Monitor alarm fatigue: standardizing use of physiological monitors and decreasing nuisance alarms

Enhancing Patient Safety Through an Integrated Internet of Things Patient Care System: Large Quasi-Experimental Study on Fall Prevention

BACKGROUND

The challenge of preventing in-patient falls remains one of the most critical concerns in health care.

OBJECTIVE

This study aims to investigate the effect of an integrated Internet of Things (IoT) smart patient care system on fall prevention.

METHODS

A quasi-experimental study design is used. The smart patient care system is an integrated IoT system combining a motion-sensing mattress for bed-exit detection, specifying different types of patient calls, integrating a health care staff scheduling system, and allowing health care staff to receive and respond to alarms via mobile devices. Unadjusted and adjusted logistic regression models were used to investigate the relationship between the use of the IoT system and bedside falls compared with a traditional patient care system.

RESULTS

In total, 1300 patients were recruited from a medical center in Taiwan. The IoT patient care system detected an average of 13.5 potential falls per day without any false alarms, whereas the traditional system issued about 11 bed-exit alarms daily, with approximately 4 being false, effectively identifying 7 potential falls. The bedside fall incidence during hospitalization was 1.2% (n=8) in the traditional patient care system ward and 0.1% (n=1) in the smart ward. We found that the likelihood of bedside falls in wards with the IoT system was reduced by 88% (odds ratio 0.12, 95% CI 0.01-0.97; P=.047).

CONCLUSIONS

The integrated IoT smart patient care system might prevent falls by assisting health care staff with efficient and resilient responses to bed-exit detection. Future product development and research are recommended to introduce IoT into patient care systems combining bed-exit alerts to prevent inpatient falls and address challenges in patient safety.

AF among Nurses Working in Neonatal and Paediatric Intensive Care Units: A Cross-Sectional Study

Aim: This research study aims to determine nurses' alarm fatigue (AF) levels in paediatric critical care units in two governmental hospitals and to examine the significant differences in the mean between nurses' attributes, nurses' working environment, and nurses' alarm management with the level of fatigue caused by the alarm. Background: In recent years, AF has become a significant and growing concern among nurses. However, in the Saudi Arabian paediatrics context, the impact of AF on nurses working in intensive care units remains unexplored. Method: A descriptive cross-sectional survey was conducted using a non-probability purposive sampling method. Data were collected from 216 nurses in two governmental hospitals through self-administered questionnaires comprised of four sections: individual attributes, work environment, alarm management, and AF scale. Data analysis: The Statistical Package of Social Science (SPSS) was used to analyse the data, and ANOVA was utilised to describe the sample's demographic characteristics and determine any differences. Results: Most participants were female, held a bachelor's degree, and were aged 31 to 35. Of the participants, 62.5% reported experiencing a medium level of AF, 29.2% reported a low level, and 8.3% reported a high level. Participants expressed that recurrent false alarms disrupt patient care and decrease trust in alarm systems. Significant differences in AF levels were observed based on marital status and the percentage of non-actionable alarms. Conclusions: Nurses working in paediatric critical units with high rates of false alarms, the frequent de-activation of alarms, and decreased trust in alarm systems are more likely to experience AF. Addressing AF is crucial for patient safety; nurse training on alarm management, the collaboration between biomedical and nursing staff, and technological advancements can help mitigate this issue. Implications for Practice: To minimise the adverse effects of AF, policymakers, biomedical experts, and nursing administrators must establish comprehensive policies and protocols concerning alarms. These measures aim to ensure secure and efficient care for the well-being of patients and nurses.

Alarm fatigue and sleep quality in medical staff-a Polish-Czech-Slovak study on workplace ergonomics

Background

Alarms are crucial in informing Healthcare Workers (HCWs) about critical patient needs, but unmanaged frequency and noise of alarms can de-sensitize medical staff and compromise patient safety. Alarm fatigue is identified as the major cause of the clinical alarm management problem. It occurs when the medical staff is overwhelmed by the number of clinical alarms.

Methods

The survey was conducted online using Google's form-making tools from June to July 2023. There were three parts to the survey used in the study: a socio-demographic metric, the Alarm Fatigue Assessment Questionnaire (AFAQ), and The Pittsburgh Sleep Quality Index (PSQI). A significance level of 0.05 was used in the analysis.

Results

The survey included 756 medical professionals from three European countries (Slovakia, the Czech Republic and Poland). The participants in the study were 42 years old on average, and they had 12 years of work experience. 603 out of 756 survey participants had poor sleep quality, 147 had good sleep quality, and 6 did not provide an answer. This study analyzed the alarm fatigue levels of respondents in every country. In the Czech Republic, Poland and Slovakia, a statistically significant association (p = 0.039, p = 0.001, p < 0.001) was found between alarm fatigue and sleep quality in medical staff.

Conclusion

Based on our study, alarm fatigue and sleep quality of HCWs are correlated. Therefore, alarm fatigue and sleep hygiene should be monitored.

Lipopolysaccharide induced systemic inflammation and heart rate variability in a term newborn piglet model

BACKGROUND

Early biomarkers are needed to improve diagnosis and support antibiotic stewardship in neonatal sepsis. Heart rate variability (HRV) is proposed as such a biomarker. However, there is a lack of studies in term newborns. Infusion of lipopolysaccharide (LPS) from Escherichia coli induces systemic inflammation comparable to sepsis in newborns. We aimed to study the effect of systemic LPS induced inflammation on HRV in term newborn piglets.

METHODS

Baseline HRV was recorded for 1 h. This control period was compared to the hourly HRV for each piglet (n = 9) during 4 h of LPS infusion. For comparison, we used a mixed-effects regression model.

RESULTS

Systemic inflammation induced by LPS was found to reduce HRV. Compared to baseline, most measures of HRV decreased to lower values compared to baseline at 2 h, 3 h, and 4 h after initiation of LPS infusion. Heart rate (HR) was increased at 2 h, 3 h, and 4 h. When adjusting for HR in the mixed-effects regression model all reductions in HRV were explained by the increase in HR.

CONCLUSIONS

Reduced HRV may be an early biomarker of neonatal sepsis. However, an increase in HR alone could be an already available, more accessible, and interpretable biomarker of sepsis in term neonates.

IMPACT

In a term newborn piglet model, systemic inflammation induced by lipopolysaccharide from Escherichia coli reduced heart rate variability measures and increased heart rate. All reductions in heart rate variability were mediated by heart rate. While heart rate variability may be a biomarker of sepsis in term newborns, changes in heart rate alone could be a more readily available biomarker.

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.

Machine Alarm Fatigue among Hemodialysis Nurses in 29 Tertiary Hospitals

OBJECTIVES

 To understand the status quo and related influencing factors of machine alarm fatigue of hemodialysis nurses in tertiary hospitals in Liaoning Province.

