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 amount of alarms regularly overwhelms staff, and induces alarm fatigue that endangers 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 analyse 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 on a framework consisting of five dimensions, each with specific metrics: alarm load, ie, alarms per bed per day, alarm flood conditions, alarm per device and per criticality; avoidable alarms, ie, the amount of technical alarms; responsiveness and alarm handling, ie response time; sensing, ie, usage of the alarm pause function; and exposure, ie, alarms per room type. Results were visualized using the R package ggplot2 to provide detailed temporal insights into the ICU’s alarm situation.
RESULTS
We developed step by step DIY instructions for self-analysis of patient monitoring data, including the scripts for data preparation and analysis. The alarm load in the respective ICU was quantified by 152.5 alarms per bed per day on average (SD 42.2), and alarm flood conditions with on average 69.55 per day (SD 31.12) 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). In regard to avoidable alarms, technical alarms by the ECG were the most frequent (eg, lead fallen off). The median response time to alarms yielded 8s (range 0-600). The alarm pause function was applied 10.86 times per bed per day (SD 2.6), and in 91% (19,334/21,194) was not actively terminated, resulting in a proper pause to pause ratio of 0.09:1. The exposure to alarms per bed per day was higher in single rooms (26%, mean 172.9/137.2 alarms per day per bed). 69% of all alarms (2,199.9/3,202.4, SD 651.2) were on average issued by 7.6 of 21 beds per day (36%).
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.
CLINICALTRIAL
NCT04661735
An Intelligent Intensive Care Unit for Patient Monitoring System