HEALTH SEVERITY SCORE PREDICTIVE MODEL

摘要

A computerized health severity score predictive model for assigning a health severity score to a member of a health insurance member population is disclosed. The computerized system and method comprises a predictive model for scoring members. The predictive model is developed based on health insurance claim data. Member claim data may comprise eligibility, demographics, medical claims, pharmacy claims, pharmacy benefit management, laboratory test results, and disease management data. A utilization transition pattern is identified from a comparison of costs observed during a first year and a subsequent year. Members are segmented into groups according to predetermined segmenting rules derived from a segmentation model that applies the utilization transition pattern. The health severity score is thus based on demographic and clinical data as well as utilization transition pattern (or cost transition) data.

主权项

1. A computerized method for assigning health severity scores to members of a health insurance population comprising:
(a) extracting at a server from at least one health claims database utilization data for a plurality of members of said health insurance population, said utilization data comprising for each member cost data for said member; (b) identifying at said server for each of said plurality of members a utilization transition pattern based on a comparison of a first year of cost data and a second year of cost data; (c) segmenting members at said server in to a plurality of groups based on data driven rules derived from a supervised segmentation model; (d) integrating at said server a predictive model trained on member data and observed utilization transition patterns with a model software application that:
(1) applies said predictive model to member data for said plurality of members; and(2) assigns to each of said plurality of members a health severity score based on application of said predictive model; and(3) outputs for each member said health severity score.