Alarm Fatigue Research Articles

Real-Time Accuracy Evaluation of Arterial Catheter Transducer Systems

IntroductionArterial pressure is currently monitored in ICU with a catheter–transducer fluid line. This fluid filled tubing line distorts the original waveform due to its dynamic characteristics (natural frequency, Fn, and damping coefficient, z), introducing potentially significant errors when calculating the cardiac output from pulse contour signal analysis. MethodsIn our study, we cross-compared Fn and z obtained with our new Fast External Pressure Test (FEPT) and with the Fast Flush Test (FFLT), to the reference technique (Sine wave variable Frequency Analysis Test - SFAT). It was carried on a testbench for 48 hours. Fn and z were measured using the three techniques with two fluid-filled tubing lines (standard, STD, and blood conserving device, BCD). ResultsFn measurements with FEPT and FFLT present similar biases (0.79 vs 0.83 Hz), but lower variability for FEPT, with limits of agreement (LOA) ranging from −3.35 to +4.99 Hz for FFLT vs −2.29 to +3.86 Hz (p<0.0001) for FEPT. For the measurement of z, FEPT has a bias of 0.047 and LOA of −0.063 to +0.157, much lower (p<0.0001) than those measured with the FFLT (bias 0.139 and LOA −0.028 to +0.306). ConclusionWhen automated, the FEPT method will detect potential situations of over/under estimations occurrences. This will prevent false alarms, alarm fatigue and therefore consequences on patient care. Eventually, FEPT turns to be more accurate than FFLT, less scattered, less time-consuming, less invasive and so well suited for use in clinical settings.

Alarm fatigue and sleep quality in healthcare workers

Abstract Background Alarm fatigue, a prevalent issue in medical settings, arises when healthcare professionals are exposed to an overwhelming number of clinical alarms, leading to desensitization and decreased responsiveness. The constant alertness required due to medical device alarms in hospitals can severely disrupt the quality of sleep for medical personnel. This study aims to demonstrate whether there is a relationship between alarm fatigue and sleep quality in medical staff. For the research project, it was decided to carry out the study in three European neighbouring countries: Poland, Slovakia and the Czech Republic Methods The the survey was carried out in three European countries: Poland, Slovakia, and the Czechia from June to July 2023. The study was attended by a total of 756 participants. The study used: a socio-demographic metric; Alarm Fatigue Assessment Questionnaire and The Pittsburgh Sleep Quality Index to evaluates sleep quality. Results Among the 756 survey participants, 603 (79.76%) had poor sleep quality (6-21 PSQI points), 147 (19.44%) had good sleep quality (0-5 PSQI points), and 6 (0.79%) did not answer. The analysis of sleep quality scores for participating countries shows that sleep problems were significantly higher in Slovakia and Poland than in the Czech Republic. Alarm fatigue levels were determined for respondents in every country in this study. The statistical difference between countries is significant. In Slovakia and Poland, the levels of alarm fatigue caused by medical devices were significantly higher than in the Czech Republic. With regards to sleep quality, there is a positive correlation between alarm fatigue and sleep problems. A statistically significant positive correlation was obtained for all three countries. Conclusions The study found that workers in the health system are exposed to alarm fatigue, and that this in turn can reduce sleep quality. The consequences of this may be reflected in patient safety. Key messages • The results of the study clearly indicate that quality of work should be ensured by developing protocols for managing medical device alarms. • Combatting alarm fatigue among medical personnel is vital for public health as it reduces medical errors caused by sleep deprivation.

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.

Original Research: Alarm Fatigue: Exploring the Adaptive and Maladaptive Coping Strategies of Nurses.

