Nurses' alarm fatigue, influencing factors, and its relationship with burnout in the critical care units: A cross-sectional study

BACKGROUND

Excessive number of alarms and false and nonactionable alarms may lead to alarm fatigue. Alarm fatigue could easily contribute to burnout. Burnout may reduce nurses' sensitivity to alarms, thus affecting patients' safety due to insufficient response to the alarms. However, no study has examined nurses' alarm fatigue in Ghana.

OBJECTIVES

The objective of this study was to investigate the level of alarm fatigue and its associated factors, as well as determine its relationship with burnout among nurses working in the critical care units of hospitals in Ghana.

METHODS

The cross-sectional study was conducted in critical care units of five hospitals in Ghana from November 2021 to January 2022. A total of 364 nurses were recruited and completed the questionnaire. Alarm fatigue was assessed by the alarm fatigue questionnaire, which was originally developed in Chinese and was translated into English using a standard protocol. Burnout was assessed using the Maslach Burnout Inventory.

RESULTS

The overall alarm fatigue score was 76.43 ± 27.80 out of 124. Longer years working at the critical care unit (B = -2.50, 95% confidence interval [CI]: -4.62, -0.37) and having policies related to alarm management (B = -10.77, 95% CI: -3.50, -18.04) were associated with a decreased risk of alarm fatigue, while working in neonatal intensive care unit (B = 16.35, 95% CI: 2.48, 30.21) and postanesthesia care unit (B = 15.16; 95% CI: 0.32, 30.01), and having anxiety and stress (B = 8.15, 95% CI: 1.30, 15.00) were associated with an increased risk of alarm fatigue. In addition, alarm fatigue was positively associated with emotional exhaustion (r = 0.52, P < 0.001) and depersonalisation (r = 0.43, P < 0.001) but not personal accomplishment (r = -0.09, P = 0.100).

CONCLUSION

Critical care nurses in Ghana experienced higher levels of alarm fatigue, which is affected by multiple factors. There is a significant link between nurses' alarm fatigue and burnout. Our findings provide important guidance for future intervention programs to improve critical care nurses' alarm fatigue by introducing policies on alarm management and improving nurses' psychological health, with a special focus on nurses with shorter working years and working in neonatal intensive care unit and postanesthesia care unit.

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.

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.

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.

Adaptation and validation of the Turkish version of the alarm fatigue assessment questionnaire

OBJECTIVE

Alarm fatigue may endanger the safety of patients by negatively affecting nurses' concentration and ability to provide effective care. Identifying alarm fatigue and taking appropriate measures are critical in preventing medical errors and for nurses to work with high motivation. This study aimed to test the psychometric properties of the Turkish version of the 23-item Alarm Fatigue Assessment Questionnaire (AFAQ).

METHOD

The study was conducted between February 2022 and April 2022 and included nurses with at least one year of clinical or intensive care experience. The data were collected via a web-based questionnaire. During the adaptation of AFAQ, language, content, and construct validity were evaluated; reliability was examined by internal consistency analysis.

RESULTS

The item and scale content validity index of AFAQ were found to be high (>0.80). The Kaiser-Meyer-Olkin measure of sampling adequacy indicated an adequate sampling (0.85); Bartlett's test of sphericity χ2 was 1935.074, p<0.001. Exploratory factor analysis (EFA) showed that the 21-item scale had a five-factor structure, explaining 51.606% of the total variance, and the factor loadings of the items were >0.30 (0.422-0.803). Confirmatory factor analysis (CFA) showed that the five-factor model had a good fit index (χ2/df=1.855, SRMR=0.039, RMSEA=0.048, CFI=0.915, and TLI=0.908) and appropriate factor loadings (>0.30). The internal consistency of AFAQ (Cronbach's alpha coefficient) was 0.85, and the corrected item-total correlations were between 0.32-0.55.

CONCLUSION

The results indicated that the Turkish version of the Alarm Fatigue Assessment Questionnaire was sufficiently valid and reliable to measure alarm fatigue in nurses.

Battling Alarm Fatigue in the Pediatric Intensive Care Unit

Pediatric intensive care unit nurses can be exposed to hundreds of alarms per patient they care for each shift. The exposure to so many alarms can cause nurses to be desensitized to future alarms and thus increase the time to respond to alarms. This is one of the largest patient safety concerns within health care today. Steps should be taken to mitigate the number of alarms nurses experience so that they can properly respond to actionable alarms.

Datawarehouse-enabled quality control of atrial fibrillation detection in the stroke unit setting

Objective

(1) To assess the accuracy of a standard operating procedure (SOP) regarding the utilization of atrial fibrillation (AF) alarms in everyday clinical practice, and (2) to evaluate the performance of automated continuous surveillance for atrial fibrillation (AF) in hospitalized acute stroke patients.

Design

Retrospective cohort study.

Setting

Two stroke units from two tertiary care hospitals in Berlin, Germany.

Participants

We identified 635 patients with ischemic stroke diagnosis for the time period between 01. January and 30. September 2021 of which 176 patients had recorded AF alarms during monitoring. Of those, 115 patients were randomly selected for evaluation. After excluding 6 patients with hemorrhagic stroke in their records, 109 patients (mean age: 79.1 years, median NIHSS at admission: 6, 57% female) remained for analysis.

Intervention

Using a clinical data warehouse for comprehensive data storage we retrospectively downloaded and visualized ECG data segments of 65 s duration around the automated AF alarms. We restricted the maximum number of ECG segments to ten per patient. Each ECG segment plot was uploaded into a REDCap database and categorized as either AF, non-AF or artifact by manual review. Atrial flutter was subsumed as AF. These classifications were then matched with 1) medical history and known diseases before stroke, 2) discharge diagnosis, and 3) recommended treatment plan in the medical history using electronic health records.

Main outcome measures

The primary outcome was the proportion of previously unknown AF diagnoses correctly identified by the monitoring system but missed by the clinical team during hospitalization. Secondary outcomes included the proportion of patients in whom a diagnosis of AF would likely have led to anticoagulant therapy. We also evaluated the accuracy of the automated detection system in terms of its positive predictive value (PPV).

Results

We evaluated a total of 717 ECG alarm segments from 109 patients. In 4 patients (3.7, 95% confidence interval [CI] 1.18-9.68%) physicians had missed AF despite at least one true positive alarm. All four patients did not receive long-term secondary prevention in form of anticoagulant therapy. 427 out of 717 alarms were rated true positives, resulting in a positive predictive value of 0.6 (CI 0.56-0.63) in this cohort.

Conclusion

By connecting a data warehouse, electronic health records and a REDCap survey tool, we introduce a path to assess the monitoring quality of AF in acute stroke patients. We find that implemented standards of procedure to detect AF during stroke unit care are effective but leave room for improvement. Such data warehouse-based concepts may help to adjust internal processes or identify targets of further investigations.

