Machine Alarm Fatigue among Hemodialysis Nurses in 29 Tertiary Hospitals

Abstract

Objectives To understand the status quo and related influencing factors of machine alarm fatigue of hemodialysis nurses in tertiary hospitals in Liaoning Province.

Methods This cross-sectional study employed convenience sampling to select 460 nurses from 29 tertiary hospitals in Liaoning Province, who are involved in hemodialysis care. Surveys were conducted using the General Information Questionnaire, Alarm Fatigue Scale, National Aeronautics and Space Administration Task Load Index, and Maslach Burnout Inventory Scale.

Results The overall machine alarm fatigue score for 460 hemodialysis nurses from 29 tertiary hospitals in Liaoning Province was 17.04 ± 3.21, indicating a moderate level. The multiple linear regression analysis shows that years of experience in hemodialysis nursing, the number of patients managed per shift, whether specialized nursing training has been received, self-reported health status, emotional exhaustion, and workload have statistically significant associations with alarm fatigue among hemodialysis nurses (p < 0.05). Among them, the years of experience in hemodialysis nursing are negatively correlated with alarm fatigue among hemodialysis nurses, whereas the number of patients managed per shift and workload are positively correlated with alarm fatigue among hemodialysis nurses.

Conclusion This study indicates that certain demographic factors, workload, and occupational burnout are associated with machine alarm fatigue among hemodialysis nurses. Therefore, hemodialysis-related managers should establish a Machine Alarm Management System, implement Personalized Thresholds and Delayed Alarms, ensure reasonable staffing arrangements, improve compassion fatigue, and enhance anticipatory care. Our findings have implications for improving the health and well-being of hemodialysis nurses, providing a conducive environment for professional training in hemodialysis, and ultimately addressing the current situation of machine alarm fatigue among hemodialysis nurses.

 

MissingLinkText

Keywords

machine alarm fatigue - hemodialysis - nurses - workload - occupational burnout

Background and Significance

Maintenance hemodialysis is a crucial renal replacement therapy for end-stage kidney disease patients, with hemodialysis patients accounting for approximately 86% of the total dialysis population.[1] Hemodialysis is a highly specialized technique that places significant demands on nursing staff, medical equipment, and treatment environments.[2] Presently, there are over 4,000 registered hemodialysis centers in China, primarily affiliated with public comprehensive hospitals.[3] These centers typically consist of multiple dialysis units and conduct dialysis treatments in a centralized space. Therefore, in a relatively limited space, the continuous alarms from patients and dialysis machines push the nurses to highly focused work. Additionally, the advance of the technique of the dialysis machine makes the machine more and more sensitive to the change in blood circulation, dialysate, air, and patient's medical condition. Any deviation from normal values triggers alarms in various forms, such as visual, auditory, or electronic alerts.[4] Hemodialysis nurses play a crucial role in the medical environment, being responsible for monitoring patients' conditions, carrying out medical orders, and providing basic care. Due to the unique nature of hemodialysis procedures, different parameters are generated at the beginning, during, and at the end of dialysis treatment, all of which require nurses to adjust. Finally, although dialysis machines are well advanced, they still need the nurse to continuously survey and monitor and make the adjustment manually. The research indicates that alarm safety issues with medical equipment have become the “top among the 10 hazards in medical technology.”[5] The hemodialysis machine generates numerous alarms due to various reasons, and frequent alarms can significantly reduce nurses' sensitivity, leading to delayed responses, distrust, and fatigue, known as alarm fatigue.[6] Alarm fatigue refers to a phenomenon within medical institutions where a large number of alarms from medical devices, including both valid alarms and false alarms, leading to a decline in health care workers' responsiveness and efficiency in handling them.[7] This increases the risk of errors among hemodialysis nurses, potentially endangering patient safety and lives, and resulting in decreased patient satisfaction with care.[8] Additionally, alarm fatigue may increase nurses' workload, negatively impact their physical and mental health, reduce job satisfaction, elevate anxiety levels, consequently leading to compassion fatigue and occupational burnout. Compassion fatigue is emotional, physical, and psychological exhaustion resulting from prolonged exposure to work-related stressors among health care providers.[9] Anxiety may further disrupt their work, leading health care professionals to become immune, ignore, disregard, or even silence alarms,[10] [11] ultimately posing significant risks to patient safety, potentially resulting in serious adverse events.[12] At present, research on nurse alarm fatigue with medical equipment both domestically and internationally is mainly focused on intensive care unit (ICU), pediatric units, and departments with higher levels of noise. Furthermore, several studies have indicated that the alarm rate of dialysis machines during the dialysis process can be as high as 40%.[4] However, there is limited research on hemodialysis nurses' alarm fatigue related to medical equipment. Therefore, this study aims to investigate the situation of alarm fatigue among hemodialysis nurses in 29 tertiary hospitals, analyze the causes of alarm fatigue among hemodialysis nurses, and provide a basis for improving intervention measures for nurse alarm fatigue (such as establishing a machine alarm management system, setting personalized thresholds, multidisciplinary team collaboration, improving empathy fatigue, and anticipatory care, etc.), reducing alarm fatigue among hemodialysis nurses, and enhancing patient safety and nursing satisfaction.

 

MissingLinkText

Objectives

The purpose of this study is to investigate the current status of alarm fatigue among hemodialysis nurses and its association with demographic factors, workload, and occupational burnout. Subsequently, appropriate interventions will be proposed to prevent or decrease alarm fatigue.

