JMIR MEDICAL INFORMATICS

Wunderlich et al

Original Paper

Alarm Management in Provisional COVID-19 Intensive Care Units:
Retrospective Analysis and Recommendations for Future
Pandemics
Maximilian Markus Wunderlich1*, MSc; Nicolas Frey1*, MSc; Sandro Amende-Wolf1; Carl Hinrichs2, Dr med; Felix
Balzer1*, Prof Dr med, Dr rer nat; Akira-Sebastian Poncette1*, PD, Dr med
1

Institute of Medical Informatics, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin,
and Berlin Institute of Health, Berlin, Germany
2

Department of Nephrology and Medical Intensive Care, Charité – Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin,
Humboldt-Universität zu Berlin, and Berlin Institute of Health, Berlin, Germany*

these authors contributed equally

Corresponding Author:
Akira-Sebastian Poncette, PD, Dr med
Institute of Medical Informatics
Charité – Universitätsmedizin Berlin
Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of HealthInvalidenstraße 90
Berlin, 10115 Berlin
Germany
Phone: 49 030 450 581 018
Email: akira-sebastian.poncette@charite.de

Abstract
Background: In response to the high patient admission rates during the COVID-19 pandemic, provisional intensive care units(ICUs) were set up, equipped with temporary monitoring and alarm systems. We sought to find out whether the provisional ICU
setting led to a higher alarm burden and more staff with alarm fatigue.
Objective: We aimed to compare alarm situations between provisional COVID-19 ICUs and non–COVID-19 ICUs during thesecond COVID-19 wave in Berlin, Germany. The study focused on measuring alarms per bed per day, identifying medical devices
with higher alarm frequencies in COVID-19 settings, evaluating the median duration of alarms in both types of ICUs, and assessing
the level of alarm fatigue experienced by health care staff.
Methods: Our approach involved a comparative analysis of alarm data from 2 provisional COVID-19 ICUs and 2 standard
non–COVID-19 ICUs. Through interviews with medical experts, we formulated hypotheses about potential differences in alarmload, alarm duration, alarm types, and staff alarm fatigue between the 2 ICU types. We analyzed alarm log data from the patientmonitoring systems of all 4 ICUs to inferentially assess the differences. In addition, we assessed staff alarm fatigue with aquestionnaire, aiming to comprehensively understand the impact of the alarm situation on health care personnel.
Results: COVID-19 ICUs had significantly more alarms per bed per day than non–COVID-19 ICUs (P<.001), and the majorityof the staff lacked experience with the alarm system. The overall median alarm duration was similar in both ICU types. We found
no COVID-19–specific alarm patterns. The alarm fatigue questionnaire results suggest that staff in both types of ICUs experienced
alarm fatigue. However, physicians and nurses who were working in COVID-19 ICUs reported a significantly higher level ofalarm fatigue (P=.04).
Conclusions: Staff in COVID-19 ICUs were exposed to a higher alarm load, and the majority lacked experience with alarm
management and the alarm system. We recommend training and educating ICU staff in alarm management, emphasizing theimportance of alarm management training as part of the preparations for future pandemics. However, the limitations of our study
design and the specific pandemic conditions warrant further studies to confirm these findings and to explore effective alarm
management strategies in different ICU settings.
(JMIR Med Inform 2024;12:e58347) doi: 10.2196/58347

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 1
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Wunderlich et al

KEYWORDS
patient monitoring; intensive care unit; ICU; alarm fatigue; alarm management; patient safety; alarm system; alarm system quality;
medical devices; clinical alarms; COVID-19

Introduction
Background
Patients critically ill with COVID-19 frequently experience
multiple organ failure and need life-sustaining measures suchas continuous renal replacement therapy, mechanical ventilation,or even extracorporeal membrane oxygenation [1]. Therefore,
during major COVID-19 outbreaks (eg, in 2019 and 2020),
intensive care units (ICUs) worldwide saw an increase inadmission rates of up to 300% [2]. Intensive care capacities
became stretched; in some regions, treatment facilities were
fully occupied [3]. Hospitals thus had to increase their intensive
care capacities to cope with the proliferating number of patientswith COVID-19 [4]. As a consequence, in many countries,
provisional ICUs were set up [5-9].
In provisional COVID-19 ICUs, as in standard ICU settings,
patients require continuous monitoring of their vital signs, which
include heart rate, cardiac rhythm, and peripheral oxygen
saturation (SpO2) via pulse oximetry. If an abnormal situation
occurs (eg, a vital sign exceeds a predetermined threshold), an
alarm is issued to alert clinicians [10]. However, ICU staff today
often experience alarm fatigue [11] because of high rates offalse positive or nonactionable alarms [12]. Alarm fatigue canbe hazardous for patient safety, especially when critical alarms
are missed [13,14]. The constant noise can cause high levels ofstress among staff and disrupt patients’ sleep-wake cycles,
increasing the risk of delirium and potentially poorer recovery
outcomes [15,16].
In COVID-19 ICUs, the alarm situation might have been evenworse: ICU staff, likely overworked due to an overwhelming
patient load and a massive workload, had to cope with thepsychological burden of the fear of infection and the distressing
reality of witnessing numerous patient deaths. ICU staff
experienced poor mental health during the COVID-19 pandemic[17-19], and more errors in clinical settings occurred [20].
Moreover, the makeshift nature of the ICUs, which were set up
in haste to handle the surge in patients, likely exacerbated these
problems. The often improvised and provisional nature of these
ICUs likely introduced additional operational complexities andstressors, such as limited space and insufficient technicalequipment, further taxing the already strained staff. All theseissues may have been compounded by a loud and potentiallyunreliable alarm system.

Objectives
This study systematically assesses the alarm situations in 2
provisional COVID-19 ICUs and 2 non–COVID-19 ICUs,
focusing on both the actual alarm rates and the extent of alarm
fatigue experienced by ICU staff. By incorporating expert
interviews with medical professionals who have worked in
COVID-19 ICUs, alongside quantitative analyses of alarm log
data from the bedside monitors [21-23] and results from thehttps://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

alarm fatigue questionnaire [24], this investigation follows amultimodal approach.

Methods
Study Design
In this study, the alarm situations in 2 provisional COVID-19
ICUs were compared with those in 2 non–COVID-19 ICUs; all
4 ICUs were situated on the same campus at a tertiary care
university hospital in Berlin, Germany. A retrospective
observational analytical study design was used, with thecomparison being approached from three perspectives: (1)hypotheses were derived and tested based on interviews withmedical experts, exploring potential differences in alarm
situations between COVID-19 and non–COVID-19 ICUs; (2)alarm log data from both types of ICUs were analyzed; and (3)the results from an alarm fatigue questionnaire, which wasadministered as part of another study [24] during the same
period, were collected and analyzed.
The non–COVID-19 ICUs consist of an interdisciplinary ICU
with patients who have recently undergone surgery or are in the
perioperative stage (hereinafter surgical ICU); and an ICU witha focus on internal medicine (hereinafter medical ICU) where
patients with severe infection, cardiac diseases, kidney failure,or other single- or multiple-organ failures are treated.
The medical and surgical ICUs have single and multiple
bedrooms and contain 20 and 21 beds, respectively. The
COVID-19 ICU was divided into 2 separate units: COVID-19
ICU-A had single bedrooms with 16 beds, and COVID-19
ICU-B had single and multiple bedrooms with 24 beds.

