Sounds good: the bright future of clinical alarm management initiatives

Mitigating Alarm Fatigue and Improving the Bedside Experience by Reducing Non-actionable Alarms

OBJECTIVES

To assess whether conditional bedside alarm triggers can reduce the frequency of non-actionable alarms without compromising patient safety and enhance nursing and family satisfaction.

STUDY DESIGN

Single center, quality improvement initiative in an acute care cardiac unit (ACCU) and pediatric intensive care unit (PICU). Following the 4-week pre-intervention baseline period, bedside monitors were programmed with hierarchical time delay and conditional alarm triggers. Bedside alarms were tallied for 4 weeks each in the immediate post intervention period and 2-year follow-up. The primary outcome was alarms per monitored patient day. Nurses and families were surveyed pre- and post-intervention.

RESULTS

A total of 1509 patients contributed to 2034, 1968, and 2043 monitored patient days which were evaluated in the baseline, follow-up, and 2-year follow-up periods, respectively. The median number of alarms per monitored patient day decreased by 75% in the PICU (p<0.001) and 82% in the ACCU (p<0.001) with sustained effect at 2-year follow-up. No increase of rapid response calls, emergent transfers, or code events occurred in either unit. Nursing surveys reported an improved capacity to respond to alarms and fewer perceived non-actionable alarms. Family surveys, however, did not demonstrate improved sleep quality.

CONCLUSIONS

Implemented changes to bedside monitor alarms decreased total alarm frequency in both the acute care cardiac unit and pediatric intensive care unit, improving the care provider experience without compromising safety.

Nurses’ clinical alarm-related behaviors and influencing factors in China†

Objective

To explore the nurses’ behaviors regarding clinical alarms, analyze the related influencing factors, and provide rationales for alarm management.

Methods

A cross-sectional survey was conducted in China. The self-made questionnaire of nurses’ clinical alarm-related knowledge, attitude, and behavior (NCAKAB) was used.

Results

The valid response rate was 98.66% (n = 2368). The average nurses’ clinical alarm-related behaviors (NCAB) score was 65.14 ± 7.95 (out of 85). The dimension scores of NCAB from high to low were alarm learning (4.02 ± 0.85, out of 5), alarm response (27.99 ± 3.64, out of 35), alarm setting (19.24 ± 3.88, out of 25), alarm recognition (7.63 ± 1.68, out of 10) and alarm notification (6.25 ± 1.84, out of 10). There were significant differences in alarm behavior scores between nurses of different ages (F = 4.619, P = 0.000), nursing stints (F = 9.564, P = 0.000), professional titles (F = 4.425, P = 0.004), departments (F = 9.166, P = 0.000), and hospital levels (t = 2.705, P = 0.007). The study showed that nurses’ total alarm behavior scores were positively correlated with the total alarm knowledge score (r = 0.267; P < 0.001) and the total alarm attitude score (r = 438; P < 0.001).

Conclusions

Nurses scored highest in alarm learning, followed by alarm response, alarm setting, alarm recognition, and alarm notification behavior. The factors that influenced alarm behavior included age, title, department, nursing stint, hospital level, professional title, alarm-related training, willingness to participate in alarm-related training, whether or not departments have improved alarm management over the last 3 years, and whether or not departments have formulated norms for alarm management. Nurses with higher scores for clinical alarm knowledge had higher correlating scores for alarm behavior; similarly, nurses with higher scores for clinical alarm attitude had higher scores for alarm-related behavior.

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

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.