Entity Record Association Using Temporal Data Analysis
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Solution Overview
Problem
Healthcare networks face complexity in associating data records across multiple source systems due to data governance restrictions and the need for accurate entity matching, which is processing-intensive and often inefficient.
Innovation Solution
A system utilizing supplemental temporal information to link data objects associated with a common entity by comparing and analyzing data objects within source systems, employing algorithms to determine likelihood scores based on both static and temporal features, and implementing a distributed computing environment for scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional data matching methods are used to associate records across multiple source systems, then data association can be achieved, but the processing is intensive and inefficient
Solution Approach 1:
The patent segments the data matching process into distinct phases: blocking (grouping records by common attributes), scoring (evaluating match likelihood), and resolving (finalizing associations). This segmentation allows each phase to be optimized independently and processed in parallel, significantly improving processing efficiency while reducing overall processing time for associating records across multiple source systems.
2Measurement precision
If comprehensive data comparison is performed to ensure accurate entity matching, then matching accuracy improves, but processing complexity increases
Solution Approach 1:
The patent applies local quality by using blocking to group records with similar characteristics before detailed comparison. This ensures that comprehensive data comparison is performed only on relevant subsets of records rather than all possible pairs, maintaining high matching accuracy while reducing processing complexity through localized, targeted analysis.
Solution Approach 2:
The patent transforms the matching problem by changing parameters from direct full-record comparison to a multi-stage process using blocking keys and scoring thresholds. This parameter transformation maintains matching accuracy by preserving all necessary comparison criteria while reducing complexity through structured progression from coarse to fine-grained analysis.
3Reliability
If data governance restrictions are enforced across multiple healthcare systems, then data security and compliance improve, but the ability to associate records accurately deteriorates
Solution Approach 1:
The patent introduces an intermediary matching layer that operates within data governance constraints. The blocking and scoring mechanisms serve as intermediaries that can process restricted data locally and produce match results without requiring unrestricted access to sensitive information across systems, thereby maintaining both compliance and matching accuracy through controlled intermediate processing.
Data Source
AI summary
A system links data objects associated with a common entity and includes at least one processor. The system compares data objects within one or more source systems to identify candidate data objects associated with a corresponding common entity based on information within those data objects. The candidate data objects are analyzed based on supplemental temporal information within the one or more source systems pertaining to the candidate data objects to determine resulting data objects associated with the corresponding common entity. The resulting data objects are linked to form a set of data objects for the common entity. Embodiments of the present invention further include a method and computer program product for linking data objects associated with a common entity.