Benefits Selection Optimization via Census Segmentation and ML
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Solution Overview
Problem
Current systems lack an efficient method to generate and optimize subscription product recommendations for clients, particularly in health insurance, considering both member demographics and provider preferences, leading to suboptimal product offerings and increased complexity in decision-making.
Innovation Solution
An automated system that processes client intake forms, consolidates member demographic information, calculates selection probabilities, and presents optimized product offerings based on financial, employee perception, and market competitiveness goals, using machine learning algorithms and dynamic GUI interfaces to adjust and filter results in real-time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual product selection processes are used considering member demographics and provider preferences, then personalized recommendations can be generated, but the decision-making complexity and time consumption increase significantly
Solution Approach 1:
The system segments the complex decision-making process into distinct modular components: demographic data processing module, preference analysis module, probability calculation module, and recommendation generation module. Each module handles a specific aspect of the product selection process, making the overall system more manageable and less complex while maintaining personalized recommendation capabilities.
Solution Approach 2:
The patent introduces an automated recommendation system as an intermediary between the raw data (demographics and preferences) and the final product selection. This intermediary processes the complex interactions between multiple factors using algorithms and machine learning models, shielding users from the underlying complexity while delivering personalized recommendations.
2Measurement precision
If comprehensive demographic data and multiple product options are analyzed, then recommendation accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and segmenting demographic data into census divisions before the actual recommendation process. Probabilities are pre-calculated for different demographic segments, and product options are pre-filtered based on provider preferences. This preparation work reduces the computational burden during real-time recommendation generation, maintaining accuracy while reducing processing time.
Solution Approach 2:
The patent transforms the complex multi-dimensional analysis problem into a probability calculation framework by changing the parameters from analyzing raw demographic data directly to working with pre-calculated selection probabilities for different census divisions. This parameter transformation simplifies the computational process while preserving recommendation accuracy.
3Ease of operation
If real-time adjustments and filtering are implemented in the product recommendations, then client satisfaction improves, but system complexity and computational load increase
Solution Approach 1:
The recommendation system is designed to be dynamic, allowing real-time adjustments based on client feedback and changing conditions. The system can filter and re-rank product recommendations on-the-fly without requiring a complete re-analysis of all demographic and preference data. This dynamic capability enhances client satisfaction while managing system complexity through efficient incremental updates.
Solution Approach 2:
The system incorporates feedback mechanisms where client responses to recommendations are captured and used to refine future recommendations. This feedback loop allows the system to learn from interactions and improve personalization over time, enhancing client satisfaction while the feedback processing is handled by dedicated modules that manage the computational load efficiently.
Data Source
AI summary
The present disclosure relates to systems and methods for optimizing benefits plan options offered by an organization through balancing derived population preferences with organizational preferences by analyzing historical selections made by individuals. Census data dividing members of an organization into census divisions may be applied to machine learning algorithm(s) to derive estimated selection preferences of the members. Using selection preferences, costs of various product offering scenarios and overall member satisfaction estimates of the scenarios may be calculated. Product offering scenarios meeting member preference criteria and organizational budget criteria may be presented for review.