Method of personalizing, individualizing, and automating the management of healthcare fraud-waste-abuse to unique individual healthcare providers

At least one network server for healthcare fraud detection, the at least one network server comprising:

• one or more processors;
• non-transitory computer-readable storage media having computer-executable instructions stored thereon, wherein when executed by the one or more processors the computer-readable instructions cause the one or more processors to—
• receive a plurality of healthcare payment requests corresponding to a plurality of healthcare providers;
• match the plurality of healthcare payment requests to corresponding ones of a plurality of predictive models, each of the plurality of predictive models including—
• a classification model configured based on historical payment request data for a group of the plurality of healthcare providers, the classification model being constructed according to one or more of:
• a neural network, case based reasoning, a decision tree, a genetic algorithm, fuzzy logic, and rules and constraints,
• a smart agent having a profile comprising a representation of historical data of the corresponding healthcare provider, and
• for each of the healthcare payment requests—
• compare the contents of at least one data field of the healthcare payment request against the profile of the corresponding smart agent and, based at least in part on the comparison, generating a first output,
• process the contents of at least one data field of the healthcare payment request via the corresponding classification model to generate a second output,
• analyze the first output and the second output to generate a decision on the healthcare payment request.

Non-transitory computer-readable storage media having computer-executable instructions for healthcare fraud detection, wherein when executed by at least one processor the computer-readable instructions cause the at least one processor to:

• receive a plurality of healthcare payment requests corresponding to a plurality of healthcare providers;
• match the plurality of healthcare payment requests to corresponding ones of a plurality of predictive models, each of the plurality of predictive models including—
• a classification model configured based on historical payment request data for a group of the plurality of healthcare providers, the classification model being constructed according to one or more of:
• a neural network, case based reasoning, a decision tree, a genetic algorithm, fuzzy logic, and rules and constraints,
• a smart agent having a profile comprising a representation of historical data of the corresponding healthcare provider, and
• for each of the healthcare payment requests—
• compare the contents of at least one data field of the healthcare payment request against the profile of the corresponding smart agent and, based at least in part on the comparison, generating a first output,
• process the contents of at least one data field of the healthcare payment request via the corresponding classification model to generate a second output,
• analyze the first output and the second output to generate a decision on the healthcare payment request.