Expert Interviews for Hypothesis Design
To derive hypotheses regarding how alarm situations might
differ between COVID-19 and non–COVID-19 ICUs, 5 medical
experts (physicians: n=2, 40%; nurses: n=3, 60%) wereinterviewed from July to December 2021. The interviews weresemistructured [25,26]. The foundational questions weregrounded in our overarching research question: “Whatdistinguishes the alarm situations in COVID-19 ICUs fromthose in non–COVID-19 ICUs?” A detailed list of thesequestions can be found in Table S1 in Multimedia Appendix 1.
In addition, 2 job shadowing sessions were conducted (one on
October 1, 2021, in the medical ICU; and another on November
12, 2021, in COVID-19 ICU-B). During the second session, aninterview was conducted in COVID-19 ICU-B. The remaining
4 interviews were held online via Microsoft Teams [27]. An
integral aspect of the interviews was that beyond the structured
questions, the medical experts were proactively asked about
potential hypotheses regarding the differences in alarm situations
between the ICU types.
Subsequent to the interviews, the collected data, includingtranscripts and notes, were meticulously examined. The

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 2
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
hypotheses were derived through an in-depth examination andcollective discussion within an interdisciplinary team.

Statistical Analysis
Data Analysis
The alarm logs were processed and analyzed as previously
described [22], using R (R Foundation for Statistical Computing)[28] with RStudio (Posit Software, PBC) [29] in combination
with the following packages: lubridate [30], ggplot2 [31], anddplyr [32]. The logs were extracted from the Philips IntelliVue
patient monitoring systems (in the surgical and medical ICUs:
MX800, software version m; in the COVID-19 ICUs: MX750,
software version p) as CSV files from the non–COVID-19 ICUs
and as XML files from the COVID-19 ICUs (structure of thecleaned alarm log data frames is listed in Table S2 Multimedia
Appendix 1). Alarm signals from mechanical ventilation devices
were not correctly stored in the alarm log data due totransmission errors and were therefore excluded from theanalysis. The included alarm signals and the assignment to their
medical device are listed in Table S3 in Multimedia Appendix
1. The logs range across 113 days (from November 19, 2020,
to March 11, 2021).

Hypothesis Testing
To test for statistical differences between COVID-19 andnon–COVID-19 ICUs, the units were grouped accordingly. All
tests were 1-tailed, with a significance level of α=.05. P values
were adjusted using the Bonferroni correction. The first
hypothesis (H1) proposed that the alarm load is higher in
COVID-19 ICUs than in non–COVID-19 ICUs. This hypothesis
was further subdivided into total alarm load, clinical red alarms,
clinical yellow alarms, and technical alarms.
The second hypothesis (H2) posited that more alarms are issued
from specific medical devices, namely electrocardiogram (ECG)
and invasive blood pressure (IBP) devices, in COVID-19 ICUs
than in non–COVID-19 ICUs. The alarms from these devices
were further categorized based on the alarm color (red:potentially life-threatening events; yellow: vital signs exceed
predetermined thresholds).
The third hypothesis (H3) proposed that the alarm duration islonger in COVID-19 ICUs than in non–COVID-19 ICUs. For
this hypothesis, the alarm durations from clinical alarms were
subdivided into medical devices (non-IBP [NIBP], temperature,
SpO2, ECG, and IBP devices) and alarm colors (red and yellow).
For H1 and H2, the alarm load was quantified in alarms per bed
per day. Given that the distributions were skewed to the rightand approximately gamma distributed, a dummy-coded
no-intercept generalized linear model with a log link function
was used. Cohen d was used as the effect size measure and was
calculated using the package effsize developed by Torchiano[33].
For H3, testing was conducted with median alarm durations.
The distributions of alarm durations were highly skewed to theright and approximately exponentially distributed; therefore,
nonparametric bootstrapping with the boot package developed
by Canty et al [34] was used with an a priori estimation of thedifference in alarm duration with equation 1.
https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

Wunderlich et al
H0: μ1 – μ2 ≤ 3 (1)HA: μ1 – μ2 > 3
Differences of 3 seconds were considered significant, whilesmaller effects that could already be significant due to the large
sample were considered not significant. A median-based
estimator for Cohen d–type effect size (equation 2) with anestimation of variance (equation 3) was used, where k is thenumber of units and l is the number of alarms.

To test the differences in alarm fatigue experienced by ICU
staff between COVID-19 and non–COVID-19 ICUs (H4), an
unpaired 1-tailed t test using Cohen d as the effect size measure
was conducted.

Exploratory Data Analysis
Metrics defined in our previous study [22] were used for theevaluation of alarm situations in the ICUs: alarms per bed perday, critical alarms, alarms per device, alarm flood conditions(≥10 alarms within 10 minutes), use of the alarm pause function
per bed per day, proper pause-to-pause ratio, and concurrent
alarm duration per bed per day. The last metric was first
introduced by Varisco et al [12] and is calculated by summing
the number of active parallel alarms. Specifically, if 2 alarms
sound simultaneously within a second, this is counted as 1
second of concurrent alarm duration, and if 3 alarms soundsimultaneously within a second, 2 seconds are counted.
The metrics were related to bed occupancy and time period tocompare the results between the different ICUs. Due to theabsence of information regarding the cause of termination in
the data, it was not possible to determine the alarm response
time. Therefore, alarm duration was used instead.
In the calculations of alarm flood conditions and concurrent
alarm duration, only alarms with an auditory modality,
specifically yellow and red alarms, were included. For the alarm
duration used in calculating concurrent alarm duration, a cutoff
was set at 1800 seconds. Alarm durations exceeding this limit
were considered outliers and were therefore excluded from theanalysis.

Alarm Fatigue Questionnaire
The questionnaire data were taken from a separate study [24]that coincided with our data collection phase. The questionnaire,
distributed as a web-based survey via REDCap (ResearchElectronic Data Capture; Vanderbilt University) [35] to ICU
staff at the same German hospital between April and June 2021,
provided responses from COVID-19 ICU-A, COVID-19 ICU-B,
a third COVID-19 ICU (COVID-19 ICU-C), and the 2
non–COVID-19 ICUs (medical and surgical ICUs). The originalquestionnaire consisted of 27 items; however, only thosealigning with the 9-item questionnaire developed by Wunderlich
et al [36] were included in this analysis. Each item was measured
on a 5-point Likert scale, ranging from I strongly agree to I
strongly disagree.
JMIR Med Inform 2024 | vol. 12 | e58347 | p. 3
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
Demographic questions about work experience, place of work,and position (nurses and physicians, as well as support staff, ie,
students or nurses from general wards) were also part of thequestionnaire. Only responses from participants who consented
to data analysis were included in the study. Submissions with
1 or 2 missing items were imputed at random based on thepredictive mean matching algorithm using 1 imputation withthe mice package [37]. To calculate an alarm fatigue score, theitems were scored from –2 (I strongly disagree) to 2 (I strongly
agree). I partly agree was scored with 0. Four items were scoredreversely. The sum of all Likert items results in the alarm fatigue
score, which ranges from –18 to 18. A score of –18 would
indicate that the staff members are not experiencing alarm
fatigue at all, while a score of 18 would mean that the staff
members are experiencing extreme alarm fatigue; the midpoint
is 0. We report the alarm fatigue in percentage as recommended
by Wunderlich et al [24] with equation 4.

Ethical Considerations
Ethics approval for this study was granted by the ethics
commissionofCharité–Universitätsmedizin
Berlin(EA4/218/20). All participants provided consent before thestudy. Data confidentiality was ensured through anonymizationin compliance with General Data Protection Regulation. No
compensation was provided to participants.

Results
Beginning with insights from expert interviews to formulate
our hypotheses, we proceeded to test them empirically using
alarm log data and an alarm fatigue questionnaire, concluding
with insights from our exploratory data analysis.

