Medical Terminology Cross-Map Conversion System
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Solution Overview
Problem
The use of multiple medical terminologies across different institutions leads to confusion and inefficiency in data review, as manual conversions between terminologies are slow, expensive, and often result in incorrect mappings, with a single term potentially corresponding to numerous targets without identifying a specific best match.
Innovation Solution
A method and system for converting medical terms using cross maps, probability data, and lexical matching algorithms to identify the most relevant target term in a different terminology, allowing for automated and accurate mapping of medical codes between various terminologies such as ICD-9 and SNOMED CT.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If manual conversion between medical terminologies is performed, then flexibility in handling different terminologies is maintained, but conversion speed and efficiency deteriorate significantly
Solution Approach 1:
The system pre-establishes cross-mapping relationships between different medical terminologies (e.g., ICD-9 to SNOMED CT) and pre-calculates probability data and lexical matches. When conversion is needed, the system directly queries these pre-computed mappings rather than performing manual analysis, thus achieving fast automated conversion while maintaining adaptability across multiple terminology systems.
2Reliability
If a single source term is mapped to all possible target terms, then completeness of mapping is achieved, but identification of the best specific match deteriorates
Solution Approach 1:
The system uses probability data derived from frequency analysis and lexical matching algorithms to evaluate and rank multiple possible target terms. This feedback mechanism allows the system to identify the most likely correct match among all possible mappings, improving precision while maintaining completeness by considering all candidates before selecting the best match.
3Ease of operation
If manual conversion processes are used, then customization and control over mapping decisions are maintained, but conversion cost and time consumption deteriorate
Solution Approach 1:
The system performs automated conversion by itself using pre-established cross-mappings, probability data, and lexical matching algorithms. The system independently identifies the best match without requiring manual intervention, thus eliminating time consumption and high costs associated with manual conversion while maintaining control through algorithmic decision-making.
4Adaptability or versatility
If multiple medical terminologies are used across different institutions, then adaptability to various institutional standards is improved, but data confusion and conversion complexity deteriorate
Solution Approach 1:
The system implements a universal cross-mapping framework that can handle conversions between multiple medical terminologies (ICD-9, ICD-10, SNOMED CT, CPT, etc.). By establishing a common mapping structure and using standardized probability and lexical matching approaches, the system simplifies the complexity of handling diverse institutional standards while maintaining adaptability to each specific terminology.
Data Source
AI summary
Methods, systems, and computer storage media are provided for converting a source in a first terminology to a target in a different terminology. A source in a first terminology may be converted to a target in a second terminology using cross maps that map sources to all possible matching targets, probability data derived from frequency data illustrating a frequency of selection for targets in relation to specific sources, lexical matching algorithms indicating targets with highest lexical matches for specific sources, or a combination thereof. Any one, or a combination, of the data above may be used to identify a probabilistically most relevant target in a desired terminology.