Background In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using four federated Common Data Models. N3C Data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source CDM conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for data quality improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multi-site data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.
There is growing use of technology-enabled contact tracing, the process of identifying potentially infected COVID-19 patients by notifying all recent contacts of an infected person. Governments, technology companies, and research groups alike have been working towards releasing smartphone apps, using IoT devices, and distributing wearable technology to automatically track "close contacts" and identify prior contacts in the event an individual tests positive. However, there has been significant public discussion about the tensions between effective technology-based contact tracing and the privacy of individuals. To inform this discussion, we present the results of seven months of online surveys focused on contact tracing and privacy, each with 100 participants. Our first surveys were on April 1 and 3, before the first peak of the virus in the US, and we continued to conduct the surveys weekly for 10 weeks (through June), and then fortnightly through November, adding topical questions to reflect current discussions about contact tracing and COVID-19. Our results present the diversity of public opinion and can inform policy makers, technologists, researchers, and public health experts on whether and how to leverage technology to reduce the spread of COVID-19, while considering potential privacy concerns.
There is growing use of technology-enabled contact tracing, the process of identifying potentially infected COVID-19 patients by notifying all recent contacts of an infected person. Governments, technology companies, and research groups alike have been working towards releasing smartphone apps, using IoT devices, and distributing wearable technology to automatically track "close contacts" and identify prior contacts in the event an individual tests positive. However, there has been significant public discussion about the tensions between effective technology-based contact tracing and the privacy of individuals. To inform this discussion, we present the results of seven months of online surveys focused on contact tracing and privacy, each with 100 participants. Our first surveys were on April 1 and 3, before the first peak of the virus in the US, and we continued to conduct the surveys weekly for 10 weeks (through June), and then fortnightly through November, adding topical questions to reflect current discussions about contact tracing and COVID-19. Our results present the diversity of public opinion and can inform policy makers, technologists, researchers, and public health experts on whether and how to leverage technology to reduce the spread of COVID-19, while considering potential privacy concerns. We are continuing to conduct longitudinal measurements and will update this report over time; citations to this version of the report should reference Report Version 2.0, December 4, 2020.
Computer-aided design/manufacturing (CAD/CAM) systems intended to support automated design and manufacturing applications such as shape generation and solid free-form fabrication (SFF) must provide not only methods for creating and editing models of objects to be manufactured, but also methods for interrogating the models. Interrogation refers to any process that derives information from the model. Typical interrogation tasks include determine surface area, volume or inertial properties, computing surface points and normals for rendering, and computing slice descriptions for SFF. While currently available commercial modeling systems generally employ a boundary representation (B-rep) implementation of solid modeling, research efforts have considered implicit modeling schemes as a potential source of improved robustness. Implicit implementations are available for a broad range of modeling operations, but interrogation operations have been widely considered too costly for many applications. This paper describes a method based on interval analysis for interrogating implicit solid models that aims at achieving both robustness and efficiency.
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