Medical Claims Analytics for Automated Payer Rule Verification
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Solution Overview
Problem
Medical claims processing is complex and involves numerous entities and databases, requiring confirmation of patient and payer information, and determining correct billing amounts, which current systems struggle to automate efficiently.
Innovation Solution
A predictive analytics computing platform that uses machine learning classifiers to verify claims data, identify missing or invalid information, and provide predictive analytic output, integrating with claims processing systems through SMART® on FHIR® and HL7® standards, and generating reports for medical providers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual verification of claims data is performed, then accuracy of verification can be maintained through human judgment, but processing time and labor costs increase significantly
Solution Approach 1:
The patent replaces manual mechanical verification processes with an automated machine learning-based verification system. The system uses trained models to automatically validate claims data against payer requirements, eliminating the need for human reviewers while maintaining high accuracy through algorithmic pattern recognition and decision rules.
Solution Approach 2:
The verification system performs self-validation by automatically comparing claims data against stored payer requirements and requirements templates. The system independently identifies missing or invalid information without requiring external human intervention, enabling claims to be verified autonomously and rapidly.
2Reliability
If comprehensive verification of all claims data fields is performed, then completeness and accuracy of claims processing is improved, but system complexity and processing overhead increase
Solution Approach 1:
The system performs preliminary verification by checking claims data against payer requirements before full processing occurs. The machine learning models pre-validate critical fields and identify potential issues early in the workflow, preventing incomplete claims from advancing further and reducing the need for complex downstream corrections.
Solution Approach 2:
The verification process is segmented into multiple independent validation checks, each targeting specific claims fields or requirements. The system divides comprehensive verification into manageable validation modules that can be executed separately, reducing overall system complexity while maintaining complete coverage of all necessary verification points.
3Reliability
If multiple payer-specific requirements are manually tracked, then compliance with each payer's unique rules can be ensured, but administrative burden and error rates increase
Solution Approach 1:
The system implements a universal verification framework that handles multiple payer-specific requirements through a single integrated platform. The machine learning models are trained on requirements from various payers and can automatically adapt to different payer rules, eliminating the need for separate manual tracking processes for each payer while ensuring comprehensive compliance.
Solution Approach 2:
The verification system provides automated feedback to claims processors about compliance status with specific payer requirements. When claims data fails to meet payer-specific criteria, the system generates targeted feedback indicating what corrections are needed, enabling rapid compliance adjustments without requiring manual interpretation of complex payer rules.
Data Source
AI summary
In an illustrative embodiment, a system for providing automation and virtual assistance to medical claims processing includes a predictive analytics platform configured to receive patient information for a medical claim from a claims processing system, cross-reference the patient information with stored data to identify a patient record, apply the patient information to machine learning classifier(s) to estimate a likelihood of match between the patient information and the patient record, provide patient record information to the claims system, receive claims data from the claims system, access, from a data universe, requirements corresponding to a payer corresponding to the medical claim, the requirements having been generated through training machine learning classifier(s) with claims data corresponding to claims denied by the payer, verify the claims data in view of the requirements, and provide an indication of missing claims information and/or invalid claims information to the claims system.