To improve patient safety, hospitals use alarm notification systems to increase nurses' real-time situational awareness of a patient's condition. Such alarms are critical to nurses' clinical decision-making and prioritization, thus helping to improve patient care and care efficiency. But the frequent and often simultaneous ringing of alarms, including many that are false, nonemergent, or nonactionable, has led to overwhelm, alarm distrust, and desensitization, resulting in alarm fatigue. This study aimed to explore oncology nurses' lived experiences with alarms and the adaptive and maladaptive strategies they use to cope with alarm fatigue. This qualitative, phenomenological study was guided by the theoretical framework of the Roy Adaptation Model. A purposive sample of nine nurses was recruited from two oncology units at a large midwestern Magnet hospital in the United States. Qualitative data were collected using a six-question, semistructured interview guide. Interviews were conducted either face-to-face in a private conference room on the unit or via the online videoconferencing platform Zoom. Data analysis yielded five themes, the most prominent being the high volume and frequency of alarms . Nurse participants reported adopting more maladaptive than adaptive coping strategies. Overall, they felt that the high frequency of false, nonemergent, and nonactionable alarms disrupted their workflow and contributed to a general desensitization to alarms. This study's findings offer valuable insight into the problem of alarm fatigue among nurses. Practical measures are urgently needed to reduce nurses' cognitive overload; shift nonnursing responsibilities to other staff; and implement efficiency-focused process changes, such as reengineering workflows to minimize interruptions. Every effort should be made to redesign protocols to reduce alarm fatigue, including by decreasing the number of false, nonemergent, and nonactionable calls and alarms.

Quieter Nurse Call System to Reduce Alarm Noise in Hospitals.

Healthcare staff are typically exposed to hundreds of alarms each shift; researchers believe that the number of alarms may be more than 1000 alarms per shift. This may lead to staff becoming desensitised to the alarm, leading to a delayed or inadequate response to the alarm, known as alarm fatigue. Clinical-grade wireless smartphones are now carried by clinical staff to receive calls and notifications from a nurse call system.

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.

Implementing an obstetric sepsis bundle in a large academic hospital system

For the purposes of this review, obstetric sepsis refers to sepsis (from all causes, including non-obstetric such as pneumonia) in pregnant or postpartum patients, which was previously described as maternal sepsis. Obstetric sepsis poses a significant threat to pregnant, birthing, and postpartum individuals, contributing prominently to maternal mortality and morbidity despite being largely preventable1. In response to identified gaps in sepsis management, particularly the lack of specific protocols tailored to obstetric populations, New York-Presbyterian undertook a system-wide initiative to implement a comprehensive sepsis bundle. This initiative included the development of new criteria for identifying sepsis in obstetric patients, the creation of electronic medical record (EMR) alerts aligned with obstetric-specific indicators, and the establishment of a structured sepsis management algorithm.The project involved collaboration across eight hospital campuses within the New York-Presbyterian system, aiming to standardize and improve the early recognition and treatment of sepsis in maternal care. Key components included rigorous data analysis to select appropriate sepsis criteria, simulation-based training to familiarize clinical teams with the new algorithm, and continuous refinement of alert systems to mitigate alarm fatigue and enhance responsiveness.Post-implementation evaluation revealed a significant reduction in preventable morbidity related to sepsis, accompanied by the identification of additional gaps in fever and chorioamnionitis management. These findings prompted the development of new clinical guidelines to further enhance patient safety. Challenges encountered included adapting sepsis criteria to balance sensitivity and specificity, as well as integrating trauma-informed care principles into clinical practice.This project underscores the effectiveness of tailored quality improvement efforts in maternal health, emphasizing the critical role of proactive interventions in enhancing patient outcomes and safety within obstetric settings. Ongoing efforts focus on monitoring process metrics through a dedicated sepsis dashboard and advancing education on trauma-informed care principles, highlighting the continued commitment to sustained improvement in maternal health outcomes.

