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首页> 外文会议>IEEE EMBS International Conference on Biomedical and Health Informatics >Applying machine learning methods to predict hand hygiene compliance characteristics
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Applying machine learning methods to predict hand hygiene compliance characteristics

机译：应用机器学习方法预测手卫生合规性特征

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Increasing hospital re-admission rates due to Hospital Acquired Infections (HAIs) are a concern at many healthcare facilities. To prevent the spread of HAIs, caregivers should comply with hand hygiene guidelines, which require reliable and timely hand hygiene compliance monitoring systems. The current standard practice of monitoring compliance involves the direct observation of caregivers' hand cleaning as they enter or exit a patient room by a trained observer, which can be time-consuming, resource-intensive, and subject to bias. To alleviate tedious manual effort and reduce errors, this paper describes how we applied machine learning to study the characteristics of compliance that can later be used to (1) assist direct observation by deciding when and where to station manual auditors and (2) improve compliance by providing just-in-time alerts or recommending training materials to non-compliant staff. The paper analyzes location and handwashing station activation data from a 30-bed intensive care unit study and uses machine learning to assess if location, time-based factors, or other behavior data can determine what characteristics are predictive of handwashing non-compliance events. The results of this study show that a care provider's entry compliance is highly indicative of the same provider's exit compliance. Moreover, compliance of the most recent patient room visit can also predict entry compliance of a provider's current patient room visit.
机译：许多医疗机构都担心因医院获得性感染（HAI）而增加的医院再入院率。为防止HAI扩散，护理人员应遵守手部卫生指南，该指南要求可靠且及时的手部卫生合规性监视系统。当前监视合规性的标准做法是由经过培训的观察员直接观察护理人员进入或离开病房时的手部清洁情况，这很费时，资源密集并且容易产生偏差。为了减轻繁琐的人工工作并减少错误，本文介绍了我们如何应用机器学习来研究合规性特征，这些特征以后可用于（1）通过确定何时何地派驻手动审核员来辅助直接观察，以及（2）改善合规性通过提供及时警报或向不合规的员工推荐培训材料。该论文分析了来自30张病床的重症监护室研究的位置和洗手站激活数据，并使用机器学习来评估位置，基于时间的因素或其他行为数据是否可以确定哪些特征可预测洗手不合规事件。这项研究的结果表明，护理提供者的进入合规性高度表明了同一提供者的退出合规性。此外，最近对患者病房就诊的依从性也可以预测提供者当前对患者病房就诊的入境依从性。

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