Conception and Development of a Targeted Alert System: Multisystem Considerations

Alerting systems have a strong potential to improve quality of care in hospital by ensuring that clinicians provide more effective and timely care to their patients. Many systems have been implemented but often fail to unleash their full potential due to the problem of alert fatigue. As an attempt to reduce this fatigue we have developed a targeted alerting system ensuring only the concerned clinicians receives the alerts. The conception of the system went through several steps going from the identification of the requirement, the prototyping and implementation into several systems. The results present the different parameters taken into consideration and developed frontends. We finally discuss the important considerations of alerting system, such as the necessity of a governance. The system still needs a formal evaluation to validate that it responds to its promises before being deployed more largely.

Comparing Continuous with Periodic Vital Sign Scoring for Clinical Deterioration Using a Patient Data Model

To evaluate a minute-by-minute monitoring algorithm against a periodic early warning score (EWS) in detecting clinical deterioration and workload. Periodic EWSs suffer from large measurement intervals, causing late detection of deterioration. This might be prevented by continuous vital sign monitoring with a real-time algorithm such as the Visensia Safety Index (VSI). This prospective comparative data modeling cohort study (NCT04189653) compares continuous algorithmic alerts against periodic EWS in continuous monitored medical and surgical inpatients. We evaluated sensitivity, frequency, number of warnings needed to evaluate (NNE) and time of initial alert till escalation of care (EOC): Rapid Response Team activation, unplanned ICU admission, emergency surgery, or death. Also, the percentage of VSI alerting minutes was compared between patients with or without EOC. In 1529 admissions continuous VSI warned for 55% of EOC (95% CI: 45-64%) versus 51% (95% CI: 41-61%) by periodic EWS. NNE for VSI was 152 alerts per detected EOC (95% CI: 114-190) compared to 21 (95% CI: 17-28). It generated 0.99 warnings per day per patient compared to 0.13. Time from detection score till escalation was 8.3 hours (IQR: 2.6-24.8) with VSI versus 5.2 (IQR: 2.7-12.3) hours with EWS (P=0.074). The percentage of warning VSI minutes was higher in patients with EOC than in stable patients (2.36% vs 0.81%, P<0.001). Although sensitivity of detection was not significantly improved continuous vital sign monitoring shows potential for earlier alerts for deterioration compared to periodic EWS. A higher percentage of alerting minutes may indicate risk for deterioration.

Multisensory alarm to benefit alarm identification and decrease workload: a feasibility study

The poor design of conventional auditory medical alarms has contributed to alarm desensitization, and eventually, alarm fatigue in medical personnel. This study tested a novel multisensory alarm system which aims to help medical personnel better interpret and respond to alarm annunciation during periods of high cognitive load such as those found within intensive care units. We tested a multisensory alarm that combined auditory and vibrotactile cues to convey alarm type, alarm priority, and patient identity. Testing was done in three phases: control (conventional auditory), Half (limited multisensory alarm), and Full (complete multisensory alarm). Participants (N = 19, undergraduates) identified alarm type, priority, and patient identity (patient 1 or 2) using conventional and multisensory alarms, while simultaneously completing a cognitively demanding task. Performance was based on reaction time (RT) and identification accuracy of alarm type and priority. Participants also reported their perceived workload. RT was significantly faster for the Control phase (p < 0.05). Participant performance in identifying alarm type, priority, and patient did not differ significantly between the three phase conditions (p = 0.87, 0.37, and 0.14 respectively). The Half multisensory phase produced the lowest mental demand, temporal demand, and overall perceived workload score. These data suggest that implementation of a multisensory alarm with alarm and patient information may decrease perceived workload without significant changes in alarm identification performance. Additionally, a ceiling effect may exist for multisensory stimuli, with only part of an alarm benefitting from multisensory integration.

Improved Patient Monitoring with a Novel Multisensory Smartwatch Application

The design of medical alarms has been heavily criticized in the past decade. Auditory medical alarms have poor learnability, discernibility, and relevance, leading to poor patient outcomes, and alarm fatigue, and overall poor informatic system design. We developed a novel trimodal patient monitoring smartwatch application for patient monitoring. Participants completed two phases: (1) control and (2) our novel trimodal system while identifying alarms (heart rate, oxygenation, and blood pressure) and completing a cognitively demanding task. Alarms were auditory icons presented as either solo or co-alarms. Participant performance was assessed by accuracy and response time (RT) of alarm identification. Using the novel system, accuracy was significantly improved overall (p < 0.01) and in co-alarm situations (p < 0.01), but not for solo alarms (p = 0.484). RT was also significantly faster (p < 0.01) while using the novel system for all alarm types. Participants reported decreased mental workload using the novel system. This feasibility study shows that our novel alarm system performs better than current standards. Improvements in accuracy, RT and perceived mental workload indicate the potential of this system to have a positive impact on medical informatic systems and clinical monitoring, for both the patient and the clinician.

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.

ECG alarms during left ventricular assist device (LVAD) therapy in the ICU

BACKGROUND

In hospitalized patients with left ventricular assist device (LVAD), electrical interference and low amplitude QRS complexes are common, which could impact the accuracy of electrocardiographic (ECG) arrhythmia detection and create technical alarms. This could contribute to provider alarm fatigue and threaten patient safety.

OBJECTIVES

We examined three LVAD patients in the cardiac intensive care unit (ICU) to determine: 1) the frequency and accuracy of audible arrhythmia alarms; 2) occurrence rates of technical alarms; and 3) alarm burden (# alarms/hour of monitoring) METHODS: Secondary analysis.

RESULTS

During 593 h, there were 549 audible arrhythmia alarms and 98% were false. There were 25,232 technical alarms and 93% were for artifact, which was configured as an inaudible text alert.

CONCLUSION

False-arrhythmia and technical alarms are frequent in LVAD patients. Future studies are needed to identify both clinical and algorithm-based strategies to improve arrhythmia detection and reduce technical alarms in LVAD patients.

