Graph Node Embeddings for Medical Billing Anomaly Detection
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Solution Overview
Problem
Existing methods for detecting procedure and diagnosis code anomalies in provider service data are myopic, focusing on internal inconsistencies and failing to reveal relationships in lower-dimensional embedded spaces, thus missing undetectable correlations and anomalies.
Innovation Solution
The method involves generating a co-occurrence adjacency matrix from provider service data, creating graph node embeddings, and assigning them to clusters to evaluate health insurance claims for excessive billing by identifying the number of clusters represented in the claim.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods focus on internal inconsistencies using internal entity profiles and data tables, then detection simplicity is maintained, but detection completeness deteriorates by missing undetectable correlations and anomalies
Solution Approach 1:
The patent transforms provider service data from traditional tabular formats into graph-based representations, adding a dimensional perspective that reveals correlations and anomalies invisible in conventional analyses. By constructing co-occurrence graphs where nodes represent diagnoses/procedures and edges represent their co-occurrence relationships, the system detects anomalies through structural patterns in this new dimensional space, thereby improving detection completeness without merely increasing complexity in the traditional sense.
2Loss of information
If graph node embeddings and clustering techniques are applied to transform matrix data, then correlation detection capability is improved, but computational complexity increases
Solution Approach 1:
The patent introduces graph node embeddings as an intermediary representation that bridges raw co-occurrence data and final anomaly detection. The embedding process transforms high-dimensional co-occurrence matrices into lower-dimensional vector spaces that preserve essential correlation structures. This intermediary representation enables efficient clustering and anomaly detection while reducing the computational burden of directly processing the full co-occurrence matrix, thus recovering correlation information without proportionally increasing complexity.
3Reliability
If co-occurrence adjacency matrices are generated from provider service data, then data relationship visibility is improved, but data processing time increases
Solution Approach 1:
The patent performs preliminary actions by pre-computing co-occurrence adjacency matrices and generating node embeddings from historical provider service data. These pre-computed representations are stored and can be efficiently queried for anomaly detection without re-processing the entire dataset. This preliminary processing establishes the correlation structure in advance, enabling faster real-time anomaly detection while maintaining high accuracy through the pre-analyzed relationship patterns.
Data Source
AI summary
Computer implemented method for detecting procedure and diagnosis code anomalies in provider service data. The method includes generating a co-occurrence adjacency matrix from service provider data of a plurality of providers. The adjacency matrix includes counts of the number of co-occurrences of a plurality of diagnoses and a plurality of procedures in the service provider data. A plurality of graph node embeddings is created based on the adjacency matrix. Each of the plurality of graph node embeddings is assigned to one of a plurality of clusters. A health insurance claim is evaluated for excessive billing based on how many of the plurality of clusters is represented in the claim.