Alarm Management Research Articles

Coping strategies of intensive care units nurses in alarm management: a qualitative research study

BackgroundIntensive care units are critical environments where various alarm systems play a pivotal role in patient monitoring and safety. Alarm fatigue can lead to slower response times and missed alarms, compromising patient safety and increasing stress and burnout among intensive care unit nurses. Understanding how intensive care unit nurses respond to and manage these alarms is crucial in evaluating their impact on patient care and nursing well-being.MethodsThis descriptive qualitative study explored the experiences of intensive care unit nurses in alarm management. Conducted in the medical and surgical intensive care units of a Northern Taiwan medical center, the study involved 15 nurses. Semi-structured interviews were utilized to investigate the working experiences of ICU nurses in alarm management and to identify their coping strategies for dealing with the constant inundation of medical device alarms. The interviews were transcribed, and content analysis was applied to identify key themes in the responses.ResultsThe study revealed five main themes in intensive care unit nurses’ strategies for managing alarms: (1) Mastering alarm signals and acting; (2) Team monitoring for life preservation; (3) Enhancing senses and distinguishing carefully; (4) Learning from the lessons of incidents for vigilant reflection; and (5) Detach alarms’ influence on daily life. These coping strategies are effective in alarm management, safeguarding patients’ lives, enhancing the serenity of the clinical environment, and mitigating the physical and mental exhaustion caused by alarm fatigue.ConclusionsIntensive Care Unit nurses develop various coping strategies to manage medical device alarms, based on their experience. These strategies are crucial in maintaining patient safety and reducing nurse alarm fatigue. They can also be used for nursing education and clinical training.

Alarm management approach for supervision of Green Hydrogen Plants

Amid the increasing significance of renewable energy sources, Green Hydrogen Plants (GHPs) have emerged as pivotal contributors to sustainable energy solutions. However, efficient and reliable alarm management in such complex systems remains a significant challenge. This paper presents a novel methodology called V-nets-based alarm management (VBAM), designed to improve supervision by addressing the intricacies of discrete event management in industrial applications such as GHPs. VBAM offers a powerful formalism that integrates visual modeling and temporal patterns, enabling the precise detection and handling of alarms. The proposed methodology is applied to a case study in a GHP, where critical operational parameters are continuously monitored. By analyzing discrete events and their temporal patterns, V-nets facilitate early fault detection, minimize false positives, and optimize alarm response. The theoretical application of VBAM demonstrates its efficacy in improving system safety, reducing downtime, and enhancing overall operational efficiency within GHPs. The contributions of this work to the state of the art include the development of a comprehensive VBAM methodology tailored to GHPs, as well as the theoretical and practical demonstration of its potential impact on alarm management within GHP operations. The outcomes from the experiments showcase the adaptability and effectiveness of VBAM in addressing the complexities of alarm management in GHPs, thereby contributing to enhanced safety, efficiency, and resilience in the operation of GHPs.

Crying wolf, alarm safety and management in paediatrics: A scoping review.

To provide a contemporaneous evidentiary overview of neonatal and paediatric studies investigating alarm-related patient safety and alarm system management. Furthermore, to describe how clinical alarm burden is captured and reported, to identify clinical devices that contribute to alarm burden, to explore alarm-related and patient safety measures and terminologies and to review alarm management initiatives. Scoping review. A systematic search of PubMed, Embase, Cumulative Index to Nursing and Allied Health Literature, Scopus and EBSCOhost was conducted from 2013 to 2023 using predetermined search terms, index terms, medical subject headings and truncation. Observational and qualitative studies with neonatal and paediatric populations reporting monitoring and alarm practices; and interventional studies reporting the success of alarm safety interventions were included. The quality of the included studies was assessed using the mixed methods appraisal tool. The search yielded 37 studies of acceptable quality. The majority explored alarm burden associated with physiological monitoring (n = 35; 95%). Alarm definitions were reported in 46% (n = 17) of studies, and commonly included what constituted actionable and non-actionable alarms. While 32% (n = 12) of studies considered alarms in relation to clinical outcomes surrounding patient safety, clinician response to alarms was only reported in 19% (n = 7) of studies. Alarm and monitoring interventions were assessed in 51% (n = 19) of included studies, with categorization into six domains: changing alarm parameters, clinician education, communication and planning, technology, alarm ordering and standardization or guidelines. This review has demonstrated the enormity of alarms in clinical settings, heterogeneity of alarm definitions and outlined interventions associated with alarm burden and patient safety. Strategies to ensure appropriate alarm limits are set and clinicians are empowered through education to recognize and respond appropriately to alarms can maximize patient safety. This review adheres to the preferred reporting items for systematic reviews and meta-analysis protocols extension for scoping reviews. No patient or public contribution.