 

MissingLinkText

Methods

Study Design and Participant Population

This study was a multicenter and cross-sectional clinical analysis. A convenience sampling method was used to recruit hemodialysis nurses from the hemodialysis centers of 29 tertiary hospitals as research subjects in Liaoning Province, China between August and October 2021. Our study is in line with the Helsinki declaration and approved by the Ethics Committees of the Second Hospital of Dalian Medical University where the study was conducted. Written informed consent was obtained from all the participants. Inclusion criteria were (1) registered nurses in practice; (2) 1 year or more of experience in hemodialysis specialization; (3) participants who provided informed consent to participate in this study. Study nurses, rotation nurses, or interns undergoing standardized training in hemodialysis departments were excluded.

 

MissingLinkText

Study Variables and Measures

General Information Questionnaire

The researcher designed the questionnaire after reviewing the literature, which included information such as gender, age, education background, department position, income, marital status, professional title, years of hemodialysis specialty work, the method of appointment, number of patients managed per shift, number of dialysis machines in the hemodialysis center, managing fixed patients per shift, frequency of using mobile communication devices to handle work affairs after off-duty hours, engaging in scientific research and teaching duties alongside clinical work, hobbies, received specialized nursing training, self-reported personality type, and self-reported health status.

 

MissingLinkText

Alarm Fatigue Scale

The scale was adapted from a fatigue scale developed by Kim and Sung[13] in 1998. The Chinese version was translated and applied by Chinese scholars, including Wei Chen et al[14] to assess the level of alarm fatigue experienced by nurses in China when dealing with monitor alarms. The scale consists of seven items: (1) alarms from the equipment make me feel tired of everything; (2) alarms from the equipment make me feel anxious and uneasy; (3) alarms from the equipment make me feel powerless; (4) alarms from the equipment make it difficult for me to concentrate; (5) alarms from the equipment make me easily forget what I was originally supposed to do; (6) alarms from the equipment make me feel very bad; (7) alarms from the equipment give me a headache.

The scale employs a Likert 5-point rating system, ranging from “strongly disagree” to “strongly agree,” with 1 to 5 points assigned, respectively. The minimum score on the scale is 7 points, indicating the lowest level of alarm fatigue, whereas the maximum score is 35 points, representing the highest degree of alarm fatigue. The Chinese version of the scale has a Cronbach's α coefficient of 0.78 and a content validity of 0.89.

 

MissingLinkText

National Aeronautics and Space Administration-Task Load Index

The scale, developed by the National Aeronautics and Space Administration (NASA) in 1988, determines six dimensions of psychological workload through surveys of pilots and is widely utilized abroad as a subjective assessment tool for psychological workload.[15] The Chinese version, introduced for measuring subjective psychological workload among nursing populations, was translated and revised by Chinese scholars Liang et al.[16] After revision, the scale includes six items: mental demands, physical demands, temporal demands, self-performance, effort level, and frustration, constituting a single dimension, which are represented on a 20-point line scale ranging from 0 to 100, indicating psychological workload from “low” to “high.” The “self-performance” item, for example, ranges from “perfect” on the left to “failure” on the right, meaning that higher scores indicate worse self-performance and a higher task workload. The scores of the six items are added together and averaged to obtain the total psychological workload score for the survey participants. The total score is 100 points, with higher scores indicating a heavier psychological workload. The Chinese version of the scale has a Cronbach's α coefficient of 0.71 and the test–retest reliability of 0.81.

 

MissingLinkText

Maslach Burnout Inventory Scale

The scale, developed by American psychologists Maslach and Jackson in 1981,[17] is designed to assess emotional responses triggered by job stress, attitudes toward service recipients, and perceptions of one's own work. The Chinese version was translated into Chinese by Professor Peng Meici of The Hong Kong Polytechnic University in China. It consists of a total of 22 items, comprising three dimensions: emotional exhaustion (9 items), depersonalization (5 items), and personal accomplishment (8 items). The scale employs a Likert 7-point rating system, ranging from “never” to “every day,” with 0 to 6 points assigned, respectively. Burnout determination criteria: if any one of the three dimensions meets the following criteria, it is considered as experiencing occupational burnout: (1) Emotional Exhaustion dimension score ≥ 27 points; (2) Depersonalization score ≥ 8 points; (3) Personal Accomplishment score ≤ 24 points. The Cronbach's α coefficient for the Chinese version of the scale is 0.93, with Cronbach's α coefficients for the dimensions of Emotional Exhaustion, Depersonalization, and Personal Accomplishment being 0.91, 0.81, and 0.84, respectively.

 

MissingLinkText

 

MissingLinkText

Data Collection

We utilized an online survey platform called Wenjuanxing to generate the questionnaire's web link. The link was distributed anonymously to head nurse in each hospital via WeChat, a popular social app in China, who then forwarded it to the nurses for completion. To prevent duplicate responses, each questionnaire could only be submitted once from a single Internet Protocol address. Before distributing the questionnaires, the primary researcher provided training to two assistants. These assistants were deemed qualified if they could accurately explain the details of each questionnaire item to the primary researcher. Subsequently, they sent the questionnaire links to head nurse who had WeChat contact information. If participating nurses had any questions about the questionnaire, they could contact the researchers via email, phone, or WeChat. Each participant had the right to decide whether to participate in the study and could withdraw at any time. Before presenting the questionnaire content, the purpose of the study was explained, and participants' informed consent was obtained. If participants provided informed consent, they could proceed to complete the questionnaire. Finally, the principal investigator reviewed each questionnaire, checked for omissions and errors, promptly confirmed any necessary modifications with the nurses to ensure the quality and completeness of the questionnaire responses. This study received a total of 488 completed questionnaires, and after excluding those that did not meet the inclusion criteria and those with more than 20% missing items, the final valid number of questionnaires was 460.

