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Published in final edited form as: Nurs Outlook. 2024 Oct 17;72(6):102288. doi: 10.1016/j.outlook.2024.102288

The association between alarm burden and nurse burnout in US hospitals

Halley Ruppel 1, Maura Dougherty 2, Mahima Kodavati 3, Karen B Lasater 4
PMCID: PMC12086589  NIHMSID: NIHMS2029928  PMID: 39413565

Abstract

Alarms pervade the hospital environment, often increasing nurses’ workload. Hospital nurses are experiencing burnout at unprecedented rates. This study examined the association between nurses’ experience of alarms and burnout using survey data from US nurses (n =2,131). Nurses who frequently/occasionally experienced overwhelm from alarms had 2.47 (CI 1.93, 3.16) greater odds of high burnout than those who rarely/never experienced alarm overwhelm; those who frequently/occasionally had to delay alarm response had 2.13 (1.67, 2.70) greater odds of high burnout than those who rarely/never did; and those who frequently/occasionally encountered situations where no one responded to an urgent alarm had 2.5 (2.07, 3.03) greater odds of high burnout than those who rarely/never encountered such situations. The associations remained largely unchanged after adjusting for hospital characteristics, nurse practice environment, and nurse staffing. Although this study was cross-sectional, the potential impact of alarms on nurses’ well-being is an important consideration as technology advances.

Keywords: Alarm fatigue, nurse burnout, alarm burden, clinical alarms (MeSH), professional burnout (MeSH)

Introduction

Although the demands of the COVID-19 pandemic brought more public awareness to the problem of burnout among healthcare providers, almost half of nurses working in hospitals (48%) were burned out prior to the pandemic (Aiken, Sloane, McHugh, Pogue, & Lasater, 2023). Nurse burnout has implications for job turnover and increases the risk of adverse events, such as medication errors, falls, and infection (Dall'Ora, Ball, Reinius, & Griffiths, 2020) and other poor outcomes for patients (Jun, Ojemeni, Kalamani, Tong, & Crecelius, 2021; Schlak, Aiken, Chittams, Poghosyan, & McHugh, 2021). Nurse burnout is multifactorial, stemming from emotionally intensive work in poorly designed systems without adequate support (Dall'Ora et al., 2020; Lake et al., 2019; Shah et al., 2021).

Patient care technology is often lauded as an opportunity to reduce nurse workload and improve systems of care (Gandhi et al., 2023). However, when appropriate resources are not available to support integration of technology into clinical workflows, technologies may paradoxically harm patients and clinicians. For more than a decade, clinicians, researchers, and policy makers have expressed concern over the incessant alerts and alarms generated by clinical technologies both in terms of patient safety and the impact on hospital nurses’ well-being (Albanowski, Burdick, Bonafide, Kleinpell, & Schlesinger, 2023; National Academies of Sciences, 2021; Sendelbach & Funk, 2013). Although alarms play an important role in drawing attention to a clinical or technical issue, medical devices generate hundreds of alarms each day, and more than 85% of them may be uninformative or invalid (Bonafide et al., 2015; Drew et al., 2014; Rayo & Moffatt-Bruce, 2015). Alarms increase nurse workload (Rasooly et al., 2021) and managing uninformative alarms likely detracts from time spent in meaningful patient care. Uninformative alarms also create conditions for nurses’ desensitization to alarms and the downstream risk for a patient safety event in which an important alarm is missed or ignored (Kowalczyk L, 2012; Sendelbach & Funk, 2013). Moreover, alarms contribute to high noise levels, which has also been linked to burnout and diminished quality of life for nurses (McCullagh, Xu, Dickson, Tan, & Lusk, 2022; Topf & Dillon, 1988).

Given the prevalence of alarms in the hospital environment, we hypothesized that alarm burden may contribute to nurse burnout (see Figure 1). Our previous findings suggested that nurses in medical-surgical units experienced more alarm burden than those in intensive care units (ICU) (Ruppel, Dougherty, Bonafide, & Lasater, 2023), which may be attributable to the growing use of continuous monitoring outside the ICU and high patient-to-nurse ratios. A few studies have explored the association between alarms and burnout in critical care nurses (Ding et al., 2023; Storm & Chen, 2020). We sought to contribute to the existing evidence by exploring the association in nurses working outside the ICU, using data from a large survey of nurses employed in hundreds of hospitals in two US states (New York, Illinois). Specifically, we examined whether nurses’ experience of alarm burden was associated with increased odds of high burnout, while accounting for hospital characteristics, the nurse practice environment, and nurse staffing.

