Machine Learning Dental Claim Processing System
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Solution Overview
Problem
Traditional dental insurance claim processing is inefficient due to inconsistent decision-making and errors caused by the large volume of materials, requiring multiple reviewers and taking several hours to complete, even when expedited.
Innovation Solution
A computer-implemented method for point of care processing of dental insurance claims using machine learning models to process dental image and patient data, determining claim decisions in real-time, and communicating them to dental clinics, optionally involving rule engines and machine learning systems for pre-authorization and treatment planning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional claim processing is used with multiple reviewers, then decision-making can be performed, but the process takes several hours to complete and produces inconsistent decisions
Solution Approach 1:
The patent replaces the mechanical human review system with an automated machine learning-based claim processing system. The machine learning model automatically analyzes claims and supporting materials, eliminating the need for multiple human reviewers and their associated inconsistencies and time delays, while providing consistent and rapid decision-making.
Solution Approach 2:
The system enables self-service claim processing where the machine learning model independently evaluates claims without requiring human intervention. The automated system processes claims autonomously, providing timely decisions while maintaining consistency through algorithmic rather than human-based judgment.
2Productivity
If multiple reviewers are used to assess claims, then claims can be processed, but errors increase due to the large volume of materials and reviewer variability
Solution Approach 1:
The patent substitutes the human review mechanism with an automated machine learning system that processes claims consistently and accurately. The machine learning model analyzes the large volume of supporting materials without the variability and errors inherent in human review, maintaining high productivity while improving decision accuracy through standardized algorithmic evaluation.
3Loss of time
If expedited review is implemented, then processing time is reduced, but the complexity of reviewing large volumes of materials remains high
Solution Approach 1:
The patent replaces the complex human review process with an automated machine learning system that efficiently processes large volumes of materials. The machine learning model handles the complexity of analyzing extensive supporting materials automatically, providing expedited processing without increasing operational complexity for the user.
Data Source
AI summary
A computer-implemented method and system provide point of care processing of an insurance claim relating to oral care delivered to a subject patient during a visit of the patient to a dental clinic. The method includes processing, by a computer system, of dental image data and patient data, using a set of machine learning models, to extract output representative of diagnostic data characterizing the dental image data; determining, by a decision support system a claim decision based on the diagnostic data; and communicating the claim decision in real time to an endpoint located in the dental clinic.