Clinical Coding Graph Execution for Accurate Document Transmission
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Solution Overview
Problem
The integration of clinical data across different healthcare systems is hindered by interoperability issues, varying systems and protocols, and a lack of standardized processing and management of clinical documents, leading to inefficiencies in data processing and routing.
Innovation Solution
A context-based method for clinical knowledge extraction using a classifier to identify the clinical context of a document, followed by an executable coding graph to convert unstructured data into structured clinical information, which includes a directed acyclic graph with branch and coding nodes for semantic analysis and data assignment, enabling automated data extraction and transmission.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If automated processing systems are implemented to handle clinical documents, then productivity and efficiency are improved, but device complexity increases due to the need for multiple processing components and integration layers
Solution Approach 1:
The system segments the clinical document processing into distinct functional modules: a classification module that identifies document types, a coding graph module that applies context-specific extraction rules, and an execution module that performs the actual data extraction. This segmentation allows each module to be independently optimized and maintained, reducing overall system complexity while improving processing efficiency.
Solution Approach 2:
The patent introduces an intermediary coding graph structure that mediates between the classification module and the execution module. The coding graph acts as a configurable intermediary that translates high-level document classifications into specific extraction operations, enabling flexible adaptation to different document types without increasing the complexity of the core processing architecture.
2Measurement precision
If context-specific coding graphs are used to improve data extraction accuracy, then measurement precision is improved, but device complexity increases due to the need for multiple graphs and selection logic
Solution Approach 1:
The system dynamically selects and executes different coding graphs based on the document classification results. Instead of using a static set of extraction rules, the coding graph module adapts its behavior by selecting the appropriate graph corresponding to the identified document type, enabling high-precision extraction for different contexts without requiring all possible extraction rules to be simultaneously active.
Solution Approach 2:
The coding graph module is designed as a universal component that can handle multiple document types through a single unified interface. The classification module feeds into the coding graph module, which then routes to the appropriate execution logic, allowing one multi-functional component to serve multiple purposes across different clinical document types.
3Reliability
If manual review and verification of extracted data is performed, then reliability is improved, but loss of time increases due to the additional review step
Solution Approach 1:
The system incorporates feedback mechanisms where the execution module can flag uncertain extractions or inconsistencies for review. This selective feedback approach allows automated processing to handle the majority of straightforward cases while automatically routing only the uncertain or complex cases to human review, thereby maintaining high reliability without requiring manual verification of all extracted data.
Data Source
AI summary
Aspects provide a method for context-based clinical knowledge extraction and automatic transmission of clinical documents. A text-based representation of a document having a clinical context is obtained and an identifier which uniquely identifies the clinical context of the document is determined. An executable coding graph is identified from a plurality of executable coding graphs based on the identifier of the clinical context. The executable coding graph is indicative of a procedure for coding the document according to the clinical context and comprises a network of branch nodes interconnected with a plurality of coding nodes thereby forming a directed acyclic graph. The executable coding graph is executed on the text based representation of the document thereby generating a structured set of clinical information linked to the document enabling the automatic transmission of a clinical document based on the data extracted from the clinical document.