Figure 1.

Figure 1.

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Conceptual model for relationships between alarms and burnout.

Methods

Study Design

We conducted a cross-sectional secondary analysis of data that were collected as part of the RN4CAST-NY/IL survey, which was distributed via email to all licensed nurses in the states of NY and IL in spring of 2021 (Aiken et al., 2023; Ruppel et al., 2023). Survey items included nurses’ appraisals of hospital characteristics, practice environment, nurse job outcomes, including burnout, and included several items on alarm burden. The current study was determined to not meet criteria for human subjects research by our institutional review board.

Sample

We included nurses working in direct patient care on medical-surgical, oncology, or stepdown inpatient units from hospitals with ≥5 nurse respondents to the survey. Nurses completing the survey were asked to self-report the type of unit in which they worked from a list of unit types. By including only those nurses from hospitals with ≥5 respondents, we were able to average staffing and practice environment variables to obtain hospital-level metrics (as described below). We also excluded respondents who did not complete all the survey items of interest.

Variables

Nurse Demographics

We described nurse respondents by their highest level of education in nursing and their years of experience as a registered nurse.

Outcome: Burnout

The outcome of interest was nurse burnout, measured using the Emotional Exhaustion subscale from Maslach Burnout Inventory-Human Services Survey, a 9-item scale (Cronbach alpha 0.92) (Maslach, Jackson, & Leiter, 1986; Maslach, Schaufeli, & Leiter, 2001). Consistent with prior research, we dichotomized burnout into those with high vs. not-high burnout, based on a cut point score of ≥27 (Aiken, Clarke, Sloane, Sochalski, & Silber, 2002; Aiken et al., 2018; Lasater et al., 2021).

Independent Variables of Interest: Alarm Burden Items

The independent variables of interest were three survey items related to the negative consequences of patient care alarms (e.g., alarms from monitors, ventilators, pumps, call bells), which our team hypothesized to be reasons that excessive alarm burden may be associated with burnout: 1. “I feel overwhelmed by the number of alarms I experience”; 2. “I delay my response to alarms longer than I would like because I can’t step away from another patient or task”; 3. “I encounter situations where a patient needed urgent attention, but no one responded to the alarm”. Response options included frequently, occasionally, rarely, and never, and were dichotomized for analysis (frequently/occasionally vs rarely/never).

These items were developed based on study team member expertise in alarm management and review of the literature, as previously described (Ruppel et al., 2023). Because the items were not designed to be a comprehensive measure of “alarm fatigue” or other discrete construct, we did not create a composite score from the items; instead, each item was evaluated separately. We refer to the items collectively as “alarm burden items” in this article for simplicity.

Covariates

Hospital Characteristics.

We obtained hospital characteristics by linking the study hospitals in the survey dataset to the American Hospital Association Annual Survey data. The hospital characteristics used in this study were: teaching status (number of residents per bed), hospital size (number of beds), and technology status, which was dichotomized as “high” or “not high” (based on open heart surgery and/or major organ transplant capability).

Hospital-Level Patient-to-Nurse Staffing.

Respondents were asked to report the number of nurses and patients on their unit on their last shift, from which we calculated a patient-to-nurse ratio (Aiken et al., 2011). Responses were averaged across all nurses from that hospital to create a hospital-level measure of patient-to-nurse staffing.

Hospital-Level Practice Environment.

The survey included an abbreviated 5-item Practice Environment Scale of the Nursing Work Index (PES-NWI) as a measure of the nurse practice environment (Lake, 2002; Lasater et al., 2021). For this study, we used the results of the abbreviated PES-NWI measure that did not include the staffing item, as we had a separate staffing variable (described above). The practice environment score is derived by taking the mean of the 4 items (“administration that listens and responds to nurse concerns”, “a supervisor who is a good manager and leader”, “good team work between nurses and physicians”, and “a clear philosophy of nursing that pervades the patient care environment”). Scores can range from 1-4, with a higher score indicating a more favorable practice environment. We used a hospital-level aggregate score obtained by taking the mean practice environment score for all nurses within the same hospital.

