Abstract: A method of generating predictor rules using a genetic algorithm for predicting at least one target event associated with a given entity, the entity having a combination of an entity type and one or more attributes. The method comprises partitioning records data into a model generation set and a model testing set. A first generation of predictor rules is determined using the records in the model generation set, and subsequent generations are constructed by iteratively identifying a first subset of predictor rules based on a precision measure of each predictor rule and identifying a second subset of predictor rules based on a recall measure of each predictor rule and generating the subsequent generation by OR combining the predictor rules of the first subset and by AND combining the predictor rules of the second subset. Construction of the predictor rule population is terminated in response to a termination criteria being met.

Abstract: Embodiments of methods and systems for predicting provider negotiated rates are disclosed. One method includes obtaining claims data and provider data, grouping the claims data into priceable units, computing prices for each of the priceable units based on the claims data and the provider data, and estimating provider negotiated rates based on the priceable units and the computed prices for the priceable units.

Abstract: Embodiments of computer-methods and computer systems for predicting out-of-pocket expense for a patient are disclosed. One computer-method includes obtaining claims data and provider data and grouping the claims data into priceable units. A server computes prices for each of the priceable units based on the claims data and the provider data. The priceable units are collected into bundled services as required for predicting the out-of-pocket expense of an episode. Attributes of a health plan of the patient are applied, and an out-of-pocket calculator predicts the out-of-pocket expense based on the collected priceable units and the computed prices for the collected priceable units. The out-of-pocket expense is communicated to patient through a network.

Abstract: Embodiments of methods and systems for predicting provider negotiated rates are disclosed. One method includes obtaining claims data and provider data, grouping the claims data into priceable units, computing prices for each of the priceable units based on the claims data and the provider data, and estimating provider negotiated rates based on the priceable units and the computed prices for the priceable units.

Abstract: Embodiments of methods and systems for providing a payer with a platform for reference pricing of health care deliverables are disclosed. One method includes identifying a set of one or more health care deliverables to which reference pricing is to be applied, and setting a reference price for each health care deliverable of the set. For this embodiment, setting the reference price includes providing the payer with analysis of negotiated rates for the health care deliverable across at least one of geographies, demographics, and centers of excellence, and allowing the payer to set the reference price for each health care deliverable based on the provided analysis. The method further includes receiving a health care deliverable request from a user who is covered by the payer, retrieving the set reference price corresponding to the health care deliverable request, and providing the set reference price or out-of-pocket cost to the user.