Alarm Fatigue Research Articles

ARTEMIS: An alarm threshold and policy mining system for the intensive care unit

BackgroundAlarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many – mostly unnecessary – alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety. ObjectiveWe present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems. MethodsPolicy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models – for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads. ResultsARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric. ConclusionARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike – with organisational, economic, and clinical benefits for the implementing hospital.

Investigation of Medical Accidents for the Development of Alert System to Reduce Alert Fatigue.

Alert fatigue, a decrease in sensitivity to alerts, is a problem in the medical field. In this study, a survey was conducted on medical accidents in order to develop an alert that could be expected to reduce alert fatigue. As a result, medical accidents related to drugs are common worldwide, and the need for an alert system that can detect the implementation of medical treatment was found.

Executive Summary: Society of Critical Care Medicine Guidelines on Recognizing and Responding to Clinical Deterioration Outside the ICU.

Clinical deterioration of patients hospitalized outside the ICU is a source of potentially reversible morbidity and mortality. To address this, some acute care facilities have implemented systems aimed at detecting and responding to such patients. To provide evidence-based recommendations for hospital clinicians and administrators to optimize recognition and response to clinical deterioration in non-ICU patients. The 25-member panel included representatives from medicine, nursing, respiratory therapy, pharmacy, patient/family partners, and clinician-methodologists with expertise in developing evidence-based clinical practice guidelines. We generated actionable questions using the Population, Intervention, Control, and Outcomes format and performed a systematic review of the literature to identify and synthesize the best available evidence. We used the Grading of Recommendations Assessment, Development, and Evaluation approach to determine certainty in the evidence and to formulate recommendations and good practice statements (GPSs). The panel issued 10 statements on recognizing and responding to non-ICU patients with critical illness. Healthcare personnel and institutions should ensure that all vital sign acquisition is timely and accurate (GPS). We make no recommendation on the use of continuous vital sign monitoring among "unselected" patients due to the absence of data regarding the benefit and the potential harms of false positive alarms, the risk of alarm fatigue, and cost. We suggest focused education for bedside clinicians in signs of clinical deterioration, and we also suggest that patient/family/care partners' concerns be included in decisions to obtain additional opinions and help (both conditional recommendations). We recommend hospital-wide deployment of a rapid response team or medical emergency team (RRT/MET) with explicit activation criteria (strong recommendation). We make no recommendation about RRT/MET professional composition or inclusion of palliative care members on the responding team but suggest that the skill set of responders should include eliciting patients' goals of care (conditional recommendation). Finally, quality improvement processes should be part of a rapid response system (GPS). The panel provided guidance to inform clinicians and administrators on effective processes to improve the care of patients at-risk for developing critical illness outside the ICU.

Meaning and prediction of 'excess mortality': a comparison of Covid-19 and pre-Covid-19 mortality data in 31 Eurostat countries from 1965 to 2021.

Determining 'excess mortality' makes it possible to compare the burden of disasters between countries and over time, and thus also to evaluate the success of mitigation measures. However, the debate on coronavirus disease 2019 (Covid-19) has exposed that calculations of excess mortalities vary considerably depending on the method and its specification. Moreover, it is often unclear what exactly is meant by 'excess mortality'. We define excess mortality as the excess over the number of deaths that would have been expected counter-factually, that is without the catastrophic event in question. Based on this definition, we use a very parsimonious calculation method, namely the linear extrapolation of death figures from previous years to determine the excess mortality during the Covid-19 pandemic. But unlike most other literature on this topic, we first evaluated and optimized the specification of our method using a larger historical data set in order to identify and minimize estimation errors and biases. The result shows that excess mortality rates in the literature are often inflated. Moreover, they would have exhibited considerable excess mortalities in the period before Covid-19, if this value had already been of public interest at that time. Three conclusions can be drawn from this study and its findings: (i) All calculation methods for current figures should first be evaluated against past figures. (ii) To avoid alarm fatigue, thresholds should be introduced which would differentiate between 'usual fluctuations' and 'remarkable excess'. (iii) Statistical offices could provide more realistic estimates.

