Deepen electronic health record diffusion beyond breadth: game changers and decision drivers

Cloud computing, financial incentive and patient-centered care are the game changers that deepen EHR diffusion beyond breadth. Based on the innovation diffusion theory (IDT), technology-organization-environment (TOE) framework and alignment literature, this study examines how these changes shape business requirement, service value and society need that drive different phases of EHR diffusion in terms of planning, adoption, usage and upgrade. A longitudinal analysis with the USA National Ambulatory Medical Care Survey (NAMCS) reveals the impacts of different drivers on EHR diffusion. In addition to quantitative results, interview observations corroborate the relationships among game changers, decision drivers and EHR diffusion. The findings provide healthcare providers, system vendors and policy-makers the insights on the best practices of promoting EHR diffusion for long-term success

Beyond Fixed Grid: Learning Geometric Image Representation with a Deformable Grid

In modern computer vision, images are typically represented as a fixed uniform grid with some stride and processed via a deep convolutional neural network. We argue that deforming the grid to better align with the high-frequency image content is a more effective strategy. We introduce \emph{Deformable Grid} DefGrid, a learnable neural network module that predicts location offsets of vertices of a 2-dimensional triangular grid, such that the edges of the deformed grid align with image boundaries. We showcase our DefGrid in a variety of use cases, i.e., by inserting it as a module at various levels of processing. We utilize DefGrid as an end-to-end \emph{learnable geometric downsampling} layer that replaces standard pooling methods for reducing feature resolution when feeding images into a deep CNN. We show significantly improved results at the same grid resolution compared to using CNNs on uniform grids for the task of semantic segmentation. We also utilize DefGrid at the output layers for the task of object mask annotation, and show that reasoning about object boundaries on our predicted polygonal grid leads to more accurate results over existing pixel-wise and curve-based approaches. We finally showcase DefGrid as a standalone module for unsupervised image partitioning, showing superior performance over existing approaches. Project website: http://www.cs.toronto.edu/~jungao/def-gridComment: ECCV 202

1,1-Dimethyl­hydrazin-1-ium picrate

In the title compound, C2H9N2 +·C6H2N3O7 −, the dihedral angles between the mean planes of the three nitro groups and the benzene ring are 63.5 (3), 10.5 (2) and 10.4 (2)°. In the crystal, mol­ecules are linked by N—H⋯O hydrogen bonds into a two-dimensional network parallel to (001)

Surface micromachined leakage proof Parylene check valve

We report here a surface micromachined leakage proof Parylene MEMS check valve that has nearly ideal performance. This new check valve has a unique design that its valve membrane has a sealing ring directly deposited on, and hence in direct contact with, the gold-coated valve seat. The adhesion between Parylene and gold is studied along with the reduction of adhesion of the sealing ring to the valve seat when self-assembled monolayer (SAM) coatings are used on gold. Testing results clearly demonstrate the effectiveness of SAM coating. Experiments show that the valves have no observable leakage in the reverse flow direction up to 30 psi and a cracking pressure less than 1 psi in the forward flow direction. Integration of this valve with microchannels in a microfluidic system is also demonstrated. Testing shows the in-channel check valve also has nearly ideal performance under both forward and reverse flow