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.

1 .- 20 . (canceled) 
     
     
         21 . A system, comprising:
 a data processing system comprising memory and one or more processors to:   predict, via input of a condition of a participant into a model trained with machine learning and historical data for conditions and services, a first metric corresponding to consumption of a service for the condition by the participant over a time period;   identify an execution of an action between a computing device linked with the participant and an electronic account;   update, via input of the execution of the action into the model, the prediction of the first metric to a second metric of the consumption of the service by the participant, wherein the first metric of the consumption is predicted for the participant over the time period in the absence of the execution of the action;   determine, based on a comparison of the first metric with the second metric, that the second metric corresponds to a reduction in the consumption of the service over the time period; and   provide, via a network to the computing device responsive to the determination of the reduction in the consumption, an alert that indicates the reduction in the consumption due to the execution of the action.   
     
     
         22 . The system of  claim 21 , comprising:
 the data processing system to detect the execution of the action between the computing device and the electronic account established by a third-party administrator device for the participant.   
     
     
         23 . The system of  claim 21 , comprising:
 the data processing system to obtain, via the network from a third-party administrator device associated with the electronic account, the condition and the time period for the participant.   
     
     
         24 . The system of  claim 21 , comprising:
 the data processing system to obtain, via the network from a third-party administrator device associated with the electronic account, a profile for the participant, the profile comprising the condition and the time period.   
     
     
         25 . The system of  claim 21 , comprising the data processing system to:
 aggregate a pattern of the consumption of the service by the participant over the time period to predict the first metric; and   update the pattern of the aggregation of the consumption of the service by the participant over the time period to predict the second metric.   
     
     
         26 . The system of  claim 21 , comprising:
 the data processing system to receive a selection of an opportunity object by the computing device, the opportunity object comprising the action.   
     
     
         27 . The system of  claim 21 , comprising:
 the data processing system to detect a selection of an opportunity object corresponding to an opportunity event provided by a third-party application that interfaces with the data processing system, the opportunity object comprising the action provided by a third-party administrator device that established the electronic account, wherein the action is established for the participant.   
     
     
         28 . The system of  claim 21 , wherein the condition comprises a health state, the first metric comprises a first monetary amount associated with the health state, and the second metric comprises a second monetary amount associated with the health state. 
     
     
         29 . The system of  claim 21 , wherein the reduction in the consumption of the service comprises a reduction by in only the service by the participant by a difference between a numeric value of the first metric and the second metric. 
     
     
         30 . The system of  claim 21 , comprising the data processing system to:
 determine the first metric based at least in part on a first rating of the service associated with the condition and a location of the participant; and   determine the second metric based at least in part on a second rating of the service, the action, and a distance heuristic associated with the location.   
     
     
         31 . A method, comprising:
 predicting, by a data processing system comprising memory and one or more processors, via input of a condition of a participant into a model trained with machine learning and historical data for conditions and services, a first metric corresponding to consumption of a service for the condition by the participant over a time period;   identifying, by the data processing system, an execution of an action between a computing device linked with the participant and an electronic account;   updating, by the data processing system via input of the execution of the action into the model, the prediction of the first metric to a second metric of the consumption of the service by the participant, wherein the first metric of the consumption is predicted for the participant over the time period in the absence of the execution of the action;   determining, by the data processing system based on a comparison of the first metric with the second metric, that the second metric corresponds to a reduction in the consumption of the service over the time period; and   providing, by the data processing system, via a network to the computing device responsive to the determination of the reduction in the consumption, an alert that indicates the reduction in the consumption due to the execution of the action.   
     
     
         32 . The method of  claim 31 , comprising:
 detecting, by the data processing system, the execution of the action between the computing device and the electronic account established by a third-party administrator device for the participant.   
     
     
         33 . The method of  claim 31 , comprising:
 obtaining, by the data processing system, via the network from a third-party administrator device associated with the electronic account, the condition and the time period for the participant.   
     
     
         34 . The method of  claim 31 , comprising:
 obtaining, by the data processing system, via the network from a third-party administrator device associated with the electronic account, a profile for the participant, the profile comprising the condition and the time period.   
     
     
         35 . The method of  claim 31 , comprising:
 aggregating, by the data processing system, a pattern of the consumption of the service by the participant over the time period to predict the first metric; and   updating, by the data processing system, the pattern of the aggregation of the consumption of the service by the participant over the time period to predict the second metric.   
     
     
         36 . The method of  claim 31 , comprising:
 receiving, by the data processing system, a selection of an opportunity object by the computing device, the opportunity object comprising the action.   
     
     
         37 . The method of  claim 31 , comprising:
 detecting, by the data processing system, a selection of an opportunity object corresponding to an opportunity event provided by a third-party application that interfaces with the data processing system, the opportunity object comprising the action provided by a third-party administrator device that established the electronic account, wherein the action is established for the participant.   
     
     
         38 . The method of  claim 31 , wherein the condition comprises a health state, the first metric comprises a first monetary amount associated with the health state, and the second metric comprises a second monetary amount associated with the health state. 
     
     
         39 . The method of  claim 31 , wherein the reduction in the consumption of the service comprises a reduction by in only the service by the participant by a difference between a numeric value of the first metric and the second metric. 
     
     
         40 . A non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to:
 predict, via input of a condition of a participant into a model trained with machine learning and historical data for conditions and services, a first metric corresponding to consumption of a service for the condition by the participant over a time period;   identify an execution of an action between a computing device linked with the participant and an electronic account;   update, via input of the execution of the action into the model, the prediction of the first metric to a second metric of the consumption of the service by the participant, wherein the first metric of the consumption is predicted for the participant over the time period in the absence of the execution of the action;   determine, based on a comparison of the first metric with the second metric, that the second metric corresponds to a reduction in the consumption of the service over the time period; and   provide, via a network to the computing device responsive to the determination of the reduction in the consumption, an alert that indicates the reduction in the consumption due to the execution of the action.