Relationship Between Alarm Fatigue and Stress Among Acute Care Nurses: A Cross-Sectional Study.

Given the vital nature of their profession, ICU nurses endure significant psychological and physical stress. Burnout, low job satisfaction, and deteriorated patient care might result from the high-stress atmosphere. This study aims to assess the level of alarm fatigue and stress among nurses who work in acute care units. A descriptive design was used to recruit nurses in acute care units. A self-administered questionnaire was used to collect the required data composed of three parts, namely demographical data; the alarm fatigue part, which was created by Torabizadeh et al. and composed of 13 items, and the perceived stress scale (PSS) which is a psychological diagnostic instrument created to assess how much people find their daily lives to be stressful. It was created by Cohen et al. and composed of 10 items. An average age of (35.3 ± 6.24) years, and an average number of years of experience of (7.63 ± 5.56), were found among the 128 nurses that were recruited. Acute care nurses had a significant degree of alarm fatigue, as indicated by the overall alarm fatigue score of (M = 30.1 ± SD = 7.47). A moderate degree of stress was also indicated by the overall perceived stress score, which was (M = 21.5 ± 5.02). Among nurses, alarm fatigue and felt stress are not correlated with any demographic feature, including sex, educational attainment, marital status, and working location. Stress and alarm fatigue are serious problems for acute care units that can jeopardize nurse and patient safety. The implementation of methods that mitigate alarm fatigue and stress, such as alarm customization, adequate staffing, and support systems, can enhance the work environment in acute care units. Healthcare companies can raise the grade of care provided to patients and enhance the general well-being and job satisfaction of their nursing staff by addressing these challenges.

The Relationship Between Alarm Fatigue and Compassion Fatigue in Intensive Care Nurses

The Relationship Between Alarm Fatigue and Compassion Fatigue in Intensive Care Nurses

Experience and knowledge key to mitigating alarm fatigue in Australian critical care nurses

Experience and knowledge key to mitigating alarm fatigue in Australian critical care nurses

Improving Telemetry use in the Acute Assessment Unit.

Despite published guidelines, telemetry use is inappropriate in 25-43% of cases. This impacts patient safety and telemetry effectiveness. QI methodology was used to review telemetry in a hospital acute medical unit with the aim of reducing inappropriate use and addressing alarm fatigue. A 'Telemetry Indication Form' was created. Eight weeks of baseline data was collated before introducing the 'Indication Form'. Four plan-do-study-act cycles were conducted. At each cycle, data was analysed using statistical process control charts. Inappropriate telemetry use significantly reduced from 32% to 4%. Total telemetry use also fell. Unfortunately, interventions to address alarm rates did not result in significant reduction in false alarms. A 'Telemetry Indication Form' has significant potential to improve patient safety through reducing inappropriate use.

The development and validation of a novel deep-learning algorithm to predict in-hospital cardiac arrest in ED-ICU (emergency department-based intensive care units): a single center retrospective cohort study

