Patient Monitoring Alarms in an Intensive Care Unit: Observational Study With Do-It-Yourself Instructions

Abstract

Background 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. Objective 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. Methods 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. Results 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). Conclusions 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.

The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study

“…The alarm logs were cleaned; only entries describing alarm start were selected, the alarms were categorized in groups depending on the origin of the alarm (electrocardiogram (ECG), ventilator etc. ), as previously described 25 . Only alarms regarding ECG, invasive and non-invasive blood pressure (IBP and NIBP), SpO2, ventilator and temperature were selected since these constitute the basic monitoring requirements for a patient in the ICU.…”

Alarm fatigue and moral distress in ICU nurses in COVID-19 pandemic

“…Desensitization to these alarms is defined as alarm fatigue [ 13 ]. According to the results of the Simpson 2019 study, most of the generated alarms are not related to the patient’s clinical condition and despite the immediate response of the nurses, did not help the patient’s clinical evaluation and caused fatigue in the nurses [ 14 ], a condition that has recently been exacerbated by COVID-19 and potentially endangers patient safety [ 15 ].…”

The clinical, humanistic, and economic outcomes, including experiencing of patient safety events, associated with admitting patients to single rooms compared with shared accommodation for acute hospital admissions. A narrative synthesis systematic literature review

“…HCPs also reported that single rooms improved patients’ sleep. (9) Poncette and colleagues(100) found that fewer alarms raised in shared accommodation reduced overall noise levels. One study also noted that patients preferred single rooms because they felt they were less likely to disturb other patients.…”

“…Significantly lower rates of falls were seen in multi-bed accommodation in two studies. (16,22) The study of Poncette et al,(100) which analysed alarm data in an ICU, found that the number of alarms per bed per day was higher in single rooms than in shared accommodation.…”

“…11 studies considered patient safety (see Figure 3 and supplementary Table 7). (8,16,22,26,(34)(35)(36)65,84,99,100). Three were before-and-after studies, four were contemporaneous studies, one used mixed design, and three were evidence syntheses.…”