Hash Index Deduplication for Fragmented Immunization Records
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Solution Overview
Problem
Current health records technologies suffer from fragmentation and errors in immunization records, making it difficult to determine a comprehensive immunization status for individuals, and existing systems struggle with processing duplicate records, reducing efficiency and increasing costs.
Innovation Solution
A health records management system that employs a hashing function to generate a hash index for health records, allowing for efficient deduplication and integration of immunization records across multiple databases, using a hash function based on record elements such as first and last names, date of birth, and character lengths to ensure accurate record matching and flagging for further processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If health records are stored in multiple separated databases across different entities, then data coverage and comprehensiveness are improved, but data fragmentation and error introduction increase
Solution Approach 1:
The patent introduces a central recording agency as an intermediary that receives, validates, and consolidates immunization records from multiple administering agents and databases. This intermediary coordinates data flow between separated databases, ensuring comprehensive coverage while maintaining data accuracy through centralized validation rules and error checking mechanisms.
2Ease of operation
If ad-hoc reporting is used to submit immunization records, then flexibility and ease of submission are improved, but error introduction and data quality deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where the central recording agency sends validation results, error messages, and correction requests back to administering agents. This feedback loop allows flexible ad-hoc submission while maintaining data quality through automated validation, error reporting, and iterative correction processes that guide users to submit accurate records.
Solution Approach 2:
The system performs preliminary validation and error checking at the point of data entry before records are fully submitted to the central database. This preliminary action catches errors early in the submission process, maintaining ease of operation while preventing poor quality data from entering the system.
3Productivity
If duplicate records are not processed, then processing speed is maintained, but data redundancy and storage inefficiency increase
Solution Approach 1:
The patent implements preliminary duplicate detection using hashing functions that generate unique identifiers for incoming records before full processing occurs. This preliminary action quickly identifies and filters duplicate records, maintaining high processing efficiency while preventing redundant data from consuming storage resources.
4Reliability
If comprehensive validation of all records is performed, then data accuracy is improved, but processing time and computational overhead increase
Solution Approach 1:
The patent applies partial validation by performing comprehensive checking only on records that fail initial hash-based duplicate detection or that are flagged as potentially problematic. Routine records undergo quick validation, while only suspicious or non-matching records receive full comprehensive validation, maintaining data accuracy while minimizing processing time overhead.
Data Source
AI summary
A networked database system including a software application for operation on a user device may comprise a processor configured for manipulation of health records associated with a user a tangible, non-transitory electronic memory in electronic communication with the processor the tangible, non-transitory memory having instructions stored thereon that, in response to execution by the processor, cause the process to perform operations comprising storing a plurality of records, applying a hashing function to the plurality of records, generating based on the hashing function, a hash index wherein each hash of the hash index is associated on a one-to-one basis with each record of the plurality of records, identifying matching hashes within the hash index, and flagging the records associated with the matching hashes of the hash index for further processing.