Attribute-Driven Temporal Clustering for Health Insurance Data Analysis
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Solution Overview
Problem
Current methods for analyzing health insurance data, such as those used by health insurance companies, are cumbersome, costly, and require excessive processing time, making it difficult to discover disease precursors and stages effectively.
Innovation Solution
A system and method for rapid generation of attribute-driven temporal clustering, which includes a server and data storage device that allows users to select attributes and determine temporal relationships between them, generating graphical representations and statistical outputs, while narrowing records by time windows and normalizing data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data mining methods are used to analyze health insurance data, then comprehensive statistical analysis can be performed, but processing time and resource requirements become excessively high
Solution Approach 1:
The patent segments the data analysis process into distinct modules: data loading module, temporal relationship determination module, and graphical representation module. Each module handles specific tasks independently, allowing for optimized processing of large health insurance datasets without requiring excessive processing time while maintaining statistical analysis accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-processing and organizing health insurance claim data into structured formats before analysis. The data is pre-loaded and indexed, allowing rapid retrieval and temporal relationship determination without compromising the comprehensiveness of statistical analysis during the actual query execution.
2Loss of information
If traditional ad-hoc analysis methods are used to discover disease precursors and stages, then thorough investigation can be conducted, but the process becomes time-consuming and cumbersome
Solution Approach 1:
The patent creates a universal analysis system that can determine temporal relationships between multiple types of attributes (diagnoses, procedures, drugs, lab tests) using a single integrated platform. This multi-functional approach maintains comprehensive disease precursor detection while simplifying operations through a unified interface that automatically handles various analysis types without requiring separate ad-hoc procedures.
Solution Approach 2:
The system incorporates feedback mechanisms where the determined temporal relationships are automatically visualized and can be further analyzed. The graphical representations provide immediate feedback on disease progression patterns, allowing researchers to quickly identify precursors and stages without manual interpretation of raw data, thus maintaining detection completeness while reducing operational complexity.
3Loss of information
If comprehensive data analysis is performed on health insurance databases, then valuable insights into disease management can be obtained, but costly resources and high processing power are required
Solution Approach 1:
The patent extracts only the essential temporal relationships between attributes that are relevant to disease management insights. Rather than performing exhaustive analysis on all possible attribute combinations, the system selectively extracts meaningful temporal patterns (such as sequence between diagnosis and treatment) and visualizes them, maintaining high insight quality while significantly reducing processing resource consumption.
Solution Approach 2:
The system changes the analysis parameters by focusing on temporal relationships rather than traditional statistical correlations. This parameter transformation allows the use of more efficient algorithms that determine sequence and timing relationships between health events, providing valuable disease management insights with lower computational resource requirements compared to comprehensive statistical analysis.
Data Source
AI summary
System and methods for rapid generation of attribute driven temporal clustering are provided. In one embodiment, the system includes a data storage device and a server. The data storage device may be configured to store a database comprising a plurality of records. The system may also include a server in data communication with the data storage device. The server may be suitably programmed to receive a first attribute and a second attribute, search a database stored on a data storage device to obtain a first group of records associated with the first attribute, search the first group of records to obtain a second group of records associated with the second attribute, determine a temporal relationship between a first index date of a first attribute and a second index date of the second attribute, and generate an output comprising a graphical representation of the temporal relationship.