评估自动计数系统检测手部卫生用品分配的准确性。

Georgia Matterson, Katrina Browne, Philip L Russo, Sonja Dawson, Hannah Kent, Brett G Mitchell
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引用次数: 0

摘要

背景:手部卫生 (HH) 是感染预防和控制计划的基本要素。直接观察是否遵守手卫生的 5 个时刻被认为是监测手卫生依从性的黄金标准。然而,由于直接观察可能会产生偏差,因此有人提出了其他策略来补充医疗机构的手卫生依从性数据。本研究评估了自动计数系统(MEZRIT™)在检测保健用品(肥皂或酒精擦手液)配发时间方面的准确性,从而衡量产品使用情况,而不是保健5时刻的依从性:在护理模拟实验室中进行了一项准实验研究,七名参与者执行了基本护理任务,其中包括进行 HH。在肥皂和酒精擦手纸分配器上安装了传感器,以记录分配产品的时间。结果:自动计数系统检测到 260 次 HH 事件,并通过视频记录予以确认。通过对视频记录的分析,计算出 5182 个非 HH 事件。自动计数系统的灵敏度为 90%(95%CI 85.8-93.1%),特异度为 100%(95%CI 99.9-100%)。该模型的阳性预测值为 100 %(95%Cl 98.4-100%),阴性预测值为 99.5 %(95%CI 99.3-99.7%):MEZRIT™系统能准确识别90%的HH事件,并排除100%的非HH事件。对 HH 产品使用情况的实时监控有助于快速应对产品使用的变化。
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Evaluating the accuracy of an automatic counting system to detect dispensing of hand hygiene product.

Background: Hand hygiene (HH) is an essential element of infection prevention and control programs. Direct observation of adherence to the 5 moments for HH is considered the gold standard in compliance monitoring. However, as direct observation introduces potential bias, other strategies have been proposed to supplement HH compliance data in healthcare facilities. This study evaluated the accuracy of an automatic counting system (MEZRIT™) to detect when a HH product (soap or alcohol-based hand rub) was dispensed, and thus measure product usage as opposed to compliance with the 5 moments for HH.

Methods: A quasi-experimental study was conducted in a nursing simulation lab where seven participants undertook basic nursing tasks which included performing HH. Sensors were attached to soap and alcohol-based hand rub dispensers to record the time at which a product was dispensed. HH events were video recorded (time-stamped) and validated against timestamps from the automatic counting system.

Results: 260 HH events were detected by the automatic counting system and confirmed by video recordings. 5182 non-HH events were calculated from analysis of the video recordings. The automatic counting system had 90 % sensitivity (95%CI 85.8-93.1 %), and 100 % specificity (95%CI 99.9-100 %). This model generated a positive predictive value of 100 % (95%Cl 98.4-100 %), and a negative predictive value of 99.5 % (95%CI 99.3-99.7 %).

Conclusion: The MEZRIT™ system accurately identified 90 % of HH events and excluded 100 % of non-HH events. The real-time monitoring of HH product usage may be beneficial in responding quickly to changes in product usage.

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