Background: Continuous physiologic monitoring commonly is used in pediatric medical-surgical (med-surg) units and is associated with high alarm burden for clinicians. Characteristics of pediatric patients generating high rates of alarms on med-surg units are not known. Objective: To describe the demographic and clinical characteristics of pediatric med-surg patients associated with high rates of clinical alarms. Methods: We conducted a cross-sectional, single-site, retrospective study using existing clinical and alarm data from a children's hospital. Continuously monitored patients from med-surg units who had available alarm data were included. Negative binomial regression models were used to test the association between patient characteristics and the rate of clinical alarms per continuously monitored hour. Results: Our final sample consisted of 1,569 patients with a total of 38,501 continuously monitored hours generating 265,432 clinical alarms. Peripheral oxygen saturation (SpO2) low alarms accounted for 57.5% of alarms. Patients with medical complexity averaged 11% fewer alarms per hour than those without medical complexity (P < 0.01). Patients older than 5 years had up to 30% fewer alarms per hour than those who were younger than 5 years (P < 0.01). Patients using supplemental oxygen averaged 39% more alarms per hour compared with patients who had no supplemental oxygen use (P < 0.01). Patients at high risk for deterioration averaged 19% more alarms per hour than patients who were not high risk (P = 0.01). Conclusion: SpO2 alarms were the most common type of alarm in this study. The results highlight patient populations in pediatric medical-surgical units that may be high yield for interventions to reduce alarms. Most physiologic monitor alarms in pediatric medical-surgical (med-surg) units are not informative and likely could be safely eliminated to reduce noise and alarm fatigue.1-3 However, identifying and sustaining successful alarm-reduction strategies is a challenge. Research shows that 25% of patients in pediatric med-surg units produce almost three-quarters of all alarms.4 These patients are a potential high-yield target for alarm-reduction strategies; however, we are not aware of studies describing characteristics of pediatric patients generating high rates of alarms. The patient populations seen on pediatric med-surg units are diverse. Children of all ages are cared for on these units, with diagnoses ranging from acute respiratory infections, to management of chronic conditions, and to psychiatric conditions. Not all patients on pediatric med-surg units have physiologic parameters continuously monitored,4 but among those who do, understanding patient characteristics associated with high rates of alarms may help clinicians, healthcare technology management (HTM) professionals, and others working on alarm management strategies to develop targeted interventions. We conducted an exploratory retrospective study to describe patient characteristics associated with high rates of alarms in pediatric med-surg units.

Alarm fatigue is exacerbated by frequent, nonactionable physiologic monitor alarms. Overutilization of pulse oximetry (SpO2) compounds this alarm burden. Narrow default alarm limits and overutilization of continuous (CSpO2) rather than intermittent monitoring contribute to nonactionable alarms. There were 1.12 million SpO2 alarms on included units during the baseline period, of which 41.0% were for SpO2 ≥ 88%. We aimed to decrease SpO2 alarms per patient day by 20% within 12 months. This quality improvement study included patients admitted January 2019 to June 2022. Intensive care and cardiology units were excluded. Interventions included (1) changing default alarm SpO2 limits on monitors from <90% to <88%, (2) changing SpO2 order default from continuous to intermittent, and (3) adding indication requirements for CSpO2. Outcome measures were total SpO2 alarms and alarms for SpO2 ≥ 88% per patient day. Balancing measures were high acuity transfers and code blues without CSpO2 ordered. Control charts were used for each. Our study included 120 408 patient days with 2.98 million SpO2 alarms. Total SpO2 alarms and alarms for SpO2 ≥ 88% per patient day decreased by 5.48 (30.57 to 25.09; 17.9%) and 4.48 (12.50 to 8.02; 35.8%), respectively. Special cause improvement was associated with changing default monitor alarm parameters. Balancing measures remained stable. SpO2 monitors alarm frequently at our children's hospital. Widening default alarm limits was associated with decreased SpO2 alarms, particularly nonactionable alarms (≥88%). This high-reliability intervention may be applied, when appropriate, to other monitor alarm parameters to further mitigate alarm burden.

