Multi-factorial Predictive Model for Healthcare Event Resource Optimization
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Solution Overview
Problem
Conventional systems fail to effectively model healthcare events in real-time, leading to missed opportunities due to the complexity of healthcare services and accounts, and the inability to rapidly and securely communicate with heterogeneous systems.
Innovation Solution
A multi-factorial real-time predictive model that processes data to generate participation metrics based on historical condition-service data, allowing for the identification of resource utilization reductions and providing alerts for healthcare opportunities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional systems are used to model healthcare events, then system simplicity is maintained, but real-time modeling capability and reliability are insufficient
Solution Approach 1:
The system segments healthcare event modeling into distinct modular components: condition metric processing module, participation metric generation module, opportunity event processing module, and resource utilization determination module. Each module handles specific aspects of the modeling process independently, enabling real-time processing while maintaining system manageability through functional decomposition.
Solution Approach 2:
The patent introduces intermediary computing systems and APIs that facilitate secure communication between heterogeneous healthcare systems. These intermediaries translate and standardize data exchanges between different systems, enabling real-time data sharing without requiring direct integration between all systems, thus improving reliability while controlling complexity.
2Speed
If conventional systems are used, then communication infrastructure is simple, but the ability to rapidly and securely communicate with heterogeneous systems is insufficient
Solution Approach 1:
The system implements universal communication interfaces and standardized data exchange protocols that enable a single communication infrastructure to interact with multiple heterogeneous healthcare systems. This multi-functional approach allows rapid communication across diverse systems without requiring separate dedicated connections for each system, accelerating data exchange while avoiding the complexity of point-to-point integrations.
3Adaptability or versatility
If more healthcare services and accounts are added, then service scope increases, but the ability to efficiently identify particular goods and services for individuals decreases
Solution Approach 1:
The system replaces manual or rule-based identification methods with machine learning models that automatically analyze participant data, condition metrics, and service characteristics. These computational models efficiently process large volumes of service options and participant profiles to identify relevant healthcare goods and services, maintaining high efficiency even as service scope expands to include numerous heterogeneous options.
Data Source
AI summary
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.