 

MissingLinkText

Statistical Analysis

This study employed double-checking and data entry into Excel, followed by data analysis using SPSS 25.0. To determine whether nurses experience alarm fatigue, scores for each scale and item of the Alarm Fatigue Scale, NASA-Task Load Index (NASA-TLX), and Maslach Burnout Inventory Scale were calculated, represented by means and standard deviations. Independent samples t-tests and analysis of variance were used to analyze whether there were statistically significant differences between groups in the general demographic survey variables, workload, and occupation burnout. Pearson correlation tests were conducted to examine the associations between alarm fatigue, workload, and occupation burnout. Multiple linear regression analysis was performed using a forward selection method to analyze variables with statistically significant associations with alarm fatigue.

 

MissingLinkText

 

MissingLinkText

Results

General Information of Hemodialysis Nurses

A total of 488 completed questionnaires were received for this study. After excluding questionnaires that did not meet the inclusion criteria and those with missing items exceeding 20%, the final number of valid questionnaires was 460. Among these participants, 97.4% were female (n = 448), whereas 2.6% were male (n = 12). The age was mainly concentrated between 26 and 35 years old (n = 212, 46.1%). Most were married (n = 350, 76.1%). [Table 1] describes the specific demographic characteristics of the respondents.

Table 1 Hemodialysis nurse demographics

Characteristic	Total (N = 460)	Mean ± SD	Statistic	p-Value
Age			F = 0.516	0.672
≤ 25	36	18.06 ± 3.58
26∼35	212	17.10 ± 4.87
36∼45	149	16.97 ± 4.85
≥ 46	63	17.05 ± 4.80
Gender			t = 0.415	0.520
Male	12	18.00 ± 3.25
Female	448	17.10 ± 4.79
Educational background			F = 1.218	0.302
Vocational education	20	18.10 ± 3.70
Associate's	107	17.14 ± 4.52
Bachelor's	331	17.10 ± 4.87
Master's or above	2	11.50 ± 6.36
Department position			F = 8.863	< 0.001
Charge nurse	321	17.74 ± 3.83
Nursing team leader	54	17.74 ± 5.89
Nursing supervisor	5	17.00 ± 1.41
Head nurse	42	15.76 ± 3.72
Others	38	14.08 ± 2.06
Income			F = 0.663	0.575
<3,000	86	16.98 ± 3.90
3,000–4,999	192	17.57 ± 4.01
5,000–7,999	134	17.04 ± 4.39
>8,000	48	17.02 ± 4.26
Marriage status			F = 4.993	0.007
Unmarried	93	16.08 ± 2.41
Married	350	17.10 ± 4.30
Divorced or widowed	17	19.12 ± 4.17
Professional title			F = 1.406	0.240
Registered nurse	82	16.27 ± 3.00
Senior nurse	152	17.24 ± 4.10
Supervisor nurse	203	17.12 ± 4.22
Co-chief nurse or above	23	16.26 ± 4.70
Years of hemodialysis specialty work			F = 4.022	0.003
< 3	112	17.22 ± 2.78
3–4	65	16.43 ± 3.15
5–7	74	16.41 ± 3.41
8–9	62	16.04 ± 3.54
≥ 10	147	15.55 ± 3.78
The method of appointment			t = −0.938	0.349
Regular employees	152	16.72 ± 4.04
Contractual employees	308	17.09 ± 4.01
Number of patients managed per shift			F = 11.246	< 0.001
≤ 5	167	16.07 ± 3.27
6–9	263	17.17 ± 4.26
≥ 10	30	19.43 ± 1.92
Number of dialysis machines			F = 0.778	0.506
< 20	24	15.75 ± 2.03
20∼39	95	17.49 ± 4.12
40∼59	144	16.95 ± 4.17
≥ 60	197	16.87 ± 4.03
Managed fixed patients per shift			t = 0.844	0.399
Yes	256	17.06 ± 4.07
No	204	16.75 ± 3.67
Using mobile communication devices to handle work after shifts			F = 0.809	0.520
Always	38	16.68 ± 3.24
Frequently	103	16.40 ± 4.20
Occasionally	217	17.02 ± 3.76
Sometimes	78	17.31 ± 4.25
None	24	17.38 ± 3.52
Engaging in scientific research and teaching duties			t = 0.723	0.470
Yes	133	17.13 ± 4.58
No	327	16.84 ± 3.58
Hobbies			t = −0.554	0.580
Yes	311	16.85 ± 3.75
No	149	17.07 ± 4.20
Received specialized nurse training			t = −0.272	0.007
Yes	258	15.70 ± 6.31
No	202	17.28 ± 6.50
Self-reported personality type			F = 6.494	0.002
Extroverted	102	15.75 ± 3.29
Ambivert	268	17.17 ± 4.06
Introverted	90	17.47 ± 3.21
Self-rated health status			F = 3.274	0.012
Excellent	44	16.75 ± 4.80
Good	123	16.40 ± 3.27
Fair	238	17.39 ± 3.78
Poor	51	15.84 ± 3.71
Very poor	4	20.00 ± 3.92

 

MissingLinkText

The Scores for Machine Alarm Fatigue, Workload, and Occupational Burnout among Hemodialysis Nurses

The Scores of the Machine Alarm Fatigue Scale, NASA-TLX, and Occupational Burnout Scale for Hemodialysis Nurses in 29 Tertiary Hospitals in Liaoning Province are presented in [Table 1]. The scores for Machine Alarm Fatigue, Workload, and Occupational Burnout were 17.04 ± 3.21, 74.23 ± 13.64, and 83.14 ± 14.21, respectively, indicating that alarm fatigue, workload, and occupational burnout among hemodialysis nurses in this study were at a moderately low level, as shown in [Table 2].

