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Medical equipment classification according to corrective maintenance data: a strategy based on the equipment age

机译:根据纠正性维护数据对医疗设备进行分类:基于设备使用年限的策略

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摘要

INTRODUCTION: Decision-making on medical equipment management is a daily task for clinical engineers, but it may prove difficult to easily extract relevant information from the large amount of data from computerized maintenance management systems. This article describes a simple method of medical equipment classification based on corrective maintenance indicators. METHODS: Three indicators were calculated based on the number of events, duration and cost of corrective maintenance. Three classes were defined according to the indicator values of different equipment ages: class A for 0-4 years, class B for 5-9 years, and class C for equipment older than 10 years. The method was applied to 2,134 pieces of equipment from the Health Service system of the University of Campinas. RESULTS: From the total, 51.7% of the equipment were classified as C, 4.2% as B and 44.1% as A. The infusion pump for general use was the type of equipment of which most units were in the C class (84.7%), even though almost 50% of them were acquired within less than 9 years, and would thus be expected to be classified as A and B. Among the pumps in class C, 39.5% were from a single manufacturer, although the equipments were acquired recently. CONCLUSION: The developed classification may be an important tool for raising alerts about equipment more prone to maintenance problems, as well as for identification of equipments with acceptable maintenance history, supporting decision-making on equipment replacement.
机译:简介:医疗设备管理的决策是临床工程师的日常工作,但事实证明,要从计算机维护管理系统的大量数据中轻松提取相关信息可能很困难。本文介绍了一种基于纠正性维护指标的医疗设备分类的简单方法。方法:根据事件数量,持续时间和纠正性维护成本计算了三个指标。根据不同设备使用年限的指标值定义了三个级别:对于0-4年为A级,对于5-9年为B级,对于10年以上的设备为C级。该方法已应用于坎皮纳斯大学(University of Campinas)健康服务系统的2134台设备。结果:在总设备中,有51.7%的设备归为C类,B类的为4.2%,A类的为44.1%。通用输液泵是大多数设备为C类的设备类型。 ,即使其中有近50%的产品是在不到9年的时间内购买的,因此有望被归类为A和B。在C类的泵中,有39.5%是由一家制造商提供的,尽管这些设备是最近购买的。结论:制定的分类可能是重要的工具,可用于发出有关更容易出现维护问题的设备的警报,以及识别具有可接受维护历史的设备,从而支持设备更换决策。

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