Deep Learning Claim Analysis System for Denial Prediction
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Solution Overview
Problem
Current medical insurance claim processing is subjective and prone to human error, leading to inefficiencies and biases in claim analysis, as well as delays in payment due to the need for manual review and investigation.
Innovation Solution
A claim analysis system utilizing deep learning to predict payer responses to medical insurance claims, including task-specific layers to determine denial probabilities and identify contributing claim features, thereby automating the analysis and reducing human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual review and investigation of claims is performed by humans, then subjective analysis and human error occur, but the system allows for human judgment and flexibility in complex cases
Solution Approach 1:
The patent replaces the mechanical system of manual human review with an automated deep learning model that processes claims data. The model uses neural networks with embedding layers to automatically analyze claim features, predict denial probabilities, and identify contributing factors, thereby eliminating human subjectivity and error while maintaining analytical capability through computational processing.
2Productivity
If human reviewers analyze claims manually, then patterns can be identified through experience, but the volume of claims reviewed is insufficient to identify comprehensive patterns
Solution Approach 1:
The deep learning model serves multiple functions simultaneously: it processes high volumes of claims, identifies patterns across diverse claim types, predicts denial outcomes, and provides explanatory reasoning. The model's architecture with shared embedding layers enables it to learn universal patterns from training data and apply them across different claim scenarios, achieving both high productivity and comprehensive pattern recognition.
3Productivity
If automated deep learning models are used to predict payer responses, then efficiency and pattern identification improve, but the system complexity and training requirements increase
Solution Approach 1:
The patent segments the claim analysis task into distinct functional components handled by different model elements: embedding layers for feature representation, task-specific output layers for prediction, and explanation generation components for reasoning. This segmentation allows the complex model to be developed, trained, and maintained in modular fashion, reducing the practical complexity despite the sophisticated functionality.
4Reliability
If manual claim review is performed, then claims can be investigated in detail, but payment delays occur while insurers investigate suspicious claims
Solution Approach 1:
The automated deep learning model enables continuous processing of claims without interruption or delay. Unlike manual review where claims queue up for human investigation, the model processes claims in real-time as they are submitted, providing immediate predictions and explanations. This continuous automated action eliminates waiting time while maintaining thorough analysis through the model's comprehensive feature evaluation capability.
Data Source
AI summary
Embodiments relate to system for automatically predicting payer response to claims. In an embodiment, the system receives claim data associated with a claim. The system identifies a set of claim features of the claim data, and generates an input vector with at least a portion of the set of claim features. The system applies the input vector to a trained model. A first portion of the neural network is configured to generate an embedding representing the input vector with a lower dimensionality than the input vector. A second portion of the neural network is configured to generate a prediction of whether the claim will be denied based on the embedding. The system provides the prediction for display on a user interface of a user device. The prediction may further include denial reason codes and a response date estimation to indicate if, when, and why a claim will be denied.