Alarm fatigue: impacts on patient safety

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.

Fewer emergency department alarms is associated with reduced use of medications for acute agitation

BACKGROUND AND OBJECTIVES

Patient monitoring systems provide critical information but often produce loud, frequent alarms that worsen patient agitation and stress. This may increase the use of physical and chemical restraints with implications for patient morbidity and autonomy. This study analyzes how augmenting alarm thresholds affects the proportion of alarm-free time and the frequency of medications administered to treat acute agitation.

METHODS

Our emergency department's patient monitoring system was modified on June 28, 2022 to increase the tachycardia alarm threshold from 130 to 150 and to remove alarm sounds for several arrhythmias, including bigeminy and premature ventricular beats. A pre-post study was performed lasting 55 days before and 55 days after this intervention. The primary outcome was change in number of daily patient alarms. The secondary outcomes were alarm-free time per day and median number of antipsychotic and benzodiazepine medications administered per day. The safety outcome was the median number of patients transferred daily to the resuscitation area. We used quantile regression to compare outcomes between the pre- and post-intervention period and linear regression to correlate alarm-free time with the number of sedating medications administered.

RESULTS

Between the pre- and post-intervention period, the median number of alarms per day decreased from 1332 to 845 (-37%). This was primarily driven by reduced low-priority arrhythmia alarms from 262 to 21 (-92%), while the median daily census was unchanged (33 vs 32). Median hours per day free from alarms increased from 1.0 to 2.4 (difference 1.4, 95% CI 0.8-2.1). The median number of sedating medications administered per day decreased from 14 to 10 (difference - 4, 95% CI -1 to -7) while the number of escalations in level of care to our resuscitation care area did not change significantly. Multivariable linear regression showed a 60-min increase of alarm-free time per day was associated with 0.8 (95% CI 0.1-1.4) fewer administrations of sedating medication while an additional patient on the behavioral health census was associated with 0.5 (95% CI 0.0-1.1) more administrations of sedating medication.

CONCLUSION

A reasonable change in alarm parameter settings may increase the time patients and healthcare workers spend in the emergency department without alarm noise, which in this study was associated with fewer doses of sedating medications administered.

Attention capture by own name decreases with speech compression

Auditory stimuli that are relevant to a listener have the potential to capture focal attention even when unattended, the listener's own name being a particularly effective stimulus. We report two experiments to test the attention-capturing potential of the listener's own name in normal speech and time-compressed speech. In Experiment 1, 39 participants were tested with a visual word categorization task with uncompressed spoken names as background auditory distractors. Participants' word categorization performance was slower when hearing their own name rather than other names, and in a final test, they were faster at detecting their own name than other names. Experiment 2 used the same task paradigm, but the auditory distractors were time-compressed names. Three compression levels were tested with 25 participants in each condition. Participants' word categorization performance was again slower when hearing their own name than when hearing other names; the slowing was strongest with slight compression and weakest with intense compression. Personally relevant time-compressed speech has the potential to capture attention, but the degree of capture depends on the level of compression. Attention capture by time-compressed speech has practical significance and provides partial evidence for the duplex-mechanism account of auditory distraction.

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.

A Comparison of Two Transport Monitor Systems With Regard to Efficiency and Staff Satisfaction in the Perioperative Setting

BACKGROUND

Medical research aims to improve patient safety and efficiency in the perioperative setting. One critical aspect of patient safety is the intrahospital transfer of patients. Also, reliable monitoring of vital signs is crucial to support the medical staff. This study was conducted to assess two monitoring systems in terms of the handover time and staff satisfaction.

METHODS

To assess several aspects, two monitoring systems were compared: an organizational unit-related monitoring system that needs to be changed and brought back to the initial organizational unit after the patient transfer and a patient-specific monitoring system that accompanies the patient during the whole perioperative process.

RESULTS

In total, 243 patients were included, and 375 transfers were examined to analyze economic factors, including differences in handover times and user-friendliness. To this end, 30 employees of the Heidelberg University Hospital were asked about their satisfaction with the two monitoring systems based on a systematic questionnaire. It could be shown that, especially during transfers from the operating theater to the intensive care unit or the recovery room, the time from arrival to fully centralized monitoring and the total handover time were significantly shorter with the patient-specific monitoring system (p < 0.001). Furthermore, the staff was more satisfied with the patient-specific monitor system in terms of flexibility, cleanability and usability.

CONCLUSION

The increased employee satisfaction and significant time benefits during intrahospital transports may increase patient safety and efficiency of patient care, reduce employee workload, and reduce costs in the overall context of patient care.

Alarm fatigue and perceived stress among critical care nurses in the intensive care units: Palestinian perspectives

OBJECTIVE

The frequency of alarms generated by monitors and other electro-medical devices is undeniably valuable but can simultaneously escalate the workload for healthcare professionals, potentially subjecting intensive care unit nurses to alarm fatigue. The aim of this study is to investigate alarm fatigue and stress levels among critical care nursing personnel. Additionally, the study aims to assess predictors for both alarm fatigue and perceived stress.

METHODOLOGY

A descriptive cross-sectional study recruited 187 Intensive Care Unit (ICU) nurses from hospitals located in the northern and central regions of the West Bank, Palestine. Data were gathered through online surveys due to logistic concerns using the Alarm Fatigue Scale and the Perceived Stress Scale. The research was conducted between November 2023 and January 2024.

RESULTS

The mean overall alarm fatigue score was 23.36 (SD = 5.57) out of 44. The study showed that 62.6% of the participating ICU nurses experience average to high degree of alarm fatigue, while 69.5% experience average to high levels of perceived stress. A significant positive Pearson correlation was found between stress and alarm fatigue (0.40, P < 0.01). Important predictors of alarm fatigue include perceived stress, nurse-to-patient ratio, gender, and years of experience, while important predictors of perceived stress include alarm fatigue, type of working shift and hospital unit.

CONCLUSION

Alarm fatigue can compromise the timely intervention required to prevent adverse outcomes by causing delayed responses or missed critical alarm, which can have major ramifications for patient safety. Addressing stress is crucial for mitigating alarm fatigue and fostering a supportive work environment to ensure optimal patient care. Consequently, exploring strategies to alleviate the negative impacts of alarm fatigue on critical care nurses' stress merits further investigation in future research studies.

Decision discovery using clinical decision support system decision log data for supporting the nurse decision-making process

BACKGROUND

Decision-making in healthcare is increasingly complex; notably in hospital environments where the information density is high, e.g., emergency departments, oncology departments, and psychiatry departments. This study aims to discover decisions from logged data to improve the decision-making process.