Mitigating Alarm Fatigue and Improving the Bedside Experience by Reducing Nonactionable Alarms

To assess whether conditional bedside alarm triggers can reduce the frequency of non-actionable alarms without compromising patient safety and enhance nursing and family satisfaction. Single center, quality improvement initiative in an acute care cardiac unit (ACCU) and pediatric intensive care unit (PICU). Following the 4-week pre-intervention baseline period, bedside monitors were programmed with hierarchical time delay and conditional alarm triggers. Bedside alarms were tallied for 4 weeks each in the immediate post intervention period and 2-year follow-up. The primary outcome was alarms per monitored patient day. Nurses and families were surveyed pre- and post-intervention. A total of 1509 patients contributed to 2034, 1968, and 2043 monitored patient days which were evaluated in the baseline, follow-up, and 2-year follow-up periods, respectively. The median number of alarms per monitored patient day decreased by 75% in the PICU (p<0.001) and 82% in the ACCU (p<0.001) with sustained effect at 2-year follow-up. No increase of rapid response calls, emergent transfers, or code events occurred in either unit. Nursing surveys reported an improved capacity to respond to alarms and fewer perceived non-actionable alarms. Family surveys, however, did not demonstrate improved sleep quality. Implemented changes to bedside monitor alarms decreased total alarm frequency in both the acute care cardiac unit and pediatric intensive care unit, improving the care provider experience without compromising safety.

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.

Improving sepsis prediction in intensive care with SepsisAI: A clinical decision support system with a focus on minimizing false alarms.

Prediction of sepsis using machine-learning approaches has recently gained traction. However, the lack of translation of these algorithms into clinical routine remains a major issue. Existing early sepsis detection methods are either based on the older definition of sepsis or do not accurately detect sepsis leading to the high frequency of false-positive alarms. This results in a well-known issue of clinicians' "alarm fatigue", leading to decreased responsiveness and identification, ultimately resulting in delayed clinical intervention. Hence, there is a fundamental, unmet need for a clinical decision system capable of accurate and timely sepsis diagnosis, running at the point of need. In this work, SepsisAI-a deep-learning algorithm based on long short-term memory (LSTM) networks was developed to predict the early onset of hospital-acquired sepsis in real-time for patients admitted to the ICU. The models are trained and validated with data from the PhysioNet Challenge, consisting of 40,336 patient data files from two healthcare systems: Beth Israel Deaconess Medical Center and Emory University Hospital. In the short term, the algorithm tracks frequently measured vital signs, sparsely available lab parameters, demographic features, and certain derived features for making predictions. A real-time alert system, which monitors the trajectory of the predictions, is developed on top of the deep-learning framework to minimize false alarms. On a balanced test dataset, the model achieves an AUROC, AUPRC, sensitivity, and specificity of 0.95, 0.96, 88.19%, and 96.75%, respectively at the patient level. In terms of lookahead time, the model issues a warning at a median of 6 hours (IQR 6 to 20 hours) and raises an alert at a median of 4 hours (IQR 2 to 5 hours) ahead of sepsis onset. Most importantly, the model achieves a false-alarm ratio of 3.18% for alerts, which is significantly less than other sepsis alarm systems. Additionally, on a disease prevalence-based test set, the algorithm reported similar outcomes with AUROC and AUPRC of 0.94 and 0.87, respectively, with sensitivity, and specificity of 97.05%, and 96.75%, respectively. The proposed algorithm might serve as a clinical decision support system to assist clinicians in the accurate and timely diagnosis of sepsis. With exceptionally high specificity and low false-alarm rate, this algorithm also helps mitigate the well-known issue of clinician alert fatigue arising from currently proposed sepsis alarm systems. Consequently, the algorithm partially addresses the challenges of successfully integrating machine-learning algorithms into routine clinical care.

Technical article: Overview of hospital-based data capture systems that acquire continuous ECG and physiologic data

Data capture systems that acquire continuous hospital-based electrocardiographic (ECG) and physiologic (vital signs) data can foster robust research (i.e., large sample sizes from consecutive patients). However, the application of these systems and the data generated are complex and requires careful human oversight to ensure that accurate and high quality data are procured. This technical article will describe two different data capture systems created by our research group designed to examine false alarms associated with alarm fatigue in nurses. The following aspects regarding these data capture systems will be discussed: (1) history of development; (2) summary of advantages, challenges, and important considerations; (3) their use in research; (4) their use in clinical care; and (5) future developments.