Table 2 Scores on the Machine Alarm Fatigue Scale, NASA-Task Load Index, and Occupational Burnout Scale

Characteristic	Total score mean	Item score mean
Machine Alarm Fatigue	17.04 ± 3.21	2.33 ± 1.35
NASA-Task Load Index	74.23 ± 13.64	12.46 ± 2.34
Occupational Burnout	83.14 ± 14.21	3.62 ± 0.08

 

MissingLinkText

The Impact of General Information and Occupational Burnout on the Level of Machine Alarm Fatigue among Hemodialysis Nurses

According to the results of this survey, marital status, departmental responsibilities, years of hemodialysis specialty work, number of patients managed per shift, received specialized nursing training, self-reported health status, workload, occupational burden differences were statistically significant, p < 0.05, as shown in [Tables 1] and [3]. Among them, the years of experience in hemodialysis nursing are negatively correlated with alarm fatigue among hemodialysis nurses, whereas the number of patients managed per shift and workload are positively correlated with alarm fatigue among hemodialysis nurses.

Table 3 Occupational burnout on the level of machine alarm fatigue among hemodialysis nurses

Characteristic	Total (N = 460)	Mean ± SD	Statistic	p
Emotional exhaustion			F = 3.611	0.028
Mild	62	16.16 ± 2.23
Moderate	121	17.06 ± 3.46
Severe	277	17.39 ± 3.37
Depersonalization			F = 3.240	0.040
Mild	112	16.88 ± 3.51
Moderate	249	17.25 ± 3.87
Severe	99	18.11 ± 2.98
Personal accomplishment			F = 5.007	0.007
Mild	301	17.36 ± 3.24
Moderate	73	16.71 ± 3.36
Severe	86	16.19 ± 2.79

 

MissingLinkText

Correlation Analysis of Machine Alarm Fatigue among Hemodialysis Nurses with Workload Index and Occupational Burnout Scores

[Table 4] illustrates the correlation results between alarm fatigue and workload as well as occupational burnout. Pearson correlation analysis revealed a positive correlation between machine alarm fatigue and workload scores among hemodialysis nurses (r = 0.720; p < 0.001), suggesting that higher workload scores were associated with higher alarm fatigue scores. Additionally, the analysis showed a positive correlation between machine alarm fatigue and occupational burnout scores (r = 0.116; p < 0.001), suggesting that higher occupational burnout scores were associated with higher alarm fatigue scores.

Table 4 Correlation analysis of machine alarm fatigue among hemodialysis nurses with workload index and occupational burnout scores (n = 460)

Characteristic	r-Value	p-Value
Workload	0.720	< 0.001
Occupational burnout	0.116	< 0.001

 

MissingLinkText

Multiple Linear Regression Analysis of Machine Alarm Fatigue in Hemodialysis Nurses

Using the total score of machine alarm fatigue in hemodialysis nurses as the dependent variable and the variables that showed statistical differences in the univariate analysis, as well as work-related workload and occupational burnout, as independent variables, a multiple linear regression analysis was conducted, as shown in [Table 5]. The research findings indicate that years of hemodialysis specialty work, number of patients managed per shift, received specialized nursing training, self-rated health status, workload, and occupational burnout are statistically significant predictors of machine alarm fatigue level. Specifically, machine alarm fatigue level is negatively correlated with years of hemodialysis specialty work (Β, −0.431; p < 0.05) and positively correlated with the number of patients managed per shift (0.825; p < 0.05), received specialized nursing training (0.998; p < 0.05), self-rated health status (0.725; p < 0.05), workload (0.250; p < 0.05), and occupational burnout (0.717; p < 0.05). The linear regression equation fits well and is statistically significant (F = 4.067; p < 0.01), collectively explaining 35.2% of the total variance in machine alarm fatigue among hemodialysis nurses.

Table 5 Multiple linear regression analysis of factors affecting machine alarm fatigue in hemodialysis nurses (n = 460)

Independent variables	Β	SE	β	t	p
Constant term	−5.023	1.714	–	−2.732	0.007
Department position	−0.935	0.615	−0.070	−1.555	0.128
Years of hemodialysis specialty work	−0.431	0.129	−0.092	−3.024	0.001
Number of patients managed per shift	0.825	0.419	0.061	1.999	0.049
Underwent specialized nurse training	0.998	0.458	0.054	2.351	0.029
Self-rated health status	0.725	0.317	0.091	2.254	0.022
Workload	0.250	0.028	0.493	14.021	0.000
Occupational burnout	0.717	0.322	0.090	2.312	0.026

Note: R ² = 0.374, adjusted R ² = 0.352; F = 4.067; p < 0.001.

 

MissingLinkText

 

MissingLinkText

Discussion

The Degree of Nurses Machine Alarm Fatigue Needs Improvement

The results of this study show that the average total score of machine alarm fatigue among hemodialysis nurses in 29 tertiary hospitals in Liaoning Province was 17.04 ± 3.21, which falls within the moderate range. This is slightly lower than the results of Wang Ling's study (25.01 ± 2.38).[18] The reason for this difference could be that Wang Ling's study only surveyed 80 nurses from the hemodialysis units of three tertiary hospitals. In this survey, the item with the highest fatigue score was “alarms make me feel anxious and restless,” which is consistent with the findings of Wang Ling and others regarding alarm fatigue. The reason for this could be the unique working environment in hemodialysis units, where nurses not only care for patients with dialysis syndrome but also deal with alarms from various machines in the dialysis unit. This can easily lead to nurse fatigue, increased psychological stress, and the emergence of anxiety. It is worth noting that 52% of nurses do not know how to prevent alarm fatigue.[19] Karahan et al[20] found through a survey of 592 nurses from five major cities in Turkey that more than half of the nurses exhibited alarm fatigue. Additionally, the study also discovered that alarm fatigue decreases with increasing years of work experience, consistent with the results of our study. The survey results conducted by Ding et al[21] among 236 critical care nurses from five hospitals in China similarly indicated a moderate level of alarm fatigue among critical care nurses, with the majority of nurses experiencing moderate to high levels of occupational burnout.

