Social media and population health virtual exchange for senior nursing students: an international collaboration

The authors have all engaged in using social media with students as a means for collaboration across national and international boundaries for various educational purposes. Following the explosion of big data in health the authors are now moving this concept forward within undergraduate and postgraduate nursing curricula for the development of population health virtual exchanges. Nursing has a global presence and yet it appears as though students have little knowledge of the health and social care needs and provision outside their local environment. This development will allow for explorative exchange amongst students in three countries, enhancing their understanding of their own and the selected international population health needs and solutions through asking and responding to questions amongst the learning community involved. The connection of the students will be recorded for their use in reflection; of particular interest will be the use of information included by the students to answer questions about their locality

Social media providing an international virtual elective experience for student nurses

The advances in social media offer many opportunities for developing understanding of different countries and cultures without any implications of travel. Nursing has a global presence and yet it appears as though students have little knowledge of the health and social care needs and provision outside their local environment. Our collaboration across three countries, New Zealand, United Kingdom, and the United States of America, brought the two themes together with the aim of senior student nurses having a communication channel to explore public health issues in each country. Using a closed Facebook™ page, third year undergraduate adult nursing students were invited to take part in a three month pilot study to test the feasibility of virtual collaboration through exchanging public health issues. Here we report upon the collaboration, operation of the social media, and main findings of the study. Three core areas will be reported upon, these being the student’s views of using social media for learning about international perspectives of health, seeing nursing as a global profession and recommendations for future development of this positively reviewed learning technique. To conclude consideration will be given to further development of this work by the collaborative team expanding the countries involved

What do nurses ask about? A review of nursing questions in a Brazilian telemedicine system

Purpose: Across the globe, nurses are often on the front lines for health care delivery in places where physicians are scarce or nonexistent, such as in rural areas or urban slums [2]. For example, the constitution of Brazil guarantees free public healthcare to every citizen, however, many doctors and specialists are concentrated in the large coastal cities of the country, with populations in the rural interiors drastically underserved by health care professionals [3]. The Health Ministry of Brazil implemented the Programa Telessaúde Brasil Redes to combat this issue by providing health support, advice from specialists, and permanent health education via telemedicine. Telemedicine can connect any member of the health care team across the continuum of care to enhance outcomes. This connection can entail submission of asynchronous questions via online systems for expert consultation. Rede NUTES is an extension of the Programa Telessaúde Brasil Redes based in the northeastern state of Pernambuco in Brazil. Rede NUTES offers telehealth services to its 80 municipalities by offering web seminars, telecare service, and second opinions on submitted questions via their online submission system for other health care professionals [7]. Data from the Rede NUTES submitted questions were analyzed and explored using big data methods to gain insights into how and for what Brazilian nurses utilized the system, as well as gain new understanding from the questions that nurses answered. Methods: Step 1 was to explore and analyze the data, questions submitted by nurses, nurse technicians, and the two categories combined were identified and isolated from the complete data set to understand ratios of how often nurses and nurse technicians were using the Rede NUTES system in comparison to their peers. Questions that were answered by nurses at Rede NUTES were also queried from the general data to review the frequency of responses given by this group of users. In step 2, the questions were reviewed by calendar months. This visualized usage trends over the course of a year for questions both submitted and responded to. A histogram was created to visualize this trend. Step 3 entailed determination of question themes per calendar each month. For example, a spike in gynecology questions dealing with pregnancy was typically followed by a corresponding increase in obstetrics questions nine months later. By visualizing question topics and clustering those topics in a timeline, it was possible to understand trends in the data for nurses. These question topic clusters were compared to the questions submitted and answered by the other medical professionals utilizing the online question system. In the final step, the data were reviewed for lexical relationships using Linguistic Inquiry and Word Count (LIWC) program in Portuguese [6]. LIWC returns values on dimensions such as number of self-references, level of positivity, and number of cognitive words. Results: As stated in [2], the nurses and nurse technicians in Pernambuco are indeed on the frontlines of serving their communities. Nurses and nurse technicians submitted the most questions to Rede NUTES out of any of the health practitioner categories for two out of the three years reviewed (38% in 2010 and 32% in 2011), and in all three years combined (36%). Furthermore, in the majority of the months in the years reviewed, nurses and nurse technicians submitted the largest percent of the questions from any health care practitioner category, submitting 92% of all questions in December of 2012. The top question topics across submitted concerned cardiology, gynecology, obstetrics, pediatrics, and dentistry for all three years. In 2012 and 2013, the top question theme dealt with electrocardiograms, while Another notable result was that nurses also answered the most questions during the three years reviewed. Questions that were responded to followed a similar thread as to those that were submitted, with specialized nurses responding to the majority of questions dealing with obstetrics, gynecology, and pediatrics, with the top question theme dealing with hypertension. Conclusion: Exploratory data analysis is the first step in using big data for future experiments [8]. It allows researchers to detect mistakes in the data that could skew machine learning results, while also checking intuitions about relationships in the data. Future work on this data set will explore relationships in the data for other medical fields. While asynchronous online telemedicine applications to obtain expert counsel such as Rede NUTES offers positive features, likewise negative features must be acknowledged. There are legitimate causes for concern over privacy and protection of data. Likewise, as many people fear receiving a break-up text, so should bad news of a diagnosis be conveyed in person. Future research should work to migitate these concerns while enhancing positive outcomes

