Abstract: Computer implemented systems and methods of health care claim analysis are provided by storing a claim data set in a computer database, including patient information and a DRG assignment based on at least a primary diagnosis code and one or more associated procedure codes. A nominal DRG weight is determined for the claim data set, using a processor in communication with the computer database. The processor looks up an alternate procedure code in the database, and determines an alternate DRG weight for the claim data set by swapping the associated procedure code with the alternate procedure code. A claim score is output to a user interface in communication with the processor, based at least in part on a difference between the nominal and alternate DRG weights.

Abstract: Computer implemented systems and methods of health care claim data analysis are provided by storing a claim data set in a computer database, including patient information and a DRG assignment based on at least primary and secondary diagnosis codes. A nominal DRG weight is determined for the claim data set, using a processor in communication with the computer database. An alternate DRG weight is determined for the claim data set by swapping the primary and secondary diagnosis codes. A claim rating is generated for the claim data set, based at least in part on a difference between the nominal and alternate DRG weights. The claim rating is output to a user interface in communication with the processor.

Abstract: Fraud and abuse detection in an entity's payment coding practices includes the ability to search for fraud at all levels of the hierarchical coded payment system within the context of an unsupervised model. The model uses variables derived and profiles created at any level or at all levels of the hierarchical coded payment system to create a comprehensive description of the payment coding activities submitted by the entity. That description is compared with other peer entities to determine unusual and potentially inappropriate activity. The profiles created may themselves be utilized for purposes other than the detection of fraud and abuse.

Abstract: Transaction-based behavioral profiling, whereby the entity to be profiled is represented by a stream of transactions, is required in a variety of data mining and predictive modeling applications. An approach is described for assessing inconsistency in the activity of an entity, as a way of detecting fraud and abuse, using service-code information available on each transaction. Inconsistency is based on the concept that certain service-codes naturally co-occur more than do others. An assessment is made of activity consistency looking at the overall activity of an individual entity, as well as looking at the interaction of entities. Several approaches for measuring consistency are provided, including one inspired by latent semantic analysis as used in text analysis. While the description is in the context of fraud detection in healthcare, the techniques are relevant to application in other industries and for purposes other than fraud detection.

Abstract: Transaction-based behavioral profiling, whereby the entity to be profiled is represented by a stream of transactions, is required in a variety of data mining and predictive modeling applications. An approach is described for assessing inconsistency in the activity of an entity, as a way of detecting fraud and abuse, using service-code information available on each transaction. Inconsistency is based on the concept that certain service-codes naturally co-occur more than do others. An assessment is made of activity consistency looking at the overall activity of an individual entity, as well as looking at the interaction of entities. Several approaches for measuring consistency are provided, including one inspired by latent semantic analysis as used in text analysis. While the description is in the context of fraud detection in healthcare, the techniques are relevant to application in other industries and for purposes other than fraud detection.

Abstract: Transition probability sequencing models and metrics are derived from healthcare claims data to identify potentially fraudulent or abusive practices, providers, doctors, clients, or other entities. Healthcare reimbursement claims from hospitals, skilled nursing facilities, doctors, etc., are processed to identify sequences of states, and transition probability metrics are determined from frequency information pertaining to the states. The metrics can these be further analyzed in predictive or rule based models, or other tools.

Abstract: Transaction-based behavioral profiling, whereby the entity to be profiled is represented by a stream of transactions, is required in a variety of data mining and predictive modeling applications. An approach is described for assessing inconsistency in the activity of an entity, as a way of detecting fraud and abuse, using service-code information available on each transaction. Inconsistency is based on the concept that certain service-codes naturally co-occur more than do others. An assessment is made of activity consistency looking at the overall activity of an individual entity, as well as looking at the interaction of entities. Several approaches for measuring consistency are provided, including one inspired by latent semantic analysis as used in text analysis. While the description is in the context of fraud detection in healthcare, the techniques are relevant to application in other industries and for purposes other than fraud detection.

Abstract: A system, computer program product, and method provide for reason driven data analysis in a web-based system that is configurable to various types of data and environments. The computer program product provides for navigation of a report tree of reports that are hierarchically organized with respect to reasons derived from an analysis of transactional data in a database. A frame based interface provides for easy and flexible navigation and configuration of the reports and other features. A configuration file is used to rapidly develop and customize the web-based system. Case management functionality is included to allow for management of cases being reviewed using the system.

Abstract: Computer implemented processes and software products generate profiles of entities, such as providers, clients, merchants and customers, and entities comprising interacting pairs of entities. The processes including deriving direct profiles from transaction data pertaining to an entity and enhancing the profile of one entity using the profile of another entity. Parallel and serial applications of the derive and enhance processes on various individual and multiple entites yields enhanced profiles that powerfully describe the interactions and relationship of the entities to each other, and between their members.

Abstract: Transition probability sequencing models and metrics are derived from healthcare claims data to identify potentially fraudulent or abusive practices, providers, doctors, clients, or other entities. Healthcare reimbursement claims from hospitals, skilled nursing facilities, doctors, etc., are processed to identify sequences of states, and transition probability metrics are determined from frequency information pertaining to the states. The metrics can these be further analyzed in predictive or rule based models, or other tools.

Abstract: Transition probability sequencing models and metrics are derived from healthcare claims data to identify potentially fraudulent or abusive practices, providers, doctors, clients, or other entities. Healthcare reimbursement claims from hospitals, skilled nursing facilities, doctors, etc., are processed to identify sequences of states, and transition probability metrics are determined from frequency information pertaining to the states. The metrics can these be further analyzed in predictive or rule based models, or other tools.

Abstract: Fraud and abuse detection in an entity's payment coding practices includes the ability to search for fraud at all levels of the hierarchical coded payment system within the context of an unsupervised model. The model uses variables derived and profiles created at any level or at all levels of the hierarchical coded payment system to create a comprehensive description of the payment coding activities submitted by the entity. That description is compared with other peer entities to determine unusual and potentially inappropriate activity. The profiles created may themselves be utilized for purposes other than the detection of fraud and abuse.