Alarm Management in Provisional COVID-19 Intensive Care Units: Retrospective Analysis and Recommendations for Future Pandemics.

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

A Methodological Framework for Managing the Alarms in Wind Turbine Control and Data Acquisition Systems for Failure Analysis

Renewable energies have a fundamental role in sustainability, with wind power being one of the most important due to its low production costs. Modern wind turbines are becoming bigger and more complex, and their operation and maintenance must be as optimized as possible. In this context, supervisory control and data acquisition systems provide valuable information, but there is no precise methodology for their analysis. To overcome this need, a generalized methodology is proposed to determine the recognition of critical subsystems through alarm analysis and management. The proposed methodology defines each subsystem in a precise way, shows the indicators for the alarms, and presents a theoretical framework for its application using the quantity and activation times of alarms, along with the real downtime. It also considers the transition of states when the wind turbine is operationally inactive. To highlight the proposal’s novelty, the methodology is exemplified with a case study from the Southern Cone, applying the method through a data management and analysis tool. Four critical subsystems were found, with the alarms of wind vanes, anemometers, and emergency speeds being of relevance. The indicators and the graphical tools recommended helped guide the applied analysis.

User Experience Design for Intensive Care Alarm Management.

Alarm fatigue is a pressing issue in intensive care units. Based on user experience design, including clinical shadowings and feedback loops, we developed a prototype for a redesigned patient monitor: The prototype moves away from today's threshold-based alarm systems. It combines a sleek design with machine learning driven clinical insights to mitigate alarm fatigue.

AF among Nurses Working in Neonatal and Paediatric Intensive Care Units: A Cross-Sectional Study.

Aim: This research study aims to determine nurses' alarm fatigue (AF) levels in paediatric critical care units in two governmental hospitals and to examine the significant differences in the mean between nurses' attributes, nurses' working environment, and nurses' alarm management with the level of fatigue caused by the alarm. Background: In recent years, AF has become a significant and growing concern among nurses. However, in the Saudi Arabian paediatrics context, the impact of AF on nurses working in intensive care units remains unexplored. Method: A descriptive cross-sectional survey was conducted using a non-probability purposive sampling method. Data were collected from 216 nurses in two governmental hospitals through self-administered questionnaires comprised of four sections: individual attributes, work environment, alarm management, and AF scale. Data analysis: The Statistical Package of Social Science (SPSS) was used to analyse the data, and ANOVA was utilised to describe the sample's demographic characteristics and determine any differences. Results: Most participants were female, held a bachelor's degree, and were aged 31 to 35. Of the participants, 62.5% reported experiencing a medium level of AF, 29.2% reported a low level, and 8.3% reported a high level. Participants expressed that recurrent false alarms disrupt patient care and decrease trust in alarm systems. Significant differences in AF levels were observed based on marital status and the percentage of non-actionable alarms. Conclusions: Nurses working in paediatric critical units with high rates of false alarms, the frequent de-activation of alarms, and decreased trust in alarm systems are more likely to experience AF. Addressing AF is crucial for patient safety; nurse training on alarm management, the collaboration between biomedical and nursing staff, and technological advancements can help mitigate this issue. Implications for Practice: To minimise the adverse effects of AF, policymakers, biomedical experts, and nursing administrators must establish comprehensive policies and protocols concerning alarms. These measures aim to ensure secure and efficient care for the well-being of patients and nurses.

Alarm fatigue and sleep quality in medical staff-a Polish-Czech-Slovak study on workplace ergonomics.

Alarms are crucial in informing Healthcare Workers (HCWs) about critical patient needs, but unmanaged frequency and noise of alarms can de-sensitize medical staff and compromise patient safety. Alarm fatigue is identified as the major cause of the clinical alarm management problem. It occurs when the medical staff is overwhelmed by the number of clinical alarms. The survey was conducted online using Google's form-making tools from June to July 2023. There were three parts to the survey used in the study: a socio-demographic metric, the Alarm Fatigue Assessment Questionnaire (AFAQ), and The Pittsburgh Sleep Quality Index (PSQI). A significance level of 0.05 was used in the analysis. The survey included 756 medical professionals from three European countries (Slovakia, the Czech Republic and Poland). The participants in the study were 42 years old on average, and they had 12 years of work experience. 603 out of 756 survey participants had poor sleep quality, 147 had good sleep quality, and 6 did not provide an answer. This study analyzed the alarm fatigue levels of respondents in every country. In the Czech Republic, Poland and Slovakia, a statistically significant association (p = 0.039, p = 0.001, p < 0.001) was found between alarm fatigue and sleep quality in medical staff. Based on our study, alarm fatigue and sleep quality of HCWs are correlated. Therefore, alarm fatigue and sleep hygiene should be monitored.

