Abstract: A prosthetic arm apparatus including a plurality of segments that provide a user of the prosthetic arm apparatus with substantially the same movement capability and function as a human arm. The segments are connectable to one another and connectable to a prosthetic support apparatus that may be adorned by the user. Some segments may provide movement about more than one axis using a single actuator. The prosthetic arm apparatus may include a user interface incorporated therein and may include one or more communication systems for communicating with external devices.

Abstract: A prosthetic device. The prosthetic device may include a flexure cut and/or a sensor to detect movement in accordance with a degree of movement. In an embodiment, the sensor may be disposed within the flexure cut. Other embodiments include at least one wire configured to connect a sensor located in a distal portion to a proximal portion, while annularly traversing a joint.

Abstract: Implementations directed to identifying variables affecting a cost of healthcare include actions of receiving data from a plurality of data sources, the data relating to cost of providing healthcare, providing a data model based on the data, processing the data model using non-parametric analysis to provide a non-parametric result, the non-parametric result including a first and second variable, automatically processing the data model to correlate at least the first variable of the non-parametric result to the second variable of the non-parametric result to provide a correlation result, processing the correlation result using parametric analysis to provide a parametric result including at least one variable that affects the cost more than one or more other variables of a plurality of variables of the data model, and providing at least one data visualization for display, the at least one data visualization providing at least one graphical representation of the parametric result.

Abstract: Implementations directed to identifying variables affecting a cost of healthcare include actions of receiving data from a plurality of data sources, the data relating to cost of providing healthcare, providing a data model based on the data, processing the data model using non-parametric analysis to provide a non-parametric result, the non-parametric result including a first and second variable, automatically processing the data model to correlate at least the first variable of the non-parametric result to the second variable of the non-parametric result to provide a correlation result, processing the correlation result using parametric analysis to provide a parametric result including at least one variable that affects the cost more than one or more other variables of a plurality of variables of the data model, and providing at least one data visualization for display, the at least one data visualization providing at least one graphical representation of the parametric result.

Abstract: Implementations directed to identifying variables affecting a cost of healthcare include actions of receiving data from a plurality of data sources, the data relating to cost of providing healthcare, providing a data model based on the data, processing the data model using non-parametric analysis to provide a non-parametric result, the non-parametric result including a first and second variable, automatically processing the data model to correlate at least the first variable of the non-parametric result to the second variable of the non-parametric result to provide a correlation result, processing the correlation result using parametric analysis to provide a parametric result including at least one variable that affects the cost more than one or more other variables of a plurality of variables of the data model, and providing at least one data visualization for display, the at least one data visualization providing at least one graphical representation of the parametric result.

Abstract: This document describes systems, methods, devices, and other techniques for automatically identifying and extracting medical conditions and supporting evidences from electronic health records. In some implementations, formatted text extracted from an unstructured electronic health record is obtained. The formatted text is segmented into multiple documents, wherein each document comprises a respective document type and represents a respective document encounter. Medical condition entities and supporting evidence entities referenced in each of the multiple documents are extracted. Extracted supporting evidence entities within a same document are linked to respective extracted medical condition entities from the same document using one or more of i) medical ontologies, or ii) a medical knowledge base. Output data representing linked supporting evidence entities and medical condition entities within a same document is provided.

Abstract: Implementations directed to identifying variables affecting a cost of healthcare include actions of receiving data from a plurality of data sources, the data relating to cost of providing healthcare, providing a data model based on the data, processing the data model using non-parametric analysis to provide a non-parametric result, the non-parametric result including a first and second variable, automatically processing the data model to correlate at least the first variable of the non-parametric result to the second variable of the non-parametric result to provide a correlation result, processing the correlation result using parametric analysis to provide a parametric result including at least one variable that affects the cost more than one or more other variables of a plurality of variables of the data model, and providing at least one data visualization for display, the at least one data visualization providing at least one graphical representation of the parametric result.

Abstract: The present disclosure describes techniques for performing dimensional analysis on a set of healthcare claims to identify subsets of claims that deviate from baseline metrics. One example method includes identifying a plurality of dimensions for analysis within a plurality of healthcare claims; calculating a set of baseline metrics for the plurality of healthcare claims; for each particular dimension of the plurality of dimensions: identifying dimensional subsets including healthcare claims from the plurality of healthcare claims; identifying outlier subsets from the identified dimensional subsets based on differences between the set of subset metrics for each dimensional subset and the set of baseline metrics; and providing, for output, each identified outlier subset and associated differences between the set of subset metrics for each outlier subset and the set of baseline metrics.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for processing a data model, a data file and user input to provide a populated data model, the populated data model including: a data content table having one or more navigation columns and one or more content columns, at least one navigation column including one or more key values of a key node, and at least one content column including data values of a plurality of data values, and a foreign key table having one or more navigation columns and one or more relationship columns, at least one navigation column including one or more key values of a foreign key node, and the one or more relationship columns associating at least one key node of the data content table to a respective foreign key node.

Abstract: A monitoring device and method are provided to monitor a separate device for malfunctions and to control and restore the malfunctioning monitored device to a normal functioning state. A malfunction state includes the monitored device being powered off or in a standby power state. The monitoring device includes control logic operative to determine a malfunction state of the monitored device and to control a reapplication of power to the monitored device to reboot the monitored device based on the determined malfunction state of the monitored device. The method for monitoring and controlling the monitored device comprises the steps of: determining a malfunction state of the monitored device; and controlling a reapplication of power to the monitored device to reboot the monitored device based on the determined malfunction state of the monitored device.