Abstract: Methods and systems for performing multidose packaging targeting are provided. The methods and systems perform operations comprising: receiving prescription related complexity data associated with a patient, the prescription related complexity data comprising medication regiment information for a plurality of medications and patient specific information, the prescription related complexity data representing complexity for the patient in adhering to the one or more medications; applying a model to the prescription related complexity data to generate a score for at least one medication of the plurality of medications, the score being indicative of a likelihood of converting the patient from a current dispensing process to an alternate dispensing process; and triggering, based on the generated score, a notification associated with converting the patient from the current dispensing process to the alternate dispensing process.

Abstract: A computer system includes processor hardware configured to execute instructions from memory hardware. The instructions include training a machine learning model to generate an entity expiration likelihood output, obtaining a set of multiple database entities, and processing, by the machine learning model, feature vector inputs associated with each database entry to generate an entity expiration likelihood output. The instructions include determining a subset of the database entities having the highest entity expiration likelihood outputs, and, for each database entity in the subset, determining output impact scores for parameters of the feature vector input associated with the database entity, generating a feature list based on the determined output impact scores, and automatically selecting an executable sequence according to the entity expiration likelihood output associated with the database entity.

Abstract: Methods and systems for performing multidose packaging targeting are provided. The methods and systems perform operations comprising: receiving prescription related complexity data associated with a patient, the prescription related complexity data comprising medication regiment information for a plurality of medications and patient specific information, the prescription related complexity data representing complexity for the patient in adhering to the one or more medications; applying a model to the prescription related complexity data to generate a score for at least one medication of the plurality of medications, the score being indicative of a likelihood of converting the patient from a current dispensing process to an alternate dispensing process; and triggering, based on the generated score, a notification associated with converting the patient from the current dispensing process to the alternate dispensing process.

Abstract: Data threat evaluation systems and methods are described. A data model structure includes a data subset from the plurality of data types that predate a known threat; this third data subset includes data types from both a first data subset and a second data subset. A model schema extracts, from the data subset, data types of the first subset that predicate and indicate the threat, the model schema to produce at least an individualized data threat regression model, a script originator regression model, and a script filler data threat regression model using the extracted data types. The system may use the models back on the data set to identify potential threats. The system can operate to integrate data to predict fraud, waste or abuse.

Abstract: Data threat evaluation systems and methods are described. A data model structure includes a root object query that, when executed, returns a third data subset from the plurality of data types that predate a known threat, the third data subset including data types in both the first data subset and the second data subset; and a model schema to extract, from the third data subset, data types of the first subset that predicate and indicate the threat, the model schema to produce at least an individualized data threat regression model, a script originator regression model, and a script filler data threat regression model using the extracted data types. The system may use the individualized data threat regression model, the script originator regression model, and the script filler data threat regression model back on the data set to identify potential threats. The system can be applied as a fraud, waste or abuse detector.