AI Claim Profiling for Individual and Group Fraud-Waste-Abuse Detection
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Solution Overview
Problem
Conventional methods for detecting healthcare fraud, waste, and abuse are limited in scope and fail to identify unexpected variations, leading to inefficiencies in addressing systemic issues within the healthcare system, resulting in significant financial losses.
Innovation Solution
Employing artificial intelligence machines to analyze healthcare payment request claims using predictive models and behavioral analysis, comparing individual and group provider profiles to detect fraudulent, wasteful, or abusive claims.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to detect healthcare fraud, then the detection process is simple and straightforward, but the detection accuracy is limited and cannot identify unexpected variations
Solution Approach 1:
The patent replaces conventional mechanical fraud detection methods with artificial intelligence and machine learning systems. The AI-based fraud detection system analyzes provider behavior patterns, compares individual and group profiles, and identifies fraudulent claims through sophisticated algorithms, thereby significantly improving detection accuracy while accepting increased system complexity
Solution Approach 2:
The patent introduces an intermediary AI system that acts as a mediator between conventional detection methods and fraud identification. The AI system processes provider claims data, behavioral patterns, and group comparisons to generate fraud risk scores, serving as an intelligent intermediary layer that enhances detection capabilities without completely replacing existing systems
2Measurement precision
If AI-based analysis is implemented to improve fraud detection, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-establishing provider profiles and group classifications before fraud detection is needed. The system maintains running profiles of individual providers and groups based on historical data, so when new claims are submitted, the AI system can quickly compare against pre-existing profiles rather than analyzing all historical data from scratch, thereby reducing processing time while maintaining high detection accuracy
Solution Approach 2:
The patent ensures continuity of useful action by continuously updating provider profiles and group classifications as new data becomes available. The AI system operates continuously to learn from incoming claims data, refine behavioral patterns, and improve detection algorithms over time, maintaining accurate and up-to-date profiles that enable rapid fraud detection without requiring periodic system retraining or data reprocessing
3Measurement precision
If individual provider analysis is performed, then detection precision is high, but the system cannot identify systemic issues and unexpected variations
Solution Approach 1:
The patent merges individual provider analysis with group-based analysis by combining both approaches in a unified fraud detection system. The AI system simultaneously evaluates individual provider claims against their personal running profiles and compares them against group profiles representing categories of providers. This combination allows the system to identify both individual fraudulent claims and systemic fraud patterns across groups, thereby achieving both high detection precision and adaptability to systemic issues
Solution Approach 2:
The patent adds another dimension to fraud detection by introducing group-level analysis alongside individual provider analysis. The system creates a multi-dimensional detection framework where claims are evaluated at both the individual provider level and the group/category level, enabling the identification of fraud patterns that manifest across multiple providers within a group while maintaining the ability to detect individual fraudulent claims
4Ease of operation
If third-party after-the-fact detection is used, then provider autonomy is maintained, but fraud control effectiveness is reduced leading to increased financial losses
Solution Approach 1:
The patent implements feedback by continuously monitoring provider claims and providing real-time or near-real-time fraud risk assessments. The AI system analyzes incoming claims against established profiles and patterns, generating feedback signals that indicate potential fraud risks. This feedback mechanism maintains provider autonomy by allowing them to continue operations while simultaneously improving fraud control effectiveness through continuous monitoring and risk identification, thereby reducing financial losses without disrupting provider workflows
Data Source
AI summary
A method of preventing healthcare fraud-waste-abuse uses artificial intelligence machines to limit financial losses. Healthcare payment request claims are analyzed by predictive models and their behavioral details are compared to running profiles unique to each healthcare provider submitting the claims. A decision results that the instant healthcare payment request claim is or is not fraudulent-wasteful-abusive. If it is, a second analysis of a group behavioral in which the healthcare provider is clustered using unsupervised learning algorithms and compared to a running profile unique to each group of healthcare providers submitting the claims. An overriding decision results if the instant healthcare payment request claim is or is not fraudulent-wasteful-abusive according to group behavior.