Medical Knowledge Model Generation with Standardized Nomenclature
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Solution Overview
Problem
Current medical knowledge representation systems are difficult for clinical staff to use due to lack of formalization, transparency, and adaptability, often relying on proprietary formats and black box systems, which hinder efficient automation and adherence to updated guidelines.
Innovation Solution
A device and process for generating and applying a medical knowledge model using a standardized nomenclature, allowing users to select and define elements and logic rules, with validation and digital signature capabilities to ensure consistency and authenticity, facilitating formalized and reliable medical knowledge application.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If proprietary formats and black box systems are used for medical knowledge representation, then automation can be achieved, but transparency and adaptability are reduced
Solution Approach 1:
The system segments medical knowledge into discrete, standardized elements (concepts, attributes, relationships) that can be individually selected, validated, and configured. This segmentation enables transparency by allowing users to see and verify individual knowledge components while maintaining automation through structured assembly of these segments into complete decision support models.
Solution Approach 2:
The system allows dynamic modification of knowledge model parameters such as inclusion criteria, exclusion criteria, threshold values, and logical relationships. Users can adjust these parameters to adapt the automated system to specific clinical contexts while maintaining the same automated processing framework, thus resolving the contradiction between automation and adaptability.
2Extent of automation
If Arden Syntax or proprietary formats are used to represent rules and workflows, then decision support can be implemented, but ease of operation for clinical staff is reduced
Solution Approach 1:
The system introduces an intermediary layer between clinical staff and the automated decision support system. This intermediary provides user-friendly interfaces, templates, and guided configuration tools that translate clinical expertise into standardized knowledge models without requiring staff to directly manipulate complex syntax or proprietary formats, thereby improving ease of operation while maintaining automation.
Solution Approach 2:
The system employs a universal knowledge representation framework that can accommodate multiple formats, standards, and clinical domains through a single standardized interface. This multi-functionality allows the same system to support various decision support scenarios without requiring users to learn different syntaxes or formats, enhancing ease of operation while preserving automated decision support capabilities.
3Adaptability or versatility
If standardized nomenclature and formalized knowledge models are used, then adaptability and transparency are improved, but device complexity increases
Solution Approach 1:
By segmenting the complex task of knowledge model generation into discrete, manageable steps (concept selection, attribute definition, relationship establishment, validation), the system reduces the perceived complexity for users. Each segment can be completed independently using standardized interfaces, making the overall process more manageable while maintaining high adaptability through standardized elements.
Solution Approach 2:
The system incorporates automated assistance features that perform routine tasks such as validating knowledge consistency, suggesting standard terms, and generating initial model structures. This self-service capability reduces the manual effort required from users, effectively lowering device complexity from the user's perspective while preserving adaptability through configurable parameters.
4Reliability
If updated regulations are implemented through software version updates, then knowledge accuracy is maintained, but loss of time and productivity are increased
Solution Approach 1:
The system enables dynamic updates to knowledge models without requiring complete software version releases. Users can selectively update specific knowledge elements, criteria, or parameters while maintaining the rest of the system intact. This dynamic approach allows rapid incorporation of updated regulations or guidelines, reducing the time and productivity loss associated with traditional version update cycles while maintaining knowledge accuracy.
Solution Approach 2:
By segmenting the knowledge base into independent, updatable modules, the system allows targeted updates to specific areas without affecting the entire software system. This modular segmentation enables rapid deployment of regulatory updates, minimizing the time required for knowledge accuracy maintenance and reducing the productivity impact of updates.
Data Source
AI summary
A device generating a medical knowledge model, a medical knowledge model system for a formalized application of medical knowledge, a process generating a medical knowledge model, a process for the formalized application of medical knowledge, and a computer program are provided. The device (10) for generating a medical knowledge model includes a processing device (11), which is configured for providing at least one element from a standardized nomenclature for a user, for adding the at least one element from the standardized nomenclature to the medical knowledge model according to a selection of the user, and for storing a logic or a rule with regard to the at least one element concerning at least one element parameter according to a definition of the user. The device (10) includes one or more interfaces (12), which are configured for providing the medical knowledge model and for receiving a user input.