Expert Interviews for Hypothesis Design
Of the 5 expert interviewees, 2 (40%) reported that only
approximately one-third of the staff in COVID-19 ICUs had
experience in intensive care, while the remaining two-thirds
consisted of nurses, who until then had only worked on generalwards; or individuals without specific experience, such as
medical students; and even untrained personnel. The staff wereassigned different tasks depending on their qualifications. Of
the 2 physicians, 1 (50%) suggested that staff were not trainedon how to properly apply sensors, such as ECG electrodes,
potentially leading to additional (medically irrelevant) alarms.
Of the 3 nurses, 1 (33%) suggested that alongside the alarm
burden, the high fatality rate among patients imposes
psychological strains on the staff. According to the interviewees,
the patient cohort in the COVID-19 ICUs presented a more orless homogeneous clinical picture with varying COVID-19
severity. Many patients needed mechanical ventilation andunderwent continuous renal replacement therapy with dialysis
devices that produce very loud and unpleasant alarms. However,these alarms were not recorded in the alarm logs.
Of the 2 physicians, 1 (50%) reported that patients with
COVID-19 infection often have multiple organ failure and ahigh length of stay; therefore, they are often equipped with ≥7
https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

Wunderlich et al
perfusors for medications (eg, antibiotics, catecholamines,
sedatives, and parenteral nutrition), all of which trigger
additional alarms that are also not recorded in the alarm logs.
All 3 nurses reported a higher alarm burden compared withthose working in non–COVID-19 ICUs. All interviewees
described the removal and reattachment of all patient sensors
during transition from prone to supine position or vice versa as
a possible cause of false alarms if the alarm pause function was
not used. On the basis of this information, we hypothesized asfollows: (H1) The alarm load is higher in COVID-19 ICUs thanin non–COVID-19 ICUs.
Of the 3 nurses, 2 (67%) reported that they perceived many
patients to be multimorbid, overweight, and of older age, withmany having a cardiac or pulmonary history and tachycardia.
All interviewees reported that the blood circulation of patientswith COVID-19 was extremely unstable, which led us tohypothesize that medical devices related to blood circulation(ie, the ECG, NIBP, or IBP devices) issue more alarms in
COVID-19 ICUs: (H2) More alarms are issued from ECG,
NIBP, and IBP devices in COVID-19 ICUs than in
non–COVID-19 ICUs.
Both COVID-19 ICUs featured long corridors withinward-opening doors, which were usually closed to isolatepatients who were infectious. The interviewees reported thatthe corridors, the dispensary room, and the physician’s room
were not equipped with central monitoring; thus, the health careproviders had no overview of the patients, which made itdifficult to locate the origin of the auditory alarms. Only thenurses’ room was equipped with central monitoring, but alarms
could not be turned off remotely. According to the interviewees,
temporary arrangements were made by the staff to address thisproblem, such as leaving the doors to patients’ rooms slightly
open or placing speakers linked to the monitors in the hallway.
However, to respond to or turn off an alarm, staff had to enter
the patient’s room. This required them to don personal protective
equipment (ie, gloves, a protective hood, a polypropylene
protective gown, and a face shield or goggles), which tookapproximately 30 seconds and hindered quick movement
between rooms. All 3 nurses reported that the protective
equipment did not interfere with turning off the alarms, theusability of the monitor displays, or the adjustment of monitor
settings. However, they all described it as strenuous and time
consuming. Accordingly, we derived the third hypothesis: (H3)
The alarm duration is higher in COVID-19 ICUs than in
non–COVID-19 ICUs.
Patients in COVID-19 ICUs often present with severe, complex
medical conditions that require close monitoring andinterventions. Of the 3 nurses, 1 (33%) suggested that next tothe alarm burden and heavy workload, the high fatality rate
among patients could strain and distress staff psychologically.
This mental and emotional strain could potentially affect staff
performance and cognitive abilities, potentially hindering their
response to alarms. Combining this information with ourreasoning for the previous 3 hypotheses, we formulated thefourth hypothesis as follows: (H4) Staff alarm fatigue is higher
in COVID-19 ICUs than in non–COVID-19 ICUs.

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 4
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Wunderlich et al

Having delineated the hypotheses informed by the expert
interviews, we proceeded to empirically test each of them,starting with the alarm load in different ICU settings.

Hypothesis 1: The Alarm Load Is Higher in COVID-19
ICUs Than in Non–COVID-19 ICUs
Significant differences were observed in all 4 tests, which
confirmed our hypothesis that the alarm load was higher inprovisional COVID-19 ICUs than in non–COVID-19 ICUs
(P<.001; Tables 1 and 2). COVID-19 ICUs experienced anaverage of 23% more alarms in total, 41% more red critical
alarms, and 24% more yellow alarms compared with
non–COVID-19 ICUs. The alarm load caused by technicalalarms was 109% higher in COVID-19 ICUs. The alarm load
results, subdivided by medical device and alarm color, arereported in Figure S1 in Multimedia Appendix 1. The technicalalarm signal that resulted in the most alarms per bed per day
was ECG lead off, with mean values recorded as follows:
surgical 3.42 (SD 2.10), medical 3.84 (SD 2.87), COVID-19
ICU-A 7.22 (SD 1.93), and COVID-19 ICU-B 7.77 (SD 2.41).

Table 1. Alarm load from all intensive care units (ICUs; n=113).
Alarm load (alarms per bed per day)Surgical ICU, mean (SD)

Medical ICU, mean (SD)COVID-19 ICU-A, mean (SD) COVID-19 ICU-B, mean (SD)

Total alarms

122.84 (38.95)

122.54 (27.85)

142.47 (41.49)

157.40 (42.58)

Red alarms

10.94 (2.43)

10.99 (2.82)

14.99 (5.89)

15.81 (4.01)

Yellow alarms

108.48 (37.86)

107.71 (26.68)

126.23 (37.93)

140.56 (41.08)

Technical alarms

5.10 (2.17)

5.03 (2.97)

10.30 (2.30)

10.84 (2.51)

Table 2. The results from the hypothesis testing for H1 (n=113).
Alarm load (alarms per bed per day)a

P value

Cohen dNon–COVID-19 ICUsa, mean (SD)COVID-19 ICUs, mean (SD)

Total alarms

122.21 (22.37)

151.26 (31.02)

<.001

1.04

Red alarms

10.98 (1.90)

15.51 (3.33)

<.001

1.67

Yellow alarms

108.33 (21.73)

134.59 (29.24)

<.001

1.02

Technical alarms

5.06 (1.87)

10.62 (1.86)

<.001

2.98

ICU: intensive care unit.

Hypothesis 2: More Alarms From ECG, NIBP, andIBP Devices Are Issued in COVID-19 ICUs Than in
Non–COVID-19 ICUs
Table 3 shows the results of hypothesis testing for alarms issued
by the IBP and ECG devices, subdivided by alarm color and
ICU type. In both ICU types, the IBP device was responsible
for the majority of the alarms, followed by the ECG and NIBP
devices. Yellow alarms issued by the IBP and NIBP devices
and red alarms issued by the ECG device occurred significantly

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

more often in COVID-19 ICUs (P<.001). However, we did not
find significant differences in the occurrence of yellow ECG
alarms and red IBP alarms. The results of the alarm load,
subdivided by medical device and alarm color, from all ICUs
are presented in Table S4 in Multimedia Appendix 1. Whilecertain alarm types exhibited significant differences, the overall
impact was not profound enough to affirm the second
hypothesis, with the exception of the notable difference in thefrequency of red ECG alarms (as indicated by the Cohen d valueof 1.13).

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 5
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Wunderlich et al

Table 3. Results of the hypothesis testing for H2 (n=113).
Devicesa

Alarm load (alarms per bed per day)P value

Cohen daNon–COVID-19 ICUsb, mean (SD)COVID-19 ICUs, mean (SD)ECGc (yellow)

39.01 (17.65)

38.79 (18.70)

.99

0.00

ECG (red)

2.51 (1.18)

4.60 (2.34)

<.001

1.13

IBPd (yellow)

53.87 (11.38)

66.16 (18.22)

<.001

0.81

IBP (red)

5.16 (1.14)

5.22 (1.34)

.99

0.05

NIBPe

0.34 (0.40)

0.56 (0.51)

<.001

0.49

Cohen d has been reported in absolute values.

b

ICU: intensive care unit.

c

ECG: electrocardiogram.

d

IBP: invasive blood pressure.

e

NIBP: noninvasive blood pressure.