METHODS

 This cross-sectional study employed convenience sampling to select 460 nurses from 29 tertiary hospitals in Liaoning Province, who are involved in hemodialysis care. Surveys were conducted using the General Information Questionnaire, Alarm Fatigue Scale, National Aeronautics and Space Administration Task Load Index, and Maslach Burnout Inventory Scale.

RESULTS

 The overall machine alarm fatigue score for 460 hemodialysis nurses from 29 tertiary hospitals in Liaoning Province was 17.04 ± 3.21, indicating a moderate level. The multiple linear regression analysis shows that years of experience in hemodialysis nursing, the number of patients managed per shift, whether specialized nursing training has been received, self-reported health status, emotional exhaustion, and workload have statistically significant associations with alarm fatigue among hemodialysis nurses (p < 0.05). Among them, the years of experience in hemodialysis nursing are negatively correlated with alarm fatigue among hemodialysis nurses, whereas the number of patients managed per shift and workload are positively correlated with alarm fatigue among hemodialysis nurses.

CONCLUSION

 This study indicates that certain demographic factors, workload, and occupational burnout are associated with machine alarm fatigue among hemodialysis nurses. Therefore, hemodialysis-related managers should establish a Machine Alarm Management System, implement Personalized Thresholds and Delayed Alarms, ensure reasonable staffing arrangements, improve compassion fatigue, and enhance anticipatory care. Our findings have implications for improving the health and well-being of hemodialysis nurses, providing a conducive environment for professional training in hemodialysis, and ultimately addressing the current situation of machine alarm fatigue among hemodialysis nurses.

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.

The Alarm Fatigue Challenge in the Neonatal Intensive Care Unit: A "before" and "after" Study

OBJECTIVE

Alarm fatigue (AF) happens when professionals are exposed to many alarms and they become desensitized to them. It is related to proliferation of devices, not standardized alarm limits, and high prevalence of "nonactionable alarms," i.e., false alarms (triggered by equipment issues) or nuisance alarms (physiological change not requiring clinical action). When AF happens, response time seems to be longer and important alarms could be dismissed. After evaluating the situation in our neonatal intensive care unit (NICU), an alarm management program (AMP) was developed to reduce AF. The objective of this study were to compare the proportion of true alarms, nonactionable alarms, and to measure response time to alarms in the NICU before and after implementing an AMP and also to determine variables associated with nonactionable alarms and response time.

STUDY DESIGN

This was a cross-sectional study. A total of 100 observations were collected between December 2019 and January 2020. After an AMP was implemented, 100 new observations were collected between June 2021 and August 2021. We estimated the true and nonactionable alarms proportion. Univariate analyses were performed to determine variables associated with nonactionable alarms and response time. Logistic regression was performed to assess independent variables.

RESULTS

The proportion of true alarms before and after AMP was 31 versus 57% (p = 0.001), whereas the proportion of nonactionable alarms was 69 versus 43% (p = 0.001). Median response time was significantly reduced (35 versus 12 seconds; p = 0.001). Before AMP, neonates with less intensive care needs had a higher proportion of nonactionable alarms and a longer response time. After AMP, response time was similar for true and nonactionable alarms. For both periods, the need of respiratory support was significantly associated with true alarms (p = 0.001). In the adjusted analysis, response time (p = 0.001) and respiratory support (p = 0.003) remained associated with nonactionable alarms.

CONCLUSION

AF was highly prevalent in our NICU. This study shows that after the implementation of an AMP, response time to alarms and the proportion of nonactionable alarms can be significantly reduced.

KEY POINTS

· AF happens when professionals are exposed to many alarms and they become desensitized to them.. · The presence of AF can compromise patients' safety.. · The implementation of an AMP can reduce AF..

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

Background

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

Objective

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

Methods

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

Results

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

Conclusion

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

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

BACKGROUND

Alarm fatigue is a major technology-induced hazard for patients and staff in intensive care units. Too many - mostly unnecessary - alarms cause desensitisation and lack of response in medical staff. Unsuitable alarm policies are one reason for alarm fatigue. But changing alarm policies is a delicate issue since it concerns patient safety.

OBJECTIVE

We present ARTEMIS, a novel, computer-aided clinical decision support system for policy makers that can help to considerably improve alarm policies using data from hospital information systems.

METHODS

Policy makers can use different policy components from ARTEMIS' internal library to assemble tailor-made alarm policies for their intensive care units. Alternatively, policy makers can provide even more highly customised policy components as Python functions using data the hospital information systems. This can even include machine learning models - for example for setting alarm thresholds. Finally, policy makers can evaluate their system of policies and compare the resulting alarm loads.

RESULTS

ARTEMIS reports and compares numbers of alarms caused by different alarm policies for an easily adaptable target population. ARTEMIS can compare policies side-by-side and provides grid comparisons and heat maps for parameter optimisation. For example, we found that the utility of alarm delays varies based on target population. Furthermore, policy makers can introduce virtual parameters that are not in the original data by providing a formula to compute them. Virtual parameters help measuring and alarming on the right metric, even if the patient monitors do not directly measure this metric.

CONCLUSION

ARTEMIS does not release the policy maker from assessing the policy from a medical standpoint. But as a knowledge discovery and clinical decision support system, it provides a strong quantitative foundation for medical decisions. At comparatively low cost of implementation, ARTEMIS can have a substantial impact on patients and staff alike - with organisational, economic, and clinical benefits for the implementing hospital.

Auditory Sequences Presented With Spearcons Support Better Multiple Patient Monitoring Than Single-Patient Alarms: A Preclinical Simulation

OBJECTIVE

A study of auditory displays for simulated patient monitoring compared the effectiveness of two sound categories (alarm sounds indicating general risk categories from international alarm standard IEC 60601-1-8 versus event-specific sounds according to the type of nursing unit) and two configurations (single-patient alarms versus multi-patient sequences).

BACKGROUND

Fieldwork in speciality-focused high dependency units (HDU) indicated that auditory alarms are ambiguous and do not identify which patient has a problem. We tested whether participants perform better using auditory displays that identify the relevant patient and problem.

METHOD

During simulated patient monitoring of four patients in a respiratory HDU, 60 non-clinicians heard either (a) IEC risk categories as single-patient alarm sounds, (b) event-specific categories as single-patient alarm sounds, (c) IEC risk categories in multi-patient sequences or (d) event-specific categories in multi-patient sequences. Participants performed a perceptual-motor task while monitoring patients; after detecting abnormal events, they identified the patient and the event.

