Radaelli Davide, Di Maria Stefano, Jakovski Zlatko, Alempijevic Djordje, Al-Habash Ibrahim, Concato Monica, Bolcato Matteo, D'Errico Stefano
Department of Medical Surgical and Health Sciences, University of Trieste, 34127 Trieste, Italy.
Institute of Forensic Medicine, Criminalistic and Medical Deontology, University Ss. Cyril and Methodius, 1000 Skopje, North Macedonia.
Healthcare (Basel). 2024 Oct 6;12(19):1996. doi: 10.3390/healthcare12191996.
Healthcare-associated infections are infections that patients acquire during hospitalization or while receiving healthcare in other facilities. They represent the most frequent negative outcome in healthcare, can be entirely prevented, and pose a burden in terms of financial and human costs. With the development of new AI and ML algorithms, hospitals could develop new and automated surveillance and prevention models for HAIs, leading to improved patient safety. The aim of this review is to systematically retrieve, collect, and summarize all available information on the application and impact of AI in HAI surveillance and/or prevention.
We conducted a systematic review of the literature using PubMed and Scopus to find articles related to the implementation of artificial intelligence in the surveillance and/or prevention of HAIs.
We identified a total of 218 articles, of which only 35 were included in the review. Most studies were conducted in the US (n = 10, 28.6%) and China (n = 5; 14.3%) and were published between 2021 and 2023 (26 articles, 74.3%) with an increasing trend over time. Most focused on the development of ML algorithms for the identification/prevention of surgical site infections (n = 18; 51%), followed by HAIs in general (n = 9; 26%), hospital-acquired urinary tract infections (n = 5; 9%), and healthcare-associated pneumonia (n = 3; 9%). Only one study focused on the proper use of personal protective equipment (PPE) and included healthcare workers as the study population. Overall, the trend indicates that several AI/ML models can effectively assist clinicians in everyday decisions, by identifying HAIs early or preventing them through personalized risk factors with good performance. However, only a few studies have reported an actual implementation of these models, which proved highly successful. In one case, manual workload was reduced by nearly 85%, while another study observed a decrease in the local hospital's HAI incidence from 1.31% to 0.58%.
AI has significant potential to improve the prevention, diagnosis, and management of healthcare-associated infections, offering benefits such as increased accuracy, reduced workloads, and cost savings. Although some AI applications have already been tested and validated, adoption in healthcare is hindered by barriers such as high implementation costs, technological limitations, and resistance from healthcare workers. Overcoming these challenges could allow AI to be more widely and cost-effectively integrated, ultimately improving patient care and infection management.
医疗保健相关感染是患者在住院期间或在其他医疗机构接受医疗保健时获得的感染。它们是医疗保健中最常见的负面结果，可以完全预防，并且在财务和人力成本方面构成负担。随着新的人工智能和机器学习算法的发展，医院可以开发用于医疗保健相关感染的新的自动化监测和预防模型，从而提高患者安全性。本综述的目的是系统地检索、收集和总结关于人工智能在医疗保健相关感染监测和/或预防中的应用及影响的所有可用信息。
我们使用PubMed和Scopus对文献进行了系统综述，以查找与人工智能在医疗保健相关感染监测和/或预防中的应用相关的文章。
我们共识别出218篇文章，其中只有35篇被纳入综述。大多数研究在美国（n = 10，28.6%）和中国（n = 5；14.3%）进行，发表于2021年至2023年之间（26篇文章，74.3%），且呈逐年增加趋势。大多数研究聚焦于开发用于识别/预防手术部位感染的机器学习算法（n = 18；51%），其次是一般医疗保健相关感染（n = 9；26%）、医院获得性尿路感染（n = 5；9%）和医疗保健相关肺炎（n = 3；9%）。只有一项研究关注个人防护装备（PPE）的正确使用，并将医护人员作为研究对象。总体而言，趋势表明一些人工智能/机器学习模型可以通过早期识别医疗保健相关感染或通过具有良好性能的个性化风险因素预防感染，有效地协助临床医生进行日常决策。然而，只有少数研究报告了这些模型的实际应用情况，且证明非常成功。在一个案例中，人工工作量减少了近85%，而另一项研究观察到当地医院的医疗保健相关感染发生率从1.31%降至0.58%。
人工智能在改善医疗保健相关感染的预防、诊断和管理方面具有巨大潜力，可带来提高准确性、减少工作量和节省成本等益处。尽管一些人工智能应用已经经过测试和验证，但医疗保健领域的采用受到高实施成本、技术限制和医护人员抵触等障碍的阻碍。克服这些挑战可以使人工智能更广泛且经济高效地整合，最终改善患者护理和感染管理。