Over recent years, the escalation of patient volumes in emergency departments (ED) worldwide has posed to the delivery of timely critical care. Intensive Care Unit (ICU) services became essential due to increasing acuity in EDs, and previous studies revealed a strong association between prolonged boarding times and unfavorable outcomes. Innovative strategies such as Emergency Department-based Intensive Care Units (ED-ICUs) have been introduced to optimize critical care delivery. Given the higher acuity and mortality rates in ED-ICU patients, the prediction of certain events, such as In-Hospital Cardiac Arrest (IHCA), has become abstruse. Conventional Early Warning Scores (EWSs) were developed to stratify the risk of conventional ICUs, but have never been validated in ED-ICU patients with higher acuity. Moreover, EWSs are predominantly focused on forecasting mortality and lack capability for real-time prediction. Our study aimed to develop and validate a deep-learning-based model to predict IHCA within 24 h in ED-ICU. We included 1975 patients admitted to ED-ICU. The study period was from 01 January 2019 to 31 December 2020. Our model, the Deep-ICU CMS (Central Monitoring System), uses four classic vital signs (blood pressure, heart rate, respiratory rate, and body temperature) as input. The model outperformed conventional EWSs in predicting IHCA and maintained performance even with extended prediction windows; it provided robust prediction within a 24-h window, setting it apart from models with restricted prediction horizons. It achieved notably high sensitivity and specificity, overcoming the alarm fatigue issue that is common in EWSs. This study pioneered IHCA risk stratification in ED-ICU and showcases Deep-ICU CMS as a robust prediction tool that overcomes the limitations of conventional EWSs. Prospective and external validation are now warranted to confirm the impact of Deep-ICU CMS in real-world practice. Given the scarcity of research in ED-ICU, our findings contribute valuable insights to optimizing critical care delivery.

Addressing Alert Fatigue by Replacing a Burdensome Interruptive Alert with Passive Clinical Decision Support.

Recognizing that alert fatigue poses risks to patient safety and clinician wellness, there is a growing emphasis on evaluation and governance of electronic health record clinical decision support (CDS). This is particularly critical for interruptive alerts to ensure that they achieve desired clinical outcomes while minimizing the burden on clinicians. This study describes an improvement effort to address a problematic interruptive alert intended to notify clinicians about patients needing coronavirus disease 2019 (COVID) precautions and how we collaborated with operational leaders to develop an alternative passive CDS system in acute care areas. Our dual aim was to reduce the alert burden by redesigning the CDS to adhere to best practices for decision support while also improving the percent of admitted patients with symptoms of possible COVID who had appropriate and timely infection precautions orders. Iterative changes to CDS design included adjustment to alert triggers and acknowledgment reasons and development of a noninterruptive rule-based order panel for acute care areas. Data on alert burden and appropriate precautions orders on symptomatic admitted patients were followed over time on run and attribute (p) and individuals-moving range control charts. At baseline, the COVID alert fired on average 8,206 times per week with an alert per encounter rate of 0.36. After our interventions, the alerts per week decreased to 1,449 and alerts per encounter to 0.07 equating to an 80% reduction for both metrics. Concurrently, the percentage of symptomatic admitted patients with COVID precautions ordered increased from 23 to 61% with a reduction in the mean time between COVID test and precautions orders from 19.7 to -1.3 minutes. CDS governance, partnering with operational stakeholders, and iterative design led to successful replacement of a frequently firing interruptive alert with less burdensome passive CDS that improved timely ordering of COVID precautions.

Assessment and early warning signs of deterioration in hospitalized patients

Efficient assessment methods and early warning systems are crucial to prevent events like arrest, unexpected transfers to the intensive care unit (ICU) or even death. This in-depth review delves into the aspects of evaluating and handling early warning indicators of deterioration among patients admitted to hospitals. The primary emphasis lies in identifying any signs of decline by observing and tracking indicators, like heart rate, blood pressure, respiratory rate, body temperature and oxygen saturation. Standardized assessments like the modified early warning score (MEWS) play a role in assessing the seriousness of a patient’s condition while rapid response teams (RRTs) provide interdisciplinary interventions. Personalized care plans, customized medication management and the inclusion of support highlight an approach. Continuously reevaluating patients and providing education for healthcare professionals underscores the nature of patient care. Despite advancements in technology that enable real time monitoring challenges such as alarm fatigue and the need for consistent staff training persist. This review concludes that a comprehensive strategy combining expertise standardized assessments and technological support is vital for managing early warning signs of deterioration to ultimately enhance patient outcomes in hospital environments.

