Automated Lead Summary Report Generation for Healthcare Fraud Analysis
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Solution Overview
Problem
Healthcare fraud detection in insurance claims is hindered by the overwhelming number of potential fraudulent leads, which lack context and useful information, overwhelming fraud analysts and requiring manual analysis despite automated detection methods.
Innovation Solution
A computer-implemented method and system that automatically generates lead summary reports by determining data sources, obtaining relevant healthcare claims data, and presenting it in a structured format using a report template, facilitating efficient assessment by users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated techniques are used to identify fraud leads, then the detection speed increases, but the number of leads becomes overwhelming for analysts to review manually
Solution Approach 1:
The patent extracts and presents only the most relevant information from the vast dataset of fraud leads. The system identifies key data elements that are most indicative of fraud and presents them in a prioritized manner, allowing analysts to focus on the most suspicious leads first without being overwhelmed by the complete dataset.
Solution Approach 2:
The patent introduces an intermediary processing layer between automated detection and manual analysis. This layer includes algorithms that score and rank leads based on multiple criteria, and presentation interfaces that organize leads with contextual information, serving as a bridge that transforms raw automated detection output into analyst-friendly prioritized lists.
2Loss of information
If comprehensive data is collected for each fraud lead, then the information completeness improves, but the time required to gather and analyze data increases
Solution Approach 1:
The patent performs preliminary data gathering and organization automatically before presents leads to analysts. The system pre-collects relevant contextual information, pre-calculates fraud scores, and pre-organizes data in meaningful groupings, so that when analysts review leads, the comprehensive information is already prepared and readily available without requiring additional manual data gathering time.
3Measurement precision
If manual analysis is performed on each fraud lead, then the assessment accuracy improves, but the analysis efficiency decreases due to the large volume of leads
Solution Approach 1:
The patent applies different levels of analysis depth to different leads based on their fraud probability scores. High-scoring leads receive more detailed automated analysis and are presented with comprehensive contextual information warranting full manual review, while lower-scoring leads receive streamlined presentation. This local differentiation of analysis quality maintains accuracy for suspicious leads while improving overall efficiency.
Data Source
AI summary
In an embodiment, a computer-implemented method comprises, in response to receiving lead data identifying an entity associated with a health care claim relating to suspected fraud, determining one or more data sources that were used to identify the entity or the suspected fraud; determining a subset of a plurality of data display elements, based on the determined one or more data sources, wherein each of the plurality of data display elements is configured to cause displaying health care claims data associated with the entity in a designated format; automatically obtaining, from a data repository, specific health care claims data associated with the entity for each of the plurality of data display elements in the subset; generating a lead summary report associated with the entity using a report template, the subset, and the obtained specific health care claims data.