Abstract: The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.

Abstract: The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.

Abstract: The present disclosure relates to merging database tables. Systems and methods may involve performing a comparison between the first set of records and the second set of records and identifying a plurality of record pairs based on the comparison. Each record pair may comprise a record in the first set of records and a record in the second set of records. In addition, A feature signature may be generated for each record pair by comparing field values in each record pair. The feature signature may be classified to identify at least one related record pair. A merged database table may be generated such that it comprises the at least one related record pair and comprises a set of unique records among selected from the first set of records and the second set of records.

Abstract: The present disclosure relates to dynamically merging database tables according to user specified parameters. A user may specify a threshold confidence level that relates to a likelihood that two database records represent the same real-world entity. In addition, a user may specify a merge rule such as desired fields or a manner for consolidating the variations of the information in desired fields from the related records. The original database tables are preserved so that users can iteratively create new dynamically merged database tables by varying the parameters.

Abstract: Some implementations may provide a computer-assisted method for master data management, the method including: receiving configuration information defining a model of entities, each entity encoding attributes of a prescriber of one or more healthcare products; receiving specification information defining mapping logic, searching logic, and matching logic, and merging logic for processing base entities and related entities of the model; receiving data from more than one source customer databases, the customer database including data encoding prescribers of healthcare products and being maintained by more than one organizations; translating the received data into staging data according to the mapping logic in the received specification information; generating master data by processing the staging data according to the searching logic, matching logic, and merging logic in the received specification information; and synchronizing at least a portion of the master data to at least one of the source customer database

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for handling data access request, including: receiving a request submitted by a user to access data entries stored at a primary data server; determining whether the received request is supported by a fast query service engine, the fast query service engine being different from the primary data server; in response to determining that the received request is supported by the fast query service engine, directing the request to the fast query service engine; receiving query results from the fast query service engine, the query results composed from more than one answer sets maintained in in-memory data store at the fast query service engine, the answer sets the in-memory data-store being synchronized with the primary data server; and providing the query results to the user submitting the request.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for receiving and aggregating anonymized data reports about when prescribers provide wireless device applications to patients. The disclosure discusses ways of analyzing the data reports in combination with other electronic medical information to generate useful conclusions about scenarios in the health care process.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for receiving and aggregating anonymized data reports about when prescribers provide wireless device applications to patients. The disclosure discusses ways of analyzing the data reports in combination with other electronic medical information to generate useful conclusions about scenarios in the health care process.

Abstract: Some implementations may provide a computer-assisted method for master data management, the method including: receiving configuration information defining a model of entities, each entity encoding attributes of a prescriber of one or more healthcare products; receiving specification information defining mapping logic, searching logic, and matching logic, and merging logic for processing base entities and related entities of the model; receiving data from more than one source customer databases, the customer database including data encoding prescribers of healthcare products and being maintained by more than one organizations; translating the received data into staging data according to the mapping logic in the received specification information; generating master data by processing the staging data according to the searching logic, matching logic, and merging logic in the received specification information; and synchronizing at least a portion of the master data to at least one of the source customer database

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for handling data access request, including: receiving a request submitted by a user to access data entries stored at a primary data server; determining whether the received request is supported by a fast query service engine, the fast query service engine being different from the primary data server; in response to determining that the received request is supported by the fast query service engine, directing the request to the fast query service engine; receiving query results from the fast query service engine, the query results composed from more than one answer sets maintained in in-memory data store at the fast query service engine, the answer sets the in-memory data-store being synchronized with the primary data server; and providing the query results to the user submitting the request.

Abstract: The disclosure generally describes computer-implemented methods, software, and systems for receiving and aggregating anonymized data reports about when prescribers provide wireless device applications to patients. The disclosure discusses ways of analyzing the data reports in combination with other electronic medical information to generate useful conclusions about scenarios in the health care process.

Abstract: Healthcare marketing data may be cleaned and normalized according to data validity and/or data canonicity rules prior to being merged into a healthcare marketing database. The merging process may include a fuzzy de-duplication operation utilizing a configurable field scoring table for automatically identifying matching and non-matching records. Potential duplicates that cannot be automatically resolved may be provided to an operator to provide merge or no-merge instructions.

Abstract: Provided herein are systems and methods for providing access to marketing data via a reduced-complexity marketer GUI for dynamically defining market segments for use in marketing campaigns.

Abstract: Personally-identifying, protected health information (“PHI”) is stored in encrypted form in protected data records, and hash values derived from the PHI are stored in associated search records. A healthcare marketer may identify market segments of individuals by querying the search records using hashed query predicates, identifying protected data records based on the search record results, and providing anonymized data-record results to the healthcare marketer. Once a market segment has been anonymously identified, the marketer may cause personalized marketing messages to be generated for individuals in the market-segment without the marketer having been exposed to PHI associated with those individuals.

Abstract: Provided herein are systems and methods for providing access to marketing data via a reduced-complexity marketer GUI for dynamically defining market segments for use in marketing campaigns.

Abstract: Personally-identifying, protected health information (“PHI”) is stored in encrypted form in protected data records, and hash values derived from the PHI are stored in associated search records. A healthcare marketer may identify market segments of individuals by querying the search records using hashed query predicates, identifying protected data records based on the search record results, and providing anonymized data-record results to the healthcare marketer. Once a market segment has been anonymously identified, the marketer may cause personalized marketing messages to be generated for individuals in the market-segment without the marketer having been exposed to PHI associated with those individuals.

Abstract: Provided herein are systems and methods for providing access to marketing data via a reduced-complexity marketer GUI for dynamically defining market segments for use in marketing campaigns. The systems and methods provide a searchable list of person-related and entity-related pluralities of market segmentation criteria, obtain a market segmentation definition comprising a search key corresponding to selected market segmentation criteria, and perform a series of steps to obtain a first market-segment snapshot.

Abstract: Healthcare marketing data may be cleaned and normalized according to data validity and/or data canonicity rules prior to being merged into a healthcare marketing database. The merging process may include a fuzzy de-duplication operation utilizing a configurable field scoring table for automatically identifying matching and non-matching records. Potential duplicates that cannot be automatically resolved may be provided to an operator to provide merge or no-merge instructions.

Abstract: Tabular survey data may be automatically imported into a marketing database, including determining a multi-factor score for each column of data. The multi-factor score rates a relative likelihood that a given column represents survey question and/or survey response data, as opposed to respondent-identifying data.

Abstract: Some implementations may provide a computer-assisted method for master data management, the method including: receiving configuration information defining a model of entities, each entity encoding attributes of a prescriber of one or more healthcare products; receiving specification information defining mapping logic, searching logic, and matching logic, and merging logic for processing base entities and related entities of the model; receiving data from more than one source customer databases, the customer database including data encoding prescribers of healthcare products and being maintained by more than one organizations; translating the received data into staging data according to the mapping logic in the received specification information; generating master data by processing the staging data according to the searching logic, matching logic, and merging logic in the received specification information; and synchronizing at least a portion of the master data to at least one of the source customer database