Technological Distractions (Part 2): A Summary of Approaches to Manage Clinical Alarms With Intent to Reduce Alarm Fatigue

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

Background

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.

Objective

We aimed to validate the hypothesized structure of the CAFQa and thus underpin the instrument's construct validity.

Methods

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.

Results

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).

Conclusions

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.

Hospital-Based Electrocardiographic Monitoring: The Good, the Not So Good, and Untapped Potential

Continuous electrocardiographic (ECG) monitoring was first introduced into hospitals in the 1960s, initially into critical care, as bedside monitors, and eventually into step-down units with telemetry capabilities. Although the initial use was rather simplistic (ie, heart rate and rhythm assessment), the capabilities of these devices and associated physiologic (vital sign) monitors have expanded considerably. Current bedside monitors now include sophisticated ECG software designed to identify myocardial ischemia (ie, ST-segment monitoring), QT-interval prolongation, and a myriad of other cardiac arrhythmia types. Physiologic monitoring has had similar advances from noninvasive assessment of core vital signs (blood pressure, respiratory rate, oxygen saturation) to invasive monitoring including arterial blood pressure, temperature, central venous pressure, intracranial pressure, carbon dioxide, and many others. The benefit of these monitoring devices is that continuous and real-time information is displayed and can be configured to alarm to alert nurses to a change in a patient's condition. I think it is fair to say that critical and high-acuity care nurses see these devices as having a positive impact in patient care. However, this enthusiasm has been somewhat dampened in the past decade by research highlighting the shortcomings and unanticipated consequences of these devices, namely alarm and alert fatigue. In this article, which is associated with the American Association of Critical-Care Nurses' Distinguished Research Lecture, I describe my 36-year journey from a clinical nurse to nurse scientist and the trajectory of my program of research focused primarily on ECG and physiologic monitoring. Specifically, I discuss the good, the not so good, and the untapped potential of these monitoring systems in clinical care. I also describe my experiences with community-based research in patients with acute coronary syndrome and/or heart failure.

Alarm Management in Intensive Care: Qualitative Triangulation Study

BACKGROUND

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.

OBJECTIVE

This study investigated the interaction of nurses and physicians with the patient monitoring system, their perceptions of alarm management, and smart alarm management solutions.

METHODS

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.

RESULTS

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.

CONCLUSIONS

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.

Application of Machine Learning Models to Biomedical and Information System Signals From Critically Ill Adults

BACKGROUND

Machine learning (ML)-derived notifications for impending episodes of hemodynamic instability and respiratory failure events are interesting because they can alert physicians in time to intervene before these complications occur.

RESEARCH QUESTION

Do ML alerts, telemedicine system (TS)-generated alerts, or biomedical monitors (BMs) have superior performance for predicting episodes of intubation or administration of vasopressors?

STUDY DESIGN AND METHODS

An ML algorithm was trained to predict intubation and vasopressor initiation events among critically ill adults. Its performance was compared with BM alarms and TS alerts.

RESULTS

ML notifications were substantially more accurate and precise, with 50-fold lower alarm burden than TS alerts for predicting vasopressor initiation and intubation events. ML notifications of internal validation cohorts demonstrated similar performance for independent academic medical center external validation and COVID-19 cohorts. Characteristics were also measured for a control group of recent patients that validated event detection methods and compared TS alert and BM alarm performance. The TS test characteristics were substantially better, with 10-fold less alarm burden than BM alarms. The accuracy of ML alerts (0.87-0.94) was in the range of other clinically actionable tests; the accuracy of TS (0.28-0.53) and BM (0.019-0.028) alerts were not. Overall test performance (F scores) for ML notifications were more than fivefold higher than for TS alerts, which were higher than those of BM alarms.

INTERPRETATION

ML-derived notifications for clinically actioned hemodynamic instability and respiratory failure events represent an advance because the magnitude of the differences of accuracy, precision, misclassification rate, and pre-event lead time is large enough to allow more proactive care and has markedly lower frequency and interruption of bedside physician work flows.

Decreasing inconsistent alarms notifications: a pragmatic clinical trial in a post-anesthesia care unit

BACKGROUND

Alarms alert healthcare professionals of deviations from normal/physiologic status. However, alarm fatigue may occur when their high pitch and diversity overwhelm clinicians, possibly leading to alarms being disabled, paused, and/or ignored. We aimed to determine whether a staff educational program on customizing alarm settings of bedside monitors may decrease inconsistent alarms in the Post-Anesthesia Care Unit (PACU).

METHODS

This is a prospective, analytic, quantitative, pragmatic, open-label, single-arm study. The outcome was evaluated on PACU admission before (P1) and after (P2) the implementation of the educational program. The heart rate, blood pressure, and oxygen saturation alarms were selected for clinical consistency.

RESULTS

A total of 260 patients were included and 344 clinical alarms collected, with 270 (78.4%) before (P1), and 74 (21.6%) after (P2) the intervention. Among the 270 alarms in P1, 45.2% were inconsistent (i.e., false alarms), compared to 9.4% of the 74 in P2. Patients with consistent alarms occurred in 30% in the P1 and 27% in the P2 (p = 0.08). Patients with inconsistent alarms occurred in 25.4% in the P1 and in 3.8% in the P2. Ignored consistent alarms were reduced from 21.5% to 2.6% (p = 0.004) in the P2 group. The educational program was a protective factor for the inconsistent clinical alarm (OR = 0.11 [95% CI 0.04-0.3]; p < 0.001) after adjustments for age, gender, and ASA physical status.

CONCLUSION

Customizing alarm settings on PACU admission proved to be a protective factor against inconsistent alarm notifications of multiparametric monitors.

Critical Care Alarm Fatigue and Monitor Customization: Alarm Frequencies and Context Factors

BACKGROUND

Alarm fatigue among nurses working in the intensive care unit has garnered considerable attention as a national patient safety priority. A viable solution for reducing the frequency of alarms and unnecessary noise is intensive care unit alarm monitor customization.

LOCAL PROBLEM

A 24-bed cardiovascular and thoracic surgery intensive care unit in a large academic medical center identified a high rate of alarms and associated noise as a problem contributing to nurse alarm fatigue.

METHODS

An alarm monitor quality improvement project used both alarm frequency and nurse surveys before and after implementation to determine the effectiveness of interventions. Multimodal interventions included nurse training sessions, informational flyers, organizational policies, and an alarm monitor training video. Unexpected results inspired an extensive investigation and secondary analysis, which included examining the data-capturing capabilities of the alarm monitors and the impact of context factors.

RESULTS

Alarm frequencies unexpectedly increased after the intervention. The software data-capturing features of the alarm monitors for determining frequency did not accurately measure nurse interactions with monitors. Measured increases in patient census, nurse staffing, and data input from medical devices from before to after the intervention substantially affected project results.

CONCLUSIONS

Alarm frequencies proved an unreliable measure of nurse skills and practices in alarm customization. Documented changes in context factors provided strong anecdotal evidence of changed circumstances that clarified project results and underscored the critical importance of contemporaneous collection of context data. Designs and methods used in quality improvement projects must include reliable outcome measures to achieve meaningful results.

