Multi-factorial real-time predictive model for healthcare events

Abstract

At least one aspect of this technical solution is directed to a system for generating a multi-factorial real-time predictive model for healthcare events, with a data processing system comprising memory and one or more processors to obtain a condition metric and a condition period associated with a participant object, generate, based on a model trained using historical condition-service data, a first participation metric for the participant object based on a participant service associated with the condition metric and the participant object, aggregate the first participation metric and the condition period into a first aggregated participation metric, receive, via a computing device associated with the participant object, a selection of an opportunity object associated with the condition metric, the opportunity object corresponding to an opportunity event provided via a third-party application that interfaces with the data processing system, generate, based on the model and the selection of the opportunity object, a second participation metric associated with the condition metric and the opportunity object, aggregate the second participation metric and the condition period into a second aggregated participation metric for the participant object, determine, based on the second participation metric being less than the first participation metric, a reduction in resource utilization, and provide, responsive to the determination of the reduction in resource utilization, an alert to the computing device indicating the reduction in resource utilization corresponding to the selection of the opportunity object.