AI/ML Medical Claims Platform for Denial Prediction
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Solution Overview
Problem
Existing systems struggle to accurately predict medical claim denials and resubmission outcomes, leading to inefficient resource allocation and high costs for providers due to inaccurate rule-based engines and lack of nuanced understanding of payer-specific requirements.
Innovation Solution
Implementing artificial intelligence/machine learning models to analyze healthcare claims data, predict denial probabilities, generate corrective actions, and automatically route claims to appropriate workflow queues, utilizing data standardization, linking, and regulatory compliance to enhance prediction accuracy and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If rule-based engines are used to predict medical claim denials, then the system structure is simple and easy to implement, but the prediction accuracy is low and cannot capture nuanced payer-specific requirements
Solution Approach 1:
The patent replaces traditional rule-based mechanical systems with machine learning models that can learn complex patterns from historical claims data. The ML models automatically capture nuanced payer-specific requirements without manual rule configuration, significantly improving prediction accuracy while the system handles the complexity internally through automated model training and inference.
Solution Approach 2:
The system transforms static rule-based parameters into dynamic learned parameters through machine learning. The models continuously adapt to changing payer behaviors and requirements by learning from new claims data, allowing the system to maintain high accuracy without increasing operational complexity for users.
2Productivity
If manual review and processing of medical claims is performed, then resource allocation can be optimized based on human expertise, but the processing time and operational costs are high
Solution Approach 1:
The system performs preliminary automated processing and prediction of claim outcomes before human review. By pre-identifying high-risk claims likely to be denied and prioritizing them for human expertise, the system reduces overall processing time while maintaining efficient resource allocation. Low-risk claims are handled automatically without human intervention.
Solution Approach 2:
The system enables self-service through automated claims processing and prediction. The ML models independently evaluate claims, predict denial risks, and suggest corrective actions without requiring manual human analysis for every claim, significantly improving productivity while reducing processing time for routine claims.
3Measurement precision
If comprehensive analysis of all claims is performed to improve prediction accuracy, then the prediction quality improves, but the computational resources and processing time increase
Solution Approach 1:
The system applies partial analysis by focusing computational resources on claims that require it. Instead of performing comprehensive analysis on all claims, the ML models predict denial risk and prioritize only high-risk claims for detailed examination and human review, reducing overall computational resource usage while maintaining high prediction accuracy for claims that matter most.
Data Source
AI summary
Embodiments of various systems, methods, and devices are disclosed for generating artificial intelligence or machine learning models for predicting denials of medical claims, predicting approvals of resubmitted medical claims, as well as automatic workflow clustering processes for automatically assigning medical claims to workflow queues using predictive segmentation and smart resource allocation.