Electrographic Seizure Detection by Neuro ICU Nurses via Bedside Real-Time Quantitative EEG

ABSTRACTObjective:Our primary objective was to determine the performance of real-time Neuroscience ICU (Neuro ICU) nurse interpretation of quantitative EEG (qEEG) at the bedside for seizure detection. Secondary objectives included determining nurse time to seizure detection and assessing factors that influenced nurse accuracy.Methods:Nurses caring for Neuro ICU patients undergoing cEEG were trained using a 1-hour qEEG panel (rhythmicity spectrogram and amplitude-integrated EEG) bedside display. Nurses’ hourly interpretations were compared to post-hoc cEEG review by two neurophysiologists as the gold standard. Diagnostic performance, time to seizure detection as compared to standard of care (SOC), and effects of other factors on nurse accuracy were calculated.Results:109 patients and 65 nurses were studied. Eight patients had seizures during the study period (7%). Nurse sensitivity and specificity for the detection of seizures were 74% and 92%, respectively. Mean nurse time to seizure detection was significantly shorter than SOC by 132 minutes (Cox proportional hazard ratio 6.96). Inaccurate nurse interpretation was associated with increased hours monitored and presence of brief rhythmic discharges.Conclusions:This prospective study of real-time nurse interpretation of qEEG for seizure detection in Neuro ICU patients showed clinically adequate sensitivity and specificity. Time to seizure detection was less than that of SOC.Classification of Evidence:This study provides Class I evidence that Neuro ICU nurse interpretation of qEEG detects seizures in adults with a sensitivity of 74% and specificity of 92% as compared to traditional cEEG review.Clinical Trial Registration Number: NCT02082873

Barriers for nurse participation in multidisciplinary ward rounds: An integrative review

Background & Aim: The purpose of this integrative literature review was to find, critically evaluate, and describe publications about barriers against nurse participation and collaboration in multidisciplinary ward rounds. Although multidisciplinary ward rounds are the right place for doctors and nurses to communicate, nurses’ attendance in these rounds is missed. The nurses' absence at the multidisciplinary ward rounding has negative effects on the patients, their relatives, other team members, and their care. Methods: A systematic approach to searching, screening, and analyzing the literature was applied. The original and review papers were used. This study was an integrative review based on Whittemore and Knafl’s framework. Web of Science, PubMed, Scopus, Cochrane, Magiran, and SID were searched by time limitation for ten years (2009-2019). The search was conducted between February 2019–March 2019. The language was limited to English and Persian. Results: After duplicate removal, title, and abstract review, 63 papers remained. After full-text control, finally, 7 papers chased for this review. Barriers for rounding were divided into 4 main categories: time limitation, reluctance to participate, ineffective communication, and infrastructure and administration. Nurse time limitation, feeling not being valued by medicines, lack of standard and structure, and nurse unawareness from time of round are the most repeated barriers. Conclusion: Barriers may need to be removed until nurse participation in multidisciplinary ward rounds improves. Some study needs to take place about this issue in Iran to identify the situation, facilitators, and barriers specific to our country. Based on them, a relevant intervention can be chased.

Evolution of an inflammatory bowel disease helpline and implications for service design and development

Inflammatory bowel disease (IBD) clinical nurse specialists deliver a service that is highly valued by patients. Access to a specialist nurse-led IBD helpline forms part of the UK IBD consensus standards. This retrospective cohort study analyses the workload evolution of an IBD helpline and discusses its implications for the design of future services. Helpline activity was shown to increase significantly between 2016 and 2018. While largely due to appropriate increased patient demand for acute advice, significant inefficiencies were demonstrated. These are likely to be present in other comparable helpline services, alongside opportunities for making more efficient use of specialist nurse time. In addition, helpline activity was shown to generate a significant additional workload involving patient follow-up and results. Robust methods for capturing this workload could help units with similar services with service design, job planning and reimbursement.