METHODS

The Design Science Research Methodology (DSRM) was chosen to design an artifact (algorithm) for the discovery and visualization of decisions. The DSRM's different activities are explained, from the definition of the problem to the evaluation of the artifact. During the design and development activities, the algorithm itself is created. During the demonstration and evaluation activities, the algorithm was tested with an authentic synthetic dataset.

RESULTS

The results show the design and simulation of an algorithm for the discovery and visualization of decisions. A fuzzy classifier algorithm was adapted for (1) discovering decisions from a decision log and (2) visualizing the decisions using the Decision Model and Notation standard.

CONCLUSIONS

In this paper, we show that decisions can be discovered from a decision log and visualized for the improvement of the decision-making process of healthcare professionals or to support the periodic evaluation of protocols and guidelines.

A machine learning algorithm for detecting abnormal patterns in continuous capnography and pulse oximetry monitoring

Continuous capnography monitors patient ventilation but can be susceptible to artifact, resulting in alarm fatigue. Development of smart algorithms may facilitate accurate detection of abnormal ventilation, allowing intervention before patient deterioration. The objective of this analysis was to use machine learning (ML) to classify combined waveforms of continuous capnography and pulse oximetry as normal or abnormal. We used data collected during the observational, prospective PRODIGY trial, in which patients receiving parenteral opioids underwent continuous capnography and pulse oximetry monitoring while on the general care floor [1]. Abnormal ventilation segments in the data stream were reviewed by nine experts and inter-rater agreement was assessed. Abnormal segments were defined as the time series 60s before and 30s after an abnormal pattern was detected. Normal segments (90s continuous monitoring) were randomly sampled and filtered to discard sequences with missing values. Five ML models were trained on extracted features and optimized towards an Fβ score with β = 2. The results show a high inter-rater agreement (> 87%), allowing 7,858 sequences (2,944 abnormal) to be used for model development. Data were divided into 80% training and 20% test sequences. The XGBoost model had the highest Fβ score of 0.94 (with β = 2), showcasing an impressive recall of 0.98 against a precision of 0.83. This study presents a promising advancement in respiratory monitoring, focusing on reducing false alarms and enhancing accuracy of alarm systems. Our algorithm reliably distinguishes normal from abnormal waveforms. More research is needed to define patterns to distinguish abnormal ventilation from artifacts.

Caring to care: Nurturing ICU healthcare professionals' wellbeing for enhanced patient safety

Healthcare professionals working in the Intensive Care Unit (ICU) care for patients suffering from a critical illness and their relatives. Working within a team of people with different personalities, competencies, and specialties, with constraints and demands might contribute to a working environment that is prone to conflicts and disagreements. This highlights that the ICU is a stressful place that can threaten healthcare professionals' wellbeing. This article aims to address the concept of wellbeing by describing how the stressful ICU work-environment threatens the wellbeing of health professionals and discussing how this situation jeopardizes patient safety. To promote wellbeing, it is imperative to explore actionable interventions such as improve communication skills, educational sessions on stress management, or mindfulness. Promoting ICU healthcare professionals' wellbeing through evidence-based strategies will not only increase their personal resilience but might contribute to a safer and more efficient patient care.

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.

Human-artificial intelligence interaction in gastrointestinal endoscopy

The number and variety of applications of artificial intelligence (AI) in gastrointestinal (GI) endoscopy is growing rapidly. New technologies based on machine learning (ML) and convolutional neural networks (CNNs) are at various stages of development and deployment to assist patients and endoscopists in preparing for endoscopic procedures, in detection, diagnosis and classification of pathology during endoscopy and in confirmation of key performance indicators. Platforms based on ML and CNNs require regulatory approval as medical devices. Interactions between humans and the technologies we use are complex and are influenced by design, behavioural and psychological elements. Due to the substantial differences between AI and prior technologies, important differences may be expected in how we interact with advice from AI technologies. Human–AI interaction (HAII) may be optimised by developing AI algorithms to minimise false positives and designing platform interfaces to maximise usability. Human factors influencing HAII may include automation bias, alarm fatigue, algorithm aversion, learning effect and deskilling. Each of these areas merits further study in the specific setting of AI applications in GI endoscopy and professional societies should engage to ensure that sufficient emphasis is placed on human-centred design in development of new AI technologies.

Factors Influencing Medication Administration Outcomes Among New Graduate Nurses Using Bar Code-Assisted Medication Administration

Paramount to patient safety is the ability for nurses to make clinical decisions free from human error. Yet, the dynamic clinical environment in which nurses work is characterized by uncertainty, urgency, and high consequence, necessitating that nurses make quick and critical decisions. The aim of this study was to examine the influence of human and environmental factors on the decision to administer among new graduate nurses in response to alert generation during bar code-assisted medication administration. The design for this study was a descriptive, longitudinal, observational cohort design using EHR audit log and administrative data. The study was set at a large, urban medical center in the United States and included 132 new graduate nurses who worked on adult, inpatient units. Research variables included human and environmental factors. Data analysis included descriptive and inferential analyses. This study found that participants continued with administration of a medication in 90.75% of alert encounters. When considering the response to an alert, residency cohort, alert category, and previous exposure variables were associated with the decision to proceed with administration. It is important to continue to study factors that influence nurses' decision-making, particularly during the process of medication administration, to improve patient safety and outcomes.

Levels and Factors of Nurses' Alarm Fatigue in Critical Care Settings in Saudi Arabia: A Multicenter Cross-Sectional Study

Background

A continuous and high frequency of alarms from monitoring and treatment devices can lead to nurses' sensory exhaustion and alarm fatigue in critical care settings.

Aim

The purpose of this study was to evaluate the level of alarm fatigue and determine the relationship between nurses' sociodemographic and work-related factors and the level of alarm fatigue in critical care settings in Hail City, Saudi Arabia.

Methods

Between May and July 2023, 298 nurses who worked in the emergency, intensive care, and critical care units of all the public hospitals in Hail City participated in a cross-sectional survey. Sociodemographic and work-related sheet and the Nurses' Alarm Fatigue Questionnaire were used to collect data.

Results

The total mean score of alarm fatigue was 26.38±8.30 out of 44. The highest score was observed for the item "I pay more attention to the alarms in certain", while the lowest score were observed for the items "I turn off the alarms at the beginning of every shift" with mean scores of 2.51 and 1.61, respectively. Nurses who were males, older than 30 years and Saudi citizens had significantly higher mean scores of alarm fatigue than their counterparts. In addition, significantly higher mean scores of alarm fatigue were noticed for nurses experienced for 10 years or more and who had regular morning shifts. Multiple linear regression showed that male (p=0.014), age (p=0.012), and Saudi nationality (p <0.029) were the independent factors affecting the level of fatigue alarm among nurses.