Effect of bundle set interventions on physiologic alarms and alarm fatigue in an intensive care unit: A quality improvement project

Physiologic monitor alarms occur at high rates in children's hospitals; ≤1% are actionable. The burden of alarms has implications for patient safety and is challenging to measure directly. Nurse workload, measured by using a version of the National Aeronautics and Space Administration Task Load Index (NASA-TLX) validated among nurses, is a useful indicator of work burden that has been associated with patient outcomes. A recent study revealed that 5-point increases in the NASA-TLX score were associated with a 22% increased risk in missed nursing care. Our objective was to measure the relationship between alarm count and nurse workload by using the NASA-TLX. We conducted a repeated cross-sectional study of pediatric nurses in a tertiary care children's hospital to measure the association between NASA-TLX workload evaluations (using the nurse-validated scale) and alarm count in the 2 hours preceding NASA-TLX administration. Using a multivariable mixed-effects regression accounting for nurse-level clustering, we modeled the adjusted association of alarm count with workload. The NASA-TLX score was assessed in 26 nurses during 394 nursing shifts over a 2-month period. In adjusted regression models, experiencing >40 alarms in the preceding 2 hours was associated with a 5.5 point increase (95% confidence interval 5.2 to 5.7; P < .001) in subjective workload. Alarm count in the preceding 2 hours is associated with a significant increase in subjective nurse workload that exceeds the threshold associated with increased risk of missed nursing care and potential patient harm.

BACKGROUND: Desensitisation to alarms, or alarm fatigue, is a concern for healthcare staff. Little is known about how physiotherapists relate to, or are affected by clinical alarms. This pilot study aimed to explore physiotherapists’ attitudes and practices towards physiologic monitor alarms (PMA) in critical care. METHODS: An online survey of physiotherapists with critical care experience working at a Model 4 Irish Hospital. A sample of convenience was used with all eligible physiotherapists invited to complete the online survey via email (n = 33). Demographic information was captured, as well as information on experiences, practices, and barriers and facilitators to managing PMA. RESULTS: The response rate was 76% (25/33). All respondents worked on-call and weekends, with one respondent managing a current day-to-day critical care caseload. The majority of respondents (20/25, 80%) perceived all PMA as clinically important, but a workplace distraction (19/25, 76%). Negative emotions were commonly experienced by respondents on hearing PMA. All respondents (25/25, 100%) reported to notice their patient’s PMA, feeling they had a responsibility to respond. Respondents indicated varying levels of self-confidence in responding to PMA but commonly assessed the cause of the alarm (24/25, 96%) and checked the patient’s condition (24/25, 96%). Education and training was identified as a key barrier and facilitator for physiotherapists in terms of managing alarms in critical care. CONCLUSION: This study provides preliminary data on physiotherapists’ attitudes and practices towards PMA in critical care. Additional studies are necessary in order to verify the findings of this pilot study and further explore alarm fatigue amongst critical care physiotherapists.

Evaluating the clinical impacts of healthcare alarm management systems plays a critical role in assessing newly implemented monitoring technology, exposing latent threats to patient safety, and identifying opportunities for system improvement. We describe a novel, accurate, rapidly implementable, and readily reproducible in situ simulation approach to measure alarm response times and rates without the challenges and expense of video analysis. An interprofessional team consisting of biomedical engineers, human factors engineers, information technology specialists, nurses, physicians, facilitators from the hospital's simulation center, clinical informaticians, and hospital administrative leadership worked with three units at a pediatric hospital to design and conduct the simulations. Existing hospital technology was used to transmit a simulated, unambiguously critical alarm that appeared to originate from an actual patient to the nurse's mobile device, and discreet observers measured responses. Simulation observational data can be used to design and evaluate quality improvement efforts to address alarm responsiveness and to benchmark performance of different alarm communication systems.

Clinical alarm systems safety is a national healthcare concern in the United States. Physiologic monitors are the medical devices associated with the highest number of false and clinically insignificant alarms, producing alarm fatigue and a challenge to meet the national clinical alarm systems safety goal. Modern physiologic monitors are high-tech complex devices with multimeasurement modalities and high sensitivity for alarms. This complexity hinders safe operation of the monitors by nurses and appropriate management of associated alarms. Nurses need to integrate cognitive knowledge, psychomotor skills, and critical thinking to safely operate the monitors and support clinical decisions. Limited resources are available to support clinical education for nurses on physiologic monitor use and alarm management. This toolkit presents an educational framework for physiologic monitor use and alarm safety guided by adult learning principles. The components of the program are (1) knowledge, skills, and attitude of physiologic monitor use; (2) scenario-based learning model to support the knowledge, skills, and attitude necessary for safe monitor use; and (3) a framework for evaluating the educational program. Education should be ongoing and customized per facility to ensure safe use of complex technology and to decrease alarm fatigue, the leading cause of alarm-related sentinel events.