RESULTS

Participants hearing multi-patient sequences made fewer wrong patient identifications than participants hearing single-patient alarms. Advantages of event-specific categories emerged when IEC risk category sounds indicated more than one potential event. Even when IEC and event-specific sounds indicated the same unique event, spearcons supported better event identification than did auditory icon sounds.

CONCLUSION

Auditory displays that unambiguously convey which patient is having what problem dramatically improve monitoring performance in a preclinical HDU simulation.

APPLICATION

Time-compressed speech assists development of detailed risk categories needed in specific HDU contexts, and multi-patient sound sequences allow multiple patient wellbeing to be monitored.

Surveillance and patient safety in nursing research: A bibliometric analysis from 1993 to 2023

AIMS

To identify and characterize the thematic foci, structure and evolution of nursing research on surveillance and patient safety.

DESIGN

Bibliometric analysis.

METHODS

Bibliometric methods were employed to analyse 1145 articles, using Bibliometrix and VOSviewer software.

DATA SOURCE

The Scopus bibliographic database was searched on April 7, 2023.

RESULTS

A keyword co-occurrence analysis found the most frequently occurring keywords to be: patient safety, nursing, nurses, adverse events, monitoring, critical care, quality improvement, vital signs, safety, alarm fatigue, education, nursing care, surveillance, clinical alarms, failure to rescue, evidence-based practice, acute care, clinical deterioration, communication, intensive care. Network mapping, clustering and time-tracking of the keywords revealed the focal themes, structure and evolution of the research field.

CONCLUSION

By assessing critical areas of the nursing research field, this study extends and enriches the current discourse on surveillance and patient safety for nursing researchers and practitioners. Critical challenges still have to be met by nurses, however, including the failure to rescue deteriorating patients. Further knowledge and understanding of surveillance and patient safety must be successfully translated from research to practice.

IMPLICATIONS FOR THE PROFESSION

This study highlights the gaps in nursing knowledge with regard to surveillance and patient safety and encourages nursing professionals to turn to evidence-based surveillance practices.

IMPACT

In addressing the problem of surveillance and its effect on patient safety, this study found that, in most clinical care settings, preventing failures to rescue and adverse patient outcomes still remains a challenge for the nursing profession. This study should have an impact on nursing academics' future research themes and on nursing professionals' future clinical practices.

REPORTING METHOD

Relevant EQUATOR guidelines have been adhered to by employing recognized bibliometric reporting methods.

Standard practices in cardiac monitoring: training needs of intensive care unit nurses

BACKGROUND

Enforcing practice standards for cardiac monitoring in intensive care units (ICUs) has been shown to reduce misdiagnoses and inappropriate interventions. Continuous professional development (CPD) programs are committed to aligning clinical practices with recommended standards. The crucial initial phase in CPD development involves assessing the training needs of the targeted population.

OBJECTIVE

To assess the training needs of ICU nurses in cardiac monitoring. The overarching goal was to formulate a focused Continuous Professional Development (CPD) program geared towards implementing standard practices in cardiac monitoring.

METHODS

This study employed a generic qualitative approach with a descriptive design, utilizing interviews and focus groups from July to September 2018. Involving 16 ICU nurses. Content analysis was employed, encompassing transcription, fluctuant and iterative reading, unitization, categorization, coding, description, and interpretation.

RESULTS

All nurses recognized cardiac monitoring's importance in the ICU but reported barriers to its effective implementation which were related to factors that could addressed by a CPD as insufficient knowledge and skills. Training needs were identified in both clinical and technical aspects, with recommendations for practical and theoretical activities and e-learning strategies. Barriers related to organizational aspects (equipment and communication within the healthcare team) were also mentioned.

CONCLUSION

ICU nurses presented clear and specific training needs related to cardiac monitoring as knowledge, skills, and competencies. Other organizational aspects were also reported as barriers. Addressing these learning needs through targeted CPD aligned with organizational initiatives can contribute to enhancing the quality of cardiac monitoring practices in ICUs.

Role of artificial intelligence in haemodynamic monitoring

This narrative review explores the evolving role of artificial intelligence (AI) in haemodynamic monitoring, emphasising its potential to revolutionise patient care. The historical reliance on invasive procedures for haemodynamic assessments is contrasted with the emerging non-invasive AI-driven approaches that address limitations and risks associated with traditional methods. Developing the hypotension prediction index and introducing CircEWSTM and CircEWS-lite TM showcase AI's effectiveness in predicting and managing circulatory failure. The crucial aspects include the balance between AI and healthcare professionals, ethical considerations, and the need for regulatory frameworks. The use of AI in haemodynamic monitoring will keep growing with ongoing research, better technology, and teamwork. As we navigate these advancements, it is crucial to balance AI's power and healthcare professionals' essential role. Clinicians must continue to use their clinical acumen to ensure that patient outliers or system problems do not compromise the treatment of the condition and patient safety.

Noise exposure among staff in intensive care units and the effects of unit-based noise management: a monocentric prospective longitudinal study

BACKGROUND

Intensive care units (ICUs) are often too noisy, exceeding 70-80 dBA, which can have negative effects on staff. The corresponding recommendation of the World Health Organization (average sound pressure level below 35 dBA) is often not achieved. To date there is a lack of intervention studies examining the extent to which unit-based noise management in ICUs contributes to a reduction in noise exposure for the staff. The study therefore aims to provide answers to 1) how unit-based noise management sustainably reduces the subjective noise exposure among staff, and 2) how this intervention affects other noise-related topics.

METHODS

We performed a monocentric prospective longitudinal study with three measurement points in a German university hospital in three ICUs. We collected data from different healthcare professionals and other professional groups between October 2021 and August 2022 using an online questionnaire. Data were analyzed using descriptive and inference statistics.

RESULTS

A total of n = 179 participants took part in the surveys. The majority of participants were nurses or pediatric nurses. Most participants worked more than 75% full-time equivalent. Staff on the three ICUs reported high levels of noise exposure. No significant changes in noise exposure over time were observed. Participants were already aware of the topic and believed that a behavior change could positively influence the noise environment.

CONCLUSIONS

This study provides an initial insight into how a unit-based noise management could contribute to a reduction in the subjective noise exposure among staff in ICUs. The results of this study highlight the importance of this topic. Future studies should aim to research aspects of adherence and their facilitators or barriers, which promote the sustained implementation of noise-reducing measures by staff.

TRIAL REGISTRATION

German Clinical Trials Register (DRKS): DRKS00025835; Date of registration: 12.08.2021.

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.

Requirements for Trustworthy Artificial Intelligence and its Application in Healthcare

OBJECTIVES

Artificial intelligence (AI) technologies are developing very rapidly in the medical field, but have yet to be actively used in actual clinical settings. Ensuring reliability is essential to disseminating technologies, necessitating a wide range of research and subsequent social consensus on requirements for trustworthy AI.