Applying Socio-Technical Models to Alarm Management: Tailoring Bed Exit Alerts in Medical-Surgical Units.

Integration of smartphone technology with the patient call-bell system provides the opportunity to enhance patient safety by supporting nurses' ability to communicate and prioritize care delivery directly. However, challenges are associated with achieving a balance between alarm support and alarm fatigue, including distracting nurses from patient care or desensitizing the nurse to other alarms and calls [1]. Our hospitals have quantitative and anecdotal reports of seriously high volumes of wireless alerts on the nurses' smartphones. Nurses have complained that the phones are generating too much noise to consume or timely prioritize. Preliminary alarm inventory revealed the Bed Exit wireless alert as a leading contributor of signal volume across many units and hospitals. The lack of standard policies and workflow improvement processes has increased nuisance alarms, making these Health Information Technologies less useful and safe. Using system data, workflow observations, and nursing interviews, Singh and Sittig's HIT Safety framework [2] was applied to identify and prioritize sociotechnical factors and interventions that impact the end-to-end Bed Exit alarm workflow. This study reviews the application of sociotechnical models and frameworks to reduce wireless calls without introducing risk and impacting patient care.

Alarm Management in Intensive Care: Qualitative Triangulation Study.

The high number of unnecessary alarms in intensive care settings leads to alarm fatigue among staff and threatens patient safety. To develop and implement effective and sustainable solutions for alarm management in intensive care units (ICUs), an understanding of staff interactions with the patient monitoring system and alarm management practices is essential. This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions. This explorative qualitative study with an ethnographic, multimethods approach was conducted in an ICU of a German university hospital. Using triangulation in data collection, 102 hours of field observations, 12 semistructured interviews with ICU staff members, and the results of a participatory task were analyzed. The data analysis followed an inductive, grounded theory approach. Nurses and physicians reported interacting with the continuous vital sign monitoring system for most of their work time and tasks. There were no established standards for alarm management; instead, nurses and physicians stated that alarms were addressed through ad hoc reactions, a practice they viewed as problematic. Staff members' perceptions of intelligent alarm management varied, but they highlighted the importance of understandable and traceable suggestions to increase trust and cognitive ease. Staff members' interactions with the omnipresent patient monitoring system and its alarms are essential parts of ICU workflows and clinical decision-making. Alarm management standards and workflows have been shown to be deficient. Our observations, as well as staff feedback, suggest that changes are warranted. Solutions for alarm management should be designed and implemented with users, workflows, and real-world data at the core.

238 The effectiveness of alarm management bundle in reducing alarm fatigue and burnout among nurses

238 The effectiveness of alarm management bundle in reducing alarm fatigue and burnout among nurses

Network-based visualisation of frequent sequences.

Frequent sequence pattern mining is an excellent tool to discover patterns in event chains. In complex systems, events from parallel processes are present, often without proper labelling. To identify the groups of events related to the subprocess, frequent sequential pattern mining can be applied. Since most algorithms provide too many frequent sequences that make it difficult to interpret the results, it is necessary to post-process the resulting frequent patterns. The available visualisation techniques do not allow easy access to multiple properties that support a faster and better understanding of the event scenarios. To answer this issue, our work proposes an intuitive and interactive solution to support this task, introducing three novel network-based sequence visualisation methods that can reduce the time of information processing from a cognitive perspective. The proposed visualisation methods offer a more information rich and easily understandable interpretation of sequential pattern mining results compared to the usual text-like outcome of pattern mining algorithms. The first uses the confidence values of the transitions to create a weighted network, while the second enriches the adjacency matrix based on the confidence values with similarities of the transitive nodes. The enriched matrix enables a similarity-based Multidimensional Scaling (MDS) projection of the sequences. The third method uses similarity measurement based on the overlap of the occurrences of the supporting events of the sequences. The applicability of the method is presented in an industrial alarm management problem and in the analysis of clickstreams of a website. The method was fully implemented in Python environment. The results show that the proposed methods are highly applicable for the interactive processing of frequent sequences, supporting the exploration of the inner mechanisms of complex systems.

Personalized Medicine Transformed: ChatGPT's Contribution to Continuous Renal Replacement Therapy Alarm Management in Intensive Care Units.

