Semantic Interoperability System for Healthcare Information
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Solution Overview
Problem
Current healthcare information systems face challenges in semantic interoperability, with disparate terminologies, formats, and standards leading to difficulties in exchanging and analyzing electronic health records effectively, particularly for managing chronic conditions and regulatory reporting.
Innovation Solution
A system employing semantically robust components with ontological reasoning and inferencing, including a rules management component, ontology management component, and information model management component, to provide deterministic computable semantics and achieve interoperability by dynamically binding value sets to information models, resolving ambiguities and mapping between different terminologies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple different terminology standards and formats are used in healthcare information systems, then diverse healthcare data can be captured and stored, but semantic interoperability and effective exchange of information between systems deteriorates
Solution Approach 1:
The patent introduces a terminology service as an intermediary component that mediates between diverse healthcare information systems and the underlying data exchange infrastructure. This service provides mapping capabilities between different terminology standards (such as SNOMED CT, ICD-10, LOINC) and a standardized internal representation, enabling semantic interoperability without requiring changes to existing systems. The terminology service acts as a translation layer that resolves semantic ambiguities and ensures consistent interpretation of healthcare data across different platforms.
Solution Approach 2:
The patent implements a universal information model that can represent multiple healthcare data types and terminology standards through a common framework. This information model provides a standardized structure that can accommodate diverse healthcare information (clinical data, laboratory results, imaging data, etc.) while maintaining consistent semantics. The model enables a single system architecture to handle multiple terminology types and data formats uniformly, achieving versatility without sacrificing interoperability.
2Productivity
If healthcare information is stored in electronic form with basic syntactic format, then data capture efficiency improves, but the ability to extract and transform information meaningfully deteriorates
Solution Approach 1:
The patent applies preliminary action by performing semantic enrichment and terminology mapping at the point of data capture rather than during later analysis phases. When healthcare data is initially stored in electronic form, the system immediately associates it with standardized terminology codes and semantic annotations. This preliminary structuring of data with meaningful context enables efficient capture while preserving analytical value, as the information is already organized in a machine-processable format with embedded semantic relationships.
Solution Approach 2:
The patent transforms healthcare data from basic syntactic format to semantically enriched format by changing the parameter representation. Instead of storing only raw text or simple structured data, the system enriches each data element with additional parameters including standardized terminology codes, semantic types, value sets, and relationships to other concepts. This parameter transformation maintains the efficiency of electronic data capture while enabling sophisticated querying, analysis, and transformation of meaningful information later.
3Adaptability or versatility
If standardization of terminologies is established through consensus-based efforts, then broad acceptance and adoption improve, but the quality and precision of semantic representation deteriorates
Solution Approach 1:
The patent segments the terminology standardization process into multiple hierarchical layers. At the upper layer, it adopts widely accepted consensus-based standards (SNOMED CT, ICD-10, LOINC) to ensure broad acceptance. At the lower layer, it implements a precise information model with detailed semantic relationships, value sets, and mapping rules that provide high measurement precision. This segmentation allows the system to leverage the versatility of established standards while compensating for their semantic imprecision through additional structured representations and explicit relationship definitions in the underlying model.
Data Source
AI summary
A system for managing and exchanging electronic information provides a rules management component for executing conceptual rules, an ontology management component, an information model management component, and a system configuration management component. The ontology management component manages at least one ontology and mappings between members of different ontologies. The ontologies may include a code system and a terminology. The ontology management component may manage a value set that is a subset of the terminology. The information model management component manages one or more information model schemas, each defining an information model and comprising information defining at least one slot within the information model. The system configuration management component manages configuration information on the configuration of each system component. The system configuration component utilizes services of the rules management component, information model management component and ontology management component to dynamically bind value sets to slots of the information model.