Alarms in a neurocritical care unit: a prospective study

The contemporary practice of monitoring physiologic parameters in the critical care setting is based on alarm systems with high sensitivity but low specificity. A natural consequence of this approach is a massive amount of alarms, which potentially leads to fatigue in the personnel and negatively impacts the quality of care provided. The study objective is to determine the prevalence, types, and determinants of alarms in a neurological critical care unit (NCCU) prototype. During a one-month period corresponding to 272 days of monitoring in 34 patients, nursing staff recorded the type and number of sounding alarms in a university NCCU. Alarms were categorized into three types as type-A alarms that were merely handled by the nursing staff, type-B alarms that were primarily managed by nurses, but the physician was also notified, and type-C alarms that were principally handled by NCCU physicians. There were a total of 9439 alarms, with an average of daily 34.7 alarms per bed, corresponding to one alarm every 41.4 min. Most of the alarms were type-A (57.7%), followed by type-B (39.2%) and type-C (3.1%) alarms. Alarms originated from electrocardiogram (34.6%), pulse oximeter (33.7%), noninvasive blood pressure monitoring (9.8%), respiratory monitoring (9.7%), intravenous fluid pumps (4.5%), ventilator (3.9%), enteral pumps (2.1%) and invasive blood pressure systems (1.7%). A noticeable diurnal variation was observed for type-A pulse oximeter, type-A and -B ECG alarms (increase during morning shifts), and type-A ventilator alarms (decrease during morning shifts). Alarms are highly prevalent in NCCUs and can correspond to an important portion of the workload.

Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection

BACKGROUND

Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos.

METHODS

We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices.

RESULTS

A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical.

CONCLUSIONS

The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.

False Asystole Alarms Post-Temporary Pacemaker Placement Due to Pseudo-fusion

An infant with congenital heart block and hemodynamically significant bradycardia underwent therapeutic temporary pacing wires placement. Post-operatively, frequent "asystole" alarms were observed on telemetry causing distress to both the family and the nursing staff. Investigation of these alarms showed that pacemaker malfunction led to monitor pseudo-malfunction. The alarms were alleviated with mindful setting of the pacemaker and telemetry monitor parameters. This case highlights the challenges of pacemaker placement and monitoring of very small infants in the intensive care setting. Awareness of these challenges would help in troubleshooting pacemaker and telemetry monitor issues.

The intersection of hand hygiene, infusion pump contamination, and high alarm volume in the health care environment

BACKGROUND

Researchers have found that lack of hand hygiene and environmental contamination are sources of infection transmission in the health care environment. One factor that may lead to lack of hand hygiene is alarm fatigue, the sensory overload that results when clinicians are exposed to an excessive number of alarms, causing them to silence alarms without taking proper precautions. In this study, we report hand hygiene compliance and infusion pump contamination in the context of infusion pump alarm prevalence.

METHODS

Health care worker hand hygiene audits were conducted to determine percent compliance. Cultures were obtained from infusion pumps to determine environmental contamination. The frequency of alarms from August 4, 2019 to September 7, 2019 was determined.

RESULTS

Hand hygiene compliance ranged from 50% to 87%. Pump contamination ranged from 20% to 70% per unit. A total of 116, 872 infusion pump alarms sounded in the hospital.

DISCUSSION

Pumps were contaminated primarily with skin flora. This was demonstrated in the context of poor hand hygiene compliance and a high number of alarms, indicative of alarm fatigue.

CONCLUSIONS

The intersection of a high prevalence of infusion pump alarms and poor hand hygiene resulting in bacterial contamination of pumps could be a source of health care-associated infection transmission for patients.

Thresholds for oximetry alarms and target range in the NICU: an observational assessment based on likely oxygen tension and maturity

BACKGROUND

Continuous monitoring of SpO₂ in the neonatal ICU is the standard of care. Changes in SpO₂ exposure have been shown to markedly impact outcome, but limiting extreme episodes is an arduous task. Much more complicated than setting alarm policy, it is fraught with balancing alarm fatigue and compliance. Information on optimum strategies is limited.

METHODS

This is a retrospective observational study intended to describe the relative chance of normoxemia, and risks of hypoxemia and hyperoxemia at relevant SpO₂ levels in the neonatal ICU. The data, paired SpO₂-PaO₂ and post-menstrual age, are from a single tertiary care unit. They reflect all infants receiving supplemental oxygen and mechanical ventilation during a 3-year period. The primary measures were the chance of normoxemia (PaO₂ 50-80 mmHg), risks of severe hypoxemia (PaO₂ ≤ 40 mmHg), and of severe hyperoxemia (PaO₂ ≥ 100 mmHg) at relevant SpO₂ levels.

RESULTS

Neonates were categorized by postmenstrual age: < 33 (n = 155), 33-36 (n = 192) and > 36 (n = 1031) weeks. From these infants, 26,162 SpO₂-PaO₂ pairs were evaluated. The post-menstrual weeks (median and IQR) of the three groups were: 26 (24-28) n = 2603; 34 (33-35) n = 2501; and 38 (37-39) n = 21,058. The chance of normoxemia (65, 95%-CI 64-67%) was similar across the SpO₂ range of 88-95%, and independent of PMA. The increasing risk of severe hypoxemia became marked at a SpO₂ of 85% (25, 95%-CI 21-29%), and was independent of PMA. The risk of severe hyperoxemia was dependent on PMA. For infants < 33 weeks it was marked at 98% SpO₂ (25, 95%-CI 18-33%), for infants 33-36 weeks at 97% SpO₂ (24, 95%-CI 14-25%) and for those > 36 weeks at 96% SpO₂ (20, 95%-CI 17-22%).

CONCLUSIONS

The risk of hyperoxemia and hypoxemia increases exponentially as SpO₂ moves towards extremes. Postmenstrual age influences the threshold at which the risk of hyperoxemia became pronounced, but not the thresholds of hypoxemia or normoxemia. The thresholds at which a marked change in the risk of hyperoxemia and hypoxemia occur can be used to guide the setting of alarm thresholds. Optimal management of neonatal oxygen saturation must take into account concerns of alarm fatigue, staffing levels, and FiO₂ titration practices.

Feasibility and Usability of Patch-based Continuous Cardiac Rhythm Monitoring in Comparison with Traditional Telemetry in Noncritically Ill Hospitalized Patients

Research on traditional cardiac telemetry demonstrates that excessive alarms are related to lead failures and noise-related interruptions. Patch-based continuous cardiac rhythm monitoring (CCRM) has emerged in outpatient ambulatory monitoring situations as a means to improve recording fidelity. In this study, patients hospitalized but not in the intensive care unit were simultaneously monitored via telemetry in parallel with the use of the Vital Signs Patch™ (VSP) CCRM system (LifeWatch Services, Rosemont, IL, USA), applying standardized monitoring and notifications provided by an off-site central monitoring unit (CMU). Among 11 patients (55% male; age: 66.8 ± 12.5 years), there were 42 CMU detections and 98 VSP detections. The VSP device was successfully applied by nursing with connectivity established in all 11 patients (100%). There were no VSP device–related adverse events or skin eruptions during the study. The CMU agreed with 59 (60%) of 98 VSP detections. Among those detections marked by disagreement 30 (77%) of 39 VSP detections were related to clinically meaningful arrhythmias (atrial: n = 9; ventricular: n = 7; brady-: n = 14) undetected by VSP due to noise. In two patients (18%), there were four clinically meaningful atrial fibrillation detections not recorded by the CMU. In conclusion, patch-based CCRM requires further development and review to replace traditional cardiac telemetry monitoring but could evolve into an appropriate method to detect clinically meaningful events missed by traditional methods if noise issues can be mitigated.

