Granular Context-Aware Document Code Assignment
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Solution Overview
Problem
Automated code assignment tools for documents, such as medical records, often rely on granular context that may not be universally applicable, leading to unnecessary suppressions or assignments due to context-specific reasons, resulting in inefficient rule creation and application.
Innovation Solution
A method that evaluates code assignments by identifying common attributes across multiple sets of attributes from previous assignments, creating a new set of attributes to determine code assignment, and using these attributes to establish rules for future assignments, thereby reducing unnecessary suppressions and improving assignment accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If granular context attributes are used for code assignment, then assignment accuracy may improve for specific cases, but unnecessary suppressions occur due to context-specific reasons
Solution Approach 1:
The patent extracts and eliminates attributes that are not common across multiple sets, removing context-specific attributes that cause unnecessary suppressions while retaining only the universal attributes that consistently determine code assignments across different documents
Solution Approach 2:
The patent creates a unified rule set based on common attributes that can be universally applied across different document types and contexts, making the code assignment system more generalizable and less dependent on specific contextual factors
2Measurement precision
If multiple sets of attributes are considered for code assignment, then comprehensive evaluation is achieved, but rule creation becomes inefficient
Solution Approach 1:
The patent automatically eliminates redundant and non-common attributes from multiple sets, extracting only the essential common attributes that need to be considered, thereby reducing the complexity of rule creation while maintaining evaluation comprehensiveness
Solution Approach 2:
The system performs self-optimization by automatically identifying and eliminating unnecessary attributes through comparative analysis of multiple attribute sets, reducing manual rule creation effort while maintaining comprehensive evaluation
Data Source
AI summary
A method for evaluating an assignment of a code, the method including providing a code previously assigned to a document based on a plurality of sets of attributes, each attribute in the plurality of sets of attributes previously believed to affect the assignment of the code; eliminating the attributes not common to the sets of attributes in the plurality of sets of attributes; and evaluating the assignment of the code based on the remaining attributes.