Conclusion

Nurses working in critical care settings at hospitals in Hail city are exposed to average levels of alarm fatigue, which can be influenced by sex, age, nationality, and experience of nurses. Therefore, it is imperative to manage alarm fatigue in critical care units by considering work-related and personality-related factors to ensure patient safety.

Describing Medication Administration and Alert Patterns Experienced by New Graduate Nurses During the First Year of Practice

The aim of this study was to describe medication administration and alert patterns among a cohort of new graduate nurses over the first year of practice. Medical errors related to clinical decision-making, including medication administration errors, may occur more frequently among new graduate nurses. To better understand nursing workflow and documentation workload in today's clinical environment, it is important to understand patterns of medication administration and alert generation during barcode-assisted medication administration. Study objectives were addressed through a descriptive, longitudinal, observational cohort design using secondary data analysis. Set in a large, urban medical center in the United States, the study sample included 132 new graduate nurses who worked on adult, inpatient units and administered medication using barcode-assisted medication administration. Data were collected through electronic health record and administration sources. New graduate nurses in the sample experienced a total of 587 879 alert and medication administration encounters, administering 772 unique medications to 17 388 unique patients. Nurses experienced an average medication workload of 28.09 medications per shift, 3.98% of which were associated with alerts, over their first year of practice. In addition to high volume of medication administration, new graduate nurses administer many different types of medications and are exposed to numerous alerts while using barcode-assisted medication administration.

External evaluation of the Dynamic Criticality Index: A machine learning model to predict future need for ICU care in hospitalized pediatric patients

OBJECTIVE

To assess the single site performance of the Dynamic Criticality Index (CI-D) models developed from a multi-institutional database to predict future care. Secondarily, to assess future care-location predictions in a single institution when CI-D models are re-developed using single-site data with identical variables and modeling methods. Four CI-D models were assessed for predicting care locations >6-12 hours, >12-18 hours, >18-24 hours, and >24-30 hours in the future.

DESIGN

Prognostic study comparing multi-institutional CI-D models' performance in a single-site electronic health record dataset to an institution-specific CI-D model developed using identical variables and modelling methods. The institution did not participate in the multi-institutional dataset.

PARTICIPANTS

All pediatric inpatients admitted from January 1st 2018 -February 29th 2020 through the emergency department.

MAIN OUTCOME(S) AND MEASURE(S)

The main outcome was inpatient care in routine or ICU care locations.

RESULTS

A total of 29,037 pediatric hospital admissions were included, with 5,563 (19.2%) admitted directly to the ICU, 869 (3.0%) transferred from routine to ICU care, and 5,023 (17.3%) transferred from ICU to routine care. Patients had a median [IQR] age 68 months (15-157), 47.5% were female and 43.4% were black. The area under the receiver operating characteristic curve (AUROC) for the multi-institutional CI-D models applied to a single-site test dataset was 0.493-0.545 and area under the precision-recall curve (AUPRC) was 0.262-0.299. The single-site CI-D models applied to an independent single-site test dataset had an AUROC 0.906-0.944 and AUPRC range from 0.754-0.824. Accuracy at 0.95 sensitivity for those transferred from routine to ICU care was 72.6%-81.0%. Accuracy at 0.95 specificity was 58.2%-76.4% for patients who transferred from ICU to routine care.

CONCLUSION AND RELEVANCE

Models developed from multi-institutional datasets and intended for application to individual institutions should be assessed locally and may benefit from re-development with site-specific data prior to deployment.

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.

Evaluation of the sensory environment in a large tertiary ICU

BACKGROUND

ICU survival is improving. However, many patients leave ICU with ongoing cognitive, physical, and/or psychological impairments and reduced quality of life. Many of the reasons for these ongoing problems are unmodifiable; however, some are linked with the ICU environment. Suboptimal lighting and excessive noise contribute to a loss of circadian rhythms and sleep disruptions, leading to increased mortality and morbidity. Despite long-standing awareness of these problems, meaningful ICU redesign is yet to be realised, and the 'ideal' ICU design is likely to be unique to local context and patient cohorts. To inform the co-design of an improved ICU environment, this study completed a detailed evaluation of the ICU environment, focussing on acoustics, sound, and light.

METHODS

This was an observational study of the lighting and acoustic environment using sensors and formal evaluations. Selected bedspaces, chosen to represent different types of bedspaces in the ICU, were monitored during prolonged study periods. Data were analysed descriptively using Microsoft Excel.

RESULTS

Two of the three monitored bedspaces showed a limited difference in lighting levels across the day, with average daytime light intensity not exceeding 300 Lux. In bedspaces with a window, the spectral power distribution (but not intensity) of the light was similar to natural light when all ceiling lights were off. However, when the ceiling lights were on, the spectral power distribution was similar between bedspaces with and without windows. Average sound levels in the study bedspaces were 63.75, 56.80, and 59.71 dBA, with the single room being noisier than the two open-plan bedspaces. There were multiple occasions of peak sound levels > 80 dBA recorded, with the maximum sound level recorded being > 105 dBA. We recorded one new monitor or ventilator alarm commencing every 69 s in each bedspace, with only 5% of alarms actioned. Acoustic testing showed poor sound absorption and blocking.

CONCLUSIONS

This study corroborates other studies confirming that the lighting and acoustic environments in the study ICU were suboptimal, potentially contributing to adverse patient outcomes. This manuscript discusses potential solutions to identified problems. Future studies are required to evaluate whether an optimised ICU environment positively impacts patient outcomes.

The effects of alarm fatigue on the tendency to make medical errors in nurses working in intensive care units

BACKGROUND

Alarm fatigue resulting from exposure to multiple alarms is an important problem that threatens patient safety. The fact that each device in intensive care units works with different alarm systems increases the number and variety of alarms.

AIM

The aim of this study was to determine the effects of alarm fatigue on the tendency of nurses working in intensive care units to make medical errors.

STUDY DESIGN

A cross-sectional and correlational design were used in this study. The study was carried out with 382 intensive care nurses who could be reached via an electronic questionnaire. Data were collected using a 'Personal Information Form', the 'Alarm Fatigue Scale (AFS)' and the 'Medical Error Tendency Scale in Nursing (METSN)'.