Alarm with care—a de-implementation strategy to reduce fall prevention alarm use in US hospitals: a study protocol for a hybrid 2 effectiveness-implementation trial

BackgroundFall prevention alarms are commonly used among US hospitals as a fall prevention strategy despite limited evidence of effectiveness. Further, fall prevention alarms are harmful to healthcare staff (e.g., alarm fatigue) and patients (e.g., sleep disturbance, mobility restriction). There is a need for research to develop and test strategies for reducing use of fall prevention alarms in US hospitals.MethodsTo address this gap, we propose testing the effectiveness and implementation of Alarm with Care, a de-implementation strategy to reduce fall prevention alarm use using a stepped-wedge randomized controlled trial among 30 adult medical or medical surgical units from nonfederal US acute care hospitals. Guided by the Choosing Wisely De-Implementation Framework, we will (1) identify barriers to fall prevention alarm de-implementation and develop tailored de-implementation strategies for each unit and (2) compare the implementation and effectiveness of high- versus low-intensity coaching to support site-specific de-implementation of fall prevention alarms. We will evaluate effectiveness and implementation outcomes and examine the effect of multi-level (e.g., hospital, unit, and patient) factors on effectiveness and implementation. Rate of fall prevention alarm use is the primary outcome. Balancing measures will include fall rates and fall-related injuries. Implementation outcomes will include feasibility, acceptability, appropriateness, and fidelity.DiscussionFindings from this line of research could be used to support scale-up of fall prevention alarm de-implementation in other healthcare settings. Further, research generated from this proposal will advance the field of de-implementation science by determining the extent to which low-intensity coaching is an effective and feasible de-implementation strategy.Trial registrationClinicalTrials.gov identifier: NCT06089239. Date of registration: October 17, 2023.

Effect of Implementing an Educational Intervention about Managing Alarm Fatigue on Improving Clinical Practices of Pediatric Critical Care Nurses

Effect of Implementing an Educational Intervention about Managing Alarm Fatigue on Improving Clinical Practices of Pediatric Critical Care Nurses

Predicting Cardiopulmonary Arrest with Digital Biomarkers: A Systematic Review

(1) Background: Telemetry units allow the continuous monitoring of vital signs and ECG of patients. Such physiological indicators work as the digital signatures and biomarkers of disease that can aid in detecting abnormalities that appear before cardiac arrests (CAs). This review aims to identify the vital sign abnormalities measured by telemetry systems that most accurately predict CAs. (2) Methods: We conducted a systematic review using PubMed, Embase, Web of Science, and MEDLINE to search studies evaluating telemetry-detected vital signs that preceded in-hospital CAs (IHCAs). (3) Results and Discussion: Out of 45 studies, 9 met the eligibility criteria. Seven studies were case series, and 2 were case controls. Four studies evaluated ECG parameters, and 5 evaluated other physiological indicators such as blood pressure, heart rate, respiratory rate, oxygen saturation, and temperature. Vital sign changes were highly frequent among participants and reached statistical significance compared to control subjects. There was no single vital sign change pattern found in all patients. ECG alarm thresholds may be adjustable to reduce alarm fatigue. Our review was limited by the significant dissimilarities of the studies on methodology and objectives. (4) Conclusions: Evidence confirms that changes in vital signs have the potential for predicting IHCAs. There is no consensus on how to best analyze these digital biomarkers. More rigorous and larger-scale prospective studies are needed to determine the predictive value of telemetry-detected vital signs for IHCAs.

