Critical care nurses’ knowledge of alarm fatigue and practices towards alarms: A multicentre study

Casey, Siobhán; Avalos, G; Dowling, Maura

doi:10.1016/j.iccn.2018.05.004

Cited by 55 publications

(55 citation statements)

References 34 publications

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“…The processing of only noise-free ECG intervals would avoid most of confounding factors in ECG interpretation and then, more reliable analyses, more accurate diagnoses, and smaller rates of false alarms of AF and other atrial arrhythmias could be obtained in continuous long-term ECG monitoring [ 20 , 31 ]. This is especially relevant in the screening of AF, because episodes are too short in the initial stages of the disease [ 13 ] and reliable automatic analyses could be helpful in minimizing or even removing later visual, time-consuming inspection of suspicious events, as well as in avoiding desensitization of the clinical staff responsible for this task [ 70 ]. Natural progression of the arrhythmia could then be more successfully and early prevented, hence eluding chronic stages where management of the disease is more complicated and treatments are poorly effective [ 71 ].…”

Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Herraiz

Martínez-Rodrigo

Bertomeu-González

et al. 2020

Entropy

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Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.

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Section: Discussionmentioning

confidence: 99%

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Herraiz

Martínez-Rodrigo

Bertomeu-González

et al. 2020

Entropy

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Section: Discussionmentioning

confidence: 99%

“…Disease, pain, the invasive nature of treatments and interventions, coupled with environmental issues such as excessive noise, light and social isolation combine to have a significant effect on patient well-being (Wenham & Pittard, 2009). Alarm fatigue among critical care nurses is also prevalent (Casey, Avalos, & Dowling, 2018 Drawing upon Haraway's (2000) suggestion of a 'cyborg ontology', it is argued that person-centred care can thrive in a technological environment if nurses make attempts to 'mediate this delicate balance between person-centred practice and technological knowing' (Lapum et al, 2012, p. 281). This can be achieved by adopting a hybridity where 'human and technological features of our cyborg ontology can work in a more synergistic as opposed to dualistic fashion' (Lapum et al, 2012, p. 284).…”

Section: Moderate Confidencementioning

confidence: 99%

Critical care nurses' experiences of providing care for adults in a highly technological environment: A qualitative evidence synthesis

Crilly

Dowling

Delaunois

et al. 2019

Journal of Clinical Nursing

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Aims and objectives To synthesise the available body of qualitative evidence relating to nurses' perceptions and experiences of care provision in adult critical care environments. Methods The study adhered to ENTREQ (Confidence in Evidence of Reviews of Qualitative Research) guidelines. See Appendix S1. A systematic search of the literature in nine databases was undertaken: CINAHL, Web of Science, MEDLINE, EMBASE, PsycINFO, Campbell Collaboration, ProQuest A & I, DART and Lenus. Blind screening to select relevant studies was undertaken, and each selected study was assessed for quality using the Critical Appraisal Skills Programme framework. Guided by Thomas and Harden's three‐stage approach to thematic analysis, line‐by‐line coding of participants' verbatim accounts and the researchers' interpretations in the selected studies' findings was undertaken and then organised into higher order analytical themes. Confidence in the findings was reviewed using GRADE‐CERQual. Results Twelve studies reported in thirteen papers, including 122 nurses, were selected in the final sample for synthesis. Three analytical themes were identified: (a) sometimes machines get all the attention, (b) with experience the patient becomes the focus and (c) technology can't save everybody. Conclusions Providing care for adult patients in a highly technological environment is challenging particularly for novice nurses, who face the potential of technology drawing all their attention. Experienced critical care nurses learn to keep technology in abeyance and deliver person‐centred care within the bounds of a technological environment. Relevance to clinical practice The review supports Locsin's theory of technological competence and highlights that providing care in critical care requires nurses to actively balance attention for the person while managing machines. Experienced nurses achieve this balance and can offer support to novice nurses. Critical care nurse orientation programmes should be underpinned by a holistic approach which addresses the dualism of technology and care.

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“…The control encounters were then subject to the following screening criteria: (1) match the same all patient refined diagnosis-related group (APR-DRG) or Medicare DRG; (2) the same gender; (3) within ±5 years of age; (4) in the same medical units as case encounters. There were 412 code blue encounters (2,099,026 alarms) and 4020 control encounters (12,696,925 alarms) between 2013 and 2018 selected from UCSF with mean age 60.8 ± 16.1 and 58.9% male. There were 254 code blue encounters (662,576 alarms) and 2213 matched control (5,363,019 alarms) encounters between 2010 and 2012 selected from UCLA with mean age 61.3 ± 17.9 and 54.2% male.…”

Section: A Data Descriptionmentioning

confidence: 99%

Generalizability of SuperAlarm via Cross-Institutional Performance Evaluation

Xiao

Ding³

et al. 2020

IEEE Access

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Bedside patient monitors are ubiquitous tools in modern critical care units to provide timely patient status. However, current systems suffer from high volume of false alarms leading to alarm fatigue, one of top technical hazards in clinical settings. Many studies are racing to develop improved algorithms towards precision patient monitoring, while little has been done to investigate the aspect of algorithm generalizability across different health institutions. Our group has been developing an evolving framework termed SuperAlarm that extracts multivariate patterns in data streams (monitor alarms, electronic health records and physiologic waveforms) of modern health enterprise to predict patient deterioration and has demonstrated great potential in mitigating alarm fatigue. In this study, we further investigate the generalizability of SuperAlarm by designing a comprehensive approach to achieve performance comparison in predicting in-hospital code blue (CB) events across two health institutions. SuperAlarm model trained with alarm data in one institution is tested on both internal and external test sets. Results show comparable performance with sensitivity up to 80% within one-hour window of events and over 90% in reduction of false alarms in both institutions. Cross-institutional performance agreement can be further improved by predicting a more stringent CB subtype (cardiopulmonary arrest), with internal sensitivity lying within 95% confident interval of external one up to 8-hour before event onset. The cross-institutional performance comparison offers first-hand knowledge on both advantages and challenges in generalizing a prediction algorithm across different institutions, which hold key information to guide the design of model training and deployment strategy.

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Critical care nurses’ knowledge of alarm fatigue and practices towards alarms: A multicentre study

Abstract: Alarm fatigue has the potential for serious consequences for patient safety and answering numerous alarms drains nursing resources.

Cited by 55 publications

References 34 publications

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Critical care nurses' experiences of providing care for adults in a highly technological environment: A qualitative evidence synthesis

Generalizability of SuperAlarm via Cross-Institutional Performance Evaluation

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