Analysis

We described nurse demographics using summary statistics (frequencies, percentages), by burnout status (high vs not-high). To test the association between each alarm burden item and the odds of high burnout, we used logistic regression models, accounting for clustering of nurses within hospitals. We employed a stepwise approach to sequentially adjust for hospital characteristics, the nurse practice environment, and patient-to-nurse staffing. In a post-hoc analysis, we tested patient-to-nurse staffing as a moderator for the relationship between alarm overwhelm and burnout, adjusting for covariates. We conducted analyses in Stata version 17. We considered P values <0.05 to be statistically significant.

Findings

Table 1 details the demographic characteristics of the 2,131 medical-surgical nurses from 151 hospitals, by burnout status (high vs not-high). The sample had a mean of 9.8 years of nursing experience (SD 10.9) and 79% had a bachelor’s degree or higher (n = 1,697). Nearly 64% (n = 1,356) experienced high burnout. Overall, 85% reported frequently or occasionally feeling overwhelmed by the number of alarms they experience (n = 1,817), 81% reported frequently or occasionally delaying their response to alarms because they could not step away from another patient or task (n = 1,726), and 62% reported frequently or occasionally encountering situations where a patient needed urgent attention but no one responded to the alarm (n = 1,326). The alarm burden items were more prevalent in the high burnout group than the not-high group. Nurses were evenly distributed across hospitals by teaching status (35% nonteaching, 30% minor, and 35% major). More than 80% of the sample worked in hospitals with >250 beds. The mean patient-to-nurse staffing ratio was 5.2 patients per nurse (SD 0.9) and the mean practice environment score was 2.7 (SD 0.3) out of 4.

Table 1.

Nurse and hospital characteristics by burnout status, n (%)

High Burnout
Yes
N = 1,3561 		No
N = 7751 		Overall,
N = 2,1311
BSN or higher 1,075 (79%) 612 (79%) 1,697 (79%)
RN years of experience
 Mean (SD) 9.2 (9.9) 10.8 (12.3) 9.8 (10.9)
Practice environment
 Mean (SD) 2.7 (0.3) 2.8 (0.3) 2.7 (0.3)
Staffing ratio (number of patients per 1 nurse)
 Mean (SD) 5.3 (0.9) 5.1 (0.9) 5.2 (0.9)
Teaching status hospital
 Nonteaching (no residents) 468 (35%) 278 (36%) 746 (35%)
 Minor (≤ 1 resident per 4 hospital beds) 416 (31%) 215 (28%) 631 (30%)
 Major (>1 resident per 4 hospital beds) 472 (35%) 282 (36%) 754 (35%)
Hospital size
 Small (≤ 100 beds) 14 (1%) 8 (1%) 22 (1%)
 Medium (101-250 beds) 212 (16%) 152 (20%) 364 (17%)
 Large (>250 beds) 1,127 (83%) 605 (78%) 1,732 (81%)
 Unknown 3 (0.2%) 10 (1%) 13 (1%)
Hospital technology status: High 746 (55%) 415 (54%) 1,161 (54%)
Alarm Burden Items
Overwhelm: Frequently or occasionally feel overwhelmed by the number of alarms 1,248 (92%) 569 (73%) 1,817 (85%)
Delay: Frequently or occasionally delay response to alarms because can’t step away from another patient/task 1,202 (89%) 524 (68%) 1,726 (81%)
Ignore: Frequently or occasionally encounter situations where a patient needs urgent attention but no one responds to alarm 987 (73%) 339 (44%) 1,326 (62%)
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1

n (%)

Table 2 displays the results of the stepwise logistic regression models testing the association between alarm burden and high burnout. In a model that included the three alarm burden items (Model 1), those who frequently or occasionally experienced alarm overwhelm had 2.47 (95% CI 1.93, 3.16) greater odds of burnout compared to those who rarely or never experienced alarm overwhelm; those who frequently or occasionally had to delay their response to alarms had 2.13 (1.67, 2.70) greater odds of burnout compared to those who rarely or never delayed their response; and those who frequently or occasionally encountered situations where no one responded to an urgent alarm had 2.5 (2.07, 3.03) greater odds of burnout compared to those who rarely or never encountered such situations. The odds of burnout remained largely unchanged even after adjusting for hospital characteristics, nurse practice environment, and patient-to-nurse staffing (Models 2, 3, 4). In the post hoc test we found that staffing did not moderate the relationship between alarm overwhelm and burnout.