1190. Nudging Away from Alert Fatigue: A Novel Method for Tracking the Impact of Integrated Clinical Decision Support on Meropenem Order Rates

Abstract Background The electronic health record (EHR) has streamlined quality of care and hospital prescribing; however, interruptive alerts and hard restrictions on antibiotic ordering can disrupt clinical workflow, often without significant benefit. This study aimed to determine the impact of permissive restriction on meropenem orders with embedded clinical decision support and prescriber attestation. Methods A quality improvement intervention implemented at Maine Medical Center (MMC, a 700-bed academic medical center) was evaluated through a multisite, quasiexperimental study within MaineHealth Hospitals. The intervention consisted of a provider-facing summary of guideline-directed indications for empiric and culture-directed uses of meropenem attached alongside the EHR order, and a required attestation button. To track meropenem order rates for adult patients prior to (Oct – Dec 2022) and after (Jan – Mar 2023) the intervention, the EHR was configured to fire a “silent” Best Practice Advisory behind-the-scenes when a provider opened the meropenem order. Rates of ordering were also monitored at other sites in MaineHealth, where the intervention was not implemented. The primary endpoint was the rate of “back-out” events, defined as a prescriber beginning a meropenem order but not signing it, and the secondary endpoint was the meropenem administration rate within 24 hours of opening an order. Results A meropenem order was initiated in 824 unique encounters during the study period; following the intervention at MMC, the back-out rate for orders increased from 20.3% (n =291) to 32.7% (n= 147, p = .01), while there was no significant difference during the same period at institutions where the intervention was not implemented (Table 1). The rate of patients who received meropenem within 24 hours following an order being opened decreased from 90.0% pre-intervention to 74.8% post-intervention (p < 0.01). Conclusion This study suggests that simple, non-resource intensive antimicrobial stewardship ‘nudges’ can simultaneously be implemented and monitored through creative EHR design, leading to a reduction in overall completion of meropenem orders and a decrease in inappropriate antibiotic prescribing. Similar interventions can be implemented in all fields of healthcare stewardship. Disclosures All Authors: No reported disclosures

Combating alert fatigue with AlertPro: Context-aware alert prioritization using reinforcement learning for multi-step attack detection

Alert fatigue problems can have serious consequences for the enterprise security. When analysts become overwhelmed by the sheer number of alerts, high-risk alerts may go unnoticed or receive delayed responses, exposing the organization to potential cyber threats or data breaches. While current research on alert triage primarily concentrates on reducing false positives, analysts still face a shortage of resources to investigate all true alerts. The key to resolving this issue lies in the prioritization of alerts based on their potential severity, allowing analysts to allocate their efforts effectively.This paper introduces AlertPro, an alert prioritization framework that facilitates the alert triage and validation stage of typical SOC workflow. The AlertPro framework extracts context features from alert sequences and history features from alerts previously investigated by analysts, besides basic features from raw alert data. By presenting analysts with only the top-ranked potentially high-risk alerts in each query and continually updating these rankings based on feedback, AlertPro significantly streamlines the alert investigation process. To evaluate AlertPro, we conducted experiments on five datasets that are chosen or prepared specifically because they all include multi-step attacks. The results reveal that AlertPro is able to discover a previously undisclosed attack concealed within the public dataset iscx, illustrating its potential in enhancing security posture. We also evaluate the feature importance in anomaly detection and conclude that employing context features yields better performance over basic features. The paper also explores the effectiveness of incorporating history features in active learning, achieving an average improvement of 30% in attack discovery rates. The processing time of AlertPro for re-ranking and selecting high-risk alerts is within 0.5 seconds, indicating that AlertPro can effectively work in real-time scenarios. AlertPro is limited to only using partial feedback and can be improved by incorporating richer feedback from experts. Overall, AlertPro mitigates alert fatigue, enabling security analysts to concentrate their efforts on high-priority threats.

Alert-Grouping: Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle with Alarm Fatigue in Intensive Care.