Hospitalized children generate up to 152 alarms per patient per day outside of the intensive care unit. In that setting, as few as 1% of alarms are clinically important. How nurses make decisions about responding to alarms, given an alarm's low specificity for detecting clinical deterioration, remains unclear. Our objective was to describe how bedside nurses think about and act upon monitor alarms for hospitalized children. This was a qualitative study that involved the direct observation of nurses working on a general pediatric unit at a large children's hospital. We used a structured tool that included predetermined categories to assess nurse responses to monitor alarms. Data on alarm frequency and type were pulled from bedside monitors. We conducted 61.3 patient-hours of observation with nine nurses, in which we documented 207 nurse responses to patient alarms. For 67% of alarms heard outside of the room, the nurse decided not to respond without further assessment. Nurses most commonly cited reassuring clinical context (eg, medical team in room), as the rationale for alarm nonresponse. The nurse deemed clinical intervention necessary in only 14 (7%) of the observed responses. Nurses rely on clinical and contextual details to determine how to respond to alarms. Few of the alarm responses in our study resulted in a clinical intervention. These findings suggest that multiple system-level and educational interventions may be necessary to improve the efficacy and safety of continuous monitoring.

To explore clinical reasoning about alarm customisation among nurses in intensive care units. Critical care nurses are responsible for detecting and rapidly acting upon changes in patients' clinical condition. Nurses use medical devices including bedside physiologic monitors to assist them in their practice. Customising alarm settings on these devices can help nurses better monitor their patients and reduce the number of clinically irrelevant alarms. As a result, customisation may also help address the problem of alarm fatigue. However, little is known about nurses' clinical reasoning with respect to customising physiologic monitor alarm settings. This article is an in-depth report of the qualitative arm of a mixed methods study conducted using an interpretive descriptive methodological approach. Twenty-seven nurses were purposively sampled from three intensive care units in an academic medical centre. Semi-structured interviews were conducted by telephone and were analysed using thematic analysis. Consolidated Criteria for Reporting Qualitative Research (COREQ) reporting guidelines were used. Four themes were identified from the interview data: unit alarm culture and context, nurse attributes, motivation to customise and customisation "know-how." A conceptual model demonstrating the relationship of these themes was developed to portray the factors that affect nurses' customisation of alarms. In addition to drawing on clinical data, nurses customised physiologic monitor alarms based on their level of clinical expertise and comfort. Nurses were influenced by the alarm culture on their clinical unit and colleagues' and patients' responses to alarms, as well as their own technical understanding of the physiologic monitors. The results of this study can be used to design strategies to support the application of clinical reasoning to alarm management, which may contribute to more appropriate alarm customisation practices and improvements in safety.

BackgroundClinicians in intensive care units experience alarm fatigue related to frequent false and non-actionable alarms produced by physiologic monitors. To reduce non-actionable alarms, alarm settings may need to be customized for individual patients; however, nurses may not customize alarms because of competing demands and alarm fatigue.ObjectiveTo examine the effectiveness and acceptance of physiologic monitor software to support customization of alarms.MethodsThis pre/post intervention study was conducted in a 56-bed medical intensive care unit. IntelliVue® Alarm Advisor customization support software for alarm limit violations was installed on all monitors and education on its use provided. For 2 months before and after implementation of the software, data were collected on patient characteristics from the electronic health record, alarm counts and duration from the monitoring system, and nurses’ experience of alarms from a survey.ResultsMedium-priority heart rate, respiratory rate, and arterial pressure alarms were significantly reduced after software implementation (9.3%, 11.8%, and 15.9% reduction respectively; p<0.001 for all). The duration of these alarms was also significantly shorter (7.8%, 13.3%, and 9.3% reduction respectively; p<0.05 for all). The number and duration of SpO2 alarms did not decrease (p>0.05 for both). Patients post-intervention had worse Glasgow Coma Scale scores (p = 0.014), but otherwise were comparable to those pre-intervention. Nurses reported less time spent on non-actionable alarms post-intervention than pre-intervention (p = 0.026). Also lower post-intervention were the proportions of nurses who reported that alarms disturbed their workflow (p = 0.027) and who encountered a situation where an important alarm was ignored (p = 0.043). The majority (>50%) agreed that the software supported setting appropriate alarm limits and was easy to use.ConclusionAlarm customization software was associated with a reduction in alarms. Use of software to support nurses’ recognition of trends in patients’ alarms and facilitate changes to alarm settings may add value to alarm reduction initiatives.