The accurate interpretation of CRRT machine alarms is crucial in the intensive care setting. ChatGPT, with its advanced natural language processing capabilities, has emerged as a tool that is evolving and advancing in its ability to assist with healthcare information. This study is designed to evaluate the accuracy of the ChatGPT-3.5 and ChatGPT-4 models in addressing queries related to CRRT alarm troubleshooting. This study consisted of two rounds of ChatGPT-3.5 and ChatGPT-4 responses to address 50 CRRT machine alarm questions that were carefully selected by two nephrologists in intensive care. Accuracy was determined by comparing the model responses to predetermined answer keys provided by critical care nephrologists, and consistency was determined by comparing outcomes across the two rounds. The accuracy rate of ChatGPT-3.5 was 86% and 84%, while the accuracy rate of ChatGPT-4 was 90% and 94% in the first and second rounds, respectively. The agreement between the first and second rounds of ChatGPT-3.5 was 84% with a Kappa statistic of 0.78, while the agreement of ChatGPT-4 was 92% with a Kappa statistic of 0.88. Although ChatGPT-4 tended to provide more accurate and consistent responses than ChatGPT-3.5, there was no statistically significant difference between the accuracy and agreement rate between ChatGPT-3.5 and -4. ChatGPT-4 had higher accuracy and consistency but did not achieve statistical significance. While these findings are encouraging, there is still potential for further development to achieve even greater reliability. This advancement is essential for ensuring the highest-quality patient care and safety standards in managing CRRT machine-related issues.

Machine Learning-Supported Designing of Human–Machine Interfaces

The design and functionality of the human–machine interface (HMI) significantly affects operational efficiency and safety related to process control. Alarm management techniques consider the cognitive model of operators, but mainly only from a signal perception point of view. To develop a human-centric alarm management system, the construction of an easy-to-use and supportive HMI is essential. This work suggests a development method that uses machine learning (ML) tools. The key idea is that more supportive higher-level HMI displays can be developed by analysing operator-related events in the process log file. The obtained process model contains relevant data on the relationship of the process events, enabling a network-like visualisation. Attributes of the network allow us to solve the minimisation problem of the ideal workflow–display relation. The suggested approach allows a targeted process pattern exploration to design higher-level HMI displays with respect to content and hierarchy. The method was applied in a real-life hydrofluoric acid alkylation plant, where a proposal was made about the content of an overview display.

Utilizing Data and Alarm Champions to Enhance Alarm Management: A Pediatric Quality Improvement Initiative.

Nuisance and false alarms distract clinicians from urgent alerts, raising patient safety risks. High alarm rates in a pediatric progressive care unit resulted in experiencing 180-250 alarms per day or 1 alarm every 3 to 4minutes per clinician. Through Plan-Do-Study-Act cycles, environmental, policy, and technology changes were implemented to decrease the average alarms/day/bed and percentage of time in alarm. Alarm settings tailored to patient needs using features embedded within the patient monitoring system were implemented and monitored with the assistance of alarm champions. The average number of alarms/day/bed decreased from 177.69 to 96.94 over the course of 10years, a 45.45% reduction. The percentage of time in alarm decreased from 7.52% to 2.83%, a 62.37% reduction. Arming clinicians with technology to analyze real-time clinical data made alarms meaningful and actionable, decreasing false alarms without compromising patient safety.

Root Cause Identification of Industrial Alarm Floods Using Word Embedding and Few-Shot Learning

Alarm systems are commonly deployed in modern industrial facilities to monitor process operations. However, due to the presence of nuisance alarms and alarm floods, their efficiencies are much degraded. Especially, alarm floods are among the most difficult issues in industrial alarm management and recognised as the main causes of many industrial accidents. To address alarm floods, this paper proposes a Root Cause Identification method (RCI) for industrial alarm floods based on word embedding and few-shot learning. The contributions are threefold: 1) A textual encoding method based on word embedding is proposed to convert alarm messages into numerical word vectors that can be used in the modeling of RCI; 2) an alarm priority based adaptive weighting strategy is designed to make the RCI more sensitive to alarms of higher priorities and appearing earlier; 3) a few-shot learning method based on the long short-term memory is adapted to identify root causes of alarm floods based on limited instances of labeled data. The effectiveness and superiority of the proposed method are demonstrated by a case study based on data from the Vinyl Acetate Monomer (VAM) public model.

