Objectives: Medical devices are ubiquitous in modern medical care. However, little is known about the epidemiology of medical devices in the healthcare marketplace, including the rate at which medical devices are subject to recalls or other advisories. We sought to study the epidemiology of medical devices in Canada, focusing on device recalls. In Canada, a recall may signify a variety of events, ranging from relatively minor field safety notifications, to removal of a product from the marketplace.

Methods: We used data from Health Canada to study medical device recalls in Canada from 2005 to 2015. We analyzed the risks of medical device recalls according to the risk class of the device (I lowest; IV highest) and the hazard priority of the recall (Type I highest potential harm; Type III lowest potential harm).

Results: During a 10-year period, there were 7,226 medical device recalls. Most recalls were for intermediate risk class (Class II, 40.1 percent; Class III, 38.7 percent) medical devices. Among recalled devices, 5.0 percent were judged to have a reasonable probability of serious adverse health consequences or death (Type I recall Hazard Priority classification). While the number of medical devices marketed in Canada is not known, over a similar 10-year period, 24,849 new Class II, II, and IV medical device licenses were issued by Health Canada.

Conclusions: Several hundred medical device recalls occur in Canada each year. Further research is needed to characterize the nature of medical device recalls, and to explore how consumers use information about recalls.

Interpretation of ice mass elevation changes observed by satellite altimetry demands quantification of the proportion of elevation change which is attributable to variations in firn densification. Detailed stratigraphic logging of snowpack structure and density was carried out at ~1km intervals along a 47 km transect on Devon Ice Cap, Canada, in spring (pre-melt) and autumn (during/ after melt) 2004 and 2006 to characterize seasonal snowpack variability across the full range of snow facies. Simultaneous meteorological measurements were gathered. Spring (pre-melt) snowpacks show low variability over large spatial scales, with low-magnitude changes in density. The end-of-summer/ autumn density profiles show high variability in both 2004 and 2006, with vastly different melt regimes generating dissimilar patterns of ice-layer formation over the two melt seasons. Dye-tracing experiments from spring to autumn 2006 reveal that vertical and horizontal distribution of meltwater flow within and below the annual snowpack is strongly affected by the pre-existing, often subtle stratigraphic interfaces in the snowpack, rather than its bulk properties. Strong interannual variability suggests that using a simple relationship between air temperature, elevation and snowpack densification to derive mass change from measurements of elevation change across High Arctic ice caps may be misguided. Melt timing and duration are important extrinsic factors governing snowpack densification and ice-layer formation in summer, rather than averaged air temperatures.

OBJECTIVES/SPECIFIC AIMS: To create a searchable public registry of all Quality Improvement (QI) projects. To incentivize the medical professionals at UF Health to initiate quality improvement projects by reducing startup burden and providing a path to publishing results. To reduce the review effort performed by the internal review board on projects that are quality improvement Versus research. To foster publication of completed quality improvement projects. To assist the UF Health Sebastian Ferrero Office of Clinical Quality & Patient Safety in managing quality improvement across the hospital system. METHODS/STUDY POPULATION: This project used a variant of the spiral software development model and principles from the ADDIE instructional design process for the creation of a registry that is web based. To understand the current registration process and management of quality projects in the UF Health system a needs assessment was performed with the UF Health Sebastian Ferrero Office of Clinical Quality & Patient Safety to gather project requirements. Biweekly meetings were held between the Quality Improvement office and the Clinical and Translational Science – Informatics and Technology teams during the entire project. Our primary goal was to collect just enough information to answer the basic questions of who is doing which QI project, what department are they from, what are the most basic details about the type of project and who is involved. We also wanted to create incentive in the user group to try to find an existing project to join or to commit the details of their proposed new project to a data registry for others to find to reduce the amount of duplicate QI projects. We created a series of design templates for further customization and feature discovery. We then proceed with the development of the registry using a Python web development framework called Django, which is a technology that powers Pinterest and the Washington Post Web sites. The application is broken down into 2 main components (i) data input, where information is collected from clinical staff, Nurses, Pharmacists, Residents, and Doctors on what quality improvement projects they intend to complete and (ii) project registry, where completed or “registered” projects can be viewed and searched publicly. The registry consists of a quality investigator profile that lists contact information, expertise, and areas of interest. A dashboard allows for the creation and review of quality improvement projects. A search function enables certain quality project details to be publicly accessible to encourage collaboration. We developed the Registry Matching Algorithm which is based on the Jaccard similarity coefficient that uses quality project features to find similar quality projects. The algorithm allows for quality investigators to find existing or previous quality improvement projects to encourage collaboration and to reduce repeat projects. We also developed the QIPR Approver Algorithm that guides the investigator through a series of questions that allows an appropriate quality project to get approved to start without the need for human intervention. RESULTS/ANTICIPATED RESULTS: A product of this project is an open source software package that is freely available on GitHub for distribution to other health systems under the Apache 2.0 open source license. Adoption of the Quality Improvement Project Registry and promotion of it to the intended audience are important factors for the success of this registry. Thanks goes to the UW-Madison and their QI/Program Evaluation Self-Certification Tool (https://uwmadison.co1.qualtrics.com/SE/?SID=SV_3lVeNuKe8FhKc73) used as example and inspiration for this project. DISCUSSION/SIGNIFICANCE OF IMPACT: This registry was created to help understand the impact of improved management of quality projects in a hospital system. The ultimate result will be to reduce time to approve quality improvement projects, increase collaboration across the UF Health Hospital system, reduce redundancy of quality improvement projects and translate more projects into publications.

Motions of nearby elliptical galaxies reveal a large-scale velocity flow relative to cosmic rest towards the point 1=307±10, b = 9±10. The data are fit best by a two-component flow model. The smaller component is due to Virgo, which induces a velocity at the Local Group of 250 km/s. The main flow is due to a more massive concentration located a distance of 4350±350 km/s towards 1=307, b=9, which induces a local velocity here of 570±60 km/s. This larger component falls off away from the mass concentration roughly as r−1. The Centaurus double cluster and its neighbors are in the direction of the mass concentration but are in the foreground and are falling into it. Galaxy counts, radial velocity surveys, and the motions of nearby spirals are consistent with the above model. The IRAS dipole results are less clear but may also be consistent. There is evidence that the distant mass concentration is non-spherical, with the Centaurus cloud a substantial sub-condensation in the foreground. The formal agreement of the large-scale flow with biased (b=2) cold dark matter is low, but the simple methods used so far to assess this are uncertain. The main weakness of the present data in comparing to theory is the fact that they do not penetrate far enough to show the velocity field on all sides of the mass concentration. Sphericity and total extent of the flow are therefore still unknown.

The peculiar motions for spiral galaxies and elliptical galaxies within V = 3500 km/s are compared to the model predictions of the mass concentration (MC) velocity field model of Lynden-Bell et al. The large-scale motions defined by over 600 galaxies from three independent sets of data (Aaronson et al.; de Vaucouleurs and Peters and elliptical galaxies) are in substantial agreement with this model.