Analysis of a Federated Learning Framework for Heterogeneous Medical Image Data: Privacy and Performance Perspective

The massive amount of data available in our modern world and the increase of computational efficiency and power have allowed for great advancements in several fields such as computer vision, image processing, and natural languages. At the center of these advancements lies a data-centric learning approach termed deep learning. However, in the medical field, the application of deep learning comes with many challenges. Some of the fundamental challenges are the lack of massive training datasets, unbalanced and heterogenous data between health applications and health centers, security and privacy concerns, and the high cost of wrong inference and prediction. One of the interesting questions of data-centric learning in the medical field is whether we can leverage the heterogenous data available in several medical facilities in a combined way without actually sharing the data between the institutes and preserving the security and privacy of patients. One way to address this question is through the use of the federated deep learning technique. In federated deep learning, the “learning” from each local deep learning model trained on a small, distinct dataset is shared with a global model instead of sharing the actual data and hence does not violate any security and privacy concerns. In this study, we aim to evaluate the efficiency of the federated learning approach on classification tasks in the medical image domain. Learning in the medical image domain is often more challenging and distinct than that of natural images because of heterogeneity in the data and the unavailability of clear, discernable discriminant features between images of different classes. To this end, we investigate federated learning in medical images in terms of model architecture and data complexity. Through our experiments, we will also investigate the effect that federated learning will have on each local model’s performance, and how it affects model generality to external datasets

Analysis of a Federated Learning Framework for Heterogeneous Medical Image Data: Privacy and Performance Perspective

The massive amount of data available in our modern world and the increase of computational efficiency and power have allowed for great advancements in several fields such as computer vision, image processing, and natural languages. At the center of these advancements lies a data-centric learning approach termed deep learning. However, in the medical field, the application of deep learning comes with many challenges. Some of the fundamental challenges are the lack of massive training datasets, unbalanced and heterogenous data between health applications and health centers, security and privacy concerns, and the high cost of wrong inference and prediction. One of the interesting questions of data-centric learning in the medical field is whether we can leverage the heterogenous data available in several medical facilities in a combined way without actually sharing the data between the institutes and preserving the security and privacy of patients. One way to address this question is through the use of the federated deep learning technique. In federated deep learning, the “learning” from each local deep learning model trained on a small, distinct dataset is shared with a global model instead of sharing the actual data and hence does not violate any security and privacy concerns. In this study, we aim to evaluate the efficiency of the federated learning approach on classification tasks in the medical image domain. Learning in the medical image domain is often more challenging and distinct than that of natural images because of heterogeneity in the data and the unavailability of clear, discernable discriminant features between images of different classes. To this end, we investigate federated learning in medical images in terms of model architecture and data complexity. Through our experiments, we will also investigate the effect that federated learning will have on each local model’s performance, and how it affects model generality to external datasets

