Abstract: A system for selection of a medical provider (MP) by a requesting patient based on preferences for the MP includes a first model modeling the preferences of a plurality of patients for select MPs and trained on a first training data set. A second model is provided for modeling the preferences of a plurality of MPs for select patients and trained on a second training data set. A controller controls the first and second models to select at least a portion of available MPs in an available MP data set for input of the associated representative information to the first and second model MP inputs. A patient rank calculator accumulates the output patient determined preferences for MPs associated with the selected at least a portion of the available MPs and generates a MP ranking for each of the selected at least a portion of the available MPs.

Abstract: A system for selection of a medical provider (MP) by a requesting patient based on preferences for the MP includes a first model modeling the preferences of a plurality of patients for select MPs and trained on a first training data set. A second model is provided for modeling the preferences of a plurality of MPs for select patients and trained on a second training data set. A controller controls the first and second models to select at least a portion of available MPs in an available MP data set for input of the associated representative information to the first and second model MP inputs. A patient rank calculator accumulates the output patient determined preferences for MPs associated with the selected at least a portion of the available MPs and generates a MP ranking for each of the selected at least a portion of the available MPs.

Abstract: A system for selection of a medical provider (MP) by a requesting patient based on preferences for the MP includes a first model modeling the preferences of a plurality of patients for select MPs and trained on a first training data set comprised of patients and MPs. The patients in the first training data set have rated at least one of the MPs in the first data set by a predetermined set of patient determined preferences. A second model is provided for modeling the preferences of a plurality of MPs for select patients and trained on a second training data set comprised of MPs and patients. The MPs in the second training data set have rated at least one of the patients in the second data set by a predetermined set of MP determined preferences. An available MP data set is provided containing information representative of available MPs for selection thereof by the requesting patient.

Abstract: The disclosure includes a computer-implemented method of processing a claim. The method includes receiving information corresponding to a context free grammar expression of a provision governing claim adjudication, receiving information corresponding to a claim, and based on the received information, determining whether the at least one provision applies to the claim.

Abstract: The disclosed technology can execute a sequence of transactions to process a request associated with a health care company and/or other entities interacting in a health care industry. Execution of at least some of the transaction sequence can result in the formation of an instance of a semantic network that can be used to support such processing. The request can correspond to a change in a relationship associated with such entities, where such request corresponds to an electronic document exhibiting a structured natural language format with a fixed context and a fixed grammar.

Abstract: The disclosed technology can identify indicia associated with different entity types that interact within an industry, identify one or more relationships (e.g., contractual provisions) that can affect interactions between such entity types, and identify transactions associated with one or more of the interactions. Further, the identified transactions can be organized into one or more transaction sequences. The identified indicia, the one or more identified relationships, and the one or more transaction sequences can then be associated to form a semantic network. An instance of the semantic network can be formed in response to the execution of at least some of the transaction sequence and can serve, at least in part, as the basis for processing requests associated with the entities. The requests can correspond to interactions associated with the entities and may be represented in a natural language format, exhibiting a fixed context and a fixed grammar.

Abstract: The disclosed technology can identify indicia associated with different entity types (e.g., health care companies, health care suppliers, health care practitioners, etc.) that interact within a health care industry, identify one or more relationships (e.g., contractual provisions) that can affect interactions between such entity types, and identify transactions associated with one or more of the interactions. Further, the identified transactions can be organized into one or more transaction sequences. The identified indicia, the one or more identified relationships, and the one or more transaction sequences can then be associated to form a semantic network. An instance of the semantic network can be formed based, at least in part, on a detection of at least one interaction associated with the entities.

Abstract: The disclosed technology can identify indicia associated with different entity types that interact within an industry, identify one or more relationships (e.g., contractual provisions) that can affect interactions between such entity types, and identify transactions associated with one or more of the interactions. Further, the identified transactions can be organized into one or more transaction sequences. The identified indicia, the one or more identified relationships, and the one or more transaction sequences can then be associated to form a semantic network. An instance of the semantic network can be formed in response to the execution of at least some of the transaction sequence and can serve, at least in part, as the basis for processing requests associated with the entities. The requests can correspond to interactions associated with the entities and may be represented in a natural language format, exhibiting a fixed context and a fixed grammar.

Abstract: The disclosed technology can identify indicia associated with different entity types that interact within an industry, identify one or more relationships (e.g., contractual provisions) that can affect interactions between such entity types, and identify transactions associated with one or more of the interactions. Further, the identified transactions can be organized into one or more transaction sequences. The identified indicia, the one or more identified relationships, and the one or more transaction sequences can then be associated to form a semantic network. An instance of the semantic network can be formed in response to the execution of at least some of the transaction sequence and can serve, at least in part, as the basis for processing requests associated with the entities. The requests can correspond to interactions associated with the entities and may be represented in a natural language format, exhibiting a fixed context and a fixed grammar.