Therefore, the scientific and effective improvement of nurses' levels of machine alarm fatigue is an urgent issue that needs to be addressed.

 

MissingLinkText

Factors Correlated with Machine Alarm Fatigue among Hemodialysis Nurses

The data analysis in this study showed statistically significant correlations between machine alarm fatigue and years of hemodialysis specialty work, number of patients managed per shift, received specialized nursing training, self-reported health status among hemodialysis nurses. This may be attributed to the increasing complexity and rapid advancement of dialysis equipment, which requires nurses with less experience to quickly acquire various technical skills and become familiar with new equipment, contributing to alarm fatigue. Additionally, the centralized nature of dialysis machines means that the more patients a nurse is responsible for during a shift, the higher the number of alarms they need to handle, leading to increased fatigue. Inadequate staffing and a lack of monitoring technology can also result in missed alarms. Nurses who have received specialized training tend to have lower alarm fatigue scores, as they have a better grasp of handling complications during dialysis and can make more accurate judgments about their patients' conditions, leading to comprehensive assessments and early warnings, thus reducing the impact of alarms on their work.[22] [23] The results of this study also show that overall health condition is a significant factor in machine alarm fatigue among hemodialysis nurses. Those with poor health conditions had significantly higher alarm fatigue scores compared with those with general or good health conditions (p < 0.05). The reasons may be attributed to the significant responsibilities and workload carried by hemodialysis nurses. They often handle the transportation of dialysate (each barrel weighing ∼15 kg), which can lead to potential health issues such as spinal injuries, lumbar muscle strain, muscle sprains, and more when moving immobile dialysis patients onto dialysis beds.[24] Additionally, the prolonged and frequent exposure to alarms diminishes nurses reaction times and agility, especially when their health is compromised, exacerbating the level of alarm fatigue.[6] Furthermore, to maintain the sterile environment of the hemodialysis unit, regular disinfection of equipment and medical devices within the unit is required. Common disinfectants include hydrogen peroxide and citric acid, the vapors of which can also impact the health of nursing staff exposed to this environment.[25]

The data analysis in this study showed statistically significant correlations between machine alarm fatigue and workload among hemodialysis nurses, with higher workload correlating with higher levels of alarm fatigue. This is possibly due to the presence of multiple alarms from various medical devices in the hemodialysis room, which can reach high noise levels, exacerbated by the enclosed environment required for clinical treatment. Additionally, nurses in hemodialysis units are required to strictly follow the operational protocols of the unit, especially when dealing with patients with infectious diseases such as hepatitis B, which can result in high-stress situations. Edworthy et al[26] found that under high-intensity workload, one out of every four alarms is ignored, emphasizing the importance of reducing alarm fatigue. Hayes et al[27] argued that while the close and prolonged relationship with patients in the hemodialysis environment contributes to increased job satisfaction for nurses, over time, this relationship can lead to emotional exhaustion, particularly when patients experience deteriorating health or pass away. The recurring negative emotions in such situations are detrimental to nurses mental and emotional well-being, leading to increased emotional tension and stress.

 

MissingLinkText

Intervention Measures for Alleviating Machine Alarm Fatigue among Hemodialysis Nurses

Alarm fatigue may lead to health care personnel feeling fatigued and desensitized to alarm sounds, thereby reducing sensitivity to alarms. This may result in health care personnel responding slowly or neglecting truly urgent situations, thereby increasing the risk of patient accidents. For example, an important alarm sound may be mistaken for a common false alarm, leading to delays in crucial intervention measures. At the same time, sustained and frequent alarm sounds may cause discomfort and anxiety for patients, particularly in cases of severe illness or when urgent treatment is needed, which can lead to adverse outcomes or even death. This discomfort in such environments may decrease patient satisfaction with the medical team. Additionally, health care personnel experiencing alarm fatigue may be unable to respond promptly to patient needs, further reducing patient evaluations of nursing quality and satisfaction. To mitigate the impact of alarm fatigue on patient safety and nursing satisfaction, and to improve nurses with machine alarms fatigue, the following measures can be taken.

Establishing a Machine Alarm Management System

Koomen et al[28] and other scholars have indicated that the outdated single-device mode, where individual medical equipment generates alarms independently without any coordination or prioritization with other devices, can result in discordant sounds and important alarms may get lost amidst trivial ones. Wei et al[29] have demonstrated the implementation of a machine alarm management system based on Internet of Things technology, incorporating equipment operational status monitoring modules, hierarchical alarm modules, and data storage communication modules to transmit machine data to a central equipment management system. The system categorizes the data and alerts health care professionals promptly through smart bracelets and mobile phones using vibration, ensuring timely response to machine alarms.[30] Netherlands scholars have already applied alarm management systems in neonatal intensive care units (NICUs),[31] and the results indicate that implementing an alarm management system can safely reduce alarm fatigue in the NICU environment. Furthermore, Claudio et al[32] found that 69.2% of in-house staff considered the alarm management system useful. The alarm management system is equally applicable to operating rooms, hospital wards, and any other nursing environment using medical equipment. Although it can reduce alarm fatigue and further ensure medical quality and safety, its applicability in the field of hemodialysis still requires further exploration. Graham and Cvach[33] found that using vibration alerts instead of sound alarms or employing soft musical melodies instead of sharp and piercing alarm sounds can reduce noise pollution caused by alarms. Researchers like Roodaki et al[34] have suggested that sound interaction technology can help avoid alarm fatigue, but its applicability and effectiveness in reducing machine alarm fatigue among hemodialysis nurses still need further research.