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.

Evaluating the Construct Validity of the Charité Alarm Fatigue Questionnaire using Confirmatory Factor Analysis.

The Charité Alarm Fatigue Questionnaire (CAFQa) is a 9-item questionnaire that aims to standardize how alarm fatigue in nurses and physicians is measured. We previously hypothesized that it has 2 correlated scales, one on the psychosomatic effects of alarm fatigue and the other on staff's coping strategies in working with alarms. We aimed to validate the hypothesized structure of the CAFQa and thus underpin the instrument's construct validity. We conducted 2 independent studies with nurses and physicians from intensive care units in Germany (study 1: n=265; study 2: n=1212). Responses to the questionnaire were analyzed using confirmatory factor analysis with the unweighted least-squares algorithm based on polychoric covariances. Convergent validity was assessed by participants' estimation of their own alarm fatigue and exposure to false alarms as a percentage. In both studies, the χ2 test reached statistical significance (study 1: χ226=44.9; P=.01; study 2: χ226=92.4; P<.001). Other fit indices suggested a good model fit (in both studies: root mean square error of approximation <0.05, standardized root mean squared residual <0.08, relative noncentrality index >0.95, Tucker-Lewis index >0.95, and comparative fit index >0.995). Participants' mean scores correlated moderately with self-reported alarm fatigue (study 1: r=0.45; study 2: r=0.53) and weakly with self-perceived exposure to false alarms (study 1: r=0.3; study 2: r=0.33). The questionnaire measures the construct of alarm fatigue as proposed in our previous study. Researchers and clinicians can rely on the CAFQa to measure the alarm fatigue of nurses and physicians.

Comparison of Postoperative Continuous Wireless Cardiac Rhythm Monitoring with Traditional Telemetry in Cardiac Surgery Patients: the SMART-TEL Study.

Telemetry monitoring (conventional cardiac monitoring system [CCMS]) is a universal method for postoperative arrhythmia detection; however, the clinical challenge of alarm fatigue, primarily associated with noise or cable disconnections, persists. The introduction of wireless continuous cardiac monitoring (WCCM) represents a potential solution to enhance recording fidelity. Patients were simultaneously outfitted with both a monitoring device considered the standard of care and a novel adhesive wireless patch. A 48-h cardiac monitoring session with the two devices occurred after cardiac surgery in a unit equipped with a telemetry system. A total of 53 patients with a mean age of 60 ± 17 years were included in the trial. The number of events detected by the two systems was significantly different at 190 versus 174 for the CCMS and the WCCM system, respectively (P < .05). However, the percentage of agreement was not significantly different at 91% versus 88% (P = .37). Events were classified as follows: pause (2 events, 1%), atrial or premature ventricular contractions (18 events, 11%), atrial flutter or fibrillation (76 events, 45%), bradycardia (12 events, 7%), and tachycardia (61 events, 36%). False alarms were significantly more frequent with the CCMS (n = 21) than with the WCCM system (n = 5; P = .002). The study successfully demonstrated the feasibility and usability of wireless monitoring for patients requiring telemetry. The overall results are compelling, as the WCCM system performed satisfactorily, achieving results comparable to those obtained with the CCMS, even with significantly fewer false alarms.

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.

Navigating Alert Fatigue: A Case Study in Electronic Health Record Alert Design Optimization.

Clinical decision support (CDS) systems play a crucial role in enhancing patient outcomes, but inadequate design contributes to alert fatigue, inundating clinicians with disruptive alerts that lack clinical relevance. This case study delves into a quality improvement (QI) project addressing nursing electronic health record (EHR) alert fatigue by strategically redesigning four high-firing/low action alerts. Employing a mixed-methods approach, including quantitative analysis, empathy mapping sessions, and user feedback, the project sought to understand and alleviate the challenges posed by these alerts. Virtual empathy mapping sessions with clinical nurses provided valuable insights into user experiences. Qualitative findings, CDS design principles, and organizational practice expectations informed the redesign process, resulting in the removal of all four identified disruptive alerts and redesign of passive alerts. This initiative released 877 unactionable disruptive nursing hours, emphasizing the significance of proper alert design and the necessity for organizational structures ensuring sustained governance in healthcare system optimization.