Reducing Pulse Oximetry False Alarms Without Missing Life-Threatening Events.

Alarm fatigue has been linked to patient morbidity and mortality in hospitals due to delayed or absent responses to monitor alarms. We sought to describe alarm rates at 5 freestanding children's hospitals during a single day and the types of alarms and proportions of patients monitored by using a point-prevalence, cross-sectional study design. We collected audible alarms on all inpatient units and calculated overall alarm rates and rates by alarm type per monitored patient per day. We found a total of 147,213 alarms during the study period, with 3-fold variation in alarm rates across hospitals among similar unit types. Across hospitals, onequarter of monitored beds were responsible for 71%, 61%, and 63% of alarms in medical-surgical, neonatal intensive care, and pediatric intensive care units, respectively. Future work focused on addressing nonactionable alarms in patients with the highest alarm counts may decrease alarm rates.

Monitor alarms occur frequently but rarely warrant intervention. This study aimed to determine if a safety huddle-based intervention reduces unit-level alarm rates or alarm rates of individual patients whose alarms are discussed, as well as evaluate implementation outcomes. Unit-level, cluster randomized, hybrid effectiveness-implementation trial with a secondary patient-level analysis. Children's hospital. Unit-level: all patients hospitalized on 4 control (n = 4177) and 4 intervention (n = 7131) units between June 15, 2015 and May 8, 2016. Patient-level: 425 patients on randomly selected dates postimplementation. Structured safety huddle review of alarm data from the patients on each unit with the most alarms, with a discussion of ways to reduce alarms. Unit-level: change in unit-level alarm rates between baseline and postimplementation periods in intervention versus control units. Patient-level: change in individual patients' alarm rates between the 24 hours leading up to huddles and the 24 hours after huddles in patients who were discussed versus not discussed in huddles. Alarm data informed 580 huddle discussions. In unit-level analysis, intervention units had 2 fewer alarms/patient-day (95% CI: 7 fewer to 6 more, P = .50) compared with control units. In patient-level analysis, patients discussed in huddles had 97 fewer alarms/patientday (95% CI: 52-138 fewer, P < .001) in the posthuddle period compared with patients not discussed in huddles. Implementation outcome analysis revealed a low intervention dose of 0.85 patients/unit/day. Safety huddle-based alarm discussions did not influence unit-level alarm rates due to low intervention dose but were effective in reducing alarms for individual children.

Bedside monitor alarms alert nurses to life-threatening physiologic changes among patients, but the response times of nurses are slow. To identify factors associated with physiologic monitor alarm response time. This prospective cohort study used 551 hours of video-recorded care administered by 38 nurses to 100 children in a children's hospital medical unit between July 22, 2014, and November 11, 2015. Patient, nurse, and alarm-level factors hypothesized to predict response time. We used multivariable accelerated failure-time models stratified by each nurse and adjusted for clustering within patients to evaluate associations between exposures and response time to alarms that occurred while the nurse was outside the room. The study participants included 38 nurses, 100% (n = 38) of whom were white and 92% (n = 35) of whom were female, and 100 children, 51% (n = 51) of whom were male. The race/ethnicity of the child participants was 45% (n = 45) black or African American, 33% (n = 33) white, 4% (n = 4) Asian, and 18% (n = 18) other. Of 11 745 alarms among 100 children, 50 (0.5%) were actionable. The adjusted median response time among nurses was 10.4 minutes (95% CI, 5.0-15.8) and varied based on the following variables: if the patient was on complex care service (5.3 minutes [95% CI, 1.4-9.3] vs 11.1 minutes [95% CI, 5.6-16.6] among general pediatrics patients), whether family members were absent from the patient's bedside (6.3 minutes [95% CI, 2.2-10.4] vs 11.7 minutes [95% CI, 5.9-17.4] when family present), whether a nurse had less than 1 year of experience (4.4 minutes [95% CI, 3.4-5.5] vs 8.8 minutes [95% CI, 7.2-10.5] for nurses with 1 or more years of experience), if there was a 1 to 1 nursing assignment (3.5 minutes [95% CI, 1.3-5.7] vs 10.6 minutes [95% CI, 5.3-16.0] for nurses caring for 2 or more patients), if there were prior alarms requiring intervention (5.5 minutes [95% CI, 1.5-9.5] vs 10.7 minutes [5.2-16.2] for patients without intervention), and if there was a lethal arrhythmia alarm (1.2 minutes [95% CI, -0.6 to 2.9] vs 10.4 minutes [95% CI, 5.1-15.8] for alarms for other conditions). Each hour that elapsed during a nurse's shift was associated with a 15% longer response time (6.1 minutes [95% CI, 2.8-9.3] in hour 2 vs 14.1 minutes [95% CI, 6.4-21.7] in hour 8). The number of nonactionable alarms to which the nurse was exposed in the preceding 120 minutes was not associated with response time. Response time was associated with factors that likely represent the heuristics nurses use to assess whether an alarm represents a life-threatening condition. The nurse to patient ratio and physical and mental fatigue (measured by the number of hours into a shift) represent modifiable factors associated with response time. Chronic alarm fatigue resulting from long-term exposure to nonactionable alarms may be a more important determinant of response time than short-term exposure.

