作为重症监护病房(ICU)最重要的技术组成部分之一,当参数偏离正常范围时,通过警报提醒工作人员,连续监测患者的重要参数显著提高了患者的安全性。然而,大量的警报经常使员工不知所措,并可能导致警报疲劳,最近COVID-19加剧了这种情况,并可能危及患者。
本研究的重点是对ICU患者监测系统的报警数据进行完整且可重复的分析。我们旨在为精通技术的ICU员工开发自己动手(DIY)说明,以自己分析其监测数据,这是开发高效和有效的报警优化策略的基本要素。
这项观察性研究是使用从2019年21张病床的外科ICU的患者监测系统中提取的警报日志数据进行的。DIY指令在非正式的跨学科小组会议中反复开发。数据分析基于由5个维度组成的框架,每个都有特定的指标:报警负载(例如,每天每张床的警报,报警洪水条件,每个设备和每个临界性的警报),可避免的警报,(eg,技术警报的数量),响应性和警报处理(例如警报持续时间),传感(例如,使用警报暂停功能),和暴露(例如,每个房间类型的警报)。使用R包ggplot2对结果进行可视化,以提供对ICU警报情况的详细了解。
我们开发了6种DIY指令,应该一步一步迭代地遵循。在收集和分析警报日志数据之前,应(重新)定义警报负载度量。接下来应创建警报指标的直观可视化,并将其呈现给员工,以帮助识别警报数据中的模式,以设计和实施有效的警报管理干预措施。我们提供用于数据准备的脚本和一个R-Markdown文件来创建全面的警报报告。各ICU中的警报负荷通过平均每天每床152.5(SD42.2)警报和警报洪水条件进行量化,平均而言,每天69.55(SD31.12),两者大多发生在早班。大多数警报是由呼吸机发出的,有创血压装置,和心电图(即,高血压和低血压,高呼吸率,低心率)。单人间每天每张床的警报暴露量较高(26%,每张床每天平均172.9/137.2报警)。
分析ICU警报日志数据可提供对当前警报情况的宝贵见解。我们的结果要求报警管理干预措施,有效地减少报警的数量,以确保患者的安全和ICU工作人员的工作满意度。我们希望我们的DIY说明鼓励其他人遵循分析和发布他们的ICU警报数据。
As one of the most essential technical components of the intensive care unit (ICU), continuous monitoring of patients\' vital parameters has significantly improved patient safety by alerting staff through an alarm when a parameter deviates from the normal range. However, the vast number of alarms regularly overwhelms staff and may induce alarm fatigue, a condition recently exacerbated by COVID-19 and potentially endangering patients.
This study focused on providing a complete and repeatable analysis of the alarm data of an ICU\'s patient monitoring system. We aimed to develop do-it-yourself (DIY) instructions for technically versed ICU staff to analyze their monitoring data themselves, which is an essential element for developing efficient and effective alarm optimization strategies.
This observational study was conducted using alarm log data extracted from the patient monitoring system of a 21-bed surgical ICU in 2019. DIY instructions were iteratively developed in informal interdisciplinary team meetings. The data analysis was grounded in a framework consisting of 5 dimensions, each with specific metrics: alarm load (eg, alarms per bed per day, alarm flood conditions, alarm per device and per criticality), avoidable alarms, (eg, the number of technical alarms), responsiveness and alarm handling (eg alarm duration), sensing (eg, usage of the alarm pause function), and exposure (eg, alarms per room type). Results were visualized using the R package ggplot2 to provide detailed insights into the ICU\'s alarm situation.
We developed 6 DIY instructions that should be followed iteratively step by step. Alarm load metrics should be (re)defined before alarm log data are collected and analyzed. Intuitive visualizations of the alarm metrics should be created next and presented to staff in order to help identify patterns in the alarm data for designing and implementing effective alarm management interventions. We provide the script we used for the data preparation and an R-Markdown file to create comprehensive alarm reports. The alarm load in the respective ICU was quantified by 152.5 (SD 42.2) alarms per bed per day on average and alarm flood conditions with, on average, 69.55 (SD 31.12) per day that both occurred mostly in the morning shifts. Most alarms were issued by the ventilator, invasive blood pressure device, and electrocardiogram (ie, high and low blood pressure, high respiratory rate, low heart rate). The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed).
Analyzing ICU alarm log data provides valuable insights into the current alarm situation. Our results call for alarm management interventions that effectively reduce the number of alarms in order to ensure patient safety and ICU staff\'s work satisfaction. We hope our DIY instructions encourage others to follow suit in analyzing and publishing their ICU alarm data.