METHODS

This review divided the requirements for trustworthy medical AI into explainability, fairness, privacy protection, and robustness, investigated research trends in the literature on AI in healthcare, and explored the criteria for trustworthy AI in the medical field.

RESULTS

Explainability provides a basis for determining whether healthcare providers would refer to the output of an AI model, which requires the further development of explainable AI technology, evaluation methods, and user interfaces. For AI fairness, the primary task is to identify evaluation metrics optimized for the medical field. As for privacy and robustness, further development of technologies is needed, especially in defending training data or AI algorithms against adversarial attacks.

CONCLUSIONS

In the future, detailed standards need to be established according to the issues that medical AI would solve or the clinical field where medical AI would be used. Furthermore, these criteria should be reflected in AI-related regulations, such as AI development guidelines and approval processes for medical devices.

Ten Years Later, Alarm Fatigue Is Still a Safety Concern

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

The impact of the use of continuous pulse oximetry monitoring to monitor patients at high risk of respiratory depression on nursing practice

AIM

To describe the impact on nursing practice of using continuous pulse oximetry monitoring to monitor patients at high risk for respiratory depression after surgery.

DESIGN

A convergent mixed method design.

METHODS

Thirty (30) hours of non-participant structured observation and explanatory interviews were conducted with 10 nurses from the surgery care unit and intensive care unit.

RESULTS

We found that nursing practice to evaluate and monitor at-risk patients through continuous pulse oximetry monitoring is mainly linked to technical care. Nurses generally meet the frequency of bedside monitoring required by established protocols. During the structured non-participant observation periods, it was observed that 90% of the alarms were false (unsustained desaturations). This was confirmed by the nurses during the explanatory interviews. Noisy environments, high number of false alarms, poor communication between nurses and various operational failures might have a negative impact on nursing practice.

CONCLUSION

Several challenges must be overcome for this technology to achieve the desired outcomes of continuous surveillance and rapid detection of respiratory depression episodes for post-surgical patients. No Patient or Public Contribution.

Anxiety and Insomnia Mediate the Association of Fear of Infection and Fatigue: A Cross-Sectional Survey of Nurses Deployed to a COVID-19 Epicenter in China

Background

This study aimed to test the mediating role of anxiety and insomnia in the association between fear of infection and fatigue.

Methods

A cross-sectional questionnaire survey was conducted on the nurses deployed to Heihe. A serial multiple mediation model was established to determine the role of anxiety and insomnia in the association between fear of infection and fatigue.

Findings

Over half (53.0%) of the study participants reported experiencing fear of infection despite stringent personal protection measures. The scores of anxiety (11.87±5.19), insomnia (16.33±5.95), and fatigue (45.94±12.93) were moderately correlated, with a Pearson correlation coefficient ranging from 0.501 to 0.579. Anxiety, either alone or in combination with insomnia, mediated the association between fear of infection and fatigue.

Conclusion

The findings suggest that anxiety and insomnia play a mediating role in the relationship between fear of infection and fatigue. These results emphasize the importance of implementing targeted mental health interventions and work arrangements to address the well-being of healthcare professionals.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

TRIAL REGISTRATION

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

Technological Intervention to Improve Alarm Management in Acute Care Telemetry Units

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

Perceptually Discriminating the Highest Priority Alarms Reduces Response Time: A Retrospective Pre-Post Study at Four Hospitals

OBJECTIVE

Reduce nurse response time for emergency and high-priority alarms by increasing discriminability between emergency and all other alarms and suppressing redundant and likely false high-priority alarms in a secondary alarm notification system (SANS).

BACKGROUND

Emergency alarms are the most urgent, requiring immediate action to address a dangerous situation. They are clinician-triggered and have higher positive predictive value (PPV). High-priority alarms are automatically triggered and have lower PPV.

METHOD

We performed a retrospective pre-post study, analyzing data 15 months before and 25 months after a SANS redesign was implemented in four hospitals. For emergency alarms, we incorporated digitized human speech to distinguish them from automatically triggered alarms, leaving their onset and escalation pathways unchanged. For automatically triggered alarms, we suppressed some by delaying initial onset and escalation by 20 s. We used linear mixed models to assess the change in response time, Fisher's exact test for the proportion of response times longer than 120 s, and control charts for process stability.

RESULTS

Response time for emergency alarms decreased at all hospitals (main, from 26.91 s to 22.32 s, p < .001; cardiac, from 127.10 s to 52.43 s, p < .001; cancer, from 18.03 s to 15.39 s, p < .001). Improvements were sustained. Automatically triggered alarms decreased 25.0%. Response time for the three automatically triggered cardiac alarms increased at the four hospitals.

CONCLUSION

Auditory sound disambiguation was associated with a sustained reduced nurse response time for emergency alarms, but suppressing some high-priority automatically triggered alarms was not.

APPLICATION

Distinguishing and escalating urgent, actionable alarms with higher PPV improves response time.

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.

The development and preliminary evaluation of a clinician e-learning training platform for a neonatal sepsis risk monitor for use in ICU settings

BACKGROUND

Training clinicians on the use of hospital-based patient monitoring systems (PMS) is vital to mitigate the risk of use errors and of frustration using these devices, especially when used in ICU settings. PMS training is typically delivered through face-to-face training sessions in the hospital. However, it is not always feasible to deliver training in this format to all clinical staff given some constraints (e.g., availability of staff and trainers to attend in-person training sessions and the costs associated with face-to-face training).

OBJECTIVE

The literature indicates that E-learning has the potential to mitigate barriers associated with time restrictions for trainers and trainees and evidence shows it to be more flexible, and convenient for learners in healthcare settings. This study aimed to develop and carry out a preliminary evaluation via a case study of an e-learning training platform designed for a novel neonatal sepsis risk monitor system (Digi-NewB).

METHODS

A multi-modal qualitative research case study approach was used, including the analysis of three qualitative data sources: (i) audio/video recordings of simulation sessions in which participants were asked to operate the system as intended (e.g., update the clinical observations and monitor the sepsis risk), (ii) interviews with the simulation participants and an attending key opinion leader (KOL), who observed all simulation sessions, and (iii) post-simulation survey.

RESULTS

After receiving ethical approval for the study, nine neonatal intensive care unit (NICU) nurses completed the online training and participated in the simulation and follow-up interview sessions. The KOL was also interviewed, and seven out of the nine NICU nurses answered the post-simulation survey. The video/audio analysis of the simulations revealed that participants were able to use and interpret the Digi-NewB interface. Interviews with simulation participants and the KOL, and feedback extracted from the survey, revealed that participants were overall satisfied with the training platform and perceived it as an efficient and effective method to deliver medical device training.