RESULTS

The mean age of the nurses who were included in the study was 31.52 ± 5.66. While 70.2% of the participants were women, 67% had bachelor's degrees, and 65.4% had been working in the intensive care unit for 1-5 years. The mean total METSN score of the participants was 229.29 ± 15.32, and their mean total AFS score was 20.02 ± 6.15. A negative and weak significant correlation was found between the total mean AFS and METSN scores of the participants (r = -0.275; p < .001). As the alarm fatigue levels of the participants increased, their medical error tendencies increased. It was determined that a one-unit increase in the alarm fatigue level of intensive care nurses increased their tendency to make medical errors by 0.263 units (p < .001).

CONCLUSIONS

It was found that the nurses had a low tendency to make medical errors and moderate levels of alarm fatigue, and an increase in their alarm fatigue levels significantly increased their medical error tendencies.

RELEVANCE TO CLINICAL PRACTICE

Institutions should establish alarm management procedures in units with multiple alarm systems, such as intensive care units, and examine the effects of alarms on employees.

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.

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.

Retrospective cohort observation on psychotropic drug-drug interaction and identification utility from 3 databases: Drugs.com®, Lexicomp®, and Epocrates®

BACKGROUND

Pharmacotherapy is necessary for many people with psychiatric disorders and polypharmacy is common. The psychotropic drug-drug interaction (DDI) should be concerned and efficiently monitored by a proper instrument.

OBJECTIVES

This study aimed to investigate the prevalence and associated factors of psychotropic DDI and to compare the identification utility from three databases: Drugs.com®, Lexicomp®, and Epocrates®.

METHODS

This was a retrospective cohort design. We collected demographic and clinical data of all patients hospitalised in the psychiatric inpatient unit in 2020. Psychotropic DDI profiles were examined through three databases. Descriptive statistics were used to report comprehensiveness of each database and prevalence of psychotropic DDI. The Fleiss' kappa index would be analysed to indicate agreement strength of DDI severity classification among three databases.

RESULTS

From 149 total admissions, the psychotropic DDIs were found in 148 admissions (99.3%). Thorough the study, there were 182 of both psychotropic and other agents prescribed under 1,357 prescriptions. In total, 2,825 psychotropic DDIs were identified by using Drugs.com® 2,500 times, Epocrates® 2,269 times, and Lexicomp® 2,265 times. Interactions with clonazepam was the three most frequent agents when co-administrated with quetiapine (n = 56), risperidone (n = 36), and valproic acid and derivatives (n = 36). Serious DDIs were comparatively lower in incidence and there was no evidence of its association with reported clinical adverse consequences. The study revealed slight and fair agreement regarding severity classification among the three databases was found. DDI events detected by Drugs.com® were greatest in number, but Lexicomp® provided the broadest list of medications prescribed in our study.

CONCLUSION

Among three databases, interactions detected by Drugs.com® were greatest in number, whereas Lexicomp® provided the broadest list of medications. Development of such databases, based on both theoretical and clinical conceptions, should be focused to balance safety of patients and weariness of healthcare providers.

An integrative review on the acceptance of artificial intelligence among healthcare professionals in hospitals

Artificial intelligence (AI) in the domain of healthcare is increasing in prominence. Acceptance is an indispensable prerequisite for the widespread implementation of AI. The aim of this integrative review is to explore barriers and facilitators influencing healthcare professionals' acceptance of AI in the hospital setting. Forty-two articles met the inclusion criteria for this review. Pertinent elements to the study such as the type of AI, factors influencing acceptance, and the participants' profession were extracted from the included studies, and the studies were appraised for their quality. The data extraction and results were presented according to the Unified Theory of Acceptance and Use of Technology (UTAUT) model. The included studies revealed a variety of facilitating and hindering factors for AI acceptance in the hospital setting. Clinical decision support systems (CDSS) were the AI form included in most studies (n = 21). Heterogeneous results with regard to the perceptions of the effects of AI on error occurrence, alert sensitivity and timely resources were reported. In contrast, fear of a loss of (professional) autonomy and difficulties in integrating AI into clinical workflows were unanimously reported to be hindering factors. On the other hand, training for the use of AI facilitated acceptance. Heterogeneous results may be explained by differences in the application and functioning of the different AI systems as well as inter-professional and interdisciplinary disparities. To conclude, in order to facilitate acceptance of AI among healthcare professionals it is advisable to integrate end-users in the early stages of AI development as well as to offer needs-adjusted training for the use of AI in healthcare and providing adequate infrastructure.

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.

Deep Learning Approach for Negation and Speculation Detection for Automated Important Finding Flagging and Extraction in Radiology Report: Internal Validation and Technique Comparison Study

BACKGROUND

Negation and speculation unrelated to abnormal findings can lead to false-positive alarms for automatic radiology report highlighting or flagging by laboratory information systems.

OBJECTIVE

This internal validation study evaluated the performance of natural language processing methods (NegEx, NegBio, NegBERT, and transformers).

METHODS

We annotated all negative and speculative statements unrelated to abnormal findings in reports. In experiment 1, we fine-tuned several transformer models (ALBERT [A Lite Bidirectional Encoder Representations from Transformers], BERT [Bidirectional Encoder Representations from Transformers], DeBERTa [Decoding-Enhanced BERT With Disentangled Attention], DistilBERT [Distilled version of BERT], ELECTRA [Efficiently Learning an Encoder That Classifies Token Replacements Accurately], ERNIE [Enhanced Representation through Knowledge Integration], RoBERTa [Robustly Optimized BERT Pretraining Approach], SpanBERT, and XLNet) and compared their performance using precision, recall, accuracy, and F₁-scores. In experiment 2, we compared the best model from experiment 1 with 3 established negation and speculation-detection algorithms (NegEx, NegBio, and NegBERT).

RESULTS

Our study collected 6000 radiology reports from 3 branches of the Chi Mei Hospital, covering multiple imaging modalities and body parts. A total of 15.01% (105,755/704,512) of words and 39.45% (4529/11,480) of important diagnostic keywords occurred in negative or speculative statements unrelated to abnormal findings. In experiment 1, all models achieved an accuracy of >0.98 and F₁-score of >0.90 on the test data set. ALBERT exhibited the best performance (accuracy=0.991; F₁-score=0.958). In experiment 2, ALBERT outperformed the optimized NegEx, NegBio, and NegBERT methods in terms of overall performance (accuracy=0.996; F₁-score=0.991), in the prediction of whether diagnostic keywords occur in speculative statements unrelated to abnormal findings, and in the improvement of the performance of keyword extraction (accuracy=0.996; F₁-score=0.997).

CONCLUSIONS

The ALBERT deep learning method showed the best performance. Our results represent a significant advancement in the clinical applications of computer-aided notification systems.

