ML Health Plan Recommendation System
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Solution Overview
Problem
Healthcare enrollment is complex for individuals with little experience in health care plan options, leading to frustration due to the lack of personalized guidance and awareness of relevant factors impacting plan selection.
Innovation Solution
A computerized method using a machine learning model that generates personalized health plan recommendations by combining user profile data with supplemental data such as medical history, demographics, and environmental factors to provide tailored options based on user preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is used to generate personalized recommendations, then the quality of recommendation output is improved, but the device complexity increases
Solution Approach 1:
The patent introduces a machine learning model as an intermediary component that processes user profile data, supplemental data, and option identifiers to generate personalized recommendations. This intermediary handles the complex analysis and matching logic, isolating the complexity from the user-facing system while improving recommendation quality through sophisticated pattern recognition and scoring mechanisms.
2Adaptability or versatility
If multiple data sources are integrated for personalized recommendations, then the adaptability is improved, but the device complexity increases
Solution Approach 1:
The patent merges multiple data sources including user profile data, supplemental data from external sources, and option identifier information into a unified recommendation framework. The machine learning model integrates these diverse data streams by processing them through standardized input layers, enabling comprehensive personalization while managing integration complexity through a consolidated processing architecture.
3Measurement precision
If historical data is used for training the machine learning model, then the measurement precision is improved, but the loss of time increases
Solution Approach 1:
The patent performs preliminary action by training the machine learning model in advance using historical profile data, supplemental data, and option identifiers. The model is trained offline on comprehensive historical datasets to learn optimal recommendation patterns, then deployed for rapid real-time recommendations. This separates the time-consuming training phase from the operational phase, improving recommendation accuracy while minimizing time loss during actual use.
Data Source
AI summary
A computer-implemented method includes determining whether historical profile data structures are stored in a database with corresponding structured supplemental data and selected health care plan option identifiers of a set of health care plan option identifiers. The method includes generating historical feature vectors using the historical data structures stored in the database or generating the historical feature vectors using created sample profile data structures. The method includes training machine learning models using the generated historical feature vectors, selecting one of the machine learning models for use in generating recommendation outputs, presenting an interactive voice interface to an entity to generate audio questions and prompts for obtaining response data from the entity, classifying voice survey responses of the entity, and generating feature vectors. The method includes processing, using the selected machine learning model, the feature vectors to generate the recommendation outputs, and transforming a user interface to display the recommendation output.