van der Werff Suzanne D, van Rooden Stephanie M, Henriksson Aron, Behnke Michael, Aghdassi Seven J S, van Mourik Maaike S M, Nauclér Pontus
Department of Medicine Solna, Division of Infectious Diseases, Karolinska Institutet, Stockholm, Sweden.
Department of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden.
J Intern Med. 2025 Aug;298(2):54-77. doi: 10.1111/joim.20100. Epub 2025 Jun 5.
Healthcare-associated infections (HAIs) are common adverse events, and surveillance is considered a core component of effective HAI reduction programmes. Recently, efforts have focused on automating the traditional manual surveillance process by utilizing data from electronic health record (EHR) systems. Using EHR data for automated surveillance, algorithms have been developed to identify patients with (ventilator-associated) pneumonia and (catheter-related) bloodstream, surgical site, (catheter-associated) urinary tract and Clostridioides difficile infections (sensitivity 54.2%-100%, specificity 63.5%-100%). Mostly methods based on natural language processing have been applied to extract information from unstructured clinical information. Further developments in artificial intelligence (AI), such as large language models, are expected to support and improve different aspects within the surveillance process; for example, more precise identification of patients with HAI. However, AI-based methods have been applied less frequently in automated surveillance and more frequently for (early) prediction, particularly for sepsis. Despite heterogeneity in settings, populations, definitions and model designs, AI-based models have shown promising results, with moderate to very good performance (accuracy 61%-99%) and predicted sepsis within 0-40 h before onset. AI-based prediction models detecting patients at risk of developing different HAIs should be explored further. The continuous evolution of AI and automation will transform HAI surveillance and prediction, offering more objective and timely infection rates and predictions. The implementation of (AI-supported) automated surveillance and prediction systems for HAI in daily practice remains scarce. Successful development and implementation of these systems demand meeting requirements related to technical capabilities, governance, practical and regulatory considerations and quality monitoring.
医疗保健相关感染（HAIs）是常见的不良事件，监测被视为有效减少HAIs计划的核心组成部分。最近，人们致力于通过利用电子健康记录（EHR）系统的数据来实现传统人工监测过程的自动化。利用EHR数据进行自动监测，已开发出算法来识别患有（呼吸机相关性）肺炎、（导管相关）血流感染、手术部位感染、（导管相关性）尿路感染和艰难梭菌感染的患者（敏感性为54.2%-100%，特异性为63.5%-100%）。大多数基于自然语言处理的方法已被应用于从非结构化临床信息中提取信息。人工智能（AI）的进一步发展，如大语言模型，有望支持和改进监测过程的不同方面；例如，更精确地识别患有HAI的患者。然而，基于AI的方法在自动监测中的应用频率较低，而在（早期）预测中应用更频繁，尤其是对于脓毒症。尽管在设置、人群、定义和模型设计方面存在异质性，但基于AI的模型已显示出有前景的结果，具有中等至非常好的性能（准确率为61%-99%），并在发病前0-40小时内预测脓毒症。应进一步探索基于AI的预测模型，以检测有发生不同HAIs风险的患者。AI和自动化的不断发展将改变HAI监测和预测，提供更客观和及时的感染率及预测。在日常实践中，（AI支持的）HAI自动监测和预测系统的实施仍然很少。这些系统的成功开发和实施需要满足与技术能力、治理、实践和监管考虑以及质量监测相关的要求。
