EHR Reference Data Mapping Using Vector Embeddings
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Solution Overview
Problem
Healthcare systems face challenges in achieving semantic interoperability due to the use of multiple reference codes or entities to identify the same concept, leading to ambiguous textual embeddings in the form of synonyms, acronyms, and abbreviations, which creates variance and inconsistency in healthcare data across different systems.
Innovation Solution
A system that generates vector embeddings for entities in both model and comparison domains, compares them using similarity metrics, and recommends candidate mappings based on thresholds, allowing users to confirm matches, thereby updating the model domain for improved data exchange between healthcare systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple reference codes or entities are used to identify the same concept in different healthcare systems, then each system can maintain its own terminology standards and coding conventions, but semantic interoperability and data consistency across systems deteriorate due to ambiguous textual embeddings
Solution Approach 1:
The patent introduces vector embeddings as an intermediary representation layer between different healthcare system terminologies. Each reference code or entity is transformed into a vector embedding that captures its semantic meaning, allowing systems to compare and match concepts across different terminology standards through vector similarity metrics rather than direct text comparison.
Solution Approach 2:
The patent transforms discrete reference codes and textual embeddings into continuous vector space representations. By changing the parameter space from discrete code values to continuous vector coordinates, the system enables mathematical operations and similarity measurements that facilitate automatic mapping and matching between different healthcare terminologies.
2Measurement precision
If vector embeddings are generated and compared for all entities across model and comparison domains, then mapping accuracy improves through similarity metrics, but computational complexity and processing time increase
Solution Approach 1:
The patent implements threshold-based filtering where vector embeddings are compared only up to a specified similarity threshold. Once entities exceed this threshold in similarity, the comparison process can be terminated early, avoiding unnecessary computational effort while maintaining mapping accuracy for high-confidence matches.
Solution Approach 2:
The patent performs preliminary vector embedding generation for all entities in the model domain before comparison with comparison domain entities. This pre-computation allows the system to reuse these embeddings across multiple comparison operations, reducing redundant calculations and improving overall processing efficiency.
Data Source
AI summary
Techniques for generating recommendations of model domain entities from a model domain for mapping to comparison domain entities from a comparison domain are provided. A model domain includes a code set of standard references codes. A comparison domain includes a code set of reference codes that include non-standard reference codes. The reference codes represent clinical and non-clinical health concepts and are represented by one or more attributes. The system generates vector embeddings for entities of the comparison and model domains by applying a vector embedding function to the attributes fields of the comparison and model domain entities. The system compares the vector embeddings of the comparison domain entity to the vector embeddings of the model domain entity to compute similarity metrics for the entity pairs. The entity pairs are presented to a user based on the similarity metrics. A selected model domain entity is mapped to the comparison domain entity.