Business Intelligence Competencies: Making Healthcare Data Come Alive

Business Intelligence Competencies: Making Healthcare Data Come Alive While a wealth of healthcare related data exists, nurse leaders (NL) have yet to understand its implications and adopt analytical skills to lead in the transformation of care delivery. Information science is at a new frontier for nursing to embrace. It is critical for nursing leadership to advance and support business intelligence (BI) and interactive data visualization (IDV) skills across the organization and advocate for greater engagement of nurses in health system decision making. With these new tools and competencies, nursing and other health professions can innovate best practices, providing enhanced quality, safety, and value in healthcare. The aim of this Doctor of Nursing evidence-based project was to engage NL’s to improve and extend competencies in BI and IDV. A survey was administered to NL’s to assess their knowledge and use of these analytic tools and then guide a process for skill development via two workshops presenting an overview of BI and IDV to NL’s. The use of BI is still in its’ infancy, dashboards tools are beginning to be deployed across healthcare organization, however, data in real time is not readily available, nor is the ability to interact and conduct data discovery. The effectiveness of the education program was evaluated by the attendees’ willingness to participate in workshops covering the basic uses of BI and IDV and understanding of the opportunities to incorporate them into their current leadership role

Towards a Theoretical Framework of Acceptance of Virtual Reality Technology: Evidence from 360-Video Concert

We examine the use of 360-degree video technology in a live music event with the aim to explore the factors leading to acceptance of the VR use case and technology, to reduce the knowledge gap about this topic. We collected self-reported, quantitative data from 23 participants and investigated the user experience during the VR mediated 360-video concert and the acceptance of the 360-video for concert participation and VR technology use. We found that acceptance of the novel VR-based communication approach was correlated mainly with perceived usefulness. Furthermore, the perceived usefulness was only correlated with fun, but not flow and immersion. We outline the results in a new theoretical framework for studying and predicting the relationships between individual characteristics, user experience, VR evaluation, content and device, and the acceptance of 360-video mediated musical events and VR technology. Implications for VR acceptance theory and design practice are discussed

Surgical Data Science - from Concepts toward Clinical Translation

Recent developments in data science in general and machine learning in particular have transformed the way experts envision the future of surgery. Surgical Data Science (SDS) is a new research field that aims to improve the quality of interventional healthcare through the capture, organization, analysis and modeling of data. While an increasing number of data-driven approaches and clinical applications have been studied in the fields of radiological and clinical data science, translational success stories are still lacking in surgery. In this publication, we shed light on the underlying reasons and provide a roadmap for future advances in the field. Based on an international workshop involving leading researchers in the field of SDS, we review current practice, key achievements and initiatives as well as available standards and tools for a number of topics relevant to the field, namely (1) infrastructure for data acquisition, storage and access in the presence of regulatory constraints, (2) data annotation and sharing and (3) data analytics. We further complement this technical perspective with (4) a review of currently available SDS products and the translational progress from academia and (5) a roadmap for faster clinical translation and exploitation of the full potential of SDS, based on an international multi-round Delphi process

An informatics based approach to respiratory healthcare.

By 2005 one person in every five UK households suffered with asthma. Research has shown that episodes of poor air quality can have a negative effect on respiratory health and is a growing concern for the asthmatic. To better inform clinical staff and patients to the contribution of poor air quality on patient health, this thesis defines an IT architecture that can be used by systems to identify environmental predictors leading to a decline in respiratory health of an individual patient. Personal environmental predictors of asthma exacerbation are identified by validating the delay between environmental predictors and decline in respiratory health. The concept is demonstrated using prototype software, and indicates that the analytical methods provide a mechanism to produce an early warning of impending asthma exacerbation due to poor air quality. The author has introduced the term enviromedics to describe this new field of research. Pattern recognition techniques are used to analyse patient-specific environments, and extract meaningful health predictors from the large quantities of data involved (often in the region of '/o million data points). This research proposes a suitable architecture that defines processes and techniques that enable the validation of patient-specific environmental predictors of respiratory decline. The design of the architecture was validated by implementing prototype applications that demonstrate, through hospital admissions data and personal lung function monitoring, that air quality can be used as a predictor of patient-specific health. The refined techniques developed during the research (such as Feature Detection Analysis) were also validated by the application prototypes. This thesis makes several contributions to knowledge, including: the process architecture; Feature Detection Analysis (FDA) that automates the detection of trend reversals within time series data; validation of the delay characteristic using a Self-organising Map (SOM) that is used as an unsupervised method of pattern recognition; Frequency, Boundary and Cluster Analysis (FBCA), an additional technique developed by this research to refine the SOM

Review : Deep learning in electron microscopy

Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy

Quantifying Quality of Life

Describes technological methods and tools for objective and quantitative assessment of QoL Appraises technology-enabled methods for incorporating QoL measurements in medicine Highlights the success factors for adoption and scaling of technology-enabled methods This open access book presents the rise of technology-enabled methods and tools for objective, quantitative assessment of Quality of Life (QoL), while following the WHOQOL model. It is an in-depth resource describing and examining state-of-the-art, minimally obtrusive, ubiquitous technologies. Highlighting the required factors for adoption and scaling of technology-enabled methods and tools for QoL assessment, it also describes how these technologies can be leveraged for behavior change, disease prevention, health management and long-term QoL enhancement in populations at large. Quantifying Quality of Life: Incorporating Daily Life into Medicine fills a gap in the field of QoL by providing assessment methods, techniques and tools. These assessments differ from the current methods that are now mostly infrequent, subjective, qualitative, memory-based, context-poor and sparse. Therefore, it is an ideal resource for physicians, physicians in training, software and hardware developers, computer scientists, data scientists, behavioural scientists, entrepreneurs, healthcare leaders and administrators who are seeking an up-to-date resource on this subject