Evaluation of clinician interaction with alerts to enhance performance of the tele-critical care medical environment

OBJECTIVE

Identify opportunities to improve the interaction between clinicians and Tele-Critical Care (Tele-CC) programs through an analysis of alert occurrence and reactivation in a specific Tele-CC application.

MATERIALS AND METHODS

Data were collected automatically through the Philips eCaremanager® software system used at multiple hospitals in the Avera health system. We evaluated the distribution of alerts per patient, frequency of alert types, time between consecutive alerts, and Tele-CC clinician choice of alert reactivation times.

RESULTS

Each patient generated an average of 79.8 alerts during their ICU stay (median 31.0; 25th - 75th percentile 10.0-89.0) with 46.4 for blood pressure and 38.4 for oxygenation. The most frequent alerts for continuous physiological parameters were: MAP limit (28.9 %), O₂/RR (26.4 %), MAP trend (16.5 %), HR trend (12.1 %), and HR limit (11.3 %). The median time between consecutive alerts for one parameter was less than 10 min for 86 % of patients. Tele-CC providers responded to all alert types with immediate reactivation 47-88 % of the time. Limit alerts had longer reactivation times than their trend alert counterparts (p-value < .001).

CONCLUSIONS

The alert type specific differences in frequency, time occurrence and provider choice of reactivation time provide insight into how clinicians interact with the Tele-CC system. Systems engineering enhancements to Tele-CC software algorithms may reduce alert burden and thereby decrease clinicians' cognitive workload for alert assessment. Further study of Tele-CC alert generation, alert presentation to clinicians, and the clinicians' options to respond to these alerts may reduce provider workload, minimize alert desensitization, and optimize the ability of Tele-CC clinicians to provide efficient and timely critical care management.

Customizing Physiologic Alarms in the Emergency Department: A Regression Discontinuity, Quality Improvement Study

INTRODUCTION

Clinical alarms promote patient safety by alerting clinicians when there is an indication or change in a condition requiring a response. An excessive volume of alarm fires, however, contributes to sensory overload and desensitization, referred to as alarm fatigue, which has significant implications when alarms are missed. This evidence-based, practice project aimed to implement and evaluate a program that reduces the number of clinically nonactionable, physiologic alarms in an emergency department. Although alarm fatigue is an important negative consequence, the focus of this project is not on alarm fatigue but on measures to reduce the volume of clinically nonactionable alarms that lead to alarm fatigue. The Iowa Model was used as a conceptual framework.

METHODS

This project involved adjusting default alarm settings and implementing an education plan on the safe use of alarms. The sample population included all patients on physiologic monitors at an emergency department. Retrospective data were collected, and regression discontinuity design was applied to compare the rate of alarm fires triggered by the physiologic monitor between pre- and postimplementation of an alarm protocol.

RESULTS

A significant change in the rate of alarm fires occurred with an estimated reduction of 14.96 (P = 0.003). There were no reports of adverse outcomes such as a delay in responding to a change in patient condition or delay leading to cardiopulmonary arrest.

DISCUSSION

A reduction in nonactionable, physiologic alarms was attained after implementing multimodal strategies inclusive of adjusting default settings, staff education on managing alarms, and emphasis on staff accountability.

Predictive Monitoring of Critical Cardiorespiratory Alarms in Neonates Under Intensive Care

We aimed at reducing alarm fatigue in neonatal intensive care units by developing a model using machine learning for the early prediction of critical cardiorespiratory alarms. During this study in over 34,000 patient monitoring hours in 55 infants 278,000 advisory (yellow) and 70,000 critical (red) alarms occurred. Vital signs including the heart rate, breathing rate, and oxygen saturation were obtained at a sampling frequency of 1 Hz while heart rate variability was calculated by processing the ECG - both were used for feature development and for predicting alarms. Yellow alarms that were followed by at least one red alarm within a short post-alarm window constituted the case-cohort while the remaining yellow alarms constituted the control cohort. For analysis, the case and control cohorts, stratified by proportion, were split into training (80%) and test sets (20%). Classifiers based on decision trees were used to predict, at the moment the yellow alarm occurred, whether a red alarm(s) would shortly follow. The best performing classifier used data from the 2-min window before the occurrence of the yellow alarm and could predict 26% of the red alarms in advance (18.4s, median), at the expense of 7% additional red alarms. These results indicate that based on predictive monitoring of critical alarms, nurses can be provided a longer window of opportunity for preemptive clinical action. Further, such as algorithm can be safely implemented as alarms that are not algorithmically predicted can still be generated upon the usual breach of the threshold, as in current clinical practice.

Alarm Fatigue: Using Alarm Data from a Patient Data Monitoring System on an Intensive Care Unit to Improve the Alarm Management

Excessive numbers of clinical alarms reduce the awareness of caregivers. Frequent alarms, many of which are non-actionable, can lead to cognitive overload, stress, and desensitization to alarms, called "Alarm Fatigue", which can severely impact patient safety. Due to the multifactorial nature of excessive alarming quantitative data about many facets of alarm generation and management are required in order to tackle the problem efficiently and effectively. Since there is no system available which would provide said data, we set out to develop one in the form of a data warehouse based on a thorough understanding of clinicians' needs. The developed system answers the users' needs in terms of readily providing them information on a daily basis, but also serves as a data source for further research. Further work is needed to include alarm sources from outside the patient monitoring infrastructure.

SAVIOR ICU: sonification and vibrotactile interface for the operating room and intensive care unit

Alarm fatigue is an issue for healthcare providers in the intensive care unit, and may result from desensitization of overbearing and under-informing alarms. To directly increase the overall identification of medical alarms and potentially contribute to a downstream decrease in the prevalence of alarm fatigue, we propose advancing alarm sonification by combining auditory and tactile stimuli to create a multisensory alarm. Participants completed four trials-two multisensory (auditory and tactile) and two unisensory (auditory). Analysis compared the unisensory trials to the multisensory trials based on the percentage of correctly identified point of change, direction of change and identity of three physiological parameters (indicated by different instruments): heart rate (drums), blood pressure (piano), blood oxygenation (guitar). A repeated-measures of ANOVA yielded a significant improvement in performance for the multisensory group compared to the unisensory group (p < 0.05). Specifically, the multisensory group had better performance in correctly identifying parameter (p < 0.05) and point of change (p < 0.05) compared to the unisensory group. Participants demonstrated a higher accuracy of identification with the use of multisensory alarms. Therefore, multisensory alarms may relieve the auditory burden of the medical environment and increase the overall quality of care and patient safety.