 

MissingLinkText

Personalized Thresholds and Delayed Alarms

Clinical studies have shown that the inability to reasonably set parameter thresholds for monitoring devices based on a patient's condition has become one of the significant obstacles to alarm management.[23] Monitoring systems come with default thresholds for various parameters, and these default values often do not meet the individualized medical needs of hemodialysis patients, leading to numerous false alarms.[35] Therefore, improving the rationality and usability of threshold settings is of paramount importance in mitigating alarm fatigue. One proposed approach is the use of personalized threshold settings, which are based on historical data such as venous pressure and transmembrane pressure for hemodialysis patients to calculate reference thresholds, allowing for a more personalized threshold configuration. In traditional alarm systems, once a parameter exceeds its limit, it immediately triggers audible and visual alarms. However, when parameters fluctuate around the alarm limit, alarms can become very frequent. The delayed alarm strategy involves optimizing the alarm mechanism. For instance, in the case of a high threshold, a fluctuation value is set. If the parameter value exceeds the alarm limit plus the fluctuation value, an alarm is triggered immediately. If the parameter value hovers between the alarm limit and the fluctuation value, the time spent above the alarm limit is accumulated, and an alarm is only issued when the accumulated time exceeds a threshold.

 

MissingLinkText

Multidisciplinary Team Collaboration

Xue et al[36] demonstrate that implementing multidisciplinary team collaboration management can effectively reduce nurse alarm fatigue. Hemodialysis units can establish supervisory teams composed of doctors, experienced hemodialysis nurses, and engineers. Studies[37] have indicated that reinforcing standardized training for machine usage not only benefits all health care staff in parameter settings but also, according to a randomized controlled trial conducted by Bi et al[38] with 93 ICU nurses, that machine alarm management training can effectively reduce alarm fatigue in ICU nurses. Moreover, particularly within the current resource constraints of the Chinese health care environment, increasing the number of health care team members and enhancing their professional skills can enable timely responses to alarms and adjustments, thereby reducing the occurrence of false alarms and alarm fatigue. Therefore, it is suggested that hemodialysis health care managers can make reasonable staffing arrangements and regularly conduct training exams for nursing staff on handling machine alarms in simulated real-life environments, ensuring nurses can proficiently operate the machines and respond to alarms flexibly, thereby reducing alarm fatigue.[39]

 

MissingLinkText

Improving Compassion Fatigue among Health Care Professionals

Kelly et al[40] found that nurses aged between 21 and 33 years old may experience a higher degree of fatigue, and those with more work experience are more likely to experience compassion fatigue. On the other hand, Sacco et al[41] suggested that critical care nurses aged 50 and above perceive higher levels of compassion satisfaction. If nurses can find joy in their work and approach patient care with compassion, the incidence of fatigue at work is likely to decrease. Additionally, research has shown that compassion fatigue or burnout can lead to insensitivity in patient care and clinical monitoring alarms, thereby contributing to alarm fatigue. It is recommended that nursing educators and nursing managers provide courses on addressing alarm fatigue for graduating students entering clinical practice, regularly assess nurses for compassion fatigue, burnout, and alarm fatigue, and increase on-the-job training opportunities for nurses. Nurses should not only take proactive steps to prevent or overcome alarm fatigue, compassion fatigue, and burnout but also support each other among colleagues.[42]

 

MissingLinkText

Anticipatory Care and Machine Performance Stability

Anticipatory care refers to proactive and targeted nursing measures taken by professional nurses based on relevant nursing procedures and experience. It involves a comprehensive analysis of a patient's condition, aiming to reduce foreseeable risks.[43] [44] Ruskin and Hueske-Kraus[45] suggest that checking the machine's thresholds before starting a session can prevent carrying over data from the previous patient. Proper placement of blood pressure cuffs can also reduce the occurrence of false alarms. Zhou et al[46] believe that adequately priming the tubing and filter before the session, elevating the liquid level in the venous chamber, and minimizing the air–blood contact surface can prevent clot formation and, consequently, reduce alarm fatigue. It is recommended that nurses actively engage in anticipatory care before starting a session, thoroughly inspect machine performance, closely monitor various parameters during treatment, and make timely adjustments to blood flow and ultrafiltration based on the patient's blood pressure and other factors. This approach will help minimize the occurrence of false alarms.

 

MissingLinkText

 

MissingLinkText

 

MissingLinkText

Conclusion

The dialyzer is an indispensable “artificial kidney” for hemodialysis patients, and machine alarms are an essential means of ensuring the clinical treatment, care, and efficiency of hemodialysis nurses. However, alarm fatigue diminishes the value of these alarms. The alarm fatigue of hemodialysis nurses may have a negative impact on patient outcomes and their satisfaction with care. When nurses become fatigued due to continuous exposure to alarm sounds, they may respond inadequately to critical alarms or overlook important reminders, potentially resulting in adverse events or complications for patients. Additionally, the frequent alarm sounds may create a stressful environment for both nurses and patients, thereby affecting overall satisfaction with the care provided. Therefore, addressing the alarm fatigue of hemodialysis nurses is crucial for ensuring patient safety and improving satisfaction with care. Currently, the situation of machine alarm fatigue among hemodialysis nurses in the 29 tertiary hospitals in Liaoning Province, China, is not optimistic. Hospital administrators should pay more attention to specialized training for hemodialysis nurses, alleviate their equipment fatigue, and provide higher-quality nursing services to hemodialysis patients. At the same time, it is a call to the entire nation to address the issue of machine alarms, which is a shared responsibility of nurses, health care institutions, and national organizations.