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.

The Role of Health Information Technology During Hospital to Home Transitions.

The use of healthcare information technology (HIT) is vital for storing and exchanging health information during patient transitions, playing a significant role in care coordination for sepsis survivors. The critical role of HIT was evident during the pre-implementation phase of a study to implement an evidence-based protocol supporting the timely transition of sepsis survivors to home health and outpatient care. Through 61 semi-structured interviews involving 91 stakeholders, over half of the 33 identified themes were related to HIT. Notably, electronic health record (EHR) alert systems led to over-capture and alarm fatigue. Efficient information transfer during HHC referral highlighted the need for improved EHR access. The study underscores HIT's importance and potential while emphasizing the need for collaborative policy and interface development to promote effective transitions in care.

Variations in Alarm Burden, Source, and Cause Across Inpatient Units at a Children's Hospital.

Alarms at hospitals are frequent and can lead to alarm fatigue posing patient safety risks. We aimed to describe alarm burden over a 1-year period and explored variations in alarm rates stratified by unit type, alarm source, and cause. A retrospective study of inpatient alarm and patient census data at 1 children's hospital from January 1, 2019, to December 31, 2019, including 8 inpatient units: 6 medical/surgical unit (MSU), 1 PICU, and 1 NICU. Rates of alarms per patient day (appd) were calculated overall and by unit type, alarm source, and cause. Poisson regression was used for comparisons. There were 7 934 997 alarms over 84 077 patient days (94.4 appd). Significant differences in alarm rates existed across inpatient unit types (MSU 81.3 appd, PICU 90.7, NICU 117.5). Pulse oximetry (POx) probes were the alarm source with highest rate, followed by cardiorespiratory leads (54.4 appd versus 31). PICU had lowest rate of POx alarms (33.3 appd, MSU 37.6, NICU 92.6), whereas NICU had lowest rate of cardiorespiratory lead alarms (16.2 appd, MSU 40.1, PICU 31.4). Alarms stratified by cause displayed variation across unit types where "low oxygen saturation" had the highest overall rate, followed by "technical" alarms (43.4 appp versus 16.3). ICUs had higher rates of low oxygenation saturation alarms, but lower rates of technical alarms than MSUs. Clinical alarms are frequent and vary across unit types, sources, and causes. Unit-level alarm rates and frequent alarm sources (eg, POx) should be considered when implementing alarm reduction strategies.

Towards Human-AI Teaming to Mitigate Alert Fatigue in Security Operations Centres

Security Operations Centres (SOCs) play a pivotal role in defending organisations against evolving cyber threats. They function as central hubs for detecting, analysing, and responding promptly to cyber incidents with the primary objective of ensuring the confidentiality, integrity, and availability of digital assets. However, they struggle against the growing problem of alert fatigue, where the sheer volume of alerts overwhelms SOC analysts and raises the risk of overlooking critical threats. In recent times, there has been a growing call for human-AI teaming, wherein humans and AI collaborate with each other, leveraging their complementary strengths and compensating for their weaknesses. The rapid advances in AI and the growing integration of AI-enabled tools and technologies within SOCs give rise to a compelling argument for the implementation of human-AI teaming within the SOC environment. Therefore, in this article, we present our vision for human-AI teaming to address the problem of alert fatigue in the SOC. We propose the 𝒜 2 𝒞 Framework, which enables flexible and dynamic decision making by allowing seamless transitions between automated, augmented, and collaborative modes of operation. Our framework allows AI-powered automation for routine alerts, AI-driven augmentation for expedited expert decision making, and collaborative exploration for tackling complex, novel threats. By implementing and operationalising 𝒜 2 𝒞, SOCs can significantly reduce alert fatigue while empowering analysts to efficiently and effectively respond to security incidents.