医院和长期护理机构的感染控制和监测创新技术:一个范围审查
Guglielmo Arzilli1, Erica De Vita1, Milena Pasquale1
1Department of Translational Research and New Technologies in Medicine and Surgery, University of Pisa, 56126 Pisa, Italy.
Antibiotics (Basel, Switzerland)
|January 22, 2024
概括
新的数字技术,如人工智能 (AI),通过减少工作量和改善患者护理,有望提高医疗保健相关感染 (HAI) 监测. 然而,在资源分配和整合方面仍然存在挑战.
科学领域:
- 传染病流行病学 传染病流行病学
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 医疗相关感染 (HAI) 对全球医疗保健系统构成重大挑战.
- 传统的HAI监测方法是有效的,但资源密集.
- 数字技术的进步,包括人工智能,通过分析庞大的健康数据集,有可能增强传统监控.
研究的目的:
- 进行对应于高血压感染监测,控制和预防的新数字技术的范围审查.
- 确定数字技术在HAI监控中的趋势,应用和结果.
- 探索与患者赋权,工作量/成本降低以及提高监控灵敏度和个性化相关的主题.
主要方法:
- 根据PRISMA-ScR指南进行了范围审查.
- 文献搜索的重点是2018年至2023年11月发表的关于医院和长期护理设施 (LTCF) 中HAI监视数字技术的研究.
- 在选1292个初始记录后包括43篇文章;进行了主题分析.
主要成果:
- 手术部位感染 (SSI) 是最常被调查的HAI,机器学习是应用最多的技术.
- 确定的主要主题包括患者赋权,减少工作量和成本,提高监控灵敏度和个性化.
- 与传统方法相比,机器学习方法通常使用更大的数据集来训练AI算法.
结论:
- 数字工具,特别是人工智能和机器学习,显示出改善HAI监控的潜力,特别是在SSI方面.
- 在医疗保健机构的资源分配和跨学科融合方面存在持续的挑战.
- 持续的开发和战略实施对于最大限度地利用数字技术在HAI监控中的好处至关重要.
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