This study has several limitations that need to be acknowledged. First, due to the cross-sectional study design, causal relationships between alarm fatigue and various factors should be interpreted with caution. To explore causal relationships, future cohort studies are needed. Second, due to the convenience sampling, the generalizability of the study results is limited. Additionally, the small sample size and the predominantly tertiary hospital-based sample further constrain the findings. Therefore, future prospective cohort studies with larger sample sizes are necessary to further refine and improve model fit, facilitating the advancement of nursing knowledge regarding alarm fatigue. Furthermore, some variables in the General Information Questionnaire were self-reported and not measured using standardized and validated scales. Finally, the measurement of alarm fatigue was based on a single occasion, which may provide insight into participants views on that particular day. However, due to the complexity of alarm fatigue, it may not represent hemodialysis nurses experiences in other situations and at different times. Future research should involve more comprehensive and ongoing assessments of alarm fatigue. Therefore, recommendations for future research on alarm fatigue in nursing should consider using larger sample sizes, different populations, or mixed methods. Additionally, factors such as dialysis machine usage time, maintenance conditions, patient condition assessment, and prevention should be taken into account. In the future, collaborative research involving multiple hemodialysis centers will be conducted with larger samples to further improve and enhance the model's fit, promoting knowledge in nursing related to alarm fatigue.

 

MissingLinkText

Clinical Relevance Statement

Hemodialysis is a complex medical procedure involving the monitoring of multiple parameters and indicators. Dialysis equipment is typically equipped with various alarm systems to alert nurses to potential issues. However, prolonged exposure to frequent alarms may lead to alarm fatigue among hemodialysis nurses, thereby reducing their sensitivity and responsiveness to alarms. This could increase the risk of complications or accidents during the hemodialysis process, posing a potential threat to patient safety. Additionally, it can have a negative impact on the work environment for nurses, potentially affecting their focus, efficiency, job satisfaction, and overall physical and mental health. Therefore, by gaining a deeper understanding of the impact of alarm fatigue on nurses and patients, appropriate solutions and strategies can be developed to improve alarm management and nursing quality during hemodialysis. Alleviating nurse alarm fatigue helps enhance their sensitivity to alarms, strengthen their response to real emergencies, and ultimately improve patient safety and nursing satisfaction.

 

MissingLinkText

Multiple-Choice Questions

1. What was the average score for machine alarm fatigue in this study?

   • 16.43 ± 6.44

   • 2.35 ± 1.15

   • 12.36 ± 2.24

   • 3.77 ± 0.08

   Correct Answer: The correct answer is option b. The results show that the average score of machine alarm fatigue is 2.35 ± 1.15.

2. According to the research results, which factor below is significantly correlated with machine alarm fatigue among hemodialysis nurses?

   • Hemodialysis machines

   • Hemodialysis rooms

   • Hemodialysis techniques

   • Overall health condition

   Correct Answer: The correct answer is option d. The results indicate a significant correlation between overall health status and machine alarm fatigue in hemodialysis nurses.

 

MissingLinkText

 

MissingLinkText

Conflict of Interest

None declared.

Acknowledgments

Thanks for the valuable contributions made by the staff from 29 tertiary hospitals in Liaoning Province.

Protection of Human and Animal Subjects

The study was performed in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects and was reviewed by the Second Hospital of Dalian Medical University Review Board.

^† Co-first author.