Infusing behavior science into large language models for activity coaching

Large language models (LLMs) have shown promise for task-oriented dialogue across a range of domains. The use of LLMs in health and fitness coaching is under-explored. Behavior science frameworks such as COM-B, which conceptualizes behavior change in terms of capability (C), Opportunity (O) and Motivation (M), can be used to architect coaching interventions in a way that promotes sustained change. Here we aim to incorporate behavior science principles into an LLM using two knowledge infusion techniques: coach message priming (where exemplar coach responses are provided as context to the LLM), and dialogue re-ranking (where the COM-B category of the LLM output is matched to the inferred user need). Simulated conversations were conducted between the primed or unprimed LLM and a member of the research team, and then evaluated by 8 human raters. Ratings for the primed conversations were significantly higher in terms of empathy and actionability. The same raters also compared a single response generated by the unprimed, primed and re-ranked models, finding a significant uplift in actionability and empathy from the re-ranking technique. This is a proof of concept of how behavior science frameworks can be infused into automated conversational agents for a more principled coaching experience.

Performance comparison of 6 in-hospital patient monitoring systems in the detection and alarm of ventricular cardiac arrhythmias

Background

Patient monitoring devices are critical for alerting of potential cardiac arrhythmias during hospitalization; however, there are concerns of alarm fatigue due to high false alarm rates.

Objective

The purpose of this study was to evaluate the sensitivity and false alarm rate of hospital-based continuous electrocardiographic (ECG) monitoring technologies.

Methods

Six commonly used multiparameter bedside monitoring systems available in the United States were evaluated: B125M (GE HealthCare), ePM10 and iPM12 (Mindray), Efficia and IntelliVue (Philips), and Life Scope (Nihon Kohden). Sensitivity was tested using ECG recordings containing 57 true ventricular tachycardia (VT) events. False-positive rate testing used 205 patient-hours of ECG recordings containing no cardiac arrhythmias. Signals from ECG recordings were fed to devices simultaneously; high-severity arrhythmia alarms were tracked. Sensitivity to true VT events and false-positive rates were determined. Differences were assessed using Fisher exact tests (sensitivity) and Z-tests (false-positive rates).

Results

B125M raised 56 total alarms for 57 annotated VT events and had the highest sensitivity (98%; P <.05), followed by iPM12 (84%), Life Scope (81%), Efficia (79%), ePM10 (77%), and IntelliVue (75%). B125M raised 20 false alarms, which was significantly lower (P <.0001) than iPM12 (284), Life Scope (292), IntelliVue (304), ePM10 (324), and Efficia (493). The most common false alarm was VT, followed by nonsustained VT.

Conclusion

We found significant performance differences among multiparameter bedside ECG monitoring systems using previously collected recordings. B125M had the highest sensitivity in detecting true VT events and lowest false alarm rate. These results can assist in minimizing alarm fatigue and optimizing patient safety by careful selection of in-hospital continuous monitoring technology.

Effect of Best Practice Alert (BPA) on Post-Discharge Opioid Prescribing After Minimally Invasive Hysterectomy: A Quality Improvement Study

Purpose

The aim of this study was to describe the effectiveness of an electronic health record best practice alert (BPA) in decreasing gynecologic post-discharge opioid prescribing following benign minimally invasive hysterectomy.

Patients and Methods

The BPA triggered for opioid orders >15 tablets. Prescribers' options included (1) decrease to 15 ≤ tablets; (2) remove the order/utilize a defaulted order set; or (3) override the alert.

Results

332 patients were included. The BPA triggered 29 times. The following actions were taken among 16 patients for whom the BPA triggered: "override the alert" (n=13); "cancel the alert" (n=2); and 'remove the opioid order set' (n=1). 12/16 patients had discharge prescriptions: one patient received 20 tablets; two received 10 tablets; and nine received 15 tablets. Top reasons for over prescribing included concerns for pain control and lack of alternatives.

Conclusion

Implementing a post-discharge opioid prescribing BPA aligned opioid prescribing following benign minimally invasive hysterectomy with guideline recommendations.

Machine Learning Approaches to Predict Asthma Exacerbations: A Narrative Review

The implementation of artificial intelligence (AI) and machine learning (ML) techniques in healthcare has garnered significant attention in recent years, especially as a result of their potential to revolutionize personalized medicine. Despite advances in the treatment and management of asthma, a significant proportion of patients continue to suffer acute exacerbations, irrespective of disease severity and therapeutic regimen. The situation is further complicated by the constellation of factors that influence disease activity in a patient with asthma, such as medical history, biomarker phenotype, pulmonary function, level of healthcare access, treatment compliance, comorbidities, personal habits, and environmental conditions. A growing body of work has demonstrated the potential for AI and ML to accurately predict asthma exacerbations while also capturing the entirety of the patient experience. However, application in the clinical setting remains mostly unexplored, and important questions on the strengths and limitations of this technology remain. This review presents an overview of the rapidly evolving landscape of AI and ML integration into asthma management by providing a snapshot of the existing scientific evidence and proposing potential avenues for future applications.

The effect of intelligent management interventions in intensive care units to reduce false alarms: An integrative review

Objective

In intensive care units (ICU), frequent false alarms from medical equipment can cause alarm fatigue among nurses, which might lead to delayed or missed responses and increased risk of adverse patient events. This review was conducted to evaluate the effectiveness of intelligent management interventions to reduce false alarms in ICU.

Method

Following the framework of Whitmore and Knafl, the reviewers systematically searched six databases: PubMed, EMBASE, CINAHL, OVID, Cochrane Library, and Scopus, and studies included intelligent management of clinical alarms published in the English or Chinese language from the inception of each database to December 2022 were retrieved. The researchers used the PICOS framework to formulate the search strategy, developed keywords, screened literature, and assessed the studies' quality using the Joanna Briggs Institute-Meta-Analysis of Statistics, Assessment, and Review Instrument (JBI-MAStARI). The review was preregistered on PROSPERO (CRD42023411552).

Results

Seven studies met the inclusion criteria. The results showed that different interventions for intelligent management of alarms were beneficial in reducing the number of false alarms, the duration of alarms, the response time to important alarms for nurses, and the alarm fatigue levels among nurses. Positive results were found in practice after the application of the novel alarm management approaches.

Conclusion

Intelligent management intervention may be an effective way to reduce false alarms. The application of systems or tools for the intelligent management of clinical alarms is urgent in hospitals. To ensure more effective patient monitoring and less distress for nurses, more alarm management approaches combined with artificial intelligence will be needed in the future to enable accurate identification of critical alarms, ensure nurses are responding accurately to alarms, and make a real difference to alarm-ridden healthcare environments.

Interdisciplinary collaboration in critical care alarm research: A bibliometric analysis

BACKGROUND

Alarm fatigue in nurses is a major patient safety concern in the intensive care unit. This is caused by exposure to high rates of false and non-actionable alarms. Despite decades of research, the problem persists, leading to stress, burnout, and patient harm resulting from true missed events. While engineering approaches to reduce false alarms have spurred hope, they appear to lack collaboration between nurses and engineers to produce real-world solutions. The aim of this bibliometric analysis was to examine the relevant literature to quantify the level of authorial collaboration between nurses, physicians, and engineers.

METHODS

We conducted a bibliometric analysis of articles on alarm fatigue and false alarm reduction strategies in critical care published between 2010 and 2022. Data were extracted at the article and author level. The percentages of author disciplines per publication were calculated by study design, journal subject area, and other article-level factors.

RESULTS

A total of 155 articles with 583 unique authors were identified. While 31.73 % (n = 185) of the unique authors had a nursing background, publications using an engineering study design (n = 46), e.g., model development, had a very low involvement of nursing authors (mean proportion at 1.09 %). Observational studies (n = 58) and interventional studies (n = 33) had a higher mean involvement of 52.27 % and 47.75 %, respectively. Articles published in nursing journals (n = 32) had the highest mean proportion of nursing authors (80.32 %), while those published in engineering journals (n = 46) had the lowest (9.00 %), with 6 (13.04 %) articles having one or more nurses as co-authors.