Informing Healthcare Alarm Design and Use: A Human Factors Cross-Industry Perspective

Background: Alarms are signals intended to capture and direct human attention to a potential issue that may require monitoring, assessment, or intervention and play a critical safety role in high-risk industries. Healthcare relies heavily on auditory and visual alarms. While there are some guidelines to inform alarm design and use, alarm fatigue and other alarm issues are challenges in the healthcare setting. Automotive, aviation, and nuclear industries have used the science of human factors to develop alarm design and use guidelines. These guidelines may provide important insights for advancing patient safety in healthcare. Methods: We identified documents containing alarm design and use guidelines from the automotive, aviation, and nuclear industries that have been endorsed by oversight agencies. These guidelines were reviewed by human factors and clinical experts to identify those most relevant to healthcare, qualitatively analyze the relevant guidelines to identify meaningful topics, synthesize the guidelines under each topic to identify key commonalities and differences, and describe how the guidelines might be considered by healthcare stakeholders to improve alarm design and use. Results: A total of 356 guidelines were extracted from industry documents (2012–present) and 327 (91.9%) were deemed relevant to healthcare. A qualitative analysis of relevant guidelines resulted in nine distinct topics: Alarm Reduction, Appropriateness, Context-Dependence, Design Characteristics, Mental Model, Prioritization, Specificity, Urgency, and User Control. There were several commonalities, as well as some differences, across industry guidelines. The guidelines under each topic were found to inform the auditory or visual modality, or both. Certain guidelines have clear considerations for healthcare stakeholders, especially technology developers and healthcare facilities. Conclusion: Numerous guidelines from other high-risk industries can inform alarm design and use in healthcare. Healthcare facilities can use the information presented as a framework for working with their technology developers to appropriately design and modify alarming technologies and can evaluate their clinical environments to see how alarming technologies might be improved.

Development and multi-institutional validation of a convolutional neural network to detect vertebral body mis-alignments in 2D x-ray setup images

BACKGROUND

Misalignment to the incorrect vertebral body remains a rare but serious patient safety risk in image-guided radiotherapy (IGRT).

PURPOSE

Our group has proposed that an automated image-review algorithm be inserted into the IGRT process as an interlock to detect off-by-one vertebral body errors. This study presents the development and multi-institutional validation of a convolutional neural network (CNN)-based approach for such an algorithm using patient image data from a planar stereoscopic x-ray IGRT system.

METHODS

X-rays and digitally reconstructed radiographs (DRRs) were collected from 429 spine radiotherapy patients (1592 treatment fractions) treated at six institutions using a stereoscopic x-ray image guidance system. Clinically-applied, physician approved, alignments were used for true-negative, "no-error" cases. "Off-by-one vertebral body" errors were simulated by translating DRRs along the spinal column using a semi-automated method. A leave-one-institution-out approach was used to estimate model accuracy on data from unseen institutions as follows: All of the images from five of the institutions were used to train a CNN model from scratch using a fixed network architecture and hyper-parameters. The size of this training set ranged from 5700 to 9372 images, depending on exactly which five institutions were contributing data. The training set was randomized and split using a 75/25 split into the final training/ validation sets. X-ray/ DRR image pairs and the associated binary labels of "no-error" or "shift" were used as the model input. Model accuracy was evaluated using images from the sixth institution, which were left out of the training phase entirely. This test set ranged from 180 to 3852 images, again depending on which institution had been left out of the training phase. The trained model was used to classify the images from the test set as either "no-error" or "shifted", and the model predictions were compared to the ground truth labels to assess the model accuracy. This process was repeated until each institution's images had been used as the testing dataset.

RESULTS

When the six models were used to classify unseen image pairs from the institution left out during training, the resulting receiver operating characteristic area under the curve values ranged from 0.976 to 0.998. With the specificity fixed at 99%, the corresponding sensitivities ranged from 61.9% to 99.2% (mean: 77.6%). With the specificity fixed at 95%, sensitivities ranged from 85.5% to 99.8% (mean: 92.9%).

CONCLUSION

This study demonstrated the CNN-based vertebral body misalignment model is robust when applied to previously unseen test data from an outside institution, indicating that this proposed additional safeguard against misalignment is feasible.

Tiered escalation response systems in practice: A post hoc analysis examining the workload implications

Objective

Many rapid response systems now have multiple tiers of escalation in addition to the traditional single tier of a medical emergency team. Given that the benefit to patient outcomes of this change is unclear, we sought to investigate the workload implications of a multitiered system, including the impact of trigger modification.

Design

The study design incorporated a post hoc analysis using a matched case-control dataset.

Setting

The study setting was an acute, adult tertiary referral hospital.

Participants

Cases that had an adverse event (cardiac arrest or unanticipated intensive care unit admission) or a rapid response team (RRT) call participated in the study. Controls were matched by age, gender, ward and time of year, and no adverse event or RRT call. Participants were admitted between May 2014 and April 2015.

Main outcome measures

The main outcome measure were the number of reviews, triggers, and modifications across three tiers of escalation; a nurse review, a multidisciplinary review (MDT-admitting medical team review), and an RRT call.

Results

There were 321 cases and 321 controls. Overall, there were 1948 nurse triggers, of which 1431 (73.5%) were in cases and 517 (26.5%) in controls, 798 MDT triggers (660 [82.7%] in cases and 138 [17.3%] in controls), and 379 RRT triggers (351 [92.6%] in cases and 28 [7.4%] in controls). Per patient per 24 h, there were 3.03 nurse, 1.24 MDT, and 0.59 RRT triggers. Accounting for modifications, this reduced to 2.17, 0.88, and 0.42, respectively. The proportion of triggers that were modified, so as not to trigger a review, was similar across all the tiers, being 28.6% of nurse, 29.6% of MDT, and 28.2% of RRT triggers. Per patient per 24 h, there were 0.61 nurse reviews, 0.52 MDT reviews, and 0.08 RRT reviews.

Conclusions

Lower-tier triggers were more prevalent, and modifications were common. Modifications significantly mitigated the escalation workload across all tiers of a multitiered system.