Effect of Simulation-Based Ventilator Alarm Management Training on Nurse’s Knowledge and Practices in a Tertiary Care Hospital

This study aimed to evaluate the effectiveness of simulation-based ventilator alarm management training on knowledge and practice among nurses. It was conducted among 50 nurses of neurosurgery department AIIMS, New Delhi. Assessment of knowledge and practice regarding ventilator alarm management was done using researcher-developed knowledge questionnaire and observational checklist. Didactic teaching of 15- 20 minutes was done regarding various ventilator alarms following the baseline assessment. Hence, simulationbased teaching on managing ventilator alarm was conducted in a group of 3-4 participants which consisted of pre-brief (3-5 min explanation regarding the situation the participant was to experience), scenario run (7-10 min where the response and measures of managing different ventilator alarms are performed) and debrief (15-20 min of discussing the positive and right performance of participants followed by different areas requiring improvement). A low tech manikin (half body Laerdal manikin) was used for the simulation and the simulation was conducted in neuro ward of AIIMS, New Delhi. The entire intervention lasts for 40-45 min after which immediate post-test was done for knowledge and skill. Retention of knowledge was assessed 7 days after the intervention. Nurse’s knowledge range regarding mechanical ventilator alarm management in pre-test was 11 (8, 15) MED (IQR), post-test 1 was 15 (13, 17) MED (IQR), post-test 2 range was 15 (13.16) MED (IQR). Thus, there was significant improvement in the knowledge level of nurses regarding the ventilator alarm management after the intervention (p &lt;0.001). The pre-practice score of nurse’s skill in managing mechanical ventilator alarm management was 3.5 (2, 5) MED (IQR) and postpractice score minimum maximum range was 10.5 (2, 14) MED (IQR). There was significant improvement in the skill level of nurses regarding the ventilator alarm management after the intervention (p &lt;0.01). Simulation-based education improves nurse’s knowledge and practice in recognising and managing the ventilator alarms.

Alarm correlation analysis with applications to industrial alarm management

Alarm systems are essential for the safe and efficient operation of process industries. However, complex plant connectivity and process interactions could cause many correlated alarms in practice and thus compromise alarm system performance. To address correlated alarms, it is desired that alarm correlations are discovered from historical Alarm and Event (A&E) logs, so the obtained results could help improve alarm configurations or design suppression strategies. Motivated by this problem, a systematic method to extract alarm correlation is proposed in this work and the contributions are: (1) Correlated alarms and their occurrence orders are captured as correlation patterns through pattern mining, and such patterns are characterized by statistical features. (2) Alarm correlations and their statistical features are visualized as network graphs to indicate process interactions and identify alarms for prioritized analysis. To demonstrate the effectiveness of the proposed method, case studies are provided using an industrial simulation benchmark Vinyl Acetate Monomer (VAM) plant model.

Improving ambulatory infusion drug delivery through alarm management using a quality improvement model and team learning.

472 Background: A quality outpatient cancer program depends on the accurate delivery of chemotherapy using a computerized ambulatory infusion pump to deliver a therapeutic dose of chemotherapy for patients. Between April and July 2021, our cancer center hotline reported an excessive rate of 60% of ambulatory pump were generating alarms. This resulted in interruptions in dose delivery, disruptions in clinical workflows, and negatively impacted patient satisfaction. Methods: A multidisciplinary team of frontline nurses, pharmacists, nurse leaders, and quality specialists was assembled to analyze six months of alarm data using a plan-do-study-act (PDSA) model. Quality tools were utilized to determine the root cause of the alarms and arrive at solutions to improve drug delivery by reducing alarms and calls to the infusion hotline. The team discovered 49% (n=22/45) of alarm could not be resolved, resulting in a dose interruption of the planned chemotherapy infusions. Patients returned to the infusion suite without receiving the total dose of chemotherapy as ordered. A full 70% of the hotline calls (n=16/22) were due to a “No Disposable” alarm code. Analysis of these returned units by both our group and the vendor led us to recommend a more advanced replacement model which was implemented in June/July of 2022. Results: Between July and December 2022, MCI outpatient ambulatory pump medication variances related to “No Disposable” improved by 100%. A total of 34 calls were received by the Hotline Nurse with 91% (31/34) of calls were successfully resolved. None of the ambulatory patients experienced an interruption in chemotherapy drug delivery. Conclusions: Forming a multidisciplinary working group resulted in a timely and significant improvement in chemotherapy safety using the PDSA model of quality improvement. Most importantly, confidence in chemotherapy drug delivery using ambulatory infusion pump was expressed by infusion nurses, pharmacists, and medical providers.

Pediatric Characteristics Associated With Higher Rates of Monitor Alarms.

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.