Supporting multiple patient monitoring with head-worn displays and spearcons

In hospitals, clinicians often need to monitor several patients while performing other tasks. However, visual displays that show patients' vital signs are in fixed locations and auditory alarms intended to alert clinicians may be missed. Information such as spearcons (time-compressed speech earcons) that 'travels' with the clinician and is delivered by earpiece and/or head-worn displays (HWDs), might overcome these problems. In this study, non-clinicians monitored five simulated patients in three 10-min scenarios while performing a demanding tracking task. Monitoring accuracy was better for participants using spearcons and a HWD (88.7%) or a HWD alone (86.2%) than for participants using spearcons alone (74.1%). Participants using the spearcons and HWD (37.7%) performed the tracking task no differently from participants using spearcons alone (37.1%) but participants using the HWD alone performed worse overall (33.1%). The combination of both displays may be a suitable solution for monitoring multiple patients.

Evaluation of two SpO2 alarm strategies during automated FiO2 control in the NICU: a randomized crossover study

Background

Changes in oxygen saturation (SpO₂) exposure have been shown to have a marked impact on neonatal outcomes and therefore careful titration of inspired oxygen is essential. In routine use, however, the frequency of SpO₂ alarms not requiring intervention results in alarm fatigue and its corresponding risk. SpO₂ control systems that automate oxygen adjustments (Auto-FiO₂) have been shown to be safe and effective. We speculated that when using Auto-FiO₂, alarm settings could be refined to reduce unnecessary alarms, without compromising safety.

Methods

An unblinded randomized crossover study was conducted in a single NICU among infants routinely managed with Auto-FiO₂. During the first 6 days of respiratory support a tight and a loose alarm strategy were switched each 24 h. A balanced block randomization was used. The tight strategy set the alarms at the prescribed SpO₂ target range, with a 30-s delay. The loose strategy set the alarms 2 wider, with a 90-s delay. The effectiveness outcome was the frequency of SpO₂ alarms, and the safety outcomes were time at SpO₂ extremes (< 80, > 98%). We hypothesized that the loose strategy would result in a marked decrease in the frequency of SpO₂ alarms, and no increases at SpO₂ extremes with 20 subjects. Within subject differences between alarm strategies for the primary outcomes were evaluated with Wilcoxon signed-rank test.

Results

During a 13-month period 26 neonates were randomized. The analysis included 21 subjects with 49 days of both tight and loose intervention. The loose alarm strategy resulted in a reduction in the median rate of SpO₂ alarms from 5.2 to 1.6 per hour (p <  0.001, 95%-CI difference 1.6–3.7). The incidence of hypoxemia and hyperoxemia were very low (less than 0.1%-time) with no difference associated with the alarm strategy (95%-CI difference less than 0.0–0.2%).

Conclusions

In this group of infants we found a marked advantage of the looser alarm strategy. We conclude that the paradigms of alarm strategies used for manual titration of oxygen need to be reconsidered when using Auto-FiO₂. We speculate that with optimal settings false positive SpO₂ alarms can be minimized, with better vigilance of clinically relevant alarms.

Trial registration

Retrospectively registered 15 May 2018 at ISRCTN (49239883).

Electronic supplementary material

The online version of this article (10.1186/s12887-019-1496-5) contains supplementary material, which is available to authorized users.

Incorporating Observed Physiological Data to Personalize Pediatric Vital Sign Alarm Thresholds

Bedside monitors are intended as a safety net in patient care, but their management in the inpatient setting is a significant patient safety concern. The low precision of vital sign alarm systems leads to clinical staff becoming desensitized to the sound of the alarm, a phenomenon known as alarm fatigue. Alarm fatigue has been shown to increase response time to alarms or result in alarms being ignored altogether and has negative consequences for patient safety. We present methods to establish personalized thresholds for heart rate and respiratory rate alarms. These thresholds are first chosen based on patient characteristics available at the time of admission and are then adapted to incorporate vital signs observed in the first 2 hours of monitoring. We demonstrate that the adapted thresholds are similar to those chosen by clinicians for individual patients and would result in fewer alarms than the currently used age-based thresholds. Personalized vital sign alarm thresholds can help to alleviate the problem of alarm fatigue in an inpatient setting while ensuring that all critical vital signs are detected.

The impact of workload on the ability to localize audible alarms

Very little is known about people's ability to localize sound under varying workload conditions, though it would be expected that increasing workload should degrade performance. A set of eight auditory clinical alarms already known to have relatively high localizability (the ease with which their location is identified) when tested alone were tested in six conditions where workload was varied. Participants were required to indicate the location of a series of alarms emanating at random from one of eight speaker locations. Additionally, they were asked to read, carry out mental arithmetic tasks, be exposed to typical ICU noise, or carry out either the reading task or the mental arithmetic task in ICU noise. Performance in the localizability task was best in the control condition (no secondary task) and worst in those tasks which involved both a secondary task and noise. The data does therefore demonstrate the typical pattern of increasing workload affecting a primary task in an area where there is little data. In addition, the data demonstrates that performance in the control condition results in a missed alarm on one in ten occurrences, whereas performance in the heaviest workload conditions results in a missed alarm on every fourth occurrence. This finding has implications for the understanding of both 'inattentional deafness' and 'alarm fatigue' in clinical environments.

Nurse-Technology Interactions and Patient Safety

Nurses are the end-users of most technology in intensive care units, and the ways in which they interact with technology affect quality of care and patient safety. Nurses' interactions include the processes of ensuring proper input of data into the technology as well as extracting and interpreting the output (clinical data, technical data, alarms). Current challenges in nurse-technology interactions for physiologic monitoring include issues regarding alarm management, workflow interruptions, and monitor surveillance. Patient safety concepts, like high reliability organizations and human factors, can advance efforts to enhance nurse-technology interactions.

Critical Care Nurses' Cognitive Ergonomics Related to Medical Device Alarms

This study uniquely gained insight into the intricacy of intensive care nurses' decision-making process when responding to and managing device alarms. Difficulty in responding to alarms included low staffing, multiple job responsibilities, and competing priority tasks. Novice nurses are more tolerant of alarms sounding owing to a lower threshold of comfort with resetting or silencing alarms; more experienced nurses are more comfortable resetting alarm limits to the patient's baseline. Understanding the decision-making process used by nurses can guide the development of policies and learning experiences that are crucial clinical support for alarm management.

Alarm Safety and Alarm Fatigue

Clinical alarm systems have received significant attention in recent years following warnings from hospital accrediting and health care technology organizations regarding patient harm caused by unsafe practices. Alarm desensitization or fatigue from frequent, false, or unnecessary alarms, has led to serious events and even patient deaths. Other concerns include settings inappropriate to patient population or condition, inadequate staff training, and improper use or disabling. Research on human factors in alarm response and of functionality of medical devices will help clinicians develop appropriate policies, practices, and device settings for clinical alarms in neonatal intensive care units.

