










































37 J Global Clinical Engineering Special Issue 1: 37-41; 2018

Received February 8, 2018, accepted March 12, 2018, date of publication March 25, 2018.

Survey and Analysis of Current State of Ventilator 
Alarms in the Intensive Care Unit

By ZK Lin, K. Zheng, YM Shen, CX Zheng, and YY Wu

The Children’s Hospital Zhejiang University School of Medicine, Hangzhou, Zhejiang Province, China

Correspondence may be directed to ZK Lin: 291567468@qq.com.

ABSTRACT

This article reports on a survey and analysis of ventilator alarm state in a children hospital. Based on the evaluation 
of the alarm effectiveness, we designed a survey statistical table for ventilator alarm investigation. We evaluated the 
alarm situation synthetically through investigation and statistical methods. Result shows that the current ventilator 
alarms are not sufficiently effective, 26.84% of them are meaningless alarms and those leading to clinician’s interven-
tion make up only 2.26% of all the alarms generated. The reliability of statistical data was also analyzed. According 
to the survey results, we identified and analyzed the causes of the problem and proposed the corresponding alarm 
management methods.

Keywords – ventilator alarm information, alarm effectiveness, alarm management, survey statistics

INTRODUCTION
The intensive care unit (ICU) is one of the most critical 

clinical departments for patients in a tertiary hospital. 
Yet, the volume of medical equipment equipped in this 
clinical area also presents great challenges in terms of 
alarm fatigue due to overwhelming alarm information 
generated during daily operation. There are simply too 
many alarms that do not result in medical intervention in 
the ICU.1 It was reported that an alarm sounded every 92 
seconds in the ICU in 2006.2 This was shortened to every 
66 seconds by 2010,3 and shortened further to every 42 
seconds by 2014.4 Too many alarms bring about audi-
tory and visual confusion for medical staff.1 They can’t 
identify the sources of the alarms effectively,5 which is 
a serious threat to the safety of patient care.6 According 
to one report from the ECRI Institute, the number of ad-
verse events related to alarm management is increasing 

yearly.7 A hospital may experience tens of thousands of 
alarm messages every day, but 85–99% of them are nui-
sance alarms or do not need clinical intervention.8 The 
presence of these alarms leads medical staff a to state of 
alarm fatigue and can cause alarm ignorance or even the 
turning off of the alarm function.9

However, above reports mainly focus on alarm issues 
for adult patients, there are few reports based on the same 
issue for pediatric patients. Children, especially newborns, 
with their language, awareness, and behavioral abilities 
not yet fully developed, bring more challenges to a health 
care team. Based on the above background, this paper 
presents an analysis method that integrates the statistical 
design of the survey, the investigation experiment, and the 
statistical analysis of the data, and analyzes the state of 
ventilator alarms in the neonatal ICU in a children hospital.

http://www.globalce.org
http://www.globalce.org
mailto:291567468@qq.com


Lin, Zheng, Shen, Zheng, and Wu: Survey and Analysis of Current State of Ventilator Alarms in the Intensive Care Unit

J Global Clinical Engineering Special Issue 1: 37-41; 2018  38

MATERIALS AND METHOD
Based on the literature review and our experience, 

alarms can be categorized into meaningful alarms and 
meaningless alarms. Meaningful alarms are those alarms 
that require a clinician’s quick attendance due to changes 
in patient condition or those technical alarms originating 
from equipment malfunctions that require timely cor-
rection. Meaningless alarms are those that don’t reflect 
the true changes of a patient’s condition, do not improve 
patient management, and may be caused by false alarm, 
improper alarm settings, or recoverable transient artifacts.

In order to carry out an assessment of the common 
ventilator alarms, we first consulted with clinicians to cat-
egorize the three main alarm interventions for ventilators in 
their routine practice: (1) clinician’s medical intervention, 
(2) clinical engineering and nurse’s equipment correction; 
and (3) alarm elimination by silencing. Clinician’s medi-
cal intervention means patients with clinically changed 
conditions requiring timely intervention of medical staff; 
clinical engineering and nurse’s equipment correction 
means a technical problems with the ventilator occurred 
requiring clinical engineering or nurse’s action such as 
immediate repair; while alarm elimination by silencing 
means that both the patient and instrument were OK and 
the alarm did not recur after silencing. We also collected 
and analyzed common alarm contents, common alarm 

intervention measures, and alarm causes. Since alarm 
limit settings are highly relevant with alarm occurrence, 
it is also important to record common alarm limit values 
accurately. Based on the key elements mentioned above, 
we design the Statistics of Clinical Meaningful Alarms, as 
shown in Table 1.

