ML-Driven API Coordination for Insurance Benefits
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current approaches for coordinating health insurance benefits between multiple insurers are manually driven, dependent on precise member data, and require human intervention, leading to inefficiencies and inaccuracies in identifying primary insurers and avoiding duplicate payments.
Innovation Solution
An automated system using machine learning models to predict the likelihood of additional insurance coverage and identify the likely insurer, generating API-based requests to verify coverage through pre-defined templates, reducing the need for manual processes and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual processes are used for coordinating benefits, then human intervention can handle complex verification, but the process is slow and inefficient
Solution Approach 1:
The patent replaces manual mechanical processes with an automated machine learning system. The ML model automatically analyzes member data, predicts additional insurance coverage, identifies likely insurers, and generates API requests without human intervention, thereby increasing productivity and reducing processing time while maintaining accuracy through algorithmic analysis
2Reliability
If manual investigative processes are used, then verification can be performed with available data, but the process requires human resources and is dependent on data availability
Solution Approach 1:
The system performs self-service by automatically conducting the entire verification process. The machine learning model independently analyzes data, makes predictions, identifies insurers, and generates requests without requiring human investigators. This reduces process complexity while maintaining or improving reliability through consistent algorithmic application
Solution Approach 2:
The patent changes the parameters of the verification process by transitioning from manual human analysis to automated machine learning analysis. This parameter change enables the system to process data more consistently and reliably while reducing the complexity of human coordination and intervention
3Measurement precision
If telephone verification is used, then direct confirmation can be obtained, but the process is time-consuming and resource-intensive
Solution Approach 1:
The patent substitutes telephone verification with automated API-based electronic verification. The machine learning system generates structured API requests to insurer databases, obtaining precise verification data automatically. This maintains measurement precision through direct database queries while dramatically increasing processing throughput by eliminating manual telephone calls
4Measurement precision
If comprehensive data analysis is performed, then accurate predictions can be made, but computational resources and network traffic increase
Solution Approach 1:
The system applies partial action by analyzing only the most relevant features and data points necessary for accurate prediction. The machine learning model is trained to identify and process only critical indicators of additional insurance coverage, rather than comprehensively analyzing all available data. This maintains prediction accuracy while reducing computational resource usage and network traffic
Data Source
AI summary
Methods, apparatus, systems, computing devices, computing entities, and/or the like for verifying the coordination of benefits information with an end-to-end automated process. First, one or more machine-learning models generate predictions for members who are likely to have insurance with another insurer. The members identified are processed through another one or more machine learning models that generate predictions for who the likely other insurers are. Each insurer is associated with an insurer record/profile that identifies one or more application programming interface templates. The API-based eligibility request templates can be automatically populated to generate eligibility API-based eligibility requests. And in turn, eligibility responses are received and used to update corresponding member profiles and process claims accordingly.