The frequency of physiologic monitor alarms in a children's hospital.

Concerns about alarm fatigue prompted The Joint Commission to issue a Sentinel Event Alert urging hospitals to minimize alarms. We previously conducted a quality improvement project on a single unit that reduced time on continuous pulse oximetry, a common source of physiologic monitor alarms, for patients with wheezing (ie, asthma and bronchiolitis, wheezing-associated respiratory infections). To study the impact of our improvement work on overall physiologic monitor alarm frequency for these patients. This was a retrospective cohort study at a freestanding children's hospital over an 8-week period. We compared alarm count, including respiratory, cardiac, and pulse oximetry alarms, for patients admitted to the intervention unit with the alarm count for similar patients on a control unit by using the Wilcoxon rank sum test. We used negative binomial regression to evaluate differences in alarm count between the units, adjusting for age, medical comorbidity, and length of stay. There were 101 patients on the intervention unit and 46 patients on the control unit. The percentage of patients with medical comorbidities was significantly higher on the intervention unit (P=.01). Median alarm count per day for patients on the intervention unit was lower; however, this difference was not statistically significant (71 vs 76 alarms per patient-day, P=.29). The multivariable model estimated a nonsignificant 6.4-count decrease in alarms for patients on the intervention unit. Reducing continuous pulse oximetry use alone may not make substantial reductions in overall alarm counts. Even on our intervention unit, alarm burden remained quite high.

Alarm fatigue from frequent nonactionable physiologic monitor alarms is frequently named as a threat to patient safety. To critically examine the available literature relevant to alarm fatigue. Articles published in English, Spanish, or French between January 1980 and April 2015 indexed in PubMed, Cumulative Index to Nursing and Allied Health Literature, Scopus, Cochrane Library, Google Scholar, and ClinicalTrials.gov. Articles focused on hospital physiologic monitor alarms addressing any of the following: (1) the proportion of alarms that are actionable, (2) the relationship between alarm exposure and nurse response time, and (3) the effectiveness of interventions in reducing alarm frequency. We extracted data on setting, collection methods, proportion of alarms determined to be actionable, nurse response time, and associations between interventions and alarm rates. Our search produced 24 observational studies focused on alarm characteristics and response time and 8 studies evaluating interventions. Actionable alarm proportion ranged from <1% to 36% across a range of hospital settings. Two studies showed relationships between high alarm exposure and longer nurse response time. Most intervention studies included multiple components implemented simultaneously. Although studies varied widely, and many had high risk of bias, promising but still unproven interventions include widening alarm parameters, instituting alarm delays, and using disposable electrocardiographic wires or frequently changed electrocardiographic electrodes. Physiologic monitor alarms are commonly nonactionable, and evidence supporting the concept of alarm fatigue is emerging. Several interventions have the potential to reduce alarms safely, but more rigorously designed studies with attention to possible unintended consequences are needed.