• References

• 1 Fila B. Quality indicators of vascular access procedures for hemodialysis. Int Urol Nephrol 2021; 53 (03) 497-504
• 2 Cao W, Feng H, Zeng X. et al. Preliminary discussion on cost and quality assessment of primary dialysis units using big data. Chin J Blood Purification 2017; 16 (06) 383-386
• 3 Wu H, Wang K, Zheng Q. Thoughts on “independent hemodialysis centers inclusion in medical insurance management”. China Social Security 2018; (07) 40-41
• 4 Zhao J, Lu Y, Ding L. et al. Analysis and guidance of 3518 cases of CRRT treatment failure alarms. Chin J Blood Purification 2014; 13 (04) 353-354
• 5 ECRI Institute. Top 10 health technology hazards for 2015. Accessed on January 22, 2017 at: http://www.ecri.org/2015hazar -ds
• 6 Wang D, Zhang Y, Luo L. et al. Investigation on the status and influencing factors of alarm fatigue in ICU nurses' medical equipment. J Nurs (Luton) 2018; 33 (13) 19-21
• 7 Harris RM, Manavizadeh J, McPherson DJ, Smith L. Do you hear bells? The increasing problem of alarm fatigue. Pa Nurse 2011; 66 (01) 10-13
• 8 Oliveira AEC, Machado AB, Santos EDD, Almeida ÉB. Alarm fatigue and the implications for patient safety. Rev Bras Enferm 2018; 71: 3035-3040
• 9 Joinson C. Coping with compassion fatigue. Nursing 1992; 22 (04) 116-120 , 118–119, 120
• 10 Rayo MF, Moffatt-Bruce SD. Diagnosing and treating “alarm fatigue”: pragmatic and evidence-based approaches needed. Jt Comm J Qual Patient Saf 2016; 42 (07) 291-292
• 11 Ryherd EE, Waye KP, Ljungkvist L. Characterizing noise and perceived work environment in a neurological intensive care unit. J Acoust Soc Am 2008; 123 (02) 747-756
• 12 Ding X. Analysis of the reasons for the decreased sensitivity of young nurses to monitor alarms and countermeasures. General Nursing 2018; 16 (10) 1239-1240
• 13 Kim SJ, Sung MS. Subjective symptoms on fatigue in hospital nurses. J Korean Acad Nurs 1998; 28 (04) 908-919
• 14 Wei Chen FX, Yulan X, Fan H, Qi H, Ye D. Multifactor logistic regression analysis and improvement measures of alarm fatigue in our hospital's monitor. Chin Med Equipment 2020; 35 (01) 138-144
• 15 Hart SG, Staveland LE. Development of NASA-TLX (Task Load Index): results of empirical and theoretical research. Adv Psychol 1988; 52: 139-183
• 16 Liang L, Zhao L, Deng J. et al. Chinese version of the NASA-TLX Scale: translation and validation. Nurs Res 2019; 33 (05) 734-737
• 17 Maslach C. The measurement of experienced burnout. J Occupl Behav 1981; 2 (02) 99-113
• 18 Wang L, Liu H. The status quo of alarm fatigue in hemodialysis room nurses and its influencing factors. Int J Nurs (N Y) 2018; 37 (22) 3051-3054
• 19 Lewandowska K, Weisbrot M, Cieloszyk A, Mędrzycka-Dąbrowska W, Krupa S, Ozga D. Impact of alarm fatigue on the work of nurses in an intensive care environment-a systematic review. Int J Environ Res Public Health 2020; 17 (22) 8409
• 20 Karahan A, Kav S, Çevik B, Çıtak EA, Uğurlu Z, Fulser B. Alarm fatigue among nurses working in intensive care and other inpatient clinics. Work 2023; 76 (02) 793-801
• 21 Ding S, Huang X, Sun R. et al. The relationship between alarm fatigue and burnout among critical care nurses: a cross-sectional study. Nurs Crit Care 2023; 28 (06) 940-947
• 22 Luo L, Wang Y, Yang M. et al. Study on the status and influencing factors of ICU nurses' alarm fatigue. General Nursing 2022; 20 (16) 2166-2170
• 23 Yin Q. Research progress on alarm fatigue in ICU medical staff. Nurs Res 2018; 32 (21) 3325-3328
• 24 Jiang S. Physical injury causes and protective measures for hemodialysis room nurses. Worlds Latest Med Inform Digest 2019; 19 (34) 203-206
• 25 Yin H, Lan C, Zhou Q. Investigation and analysis of stress sources in 258 blood purification nurses in Jiangxi Province. Jiangxi Medicine 2019; 54 (04) 402-404
• 26 Edworthy J, Reid S, Peel K. et al. The impact of workload on the ability to localize audible alarms. Appl Ergon 2018; 72: 88-93
• 27 Hayes B, Bonner A, Douglas C. Haemodialysis work environment contributors to job satisfaction and stress: a sequential mixed methods study. BMC Nurs 2015; 14: 58
• 28 Koomen E, Webster CS, Konrad D. et al. Reducing medical device alarms by an order of magnitude: a human factors approach. Anaesth Intensive Care 2021; 49 (01) 52-61
• 29 Wei A, Zhang H, Yin J, Liu W, Yan L. A design of smart alarm management system of ICU. China Med Equipment 2018; 15 (11) 133-135
• 30 Short K, Chung Jr YJ. Solving alarm fatigue with smartphone technology. Nursing 2019; 49 (01) 52-57
• 31 Varisco G, van de Mortel H, Cabrera-Quiros L. et al. Optimisation of clinical workflow and monitor settings safely reduces alarms in the NICU. Acta Paediatr 2021; 110 (04) 1141-1150
• 32 Claudio D, Deb S, Diegel E. A framework to assess alarm fatigue indicators in critical care staff. Crit Care Explor 2021; 3 (06) e0464
• 33 Graham KC, Cvach M. Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms. Am J Crit Care 2010; 19 (01) 28-34 , quiz 35
• 34 Roodaki H, Navab N, Eslami A, Stapleton C, Navab N. SonifEye: sonification of visual information using physical modeling sound synthesis. IEEE Trans Vis Comput Graph 2017; 23 (11) 2366-2371
• 35 Poole S, Shah N. Addressing vital sign alarm fatigue using personalized alarm thresholds. Pac Symp Biocomput 2018; 23: 472-483
• 36 Xue Y, Zhong J, Zheng J. Application of multidisciplinary team on reducing ECG monitor alarm fatigue of nurses in cardiac surgery ICU. J Nurs Adm 2018; 18 (08) 601
• 37 Bridi AC, Louro TQ, da Silva RC. Clinical alarms in intensive care: implications of alarm fatigue for the safety of patients. Rev Lat Am Enfermagem 2014; 22 (06) 1034-1040
• 38 Bi J, Yin X, Li H. et al. Effects of monitor alarm management training on nurses' alarm fatigue: a randomised controlled trial. J Clin Nurs 2020; 29 (21–22): 4203-4216
• 39 Chen T. Research progress on the intervention of ICU instrument alarm. Publ Med Forum Magazine 2017; 21 (15) 1971-1972
• 40 Kelly L, Runge J, Spencer C. Predictors of compassion fatigue and compassion satisfaction in acute care nurses. J Nurs Scholarsh 2015; 47 (06) 522-528
• 41 Sacco TL, Ciurzynski SM, Harvey ME, Ingersoll GL. Compassion satisfaction and compassion fatigue among critical care nurses. Crit Care Nurse 2015; 35 (04) 32-43 , quiz 1p, 43
• 42 Storm J, Chen HC. The relationships among alarm fatigue, compassion fatigue, burnout and compassion satisfaction in critical care and step-down nurses. J Clin Nurs 2021; 30 (3-4): 443-453
• 43 Bi S. Application of predictive nursing in orthopedics nursing. Nursing Res China 2017; 31 (06) 754-755
• 44 Nian S, Li H, Li E, Shen Q. Predictive nursing in the prevention of hypoglycemia in hemodialysis. J Nurs Adm 2020; 20 (08) 597
• 45 Ruskin KJ, Hueske-Kraus D. Alarm fatigue: impacts on patient safety. Curr Opin Anaesthesiol 2015; 28 (06) 685-690
• 46 Zhou Q, Hu T, Yi R. To observe the application effect of predictive nursing in preventing extracorporeal circulation tube plugging. Today Nurse 2021; 28 (26) 98-99

Publication History

Received: 03 December 2023

Accepted: 26 March 2024

Accepted Manuscript online:
01 April 2024

Article published online:
10 July 2024