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Multi-parameter vital sign database to assist in alarm optimization for general care units

机译:多参数生命体征数据库可帮助优化一般护理部门的警报

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摘要

Continual vital sign assessment on the general care, medical-surgical floor is expected to provide early indication of patient deterioration and increase the effectiveness of rapid response teams. However, there is concern that continual, multi-parameter vital sign monitoring will produce alarm fatigue. The objective of this study was the development of a methodology to help care teams optimize alarm settings. An on-body wireless monitoring system was used to continually assess heart rate, respiratory rate, SpO2 and noninvasive blood pressure in the general ward of ten hospitals between April 1, 2014 and January 19, 2015. These data, 94,575 h for 3430 patients are contained in a large database, accessible with cloud computing tools. Simulation scenarios assessed the total alarm rate as a function of threshold and annunciation delay (s). The total alarm rate of ten alarms/patient/day predicted from the cloud-hosted database was the same as the total alarm rate for a 10 day evaluation (1550 h for 36 patients) in an independent hospital. Plots of vital sign distributions in the cloud-hosted database were similar to other large databases published by different authors. The cloud-hosted database can be used to run simulations for various alarm thresholds and annunciation delays to predict the total alarm burden experienced by nursing staff. This methodology might, in the future, be used to help reduce alarm fatigue without sacrificing the ability to continually monitor all vital signs.Electronic supplementary materialThe online version of this article (doi:10.1007/s10877-015-9790-8) contains supplementary material, which is available to authorized users.
机译:一般护理,外科手术地板上的持续生命体征评估有望为患者恶化提供早期迹象,并提高快速反应团队的效率。然而,令人担忧的是,连续的多参数生命体征监测将产生警报疲劳。这项研究的目的是开发一种方法,以帮助护理团队优化警报设置。在2014年4月1日至2015年1月19日之间,使用了一种无线监测系统来连续评估10家医院的普通病房的心率,呼吸频率,SpO2和无创血压。这些数据为3,430名患者94,575小时包含在大型数据库中,可通过云计算工具访问。仿真方案评估了总警报率与阈值和通知延迟的关系。从云托管数据库预测的十个警报/患者/天的总警报率与独立医院进行十天评估的总警报率(36个患者为1550小时)相同。云托管数据库中的生命体征分布图与不同作者发布的其他大型数据库相似。云托管数据库可用于运行各种警报阈值和通知延迟的模拟,以预测护理人员所承受的总警报负担。将来可能会使用这种方法来帮助减轻警报疲劳而又不牺牲持续监视所有生命体征的能力。电子补充材料本文的在线版本(doi:10.1007 / s10877-015-9790-8)包含补充材料,可供授权用户使用。

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