Data analytics systems and methods are disclosed herein. A parser can parse reference data from various data sources to store in a data structure. An uploader can receive study data designated by a researcher and store the study data in the data structure. A matcher can compare analyte nameset data in the study data with analyte nameset data from the reference data to generate one or more links each correlating an instance of an analyte in the study data with an instance of that analyte in the reference data. Library overlays each include one or more modules to access reference data to generate organized associations of reference data. A calculation engine can receive a selection of one or more library overlay(s) and manipulate the reference data and study data according to the organized associations of the selected library overlay(s) to generate configured data stored in a collection of data caches for presentation to a researcher via a user interface.

A method includes receiving a selection of a subgroup of participants from a group of potential focus group participants, placing each participant in the subgroup of participants in a waiting room environment, establishing a respective testing video conference between each participant in the subgroup of participants and the second user and confirming, based on the respective testing video conference, a respective technical ability to carry out a video conference. The method includes establishing the video conference comprising each respective confirmed participant and the second user, receiving respective data as part of the focus group from each respective confirmed participant and presenting live data associated with the video conference of the focus group to the first user at a user device. The method and associated network server enable a quick way to establish a focus group and then carry out a focus group on a topic of interest.

Embodiments are directed to data ingestion over a network. Raw data and integrated data associated with a plurality of separate data sources may be provided such that the raw data includes content associated with a plurality of subjects. Categorization models may be employed to categorize the raw data based on various features, such as, format, structure, data source, variability, volume, or associated entities. Matching models may be determined based on the categorization of the of the raw data, the integrated data and the content associated with the plurality of subjects. Matching models may generate a plurality of unified facts based on the raw data and the integrated data such that each unified fact is associated with a score associated with a quality of its match with a unified schema.

Embodiments are directed to data ingestion over a network. Raw data and integrated data associated with a plurality of separate data sources may be provided such that the raw data includes content associated with a plurality of subjects. Categorization models may be employed to categorize the raw data based on various features, such as, format, structure, data source, variability, volume, or associated entities. Matching models may be determined based on the categorization of the of the raw data, the integrated data and the content associated with the plurality of subjects. Matching models may generate a plurality of unified facts based on the raw data and the integrated data such that each unified fact is associated with a score associated with a quality of its match with a unified schema.

Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts (e.g., machine learning models) to determine predicted taxonomy-based classification scores for claims and dynamically update data fields based on the same.

Methods, apparatus, systems, computing devices, computing entities, and/or the like for using machine-learning concepts (e.g., machine learning models) to determine predicted taxonomy-based classification scores for claims and dynamically update data fields based on the same.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

A method, computer program product, and computing system for proactive encounter scanning is executed on a computing device and includes obtaining encounter information of a patient encounter. The encounter information is proactively processed to determine if the encounter information is indicative of one or more medical conditions and to generate one or more result set. The one or more result sets are provided to the user.

A set of unstructured data is analyzed to infer structural elements from the unstructured data, and quantized data quality levels, indicative of data quality in the structural elements, are assigned to the structural elements. A set of structured data is generated to include the structural elements inferred from the unstructured data and associations between respective ones of the structural elements in the set of structured data and the corresponding quantized quality levels assigned to the structural elements. The set of structured data, including the associations between respective ones of the structural elements and the corresponding quantized quality levels assigned to the structural elements, is provided to a user interface application to enable the user interface application to visually display varying data qualities in the set of structured data.

A set of unstructured data is analyzed to infer structural elements from the unstructured data, and quantized data quality levels, indicative of data quality in the structural elements, are assigned to the structural elements. A set of structured data is generated to include the structural elements inferred from the unstructured data and associations between respective ones of the structural elements in the set of structured data and the corresponding quantized quality levels assigned to the structural elements. The set of structured data, including the associations between respective ones of the structural elements and the corresponding quantized quality levels assigned to the structural elements, is provided to a user interface application to enable the user interface application to visually display varying data qualities in the set of structured data.