Clinical Alarms in a Gynaecological Surgical Unit: A Retrospective Data Analysis

Alarm fatigue refers to the desensitisation of medical staff to patient monitor clinical alarms, which may lead to slower response time or total ignorance of alarms and thereby affects patient safety. The reasons behind alarm fatigue are complex; the main contributing factors include the high number of alarms and the poor positive predictive value of alarms. The study was performed in the Surgery and Anaesthesia Unit of the Women's Hospital, Helsinki, by collecting data from patient monitoring device clinical alarms and patient characteristics from surgical operations. We descriptively analysed the data and statistically analysed the differences in alarm types between weekdays and weekends, using chi-squared, for a total of eight monitors with 562 patients. The most common operational procedure was caesarean section, of which 149 were performed (15.7%). Statistically significant differences existed in alarm types and procedures between weekdays and weekends. The number of alarms produced was 11.7 per patient. In total, 4698 (71.5%) alarms were technical and 1873 (28.5%) were physiological. The most common physiological alarm type was low pulse oximetry, with a total of 437 (23.3%). Of all the alarms, the number of alarms either acknowledged or silenced was 1234 (18.8%). A notable phenomenon in the study unit was alarm fatigue. Greater customisation of patient monitors for different settings is needed to reduce the number of alarms that do not have clinical significance.

Polish Adaptation of the Alarm Fatigue Assessment Questionnaire as an Element of Improving Patient Safety

Medical personnel, working in medical intensive care units, are exposed to fatigue associated with alarms emitted by numerous medical devices used for diagnosing, treating, and monitoring patients. Alarm fatigue is a safety and quality problem in patient care and actions should be taken to reduce this by, among other measures, building an effective safety culture. In the present study, an adaptation of a questionnaire to assess alarm fatigue was carried out. The study obtained good reliability of the questionnaire at Cronbach's alpha level of 0.88. The Polish research team has successfully adapted the Alarm Fatigue Assessment Questionnaire so that it can be used in healthcare settings as a tool to improve patient safety.

Determinants of Medical Equipment Alarm Fatigue in Practicing Nurses: A Systematic Review

Objective

This study aimed to systematically evaluate the level of medical equipment alarm fatigue and its influencing factors among clinical nurses.

Methods

PubMed, Embase, CNKI, and Wanfang databases were systematically searched to identify articles on alarm fatigue of clinical nurses published before September 25, 2022. According to the evaluation criteria of prevalence studies recommended by JBI Evidence-Based Health Care Center, the quality of the literature meeting the inclusion criteria was evaluated, and Stata MP17 software was used for meta-analysis.

Results

A total of 14 cross-sectional studies were included, with a total sample of 2,848 nurses. The results showed that the alarm fatigue score of clinical nurses was 21.76 (95% CI [20.27, 23.25]). Subgroup analysis showed that the nurses who worked night shift and had lower professional title had higher alarm fatigue.

Conclusion

The alarm fatigue of clinical nurses was at a moderate level. To reduce the alarm fatigue level of clinical nurses, nursing managers should strengthen the alarm safety awareness of nurses, rationally arrange nurse manpower, carry out training to actively improve the alarm management ability of nurses, and optimize the alarm level and frequency of alarm equipment.

Assessment of Alarm Fatigue Risk Among Oncology Nurses in Jordan

Introduction

Using technology in the clinical setting where clinical alarms frequently occur, resulting in many false alarms, which is called alarm fatigue, alarm fatigue may increase nurses' distraction, and that might negatively affect patient safety.

Objective

This study aimed to assess alarm fatigue among oncology nurses in Jordan.

Methods

A descriptive cross-sectional design was used in a non-profit specialized cancer center. A self-reported questionnaire was answered by nurses who participated in the study.

Results

A total of 222 questionnaires were analyzed with a more than 95% response rate. More than half of the sample (60.4%) were females. The participants were young nurses with a mean age of 25.18 ± 3.33 years. The total mean score of alarm fatigue was 31.62 ± 7.14 on a scale ranging from zero to 52. Post-hoc analysis showed that the palliative unit (25.73 ± 7.22) and emergency room (28.73 ± 6.62) had low scores of total mean alarm fatigue than remaining area of practice, such as the ICU (33.92 ± 6.99); p-value: .004.

Conclusion

Alarm fatigue is a global issue affecting many practice areas. An educational program is recommended for nurses to learn how to deal with alarm fatigue. In order to effectively manage alarms, nurses' education and individual training are crucial.

The influence of patient characteristics on the alarm rate in intensive care units: a retrospective cohort study

Intensive care units (ICU) are often overflooded with alarms from monitoring devices which constitutes a hazard to both staff and patients. To date, the suggested solutions to excessive monitoring alarms have remained on a research level. We aimed to identify patient characteristics that affect the ICU alarm rate with the goal of proposing a straightforward solution that can easily be implemented in ICUs. Alarm logs from eight adult ICUs of a tertiary care university-hospital in Berlin, Germany were retrospectively collected between September 2019 and March 2021. Adult patients admitted to the ICU with at least 24 h of continuous alarm logs were included in the study. The sum of alarms per patient per day was calculated. The median was 119. A total of 26,890 observations from 3205 patients were included. 23 variables were extracted from patients' electronic health records (EHR) and a multivariable logistic regression was performed to evaluate the association of patient characteristics and alarm rates. Invasive blood pressure monitoring (adjusted odds ratio (aOR) 4.68, 95%CI 4.15-5.29, p < 0.001), invasive mechanical ventilation (aOR 1.24, 95%CI 1.16-1.32, p < 0.001), heart failure (aOR 1.26, 95%CI 1.19-1.35, p < 0.001), chronic renal failure (aOR 1.18, 95%CI 1.10-1.27, p < 0.001), hypertension (aOR 1.19, 95%CI 1.13-1.26, p < 0.001), high RASS (aOR 1.22, 95%CI 1.18-1.25, p < 0.001) and scheduled surgical admission (aOR 1.22, 95%CI 1.13-1.32, p < 0.001) were significantly associated with a high alarm rate. Our study suggests that patient-specific alarm management should be integrated in the clinical routine of ICUs. To reduce the overall alarm load, particular attention regarding alarm management should be paid to patients with invasive blood pressure monitoring, invasive mechanical ventilation, heart failure, chronic renal failure, hypertension, high RASS or scheduled surgical admission since they are more likely to have a high contribution to noise pollution, alarm fatigue and hence compromised patient safety in ICUs.

