Introduction
Amidst COVID-19 crisis, confusion exists over current radiology operations due to influx of new data and new protocols. In order to decrease confusion and reduce imaging facility related COVID-19 transmissions, we created a dedicated radiology COVID-19 call center and dedicated out-patient COVID-19 imaging sites (referred to “HOT” sites).
Materials and Methods
We created a central radiology call center hotline, staffed by our radiology technologists, to answer all radiology questions related to COVID-19 and help with scheduling exams. All out-patient x-ray exams became mandatory to schedule through the call center so proper COVID-19 screening could occur. If positive for COVID-19 symptoms, they are sent to “HOT” sites. Various statistical analyses were performed.
Results
A total of 2548 calls were received over 7 weeks with linear increase in calls during this period (R 2 =0.17, p=0.003). Most common reasons for calling were related to scheduling (n=2336, 92%) and radiology operations (n=145, 6%). At our main “HOT” site, from a total of 371 separate patient encounters by date of study, 72 patient encounters (19%) were COVID-19 positive at time of exam.
Discussion
This project provides efficient and reassuring radiology operations during an emergency situation by providing a single reliable point of contact and a source of truth for all facets of radiology. In doing so, we facilitate high quality patient centered care while protecting the health of our patients and staff.
Understanding and optimizing memory usage of software applications is a difficult task, usually involving the analysis of large amounts of memory-related complex data. Over the years, numerous software visualizations have been proposed to help developers analyze the memory usage information of their programs.
This article reports a systematic literature review of published works centered on software visualizations for analyzing the memory consumption of programs. We have systematically selected 46 articles and categorized them based on the tasks supported, data collected, visualization techniques, evaluations conducted, and prototype availability. As a result, we introduce a taxonomy based on these five dimensions to identify the main challenges of visualizing memory consumption and opportunities for improvement. Despite the effort to evaluate visualizations, we also find that most articles lack evidence regarding how these visualizations perform in practice. We also highlight that few articles are available for developers willing to adopt a visualization for memory consumption analysis. Additionally, we describe a number of research areas that are worth exploring.
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