Intelligent use of advanced capabilities of diagnostic ECG algorithms in a monitoring environment

A large number of ST-elevation notifications are generated by cardiac monitoring systems, but only a fraction of them is related to the critical condition known as ST-segment elevation myocardial infarction (STEMI) in which the blockage of coronary artery causes ST-segment elevation. Confounders such as acute pericarditis and benign early repolarization create electrocardiographic patterns mimicking STEMI but usually do not benefit from a real-time notification. A STEMI screening algorithm able to recognize those confounders utilizing capabilities of diagnostic ECG algorithms in variation analysis of ST segments helps to avoid triggering a non-actionable ST-elevation notification. However, diagnostic algorithms are generally designed to analyze short ECG snapshots collected in low-noise resting position and hence are susceptible to high levels of noise common in a monitoring environment. We developed a STEMI screening algorithm which performs a real-time signal quality evaluation on the ECG waveform to select the segments with quality high enough for subsequent analysis by a diagnostic ECG algorithm. The STEMI notifications generated by this multi-stage STEMI screening algorithm are significantly fewer than ST-elevation notifications generated by a continuous ST monitoring strategy.

A multidisciplinary approach to reducing alarm fatigue and cost through appropriate use of cardiac telemetry

BACKGROUND

Alarm fatigue (AF) is a distressing factor for staff and patients in the hospital. Using cardiac telemetry (CT) without clinical indications can create unnecessary alarms, and increase AF and cost of healthcare. We sought to reduce AF and cost associated with CT monitoring.

METHODS

After implementing a new protocol for CT placement, data were collected on telemetry orders, alarms and bed cost for 13 weeks from 1 January 2015 through 31 March 2015. We also retrospectively collected data on the same variables for the 13 weeks prior to the intervention. A survey was administered to nurses to assess past and present perceptions of AF. Interventions included protocol creation and education for participants.

RESULTS

At baseline, 77% of patients were monitored with CT. A total of 145 (31%) order discrepancies were discovered during data collection, of which 72% had no indication for CT, so CT was discontinued. The other 28% had indications, so orders were placed. A total of 8336 alarms were recorded during 4 weeks of data collection, of which 333 (4%) were classified as true actionable alarms. Postintervention data showed 67% CT assignment with 10% reduction in CT usage, with no increase in mortality (p<0.001 and >0.05, respectively). A 42% cost reduction was achieved after adjusting the patient status. Nurses reported 27% perceived reduction in AF. One-year follow-up revealed that 69% of patients were being monitored by CT, and the rate of order discrepancies due to lack of indication was 9%.

CONCLUSION

All hospital units may benefit from the protocols created during this study. If applied appropriately, these protocols can lead to reduced AF and cost per episode of care.

Nurse Competence on Physiologic Monitors Use: Toward Eliminating Alarm Fatigue in Intensive Care Units

BACKGROUND

Studies on nurse competence on alarm management are a few and tend to be focused on limited skills. In response to Phase II of implementing the National Patient Safety Goal on clinical alarm systems safety, this study assessed nurses' perceived competence on physiologic monitors use in intensive care units (ICUs) and developed and validated a tool for this purpose.

METHODS

This descriptive study took place in a Magnet hospital in a Southwestern state of the U.S. A Nurse Competence on Philips Physiologic Monitors Use Survey was created and went through validation by 13 expert ICU nurses. The survey included 5 subscales with 59 rated items and two open-ended questions. Items on the first 4 subscales reflect most common tasks nurses perform using physiologic monitors. Items on the fifth subscale (advanced functions) reflect rarely used skills and were included to understand the scope of utilizing advanced physiologic monitors' features. Thirty nurses from 4 adult ICUs were invited to respond to the survey.

RESULTS

Thirty nurses (100%) responded to the survey. The majority of nurses were from Neuro (47%) and Surgical Trauma (37%) ICUs. The data supported the high reliability and construct validity of the survey. At least one (3%) to 8 nurses (27%) reported lack of confidence on each item on the survey. On the first four subscales, 3% - 40% of the nurses reported they had never heard of or used 27 features/functions on the monitors. No relationships were found between subscales' scores and demographic characteristics (p > .05). Nurses asked for training on navigating the central-station monitor and troubleshooting alarms, and the use of unit-specific super users to tailor training to users' needs.

CONCLUSION

This is the first study to create and test a list of competencies for physiologic monitors use. Rigorous, periodic and individualized training is essential for safe and appropriate use of physiologic monitors and to decrease alarm fatigue. Training should be comprehensive to include all necessary skills and should not assume proficiency on basic skills. Special attention should be focused on managing technical alarms. Increasing the number of super users is a recommended strategy for individualized and unit-specific training. There is a need for a usability testing of complex IT-equipped medical devices, such as physiologic monitors, for effective, efficient and safe navigation of the monitors.

Alarm Fatigue: Causes and Effects

The term "Alarm fatigue" is commonly used to describe the effect which a high number of alarms can have on caregivers: Frequent alarms, many of which are avoidable, can lead to inadequate responses, severely impacting patient safety. In the first step of a long-term effort to address this problem, both the direct and indirect impact of alarms, as well as possible causes of unnecessary alarms were focused. Models of these causes and impacts were developed using a scoping review which included guided interviews with experts from medical informatics, clinicians and medical device manufacturers. These models can provide the methodical grounds for the definition of targeted interventions and the assessment of their effects.

US and Dutch nurse experiences with fall prevention technology within nursing home environment and workflow: A qualitative study

Falls remain a major geriatric problem, and the search for new solutions continues. We investigated how existing fall prevention technology was experienced within nursing home nurses' environment and workflow. Our NIH-funded study in an American nursing home was followed by a cultural learning exchange with a Dutch nursing home. We constructed two case reports from interview and observational data and compared the magnitude of falls, safety cultures, and technology characteristics and effectiveness. Falls were a high-magnitude problem at the US site, with a collectively vigilant safety culture attending to non-directional audible alarms; falls were a low-magnitude problem at the NL site which employed customizable, infrared sensors that directed text alerts to assigned staff members' mobile devices in patient-centered care culture. Across cases, 1) a coordinated communication system was essential in facilitating effective fall prevention alert response, and 2) nursing home safety culture is tightly associated with the chosen technological system.