CONCLUSION

Minimal involvement of nursing expertise in alarm research utilizing engineering methodologies may be one reason for the lack of successful, real-world solutions to ameliorate alarm fatigue. Fostering a collaborative, interdisciplinary research culture can promote a common publication culture across fields and may yield sustainable implementation of technological solutions in healthcare.

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 Safety framework to inform alarm system design and improvement.

Ventricular tachycardia and in-hospital mortality in the intensive care unit

Background

Continuous electrocardiographic (ECG) monitoring is used to identify ventricular tachycardia (VT), but false alarms occur frequently.

Objective

The purpose of this study was to assess the rate of 30-day in-hospital mortality associated with VT alerts generated from bedside ECG monitors to those from a new algorithm among intensive care unit (ICU) patients.

Methods

We conducted a retrospective cohort study in consecutive adult ICU patients at an urban academic medical center and compared current bedside monitor VT alerts, VT alerts from a new-unannotated algorithm, and true-annotated VT. We used survival analysis to explore the association between VT alerts and mortality.

Results

We included 5679 ICU admissions (mean age 58 ± 17 years; 48% women), 503 (8.9%) experienced 30-day in-hospital mortality. A total of 30.1% had at least 1 current bedside monitor VT alert, 14.3% had a new-unannotated algorithm VT alert, and 11.6% had true-annotated VT. Bedside monitor VT alert was not associated with increased rate of 30-day mortality (adjusted hazard ratio [aHR] 1.06; 95% confidence interval [CI] 0.88-1.27), but there was an association for VT alerts from our new-unannotated algorithm (aHR 1.38; 95% CI 1.12-1.69) and true-annotated VT(aHR 1.39; 95% CI 1.12-1.73).

Conclusion

Unannotated and annotated-true VT were associated with increased rate of 30-day in-hospital mortality, whereas current bedside monitor VT was not. Our new algorithm may accurately identify high-risk VT; however, prospective validation is needed.

A grounded theory study of alarm fatigue among nurses in intensive care units

OBJECTIVES

The aim of this study was to explore the process of how nurses experienced and dealt with alarm fatigue in intensive care units based on Iranian nurses' perceptions and experiences.

BACKGROUND

Alarm fatigue is the overstimulation of senses due to the constant ringing of alarms in intensive care units. It is associated with nurses' desensitization to critical alarms that can directly influence patient safety and quality of care.

METHODS

A qualitative exploratory study using the grounded theory approach by Strauss and Corbin was carried out. Participants were 20 nurses working in intensive care units. The sampling process was started purposively and continued theoretically. Data were collected using semi-structured, in-depth, and individual interviews and continued to data saturation. The constant comparative analysis approach was used consisting of the following steps: open coding, developing concepts, analysing the context, entering the process into data analysis, integrating categories.

FINDINGS

The participants' main concern in the exposure to alarm fatigue was 'threat to personal balance'. The core category in this research was 'trying to create a holistic balance', which reflected a set of strategies that the nurses consistently and continuously used to deal with alarm fatigue and consisted of four main categories as follows: 'smart care', 'deliberate balancing', 'conditional prioritisation', and 'negligent performance'. Threat to personal balance was strengthened by 'inappropriate circuit of individual roles', 'distortion of the organisational structure', and 'insecurity of the infrastructure'. The consequences of this process was harm to the patient, burnout among nurse, and damage to the healthcare organisation.

CONCLUSIONS

The research findings have practical implications for healthcare management, policymaking, nursing education, research, and clinical practice. Mitigating staff shortages, improving staff competencies, enhancing nurses' authority for responding to alarms, modifying care routines, improving the physical environment, and removing problems related to alarm equipment can prevent alarm fatigue and its unappropriated consequences.

Agreement of Computerized QT and QTc Interval Measurements Between Both Bedside and Expert Nurses Using Electronic Calipers

BACKGROUND

In hospitalized patients, QT/QTc (heart rate corrected) prolongation on the electrocardiogram (ECG) increases the risk of torsade de pointes. Manual measurements are time-consuming and often inaccurate. Some bedside monitors automatically and continuously measure QT/QTc; however, the agreement between computerized versus nurse-measured values has not been evaluated.

OBJECTIVE

The aim of this study was to examine the agreement between computerized QT/QTc and bedside and expert nurses who used electronic calipers.

METHODS

This was a prospective observational study in 3 intensive care units. Up to 2 QT/QTc measurements (milliseconds) per patient were collected. Bland-Altman test was used to analyze measurement agreement.

RESULTS

A total of 54 QT/QTc measurements from 34 patients admitted to the ICU were included. The mean difference (bias) for QT comparisons was as follows: computerized versus expert nurses, -11.04 ± 4.45 milliseconds (95% confidence interval [CI], -2.3 to -19.8; P = .016), and computerized versus bedside nurses, -13.72 ± 6.70 (95% CI, -0.70 to -26.8; P = .044). The mean bias for QTc comparisons was as follows: computerized versus expert nurses, -12.46 ± 5.80 (95% CI, -1.1 to -23.8; P = .035), and computerized versus bedside nurses, -18.49 ± 7.90 (95% CI, -3.0 to -33.9; P = .022).

CONCLUSION

Computerized QT/QTc measurements calculated by bedside monitor software and measurements performed by nurses were in close agreement; statistically significant differences were found, but differences were less than 20 milliseconds (on-half of a small box), indicating no clinical significance. Computerized measurements may be a suitable alternative to nurse-measured QT/QTc. This could reduce inaccuracies and nurse burden while increasing adherence to practice recommendations. Further research comparing computerized QT/QTc from bedside monitoring to standard 12-lead electrocardiogram in a larger sample, including non-ICU patients, is needed.

Alarm burden and the nursing care environment: a 213-hospital cross-sectional study

BACKGROUND

High rates of medical device alarms in hospitals are a well-documented threat to patient safety. Little is known about organisational features that may be associated with nurses' experience of alarm burden.

AIMS

To evaluate the association between nurse-reported alarm burden, appraisals of patient safety, quality of care and hospital characteristics.

METHODS

Secondary analysis of cross-sectional survey data from 3986 hospital-based direct-care registered nurses in 213 acute care hospitals in New York and Illinois, USA. We evaluated associations of alarm burden with appraisals of patient safety and quality of care and hospital characteristics (work environment, staffing adequacy, size, teaching status) using χ2 tests.

RESULTS

The majority of respondents reported feeling overwhelmed by alarms (83%), delaying their response to alarms because they were unable to step away from another patient/task (76%), and experiencing situations where a patient needed urgent attention but no one responded to an alarm (55%). Nurses on medical-surgical units reported these experiences at higher rates than nurses working in intensive care units (p<0.001). Alarm burden items were significantly associated with poorer nurse-reported patient safety, quality of care, staffing and work environment. Findings were most pronounced for situations where a patient needed urgent attention but no one responded to the alarm, which was frequently/occasionally experienced by 72% of those who rated their hospital's safety as poor versus 38% good, p<0.001; 80% who rated overall quality of care poor/fair versus 46% good/excellent, p<0.001 and 65% from poor work environments versus 42% from good work environments, p<0.001.

CONCLUSION

Most nurses reported feeling overwhelmed by medical device alarms, and our findings suggest that alarm burden may be more pronounced in hospitals with unfavourable working conditions and suboptimal quality and safety. Because this was a cross-sectional study, further research is needed to explore causal relationships and the role of modifiable systems factors in reducing alarm burden.

