Lorenzoni Giulia, Garbin Anna, Brigiari Gloria, Papappicco Cinzia Anna Maria, Manfrin Vinicio, Gregori Dario
Unit of Biostatistics, Epidemiology and Public Health, Department of Cardiac, Thoracic, Vascular Sciences and Public Health, University of Padova, 35131 Padova, Italy.
Infectious Disease Unit, San Bortolo Hospital, ULSS 8, 36100 Vicenza, Italy.
Healthcare (Basel). 2025 Apr 11;13(8):879. doi: 10.3390/healthcare13080879.
Healthcare-associated infections (HAIs), including sepsis, represent a major challenge in clinical practice owing to their impact on patient outcomes and healthcare systems. Large language models (LLMs) offer a potential solution by analyzing clinical documentation and providing guideline-based recommendations for infection management. This study aimed to evaluate the performance of LLMs in extracting and assessing clinical data for appropriateness in infection prevention and management practices of patients admitted to an infectious disease ward. This retrospective proof-of-concept study analyzed the clinical documentation of seven patients diagnosed with sepsis and admitted to the Infectious Disease Unit of San Bortolo Hospital, ULSS 8, in the Veneto region (Italy). The following five domains were assessed: antibiotic therapy, isolation measures, urinary catheter management, infusion line management, and pressure ulcer care. The records, written in Italian, were anonymized and paired with international guidelines to evaluate the ability of LLMs (ChatGPT-4o) to extract relevant data and determine appropriateness. The model demonstrated strengths in antibiotic therapy, urinary catheter management, the accurate identification of indications, de-escalation timing, and removal protocols. However, errors occurred in isolation measures, with incorrect recommendations for contact precautions, and in pressure ulcer management, where non-existent lesions were identified. The findings underscore the potential of LLMs not merely as computational tools but also as valuable allies in advancing evidence-based practice and supporting healthcare professionals in delivering high-quality care.
医疗保健相关感染（HAIs），包括败血症，由于其对患者预后和医疗系统的影响，在临床实践中构成了重大挑战。大语言模型（LLMs）通过分析临床文档并为感染管理提供基于指南的建议，提供了一种潜在的解决方案。本研究旨在评估大语言模型在提取和评估临床数据以确定其在传染病病房住院患者感染预防和管理实践中的适宜性方面的表现。这项回顾性概念验证研究分析了七名被诊断为败血症并入住意大利威尼托地区ULSS 8的圣博尔托洛医院传染病科的患者的临床文档。评估了以下五个领域：抗生素治疗、隔离措施、导尿管管理、输液管管理和压疮护理。用意大利语书写的记录经过匿名处理，并与国际指南配对，以评估大语言模型（ChatGPT-4o）提取相关数据并确定适宜性的能力。该模型在抗生素治疗、导尿管管理、准确识别适应症、降阶梯时机和拔除方案方面表现出优势。然而，在隔离措施方面出现了错误，接触预防措施的建议不正确，在压疮管理方面也出现了错误，识别出了不存在的损伤。研究结果强调了大语言模型不仅作为计算工具，而且作为推进循证实践和支持医疗保健专业人员提供高质量护理的宝贵盟友的潜力。
