We evaluate the performance and usability of mouse-based, touchbased, and tangible interaction for manipulating objects in a 3D virtual environment. This comparison is a step toward a better understanding of the limitations and benefits of these existing interaction techniques, with the ultimate goal of facilitating the integration of different 3D data exploration environments into a single interaction continuum. For this purpose we analyze participants' performance in 3D manipulation using a docking task. We measured completion times, docking precision, as well as subjective criteria such as fatigue, workload, and preference. Our results show that the three input modalities provide similar levels of precision but require different interaction times. We also discuss our qualitative observations as well as people's preferences and put our findings into context of the practical application domain of 3D data analysis environments.

We present an observational study with domain experts to understand how augmented reality (AR) extensions to traditional PC-based data analysis tools can help particle physicists to explore and understand 3D data. Our goal is to allow researchers to integrate stereoscopic AR-based visual representations and interaction techniques into their tools, and thus ultimately to increase the adoption of modern immersive analytics techniques in existing data analysis workflows. We use Microsoft's HoloLens as a lightweight and easily maintainable AR headset and replicate existing visualization and interaction capabilities on both the PC and the AR view. We treat the AR headset as a second yet stereoscopic screen, allowing researchers to study their data in a connected multi-view manner. Our results indicate that our collaborating physicists appreciate a hybrid data exploration setup with an interactive AR extension to improve their understanding of particle collision events.

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Highlights France, Belgium and Canada have varied degrees of governance. Higher data integration and resource coordination is achieved in centralized France. But decentralized Canada built testing capacity quickly, with variation across provinces. Shared decision making between national and subnational in Belgium may be least effective.

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We discuss spatial selection techniques for three‐dimensional datasets. Such 3D spatial selection is fundamental to exploratory data analysis. While 2D selection is efficient for datasets with explicit shapes and structures, it is less efficient for data without such properties. We first propose a new taxonomy of 3D selection techniques, focusing on the amount of control the user has to define the selection volume. We then describe the 3D spatial selection technique Tangible Brush, which gives manual control over the final selection volume. It combines 2D touch with 6‐DOF 3D tangible input to allow users to perform 3D selections in volumetric data. We use touch input to draw a 2D lasso, extruding it to a 3D selection volume based on the motion of a tangible, spatially‐aware tablet. We describe our approach and present its quantitative and qualitative comparison to state‐of‐the‐art structure‐dependent selection. Our results show that, in addition to being dataset‐independent, Tangible Brush is more accurate than existing dataset‐dependent techniques, thus providing a trade‐off between precision and effort.

We investigate the effects on health care costs and utilization of team-based primary care delivery: Quebec's Family Medicine Groups (FMGs). FMGs include extended hours, patient enrolment and multidisciplinary teams, but they maintain the same remuneration scheme (fee-for-service) as outside FMGs. In contrast to previous studies, we examine the impacts of organizational changes in primary care settings in the absence of changes to provider payment and outside integrated care systems. We built a panel of administrative data of the population of elderly and chronically ill patients, characterizing all individuals as FMG enrollees or not. Participation in FMGs is voluntary and we address potential selection bias by matching on GP propensity scores, using inverse probability of treatment weights at the patient level, and then estimating difference-in-differences models. We also use appropriate modelling strategies to account for the distributions of health care cost and utilization data. We find that FMGs significantly decrease patients' health care services utilization and costs in outpatient settings relative to patients not in FMGs. The number of primary care visits decreased by 11% per patient per year among FMG enrolees and specialist visits declined by 6%. The declines in costs were of roughly equal magnitude. We found no evidence of an effect on hospitalizations, their associated costs, or the costs of ED visits. These results provide support for the idea that primary care organizational reforms can have impacts on the health care system in the absence of changes to physician payment mechanisms. The extent to which the decline in GP visits represents substitution with other primary care providers warrants further investigation.

Research in diabetes, especially when it comes to building data-driven models to forecast future glucose values, is hindered by the sensitive nature of the data. Because researchers do not share the same data between studies, progress is hard to assess. This paper aims at comparing the most promising algorithms in the field, namely Feedforward Neural Networks (FFNN), Long Short-Term Memory (LSTM) Recurrent Neural Networks, Extreme Learning Machines (ELM), Support Vector Regression (SVR) and Gaussian Processes (GP). They are personalized and trained on a population of 10 virtual children from the Type 1 Diabetes Metabolic Simulator software to predict future glucose values at a prediction horizon of 30 minutes. The performances of the models are evaluated using the Root Mean Squared Error (RMSE) and the Continuous Glucose-Error Grid Analysis (CG-EGA). While most of the models end up having low RMSE, the GP model with a Dot-Product kernel (GP-DP), a novel usage in the context of glucose prediction, has the lowest. Despite having good RMSE values, we show that the models do not necessarily exhibit a good clinical acceptability, measured by the CG-EGA. Only the LSTM, SVR and GP-DP models have overall acceptable results, each of them performing best in one of the glycemia regions.