Deep learning classification of capnography waveforms: secondary analysis of the PRODIGY study

Capnography monitors trigger high priority 'no breath' alarms when CO2 measurements do not exceed a given threshold over a specified time-period. False alarms occur when the underlying breathing pattern is stable, but the alarm is triggered when the CO2 value reduces even slightly below the threshold. True 'no breath' events can be falsely classified as breathing if waveform artifact causes an aberrant spike in CO2 values above the threshold. The aim of this study was to determine the accuracy of a deep learning approach to classifying segments of capnography waveforms as either 'breath' or 'no breath'. A post hoc secondary analysis of data from 9 North American sites included in the PRediction of Opioid-induced Respiratory Depression In Patients Monitored by capnoGraphY (PRODIGY) study was conducted. We used a convolutional neural network to classify 15 s capnography waveform segments drawn from a random sample of 400 participants. Loss was calculated over batches of 32 using the binary cross-entropy loss function with weights updated using the Adam optimizer. Internal-external validation was performed by iteratively fitting the model using data from all but one hospital and then assessing its performance in the remaining hospital. The labelled dataset consisted of 10,391 capnography waveform segments. The neural network's accuracy was 0.97, precision was 0.97 and recall was 0.96. Performance was consistent across hospitals in internal-external validation. The neural network could reduce false capnography alarms. Further research is needed to compare the frequency of alarms derived from the neural network with the standard approach.

Ten Years Later, Alarm Fatigue Is Still a Safety Concern

Ten years after the publication of a landmark article in AACN Advanced Critical Care, alarm fatigue continues to be an issue that researchers, clinicians, and organizations aim to remediate. Alarm fatigue contributes to missed alarms and medical errors that result in patient death, increased clinical workload and burnout, and interference with patient recovery. Led by the American Association of Critical-Care Nurses, national patient safety organizations continue to prioritize efforts to battle alarm fatigue and have proposed alarm management strategies to mitigate the effects of alarm fatigue. Similarly, clinical efforts now use simulation studies, individualized alarm thresholds, and interdisciplinary teams to optimize alarm use. Finally, engineering research efforts have innovated the standard alarm to convey information more effectively for medical users. By focusing on patient and provider safety, clinical workflow, and alarm technology, efforts to reduce alarm fatigue over the past 10 years have been grounded in an evidence-based and personnel-focused approach.

Facilitators and Barriers to Interacting With Clinical Decision Support in the ICU: A Mixed-Methods Approach

OBJECTIVES

Clinical decision support systems (CDSSs) are used in various aspects of healthcare to improve clinical decision-making, including in the ICU. However, there is growing evidence that CDSS are not used to their full potential, often resulting in alert fatigue which has been associated with patient harm. Clinicians in the ICU may be more vulnerable to desensitization of alerts than clinicians in less urgent parts of the hospital. We evaluated facilitators and barriers to appropriate CDSS interaction and provide methods to improve currently available CDSS in the ICU.

DESIGN

Sequential explanatory mixed-methods study design, using the BEhavior and Acceptance fRamework.

SETTING

International survey study.

PATIENT/SUBJECTS

Clinicians (pharmacists, physicians) identified via survey, with recent experience with clinical decision support.

INTERVENTIONS

An initial survey was developed to evaluate clinician perspectives on their interactions with CDSS. A subsequent in-depth interview was developed to further evaluate clinician (pharmacist, physician) beliefs and behaviors about CDSS. These interviews were then qualitatively analyzed to determine themes of facilitators and barriers with CDSS interactions.

MEASUREMENTS AND MAIN RESULTS

A total of 48 respondents completed the initial survey (estimated response rate 15.5%). The majority believed that responding to CDSS alerts was part of their job (75%) but felt they experienced alert fatigue (56.5%). In the qualitative analysis, a total of five facilitators (patient safety, ease of response, specificity, prioritization, and feedback) and four barriers (excess quantity, work environment, difficulty in response, and irrelevance) were identified from the in-depth interviews.

CONCLUSIONS

In this mixed-methods survey, we identified areas that institutions should focus on to improve appropriate clinician interactions with CDSS, specific to the ICU. Tailoring of CDSS to the ICU may lead to improvement in CDSS and subsequent improved patient safety outcomes.

Staff perspectives on the influence of patient characteristics on alarm management in the intensive care unit: a cross-sectional survey study

BACKGROUND

High rates of clinical alarms in the intensive care unit can result in alarm fatigue among staff. Individualization of alarm thresholds is regarded as one measure to reduce non-actionable alarms. The aim of this study was to investigate staff's perceptions of alarm threshold individualization according to patient characteristics and disease status.

METHODS

This is a cross-sectional survey study (February-July 2020). Intensive care nurses and physicians were sampled by convenience. Data was collected using an online questionnaire.

RESULTS

Staff view the individualization of alarm thresholds in the monitoring of vital signs as important. The extent to which alarm thresholds are adapted from the normal range varies depending on the vital sign monitored, the reason for clinical deterioration, and the professional group asked. Vital signs used for hemodynamic monitoring (heart rate and blood pressure) were most subject to alarm individualizations. Staff are ambivalent regarding the integration of novel technological features into alarm management.

CONCLUSIONS

All relevant stakeholders, including clinicians, hospital management, and industry, must collaborate to establish a "standard for individualization," moving away from ad hoc alarm management to an intelligent, data-driven alarm management. Making alarms meaningful and trustworthy again has the potential to mitigate alarm fatigue - a major cause of stress in clinical staff and considerable hazard to patient safety.

TRIAL REGISTRATION

The study was registered at ClinicalTrials.gov (NCT03514173) on 02/05/2018.

Technological Intervention to Improve Alarm Management in Acute Care Telemetry Units

Background: Telemetry monitoring is intended to improve patient safety and reduce harm. However, excessive monitor alarms may have the undesired effect of staff ignoring, silencing, or delaying a response due to alarm fatigue. Outlier patients, or those patients who are responsible for generating the most monitor alarms, contribute to excessive monitor alarms. Methods: Daily alarm data reports at a large academic medical center indicated that one or two patient outliers generated the most alarms daily. A technological intervention aimed at reminding registered nurses (RNs) to adjust alarm thresholds for patients who triggered excessive alarms was implemented. The notification was sent to the assigned RN's mobile phone when a patient exceeded the unit's seven-day average of alarms per day by greater than 400%. Results: A reduction in average alarm duration was observed across the four acute care telemetry units (P < 0.001), with an overall decrease of 8.07 seconds in the postintervention versus preintervention period. However, alarm frequency increased significantly (χ23 = 34.83, P < 0.001). Conclusion: Implementing a technological intervention to notify RNs to adjust alarm parameters may reduce alarm duration. Reducing alarm duration may improve RN telemetry management, alarm fatigue, and awareness. More research is needed to support this conclusion, as well as to determine the cause of the observed increase in alarm frequency.

Determining Factors of Alarm Fatigue among Nurses in Intensive Care Units-A Polish Pilot Study

INTRODUCTION

With the development of medical technology, clinical alarms from various medical devices, which are rapidly increasing, are becoming a new problem in intensive care units. The aim of this study was to evaluate alarm fatigue in Polish nurses employed in Intensive Care Units and identify the factors associated with alarm fatigue.

METHODS

A cross-sectional study. The study used the nurses' alarm fatigue questionnaire by Torabizadeh. The study covered 400 Intensive Care Unit nurses. The data were collected from February to June 2021.

RESULTS

The overall mean score of alarm fatigue was 25.8 ± 5.8. Participation in training programs related to the use of monitoring devices available in the ward, both regularly (ß = -0.21) and once (ß = -0.17), negatively correlated with nurses' alarm fatigue. On the other hand, alarm fatigue was positively associated with 12 h shifts [vs. 8 h shifts and 24 h shifts] (ß = 0.11) and employment in Intensive Cardiac Surveillance Units-including Cardiac Surgery [vs. other Intensive Care Units] (ß = 0.10).