CONCLUSIONS

This study developed an online training platform to train clinicians in the use of a critical care medical device and carried out a preliminary evaluation of the platform via a case study. The e-learning platform was designed to supplement and enhance other training approaches. Further research is required to evaluate the effectiveness of this approach.

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.

Tell-a provider about tele: Reducing overuse of telemetry across 10 hospitals in a safety net system

BACKGROUND

Telemetry is often a scarce resource at hospitals and is important for arrhythmia and myocardial ischemia detection. Overuse of telemetry monitoring leads to alarm fatigue resulting in failure to respond to arrhythmias, patient harm, and possible unnecessary testing.

METHODS

This quality improvement initiative was implemented across NYC Health and Hospitals, an 11-hospital urban safety net system. The electronic health record intervention involved the addition of a mandatory indication in the telemetry order and a best practice advisory (BPA) that would fire after the recommended time period for reassessment had passed.

RESULTS

The average telemetry hours per patient encounter went from 60.1 preintervention to 48.4 postintervention, a 19.5% reduction (p < .001). When stratified by the 11 hospitals, decreases ranged from 9% to 30%. The BPA had a 53% accept rate and fired 52,682 times, with 27,938 "discontinue telemetry" orders placed. The true accept rate was 50.4%, as there was a 2.6% 24-h reorder rate. There was variation based on clinician specialty and clinician type (attending, fellow, resident, physician associate, nurse practitioner).

CONCLUSION

We successfully reduced telemetry monitoring across a multisite safety net system using solely an electronic health record (EHR) intervention. This expands on previous telemetry monitoring reduction initiatives using EHR interventions at single academic sites. Further study is needed to investigate variation across clinician type, specialty, and post-acute sites.

The Mediating Role of Depression and of State Anxiety οn the Relationship between Trait Anxiety and Fatigue in Nurses during the Pandemic Crisis

The coronavirus pandemic (COVID-19) is a global health crisis with a particular emotional and physical impact on health professionals, especially nurses. The aim of this study was to investigate the prevalence of anxiety, depression and fatigue and their possible relationships among nurses during the pandemic. The study population consisted of nurses from five tertiary-level public hospitals in Athens who completed the Fatigue Assessment Scale (FAS), Beck Depression Inventory (BDI) and State-Trait Anxiety Inventory (STAI) questionnaires. Gender, age and years of work experience were recorded. The study was conducted from mid-November to mid-December 2021. The sample included 404 nurses (69 males and 335 females) with a mean age of 42.88 years (SD = 10.90) and 17.96 (SD = 12.00) years of work experience. Symptoms of fatigue were noted in 60.4% of participants, while 39.7% had symptoms of depression, 60.1% had abnormal scores on state anxiety and 46.8% on trait anxiety, with females showing higher scores on all scales (p < 0.05). High positive correlations (p < 0.01) were found between the FAS, BDI, State Anxiety and Trait Anxiety scales. Regression analysis showed that 51.7% of the variance in FAS scores can be explained by trait anxiety, an additional 6.2% by the BDI and 1.2% by state anxiety. Mediation analysis showed that state anxiety and BDI mediate the relationship between trait anxiety and FAS. Finally, BDI was found to exert a moderating role in the relationship between trait anxiety and fatigue. In conclusion, our study showed that nurses continue to experience high rates of anxiety, depression and fatigue. The variation in fatigue appears to be significantly dependent on trait anxiety. Depressive symptomatology and state anxiety exert a parallel positive mediation on the relationship between trait anxiety and fatigue, with depression exhibiting a moderating role in this relationship.

The Association Between Catheter Type and Dialysis Treatment: A Retrospective Data Analysis at Two U.S.-Based ICUs

Dialysis catheter type may be associated with differences in continuous renal replacement therapy (CRRT) treatment in the critically ill, with potential implications for patient outcomes and healthcare costs.

OBJECTIVES

To evaluate the association between the catheter type and multiple dialysis treatment outcomes among the critically ill.

DESIGN

Retrospective, observational study.

SETTING

Two U.S.-based ICUs.

PARTICIPANTS

Critically ill patients receiving CRRT between April 1, 2018, and July 1, 2020. A total of 1,037 CRRT sessions were analyzed.

MAIN OUTCOMES AND MEASURES

Circuit life, alarm interruption frequency (including a subset of vascular access [VA]-related alarms), termination type (elective vs nonelective), and blood flow rates. Pre- (n = 530) and post-catheter change (n = 507) periods were assessed, and the post-change period was further divided into intervals of pre-COVID (n = 167) and COVID contemporaneous (n = 340) to account for the pandemic's impact.

RESULTS

Compared with pre-change sessions, post-change sessions had 31% longer circuit life (95% CI, 1.14-1.49; p < 0.001), 3% higher blood flow rate (1.01-1.05; p < 0.01), and lower proportion of nonelective terminations (adjusted odds ratio [OR], 0.42 [0.28-0.62]; p < 0.001). There were fewer interruptions for all alarms (adjusted count ratio, 0.95 [0.87-1.05]; p = 0.31) and VA-related alarms (0.80 [0.66-0.96]; p = 0.014). The sessions during COVID period were statistically similar to pre-COVID sessions for all outcomes except a lower proportion of nonelective terminations (adjusted OR, 0.39 [0.22-0.70]; p < 0.01).

CONCLUSIONS

A change in catheter type was associated with longer CRRT sessions with fewer interruptions and unexpected terminations in a population of critical patients.

Assessment of Remote Vital Sign Monitoring and Alarms in a Real-World Healthcare at Home Dataset

The importance of vital sign monitoring to detect deterioration increases during healthcare at home. Continuous monitoring with wearables increases assessment frequency but may create information overload for clinicians. The goal of this work was to demonstrate the impact of vital sign observation frequency and alarm settings on alarms in a real-world dataset. Vital signs were collected from 76 patients admitted to healthcare at home programs using the Current Health (CH) platform; its wearable continuously measured respiratory rate (RR), pulse rate (PR), and oxygen saturation (SpO₂). Total alarms, alarm rate, patient rate, and detection time were calculated for three alarm rulesets to detect changes in SpO₂, PR, and RR under four vital sign observation frequencies and four window sizes for the alarm algorithms' median filter. Total alarms ranged from 65 to 3113. The alarm rate and early detection increased with the observation frequency for all alarm rulesets. Median filter windows reduced alarms triggered by normal fluctuations in vital signs without compromising the granularity of time between assessments. Frequent assessments enabled with continuous monitoring support early intervention but need to pair with settings that balance sensitivity, specificity, clinical risk, and provider capacity to respond when a patient is home to minimize clinician burden.

Development and Comparative Performance of Physiologic Monitoring Strategies in the Emergency Department

IMPORTANCE

Accurate and timely documentation of vital signs affects all aspects of triage, diagnosis, and management. The adequacy of current patient monitoring practices and the potential to improve on them are poorly understood.

