Dynamic Healthcare Cost Forecasting Using Machine Learning
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Solution Overview
Problem
Current healthcare cost forecasting models are static and inaccurate, leading to unreliable financial projections for individuals and families, especially over long periods, as they fail to account for dynamic factors influencing out-of-pocket expenses.
Innovation Solution
A system that uses machine learning and dynamic database modules to generate personalized healthcare cost forecasts by analyzing historical data, incorporating factors like location, individual health, plan attributes, and regulatory changes, and continuously learning to improve predictions through reinforcement learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static models and indicators are used for healthcare cost forecasting, then the system is simple and easy to operate, but the prediction accuracy deteriorates over long periods
Solution Approach 1:
The patent transitions from static forecasting models to a dynamic machine learning system that continuously learns and adapts to changing healthcare cost patterns. The system processes historical claims data, updates predictive models over time, and generates dynamic cost projections that reflect evolving healthcare trends, thereby maintaining high prediction accuracy without sacrificing ease of use through automated operations.
Solution Approach 2:
The system changes the parameters of the forecasting model from fixed static values to dynamic variables that are continuously updated based on historical data analysis. Machine learning algorithms adjust model parameters automatically as new data becomes available, allowing the system to adapt to changing healthcare cost structures while maintaining operational simplicity through automation.
2Device complexity
If static forecasting models are used, then the system complexity is low, but the reliability of long-term projections deteriorates
Solution Approach 1:
The patent implements a dynamic machine learning-based forecasting system that continuously adapts to changing healthcare cost patterns. The system processes historical claims data, identifies trends, and updates predictive models over time, thereby maintaining high reliability of long-term projections. The increased complexity is managed through automated processes that handle data collection, processing, and model updating without requiring manual intervention.
Solution Approach 2:
The machine learning system performs self-updating and self-optimization by automatically processing historical data, identifying patterns, and adjusting predictive models without external intervention. This self-service capability maintains high projection reliability while managing system complexity through automated operations that handle the computational burden internally.
3Ease of manufacture
If static models are used for healthcare cost calculation, then the system is simple to implement, but the accuracy of out-of-pocket expenditure projections deteriorates
Solution Approach 1:
The patent replaces static calculation models with a dynamic machine learning system that continuously learns from historical claims data to improve expenditure projection accuracy. The system adapts to changing healthcare cost patterns, plan variations, and individual factors, providing accurate out-of-pocket expenditure predictions while maintaining ease of implementation through automated processing and user-friendly interfaces.
Solution Approach 2:
The system transforms fixed parameters in traditional models into dynamic variables that are continuously updated based on historical data analysis. Machine learning algorithms automatically adjust expenditure projection parameters as new data becomes available, significantly improving the accuracy of out-of-pocket expenditure predictions while the system remains easy to implement through automated operations.
Data Source
AI summary
The disclosed technology includes a system for modeling progression of lifetime healthcare expenses, including healthcare events resulting in an out-of-pocket expenditure per a given healthcare plan, wherein each healthcare event is associated with a group of healthcare events. Each group of healthcare events is associated with different sets of certainty with different groups of individuals. A central computing device communicates with healthcare event data generation sources to obtain the healthcare event data. A static database module stores the healthcare event data in hierarchical layered graphs. A dynamic database module dynamically generates data that depicts different future expenditures over a life span based on the healthcare event data in the static database module. A computer modeling module generates the most likely set of future expenditures using the different future expenditures data in the dynamic database module.