In Intensive Care Units (ICUs), patients are monitored using various devices that generate alerts when specific metrics, such as heart rate and oxygen saturation, exceed predetermined thresholds. However, these alerts can be inaccurate and lead to alert fatigue, resulting in errors and inaccurate diagnoses. We propose Alert grouping, a "Smart Personalization of Monitoring System Thresholds to Help Healthcare Teams Struggle Alarm Fatigue in Intensive Care" model. The alert grouping looks at patients at the individual and cluster levels, and healthcare-related constraints to assist medical and nursing teams in setting personalized alert thresholds of vital parameters. By simulating the function of ICU patient bed devices, we demonstrate that the proposed alert grouping model effectively reduces the number of alarms overall, improving the alert system's validity and reducing alarm fatigue. Implementing this personalized alert model in ICUs boosts medical and nursing teams' confidence in the alert system, leading to better care for ICU patients by significantly reducing alarm fatigue, thereby improving the quality of care for ICU patients.

Implementation of a Continuous Patient Monitoring System in the Hospital Setting: A Qualitative Study

Technology can improve care delivery, patient outcomes, and staff satisfaction, but integration into the clinical workflow remains challenging. To contribute to this knowledge area, this study examined the implementation continuum of a contact-free, continuous monitoring system (CFCM) in an inpatient setting. CFCM monitors vital signs and uses the information to alert clinicians of important changes, enabling early detection of patient deterioration. Data were collected throughout the entire implementation continuum at a community teaching hospital. Throughout the study, 3 group and 24 individual interviews and five process observations were conducted. Postimplementation alarm response data were collected. Analysis was conducted using triangulation of information sources and two-coder consensus. Preimplementation perceived barriers were alarm fatigue, questions about accuracy and trust, impact on patient experience, and challenges to the status quo. Stakeholders identified the value of CFCM as preventing deterioration and benefitting patients who are not good candidates for telemetry. Educational materials addressed each barrier and emphasized the shared CFCM values. Mean alarm response times were below the desired target of two minutes. Postimplementation interview analysis themes revealed lessened concerns of alarm fatigue and improved trust in CFCM than anticipated. Postimplementation challenges included insufficient training for secondary users and impact on patient experience. In addition to understanding the preimplementation anticipated barriers to implementation and establishing shared value before implementation, future recommendations include studying strategies for optimal tailoring of education to each user group, identifying and reinforcing positive process changes after implementation, and including patient experience as the overarching element in frameworks for digital tool implementation.

Alarm fatigue among nurses working in intensive care and other inpatient clinics.

Alarm fatigue is an important technological hazard that adversely affects patient safety and the healthcare team. Nurses can be exposed to an excessive amount of alarms during their work which may lead to alarm fatigue. To determine the experiences of alarm fatigue among nurses working in intensive care units and other inpatient clinics. This descriptive study was conducted in university hospitals in five cities in Turkey between August and December 2019. A total of 592 nurses participated in this study. The data was collected using questionnaires and the Visual Analog Scale (0 to 10 points) was used to determine the level of alarm fatigue. More than half of the nurses experienced problems, especially false alarms, caused by devices. Alarm fatigue decreased with increasing age and working years. Nurses reported appropriate actions in solving problems, but also had practices that may increase the risk of error, such as turning off or muting alarms or turning off equipment. Alarm fatigue is mostly caused by false alarms. It can lead to physical fatigue, increased workload and decreased concentration, resulting in an increased possibility of error. Management of alarm fatigue is necessary in preventing a compromise in patients' safety and improving quality of care.

Observing sources of system resilience using in situ alarm simulations.

Alarm fatigue (and resultant alarm nonresponse) threatens the safety of hospitalized patients. Historically threats to patient safety, including alarm fatigue, have been evaluated using a Safety I perspective analyzing rare events such as failure to respond to patients'critical alarms. Safety II approaches call for learning from the everyday adaptations clinicians make to keep patients safe. To identify such sources of resilience in alarm systems, we conducted 59 in situ simulations of a critical hypoxemic-event alarm in medical/surgical and intensive care units at a tertiary care pediatric hospital between December 2019 and May 2022. Response timing, observations of the environment, and postsimulation debrief interviews were captured. Four primary means of successful alarm responses were mapped to domains of Systems Engineering Initiative for Patient Safetyframework to inform alarm system design and improvement.