Geographic Condition Zone Identification Using Aggregated Claim Data
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Solution Overview
Problem
Current systems lack an efficient method to identify and visualize geographic condition zones based on aggregated claim data, which hinders the ability to detect localized trends and analyze geographic-specific information effectively.
Innovation Solution
A system comprising a geographic repository, an aggregation engine, and a mapping engine that filters claim data using predefined conditions and maps it to appropriate locations in a geographic context, enabling the identification of geographic condition zones.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If claim data is aggregated and presented in a geographic setting using GIS, then users can visualize trends by geographic location, but the system complexity increases due to the need for geographic mapping and data aggregation infrastructure
Solution Approach 1:
The patent introduces a geographic context as an intermediary layer between the claim data and the user interface. The mapping engine translates claim data into geographic contexts, which then serve as the visualization medium. This intermediary structure enables geographic visualization without requiring users to directly interact with complex GIS infrastructure, thus reducing perceived system complexity while preserving geographic information.
Solution Approach 2:
The system creates a virtual copy of claim data mapped onto geographic contexts rather than requiring direct manipulation of raw data through complex GIS tools. The mapping engine generates simplified geographic representations (copies) of the claim data that can be easily visualized and analyzed, separating the complexity of data aggregation from the simplicity of the user interface.
2Measurement precision
If claim data is filtered using predefined conditions and mapped to geographic locations, then localized trends can be detected, but the processing time and computational resources increase
Solution Approach 1:
The system pre-processes claim data by filtering it according to predefined conditions before mapping to geographic locations. This preliminary filtering action separates the data processing steps from the visualization steps, allowing for more efficient processing. By pre-filtering the data, the system reduces the amount of data that needs to be mapped and visualized, thereby reducing overall processing time while maintaining measurement precision for localized trend detection.
Solution Approach 2:
The patent segments the data processing into distinct phases: filtering claim data according to predefined conditions, then mapping only the filtered data to geographic locations. This segmentation allows each phase to be optimized independently, improving overall processing efficiency while maintaining the ability to detect localized trends with precision.
3Reliability
If aggregated medical data is presented in a geographic setting to determine disease outbreaks, then public health analysis is improved, but the data aggregation and mapping requirements increase system complexity
Solution Approach 1:
The mapping engine serves as an intermediary that translates complex claim data into simplified geographic contexts suitable for public health analysis. This intermediary layer handles the data aggregation and mapping complexity internally, presenting simplified visualizations to users without exposing them to the underlying infrastructure complexity, thus maintaining reliable disease outbreak detection while hiding system complexity.
Solution Approach 2:
The geographic context framework serves multiple functions: it visualizes disease outbreaks, filters data by predefined conditions, and maps claim data to geographic locations. This multi-functionality reduces the need for separate specialized systems, thereby reducing overall system complexity while maintaining reliable public health analysis capabilities.
Data Source
AI summary
A method for identifying a geographic condition zone, involving receiving a request for claim data, obtaining the claim data associated with a plurality of users, wherein the claim data comprises location data, filtering the claim data using a predefined condition to obtain filtered claim data, mapping the filtered claim data and the location data to appropriate locations in a geographic context to obtain mapped filtered claim data, and transmitting the geographic context comprising the mapped filtered claim data.