Medical Coding System Using Context-Based Auto-Coding
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Solution Overview
Problem
Current medical coding systems face challenges such as manual setup, inability to update medical codes, lack of context-based coding, inability to auto-code selectively, and limited analytics, leading to inconsistencies and reliance on user experience for accurate medical coding.
Innovation Solution
A device and method that automatically create and test data structures, update medical codes based on dictionary changes, code using context information, selectively auto-code medical terms, attach comments, and perform report analytics to improve coding accuracy and consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual coding is used by users, then flexibility in handling complex medical terms is maintained, but coding accuracy and consistency deteriorate due to reliance on user experience
Solution Approach 1:
The system performs self-learning by automatically analyzing coded medical terms and their corresponding codes, building its own knowledge base without requiring manual programming. The system serves itself by continuously improving its coding accuracy through automated learning from user corrections and validations.
Solution Approach 2:
The patent replaces manual mechanical coding processes with an automated intelligent system that uses machine learning algorithms to determine medical codes. The system substitutes human cognitive judgment with automated computational analysis of medical terms, context information, and coding patterns.
2Productivity
If automated coding is implemented, then coding efficiency is improved, but coding accuracy deteriorates due to lack of context understanding
Solution Approach 1:
The system adds contextual dimensions to the coding process by analyzing not just the medical term itself but also surrounding text, document type, section headers, and other contextual elements. This multi-dimensional analysis enables accurate automated coding by understanding the context in which medical terms appear.
Solution Approach 2:
The system introduces an intermediary learning layer between the medical term and the code assignment. This intermediary layer learns the relationship between terms and codes through analysis of coded data, acting as a intelligent mediator that understands context and nuances before assigning appropriate codes.
3Adaptability or versatility
If data structures are manually created and updated, then system simplicity is maintained, but adaptability to dictionary changes deteriorates
Solution Approach 1:
The system implements dynamic data structures that automatically adapt to dictionary changes. When medical dictionaries are updated with new terms or codes, the system dynamically learns these changes and updates its internal knowledge base without requiring manual reconfiguration of data structures.
Solution Approach 2:
The system performs preliminary analysis and learning when dictionary updates occur, preparing the data structures in advance for future coding tasks. By proactively incorporating dictionary changes into its knowledge base, the system ensures readiness for accurate coding with updated terminology.
Data Source
AI summary
A device may receive information that identifies a first medical term and may determine whether the first medical term corresponds to a first medical code, which may be a medical code previously input by a first user in association with a second medical term. Or, the first medical code may be included in a dictionary. The device may determine a valid code corresponding to the first medical term based on determining whether the first medical term corresponds to the first medical code. The valid code may be determined based on the first medical code when the first medical term corresponds to the first medical code. The valid code may be determined based on a second medical code, input by a second user, when the first medical term does not correspond to the first medical code. The device may provide information that identifies the valid code.