CONCLUSION

Monitoring device alarms constitute a significant burden on Polish Intensive Care Unit nurses, in particular those who do not take part in training on the operation of monitoring devices available in their ward. It is necessary to improve Intensive Care Unit personnel's awareness of the consequences of overburdening and alarm fatigue, as well as to identify fatigue-related factors.

Clinician Perspectives on Specifications for Metrics to Inform Pediatric Alarm Management

Background: Ongoing management of monitor alarms is important for reducing alarm fatigue among clinicians (e.g., nurses, physicians). Strategies to enhance clinician engagement in active alarm management in pediatric acute care have not been well explored. Access to alarm summary metrics may enhance clinician engagement. Objective: To lay the foundation for intervention development, we sought to identify functional specifications for formulating, packaging, and delivering alarm metrics to clinicians. Methods: Our team of clinician scientists and human factors engineers conducted focus groups with clinicians from medical-surgical inpatient units in a children's hospital. We inductively coded transcripts, developed codes into themes, and grouped themes into "current state" and "future state." Results: We conducted five focus groups with 13 clinicians (eight registered nurses and five doctors of medicine). In the current state, information exchanged among team members about alarm burden is initiated by nurses on an ad hoc basis. For a future state, clinicians identified ways in which alarm metrics could help them manage alarms and described specific information, such as alarm trends, benchmarks, and contextual data, that would support decision-making. Conclusion: We developed four recommendations for future strategies to enhance clinicians' active management of patient alarms: (1) formulate alarm metrics for clinicians by categorizing alarm rates by type and summarizing alarm trends over time, (2) package alarm metrics with contextual patient data to facilitate clinicians' sensemaking, (3) deliver alarm metrics in a forum that facilitates interprofessional discussion, and (4) provide clinician education to establish a shared mental model about alarm fatigue and evidence-based alarm-reduction strategies.

An annotated ventricular tachycardia (VT) alarm database: Toward a uniform standard for optimizing automated VT identification in hospitalized patients

BACKGROUND

False ventricular tachycardia (VT) alarms are common during in-hospital electrocardiographic (ECG) monitoring. Prior research shows that the majority of false VT can be attributed to algorithm deficiencies.

PURPOSE

The purpose of this study was: (1) to describe the creation of a VT database annotated by ECG experts and (2) to determine true vs. false VT using a new VT algorithm created by our group.

METHODS

The VT algorithm was processed in 5320 consecutive ICU patients with 572,574 h of ECG and physiologic monitoring. A search algorithm identified potential VT, defined as: heart rate >100 beats/min, QRSs > 120 ms, and change in QRS morphology in >6 consecutive beats compared to the preceding native rhythm. Seven ECG channels, SpO₂ , and arterial blood pressure waveforms were processed and loaded into a web-based annotation software program. Five PhD-prepared nurse scientists performed the annotations.

RESULTS

Of the 5320 ICU patients, 858 (16.13%) had 22,325 VTs. After three levels of iterative annotations, a total of 11,970 (53.62%) were adjudicated as true, 6485 (29.05%) as false, and 3870 (17.33%) were unresolved. The unresolved VTs were concentrated in 17 patients (1.98%). Of the 3870 unresolved VTs, 85.7% (n = 3281) were confounded by ventricular paced rhythm, 10.8% (n = 414) by underlying BBB, and 3.5% (n = 133) had a combination of both.

CONCLUSIONS

The database described here represents the single largest human-annotated database to date. The database includes consecutive ICU patients, with true, false, and challenging VTs (unresolved) and could serve as a gold standard database to develop and test new VT algorithms.

Artificial and human intelligence for early identification of neonatal sepsis

Artificial intelligence may have a role in the early detection of sepsis in neonates. Machine learning can identify patterns that predict high or increasing risk for clinical deterioration from a sepsis-like illness. In developing this potential addition to NICU care, careful consideration should be given to the data and methods used to develop, validate, and evaluate prediction models. When an AI system alerts clinicians to a change in a patient's condition that warrants a bedside evaluation, human intelligence and experience come into play to determine an appropriate course of action: evaluate and treat or wait and watch closely. With intelligently developed, validated, and implemented AI sepsis systems, both clinicians and patients stand to benefit. IMPACT: This narrative review highlights the application of AI in neonatal sepsis prediction. It describes issues in clinical prediction model development specific to this population. This article reviews the methods, considerations, and literature on neonatal sepsis model development and validation. Challenges of AI technology and potential barriers to using sepsis AI systems in the NICU are discussed.

Machine learning did not beat logistic regression in time series prediction for severe asthma exacerbations

Early detection of severe asthma exacerbations through home monitoring data in patients with stable mild-to-moderate chronic asthma could help to timely adjust medication. We evaluated the potential of machine learning methods compared to a clinical rule and logistic regression to predict severe exacerbations. We used daily home monitoring data from two studies in asthma patients (development: n = 165 and validation: n = 101 patients). Two ML models (XGBoost, one class SVM) and a logistic regression model provided predictions based on peak expiratory flow and asthma symptoms. These models were compared with an asthma action plan rule. Severe exacerbations occurred in 0.2% of all daily measurements in the development (154/92,787 days) and validation cohorts (94/40,185 days). The AUC of the best performing XGBoost was 0.85 (0.82-0.87) and 0.88 (0.86-0.90) for logistic regression in the validation cohort. The XGBoost model provided overly extreme risk estimates, whereas the logistic regression underestimated predicted risks. Sensitivity and specificity were better overall for XGBoost and logistic regression compared to one class SVM and the clinical rule. We conclude that ML models did not beat logistic regression in predicting short-term severe asthma exacerbations based on home monitoring data. Clinical application remains challenging in settings with low event incidence and high false alarm rates with high sensitivity.

Understanding the “alarm problem” associated with continuous physiologic monitoring of general care patients

Study Aim

Methods

Results

Conclusions

Computational approaches to alleviate alarm fatigue in intensive care medicine: A systematic literature review

Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms. With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation. The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate. Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff. The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions. However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.

Systematic Review Registration:https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42021233461, identifier: CRD42021233461.

Alarm fatigue in nurses working in intensive care units: A multicenter study

BACKGROUND: Technological progress improves health care efficiency, quality, safety, and cost, supporting clinical activity in various scenarios, such as Intensive Care Unit (ICU). A timely response to alarms from monitors and other ICU electromedical devices is therefore crucial. The number of false alarms tend to desensitize care providers increasing the risk of experiencing alarm fatigue and, at times, lead to severe consequences for patients. OBJECTIVES: To assess the psychometric properties of the Italian version of the Alarm Fatigue Questionnaire and to explore the phenomenon of alarm fatigue among nurses working in intensive care settings. METHODS: The CVI-I was calculated to evaluate the validity of the content of the tool. Construct validity was investigated through exploratory factor analysis. Cronbach’s alpha coefficient (α) was used to examine the internal consistency of the scale and Spearman’s rho coefficient to test for stability. We designed a multicentre cross-sectional survey. A convenience sample of nurses from 4 Major Italian hospitals was recruited. The nurses completed the Italian version of the Alarm Fatigue Questionnaire. RESULTS: The content validity index CVI-S of the scale (CVI-S) was 91.11%; Cronbach’s alpha coefficient was 0.71. The Italian version of the tool explained 67.18%of the overall variance. 396 nurses were enrolled (79.84%). The overall level of alarm fatigue was Me = 29 [22;30]. 42.17%of the sample reported prior experience with alarm fatigue incidents. CONCLUSIONS: The extension of alarm fatigue requires the adoption of a preventive intervention plan. The Italian version of the Alarm Fatigue Questionnaire shows promising psychometric properties.

