Geographic Mapping of Healthcare Data via Server Aggregation
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Solution Overview
Problem
Traditional healthcare data management systems fail to effectively identify causal or associative factors for diseases or conditions due to the latent nature of these factors, making it difficult to leverage environmental data for research purposes efficiently.
Innovation Solution
A method and system that aggregate patient records by geographic units based on healthcare metrics and environmental factors, annotating maps to visualize geographic variances, enabling the correlation of healthcare data with environmental data for trend analysis and visualization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If patient records are aggregated by geographic units and annotated on maps, then geographic trends and environmental correlations become visible, but data processing complexity and system requirements increase
Solution Approach 1:
The patent introduces a server computer as an intermediary component that receives patient records, aggregates them by geographic units, accesses maps, annotates maps with healthcare metrics and environmental factors, and generates visual reports. This intermediary system bridges the gap between raw data and actionable geographic insights without requiring direct complex processing at the client level, thus resolving the contradiction between information preservation and system complexity.
2Loss of information
If environmental data is aggregated and correlated with healthcare data, then causal and associative factors for diseases can be identified, but research time and computational resources increase
Solution Approach 1:
The patent performs preliminary aggregation of patient records by geographic units and pre-annotation of maps with healthcare metrics before environmental factor analysis. Environmental data is then aggregated by the same geographic units and correlated with pre-existing healthcare data visualizations. This preliminary organization of data structures and pre-computation of geographic aggregations reduces the time required for subsequent causal factor analysis and disease pattern identification.
3Reliability
If patient records are independently maintained by healthcare providers, then data security and privacy are maintained, but geographic trend analysis and environmental correlation become difficult
Solution Approach 1:
The server computer acts as a secure intermediary that receives patient records from multiple healthcare providers, aggregates them by geographic units, and generates anonymized visual reports showing geographic trends and environmental correlations. This intermediary approach allows data from independent providers to be combined for geographic analysis while maintaining the security and privacy benefits of distributed data storage, as the server processes aggregated data rather than requiring direct access to individual patient records at each provider location.
Data Source
AI summary
A method includes receiving, by a server computer, a selection of patient records. The method further includes aggregating the patient records by geographic unit based on at least one healthcare metric. In addition, the method includes accessing a map of a geographic region covered by the selected patient records. Further, the method includes annotating the map based on a geographic variance of the at least one healthcare metric. The method also includes receiving, by the server computer, a selection of at least one environmental factor. Additionally, the method includes aggregating, by the server computer, environmental data for the environmental factor by the geographic unit. The method further includes annotating the map based on a geographic variance of the environmental factor.