Medication Event Concept Mapping for Free-Text Deduplication
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Solution Overview
Problem
Existing healthcare systems face challenges in deduplicating medication events due to the use of diverse alphanumeric medication codes and medication free text, leading to redundant information across different healthcare platforms.
Innovation Solution
A system that generates vector embeddings for standard medication codes and medication free text, compares them using similarity measures, and recommends candidate standard medication codes for mapping, facilitating deduplication by storing associations between free text and standard codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If diverse alphanumeric medication codes and free text are used across different healthcare platforms, then adaptability and information richness are improved, but data redundancy and duplication increase
Solution Approach 1:
The patent introduces vector embeddings as an intermediary representation layer between diverse medication codes/free text and the deduplication process. Each medication event is transformed into a vector embedding that captures its semantic meaning, enabling comparison across different coding systems without direct code matching. This intermediary representation resolves the contradiction by allowing adaptability to multiple formats while reducing redundancy through semantic similarity comparison.
Solution Approach 2:
The patent transforms medication events from their original diverse formats (alphanumeric codes, free text) into a unified parameter space (vector embeddings). By changing the representation parameters from discrete codes to continuous vector spaces, the system enables meaningful comparison and deduplication while preserving the adaptability to handle various input formats. The similarity measures operate on these transformed parameters to identify duplicates.
2Measurement precision
If manual review and mapping of medication codes is performed, then mapping accuracy is improved, but processing time and operational complexity increase
Solution Approach 1:
The patent implements self-service through automated vector embedding generation and similarity-based candidate recommendation. The system automatically transforms medication events into vector embeddings and identifies potential duplicate candidates without requiring manual intervention for every record. This reduces processing time significantly while maintaining accuracy through the structured recommendation process that presents limited candidates for verification.
Solution Approach 2:
The patent incorporates feedback mechanisms where the automated system generates candidate matches, which are then reviewed and confirmed or rejected by users. The confirmed mappings can be fed back into the system to improve future matching accuracy. This feedback loop maintains high mapping accuracy while minimizing manual processing time, as users only need to review candidate suggestions rather than perform complete manual mapping.
3Quantity of substance
If comprehensive medication data is collected from multiple sources, then information completeness is improved, but data quality and consistency deteriorate due to duplication
Solution Approach 1:
The patent segments the medication data processing into distinct components: vector embedding generation, similarity calculation, candidate identification, and duplicate removal. By segmenting the deduplication process, the system can systematically handle comprehensive data from multiple sources while maintaining quality control at each stage. This segmentation enables the system to process complete information sets without being overwhelmed by duplication issues.
Solution Approach 2:
The patent replaces traditional mechanical deduplication methods (exact code matching, rule-based filtering) with vector-based semantic similarity comparison. This substitution allows the system to handle comprehensive medication data from multiple sources more effectively by comparing the semantic meaning of medication events rather than relying on exact format matches. The vector embedding approach maintains data quality by identifying true semantic duplicates even when formats differ, while preserving information completeness by accepting diverse input formats.
Data Source
AI summary
Techniques for generating recommendations of standard medication codes for storing in association with medication free text to facilitate deduplication of patient medication events are disclosed. Standard medication codes are alphanumeric identifiers that represent medication events. Medication free text is medication event information in natural language. The system generates vector embeddings for the standard medication codes by applying a vector embedding function to a set of attributes associated with the standard medication codes. The system generates a vector embedding for a target unmapped medication code by applying the vector embedding function to medication free text of the target unmapped medication code. The system compares the target vector embedding for the target unmapped medication code to the vector embeddings computed for each of the standard medication codes. The system presents recommended standard medication codes and groupings of similar standard medication codes to a user for mapping to the medication free text.