Fitzpatrick Fidelma, Doherty Aaron, Lacey Gerard
Department of Clinical Microbiology, The Royal College of Surgeons in Ireland, Dublin, Ireland.
Department of Microbiology, Beaumont Hospital, Dublin, Ireland.
Curr Treat Options Infect Dis. 2020;12(2):135-144. doi: 10.1007/s40506-020-00216-7. Epub 2020 Mar 19.
Artificial intelligence (AI) offers huge potential in infection prevention and control (IPC). We explore its potential IPC benefits in epidemiology, laboratory infection diagnosis, and hand hygiene.
AI has the potential to detect transmission events during outbreaks or predict high-risk patients, enabling development of tailored IPC interventions. AI offers opportunities to enhance diagnostics with objective pattern recognition, standardize the diagnosis of infections with IPC implications, and facilitate the dissemination of IPC expertise. AI hand hygiene applications can deliver behavior change, though it requires further evaluation in different clinical settings. However, staff can become dependent on automatic reminders, and performance returns to baseline if feedback is removed.
Advantages for IPC include speed, consistency, and capability of handling infinitely large datasets. However, many challenges remain; improving the availability of high-quality representative datasets and consideration of biases within preexisting databases are important challenges for future developments. AI in itself will not improve IPC; this requires culture and behavior change. Most studies to date assess performance retrospectively so there is a need for prospective evaluation in the real-life, often chaotic, clinical setting. Close collaboration with IPC experts to interpret outputs and ensure clinical relevance is essential.
人工智能(AI)在感染预防与控制(IPC)方面具有巨大潜力。我们探讨其在流行病学、实验室感染诊断和手卫生方面潜在的IPC益处。
AI有潜力在疫情暴发期间检测传播事件或预测高风险患者,从而制定针对性的IPC干预措施。AI提供了通过客观模式识别增强诊断、规范对有IPC意义的感染的诊断以及促进IPC专业知识传播的机会。AI手卫生应用可以带来行为改变,不过这需要在不同临床环境中进一步评估。然而,工作人员可能会依赖自动提醒,如果移除反馈,表现会回到基线水平。
IPC的优势包括速度、一致性以及处理无限大的数据集的能力。然而,仍存在许多挑战;提高高质量代表性数据集的可用性以及考虑现有数据库中的偏差是未来发展的重要挑战。AI本身并不能改善IPC;这需要文化和行为的改变。迄今为止,大多数研究都是回顾性评估性能,因此需要在现实生活中往往混乱的临床环境中进行前瞻性评估。与IPC专家密切合作以解释结果并确保临床相关性至关重要。