Table 2.

Association between alarm burden items and high burnout (multivariable logistic regression with robust standard errors to account for clustering by hospital).

Alarm burden
item		 Model 1: All
alarm burden
items		 Model 2: Adjusted
for Hospital
Characteristics		 Model 3:
+Adjusted for
Nurse Practice
Environment		 Model 4:
+Adjusted for
Staffing
Overwhelm 2.47
(1.93, 3.16)		 2.47
(1.93, 3.17)		 2.54
(1.97, 3.28)		 2.52
(1.96, 3.25)
Delay 2.13
(1.67, 2.70)		 2.16
(1.69, 2.75)		 2.21
(1.74, 2.81)		 2.21
(1.74, 2.81)
Ignore 2.50
(2.07, 3.03)		 2.54
(2.10, 3.07)		 2.27
(1.86, 2.77)		 2.26
(1.85, 2.75)
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Note: Odds Ratio (95% Confidence Interval) shown

Discussion and Recommendations

We found that nurses who frequently or occasionally experienced the alarm burden items (feeling overwhelmed by alarms, delaying response to alarms, and encountering ignored alarms) had significantly higher odds of burnout than those who rarely or never experienced them. Causes of burnout are multifactorial and our findings suggest that, even after controlling for other important factors, negative consequences of alarm burden may contribute to burnout. This study was cross-sectional; therefore, we cannot rule out that nurses with high burnout may be more likely to experience or report alarms as overwhelming compared to those with less burnout. Nevertheless, there is reasonable basis for hypothesizing that alarm burden could contribute to burnout among nurses.

The current use of alarms within medical-surgical units creates conditions that may predispose nurses to the experiences measured by the emotional exhaustion subscale from Maslach’s Burnout Inventory (feeling emotionally drained, fatigued, used up, and frustrated) (Maslach et al., 1986). Although more research is needed in adult medical-surgical units, pediatrics studies have shown that each monitored patient can produce dozens of alarms per day (Pater et al., 2020; Schondelmeyer et al., 2018) and more than 99% of these alarms are invalid (e.g., movement artifact) or not clinically relevant (e.g., a brief desaturation not requiring intervention) (Bonafide et al., 2015). Regular interruptions from uninformative alarms can be frustrating and draining, as they increase nurse workload and can contribute to mental fatigue (Rasooly et al., 2021). Moreover, nurses may experience moral distress over conflicting time-sensitive priorities, such as the decision to respond to an alarm or complete other high priority patient care.

To the best of our knowledge, our study is the first to describe an association between alarm burden and burnout among medical-surgical nurses, while adjusting for practice environment and staffing. Two prior studies demonstrated inconsistent results when testing the association between alarm fatigue and burnout experienced by critical care nurses (Ding et al., 2023; Storm & Chen, 2020). Neither accounted for hospital-level variables. A key challenge of this body of work is the lack of a well-established measure of “alarm fatigue”—a commonly used but poorly defined construct (Winters et al., 2018). We used three simple items reflecting nurse-reported negative consequences of alarm burden, each demonstrating a relationship with burnout, but we did not measure “alarm fatigue.”

In our study, a large percentage of those with low burnout also experienced overwhelm from alarms and delayed response to alarms, highlighting the wide-spread nature of this problem. The patient safety implications of the current state of alarms have been widely recognized, including by The Joint Commission (Sendelbach & Funk, 2013; The Joint Commission, 2013). Numerous studies and quality improvement projects report methods for reducing alarm burden, particularly in the context of physiologic monitors, such as: creating population-specific default alarm limit settings, introducing delays before an alarm is generated, regularly replacing electrodes and sensors, and customizing alarm limits for individual patients (Winters et al., 2018).

Our study extends the current state of alarm research by indicating an association between patient care alarms and nurse well-being. The results have potential implications for nurse retention and overall hospital performance, in addition to patient safety. Further research is needed to explore the causality between alarms and burnout, and the mechanisms that link excessive alarms to burnout.