In this study, we selected the SLE5000 ventilator as an 
example, where this paper applies the designed survey 
table to the collection and observation of the SLE5000 
ventilator alarms generated in daily use in the neonatal 
intensive care unit (NICU) over a period of 10 days.

RESULT

The Results of the Survey
This survey is based on 120 total questionnaires, with 

486 events of recorded alarm information from 112 valid 
questionnaires, and 12 kinds of common alarms generated. 
The specific number of alarms shown in Figure 1. Among 
them, the high-pressure alarm, low pressure alarm, and 
cycle failure occur with higher frequency. The results of 
intervention are shown in Table 2.

According to the effectiveness of the alarm and the 
definition of meaningful alarms described earlier, we 
classify 354 alarms events as meaningful alarms, and the 
calculation of meaningful alarms rate per day is shown 
in Table 3.

Table 1. Statistics of Clinical Meaningful Alarms
Ventilator Model:  Patient Hospital Number: Date:  

Set value
PEEP
H: 
L:   

Amplitude Hz Frequency
H: 
L:   

Tidal volume
H:
L:

Minute ventilation
H:
L:

Alarm content Intervention measures and their causes (multiple choice) The result of the intervention

Event hints:                           Mute Endotracheal secretions are much, should suck 
them out

 Abnormal machine and accessories The patient is 
restless

 There is water in the tube Replacement of the sensor
 Adjust the position of the endotracheal intubation
 pipeline discount, off other

 Alarm elimination by silencing
 Clinician’s medical intervention
 Clinical engineering and nurse’s equipment 

correction

…… …… ……



Lin, Zheng, Shen, Zheng, and Wu: Survey and Analysis of Current State of Ventilator Alarms in the Intensive Care Unit

39 J Global Clinical Engineering Special Issue 1: 37-41; 2018

Figure 1. Alarm name and number of alarms.

Table 2. The Result of the Intervention

Clinician’s medical 
intervention

Clinical engineering and 
nurse’s equipment correction

Alarm elimination by 
silencing 

Number of alarms 11 343 132

Proportion 2.26% 70.58% 27.16%

Table 3. Overview of Alarm Data
Time Total Meaningful alarm Meaningless alarm Rate of meaningful alarm 

1st day 50 43 7 86.00%

2nd day 46 31 15 67.39%

3rd day 36 28 8 77.78%

4th day 45 39 6 86.36%

5th day 59 46 13 77.97%

6th day 69 44 25 63.77%

7th day 48 31 17 64.58%

8th day 48 34 14 70.83%

9th day 45 29 16 64.44%

10th day 40 29 11 72.50%

Total 486 354 132 ——

Average 46.8 35.4 13.2 73.16%



Lin, Zheng, Shen, Zheng, and Wu: Survey and Analysis of Current State of Ventilator Alarms in the Intensive Care Unit

J Global Clinical Engineering Special Issue 1: 37-41; 2018  40

The rate of meaningful alarms was 73.16% of all 
alarms generated. This included those alarms that really 
reflect the changes of patient condition which need the 
clinician’s quick attendance or those technical alarms 
for equipment malfunction that require correction im-
mediately or soon. Yet, the alarms that required clinician 
medical intervention reached only 2.26%. There is quite 
a large proportion of meaningless alarms, which consists 
of 26.84% of all alarms generated. This indicates that the 
alarm conditions should and could be improved greatly.

Reliability Test
Reliability refers to the degree of questionnaire re-

sults repeatability. The coefficient of Cronbach’s Alpha is 
between 0 and 1, and the larger the value, the better the 
relevance of the items in the questionnaire and the higher 
the degree of internal consistency.10 In general, the internal 
consistency is considered excellent, good, or poor accord-
ingly if the coefficient of Cronbach’s Alpha is greater than 
0.8, within 0.6~0.8, and less than 0.6 respectively . Using 
the SPSS19.0 software to analyze the experimental data, 
the results show that the Cronbach’s Alpha coefficient of 
our survey is 0.915, which indicates that the statistical 
experiment is credible and statistically significant.