Hypothesis 3: The Alarm Duration Is Higher in
COVID-19 ICUs Than in Non–COVID-19 ICUs
All ICUs had a median clinical alarm duration of 10 seconds.
Yellow alarms issued by the NIBP device had the longest alarm
durations (Table 4; all group sizes are reported in Table S5 in
Multimedia Appendix 1). The durations of yellow alarms
triggered by IBP and ECG devices were shorter than those ofalarms from all other medical devices, a pattern consistent acrossall types of ICUs. The median duration of technical alarms in
COVID-19 ICUs was significantly longer. Yellow alarms issued

by the NIBP, temperature, and SpO2 devices had significantly
longer durations in COVID-19 ICUs (P<.001; Table 5; all groupsizes are reported in Table S5 in Multimedia Appendix 1). The
results of the median alarm duration, subdivided by medical
device and alarm color, are presented in Figure S2 in Multimedia
Appendix 1. The results show a mixed picture: <50% of thetotal alarms had a significant difference in alarm duration
between the ICU types, implying that most alarm durations did
not differ significantly between the 2 ICU types. As such, ourdata do not provide sufficient empirical evidence to support H3.

Table 4. Results of the hypothesis testing for H3 across each type of intensive care unit (ICU).
Alarm duration (s)Surgical ICU, median Medical ICU, median
(IQR)
(IQR)

COVID-19 ICU-A, median COVID-19 ICU-B, median
(IQR)
(IQR)

Clinical alarms

10 (4-27)

10 (4-25)

10 (3-28)

10 (3-29)

Technical alarms

7 (4-66)

4 (4-62)

13 (4-65)

14 (4-68)

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 6
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Wunderlich et al

Table 5. Results of the hypothesis testing for H3 across the 2 types of intensive care units (ICUs).
AlarmsP value

Cohen da type

Non–COVID-19 ICUs,
median (IQR)

COVID-19 ICUs, median(IQR)

5 (3-27)

14 (4-67)

<.001

0.016

NIBP

62 (25.00-179.75)

99 (29-337)

<.001

0.094

Temperature

24 (8-86)

32 (9-192)

<.001

0.009

SpO2c (yellow)

14 (7-32)

23 (11-57)

<.001

0.069

SpO2 (red)28 (11-89)

33 (14-75)

.11

0.004

IBPd (yellow)

13 (6-32)

11 (4-30)

.99

0.014

IBP (red)24 (9-84)

20 (9-48)

.99

0.005

e

ECG (yellow)

4 (3-9)3 (2-7)

.99

0.014

ECG (red)23 (9-119)

16 (8-35)

.99

0.005

Technicalba

Alarm duration (s)Cohen d has been reported in absolute values.

b

NIBP: noninvasive blood pressure.

c

SpO2: peripheral oxygen saturation.

d

IBP: invasive blood pressure.

e

ECG: electrocardiogram.

Hypothesis 4: Staff Alarm Fatigue Is Higher in
COVID-19 ICUs Than in Non–COVID-19 ICUs
The questionnaire was completed by 707 participants (n=78,
11% returned blank questionnaires; n=44, 6.2% returned
incomplete questionnaires). Of the 585 participants who returned
complete questionnaires, we included 144 (24.6%) in theanalysis. Of these 144 participants, 88 (61.1%) were from
non–COVID-19 ICUs (n=32, 36% from the medical ICU; n=56,
64% from the surgical ICU), and 56 (38.9%) were fromCOVID-19 ICUs (n=48, 86% from COVID-19 ICU-A; n=8,
14% from COVID-19 ICU-B). The majority of the respondents(92/144, 63.9%) were intensive care nurses (COVID-19 ICUs:
25/56, 45%; non–COVID-19 ICUs: 67/88, 76%). The
COVID-19 ICUs had a notable proportion of additional support
staff among the respondents, including nursing students andnurses from regular wards (24/56, 43%) compared with
non–COVID-19 ICUs (9/88, 10%). The least represented group
among the respondents were physicians (COVID-19 ICUs: 7/56,

12%; non–COVID-19 ICUs: 9/88, 10%). The overall alarm
fatigue score was higher in COVID-19 ICUs (mean 56.00, SD
15.80) than in non–COVID-19 ICUs (mean 55.27, SD 13.76).
Statistical testing of the alarm fatigue score revealed nosignificant differences between COVID-19 and non–COVID-19
ICUs (t105.41=0.2841; P=.39; Cohen d=0.05). Importantly, when
considering only experienced ICU staff—nurses andphysicians—the alarm fatigue scores were significantly higher
in COVID-19 ICUs than in non–COVID-19 ICUs (t109=1.7332;
P=.04; Cohen d=0.363). Figure S3 in Multimedia Appendix 1
depicts the results of the questionnaire, subdivided byprofession. Nurses and physicians reported a higher alarm
fatigue score than support staff, who generally reported an
overall low alarm fatigue score in both ICU types. Given the
results of the hypothesis testing (Table 6), we cannot
conclusively validate H4, especially when considering all staff
types; however, among ICU staff, there is evidence suggestinghigher alarm fatigue in COVID-19 ICUs than in non–COVID-19
ICUs.

Table 6. Results of the hypothesis testing for H4.
Alarm fatigue questionnaire scores

P value

Cohen dNon–COVID-19 ICUs,
mean (SD)COVID-19 ICUs, mean(SD)

All participants

55.27 (13.76)

56.00 (15.80)

.39

0.050

Clinicians (nurses and physicians)

59.00 (13.05)

64.28 (14.20)

.04

0.363

Insights From the Exploratory Data Analysis
While the surgical and medical ICUs were approximately fully
occupied over the entire period, the COVID-19 ICUs were often
only partly occupied depending on the patient load. Average
bed occupancy was 97.26% and 92.27% in the surgical andhttps://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

medical ICUs, respectively; and 88.79% and 82.75% in
COVID-19 ICU-A and COVID-19 ICU-B, respectively. Figure
S4 in Multimedia Appendix 1 displays the unit occupation overthe entire period.

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 7
(page number not for citation purposes)

JMIR MEDICAL INFORMATICSThe use of the alarm pause function per bed per day was
substantially less frequent in COVID-19 ICUs (mean 5.08, SD
1.69) compared with alarm pauses per bed per day innon–COVID-19 ICUs (mean 12.21, SD 2.21). The medical ICU
recorded the highest proper pause-to-pause ratio of 0.08,
followed by the surgical ICU and COVID-19 ICU-A, both at
0.04. COVID-19 ICU-B had the lowest pause-to-pause ratio:
0.03. Figure S5 in Multimedia Appendix 1 displays the numberof threshold changes per bed per day and profile changes per
bed per day from all ICUs.
In COVID-19 ICUs, alarm flood conditions per bed per day
occurred on average 35% more frequently (COVID-19 ICU-A:
mean 2.60, SD 1.29; COVID-19 ICU-B: mean 2.73, SD 1.47)
compared with their non–COVID-19 counterparts (surgical
ICU: mean 1.71, SD 1.33; medical ICU: mean 1.95, SD 0.92).
In addition, COVID-19 ICUs experienced on average 27% moreinstances of concurrent alarm duration per bed per day (mean5201.76, SD 1156.00) than non–COVID-19 ICUs (mean4101.42, SD 965.00).