Alarm fatigue is reported to be a major threat to patient safety, yet little empirical data support its existence in the hospital. To determine if nurses exposed to high rates of nonactionable physiologic monitor alarms respond more slowly to subsequent alarms that could represent life-threatening conditions. Observational study using video. Freestanding children's hospital. Pediatric intensive care unit (PICU) patients requiring inotropic support and/or mechanical ventilation, and medical ward patients. None. Actionable alarms were defined as correctly identifying physiologic status and warranting clinical intervention or consultation. We measured response time to alarms occurring while there were no clinicians in the patient's room. We evaluated the association between the number of nonactionable alarms the patient had in the preceding 120 minutes (categorized as 0-29, 30-79, or 80+ alarms) and response time to subsequent alarms in the same patient using a log-rank test that accounts for within-nurse clustering. We observed 36 nurses for 210 hours with 5070 alarms; 87.1% of PICU and 99.0% of ward clinical alarms were nonactionable. Kaplan-Meier plots showed incremental increases in response time as the number of nonactionable alarms in the preceding 120 minutes increased (log-rank test stratified by nurse P < 0.001 in PICU, P = 0.009 in the ward). Most alarms were nonactionable, and response time increased as nonactionable alarm exposure increased. Alarm fatigue could explain these findings. Future studies should evaluate the simultaneous influence of workload and other factors that can impact response time.

PurposePhysiologic monitors are plagued with alarms that create a cacophony of sounds and visual alerts causing “alarm fatigue” which creates an unsafe patient environment because a life-threatening event may be missed in this milieu of sensory overload. Using a state-of-the-art technology acquisition infrastructure, all monitor data including 7 ECG leads, all pressure, SpO2, and respiration waveforms as well as user settings and alarms were stored on 461 adults treated in intensive care units. Using a well-defined alarm annotation protocol, nurse scientists with 95% inter-rater reliability annotated 12,671 arrhythmia alarms.ResultsA total of 2,558,760 unique alarms occurred in the 31-day study period: arrhythmia, 1,154,201; parameter, 612,927; technical, 791,632. There were 381,560 audible alarms for an audible alarm burden of 187/bed/day. 88.8% of the 12,671 annotated arrhythmia alarms were false positives. Conditions causing excessive alarms included inappropriate alarm settings, persistent atrial fibrillation, and non-actionable events such as PVC's and brief spikes in ST segments. Low amplitude QRS complexes in some, but not all available ECG leads caused undercounting and false arrhythmia alarms. Wide QRS complexes due to bundle branch block or ventricular pacemaker rhythm caused false alarms. 93% of the 168 true ventricular tachycardia alarms were not sustained long enough to warrant treatment.DiscussionThe excessive number of physiologic monitor alarms is a complex interplay of inappropriate user settings, patient conditions, and algorithm deficiencies. Device solutions should focus on use of all available ECG leads to identify non-artifact leads and leads with adequate QRS amplitude. Devices should provide prompts to aide in more appropriate tailoring of alarm settings to individual patients. Atrial fibrillation alarms should be limited to new onset and termination of the arrhythmia and delays for ST-segment and other parameter alarms should be configurable. Because computer devices are more reliable than humans, an opportunity exists to improve physiologic monitoring and reduce alarm fatigue.

False physiologic monitor alarms are extremely common in the hospital environment. High false alarm rates have the potential to lead to alarm fatigue, leading nurses to delay their responses to alarms, ignore alarms, or disable them entirely. Recent evidence from the U.S. Food and Drug Administration (FDA) and The Joint Commission has demonstrated a link between alarm fatigue and patient deaths. Yet, very little scientific effort has focused on the rigorous quantitative measurement of alarms and responses in the hospital setting. We developed a system using multiple temporarily mounted, minimally obtrusive video cameras in hospitalized patients' rooms to characterize physiologic monitor alarms and nurse responses as a proxy for alarm fatigue. This allowed us to efficiently categorize each alarm's cause, technical validity, actionable characteristics, and determine the nurse's response time. We describe and illustrate the methods we used to acquire the video, synchronize and process the video, manage the large digital files, integrate the video with data from the physiologic monitor alarm network, archive the video to secure servers, and perform expert review and annotation using alarm "bookmarks." We discuss the technical and logistical challenges we encountered, including the root causes of hardware failures as well as issues with consent, confidentiality, protection of the video from litigation, and Hawthorne-like effects. The description of this video method may be useful to multidisciplinary teams interested in evaluating physiologic monitor alarms and alarm responses to better characterize alarm fatigue and other patient safety issues in clinical settings.