Development of Feature Based Classification of Fruit using Deep Learning

One of the national primary health care services in Malaysia is school health care. This care is very crucial as it ensures that, countrywide, the health of students from the age of five to fifteen is in a good condition. In Malaysia, nurses hold a major responsibility for delivering the school health service. However, there is no solid research investigating the nursing time required to deliver school health services. This paper presents a system dynamics model representing the specific school health services delivered by nurses. System Dynamics is a computer-aided approach to policy analysis and design. In this paper, the system dynamics model are represented by several causal loop diagrams which covers all the school health activities and is able to determine the projected total nurse time required in delivering the service. The baseline simulation result of the nurse time required for delivering school health services is about 1080000 hours in year 2030, which is equivalent to 680 full time equivalent (FTE) nurses. Furthermore, various what-if analyses are tested with the model, as it is important for policy makers to investigate various scenarios for an effective decision-making process. In other words, the theme of the study is to understand the implication of the changes in school population size and the modification of certain activities in the school health program on the nurse time spent delivering school health service by developing a dedicated forecasting system dynamics model for school health. The time horizon for the forecasting is from 2018 until 2030Fruit classification is a challenging task in image processing. Computer vision based classification method is agile and rigorous compared to human based approach. In this paper, a method is developed for feature classification using deep learning. The region with their own characteristics is classified based on deep learning convolutional neural network technique. Traditional method for diagnosis of fruit involves visual observations by experts. The interference of environmental factors needs to be considered during diagnosis process. Datasets such as VOC, PASCAL, ImageNet etc. are easily available that are used for training of several different types of objects. The proposed model introduces two pre-trained networks; AlexNet and GoogLeNet. For faster and optimized training, Rectified linear unit (ReLu) is used that maintain positive value and map negative values to zero. The model learns to perform classification directly from images. Neural network architecture is used for implementation of deep learning. Error in deep learning is minimized compared to machine learning. The high end GPU’s reduces the training time. A transfer learning technique is proposed to retrain the network that is capable of performing new recognition task.

Modeling Nurse Time for School Health Service using System Dynamics

One of the national primary health care services in Malaysia is school health care. This care is very crucial as it ensures that, countrywide, the health of students from the age of five to fifteen is in a good condition. In Malaysia, nurses hold a major responsibility for delivering the school health service. However, there is no solid research investigating the nursing time required to deliver school health services. This paper presents a system dynamics model representing the specific school health services delivered by nurses. System Dynamics is a computer-aided approach to policy analysis and design. In this paper, the system dynamics model are represented by several causal loop diagrams which covers all the school health activities and is able to determine the projected total nurse time required in delivering the service. The baseline simulation result of the nurse time required for delivering school health services is about 1080000 hours in year 2030, which is equivalent to 680 full time equivalent (FTE) nurses. Furthermore, various what-if analyses are tested with the model, as it is important for policy makers to investigate various scenarios for an effective decision-making process. In other words, the theme of the study is to understand the implication of the changes in school population size and the modification of certain activities in the school health program on the nurse time spent delivering school health service by developing a dedicated forecasting system dynamics model for school health. The time horizon for the forecasting is from 2018 until 2030

A clinically effective primary wound dressing that supports self-care for chronic and acute wounds

Wound care forms a large component of the ever-increasing workload of district and community nurses. The need for a cost-effective product that can be used on a variety of wounds and that meets multiple requirements (e.g. protease modulation, anti-microbial, peri-wound skin protection, maceration control and barrier function) is well recognised. The plethora of wound dressings available today all fulfil some, although not all, of these requirements. Choosing the correct dressing decreases healing time, provides cost-effective care and improves patient quality of life. This article looks at the important properties of wound care products, investigates the need to release nurse time and describes how patients with wounds can engage in effective self-care, with a focus on 1 Primary Wound Dressing® (1PWD), a cost effective, easy-to-use product that has already demonstrated clinical efficacy. Case studies showing the successful use of 1PWD are also presented to highlight the clinical application of this novel product.

Feasibility of a Symptom Management Intervention for Honduran Adults Undergoing Chemotherapy

Evidence-based interventions often need to be adapted to maximize their implementation potential in low-to middle-income countries. A single-arm feasibility study was conducted to determine the feasibility and acceptability of a telephone-delivered, nurse-led, symptom management intervention for adults undergoing chemotherapy in Honduras. Over the course of 6 months, nurses engaged 25 patients undergoing chemotherapy in the intervention. Each participant received an average of 16.2 attempts to contact them for telephone sessions ( SD = 8.0, range = 2-28). Collectively, the participants discussed 24 different types of symptoms. The most commonly discussed symptoms were pain (12%), nausea (7%), and constipation (5%). Qualitative and quantitative data were used to identify treatment manual modifications (i.e., adding content about different symptoms and addressing scheduling of treatment) and workplace modifications (i.e., dedicated nurse time and space) that are needed to optimize implementation of the intervention.