Preventing unrecognised oesophageal intubation: a consensus guideline from the Project for Universal Management of Airways and international airway societies

Across multiple disciplines undertaking airway management globally, preventable episodes of unrecognised oesophageal intubation result in profound hypoxaemia, brain injury and death. These events occur in the hands of both inexperienced and experienced practitioners. Current evidence shows that unrecognised oesophageal intubation occurs sufficiently frequently to be a major concern and to merit a co-ordinated approach to address it. Harm from unrecognised oesophageal intubation is avoidable through reducing the rate of oesophageal intubation, combined with prompt detection and immediate action when it occurs. The detection of 'sustained exhaled carbon dioxide' using waveform capnography is the mainstay for excluding oesophageal placement of an intended tracheal tube. Tube removal should be the default response when sustained exhaled carbon dioxide cannot be detected. If default tube removal is considered dangerous, urgent exclusion of oesophageal intubation using valid alternative techniques is indicated, in parallel with evaluation of other causes of inability to detect carbon dioxide. The tube should be removed if timely restoration of sustained exhaled carbon dioxide cannot be achieved. In addition to technical interventions, strategies are required to address cognitive biases and the deterioration of individual and team performance in stressful situations, to which all practitioners are vulnerable. These guidelines provide recommendations for preventing unrecognised oesophageal intubation that are relevant to all airway practitioners independent of geography, clinical location, discipline or patient type.

Health System Redesign of Cardiac Monitoring Oversight to Optimize Alarm Management, Safety, and Staff Engagement

OBJECTIVE

The purpose of this quality improvement project was to improve health system patient safety by creating a cardiac monitoring structure aligned with national standards.

BACKGROUND

Excessive alarms pose patient safety threats and are often false or clinically insignificant. The Joint Commission identified reduction of nonactionable alarms as a National Patient Safety Goal.

METHODS

The conversion to structured monitoring occurred in 4 phases: 1) defining health system monitoring structure and processes; 2) co-create sessions; 3) implementation and impact analysis; and 4) ongoing evaluation and optimization.

RESULTS

Twenty-two clinical units participated. At the conclusion of phase 4, total 30-day alarm rates decreased by 74% at the academic hospital and by 92% and 95% at the community hospitals and were sustained for 12 months.

CONCLUSIONS

Decreasing alarm frequency can be safely achieved in academic and community hospitals by creating a system-wide monitoring infrastructure and standardized processes that engage interdisciplinary teams.

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.

Clinical Decision Support Stewardship: Best Practices and Techniques to Monitor and Improve Interruptive Alerts

Interruptive clinical decision support systems, both within and outside of electronic health records, are a resource that should be used sparingly and monitored closely. Excessive use of interruptive alerting can quickly lead to alert fatigue and decreased effectiveness and ignoring of alerts. In this review, we discuss the evidence for effective alert stewardship as well as practices and methods we have found useful to assess interruptive alert burden, reduce excessive firings, optimize alert effectiveness, and establish quality governance at our institutions. We also discuss the importance of a holistic view of the alerting ecosystem beyond the electronic health record.

Capnography and Pulse Oximetry Improve Fast Track Extubation in Patients Undergoing Coronary Artery Bypass Graft Surgery: A Randomized Clinical Trial

Background

Use of capnography as a non-invasive method during the weaning process for fast track extubation (FTE) is controversial. We conducted the present study to determine whether pulse oximetry and capnography could be utilized as alternatives to arterial blood gas (ABG) measurements in patients under mechanical ventilation (MV) following coronary artery bypass graft (CABG) surgery.

Methods

In this randomized clinical trial, 70 patients, who were candidates for CABG surgery, were randomly assigned into two equal groups (n = 35), intervention and control group. In the intervention group, the ventilator management and weaning from MV was done using Etco2 from capnography and SpO2 from pulse oximetry. Meanwhile, in the control group, weaning was done based on ABG analysis. The length of intensive care unit (ICU) stay, time to extubation, number of manual ventilators setting changes, and alarms were compared between the groups.

Results

The end-tidal carbon dioxide (ETCO2) levels in the intervention group were completely similar to the partial pressure of carbon dioxide (PaCo2) in the control group (39.5 ± 3.1 vs. 39.4 ± 4.32, p > 0.05). The mean extubation times were significantly shorter in the intervention group compared to those in the control patients (212.2 ± 80.6 vs. 342.7 ± 110.7, p < 0.001). Moreover, the number of changes in the manual ventilator setting and the number of alarms were significantly lower in the intervention group. However, the differences in the length of stay in ICU between the two groups were not significant (p = 0.219).

Conclusion

Our results suggests that capnography can be used as an alternative to ABG. Furthermore, it is a safe and valuable monitor that could be a good alternative for ABG in this population. Further studies with larger sample sizes and on different disease states and populations are required to assess the accuracy of our findings.

Clinical Trial Registration

Current Controlled Trials, IRCT, IRCT201701016778N6, Registered 3 March 2017, https://www.irct.ir/trial/7192.

The Case for the Anesthesiologist-Informaticist

Health care has been transformed by computerization, and the use of electronic health record systems has become widespread. Anesthesia information management systems are commonly used in the operating room to maintain records of anesthetic care delivery. The perioperative environment and the practice of anesthesia generate a large volume of data that may be reused to support clinical decision-making, research, and process improvement. Anesthesiologists trained in clinical informatics, referred to as informaticists or informaticians, may help implement and optimize anesthesia information management systems. They may also participate in clinical research, management of information systems, and quality improvement in the operating room or throughout a health care system. Here, we describe the specialty of clinical informatics, how anesthesiologists may obtain training in clinical informatics, and the considerations particular to the subspecialty of anesthesia informatics. Management of perioperative information systems, implementation of computerized clinical decision support systems in the perioperative environment, the role of virtual visits and remote monitoring, perioperative informatics research, perioperative process improvement, leadership, and change management are described from the perspective of the anesthesiologist-informaticist.

Anesthesia providers' visual attention in simulated anesthesia emergencies using conventional number-based and avatar-based patient monitoring: a prospective, eye-tracking study

BACKGROUND

Inadequate situational awareness accounts for two-thirds of preventable complications in anesthesia. An essential tool for situational awareness in the perioperative setting is the patient monitor. However, the conventional monitor has several weaknesses. Avatar-based patient monitoring may address these shortcomings and promote situation awareness, a prerequisite for good decision making.

OBJECTIVE

The spatial distribution of visual attention is a fundamental process for achieving adequate situation awareness and thus a potential quantifiable surrogate for situation awareness. Moreover, measuring visual attention with a head-mounted eye-tracker may provide insights into usage and acceptance of the new avatar-based patient monitoring modality.

METHODS

This prospective eye-tracking study compared anesthesia providers' visual attention on conventional and avatar-based patient monitors during simulated critical anesthesia events. We defined visual attention, measured as fixation count and dwell time, as our primary outcome. We correlated visual attention with the potential confounders: performance in managing simulated critical anesthesia events (task performance), work experience, and profession. We used mixed linear models to analyze the results.