OBJECTIVE

To develop measures of fit between documented and actual patient vital signs throughout the visit, as determined from continuous physiologic monitoring, and to compare the performance of actual practice with alternative patient monitoring strategies.

DESIGN, SETTING, AND PARTICIPANTS

This cross-sectional study evaluated 25 751 adult visits to continuously monitored emergency department (ED) beds between August 1, 2020, and December 31, 2021. A series of monitoring strategies for the documentation of vital signs (heart rate [HR], respiratory rate [RR], oxygen saturation by pulse oximetry [Spo2], mean arterial pressure [MAP]) was developed and the strategies' ability to capture physiologic trends and vital sign abnormalities simulated. Strategies included equal spacing of charting events, charting at variable intervals depending on the last observed values, and discrete optimization of charting events.

MAIN OUTCOMES AND MEASURES

Coverage was defined as the proportion of monitor-derived vital sign measurements (at 1-minute resolution) that fall within the bounds of nursing-charted values over the course of an ED visit (HR ± 5 beats/min, RR ± 3 breaths/min, Spo2 ± 2%, and MAP ± 6 mm Hg). Capture was defined as the documentation of a vital sign abnormality detected by bedside monitor (tachycardia [HR >100 beats/min], bradycardia [HR <60 beats/min], hypotension [MAP <65 mm Hg], and hypoxia [Spo2 <95%]).

RESULTS

Median patient age was 60 years (IQR, 43-75 years), and 13 329 visits (51.8%) were by women. Monitored visits had a median of 4 (IQR, 2-5) vital sign charting events per visit. Compared with actual practice, a simple rule, which observes vital signs more frequently if the last observation fell outside the bounds of the previous values, and using the same number of observations as actual practice, produced relative coverage improvements of 31.5% (95% CI, 30.5%-32.5%) for HR, 31.0% (95% CI, 30.0%-32.0%) for MAP, 16.8% (95% CI, 16.0%-17.6%) for RR, and 7.8% (95% CI, 7.3%-8.3%) for Spo2. The same strategy improved capture of abnormalities by 38.9% (95% CI, 26.8%-52.2%) for tachycardia, 38.1% (95% CI, 29.0%-47.9%) for bradycardia, 39.0% (95% CI, 24.2%-55.7%) for hypotension, and 123.1% (95% CI, 110.7%-136.3%) for hypoxia. Analysis of optimal coverage suggested an additional scope for improvement through more sophisticated strategies.

CONCLUSIONS AND RELEVANCE

In this cross-sectional study, actual documentation of ED vital signs was variable and incomplete, missing important trends and abnormalities. Alternative monitoring strategies may improve on current practice without increasing the overall frequency of patient monitoring.

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

Study Aim

Methods

Results

Conclusions

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

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

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

The effect of educational interventions in managing nurses' alarm fatigue: An integrative review

BACKGROUND

Alarm fatigue is becoming more widely acknowledged as a serious safety concern in modern clinical practice. Nurses are not always proficient in the alarms' functions and capabilities, and they do not undertake training regularly. Educating nurses on alarms maintains their knowledge and abilities in complex clinical settings. Some education has been undertaken to improve clinical alarm response, but the evidence for evaluating the effectiveness of nurse education interventions is limited.

OBJECTIVE

To evaluate the effects of educational interventions for reducing alarm fatigue in nurses, including the reduction of excessive, false and non-actionable alarms, which are major factors causing alarm fatigue in nurses.

DATA SOURCES

PUBMED, EMBASE, CINAHL, SCOPUS and OVID databases were systematically searched from 2016 to 2021.

DESIGN

Integrative Review.

REVIEW METHODS

An integrative review of literature was performed using the PRISMA checklist. Critical appraisal was done using Joanna Briggs Institute level of evidence.

RESULTS

Thirteen studies met the inclusion criteria. The results of most studies showed that educational intervention was beneficial for reducing the total number of alarms and false alarms. Furthermore, nurses' perceptions and knowledge improved, but the reduction in nurses' alarm fatigue is uncertain. A positive effect in alarm management practices was identified after the educational intervention.

CONCLUSION

Educational intervention may be the way to manage nurses' alarm fatigue. The use of medical devices in hospitals is increasing exponentially, and for this reason, alarms are inevitable. The introduction of effective and continuous education and training programs for nurses concerning clinical alarm management as well as raising nurses' awareness of the occurrence of alarm fatigue is vital.

A pilot study to investigate real-time digital alerting from wearable sensors in surgical patients

Background

Continuous vital sign monitoring may identify changes sooner than current standard monitoring.

Objective

To investigate if the use of real-time digital alerts sent to healthcare staff can improve the time taken to identify unwell patients and those with sepsis.

Design

A prospective cohort study design.

Setting

West Middlesex University Hospital, UK.

Participants

Fifty acutely unwell surgical patients admitted to hospital.

Intervention

Patients wore a lightweight wearable sensor measuring heart rate (HR), respiratory rate (RR) and temperature every 2 min whilst standard intermittent ward monitoring of vital signs was performed by nurses. Digital alerts were sent to healthcare staff from the sensor to a smartphone device. All alerts were reviewed for recruited patients to identify the exact time on the sensor in which deterioration occurred. The time to acknowledgement was then reviewed for each action and an average time to acknowledgement calculated.

Results

There were 50 patients recruited in the pilot study, of which there were vital sign alerts in 18 patients (36%). The total number of vital sign alerts generated in these 18 patients was 51. Of these 51 alerts, there were 7 alerts for high HR (13.7%), 33 for RR (64.7%) and 11 for temperature (21.6%). Out of the 27 acknowledged alerts, there were 2 alerts for HR, 17 for RR and 8 for temperature. The average time to staff acknowledgement of the notification for all alerts was 154 min (2.6 h).

There were some patients which had shown signs of deterioration in the cohort. The frequency of routine observation monitoring was increased in 2 cases, 3 patients were referred to a senior clinician and 2 patients were initiated on the sepsis pathway.

Conclusion

This study demonstrates the evaluation of digital alerts to nurses in real time. Although not all alerts were acknowledged, deterioration on the ward observations was detected and actions were taken accordingly. Patients were started on the sepsis pathway and escalation to senior clinicians occurred. Further research is required to review why only some alerts were acknowledged and the effects of digital alerting on patient outcomes.

Trial registration

ClinicalTrials.gov, NCT04638738

Premature ventricular complexes during continuous electrocardiographic monitoring in the intensive care unit: Occurrence rates and associated patient characteristics

AIMS AND OBJECTIVES

This study examined the occurrence rate of specific types of premature ventricular complex (PVC) alarms and whether patient demographic and/or clinical characteristics were associated with PVC occurrences.

