Background: Nurses can be exposed to hundreds of alarms during their shift, contributing to alarm fatigue.
Purpose: The purposes were to explore similarities and differences in perceptions of clinical alarms by labor nurses caring for generally healthy women compared with perceptions of adult intensive care unit (ICU) and neonatal ICU nurses caring for critically ill patients and to seek nurses' suggestions for potential improvements.
Methods: Nurses were asked via focus groups about the utility of clinical alarms from medical devices.
Results: There was consensus that false alarms and too many devices generating alarms contributed to alarm fatigue, and most alarms lacked clinical relevance. Nurses identified certain types of alarms that they responded to immediately, but the vast majority of the alarms did not contribute to their clinical assessment or planned nursing care.
Conclusions: Monitoring only those patients who need it and only those physiologic values that are warranted, based on patient condition, may decrease alarm burden.
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http://dx.doi.org/10.1097/NCQ.0000000000000335 | DOI Listing |
EClinicalMedicine
February 2025
Emergency Centre, Zhongnan Hospital of Wuhan University, Wuhan, Hubei, China.
Background: Sepsis is a significant health burden on a global scale. Timely identification and treatment of sepsis can greatly improve patient outcomes, including survival rates. However, time-consuming laboratory results are often needed for screening sepsis.
View Article and Find Full Text PDFPrevious studies have shown that perceptual performance can be modulated at specific frequencies phase-locked to self-paced motor actions, but findings have been inconsistent. To investigate this effect at the population level, we tested 50 participants who performed a self-paced button press followed by a threshold-level detection task, using both fixed- and random-effects analyses. Contrary to expectations, the aggregated data showed no significant action-related modulation.
View Article and Find Full Text PDFCommun Biol
January 2025
Department of Neuroscience, University of Pennsylvania, Philadelphia, PA, USA.
Human behavior is strongly influenced by anticipation, but the underlying neural mechanisms are poorly understood. We obtained intracranial electrocephalography (iEEG) measurements in neurosurgical patients as they performed a simple sensory-motor task with variable (short or long) foreperiod delays that affected anticipation of the cue to respond. Participants showed two forms of anticipatory response biases, distinguished by more premature false alarms (FAs) or faster response times (RTs) on long-delay trials.
View Article and Find Full Text PDFSensors (Basel)
January 2025
College of Mechanical and Electrical Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
According to the physical characteristics of cotton and the work characteristics of cotton pickers in the field, during the picking process, there is a risk of cotton combustion. The cotton picker working environment is complex, cotton ignition can be hidden, and fire is difficult to detect. Therefore, in this study, we designed an improved algorithm for multi-sensor data fusion; built a cotton picker fire detection system by using infrared temperature sensors, CO sensors, and the upper computer; and proposed a BP neural network model based on improved mutation operator hybrid gray wolf optimizer and particle swarm optimization (MGWO-PSO) algorithm based on the BP neural network model.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou 310018, China.
Spectrum sensing is recognized as a viable strategy to alleviate the scarcity of spectrum resources and to optimize their usage. In this paper, considering the time-varying characteristics and the dependence on various timescales within a time series of samples composed of in-phase (I) and quadrature (Q) component signals, we propose a multi-scale time-correlated perceptual attention model named MSTC-PANet. The model consists of multiple parallel temporal correlation perceptual attention (TCPA) modules, enabling us to extract features at different timescales and identify dependencies among features across various timescales.
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