Nurses’ clinical alarm-related behaviors and influencing factors in China†

Objective

To explore the nurses’ behaviors regarding clinical alarms, analyze the related influencing factors, and provide rationales for alarm management.

Methods

A cross-sectional survey was conducted in China. The self-made questionnaire of nurses’ clinical alarm-related knowledge, attitude, and behavior (NCAKAB) was used.

Results

The valid response rate was 98.66% (n = 2368). The average nurses’ clinical alarm-related behaviors (NCAB) score was 65.14 ± 7.95 (out of 85). The dimension scores of NCAB from high to low were alarm learning (4.02 ± 0.85, out of 5), alarm response (27.99 ± 3.64, out of 35), alarm setting (19.24 ± 3.88, out of 25), alarm recognition (7.63 ± 1.68, out of 10) and alarm notification (6.25 ± 1.84, out of 10). There were significant differences in alarm behavior scores between nurses of different ages (F = 4.619, P = 0.000), nursing stints (F = 9.564, P = 0.000), professional titles (F = 4.425, P = 0.004), departments (F = 9.166, P = 0.000), and hospital levels (t = 2.705, P = 0.007). The study showed that nurses’ total alarm behavior scores were positively correlated with the total alarm knowledge score (r = 0.267; P < 0.001) and the total alarm attitude score (r = 438; P < 0.001).

Conclusions

Nurses scored highest in alarm learning, followed by alarm response, alarm setting, alarm recognition, and alarm notification behavior. The factors that influenced alarm behavior included age, title, department, nursing stint, hospital level, professional title, alarm-related training, willingness to participate in alarm-related training, whether or not departments have improved alarm management over the last 3 years, and whether or not departments have formulated norms for alarm management. Nurses with higher scores for clinical alarm knowledge had higher correlating scores for alarm behavior; similarly, nurses with higher scores for clinical alarm attitude had higher scores for alarm-related behavior.

Integrated Pulmonary Index during nurse-administered procedural sedation: Study protocol for a cluster-randomized trial

AIM

To determine if smart alarm-guided treatment of respiratory depression using the Integrated Pulmonary Index is an effective way to implement capnography during nurse-administered sedation.

DESIGN

Parallel cluster-randomized trial.

METHODS

Nurses will be randomized to use capnography with or without the Integrated Pulmonary Index enabled. Capnography alarm performance will be compared between nurses using capnography alone or with the Integrated Pulmonary Index enabled. The target sample size is 400 adult patients scheduled for elective procedures with nurse-administered sedation. The primary outcome is the number of seconds in an alert condition state without an intervention being applied. Secondary outcomes are alarm burden, number of appropriate alarms, number of inappropriate alarms, total duration of alert conditions, choice of alarm settings and adverse sedation events. This study has been funded since April 2021.

DISCUSSION

Implementing capnography into practice for respiratory monitoring during nurse-administered sedation is considered a high priority. The Integrated Pulmonary Index shows promise as a strategy to optimize the implementation of capnography for respiratory monitoring during nurse-administered sedation. If it is found in this study that using the Integrated Pulmonary Index improves the nursing management of physiologically abnormal states during nurse-administered sedation, it would provide the high-level evidence needed to support broader use of this 'smart alarm' strategy for respiratory monitoring in practice.

IMPACT

With advances in medical technology continuing to expand the indications for minimally invasive surgical techniques, the use of nurse-administered sedation during medical procedures is likely to expand in the future. The findings may be applied to other populations receiving nurse-administered sedation during medical procedures. Results from this study will help translate the usage of smart alarm-guided treatment of respiratory depression during procedural sedation.

TRIAL REGISTRATION

NCT05068700.

Pediatric sepsis screening in US hospitals

Sepsis is a major cause of morbidity and mortality in children. While adverse outcomes can be reduced through prompt initiation of sepsis protocols including fluid resuscitation and antibiotics, provision of these therapies relies on clinician recognition of sepsis. Recognition is challenging in children because early signs of shock such as tachycardia and tachypnea have low specificity while hypotension often does not occur until late in the clinical course. This narrative review highlights the important context that has led to the rapid growth of pediatric sepsis screening in the United States. In this review, we (1) describe different screening tools used in US emergency department, inpatient, and intensive care unit settings; (2) highlight details of the design, implementation, and evaluation of specific tools; (3) review the available data on the process of integrating sepsis screening into an overall sepsis quality improvement program and on the effect of these screening tools on patient outcomes; (4) discuss potential harms of sepsis screening including alarm fatigue; and (5) highlight several future directions in sepsis screening, such as novel tools that incorporate artificial intelligence and machine learning methods to augment sepsis identification with the ultimate goal of precision-based approaches to sepsis recognition and treatment. IMPACT: This narrative review highlights the context that has led to the rapid growth of pediatric sepsis screening nationally. Screening tools used in US emergency department, inpatient, and intensive care unit settings are described in terms of their design, implementation, and clinical performance. Limitations and potential harms of these tools are highlighted, as well as future directions that may lead to a more precision-based approach to sepsis recognition and treatment.

Performance of Modified Pediatric Early Warning Score in General Medical Conditions and Disease Subgroups

Numerous existing Pediatric Early Warning Scores (PEWs) have varying degrees of reliability and validity, which are used in variety diseases of patients. This study is a prospective diagnosis study which involved the pediatric nurse evaluation of patient status using modified Pediatric Early Warning Score (NU-PEWS) until the patient was discharged or transferred to PICU. A total of 824 pediatric patients were admitted, 407 participants were enrolled in this study. The NU-PEWS demonstrated the most accurate cut-off point at greater than 3, with 90.5% sensitivity and 89.1% specificity. The receiver operating characteristic (ROC) indicated positive results in the general medical condition (ROC 0.958), gastrointestinal, respiratory, and hematologic diseases (ROC 0.94-0.97) whereas lowest in neurological disease (ROC 0.843). This study validated that NU-PEWS has good performance in detecting deteriorating patients, and that prediction utilizations are good in almost every subgroup of disease, with the exception of neurological disease.

Home pulse oximetry after discharge from a quaternary-care children's hospital: Prescriber patterns and perspectives

INTRODUCTION

Pulse oximetry monitoring is prescribed to children receiving home oxygen for chronic medical conditions associated with hypoxemia. Although home pediatric pulse oximetry is supported by national organizations, there is a lack of guidelines outlining indications and prescribing parameters.

METHODS

A mixed-methods analysis of pediatric home pulse oximetry orders prescribed through the institutional home healthcare provider at a large US children's hospital 6/2018-7/2019 was retrospectively reviewed to determine prescribed alarm parameter limits and recommended interventions. Semi-structured qualitative interviews with pediatric providers managing patients receiving home oxygen and pulse oximetry were conducted to identify opportunities to improve home pulse oximetry prescribing practices. Interviews were analyzed using a modified content analysis approach to identify recurring themes.

RESULTS

A total of 368 children received home pulse oximetry orders. Orders were most frequently prescribed on noncardiac medical floors (32%). Attending physicians were the most frequent ordering providers (52%). Frequency of use was prescribed in 96% of orders, however, just 70% were provided with specific instructions for interventions when alarms occurred. Provider role and clinical setting were significantly associated with the presence of a care plan. Provider interviews identified opportunities for improvement with the device, management of alarm parameter limits, and access to home monitor data.

