530 research outputs found

    The Development and Evaluation of a Learning Electronic Medical Record System

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    Electronic medical record (EMR) systems are capturing increasing amounts of data per patient. For clinicians to efficiently and accurately understand a patient’s clinical state, better ways are needed to determine when and how to display patient data. The American Medical Association envisions EMR systems that manage information flow and adjust for context, environment, and user preferences. We developed, implemented, and evaluated a prototype Learning EMR (LEMR) system with the aim of helping make this vision a reality. A LEMR system, as we employ the term, observes clinician information seeking behavior and applies it to direct the future display of patient data. The development of this system was divided into five phases. First, we developed a prototype LEMR interface that served as a testing bed for LEMR experimentation. The LEMR interface was evaluated in two studies: a think aloud study and a usability study. The results from these studies were used to iteratively improve the interface. Second, we tested the accuracy of an inexpensive eye-tracking device and developed an automatic method for mapping eye gaze to patient data displayed in the LEMR interface. In the two studies we showed that an inexpensive eye-tracking device can perform as well as a costlier device intended for research and that the automatic mapping method accurately captures the patient information a user is viewing. Third, we collected observations of clinician information seeking behavior in the LEMR system. In three studies we evaluated different observation methods and applied those methods to collect training data. Fourth, we used machine learning on the training data to model clinician information seeking behavior. The models predict information that clinicians will seek in a given clinical context. Fifth, we applied the models to direct the display of patient data in a prospective evaluation of the LEMR system. The evaluation found that the system reduced the amount of time it takes for clinicians to prepare for morning rounds and highlighted about half of the patient data that clinicians seek

    Evaluation Of Information Visualization For Decision Making Support In An Emergency Department Information System.

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    The purpose of this dissertation is to propose an evaluation framework to assess various IV techniques in EDIS and provide recommendations for developers

    Privacy-Protecting Techniques for Behavioral Data: A Survey

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    Our behavior (the way we talk, walk, or think) is unique and can be used as a biometric trait. It also correlates with sensitive attributes like emotions. Hence, techniques to protect individuals privacy against unwanted inferences are required. To consolidate knowledge in this area, we systematically reviewed applicable anonymization techniques. We taxonomize and compare existing solutions regarding privacy goals, conceptual operation, advantages, and limitations. Our analysis shows that some behavioral traits (e.g., voice) have received much attention, while others (e.g., eye-gaze, brainwaves) are mostly neglected. We also find that the evaluation methodology of behavioral anonymization techniques can be further improved

    Image and Evidence: The Study of Attention through the Combined Lenses of Neuroscience and Art

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    : Levy, EK 2012, ‘An artistic exploration of inattention blindness’, in Frontiers Hum Neurosci, vol. 5, ISSN=1662-5161.Full version unavailable due to 3rd party copyright restrictions.This study proposed that new insights about attention, including its phenomenon and pathology, would be provided by combining perspectives of the neurobiological discourse about attention with analyses of artworks that exploit the constraints of the attentional system. To advance the central argument that art offers a training ground for the attentional system, a wide range of contemporary art was analysed in light of specific tasks invoked. The kinds of cognitive tasks these works initiate with respect to the attentional system have been particularly critical to this research. Attention was explored within the context of transdisciplinary art practices, varied circumstances of viewing, new neuroscientific findings, and new approaches towards learning. Research for this dissertation required practical investigations in a gallery setting, and this original work was contextualised and correlated with pertinent neuroscientific approaches. It was also concluded that art can enhance public awareness of attention disorders and assist the public in discriminating between medical and social factors through questioning how norms of behaviour are defined and measured. This territory was examined through the comparative analysis of several diagnostic tests for attention deficit hyperactivity disorder (ADHD), through the adaptation of a methodology from economics involving patent citation in order to show market incentives, and through examples of data visualisation. The construction of an installation and collaborative animation allowed participants to experience first-hand the constraints on the attentional system, provoking awareness of our own “normal” physiological limitations. The embodied knowledge of images, emotion, and social context that are deeply embedded in art practices appeared to be capable of supplementing neuroscience’s understanding of attention and its disorders

    Automating the eye examination using optical coherence tomography

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    Optical coherence tomography (OCT) devices are becoming ubiquitous in eye clinics worldwide to aid the diagnosis and monitoring of eye disease. Much of this uptake relates to the ability to non-invasively capture micron-resolution images, enabling objective and quantitative data to be obtained from ocular structures. Although safe and reasonably quick to perform, the costs involved with operating OCT devices are not trivial, and the requirement for OCT and other imaging in addition to other clinical measures is placing increasing demand on ophthalmology clinics, contributing to fragmented patient pathways and often extended waiting times. In this thesis, a novel “binocular optical coherence tomography” system that seeks to overcome some of the limitations of current commercial OCT systems, is clinically evaluated. This device incorporates many aspects of the eye examination into a single patient-operated instrument, and aims to improve the efficiency and quality of eye care while reducing the overall labour and equipment costs. A progressive framework of testing is followed that includes human factors and usability testing, followed by early stage diagnostic studies to assess the agreement, repeatability, and reproducibility of individual diagnostic features. Health economics analysis of the retinal therapy clinic is used to model cost effectiveness of current practice and with binocular OCT implementation. The binocular OCT and development of other low-cost OCT systems may improve accessibility, however there remains a relative shortage of experts to interpret the images. Artificial intelligence (AI) is likely to play a role in rapid and automated image classification. This thesis explores the application of AI within retinal therapy clinics to predict the onset of exudative age-related macular degeneration in fellow eyes of patients undergoing treatment in their first eye. Together with automated and simultaneous imaging of both eyes with binocular OCT and the potential for low-cost patient-facing systems, AI is likely to have a role in personalising management plans, especially in a future where preventive treatments are available

    Recent Developments in Smart Healthcare

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    Medicine is undergoing a sector-wide transformation thanks to the advances in computing and networking technologies. Healthcare is changing from reactive and hospital-centered to preventive and personalized, from disease focused to well-being centered. In essence, the healthcare systems, as well as fundamental medicine research, are becoming smarter. We anticipate significant improvements in areas ranging from molecular genomics and proteomics to decision support for healthcare professionals through big data analytics, to support behavior changes through technology-enabled self-management, and social and motivational support. Furthermore, with smart technologies, healthcare delivery could also be made more efficient, higher quality, and lower cost. In this special issue, we received a total 45 submissions and accepted 19 outstanding papers that roughly span across several interesting topics on smart healthcare, including public health, health information technology (Health IT), and smart medicine
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