BACKGROUND

Because PVCs can signal myocardial irritability, in-hospital electrocardiographic (ECG) monitors are typically configured to alert nurses when they occur. However, PVC alarms are common and can contribute to alarm fatigue. A better understanding of occurrences of PVCs could help guide alarm management strategies.

DESIGN

A secondary quantitative analysis from an alarm study.

METHODS

The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) checklist was followed. Seven PVC alarm types (vendor-specific) were described, and included isolated, couplet, bigeminy, trigeminy, run PVC (i.e. VT >2), R-on-T and PVCs/min. Negative binomial and hurdle regression analyses were computed to examine the association of patient demographic and clinical characteristics with each PVC type.

RESULTS

A total of 797,072 PVC alarms (45,271 monitoring hours) occurred in 446 patients, including six who had disproportionately high PVC alarm counts (40% of the total alarms). Isolated PVCs were the most frequent type (81.13%) while R-on-T were the least common (0.29%). Significant predictors associated with higher alarms rates: older age (isolated PVCs, bigeminy and couplets); male sex and presence of PVCs on the 12-lead ECG (isolated PVCs). Hyperkalaemia at ICU admission was associated with a lower R-on-T type PVCs.

CONCLUSIONS

Only a few distinct demographic and clinical characteristics were associated with the occurrence rate of PVC alarms. Further research is warranted to examine whether PVCs were associated with adverse outcomes, which could guide alarm management strategies to reduce unnecessary PVC alarms.

RELEVANCE TO CLINICAL PRACTICE

Targeted alarm strategies, such as turning off certain PVC-type alarms and evaluating alarm trends in the first 24 h of admission in select patients, might add to the current practice of alarm management.

Predicting arrhythmia, atrial fibrillation from electrocardiogram signals using Pivot Range Fitness Scale-Based Machine Learning Model

Purpose

Patients having ventricular arrhythmias and atrial fibrillation, that are early markers of stroke and sudden cardiac death, as well as benign subjects are all studied using the electrocardiogram (ECG). In order to identify cardiac anomalies, ECG signals analyse the heart's electrical activity and show output in the form of waveforms. Patients with these disorders must be identified as soon as possible. ECG signals can be difficult, time-consuming and subject to inter-observer variability when inspected manually.

Design/methodology/approach

There are various forms of arrhythmias that are difficult to distinguish in complicated non-linear ECG data. It may be beneficial to use computer-aided decision support systems (CAD). It is possible to classify arrhythmias in a rapid, accurate, repeatable and objective manner using the CAD, which use machine learning algorithms to identify the tiny changes in cardiac rhythms. Cardiac infractions can be classified and detected using this method. The authors want to categorize the arrhythmia with better accurate findings in even less computational time as the primary objective. Using signal and axis characteristics and their association n-grams as features, this paper makes a significant addition to the field. Using a benchmark dataset as input to multi-label multi-fold cross-validation, an experimental investigation was conducted.

Findings

This dataset was used as input for cross-validation on contemporary models and the resulting cross-validation metrics have been weighed against the performance metrics of other contemporary models. There have been few false alarms with the suggested model's high sensitivity and specificity.

Originality/value

The results of cross validation are significant. In terms of specificity, sensitivity, and decision accuracy, the proposed model outperforms other contemporary models.

An alert tool to promote lung protective ventilation for possible acute respiratory distress syndrome

Objective

Computer-aided decision tools may speed recognition of acute respiratory distress syndrome (ARDS) and promote consistent, timely treatment using lung-protective ventilation (LPV). This study evaluated implementation and service (process) outcomes with deployment and use of a clinical decision support (CDS) synchronous alert tool associated with existing computerized ventilator protocols and targeted patients with possible ARDS not receiving LPV.

Materials and Methods

We performed an explanatory mixed methods study from December 2019 to November 2020 to evaluate CDS alert implementation outcomes across 13 intensive care units (ICU) in an integrated healthcare system with >4000 mechanically ventilated patients annually. We utilized quantitative methods to measure service outcomes including CDS alert tool utilization, accuracy, and implementation effectiveness. Attitudes regarding the appropriateness and acceptability of the CDS tool were assessed via an electronic field survey of physicians and advanced practice providers.

Results

Thirty-eight percent of study encounters had at least one episode of LPV nonadherence. Addition of LPV treatment detection logic prevented an estimated 1812 alert messages (41%) over use of disease detection logic alone. Forty-eight percent of alert recommendations were implemented within 2 h. Alert accuracy was estimated at 63% when compared to gold standard ARDS adjudication, with sensitivity of 85% and positive predictive value of 62%. Fifty-seven percent of survey respondents observed one or more benefits associated with the alert.

Conclusion

Introduction of a CDS alert tool based upon ARDS risk factors and integrated with computerized ventilator protocol instructions increased visibility to gaps in LPV use and promoted increased adherence to LPV.

Practical challenges and potential approaches to predicting low-incidence diseases on farm using individual cow data: A clinical mastitis example

Clinical mastitis (CM) incidence is considerable in terms of cows affected per year, but cases are much less common in terms of detections per cow per milking. From a modeling perspective, where predictions are made every time any cow is milked, low CM incidence per cow day makes training, evaluating, and applying CM prediction models a challenge. The objective of this study was to build models for predicting CM incidence using time-series sensor data and choose models that maximize net return based on a cost matrix. Data collected from 2 university dairy farms, the University of Florida and Virginia Polytechnic Institute and State University, were used to gather representative data, including 110,156 milkings and 333 CM cases. Variables used in the models were milk yield, protein, lactose, fat, electrical conductivity, days in milk, lactation number, and activity as the number of steps, lying time, lying bouts, and lying bout duration. Models that predicted either likelihood of CM caused by gram-negative (GN) or gram-positive (GP) bacteria on each day were derived using extreme gradient boosting with weighting favoring true-positive cases, logistic responses, and log-loss errors. Model accuracies were determined using data randomly held out from the training set on each run. All variables considered were in terms of change (slope) over previous days, including the day CM was visually detected. The GN models had a median sensitivity (Se) of 52.6% and specificity (Sp) of 99.8%, whereas the GP models had a median Se of 37.5% and Sp of 99.9% when tested on the held-out data. In our models optimized to reduce cost from predictions, the Se was much less than Sp, suggesting that CM models might benefit from greater model weighting placed on Sp. Results also highlight the importance of positive predictive value (true positive cases per predicted positive case) along with Sp and Se, as models built on sparse data tend to predict too many false-positive cases. The calculated partial net return of our GN and GP models were -$0.15 and -$0.10 per cow per lactation, respectively, whereas International Organization for Standardization (ISO) standard models with Se of 80% and Sp of 99% would return -$1.32 per cow per lactation. Models chosen that minimized the cost to the farmer differed markedly from models that met ISO guidelines, showing asymmetry in targets between Sp and Se when the disease incidence rate is low. Because of the unique challenges that low-incidence diseases like CM present, we recommend that future CM predictive models consider the economic and practical implications in addition to the traditional model evaluation metrics.

