Universal Identifier Matching for Clinical Data Integration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The healthcare industry faces challenges in accurately identifying and integrating patient records across different systems, leading to inconsistencies in clinical analytics data, which hinders effective decision-making and health outcome improvements.
Innovation Solution
A system and method that involve receiving and staging health-related data from multiple sources, matching and compressing it using universal identifiers, and providing it to an analytics engine for processing, ensuring accurate data integration and analysis for improved healthcare insights.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If data from multiple sources is integrated without standardized identifiers, then data completeness is improved, but data accuracy and reliability deteriorate due to inability to properly match records to individuals
Solution Approach 1:
The patent implements a universal identifier system that can uniquely identify individuals across multiple different data sources and healthcare entities. This universal identifier serves as a common reference that enables accurate record matching while maintaining the ability to integrate diverse data types from various sources, thus resolving the contradiction between data completeness and accuracy.
Solution Approach 2:
The patent introduces an intermediary matching service that acts as a mediator between multiple data sources and the analytics engine. This service receives data from various sources, performs matching using universal identifiers and matching rules, and delivers integrated results to the analytics engine, thereby enabling both comprehensive data integration and accurate record attribution.
2Loss of information
If comprehensive health data is collected from multiple entities, then analytics usefulness is improved, but identification accuracy deteriorates due to inconsistent capture of personal attributes across systems
Solution Approach 1:
The patent transforms personal attributes from their original inconsistent formats into standardized parameters through the matching service. By applying matching rules that normalize different representations of personal information (name variations, address formats, etc.), the system maintains comprehensive data collection while ensuring accurate identification and consistent parameter representation for analytics.
3Measurement precision
If manual record identification and integration processes are used, then identification accuracy is improved, but processing speed and productivity deteriorate
Solution Approach 1:
The patent implements an automated matching service that performs record identification and integration autonomously using universal identifiers and predefined matching rules. The system self-manages the complex process of matching records across multiple sources without requiring manual intervention, thereby maintaining high identification accuracy while dramatically improving processing speed and scalability.
4Adaptability or versatility
If data is stored in multiple separate systems, then data source diversity is improved, but system complexity and difficulty of integration increase
Solution Approach 1:
The patent extracts the complexity of data integration and record matching into a separate, dedicated matching service. This service handles all the complex logic for matching records across multiple sources using universal identifiers, while the remaining systems can operate independently with simplified data access. This extraction of complexity enables diverse data sources to be integrated without increasing the complexity of individual systems.
Data Source
AI summary
Systems and process for performing analytical processes on the health-related data for a person include components and steps for processing system multiple files from multiple sources containing health-related data for numerous individual. Such processing may include: staging health-related data; matching pieces of staged data to a person using one or more matching rules; compressing the matched staged data for the person into a compressed file through assignment of a universal identifier which is associated with the person; providing the compressed file to an analytics engine; and decompressing the at least one analytics results file using universal identifier to access the analytics results for the person. Additionally, the analytics results for the person may be provided to a user for intervention into the health of the person.