Machine Learning Insurance Plan Recommendation System
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Solution Overview
Problem
Consumers face difficulties in navigating and selecting the most appropriate medical insurance plans due to the complexity of available options and confusing terminology, often leading to enrollment in plans that do not meet their medical needs or financial budgets.
Innovation Solution
A method utilizing machine-learning algorithms to identify and recommend insurance plans by extracting features from images of insurance cards, integrating historical medical information to evaluate and suggest alternative plans based on user-specific needs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If consumers manually evaluate multiple insurance plans with complicated terminology, then they can understand the details of each plan, but the process becomes overwhelming and time-consuming
Solution Approach 1:
The system automatically extracts information from insurance cards using image processing and machine learning algorithms, eliminating the need for consumers to manually read and interpret complicated insurance policy language. The system serves itself by autonomously processing images and generating plan evaluations without requiring user intervention in the analysis process.
Solution Approach 2:
The patent introduces an intermediary system that translates complex insurance terminology into simplified comparisons. The machine learning model acts as a mediator between the raw insurance card data and the consumer's decision-making process, automatically extracting relevant features and presenting them in an understandable format.
2Reliability
If consumers manually compare multiple insurance plans, then they can assess suitability for their needs, but the complexity of options makes the process difficult
Solution Approach 1:
The system extracts only the most relevant features from the insurance card images using machine learning algorithms. By isolating and extracting key information such as plan type, coverage details, and cost structures, the system simplifies the comparison process while maintaining accuracy in assessing plan suitability.
Solution Approach 2:
The patent segments the complex insurance plan evaluation into distinct analytical components. The machine learning model processes different aspects of insurance plans separately (e.g., coverage benefits, cost structures, network providers) and then integrates them into a comprehensive suitability assessment, making the overall process more manageable.
3Productivity
If consumers rely on automated image processing systems, then the process becomes faster and simpler, but accuracy in identifying plan details may be compromised
Solution Approach 1:
The system performs preliminary processing of insurance card images by extracting and pre-processing relevant features before final classification. This preliminary action includes enhancing image quality, normalizing data formats, and preparing feature vectors for machine learning analysis, which ensures high accuracy in subsequent plan identification.
Solution Approach 2:
The machine learning model incorporates feedback mechanisms to continuously improve its classification accuracy. The system processes insurance card images, compares them against trained models, and refines its predictions based on validation results, ensuring that automated processing maintains high levels of precision in identifying plan details.
Data Source
AI summary
A system including one or more processor and one or more non-transitory computer-readable media storing computing instructions that, when executed on the one or more processors, cause the one or more processors to perform: extracting one or more feature values from at least one image of an insurance card of a user; generating a feature vector associated with the at least one image of the insurance card of the user; reducing, using dimensionality reduction, an amount of data in the feature vector to a reduced set of data; determining, by a machine learning model, a first insurance plan of the user based on machine learning model input data comprising the reduced set of data; identifying at least one alternative insurance plan for the user; and sending instructions to display a recommendation of the at least one alternative insurance plan on a user interface. Other embodiments are disclosed.