RESULTS

Fifty-two teams performed 156 simulations. After a manual quality check of the eye-tracking footage, we excluded 57 simulations due to technical problems and quality issues. Participants had a median of 198 (IQR 92.5 - 317.5) fixations on the patient monitor with a median dwell time of 30.2 (IQR 14.9 - 51.3) seconds. We found no significant difference in participants' visual attention when using avatar-based patient monitoring or conventional patient monitoring. However, we found that with each percentage point of better task performance, the number of fixations decreased by about 1.39 (coefficient -1.39; 95%CI: -2.44 to -0.34; P=.02), and the dwell time diminished by 0.23 seconds (coefficient -0.23; 95%CI: -0.4 to -0.06; P=.01).

CONCLUSIONS

Using eye-tracking, we found no significant difference in visual attention when anesthesia providers used avatar-based monitoring or conventional patient monitoring in simulated critical anesthesia events. However, we identified visual attention in conjunction with task performance as a surrogate for situational awareness.

CLINICALTRIAL

Business Management System for Ethics Committees Number Req-2020-00059.

An inherently dangerous fluid warmer design

A Hotline® fluid warmer is a device commonly used by anesthesia providers in the operating room to warm and infuse blood products and large fluid volumes. The purpose of the fluid warmer is to counter heat loss, which occurs under anesthesia. Despite normal checks performed prior to its use, we discovered a breach in the fluid warming set attached to the Hotline® fluid warmer during blood administration. The breach contaminated the patient's sterile intravenous line. We describe the quality and safety processes we undertook in detail. We discuss the notion that monitoring alarms are an important safety feature of most modern devices utilized by anesthesia providers. We believe the Hotline® fluid warmer lacks a crucial monitor for detecting a breach within the fluid warming set, and therefore recommend the addition of an alarm to improve this device's safety.

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.

Voice alerting as a medical alarm modality for next-generation patient monitoring: a randomised international multicentre trial

BACKGROUND

Acoustic alarms in medical devices are vital for patient safety. State-of-the-art patient monitoring alarms are indistinguishable and contribute to alarm fatigue. There are two promising new sound modalities for vital sign alarms. Auditory icons convey alarms as brief metaphorical sounds, and voice alerts transmit information using a clear-spoken language. We compared how reliably healthcare professionals identified alarms using these two modalities.

METHODS

This investigator-initiated computer-based multicentre simulation study included 28 anaesthesia providers who were asked to identify vital sign alarms in randomised order, once with voice alerts and once with auditory icons. We further assessed time to decision, diagnostic confidence, and perceived helpfulness. We analysed the results using mixed models, adjusted for possible confounders.

RESULTS

We assessed 14 alarms for each modality, resulting in 392 comparisons across all participants. Compared with auditory icons, healthcare providers had 58 times higher odds of correctly identifying alarms using voice alerts (odds ratio 58.0; 95% confidence interval [CI]: 25.1-133.6; P<0.001), made their decisions about 14 s faster (coefficient -13.9; 95% CI: -15.8 to -12.1 s; P<0.001), perceived higher diagnostic confidence (100% [392 of 392] vs 43% [169 of 392; P<0.001]), and rated voice alerts as more helpful (odds ratio 138.2; 95% CI: 64.9-294.1; P<0.001). The participants were able to identify significantly higher proportions of alarms with voice alerts (98.5%; P<0.001) and auditory icons (54.1%; P<0.001) compared with state-of-the-art alarms (17.9%).

CONCLUSIONS

Voice alerts were superior to auditory icons, and both were superior to current state-of-the-art auditory alarms. These findings demonstrate the potential that voice alerts hold for patient monitoring.

Evaluating the appropriateness of clinical decision support alerts: A case study

RATIONALE, AIMS, AND OBJECTIVES

Clinical decision support (CDS) generates excessive alerts that disrupt the workflow of clinicians. Therefore, inefficient clinical processes that contribute to the misfit between CDS alert and workflow must be evaluated. This study evaluates the appropriateness of CDS alerts in supporting clinical workflow from a socio-technical perspective.

METHOD

A qualitative case study evaluation was conducted at a 620-bed public teaching hospital in Malaysia using interview, observation, and document analysis to investigate the features and functions of alert appropriateness and workflow-related issues in cardiological and dermatological settings. The current state map for medication prescribing process was also modelled to identify problems pertinent to CDS alert appropriateness.

RESULTS

The main findings showed that CDS was not well designed to fit into a clinician's workflow due to influencing factors such as technology (usability, alert content, and alert timing), human (training, perception, knowledge, and skills), organizational (rules and regulations, privacy, and security), and processes (documenting patient information, overriding default option, waste, and delay) impeding the use of CDS with its alert function. We illustrated how alert affect workflow in clinical processes using a Lean tool known as value stream mapping. This study also proposes how CDS alerts should be integrated into clinical workflows to optimize their potential to enhance patient safety.

CONCLUSION

The design and implementation of CDS alerts should be aligned with and incorporate socio-technical factors. Process improvement methods such as Lean can be used to enhance the appropriateness of CDS alerts by identifying inefficient clinical processes that impede the fit of these alerts into clinical workflow.

An Investigation of Speech Features, Plant System Alarms, and Operator-System Interaction for the Classification of Operator Cognitive Workload During Dynamic Work

OBJECTIVE

To investigate speech features, human-machine alarms, and operator-system interaction for the estimation of cognitive workload in full-scale realistic simulated scenarios.

BACKGROUND

Theories and models of cognitive workload are critical for the design and evaluation of human-machine systems. Unfortunately, there are very few nonintrusive cognitive workload measures available for realistic dynamic human-machine interaction.

METHOD

The study was conducted in a full-scope control room research simulator of an advanced nuclear reactor. Six crews, each consisting of three operators, participated in 12 scenarios. The operators rated their workload every second minute. Machine learning algorithms were trained to estimate operators' workload based on crew communication, operator-system interaction, and system alarms.

RESULTS

Random Forest (RF) utilizing speech and system features achieved an accuracy of 67% on test data. Utilizing speech features only, the accuracy achieved was 63%. The most important speech features were pitch, amplitude, and articulation rate. A 61% accuracy was achieved when alarms and operator-system interaction features were used. The most important features were the number of alarms and amount of operator-system interaction. Accuracy for algorithms trained for each operator ranged from 39% to 98%, with an average of 72%. For a majority of analyses performed, RF and extreme gradient boosting (XGB) outperformed other algorithms.

CONCLUSION

The results demonstrate that the features investigated and machine learning models developed provide a potential for the dynamic nonintrusive measurement of cognitive workload.

APPLICATION

The approach presented can be developed for nonintrusive workload measurement in real-world human-machine applications, simulator-based training, and research.