Remote Monitoring of Chronic Critically Ill Patients after Hospital Discharge: A Systematic Review

Background: Over the past few decades, critical care has seen many advancements. These advancements resulted in a considerable increase in the prevalence of chronically critically ill patients requiring prolonged medical care, which led to a massive increase in healthcare utilization. Methods: We performed a search for suitable articles using PubMed and Google Scholar from the inception of these databases to 15 May 2021. Results: Thirty-four articles were included in the review and analyzed. We described the following characteristics and problems with chronic critically ill patient management: the patient population, remote monitoring, the monitoring of physiological parameters in chronic critically ill patients, the anatomical location of sensors, the barriers to implementation, and the main technology-related issues. The main challenges in the management of these patients are (1) the shortage of caretakers, (2) the periodicity of vital function monitoring (e.g., episodic measuring of blood pressure leads to missing important critical events such as hypertension, hypotension, and hypoxia), and (3) failure to catch and manage critical physiological events at the right time, which can result in poor outcomes. Conclusions: The prevalence of critically ill patients is expected to grow. Technical solutions can greatly assist medical personnel and caregivers. Wearable devices can be used to monitor blood pressure, heart rate, pulse, respiratory rate, blood oxygen saturation, metabolism, and central nervous system function. The most important points that should be addressed in future studies are the performance of the remote monitoring systems, safety, clinical and economic outcomes, as well as the acceptance of the devices by patients, caretakers, and healthcare professionals.

Prediction of cardiac arrest in critically ill patients based on bedside vital signs monitoring

PURPOSE

Cardiac arrest (CA) is the most serious death-related event in critically ill patients and the early detection of CA is beneficial to reduce mortality according to clinical research. This study aims to develop and verify a real-time, interpretable machine learning model, namely cardiac arrest prediction index (CAPI), to predict CA of critically ill patients based on bedside vital signs monitoring.

METHODS

A total of 1,860 patients were analyzed retrospectively from the Medical Information Mart for Intensive Care III (MIMIC-III) database. Based on vital signs, we extracted a total of 43 features for building machine learning model. Extreme Gradient Boosting (XGBoost) was used to develop a real-time prediction model. Three-fold cross validation determined the consistency of model accuracy. SHAP value was used to capture the overall and real-time interpretability of the model.

RESULTS

On the test set, CAPI predicted 95% of CA events, 80% of which were identified more than 25 min in advance, resulting in an area under the receiver operating characteristic curve (AUROC) of 0.94. The sensitivity, specificity, area under the precision-recall curve (AUPRC) and F1-score were 0.86, 0.85, 0.12 and 0.05, respectively.

CONCLUSION

CAPI can help predict patients with CA in the vital signs monitoring at bedside. Compared with previous studies, CAPI can give more timely notifications to doctors for CA events. However, current performance was at the cost of alarm fatigue. Future research is still needed to achieve better clinical application.

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

INTRODUCTION

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

METHODS

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

RESULTS

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

DISCUSSION

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

Determining the Impact of an Alarm Management Program on Alarm Fatigue among ICU and Telemetry RNs: An Evidence Based Research Project

This evidence-based research project provides an appraisal of current research on how an alarm management program impacts alarm fatigue among registered nurses (RNs) in both intensive care units (ICUs) and telemetry units. Alarm fatigue is a major problem recognized by both the American Association of Critical-Care Nurses (AACN) and the Joint Commission. RNs are the primary caretakers of critically ill patients in ICUs and telemetry units and therefore are at the greatest risk for alarm fatigue. The researchers performed an evidence synthesis to determine the impact of an alarm management program on alarm fatigue among ICU and telemetry RNs. A literature search was conducted using scientific databases such as PubMed, CINAHL, Trip, Cochrane Review, and Google Scholar. Our search strategy included the following terms: adult registered nurse, inpatient registered nurse, ICU registered nurses, RNs, Nurse Practitioners, alarm fatigue, alarm management strategy, education, cardiac monitor alarm, alarm strategies, alarm bundle, telemetry alarm, and cardiac monitor. Any studies involving the pediatric population, pulse oximeter alarms, and ventilator alarms were excluded. Due to the lack of available randomized control trials and cohort studies, the authors included two quality improvement (QI) projects. Finally, six studies were taken into consideration for review. The authors appraised each of the six articles using the Critical Appraisal Skills Programme Checklist (CASP) Tool. This tool allowed the authors to synthesize information based on the outcomes and determine the level of the evidence of each article in order to make evidence-based practice recommendations on implementing alarm management programs. Conclusion: Despite extensive literature highlighting the astronomical prevalence of alarm fatigue in RNs, there was a lack of current high-quality data related to implementing alarm management programs. Therefore, more research is needed to support the utilization of alarm management programs in ICUs and telemetry units to improve alarm fatigue among RNs.

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

Background

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

Methods

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

Results

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

Conclusions

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

Clinical Decision Support Systems

BACKGROUND

By combining up-to-date medical knowledge and steadily increasing patient data, a new level of medical care can emerge.

SUMMARY AND KEY MESSAGES

Clinical decision support systems (CDSSs) are an arising solution to handling rich data and providing them to health care providers in order to improve diagnosis and treatment. However, despite promising examples in many areas, substantial evidence for a thorough benefit of these support solutions is lacking. This may be due to a lack of general frameworks and diverse health systems around the globe. We therefore summarize the current status of CDSSs in medicine but also discuss potential limitations that need to be overcome in order to further foster future development and acceptance.

Decaying amplitude envelopes reduce alarm annoyance: Exploring new approaches to improving auditory interfaces

Auditory alarms offer great potential for facilitating human-computer interactions in complex, rapidly changing environments. They are particularly useful in medical settings, where in theory they should afford communication in emergency rooms, operating theatres, and hospitals around the world. Unfortunately, the sounds typically used in these devices are problematic, and researchers have documented numerous shortcomings. Their ubiquity means that even incremental improvements can have significant benefits for patient care. However, solutions have proven challenging for multiple reasons-including issues of backward compatibility inherent in changing any standard. Here we present a series of three experiments showing that manipulations to one specific, understudied property can significantly lower alarm annoyance without harming learning or memory-while preserving an alarm's melodic and rhythmic structure. These results suggest promising new directions for improving the hospital's soundscape, where evidence of problems related to sound are increasingly recognized as affecting medical outcomes as well as physician well-being.

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.