Crowd-Sourced Data Aggregation for Healthcare Provider Reliability
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The reliability of shared data sets, particularly in healthcare provider information, is compromised due to incomplete and varying quality, with commercially provided data being costly and not easily integrated, and updates not being consistently reflected across multiple providers, leading to increased costs and inefficiencies for users.
Innovation Solution
A system and method for improving aggregated data sets through crowd sourcing, which involves receiving and verifying data from multiple sources, parsing and translating it into native formats, tagging with geographic attributes, and mapping into database tables, allowing for secure and efficient access based on user subscription levels, thereby enhancing data quality and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is made freely accessible from multiple providers, then data availability increases, but data reliability and quality deteriorate
Solution Approach 1:
The patent combines data from multiple providers into a single aggregated data set, allowing users to access comprehensive information from one source rather than multiple providers, thereby maintaining data availability while improving reliability through aggregation and verification
Solution Approach 2:
The system implements verification mechanisms where data from multiple sources is cross-checked and validated. Users can contribute corrections and updates, creating a feedback loop that continuously improves data quality while maintaining broad data availability
2Reliability
If commercially provided data is used to improve quality, then data reliability improves, but cost increases
Solution Approach 1:
The system enables users to contribute data corrections, updates, and verifications themselves, reducing the need for expensive manual verification services. The crowd-sourced verification model allows users to serve themselves in improving data quality without requiring costly commercial data services
Solution Approach 2:
The aggregated data set serves multiple functions: it provides baseline data access to all users, enables verification through multiple sources, and allows community contribution. This multi-functionality reduces reliance on expensive specialized commercial data services
3Quantity of substance
If data from multiple providers is aggregated, then data completeness improves, but data integration complexity increases
Solution Approach 1:
The system segments data into standardized categories and fields, organizing incoming data from multiple providers into consistent structures. This segmentation approach allows comprehensive data collection while managing integration complexity through systematic organization
Solution Approach 2:
The system transforms data from various providers by standardizing parameters, formats, and schemas. Data is converted to a common structure with standardized attributes, enabling complete data aggregation while reducing integration complexity through parameter harmonization
4Reliability
If updates are propagated across multiple providers, then data currency improves, but system synchronization complexity increases
Solution Approach 1:
The system consolidates update management into a single centralized system rather than requiring coordination across multiple independent providers. Updates are managed once in the aggregated data set and automatically reflected for all users, improving data currency while eliminating complex multi-provider synchronization
Solution Approach 2:
The aggregated data set acts as an intermediary layer between data sources and users. Updates are processed through this intermediary, which handles verification and distribution, simplifying the update propagation process compared to direct multi-provider synchronization
Data Source
AI summary
A method and system for improving aggregated data sets through crowd sourcing. The method includes organizing a plurality of data sets into an aggregated data set, providing search access to at least a portion of the aggregated data set based upon a subscription level associated with a user, and returning results to the user. Organizing the plurality of data sets into an aggregated data set includes receiving data from a plurality of sources, parsing the data, translating the parsed data into its native format and content, tagging the parsed data with attributes detailing how the data is entered, wherein the attributes comprise geographic location information, and mapping the translated data into a plurality of database tables within the aggregated data set. The system includes various components for performing the method.