DISCUSSION

This study demonstrated significant variability in home pulse oximetry prescribing practices. Provider interviews highlighted the importance of the provider-patient relationship and areas for improvement. There is an opportunity to create standardized guidelines that optimize the use of home monitoring devices for patients, families, and pulmonary providers.

State of Science in Alarm System Safety: Implications for Researchers, Vendors, and Clinical Leaders

Alarm fatigue is a complex phenomenon that needs to be assessed within the context of the clinical setting. Considering that complexity, the available information on how to address alarm fatigue and improve alarm system safety is relatively scarce. This article summarizes the state of science in alarm system safety based on the eight dimensions of a sociotechnical model for studying health information technology in complex adaptive healthcare systems. The summary and recommendations were guided by available systematic reviews on the topic, interventional studies published between January 2019 and February 2022, and recommendations and evidence-based practice interventions published by professional organizations. The current article suggests implications to help researchers respond to the gap in science related to alarm safety, help vendors design safe monitoring systems, and help clinical leaders apply evidence-based strategies to improve alarm safety in their settings. Physiologic monitors in intensive care units-the devices most commonly used in complex care environments and associated with the highest number of alarms and deaths-are the focus of the current work.

Diagnostic and prognostic significance of premature ventricular complexes in community and hospital-based participants: A scoping review

Background

While there are published studies that have examined premature ventricular complexes (PVCs) among patients with and without cardiac disease, there has not been a comprehensive review of the literature examining the diagnostic and prognostic significance of PVCs. This could help guide both community and hospital-based research and clinical practice.

Methods

Scoping review frameworks by Arksey and O’Malley and the Joanna Briggs Institute (JBI) were used. A systematic search of the literature using four databases (CINAHL, Embase, PubMed, and Web of Science) was conducted. The review was prepared adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review (PRISMA-ScR).

Results

A total of 71 relevant articles were identified, 66 (93%) were observational, and five (7%) were secondary analyses from randomized clinical trials. Three studies (4%) examined the diagnostic importance of PVC origin (left/right ventricle) and QRS morphology in the diagnosis of acute myocardial ischemia (MI). The majority of the studies examined prognostic outcomes including left ventricular dysfunction, heart failure, arrhythmias, ischemic heart diseases, and mortality by PVCs frequency, burden, and QRS morphology.

Conclusions

Very few studies have evaluated the diagnostic significance of PVCs and all are decades old. No hospital setting only studies were identified. Community-based longitudinal studies, which make up most of the literature, show that PVCs are associated with structural and coronary heart disease, lethal arrhythmias, atrial fibrillation, stroke, all-cause and cardiac mortality. However, a causal association between PVCs and these outcomes cannot be established due to the purely observational study designs employed.

SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hypoxemia would offer valuable opportunities for early and thus more effective intervention. We present SWIFT (SpO2 Waveform ICU Forecasting Technique), a deep learning model that predicts blood oxygen saturation (SpO2) waveforms 5 and 30 minutes in the future using only prior SpO2 values as inputs. When tested on novel data, SWIFT predicts more than 80% and 60% of hypoxemic events in critically ill and COVID-19 patients, respectively. SWIFT also predicts SpO2 waveforms with average MSE below .0007. SWIFT predicts both occurrence and magnitude of potential hypoxemic events 30 minutes in the future, allowing it to be used to inform clinical interventions, patient triaging, and optimal resource allocation. SWIFT may be used in clinical decision support systems to inform the management of critically ill patients during the COVID-19 pandemic and beyond.

Nursing and precision predictive analytics monitoring in the acute and intensive care setting: An emerging role for responding to COVID-19 and beyond

As the global response to COVID-19 continues, nurses will be tasked with appropriately triaging patients, responding to events of clinical deterioration, and developing family-centered plans of care within a healthcare system exceeding capacity. Predictive analytics monitoring, an artificial intelligence (AI)-based tool that translates streaming clinical data into a real-time visual estimation of patient risks, allows for evolving acuity assessments and detection of clinical deterioration while the patient is in pre-symptomatic states. While nurses are on the frontline for the COVID-19 pandemic, the use of AI-based predictive analytics monitoring may help cognitively complex clinical decision-making tasks and pave a pathway for early detection of patients at risk for decompensation. We must develop strategies and techniques to study the impact of AI-based technologies on patient care outcomes and the clinical workflow. This paper outlines key concepts for the intersection of nursing and precision predictive analytics monitoring.

Predicting Prolonged Apnea During Nurse-Administered Procedural Sedation: Machine Learning Study

BACKGROUND

Capnography is commonly used for nurse-administered procedural sedation. Distinguishing between capnography waveform abnormalities that signal the need for clinical intervention for an event and those that do not indicate the need for intervention is essential for the successful implementation of this technology into practice. It is possible that capnography alarm management may be improved by using machine learning to create a "smart alarm" that can alert clinicians to apneic events that are predicted to be prolonged.

OBJECTIVE

To determine the accuracy of machine learning models for predicting at the 15-second time point if apnea will be prolonged (ie, apnea that persists for >30 seconds).

METHODS

A secondary analysis of an observational study was conducted. We selected several candidate models to evaluate, including a random forest model, generalized linear model (logistic regression), least absolute shrinkage and selection operator regression, ridge regression, and the XGBoost model. Out-of-sample accuracy of the models was calculated using 10-fold cross-validation. The net benefit decision analytic measure was used to assist with deciding whether using the models in practice would lead to better outcomes on average than using the current default capnography alarm management strategies. The default strategies are the aggressive approach, in which an alarm is triggered after brief periods of apnea (typically 15 seconds) and the conservative approach, in which an alarm is triggered for only prolonged periods of apnea (typically >30 seconds).

RESULTS

A total of 384 apneic events longer than 15 seconds were observed in 61 of the 102 patients (59.8%) who participated in the observational study. Nearly half of the apneic events (180/384, 46.9%) were prolonged. The random forest model performed the best in terms of discrimination (area under the receiver operating characteristic curve 0.66) and calibration. The net benefit associated with the random forest model exceeded that associated with the aggressive strategy but was lower than that associated with the conservative strategy.

CONCLUSIONS

Decision curve analysis indicated that using a random forest model would lead to a better outcome for capnography alarm management than using an aggressive strategy in which alarms are triggered after 15 seconds of apnea. The model would not be superior to the conservative strategy in which alarms are only triggered after 30 seconds.

Computer Assisted Patient Monitoring: Associated Patient, Clinical and ECG Characteristics and Strategy to Minimize False Alarms

This chapter is a review of studies that have examined false arrhythmia alarms during in-hospital electrocardiographic (ECG) monitoring in the intensive care unit. In addition, we describe an annotation effort being conducted at the UCSF School of Nursing, Center for Physiologic Research designed to improve algorithms for lethal arrhythmias (i.e., asystole, ventricular fibrillation, and ventricular tachycardia). Background: Alarm fatigue is a serious patient safety hazard among hospitalized patients. Data from the past five years, showed that alarm fatigue was responsible for over 650 deaths, which is likely lower than the actual number due to under-reporting. Arrhythmia alarms are a common source of false alarms and 90% are false. While clinical scientists have implemented a number of interventions to reduce these types of alarms (e.g., customized alarm settings; daily skin electrode changes; disposable vs. non-disposable lead wires; and education), only minor improvements have been made. This is likely as these interventions do not address the primary problem of false arrhythmia alarms, namely deficient and outdated arrhythmia algorithms. In this chapter we will describe a number of ECG features associated with false arrhythmia alarms. In addition, we briefly discuss an annotation effort our group has undertaken to improve lethal arrhythmia algorithms.