A nurses' alarm fatigue questionnaire: development and psychometric properties

Alarm fatigue can adversely affect nurses' efficiency and concentration on their tasks, which is a threat to patients' safety. The purpose of the present study was to develop and test the psychometric accuracy of an alarm fatigue questionnaire for nurses. This study was conducted in two stages: in stage one, in order to establish the different aspects of the concept of alarm fatigue, the researchers reviewed the available literature-articles and books-on alarm fatigue, and then consulted several experts in a meeting to define alarm fatigue and develop statements for the questionnaire. In stage two, after the final draft had been approved, the validity of the instrument was measured using the two methods of face validity (the quantitative and qualitative approaches) and content validity (the qualitative and quantitative approaches). Test-retest, Cronbach's alpha, and Principal Component Analysis were used for item reduction and reliability analysis. Based on the results of stage one, the researchers extracted 30 statements based on a 5-point Likert scale. In stage two, after the face and content validity of the questionnaire had been established, 19 statements were left in the instrument. Based on factor loadings of the items and "alpha if item deleted" and after the second round of consultation with the expert panel, six items were removed from the scale. The test of the reliability of nurses' alarm fatigue questionnaire based on the internal homogeneity and retest methods yielded the following results: test-retest correlation coefficient = 0.99; Guttman split-half correlation coefficient = 0.79; Cronbach's alpha = 0.91. Regarding the importance of recognizing alarm fatigue in nurses, there is need for an instrument to measure the phenomenon. The results of the study show that the developed questionnaire is valid and reliable enough for measuring alarm fatigue in nurses.

Retrospective analysis of pulse oximeter alarm settings in an intensive care unit patient population

Background

The cacophony of alerts and alarms in a hospital produced by medical devices results in alarm fatigue. The pulse oximeter is one of the most common sources of alarms. One of the ways to reduce alarm rates is to adjust alarm settings at the bedside. This study is aimed to retrospectively examine individual pulse oximeter alarm settings on alarm rates and inter- and intra- patient variability.

Methods

Nine hundred sixty-two previously collected intensive care unit (ICU) patient records were obtained from the Multiparameter Intelligent Monitoring in Intensive Care II Database (Beth Israel Deaconess Medical Center, Boston, MA). Inclusion criteria included patient records that contained SpO₂ trend data sampled at 1 Hz for at least 1 h and a matching clinical record. SpO₂ alarm rates were simulated by applying a range of thresholds (84, 86, 88, and 90 %) and delay times (10 to 60 s) to the SpO₂ data. Patient records with at least 12 h of SpO₂ data were examined for the variability in alarm rate over time.

Results

Decreasing SpO₂ thresholds and increasing delay times resulted in decreased alarm rates. A limited number of patient records accounted for most alarms, and this number increased as alarm settings loosened (the top 10 % of patient records were responsible for 57.4 % of all alarms at an SpO₂ threshold of 90 % and 15 s delay and 81.6 % at an SpO2 threshold of 84 % and 45 s delay). Alarm rates were not consistent over time for individual patients with periods of high and low alarms for all alarm settings.

Conclusion

Pulse oximeter SpO₂ alarm rates are variable between patients and over time, and the alarm rate and the extent of inter- and intra-patient variability can be affected by the alarm settings. Personalized alarm settings for a patient’s current status may help to reduce alarm fatigue for nurses.

Electronic supplementary material

The online version of this article (doi:10.1186/s12912-016-0149-3) contains supplementary material, which is available to authorized users.

Reduction of clinically irrelevant alarms in patient monitoring by adaptive time delays

The problem of high rates of false alarms in patient monitoring in anesthesiology and intensive care medicine is well known but remains unsolved. False alarms desensitize the medical staff, leading to ignored true alarms and reduced quality of patient care. A database of intra-operative monitoring data was analyzed to find characteristic alarm patterns. The original data were re-evaluated to find relevant events and to rate the severity of these events. Based on this analysis an adaptive time delay was developed that individually delays the alarms depending on the grade of threshold deviation. The conventional threshold algorithm led to 4893 alarms. 3515 (71.84 %) of these alarms were annotated as clinically irrelevant. In total 81.0 % of all clinically irrelevant alarms were caused by only mild and/or brief threshold violations. We implemented the new algorithm for selected parameters. These parameters equipped with adaptive validation delays led to 1729 alarms. 931 (53.85 %) alarms were annotated as clinically irrelevant. 632 alarms indicated the 645 clinically relevant events. The positive predictive value of occurring alarms improved from 28.16 % (conventional algorithm) to 46.15 % (new algorithm). 13 events were missed. The false positive alarm reduction rate of the algorithm ranged from 33 to 86.75 %. The overall reduction was 73.51 %. The implementation of this algorithm may be able to suppress a large percentage of false alarms. The effect of this approach has not been demonstrated but shows promise for reducing alarm fatigue. Its safety needs to be proven in a prospective study.

Multi-parameter vital sign database to assist in alarm optimization for general care units

Continual vital sign assessment on the general care, medical-surgical floor is expected to provide early indication of patient deterioration and increase the effectiveness of rapid response teams. However, there is concern that continual, multi-parameter vital sign monitoring will produce alarm fatigue. The objective of this study was the development of a methodology to help care teams optimize alarm settings. An on-body wireless monitoring system was used to continually assess heart rate, respiratory rate, SpO₂ and noninvasive blood pressure in the general ward of ten hospitals between April 1, 2014 and January 19, 2015. These data, 94,575 h for 3430 patients are contained in a large database, accessible with cloud computing tools. Simulation scenarios assessed the total alarm rate as a function of threshold and annunciation delay (s). The total alarm rate of ten alarms/patient/day predicted from the cloud-hosted database was the same as the total alarm rate for a 10 day evaluation (1550 h for 36 patients) in an independent hospital. Plots of vital sign distributions in the cloud-hosted database were similar to other large databases published by different authors. The cloud-hosted database can be used to run simulations for various alarm thresholds and annunciation delays to predict the total alarm burden experienced by nursing staff. This methodology might, in the future, be used to help reduce alarm fatigue without sacrificing the ability to continually monitor all vital signs.

