ML Claim Routing for Reversible Denial Assessment
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Solution Overview
Problem
The process of identifying and appealing denied medical claims in the healthcare sector is labor-intensive and time-consuming, particularly when dealing with large volumes of claims, as it typically relies on human analysts with specialized knowledge.
Innovation Solution
Utilizing machine learning to automatically assess denied claims, predict the likelihood of reversal, and route them to appropriate handling queues or remediation processes, including decoding non-interpretable denial reasons and adapting claims based on machine learning models trained on historical data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human analysts manually review each denied medical claim, then the accuracy and specialized knowledge application is improved, but the time consumption and labor intensity increase significantly
Solution Approach 1:
The patent replaces the manual mechanical review process with an automated machine learning system. The ML model processes claim data, extracts features, and predicts reversal likelihood automatically, eliminating the need for human analysts to manually review each claim while maintaining assessment accuracy through trained models on historical data
Solution Approach 2:
The system performs self-service by automatically assessing claims and routing them to appropriate queues without requiring human intervention for the assessment function. The machine learning model independently evaluates claim data, extracts relevant features, and makes routing decisions based on predicted reversal likelihood
2Reliability
If human analysts manually assess denied claims, then the quality of claim review is improved, but the scalability of the process deteriorates
Solution Approach 1:
The patent substitutes manual human review with an automated machine learning system that can process large volumes of claims simultaneously. The system maintains review quality through trained models while achieving scalability by handling hundreds or thousands of claims without additional human resources
Solution Approach 2:
The machine learning system provides universal functionality to handle diverse claim types and scenarios that would require multiple specialized human analysts. A single automated system performs assessment, feature extraction, and routing decisions across all claim types, achieving both quality consistency and scalability
3Productivity
If machine learning models are used to automate claim assessment, then the processing speed and volume capacity are improved, but the complexity of the system increases
Solution Approach 1:
The patent segments the claim assessment system into distinct functional components: data extraction module, feature extraction module, machine learning assessment module, and routing module. This segmentation manages system complexity by dividing the automated process into manageable, independent functions while maintaining high processing speed
Solution Approach 2:
The feature extraction layer acts as an intermediary between raw claim data and the machine learning model. It processes and transforms claim data into meaningful features, simplifying the interaction between data inputs and the ML algorithm, thereby managing system complexity while maintaining automated processing capability
Data Source
AI summary
A system, method, and computer-program product includes obtaining, from a computer database, claim data associated with a digital claim that has an adverse decision, extracting, using one or more feature extractors, one or more corpora of feature vectors from the claim data associated with the digital claim, computing, using a claim assessment machine learning model, a claim assessment inference that includes a likelihood of the adverse decision being reversed for the digital claim based on the claim assessment machine learning model receiving the one or more corpora of feature vectors, and automatically routing the digital claim to a target claim handling queue of a plurality of distinct claim handling queues based on the likelihood of the adverse decision being reversed for the digital claim.