Discussion
Overview
We compared alarm situations in 2 provisional COVID-19 ICUs
with those in 2 non–COVID-19 ICUs. Interviews with nurses
and physicians who worked in the COVID-19 ICUs led us tohypothesize that COVID-19 ICUs have a higher alarm load, ahigher number of specific alarm signals, and longer-sounding
alarms than non–COVID-19 ICUs. We also hypothesized that
staff working in COVID-19 ICUs experience more alarm fatigue
than staff working in non–COVID-19 ICUs.

There Was a Higher Alarm Load in the Provisional
COVID-19 ICUs
COVID-19 ICUs had a significantly higher alarm load from
red, yellow, and technical alarms. This higher alarm load led
to an increased number of alarm flood conditions and concurrent
alarm duration, escalating the nurses’ workload and potentiallycausing sensory overload [38]. While some differences can be
attributed to the COVID-19 condition itself—such as the higher
number of red critical alarms, possibly due to the high mortality
rate and bad conditions of the patients—we suspect that mostdifferences in alarm load were due to the interaction of staff
with the alarm system (eg, not using the pause function whenturning a patient from prone to supine position or vice versa,
not adjusting thresholds specifically to patients’ conditions, notinstalling or not using monitoring profiles specific to patientswith COVID-19 infection, or improperly applying sensors).

Patients With COVID-19 Infection Had Similar Alarm
Signals as Those Without COVID-19 Infection
We anticipated that some devices—such as ECG, NIBP, and
IBP devices—would generate more alarms in COVID-19 ICUs
due to the unique physiological manifestations of COVID-19,
but this was not the case. Only red ECG alarms were notably
more frequent in COVID-19 ICUs than in non–COVID-19
ICUs; other numbers of different clinical alarm signals werehttps://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

Wunderlich et alsimilar across all ICUs and seem to be a recurring theme because
similar results were reported in previous studies [12,22].
In both ICU types, ECG lead off was the most frequent technicalalarm signal. Interestingly, it occurred more than twice as often
in COVID-19 ICUs. This also might be attributed to practices
such as moving patients between prone and supine positioning,which necessitates the removal and subsequent reattachment of
all sensors and electrodes each time.

The Alarm Duration Was Equally Long in Both ICU
Types
While our initial theory posited that alarm durations would be
longer in COVID-19 ICUs—owing to the time required for staff
to don protective equipment before entering a patient’s
room—our findings did not confirm this hypothesis. We foundno significant differences in the overall median alarm durations
between COVID-19 and non–COVID-19 ICUs, except in thecase of a few medical devices.
Devices displaying visual information such as ECG waveforms
allow staff to rapidly assess alarm urgency. By contrast, thenumerical values displayed by devices such as those measuring
temperature or NIBP require more time for staff to interpret,
potentially causing longer alarm durations.
We must also acknowledge the impact of other factors, forexample, the floor layout of the unit, different unit policies [23],nurse-patient ratio [39], and the individual traits of the staff
members [40].
Interestingly, red alarm durations were consistently longer thanyellow alarm durations across all medical devices and in both
ICU types. This may be due to health care providers promptly
turning off yellow alarms first (without checking the patient’s
condition) [39], placing more emphasis on critical alarms, andonly then checking the patient’s condition.
In our exploratory analyses, we found that the alarm pause
function was used more frequently in non–COVID-19 ICUs
than in COVID-19 ICUs. This might be explained by the factthat many nurses in COVID-19 ICUs had limited critical careexperience and therefore did not know that this function exists
or how to use it; COVID-19 ICU staff also might have entered
the patients’ rooms less often because they were required to donprotective gear, which is a time-consuming process.

ICU Staff Experienced More Alarm Fatigue in
COVID-19 ICUs
While overall alarm fatigue questionnaire scores were similar
between both types of ICUs, the situation varied among different
health care professionals. Nurses and physicians in COVID-19
ICUs had significantly higher alarm fatigue scores, whereas
support staff in both types of ICUs reported low alarm fatigue
scores. Notably, a substantial portion of questionnaire
participants in COVID-19 ICUs consisted of support staff, thatis, students or nurses from normal wards (COVID-19 ICUs:
25/56, 45%; non–COVID-19 ICUs: 9/88, 10%), reflecting therecruitment of nurses from other services and students withvarying levels of critical care experience due to the exceptional
circumstances. Unlike the experienced and well-rehearsed teams
typically found in non–COVID-19 ICUs, COVID-19 ICU teams
JMIR Med Inform 2024 | vol. 12 | e58347 | p. 8
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
often incorporated diverse teams that lacked experience in
critical care medicine, training, and familiarity with themonitoring system and alarm management. This inexperienceextended to their training and understanding of monitoring
systems and alarm management, mirroring findings fromexisting literature [41]. In some hospitals, the patient-nurse ratio
was increased, which occasionally resulted in poorer quality ofcare [42]. However, it is important to note that greater fatigue
was identified among those working with patients with
COVID-19 infection, rather than directly associating alarm
fatigue with the COVID-19 condition itself.
There seemed to have been challenges with the alarm systemin the COVID-19 ICUs that might have impeded appropriatemonitoring. Due to the absence of central monitors in thecorridors, dispensary room, and physicians’ room, it wasdifficult for health care providers to locate or swiftly identify
and respond to alarms. Interviewees mentioned the protective
gear worn in the COVID-19 ICUs as an additional burden when
responding to alarms. Protective equipment can impede swift
movement, thus slowing response times to alarms, which might
intensify the stress and sensory overload associated with alarm
fatigue. This finding aligns with the conclusions drawn by
Akturan et al [43] that suggested that personal protective
equipment could contribute to increased alarm fatigue.

Recommendations for ICU Alarm Systems in Future
Pandemics
Due to extensive international traffic, the risk of future
pandemics remains high in a globalized world [44], and the
COVID-19 pandemic will be followed by a new pandemic atsome point. Such pandemics will again likely require the setting
up of provisional ICUs to cope with rapid patient admissions.
When preparing for such events, we recommend also preparing
the alarm systems and their human operators. Even outside ofpandemics, the shortage of specialist staff is increasing thewillingness of authorities to deploy untrained personnel incertain functions in the medical field.

Wunderlich et al
Alarm management aims to reduce the number of unnecessary
alarms on the premise that a lower alarm rate decreases alarm
fatigue among staff [22]. Provisional ICUs can benefit from
effective alarm management, as do regular ICUs, where it helps
to significantly decrease the number of false alarms and theoverall alarm burden [45-49].
Unnecessary alarms stem from insufficient alarm management
knowledge, including using default instead of customized alarm
limits, rarely using the pause function during patient
manipulation or being unaware of its existence, using
insufficient consumables, or attaching electrodes improperly[50]. Previous studies [51] highlight a lack of knowledge about
these functions among nurses, pointing to a pressing need for
education on physiological monitors [52].
Given the urgency with which new staff members have to beonboarded, the training should be decentralized and easily
accessible. To cater to these requirements, we recommend
developing educational microcredentials, such as video tutorials
that provide guidance on adjusting alarm limits on specificmonitoring devices, implementing remote alarm turn-off, andtransitioning from individual to smart alarm systems. To
visualize and operationalize our approach to pandemic
preparedness in ICU alarm systems, we propose a bicyclicalstrategy, as illustrated in Figure 1. This strategy underscores
the significance of continuous learning and adaptability in alarm
management. Operating modern bedside monitors requires ablend of cognitive knowledge, psychomotor skills, criticalthinking, and an understanding of alarm systems [53]. Hence,
this educational program should use the skillmap from the study
by Sowan et al [51] as a foundation, expanding it to address thebroader technical and practical aspects of monitor use. Modular
content that teaches specific skills can be easily adapted to meet
unique situational needs; for example, tutorials can be designed
to explain how to respond to alarms while wearing protective
gear or how to mobilize patients in prone position withoutcausing alarm artifacts. These resources could be beneficial for
ICU staff training not only during pandemics but also in routine
situations.

