Abstract: A computer-based real-world evidence (CRWE) solution that can handle disparities in real-world data (RWD) that might otherwise not be combinable or that might originate from disparate or heterogeneous sources. The CRWE solution is designed such that it can build up RWD data from the ground up (for example, from the atomic level) into a canonical relational database by converting or linking the raw RWD data to its canonical versions such that reliable canonical answers can be mined from large datasets and consistently provided in response to queries to the solution for an answer. The CRWE solution is designed to gather and analyze large amounts of RWD data from heterogeneous, multi-national, and unverifiable data sources, and provide canonical results that are constituently reliable and can expose, for example, clinically-significant correlations between medical products or treatments and outcomes.

Abstract: A computer-based real-world evidence (CRWE) solution that can handle disparities in real-world data (RWD) that might otherwise not be combinable or that might originate from disparate or heterogeneous sources. The CRWE solution is designed such that it can build up RWD data from the ground up (for example, from the atomic level) into a canonical relational database by converting or linking the raw RWD data to its canonical versions such that reliable canonical answers can be mined from large datasets and consistently provided in response to queries to the solution for an answer. The CRWE solution is designed to gather and analyze large amounts of RWD data from heterogeneous, multi-national, and unverifiable data sources, and provide canonical results that are constituently reliable and can expose, for example, clinically-significant correlations between medical products or treatments and outcomes.

Abstract: A computer-based real-world evidence (CRWE) solution that can handle disparities in real-world data (RWD) that might otherwise not be combinable or that might originate from disparate or heterogeneous sources. The CRWE solution is designed such that it can build up RWD data from the ground up (for example, from the atomic level) into a canonical relational database by converting or linking the raw RWD data to its canonical versions such that reliable canonical answers can be mined from large datasets and consistently provided in response to queries to the solution for an answer. The CRWE solution is designed to gather and analyze large amounts of RWD data from heterogeneous, multi-national, and unverifiable data sources, and provide canonical results that are constituently reliable and can expose, for example, clinically-significant correlations between medical products or treatments and outcomes.

Abstract: This invention comprises a system and a method for managing a plurality of video assets via code executing in a processor. The system and method include a user interface, a controller configured to receive metadata associated with the plurality of video assets through the user interface, a database communicatively coupled to the controller in order to store the metadata, a sequence generator communicatively coupled to the database to generate a playlist from the metadata, a generator module communicatively coupled to the sequence generator module, wherein the generator module is configured to generate a uniform resource locator address (URL) that points to a playlist comprising related video assets and a communications module for providing the URL to a remote machine.