assisted function in our hospital, it gradually gained popularity. LLM shows considerable promise for enhancing the efficiency and quality of medical recor...