Dynamic Medical Term Repository System
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Solution Overview
Problem
Existing healthcare information systems require a pre-populated lexicon for medical records, which is time-consuming to install and may contain errors, and do not dynamically update terms based on usage, limiting their effectiveness in searching and maintaining accurate medical records.
Innovation Solution
A dictionary system that dynamically updates its term repository by parsing transaction messages from various healthcare information systems, adding new terms as needed, and maintaining usage counts, allowing for incremental updates to facility-specific repositories without a full installation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a pre-populated lexicon is installed manually, then the system has a complete term repository for searching, but the installation is time-consuming and may contain errors
Solution Approach 1:
The system automatically extracts terms from transaction messages and updates the term repository without requiring manual installation or intervention. The dictionary system self-populates by parsing incoming messages and identifying new terms, eliminating the time-consuming manual lexicon installation process while reducing human error.
Solution Approach 2:
The system performs preliminary term extraction and validation by analyzing transaction messages before adding terms to the repository. This preliminary processing ensures term accuracy and completeness before the terms are made available for searching, preventing errors from propagating.
2Stability of the object's composition
If a static lexicon is used, then the term repository is stable, but it does not update dynamically with new terms from usage
Solution Approach 1:
The term repository transitions from a static pre-populated lexicon to a dynamic structure that automatically updates based on transaction message analysis. The system continuously monitors incoming messages, extracts new terms, and updates the repository in real-time, allowing the term set to adapt to evolving medical terminology while maintaining organizational structure.
Solution Approach 2:
The system implements feedback loops where transaction messages are analyzed to identify new terms, which are then added to the repository. This feedback mechanism ensures the term repository continuously improves and updates based on actual usage patterns, maintaining both stability and adaptability.
3Reliability
If manual term addition is required, then term accuracy can be controlled, but the process is labor-intensive and slow
Solution Approach 1:
The manual mechanical process of adding terms is replaced with an automated electronic system that parses transaction messages and extracts terms programmatically. This substitution dramatically increases term addition speed while maintaining accuracy through systematic validation rules and structured data processing.
Solution Approach 2:
The system introduces an intermediary automated extraction process between transaction messages and the term repository. This intermediary layer analyzes messages, validates terms, and conditionally adds them to the repository, maintaining accuracy control while eliminating manual labor through automated decision-making logic.
4Adaptability or versatility
If a comprehensive lexicon is installed upfront, then all possible terms are available for searching, but the system size and complexity increase
Solution Approach 1:
The comprehensive lexicon is segmented into a core initial set plus dynamically added terms. Rather than installing all possible terms upfront, the system starts with essential terms and progressively adds specialized terms as they appear in transaction messages, reducing initial system complexity while maintaining comprehensive coverage over time.
Data Source
AI summary
A dictionary system provides a term repository supporting the operation of an enterprise, which dynamically updates its content in response to system use. An input processor acquires transaction message data in at least one of a plurality of different data formats. A data processor parses the acquired transaction message data to extract a term from the message data. The processor then compares the extracted term to terms in a first term repository. The first term repository is updated to include the extracted term if the extracted term is absent from said first term repository. A communication processor intermittently processes the content of said first term repository to be suitable for communication to a second term repository.