Automated Semantic Mapping for Healthcare Data Interoperability
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Solution Overview
Problem
Manual mapping of service codes in healthcare systems is costly, time-consuming, and error-prone due to the lack of semantic interoperability between different healthcare entities, which hinders the seamless exchange and analysis of data across various healthcare systems.
Innovation Solution
An automated method using machine learning to create semantic maps between different healthcare systems by analyzing the distribution of variables and values in transaction data, allowing for the linking of corresponding semantic meanings across systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual mapping or translation is used to exchange information between healthcare systems, then semantic interoperability is achieved, but the process becomes costly, time consuming, and error prone
Solution Approach 1:
The patent replaces the manual mechanical process of code mapping with an automated computer-based system that uses machine learning algorithms. The system automatically analyzes transaction data from different healthcare systems, identifies semantic relationships between service codes, and generates mappings without human intervention, thereby eliminating the time-consuming and error-prone manual process while maintaining high accuracy through algorithmic analysis
Solution Approach 2:
The mapping system performs self-service by automatically analyzing its own transaction data to learn semantic relationships between different healthcare systems' service codes. The system uses unsupervised machine learning to autonomously identify patterns, group equivalent codes, and generate mappings without requiring external manual input or expertise, enabling the system to improve its mapping capabilities continuously as it processes more data
2Adaptability or versatility
If manual mapping is used to translate data between different healthcare systems, then data exchange is enabled, but the process becomes costly and error prone
Solution Approach 1:
The patent creates a universal mapping system that can handle multiple healthcare systems and their diverse service codes through a single automated platform. The machine learning model is designed to learn semantic relationships across different coding systems (such as ICD-9, ICD-10, CPT, HCPCS) without requiring system-specific manual configuration, thereby enabling broad data exchange capability while reducing implementation costs through standardized automated processing
Solution Approach 2:
The system replaces expensive manual mapping processes with automated computational analysis. By using machine learning algorithms to analyze transaction data and identify semantic relationships, the system eliminates the need for costly manual expert intervention while maintaining high adaptability to different healthcare systems through data-driven learning
3Productivity
If automated mapping using machine learning is implemented, then time and cost are reduced, but the complexity of the system increases
Solution Approach 1:
The patent extracts the core mapping functionality into a separate machine learning module that operates independently from the healthcare systems it serves. This extracted component focuses solely on analyzing transaction data and generating mappings, while the surrounding infrastructure handles data exchange and system integration. This separation reduces overall system complexity by isolating the complex AI functionality into a manageable, reusable component that can be deployed across multiple systems
Data Source
AI summary
Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.