Figure 1. Two-phase approach to intensive care unit (ICU) alarm system preparedness and refinement. This figure details continuous training during
nonpandemic periods, rapid onboarding, and specialized training at the onset of a pandemic, followed by an evaluation cycle after the pandemic toimprove future alarm management strategies.

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 9
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Limitations
ICUs function as complex sociotechnical systems, making it
inherently challenging to compare them. All ICUs were
equipped with similar technical equipment (eg, mechanicalventilators and dialysis devices), but our comparison between
ICUs was complicated by the variations in the specific devices
used across units, leading to differences in alarm signals.
Therefore, we only included alarms that occurred in all units.
In addition, numerous devices in all ICUs are not connected tothe central monitoring system, meaning that the alarms could
not be recorded and evaluated. Alarms from mechanicalventilation had to be excluded from the ICU data due to atechnical error, preventing us from testing our hypothesis about
an increase in such alarms in COVID-19 ICUs.
The monitoring systems in the 2 ICU types varied in theirversion numbers and settings, further affecting thecomparability. In COVID-19 ICUs, the ECG lead off alarm wasset as a yellow alarm, while in non–COVID-19 ICUs, it was
set as a blue alarm. This discrepancy likely stemmed from alack of awareness or understanding about the settings. We had
to exclude this alarm from the yellow alarm load comparison
due to this discrepancy.
Our metrics, calculated relative to the number of occupied beds
per day, could potentially skew the results. When metrics arecalculated this way, it might not accurately reflect the real-life
impact of alarm flood conditions on health care workers. In alarger ICU (such as the one with 24 beds), the same rate ofalarms per bed would create a larger absolute number of alarms
because there are more beds. Consequently, the staff in this ICU
would be exposed to a higher number of total alarms than thestaff in a smaller ICU (one with, say, 10 beds), although therate of alarms per bed is the same. While the surgical andmedical ICUs were approximately fully occupied over the entire
period under study, the occupancy of the COVID-19 ICUs often
varied, depending on the patient load. The average bed
occupancy was 97.26% and 92.27% in the surgical and medical
ICUs, respectively; and 88.79% and 82.75% in COVID-19
ICU-A and COVID-19 ICU-B, respectively. The occupancy of
all examined ICUs is reported in Figure S2 in Multimedia
Appendix 1. The staff members interviewed from the COVID-19
ICUs were not the same as those from the non–COVID-19 ICUs,
which introduces a limitation related to team differences andexperience levels.
Regarding the alarm durations, we could not investigate theimpact of the isolation process by analyzing alarm response

Wunderlich et al
times between COVID-19 ICUs and isolation rooms in
non–COVID-19 ICUs. Due to the provisional and time-limited
nature of the COVID-19 ICUs, the alarm fatigue in health careproviders might reflect not only the conditions in the COVID-19
ICUs but also the individual exposure to alarm load during theirformer career. We cannot definitively determine whether theincreased alarm load was caused by the COVID-19 condition
itself or by the differences in ICU settings. Unfortunately, moredetailed clinical data for every patient associated with the alarm
data were not available at the time of the study. From a clinicalperspective, the patient cohorts in the COVID-19 ICUs andgeneral ICUs were comparable in terms of the severity of theirconditions. In addition, fatigue among health care professionals
working with patients with COVID-19 infection was inherently
greater due to the overall stress and workload of managing
COVID-19 cases, which cannot be attributed solely to alarmload. We suggest propensity score matching or similar statisticaltechniques as areas for future research. Similarly, determining
isolation rooms from non–COVID-19 ICUs is recommended
for further studies to gain a deeper understanding of the impactof the isolation process on alarm response times.

Conclusions
In this study, the COVID-19 ICUs registered significantly morealarms than the non–COVID-19 ICUs. The higher number ofalarms led to a higher level of alarm fatigue among the cliniciansworking in COVID-19 ICUs. We believe that this was causedby the high proportion of untrained staff who were deployed tothe temporary ICUs during the pandemic and the provisionalsetting. The absence of central monitors in individual rooms
and corridors further compounded these challenges, making it
difficult for health care providers to swiftly identify and respondto alarms. However, it is important to note that our findings arelimited by the study design and specific circumstances during
the pandemic, which might affect the strength of ourconclusions. Further studies are warranted to better understandthe broader implications of alarm management in different ICU
settings.
To mitigate alarm overload in provisional ICUs during future
pandemics, we recommend creating skill-oriented video tutorials
on alarm management and monitor use. These tutorials should
provide easily accessible training for new staff, who may berapidly recruited and could have limited or no prior ICU
experience. This educational material could equip them with
the necessary knowledge to effectively navigate the ICU alarmsystem.

Acknowledgments
The authors express their gratitude to the intensive care unit staff for their participation in this study. ASP is a participant in the
Digital Clinician Scientist Program funded by Charité–Universitätsmedizin Berlin and the Berlin Institute of Health. The cost of
publishing open access was covered by Projekt DEAL.

Data Availability
The data sets generated and analyzed during this study are available in the Zenodo open data repository [54]. The code is available
from the corresponding author on reasonable request.

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 10(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Wunderlich et al

Authors' Contributions
ASP and MMW had the idea for the study. The study was conceived by NF, MMW, ASP, and FB. NF and MMW analyzed thedata and conducted the interviews, supported by ASP. NF wrote the manuscript, supported by ASP, CH, SA-W, and MMW. FB
supervised all parts of the study. All authors critically reviewed and approved the manuscript. The paper was extracted from NF’s
MSc thesis.

Conflicts of Interest
FB reports grants from the German Federal Ministry of Education and Research, the German Federal Ministry of Health, theBerlin Institute of Health, the Hans Böckler Foundation, the Einstein Foundation, the Berlin University Alliance, and from theRobert Koch Institute, as well as personal fees from Elsevier Publishing, outside the submitted work. All other authors declareno conflicts of interest.

Multimedia Appendix 1
Alarm load and management differences between COVID-19 and non–COVID-19 intensive care units (ICUs). Figures include
alarm frequencies by device and color, unit occupancy, alarm durations, and adjustments in settings across ICUs. Table data
cover interview questions, alarm log structure, included alarm signals, hypothesis testing group sizes, and statistical analysis ofalarm load per bed per day.
[DOCX File , 1421 KB-Multimedia Appendix 1]

References
1.
2.
3.

4.

5.

6.

7.
8.

9.

10.

11.
12.

13.
14.