DISCUSSION
The survey uses the designed form to collect and analyze 

the state of the SLE5000 ventilator alarms management. 
There were 486 recorded alarm events collected over a 
time period of 10 days. Though we believe the survey only 
collect the most common relevant alarms occurring, the 
actual alarms generated by a ventilator may be higher 
than this survey collected. Yet, the internal consistency 
reliability of the 10 days’ survey data is analyzed by 
SPSS19.0 software and it shows overall survey data are 
solid and strong.

Survey results show that 26.84% of the alarm data is 
meaningless alarms, which means that those alarms did 
not contribute to better patient management and could 
have been avoided in the first place. Even some of those 
classified as meaningful alarms, in particular some techni-
cal alarms, there is still room to reduce their occurrence. 
Alarm management is teamwork. All stakeholders includ-
ing hospital leadership, medical staff, clinical engineers, 

manufacturers, and independent service organizations 
should participate. We suggest the following strategies:

• First, urge manufacturers to improve the quality and 
reliability of equipment and improve the design of 
alarm system.

• Second, assure clinical engineering staff to perform 
service and preventive maintenance of relevant medi-
cal equipment timely and appropriately.

• Third, strengthen user training in terms of medical 
equipment operation as well as alarm management 
including setting alarm limits appropriately.

• Fourth, develop and apply alarm integration and 
management systems based on IT technology.

CONCLUSIONS
The article aims are a survey and analysis of the current 

state of ventilator alarms in an ICU. The results show that 
the current ventilator alarm management in the ICU needs 
to be improved. As well. collaboration among clinicians, 
clinical engineering staff, and ventilator manufacturer is 
important and necessary in terms of providing a better 
solution based on training, smart alarm design, and alarm 
integration management.

We believe the methodology mentioned in this paper 
is not only suitable for SLE5000 ventilator alarms infor-
mation survey and assessment, but also could be used as 
reference for other types of ventilators or medical equip-
ment such as monitors, infusion pumps, etc. Nevertheless, 
the systematic management of all instruments’ alarm is a 
complex project. Further research is needed to learn best 
practices of other facilities currently and into the future.

ACKNOWLEDGEMENT
We would like to extend our sincere gratitude to Dr. 

Yadin David, director of Health Technology Task Group 
IUPESM, for his comments and suggestions. We are also 
deeply indebted to our other clinical engineering staff and 
NICU physicians and nurses who helped us to complete 
this survey.



Lin, Zheng, Shen, Zheng, and Wu: Survey and Analysis of Current State of Ventilator Alarms in the Intensive Care Unit

41 J Global Clinical Engineering Special Issue 1: 37-41; 2018

CONFLICT OF INTEREST
The authors declare that there is no conflict of interest 

involved this paper.

REFERENCES
1. Lacherez P, Donaldson L, Burt JS. Do learned alarm sounds 

interfere with working memory. J Hum Fact Ergonom Soc 
2016;58(7):1044–51.

2. Graham K C, Cvach M. Monitor alarm fatigue: standardizing 
use of physiological monitors and decreasing nuisance 
alarms. Am J Crit Care 2010;19(1):28–34.

3. Hooper J. Unpublished data. Washington, DC: Children’s 
National Medical Center; 2009.

4. Drew BJ, Harris P, Zègre-Hemsey JK, et al. Insights into the 
problem of alarm fatigue with physiologic monitor devices: a 
comprehensive observational study of consecutive intensive 
care unit patients. PloS One 2014;9(10):e110274.

5. Jacques S. Breaking barriers to patient-centric alarm man-
agement. Biomed Instrument Technol 2016;50(3):181–83.

6. Paine CW, Goel VV, Ely E, et al. Systematic review of physiologic 
monitor alarm characteristics and pragmatic interventions 
to reduce alarm frequency. J Hospit Med 2016;11(2):136–44.

7. ECRI Institute. Top 10 Technology Health Hazards For 2016. 
[Internet]. 2015. www.ecri.org/2016hazards.

8. Joint Commission. Medical device alarm safety in hospitals. 
Sentinel Event Alert 2013.

9. Criscitelli T. Alarm management: promoting safety and es-
tablishing guidelines. association of operating room nurses. 
AORN J 2016;103(5):518.

10. Wu M, Wu J, Liu Q, et al. Reliability and validity analysis of 
the practical application of the drug information service. 
Comp Syst Appl 2013;(10):198–202.

www.ecri.org

	_Ref461114133
	_Ref461114141
	_Ref461112075
	_Ref461110075
	_Ref461114197
	_Ref461114685