Nurses' use of auditory alarms and alerts in high dependency units: A field study

A fieldwork study conducted in six units of a major metropolitan Australian hospital revealed that nurses' attitudes towards alarms are influenced by each unit's physical layout and caseload. Additionally, nurses relied heavily on both non-actionable and actionable alarms to maintain their awareness of the status of their patients' wellbeing, and used auditory alarms beyond the scope of their intended design. Results suggest that before reducing or removing auditory alarms from the clinical environment to improve patient safety, it is important to understand how nurses in different clinical contexts use current alarm systems to extract meaningful information. Such an understanding could guide appropriate alarm reduction strategies and guide alternative design solutions to support nurses' situation awareness during monitoring.

Reducing Cardiac Telemetry Nuisance Alarms Through Evidence-Based Interventions

BACKGROUND

Cardiac telemetry nuisance alarms due to leads off and poor signal increase staff workflow interruptions, decrease staff trust in technology, and can compromise patient safety.

LOCAL PROBLEM

Interventions were directed at reducing nuisance alarms on a 32-bed, non-intensive care - a cardiac telemetry unit.

METHODS

A nursing staff education module with evidence-based practices for reducing nuisance alarms, a daily care protocol for patients on cardiac telemetry monitoring, and daily audits of protocol adherence were implemented.

RESULTS

Staff pre- and posttest comparisons on their knowledge relating to nuisance alarms and the evidence-based protocol demonstrated a significant mean increase of 3.02 (95% CI, 2.55-3.48). Daily audits for 7 weeks demonstrated an average of 58.46% staff adherence. Telemetry technician call volume reduction was 16% postimplementation, while nuisance alarms were not reduced significantly.

CONCLUSIONS

This rapid-cycle, quality improvement process resulted in minimal reduction in nuisance alarms but improved staff awareness of the issue and reduced workflow interruptions.

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

OBJECTIVE

To determine if the implementation of an evidence-based bundle designed to reduce the number of physiologic monitor alarms reduces alarm fatigue in intensive care nurses.

DESIGN

This quality improvement project retrospectively reviewed alarm data rates, types, and frequency to identify the top three problematic physiologic alarms in an intensive care unit. An alarm management bundle was implemented to reduce the number of alarms. The Nurses' Alarm Fatigue Questionnaire was used to measure nurses' alarms fatigue pre- and post-implementation of the bundle.

SETTING

A combined medical surgical intensive care unit at an accredited hospital in the United States.

RESULTS

The top three problematic alarms identified during the pre-implementation phase were arrhythmia, invasive blood pressure, and respiration alarms. All three identified problematic physiologic alarms had a reduction in frequency with arrhythmia alarms demonstrating the largest decrease in frequency (46.82%). When measuring alarm fatigue, the overall total scores increased from pre- (M = 30.59, SD = 5.56) to post-implementation (M = 32.60, SD = 4.84) indicating no significant difference between the two periods.

CONCLUSION

After implementing an alarm management bundle, all three identified problematic physiologic alarms decreased in frequency. Despite the reduction in these alarms, there was not a reduction in nurses' alarm fatigue.

Clinical alarms and alarm fatigue in a University Hospital Emergency Department-A retrospective data analysis

BACKGROUND

Alarm fatigue is hypothesized to be caused by vast amount of patient monitor alarms. Objectives were to study the frequency and types of patient monitor alarms, to evaluate alarm fatigue, and to find unit specific alarm threshold values in a university hospital emergency department.

METHODS

We retrospectively gathered alarm data from 9 September to 6 October 2019, in Jorvi Hospital Emergency department, Finland. The department treats surgical, internal and general medicine patients aged 16 and older. The number of patients is on average 4600 to 5000 per month. Eight out of 46 monitors were used for data gathering and the monitored modalities included electrocardiography, respiratory rate, blood pressure, and pulse oximetry.

RESULTS

Total number of alarms in the study monitors was 28 176. Number of acknowledged alarms (ie acknowledgement indicator pressed in the monitor) was 695 (2.5%). The most common alarm types were: Respiratory rate high, 9077 (32.2%), pulse oximetry low, 4572 (16.2%) and pulse oximetry probe off, 4036 (14.3%). Number of alarms with duration under 10 s was 14 936 (53%). Number of individual alarm sounds was 105 000, 469 per monitor per day. Of respiratory rate high alarms, 2846 (31.4%) had initial value below 30 breaths min^-1 . Of pulse oximetry low alarms, 2421 (53.0%) had initial value above 88%.

CONCLUSIONS

Alarm sound load, from individual alarm sounds, was nearly continuous in an emergency department observation room equipped with nine monitors. Intervention by the staff to the alarms was infrequent. More than half of the alarms were momentary.

An Evidence-Based Initiative to Reduce Alarm Fatigue in a Burn Intensive Care Unit

BACKGROUND

Alarm fatigue occurs when nurses are exposed to multiple alarms of mixed significance and become desensitized to alarms to the point that a critical alarm may receive no response or a delayed response. In burn intensive care units, reducing the risk of alarm fatigue is uniquely challenging because of the critically ill patient population and the nature of burn skin injuries. Nurses and the interdisciplinary team can become fatigued and desensitized to alarms, decreasing response rates for necessary interventions.

OBJECTIVE

To decrease the risk of alarm fatigue by using an initiative designed to reduce nonactionable and false alarms in a burn intensive care unit.

METHODS

Baseline data (alarm count per patient-day by alarm type) were collected for 1 month before education and implementation of evidence-based interventions. Data were collected every 6 months for 2 years.

INTERVENTIONS

A series of interventions included raising awareness of the risks associated with alarm fatigue, customizing alarm parameters and default settings, providing education on electrode placement and daily electrode changes, using physical reminders, and consistently sharing alarm data. The education, delivered in modules, aligned with the evidence-based interventions.

RESULTS

Preintervention baseline data were compared to postintervention data at 6, 12, 18, and 24 months. The results showed a significantly sustained reduction (P < .001) in total alarm rate over time.

CONCLUSION

A quality improvement initiative based on evidence-based practice can contribute to a sustainable reduction in nonactionable and false alarms, ultimately improving patient safety.

A Framework to Assess Alarm Fatigue Indicators in Critical Care Staff

This article examines work-related and Personality personality factors that could influence health providers in experiencing alarm fatigue. The purpose of this study is to provide a basis to determine factors that may predict the potential of alarm fatigue in critical care staff.

DESIGN

A questionnaire-based survey and an observational study were conducted to assess factors that could contribute to indicators of alarm fatigue.

INTERVENTIONS

Factors included patient-to-staff ratio, criticality of the alarm, priority of different tasks, and personality traits.

SETTING

The study was conducted at an eight-bed ICU in a mid-size hospital in Montana.

SUBJECTS

Data were collected for six day shifts and six night shifts involving 24 critical care professionals. Within each 12-hour shift, six 15-minute intervals were randomly generated through work sampling for 6 days; a total of 1,080 observations were collected.

MEASUREMENTS

Alarm fatigue was assessed with the subjective workload assessment technique and Boredom, Apathy, and Distrust Affects, which were measured through validated questionnaires. The Big Five Personality model was used to assess personality traits.

MAIN RESULTS

Work factors including task prioritization, nurse-to-patient ratio, and length of shifts were associated with indicators of alarm fatigue. Personality traits of openness, conscientiousness, and neuroticism were also associated.

CONCLUSIONS

We recommend assessing personality traits for critical care staff to be aware of how their individualities can affect their behavior towards alarm fatigue. We also recommend an examination of alternative strategies to reduce alarm fatigue, including examining the use of breaks, work rotation, or shift reduction.