Shen X, Zou X, Zhong X, Yan J, Li L. Psychological stress of ICU nurses in the time of COVID-19. Crit Care. May 06,
2020;24(1):200. [FREE Full text] [doi: 10.1186/s13054-020-02926-2] [Medline: 32375848]
Ferrer R. COVID-19 pandemic: the greatest challenge in the history of critical care. Med Intensiva (Engl Ed). Aug
2020;44(6):323-324. [FREE Full text] [doi: 10.1016/j.medin.2020.04.002] [Medline: 32376091]
Bello M, Segura V, Camputaro L, Hoyos W, Maza M, Sandoval X, et al. Hospital El Salvador: a novel paradigm of intensive
care in response to COVID-19 in Central America. The Lancet Global Health. Mar 2021;9(3):e241-e242. [doi:10.1016/s2214-109x(20)30513-1]
McCabe R, Kont MD, Schmit N, Whittaker C, Løchen A, Baguelin M, et al. Modelling intensive care unit capacity under
different epidemiological scenarios of the COVID-19 pandemic in three Western European countries. Int J Epidemiol. Jul
09, 2021;50(3):753-767. [FREE Full text] [doi: 10.1093/ije/dyab034] [Medline: 33837401]
Cai Y, Wu X, Zhang Y, Xia J, Li M, Feng Y, et al. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2)
contamination in air and environment in temporary COVID-19 ICU wards. Research Square. Preprint posted online April
6, 2020. [FREE Full text] [doi: 10.21203/rs.3.rs-21384/v1]
Hittesdorf E, Panzer O, Wang D, Stevens JS, Hastie J, Jordan DA, et al. Mortality and renal outcomes of patients withsevere COVID-19 treated in a provisional intensive care unit. J Crit Care. Apr 2021;62:172-175. [FREE Full text] [doi:10.1016/j.jcrc.2020.12.012] [Medline: 33385774]
Peters AW, Chawla KS, Turnbull ZA. Transforming ORs into ICUs. N Engl J Med. May 07, 2020;382(19):e52. [doi:10.1056/nejmc2010853]
Singh S, Ambooken GC, Setlur R, Paul SK, Kanitkar M, Singh Bhatia S, et al. Challenges faced in establishing a dedicated
250 bed COVID-19 intensive care unit in a temporary structure. Trends Anaesth Crit Care. Feb 2021;36:9-16. [FREE Fulltext] [doi: 10.1016/j.tacc.2020.10.006] [Medline: 38620737]
Uppal A, Silvestri DM, Siegler M, Natsui S, Boudourakis L, Salway RJ, et al. Critical care and emergency department
response at the epicenter of the COVID-19 pandemic. Health Aff (Millwood). Aug 01, 2020;39(8):1443-1449. [doi:10.1377/hlthaff.2020.00901] [Medline: 32525713]
Hravnak M, Pellathy T, Chen L, Dubrawski A, Wertz A, Clermont G, et al. A call to alarms: current state and future
directions in the battle against alarm fatigue. J Electrocardiol. Nov 2018;51(6S):S44-S48. [FREE Full text] [doi:10.1016/j.jelectrocard.2018.07.024] [Medline: 30077422]
Sendelbach S, Funk M. Alarm fatigue: a patient safety concern. AACN Adv Crit Care. 2013;24(4):378-386. [doi:10.1097/nci.0b013e3182a903f9]
Varisco G, van de Mortel H, Cabrera-Quiros L, Atallah L, Hueske-Kraus D, Long X, et al. Optimisation of clinical workflow
and monitor settings safely reduces alarms in the NICU. Acta Paediatr. Apr 22, 2021;110(4):1141-1150. [FREE Full text][doi: 10.1111/apa.15615] [Medline: 33048364]
Bach TA, Berglund LM, Turk E. Managing alarm systems for quality and safety in the hospital setting. BMJ Open Qual.
Jul 25, 2018;7(3):e000202. [FREE Full text] [doi: 10.1136/bmjoq-2017-000202] [Medline: 30094341]
Baker K, Rodger J. Assessing causes of alarm fatigue in long-term acute care and its impact on identifying clinical changes
in patient conditions. Inform Med Unlocked. 2020;18:100300. [doi: 10.1016/j.imu.2020.100300]

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 11(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
15.

16.
17.

18.

19.

20.

21.
22.

23.

24.

25.
26.
27.
28.
29.
30.
31.
32.

33.
34.
35.

36.

37.
38.

Poncette AS, Spies C, Mosch L, Schieler M, Weber-Carstens S, Krampe H, et al. Clinical requirements of future patientmonitoring in the intensive care unit: qualitative study. JMIR Med Inform. Apr 30, 2019;7(2):e13064. [FREE Full text][doi: 10.2196/13064] [Medline: 31038467]
Pugh RJ, Jones C, Griffiths RD. The impact of noise in the intensive care unit. In: Intensive Care Medicine. Yearbook of
Intensive Care and Emergency Medicine. Berlin, Germany. Springer; 2007:942-949.
Melnyk BM, Hsieh AP, Tan A, Teall AM, Weberg D, Jun J, et al. Associations among nurses' mental/physical health,
lifestyle behaviors, shift length, and workplace wellness support during COVID-19: important implications for health caresystems. Nurs Adm Q. 2022;46(1):5-18. [FREE Full text] [doi: 10.1097/NAQ.0000000000000499] [Medline: 34551423]
Sasangohar F, Jones SL, Masud FN, Vahidy FS, Kash BA. Provider burnout and fatigue during the COVID-19 pandemic:
lessons learned from a high-volume intensive care unit. Anesth Analg. Jul 2020;131(1):106-111. [FREE Full text] [doi:10.1213/ANE.0000000000004866] [Medline: 32282389]
Sikaras C, Ilias I, Tselebis A, Pachi A, Zyga S, Tsironi M, et al. Nursing staff fatigue and burnout during the COVID-19
pandemic in Greece. AIMS Public Health. 2022;9(1):94-105. [FREE Full text] [doi: 10.3934/publichealth.2022008][Medline: 35071671]
Galanis P, Vraka I, Fragkou D, Bilali A, Kaitelidou D. Nurses' burnout and associated risk factors during the COVID-19
pandemic: a systematic review and meta-analysis. J Adv Nurs. Aug 25, 2021;77(8):3286-3302. [FREE Full text] [doi:10.1111/jan.14839] [Medline: 33764561]
Allan SH, Doyle PA, Sapirstein A, Cvach M. Data-driven implementation of alarm reduction interventions in a cardiovascularsurgical ICU. Jt Comm J Qual Patient Saf. Feb 2017;43(2):62-70. [doi: 10.1016/j.jcjq.2016.11.004] [Medline: 28334564]
Poncette AS, Wunderlich MM, Spies C, Heeren P, Vorderwülbecke G, Salgado E, et al. Patient monitoring alarms in an
intensive care unit: observational study with do-it-yourself instructions. J Med Internet Res. May 28, 2021;23(5):e26494.
[FREE Full text] [doi: 10.2196/26494] [Medline: 34047701]
Wilken M, Hüske-Kraus D, Röhrig R. Alarm fatigue: using alarm data from a patient data monitoring system on an intensive
care unit to improve the alarm management. Stud Health Technol Inform. Sep 03, 2019;267:273-281. [doi:10.3233/SHTI190838] [Medline: 31483282]
Wunderlich MM, Amende-Wolf S, Krampe H, Kruppa J, Spies C, Weiß B, et al. A brief questionnaire for measuring alarm
fatigue in nurses and physicians in intensive care units. Sci Rep. Aug 24, 2023;13(1):13860. [FREE Full text] [doi:10.1038/s41598-023-40290-7] [Medline: 37620385]
Helfferich C. Die Qualität qualitativer Daten: Manual für die Durchführung Qualitativer Interviews. Wiesbaden, Germany.
VS Verlag für Sozialwissenschaften; 2009.
Mayring P. Qualitative Inhaltsanalyse: Grundlagen und Techniken. Weinheim, Germany. Beltz Verlag; 2022.
Microsoft Teams. Microsoft. URL: https://www.microsoft.com/de-de/microsoft-teams/group-chat-software [accessed2024-08-21]
R Core Team. R: a language and environment for statistical computing. R Foundation for Statistical Computing. 2019.
URL: https://www.R-project.org/ [accessed 2024-08-21]
RStudio Team. RStudio: integrated development environment for R. RStudio, PBC. 2020. URL: http://www.rstudio.com/[accessed 2024-08-21]
Grolemund G, Wickham H. Dates and times made easy with lubridate. J Stat Softw. Apr 07, 2011;40(3):1-25. [doi:10.18637/jss.v040.i03]
Wickham H. Ggplot2: Elegant Graphics for Data Analysis. New York, NY. Springer; 2009.
Wickham H, François R, Henry L, Müller K, Vaughan D, Posit Software, PBC. dplyr: a grammar of data manipulation.
The Comprehensive R Archive Network. Nov 17, 2023. URL: https://cran.r-project.org/web/packages/dplyr/index.html[accessed 2024-08-21]
Torchiano M. Effsize - a package for efficient effect size computation. Zenodo. Nov 13, 2016. URL: https://zenodo.org/
records/196082 [accessed 2024-08-21]
Canty A, Ripley B, Brazzale AR. boot: bootstrap functions. The Comprehensive R Archive Network. Feb 26, 2024. URL:
https://cran.r-project.org/web/packages/boot/boot.pdf [accessed 2024-08-21]
Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG. Research electronic data capture (REDCap)--a
metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed
Inform. Apr 2009;42(2):377-381. [FREE Full text] [doi: 10.1016/j.jbi.2008.08.010] [Medline: 18929686]
Wunderlich MM, Amende-Wolf S, Poncette AS, Krampe H, Kruppa J, Spies C, et al. Ein deutschsprachiger Fragebogen
zur Messung der Alarmmüdigkeit bei Pflegekräften und Ärzt:innen auf Intensivstationen. Kongress der Deutschen
Interdisziplinären Vereinigung für Intensiv- und Notfallmedizin e.V. 2021. URL: https://medinfo.charite.de/forschung/ag_intelligent_patient_monitoring/alarmzen/ [accessed 2024-08-21]
van Buuren S, Groothuis-Oudshoorn K. Mice: multivariate imputation by chained equations in R. J Stat Softw.
2011;45(3):1-67. [FREE Full text] [doi: 10.18637/jss.v045.i03]
Cosper P, Zellinger M, Enebo A, Jacques S, Razzano L, Flack MN. Improving clinical alarm management: guidance andstrategies. Biomed Instrum Technol. 2017;51(2):109-115. [doi: 10.2345/0899-8205-51.2.109] [Medline: 28296432]

