64 research outputs found
Pre-formatted written discharge summary—a step towards quality assurance in the emergency department
The Extent and Coverage of Current Knowledge of Connected Health: Systematic Mapping Study
Background: This paper examines the development of the Connected Health research landscape with a view on providing a historical perspective on existing Connected Health research. Connected Health has become a rapidly growing research field as our healthcare system is facing pressured to become more proactive and patient centred. Objective: We aimed to identify the extent and coverage of the current body of knowledge in Connected Health. With this, we want to identify which topics have drawn the attention of Connected health researchers, and if there are gaps or interdisciplinary opportunities for further research. Methods: We used a systematic mapping study that combines scientific contributions from research on medicine, business, computer science and engineering. We analyse the papers with seven classification criteria, publication source, publication year, research types, empirical types, contribution types research topic and the condition studied in the paper. Results: Altogether, our search resulted in 208 papers which were analysed by a multidisciplinary group of researchers. Our results indicate a slow start for Connected Health research but a more recent steady upswing since 2013. The majority of papers proposed healthcare solutions (37%) or evaluated Connected Health approaches (23%). Case studies (28%) and experiments (26%) were the most popular forms of scientific validation employed. Diabetes, cancer, multiple sclerosis, and heart conditions are among the most prevalent conditions studied. Conclusions: We conclude that Connected Health research seems to be an established field of research, which has been growing strongly during the last five years. There seems to be more focus on technology driven research with a strong contribution from medicine, but business aspects of Connected health are not as much studied
Migraine Ophthalmique with Reversible Scotomas after Sclerotherapy with Liquid 1% Polidocanol
A novel hand gesture-based image browsing system for the operating room
Gestix is an accurate and efficient device for medical imaging
manipulation and allows for intuitive interaction while respecting the
sterility constraint crucial in the OR environmen
Reversible Cardiac Arrest After Polidocanol Sclerotherapy of Peripheral Venous Malformation
Novel Techniques to Assess Predictive Systems and Reduce Their Alarm Burden
Machine prediction algorithms (e.g., binary classifiers) often are adopted on
the basis of claimed performance using classic metrics such as sensitivity and
predictive value. However, classifier performance depends heavily upon the
context (workflow) in which the classifier operates. Classic metrics do not
reflect the realized utility of a predictor unless certain implicit assumptions
are met, and these assumptions cannot be met in many common clinical scenarios.
This often results in suboptimal implementations and in disappointment when
expected outcomes are not achieved. One common failure mode for classic metrics
arises when multiple predictions can be made for the same event, particularly
when redundant true positive predictions produce little additional value. This
describes many clinical alerting systems. We explain why classic metrics cannot
correctly represent predictor performance in such contexts, and introduce an
improved performance assessment technique using utility functions to score
predictions based on their utility in a specific workflow context. The
resulting utility metrics (u-metrics) explicitly account for the effects of
temporal relationships on prediction utility. Compared to traditional measures,
u-metrics more accurately reflect the real world costs and benefits of a
predictor operating in a live clinical context. The improvement can be
significant. We also describe a formal approach to snoozing, a mitigation
strategy in which some predictions are suppressed to improve predictor
performance by reducing false positives while retaining event capture. Snoozing
is especially useful for predictors that generate interruptive alarms.
U-metrics correctly measure and predict the performance benefits of snoozing,
whereas traditional metrics do not.Comment: 12 pages, 5 figures, 10 table
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