Abstract: The present disclosure describes techniques for performing dimensional analysis on a set of healthcare claims to identify subsets of claims that deviate from baseline metrics. One example method includes identifying a plurality of dimensions for analysis within a plurality of healthcare claims; calculating a set of baseline metrics for the plurality of healthcare claims; for each particular dimension of the plurality of dimensions: identifying dimensional subsets including healthcare claims from the plurality of healthcare claims; identifying outlier subsets from the identified dimensional subsets based on differences between the set of subset metrics for each dimensional subset and the set of baseline metrics; and providing, for output, each identified outlier subset and associated differences between the set of subset metrics for each outlier subset and the set of baseline metrics.

Abstract: Methods and systems for processing claims (e.g., healthcare insurance claims) are described. For example, prior to payment of an unpaid claim, a prediction is made as to whether or not an attribute specified in the claim is correct. Depending on the prediction results, the claim can be flagged for an audit. Feedback from the audit can be used to update the prediction models in order to refine the accuracy of those models.

Abstract: Methods and systems for processing claims (e.g., healthcare insurance claims) are described. For example, prior to payment of an unpaid claim, a prediction is made as to whether or not an attribute specified in the claim is correct. Depending on the prediction results, the claim can be flagged for an audit. Feedback from the audit can be used to update the prediction models in order to refine the accuracy of those models.

Abstract: Methods and systems for processing claims (e.g., healthcare insurance claims) are described. For example, prior to payment of an unpaid claim, a prediction is made as to whether or not an attribute specified in the claim is correct. Depending on the prediction results, the claim can be flagged for an audit. Feedback from the audit can be used to update the prediction models in order to refine the accuracy of those models.