Towards a Hybrid Method to Categorize Interruptions and Activities in Healthcare

Objective Interruptions are known to have a negative impact on activity performance. Understanding how an interruption contributes to human error is limited because there is not a standard method for analyzing and classifying interruptions. Qualitative data are typically analyzed by either a deductive or an inductive method. Both methods have limitations. In this paper a hybrid method was developed that integrates deductive and inductive methods for the categorization of activities and interruptions recorded during an ethnographic study of physicians and registered nurses in a Level One Trauma Center. Understanding the effects of interruptions is important for designing and evaluating informatics tools in particular and for improving healthcare quality and patient safety in general. Method The hybrid method was developed using a deductive a priori classification framework with the provision of adding new categories discovered inductively in the data. The inductive process utilized line-by-line coding and constant comparison as stated in Grounded Theory. Results The categories of activities and interruptions were organized into a three-tiered hierarchy of activity. Validity and reliability of the categories were tested by categorizing a medical error case external to the study. No new categories of interruptions were identified during analysis of the medical error case. Conclusions Findings from this study provide evidence that the hybrid model of categorization is more complete than either a deductive or an inductive method alone. The hybrid method developed in this study provides the methodical support for understanding, analyzing, and managing interruptions and workflow

Traces of the invisible: how an alternative reading of The Sleeping Beauty fashioned a bookwork heightening awareness of the role of the anesthetist

This article discusses a Leverhulme residency undertaken by the author Julie Brixey-Williams in 2003-4 at the Association of Anaesthetists of Great Britain and Ireland. Notions of medical visibility were explored through practice-led investigations under the umbrella title Traces of the Invisible, that concentrated on making concrete, visible responses to the hidden or intangible elements of the anesthetist’s working life, in areas such as sleep, breath, pain and genetic markers. Rosebud is a unique nine-foot concertina bookwork created after reading the entire story of The Sleeping Beauty into an anesthetic machine. This essay expands upon the concepts and material responses that led to the making of the book, with particular reference to how the book’s structure forms a relationship to language and the body-as-site, whilst operating as a sculptural object that raises the visibility of the anesthetic profession. Fairy tales and their telling, including stories of enchanted sleep, transformational qualities, magical languages and shaman healers, will be examined alongside

Determining design freedom of linear feedback control systems

“The problem confronted in this thesis is to develop a method or procedure to be used in determining the amount of design freedom that is available in any given linear feedback control system by working with the signal flow-graph representation of the system. Literature was reviewed that deals with basic signal flow-graph theory and degrees of design freedom. Signal- flow-graph theory that forms a foundation for the development is presented. The development consists of starting with an essential signal-flow graph of order one. The sensitivity and transmittance functions are written for this essential graph in terms of graph symbols; then these functional relationships are solved to give each graph symbol in terms of graph functions. A procedure is written for the use of the derived equations in determining design freedom and examples are used to illustrate the procedure. Discussed briefly is the possibility of applying this procedure to systems represented by signal-flow graphs of order greater than one”--Abstract, page ii

Obstacles to research on the effects of interruptions in healthcare

The authors of The Institute of Medicine report ‘To Err is Human’ concluded that interruptions can contribute to medical errors. Given this risk, healthcare researchers have generally, and often solely, viewed interruptions as obstacles to work—as factors that thwart progress, create stress, increase workload, interfere with memory for current and future tasks and harm efficiency, productivity and safety. For example, researchers reported a positive association between interruptions and errors. A contrasting view is to see interruptions as promoting safety and high-quality patient care. From this view, interruptions function as interventions,6–8 such as a call to cease or change work if the interruptee is potentially committing an error.9 Other industries encourage interruptions for that reason. Many researchers investigating interruptions in healthcare cite the sterile cockpit principle as a rationale for reducing interruptions—but it is less often noted that copilots are trained to speak up with safety concerns even if it means interrupting a senior pilot's work. These different views on studying interruptions have made it difficult to draw conclusions from the research. Granted, diverse perspectives and methods can generate a greater variety of ideas and solutions than single perspectives and methods. However, such diversity also makes it more difficult to compile and compare research results or identify critical research questions. The present paper draws attention to three obstacles to research on the effects of interruptions that arise from differing views and methods: definitions, processes and data collection. We discuss possible solutions that may lead us to a better understanding of the effects of interruptions and to a multidisciplinary view on the effects of interruptions in healthcare