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

Wunderlich et al

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 12(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
39.
40.
41.

42.

43.

44.

45.

46.

47.

48.

49.
50.
51.

52.
53.
54.

Wunderlich et al

Hüske-Kraus D, Wilken M, Röhrig R. Measuring alarm system quality in ICUs (WIP). EasyChair. Preprint posted online
2018. URL: https://easychair.org/publications/preprint_open/6nzL [accessed 2024-08-21]
Deb S, Claudio D. Alarm fatigue and its influence on staff performance. IIE Trans Healthc Syst Eng. Jul 29,
2015;5(3):183-196. [doi: 10.1080/19488300.2015.1062065]
González-Gil MT, González-Blázquez C, Parro-Moreno AI, Pedraz-Marcos A, Palmar-Santos A, Otero-García L, et al.
Nurses' perceptions and demands regarding COVID-19 care delivery in critical care units and hospital emergency services.
Intensive Crit Care Nurs. Feb 2021;62:102966. [FREE Full text] [doi: 10.1016/j.iccn.2020.102966] [Medline: 33172732]
Bergman L, Falk AC, Wolf A, Larsson IM. Registered nurses' experiences of working in the intensive care unit during the
COVID-19 pandemic. Nurs Crit Care. Nov 2021;26(6):467-475. [FREE Full text] [doi: 10.1111/nicc.12649] [Medline:
33973304]
Akturan S, Güner Y, Tuncel B, Üçüncüoğlu M, Kurt T. Evaluation of alarm fatigue of nurses working in the COVID-19
Intensive Care Service: a mixed methods study. J Clin Nurs. Sep 2022;31(17-18):2654-2662. [doi: 10.1111/jocn.16190][Medline: 34985160]
Shafaati M, Chopra H, Priyanka, Khandia R, Choudhary OP, Rodriguez-Morales AJ. The next pandemic catastrophe: can
we avert the inevitable? New Microbes New Infect. Mar 2023;52:101110. [FREE Full text] [doi: 10.1016/j.nmni.2023.101110][Medline: 36937540]
Sahoo T, Joshi M, Madathil S, Verma A, Sankar MJ, Thukral A. Quality improvement initiative for reduction of false
alarms from multiparameter monitors in neonatal intensive care unit. J Educ Health Promot. Oct 24, 2019;8:203. [FREEFull text] [doi: 10.4103/jehp.jehp_226_19] [Medline: 31807593]
Seifert M, Tola DH, Thompson J, McGugan L, Smallheer B. Effect of bundle set interventions on physiologic alarms andalarm fatigue in an intensive care unit: a quality improvement project. Intensive Crit Care Nurs. Dec 2021;67:103098. [doi:10.1016/j.iccn.2021.103098] [Medline: 34393010]
Van de Pol I, Wirds J. St. Antonius hospital reduces non-actionable ICU alarms by 40% to improve patient care and staff
satisfaction. Philips Clinical Services. URL: https://www.philips.com/c-dam/b2bhc/master/articles/acute-care/
forty-percent-st-antonius-customer-story.pdf [accessed 2024-08-21]
Yeh J, Wilson R, Young L, Pahl L, Whitney S, Dellsperger KC, et al. Team-based intervention to reduce the impact of
nonactionable alarms in an adult intensive care unit. J Nurs Care Qual. 2020;35(2):115-122. [doi:10.1097/NCQ.0000000000000436] [Medline: 31513051]
Yue L, Plummer V, Cross W. The effectiveness of nurse education and training for clinical alarm response and management:a systematic review. J Clin Nurs. Sep 2017;26(17-18):2511-2526. [doi: 10.1111/jocn.13605] [Medline: 27685951]
Wilken M, Hüske-Kraus D, Klausen A, Koch C, Schlauch W, Röhrig R. Alarm fatigue: causes and effects. Stud HealthTechnol Inform. 2017;243:107-111. [Medline: 28883181]
Sowan AK, Vera AG, Fonseca EI, Reed CC, Tarriela AF, Berndt AE. Nurse competence on physiologic monitors use:
toward eliminating alarm fatigue in intensive care units. Open Med Inform J. Apr 14, 2017;11:1-11. [FREE Full text] [doi:10.2174/1874431101711010001] [Medline: 28567167]
Cvach M, Kitchens M, Smith K, Harris P, Flack MN. Customizing alarm limits based on specific needs of patients. Biomed
Instrum Technol. 2017;51(3):227-234. [doi: 10.2345/0899-8205-51.3.227] [Medline: 28530858]
Phillips J, Sowan A, Ruppel H, Magness R. Educational program for physiologic monitor use and alarm systems safety: atoolkit. Clin Nurse Spec. 2020;34(2):50-62. [doi: 10.1097/NUR.0000000000000507] [Medline: 32068633]
Wunderlich MM, Frey N, Amende-Wolf S, Hinrichs C, Balzer F, Poncette AS. Alarm management in provisional COVID-19
intensive care units: a retrospective analysis and recommendations for future pandemics. Zenodo. URL: https://zenodo.org/
records/10418595 [accessed 2024-08-21]

Abbreviations
ECG: electrocardiogram
IBP: invasive blood pressure
ICU: intensive care unit
NIBP: noninvasive blood pressure
REDCap: Research Electronic Data Capture
SpO2: peripheral oxygen saturation

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 13(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Wunderlich et al

Edited by C Lovis; submitted 14.03.24; peer-reviewed by K Bin, J-S Jerng; comments to author 24.04.24; revised version received
10.06.24; accepted 21.07.24; published 09.09.24
Please cite as:
Wunderlich MM, Frey N, Amende-Wolf S, Hinrichs C, Balzer F, Poncette A-S
Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics
JMIR Med Inform 2024;12:e58347
URL: https://medinform.jmir.org/2024/1/e58347
doi: 10.2196/58347
PMID:©Maximilian Markus Wunderlich, Nicolas Frey, Sandro Amende-Wolf, Carl Hinrichs, Felix Balzer, Akira-Sebastian Poncette.
Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 09.09.2024. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR
Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on
https://medinform.jmir.org/, as well as this copyright and license information must be included.

https://medinform.jmir.org/2024/1/e58347

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e58347 | p. 14(page number not for citation purposes)