Even as further research is conducted, manufacturers and organizational leadership can work to optimize technology use. Patient care technologies are often assumed to benefit patients and nurses by providing novel data and making work more efficient, but mounting evidence shows that there is a cost to adding technology to the clinical care environment. Technology can place unforeseen demands on clinicians when they have to manage large numbers of invalid alarms and alerts (Orenstein et al., 2021; Rasooly et al., 2021). Rather than reducing the need for nurses, some technologies may actually require additional staffing and resources to realize their intended benefit to patients. Manufacturers conducting real-world clinical studies of alarm-generating technology should evaluate the alarm system, such as frequency of invalid or nonactionable alarms, alarm audibility, and ease of resolving alarm conditions. Organizational leadership should conduct comprehensive assessments of all alarms for which nurses are responsible, and create an overall strategy for alarm management. Tools from implementation science and human factors engineering can be used to evaluate the impact of technologies on workflows, patient safety, and job outcomes (Fernandez et al., 2019; Karsh, 2004).

Limitations

As a cross-sectional study, we cannot establish causality between alarm burden and burnout. It is possible that nurses who are burned out are more likely to negatively recall their experiences with alarms. Because we used self-report survey data, we did not have an objective measure of the number of alarms to which the nurses were exposed. However, our alarm burden items allowed for assessment of both the extent to which alarms were overwhelming to nurses and process outcomes related to alarms (i.e., delayed or absent response to alarms). Our alarm burden items were not intended as an instrument to measure a single construct. For pragmatic reasons, we dichotomized response options frequently/occasionally and rarely/never, which may have resulted in loss of information. The survey was conducted in two states in the US, which could threaten generalizability; however, we have no reason to believe that the environment of alarms at hospitals in these two states would systematically differ from hospitals in the US or other countries with similar technology usage. However, even within the US, medical surgical units are varied in patient population and technologies used. Finally, variables like hospital policies, alarm management systems and other alarm-specific organizational factors could not be accounted for in this study. Still, to the best of our knowledge this is the largest study of alarms and burnout to date, and the first to account for hospital characteristics, nurse practice environment, and nurse staffing.

Conclusion

Our results show that an association exists between nurse-reported alarm burden and high burnout, even after controlling for hospital characteristics, patient-to-nurse staffing, and the nurse practice environment. As hospitals adopt more alarm-generating technologies, the potential impact of additional alarms on nurses’ work and well-being is an important consideration.

Highlights:

  • Nurses’ experience of alarm burden is significantly associated with high burnout.

  • Unaddressed alarm burden may be a threat to nurse job outcomes, unaccounted for by other known contributors like high patient-to-nurse staffing levels and unfavorable nurse work environments.

  • Consideration of alarm burden is necessary when introducing new technology into nurses’ environment and workflow.

Acknowledgements:

We thank Priscilla Cho, BSN for her support reviewing relevant literature, which she conducted as an undergraduate student at the University of Pennsylvania School of Nursing.

Acknowledgment of Funding:

This study was funded by a University of Pennsylvania University Research Foundation Grant (PI: Ruppel); the RN4CAST-NY/IL Survey was funded by a grant from the National Council of State Boards of Nursing (PI: Lasater); Maura Dougherty was funded by the NIH T32 NR007104 (PIs: Aiken, McHugh, Lake). Karen Lasater was funded by the Agency for Healthcare Research and Quality (1R01HS028978 PI: Lasater). Mahima Kodavati was supported by The Wharton School Health Care Management Department SUMR Scholars program in partnership with the Leonard Davis Institute.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Declaration of Competing Interest

None.

Contributor Information

Halley Ruppel, Department of Family and Community Health, University of Pennsylvania School of Nursing; Leonard Davis Institute for Health Economics, University of Pennsylvania; Clinical Futures, Children’s Hospital of Philadelphia Research Institute.

Maura Dougherty, Columbia University School of Nursing.

Mahima Kodavati, Yale University.

Karen B. Lasater, Center for Health Outcomes Research and Policy and Department of Biobehavioral Health Sciences, University of Pennsylvania School of Nursing; Leonard Davis Institute for Health Economics, University of Pennsylvania.

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