Fraud Detection Regression Models for Healthcare Data Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The vast amount of transaction and interaction data in healthcare and pharmaceutical services is overwhelming, making it difficult to detect fraud, waste, or abuse effectively, as existing methods struggle to derive meaningful insights from this large data set.
Innovation Solution
A system and method that utilize data threat regression models to identify and quantify the likelihood of fraud, waste, or abuse by processing prescriptions through specialized network machines, developing models that calculate variable threat values based on selected features from a database, and triggering flags for potential threats, which can be used to initiate investigations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If all transaction and interaction data is processed and analyzed, then detection accuracy of fraud, waste or abuse is improved, but data processing complexity and resource requirements worsen
Solution Approach 1:
The patent segments the overwhelming transaction and interaction data into distinct data types (prescription data, claims data, patient data, provider data, pharmacy data). By dividing the data into categorized groups, the system can process and analyze each segment separately using appropriate analytical methods, reducing overall processing complexity while maintaining comprehensive detection capability.
Solution Approach 2:
The patent extracts and identifies specific data types that are most indicative of fraud, waste, or abuse threats. Rather than processing all data equally, the system extracts relevant features from each data category (such as prescription patterns, claims anomalies, provider behavior metrics) and focuses analytical resources on these extracted indicators, improving detection efficiency.
2Reliability
If data from multiple sources is integrated, then detection capability is improved, but data integration complexity worsens
Solution Approach 1:
The patent implements a universal data integration framework that handles multiple data sources (prescription, claims, patient, provider, pharmacy data) through a common processing architecture. This multi-functional system applies consistent integration methods across all data types, enabling comprehensive detection capability while managing integration complexity through standardized approaches.
Solution Approach 2:
The patent adds a categorical dimension to data integration by organizing diverse data sources into structured data type groups. This dimensional organization transforms the integration problem from handling raw heterogeneous data to managing categorized data families, simplifying the integration process while maintaining the ability to detect cross-source fraud patterns.
3Measurement precision
If comprehensive data analysis is performed, then fraud detection accuracy is improved, but processing time worsens
Solution Approach 1:
The patent performs preliminary organization and categorization of transaction and interaction data into structured data types before detailed fraud detection analysis. By pre-segmenting data into prescription, claims, patient, provider, and pharmacy categories with relevant features identified in advance, the system reduces the computational burden during actual fraud detection, decreasing processing time while maintaining accuracy.
Data Source
AI summary
Data threat evaluation systems and methods are described. A data model structure includes a data subset from the plurality of data types that predate a known threat; this third data subset includes data types from both a first data subset and a second data subset. A model schema extracts, from the data subset, data types of the first subset that predicate and indicate the threat, the model schema to produce at least an individualized data threat regression model, a script originator regression model, and a script filler data threat regression model using the extracted data types. The system may use the models back on the data set to identify potential threats. The system can operate to integrate data to predict fraud, waste or abuse.