Subscription Recommendation System Using ML Cost Prediction
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Solution Overview
Problem
Current systems lack an efficient method for providing personalized subscription product recommendations that account for future costs and member behavior, leading to suboptimal plan selection and increased administrative complexity.
Innovation Solution
A benefits administration system utilizing trained machine learning models to identify cost-driving factors and cluster members based on their attributes, generating real-time recommendations by integrating economic and behavioral scoring to optimize subscription product choices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional rule-based recommendation systems are used, then implementation is straightforward, but they cannot accurately predict future costs and member behavior
Solution Approach 1:
The patent replaces traditional mechanical rule-based recommendation systems with machine learning models that automatically learn patterns from historical claims data. The ML models substitute manual rule creation with automated data-driven predictions, achieving superior accuracy in forecasting future costs and member behavior while handling the complexity internally through algorithms rather than explicit rules.
Solution Approach 2:
The system transforms static rule-based parameters into dynamic predictions by using machine learning models that continuously learn from historical claims data. The models adjust their internal parameters through training on multi-year claims data, enabling them to adapt to changing member behaviors and cost patterns without manual intervention.
2Measurement precision
If comprehensive claims data analysis is performed to predict future costs, then recommendation accuracy improves, but processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-training machine learning models on historical claims data before actual recommendation needs arise. The models are trained on multi-year claims data in advance, creating pre-computed knowledge structures that enable fast real-time predictions without re-processing the entire historical dataset during actual recommendation generation.
Solution Approach 2:
The system implements dynamic processing by using machine learning models that can adapt their computation based on input characteristics. The models process only relevant features from member profiles and claims data, dynamically adjusting the analysis depth based on data availability and prediction requirements, thereby reducing unnecessary processing time while maintaining accuracy.
3Productivity
If real-time recommendations are provided, then member engagement improves, but computational resources are heavily consumed
Solution Approach 1:
The patent performs preliminary computation by pre-training machine learning models on historical claims data before real-time recommendation delivery. The heavy computational work of learning patterns from multi-year claims data is completed in advance, allowing the system to deliver real-time recommendations by applying already-trained models to new member inputs, thereby reducing real-time computational resource consumption.
4Measurement precision
If multiple cost-driving factors are considered, then recommendation quality improves, but administrative complexity increases
Solution Approach 1:
The patent implements a universal machine learning framework that handles multiple cost-driving factors through a single integrated model. Rather than creating separate analysis systems for each factor (demographics, utilization patterns, cost drivers), the ML model processes all factors simultaneously through unified algorithms, reducing administrative complexity while maintaining comprehensive analysis capability.
Data Source
AI summary
In an illustrative embodiment, systems and methods for providing subscription product recommendations can identify, from trained data models, cost-driving factors impacting costs of subscription products offered by a provider. The data models can be trained with claims data from a member population with multiple years of claims data. The cost-driving factors can correspond to attributes of the claims data in a first year that predict future costs in a following year. Requests for product recommendations include responses to questions each associated with a cost-driving factor. Based on the responses, the member can be mapped to a cluster grouping associated with a projected cost to the member for a subscription product. Recommendations can be generated based on an economic equivalent score for each subscription product reflecting the respective projected cost and one or more adjustment factors indicating an impact of one or more qualitative factors on subscription product selection choices.