Abstract: A scoring model is provided that is trained using historical patient readmission data. The scoring model is used to analyze patient insurance claim data for which patients were readmitted to a healthcare facility in order to characterize whether the corresponding insurance claims are potentially fraudulent or erroneous. Related techniques, apparatus, systems, and articles are also described.

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: 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.