NLP Search Indexing for Medical Code Mapping
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Solution Overview
Problem
Current computer-assisted coding (CAC) systems face challenges in efficiently managing the complexity of medical coding and mapping due to the large number of healthcare-related concepts, varying terminologies, and the need for precise code sets, which are exacerbated by the limitations of rules-based NLP tools and the disconnect between clinician language and administrative codes.
Innovation Solution
The implementation of natural language processing (NLP) coupled with a fuzzy string search and structured data search indexing tool, utilizing Elasticsearch, to improve computer-assisted coding by generating search indices for healthcare-related concepts, processing queries, and providing visual representations of matches to facilitate accurate mapping between different code sets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If rules-based NLP tools are used for medical coding, then cost is reduced, but adaptability and generalization capability deteriorate
Solution Approach 1:
The patent introduces a hybrid architecture where an intermediary layer combines rules-based NLP tools with machine learning models. The rules-based component handles structured data and common patterns, while the ML component learns from unstructured clinical text, allowing the system to adapt to different domains without complete re-engineering.
Solution Approach 2:
The system is designed to be universally applicable across multiple medical coding domains (ICD-10, CPT, HCPCS) by implementing a unified architecture that can process different code sets and clinical specialties through the same underlying NLP and machine learning framework.
2Manufacturing precision
If rules-based NLP tools are used for each specific use case, then coding precision for that case is improved, but device complexity and maintenance effort increase
Solution Approach 1:
The patent implements a universal NLP platform that can handle multiple medical coding domains (ICD-10, CPT, HCPCS) and clinical specialties through a single system architecture, eliminating the need to create separate solutions for each use case while maintaining high coding precision.
Solution Approach 2:
The system allows dynamic adjustment of parameters such as code sets, clinical domains, and processing rules without requiring structural changes to the underlying system, enabling precise coding for different specialties through parameter configuration rather than system redesign.
3Quantity of substance
If more data from disparate sources is integrated, then data collection capability is improved, but workload complexity increases
Solution Approach 1:
The patent implements a universal data processing framework that can ingest and process multiple types of medical data from disparate sources (EHRs, claims, registries) through a single system, standardizing data formats and processing pipelines to manage complexity while handling diverse data volumes.
4Loss of information
If NLP techniques are used to extract concepts from free text, then knowledge extraction capability is improved, but mapping complexity between code sets increases
Solution Approach 1:
The patent introduces an intermediary representation layer that extracts concepts from free text and maps them to standardized medical concepts before final code assignment. This intermediary layer uses machine learning to learn mappings between different code sets, reducing the complexity of direct mapping while preserving knowledge extraction capabilities.
Data Source
AI summary
Systems and methods for using NLP techniques to improve search engine results for computer-assisted coding. The method includes building a search index for each of the concepts of a health care-related code set, separately or in parallel building a query comprising data from concepts and/or lexicals of a second health care-related code set, pre-processing the data elements derived from each code set, applying a natural language processor to one or both sets of data to generate the search index and the query, inputting the query into a search engine to evaluate the query against the search index, identifying potential matches, presenting the potential matches to the user and receiving a selection of one potential match to be the match, and mapping the concept in the health care-related code set corresponding to the match to the concept and/or lexical of the second health care-related code set corresponding to the query.