Integrating monitor alarms with laboratory test results to enhance patient deterioration prediction

Patient monitors in modern hospitals have become ubiquitous but they generate an excessive number of false alarms causing alarm fatigue. Our previous work showed that combinations of frequently co-occurring monitor alarms, called SuperAlarm patterns, were capable of predicting in-hospital code blue events at a lower alarm frequency. In the present study, we extend the conceptual domain of a SuperAlarm to incorporate laboratory test results along with monitor alarms so as to build an integrated data set to mine SuperAlarm patterns. We propose two approaches to integrate monitor alarms with laboratory test results and use a maximal frequent itemsets mining algorithm to find SuperAlarm patterns. Under an acceptable false positive rate FPRmax, optimal parameters including the minimum support threshold and the length of time window for the algorithm to find the combinations of monitor alarms and laboratory test results are determined based on a 10-fold cross-validation set. SuperAlarm candidates are generated under these optimal parameters. The final SuperAlarm patterns are obtained by further removing the candidates with false positive rate>FPRmax. The performance of SuperAlarm patterns are assessed using an independent test data set. First, we calculate the sensitivity with respect to prediction window and the sensitivity with respect to lead time. Second, we calculate the false SuperAlarm ratio (ratio of the hourly number of SuperAlarm triggers for control patients to that of the monitor alarms, or that of regular monitor alarms plus laboratory test results if the SuperAlarm patterns contain laboratory test results) and the work-up to detection ratio, WDR (ratio of the number of patients triggering any SuperAlarm patterns to that of code blue patients triggering any SuperAlarm patterns). The experiment results demonstrate that when varying FPRmax between 0.02 and 0.15, the SuperAlarm patterns composed of monitor alarms along with the last two laboratory test results are triggered at least once for [56.7-93.3%] of code blue patients within an 1-h prediction window before code blue events and for [43.3-90.0%] of code blue patients at least 1-h ahead of code blue events. However, the hourly number of these SuperAlarm patterns occurring in control patients is only [2.0-14.8%] of that of regular monitor alarms with WDR varying between 2.1 and 6.5 in a 12-h window. For a given FPRmax threshold, the SuperAlarm set generated from the integrated data set has higher sensitivity and lower WDR than the SuperAlarm set generated from the regular monitor alarm data set. In addition, the McNemar's test also shows that the performance of the SuperAlarm set from the integrated data set is significantly different from that of the SuperAlarm set from the regular monitor alarm data set. We therefore conclude that the SuperAlarm patterns generated from the integrated data set are better at predicting code blue events.

False ventricular tachycardia alarm suppression in the ICU based on the discrete wavelet transform in the ECG signal

Over the past few years, reducing the number of false positive cardiac monitor alarms (FA) in the intensive care unit (ICU) has become an issue of the utmost importance. In our work, we developed a robust methodology that, without the need for additional non-ECG waveforms, suppresses false positive ventricular tachycardia (VT) alarms without resulting in false negative alarms. Our approach is based on features extracted from the ECG signal 20 seconds prior to a triggered alarm. We applied a multi resolution wavelet transform to the ECG data 20seconds prior to the alarm trigger, extracted features from appropriately chosen scales and combined them across all available leads. These representations are presented to a L1-regularized logistic regression classifier. Results are shown in two datasets of physiological waveforms with manually assessed cardiac monitor alarms: the MIMIC II dataset, where we achieved a false alarm (FA) suppression of 21% with zero true alarm (TA) suppression; and a dataset compiled by UCSF and General Electric, where a 36% FA suppression was achieved with a zero TA suppression. The methodology described in this work could be implemented to reduce the number of false monitor alarms in other arrhythmias.

Alarm setting for the critically ill patient: a descriptive pilot survey of nurses' perceptions of current practice in an Australian Regional Critical Care Unit

AIM

The aim of this survey was to assess registered nurse's perceptions of alarm setting and management in an Australian Regional Critical Care Unit.

BACKGROUND

The setting and management of alarms within the critical care environment is one of the key responsibilities of the nurse in this area. However, with up to 99% of alarms potentially being false-positives it is easy for the nurse to become desensitised or fatigued by incessant alarms; in some cases up to 400 per patient per day. Inadvertently ignoring, silencing or disabling alarms can have deleterious implications for the patient and nurse.

METHOD

A total population sample of 48 nursing staff from a 13 bedded ICU/HDU/CCU within regional Australia were asked to participate. A 10 item open-ended and multiple choice questionnaire was distributed to determine their perceptions and attitudes of alarm setting and management within this clinical area.

RESULTS

Two key themes were identified from the open-ended questions: attitudes towards inappropriate alarm settings and annoyance at delayed responses to alarms. A significant number of respondents (93%) agreed that alarm fatigue can result in alarm desensitisation and the disabling of alarms, whilst 81% suggested the key factors are those associated with false-positive alarms and inappropriately set alarms.

CONCLUSION

This study contributes to what is known about alarm fatigue, setting and management within a critical care environment. In addition it gives an insight as to what nurses' within a regional context consider the key factors which contribute to alarm fatigue. Clearly nursing burnout and potential patient harm are important considerations for practice especially when confronted with alarm fatigue and desensitisation. Therefore, promoting and maintaining an environment of ongoing intra-professional communication and alarm surveillance are crucial in alleviating these potential problems.

A case-control study of non-monitored ECG metrics preceding in-hospital bradyasystolic cardiac arrest: implication for predictive monitor alarms

OBJECTIVES

We investigated whether additional electrocardiographic (ECG) metrics not available on current patient monitors could predict bradyasystolic cardiac arrest in hospitalized adult patients.

METHODS

A retrospective case-control design was used to study eight ECG metrics from 22 adult bradyasystolic patients and their 45 control patients. The eight ECG metrics included heart rate, QRS width, interval from P-wave peak to QRS onset (PRp), heart rate-corrected interval from QRS onset to T-wave peak (QTpc), amplitude of QRS peak (rAmp), amplitude of P-wave (pAmp), amplitude of T-wave (tAmp), and absolute difference in the ECG amplitudes at QRS onset and offset divided by rAmp, that is, relative J-point amplitude (relJAmp). We derived the maximal true-positive rate (TPR) of detecting cardiac arrest at a globally minimal false-positive rate (FPR) for each metric and for the absolute slope values resulted from a linear fitting of the time series of these metrics. We also recorded the first time crossing the detection threshold to the time of arrest as lead time.

RESULTS

Conditions of relJAmp >20% and PRp >196.6 ms, respectively, achieved a TPR of 22.7% and 27.3% with zero FPRs. The lead prediction time of these two conditions was 5.7 ± 6.8 hours and 8.0 ± 7.2 hours, respectively. Performance of triggers based on the absolute slope values depended on the window length used for linear fitting. rAmp slope of a 2-hour window, PRp slope of a 30-minute window, and relJAmp slope of a 2-hour window achieved the best TPR of 27.3% (FPR = 2.3%, lead time = 6.5 ± 5.7 hours), 14.3% (FPR=0.0%, lead time = 10.9 ± 10.9), and 18.2% (FPR = 2.3%, lead time = 8.8 ± 9.8), respectively. McNemar test showed that only relJAmp >20.0% is significantly different from the baseline trigger of HR >149.3 bpm (p=0.046). In addition, metrics-based and slope-based triggers were complementary as an "OR" combination of two single-metric triggers raised the TPR up to 45.4% with zero FPR.

CONCLUSIONS

It is feasible to compute additional metrics from continuous ECG from bedside monitors. These additional parameters can provide highly specific triggers for predicting bradyasystolic cardiac arrest. Complementary triggers based on the slope of trending of these ECG metrics can further increase the sensitivity without incurring false positives.