Medical Coding Models With Confidence Scoring for Faster Accuracy
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Solution Overview
Problem
Current medical coding processes are time-consuming, labor-intensive, prone to errors, and vulnerable to non-uniform practices, posing challenges in efficiency, accuracy, and data security.
Innovation Solution
A multi-tier model system is employed to automatically determine medical codes, utilizing machine learning models for medical procedure and classification codes, including first-level and second-level models to specialize in anatomical regions and procedure types, with confidence scoring and optimization to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual medical coding is performed by medical coders reviewing clinical documentation, then medical codes can be assigned, but the process becomes time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of medical coders reviewing clinical documentation with an automated computer-based system that uses natural language processing, machine learning models, and optical character recognition to extract data and determine medical codes automatically, thereby eliminating time-consuming manual labor while maintaining coding accuracy
Solution Approach 2:
The system enables self-service by allowing the medical coding process to be performed automatically by the computer-based system without requiring human medical coders to manually review documentation, with the system independently extracting data, selecting appropriate codes, and generating coding reports
2Reliability
If manual medical coding is performed by medical coders, then medical codes can be assigned, but the process is expensive and prone to errors
Solution Approach 1:
The patent replaces manual medical coding with an automated computer-based system that uses natural language processing and machine learning to extract clinical data and determine medical codes, eliminating human errors and improving coding efficiency while reducing costs associated with manual labor
Solution Approach 2:
The system incorporates feedback mechanisms where confidence scores are generated for each determined medical code, allowing the system to identify and review low-confidence codes, thereby continuously improving coding accuracy and reliability through iterative refinement
3Reliability
If manual medical coding is performed, then medical codes can be assigned, but non-uniform practices and data security issues arise
Solution Approach 1:
The patent implements a universal automated system that processes medical documentation consistently across different providers and settings, applying the same natural language processing and machine learning models to ensure uniform coding practices while maintaining data security through automated handling
Solution Approach 2:
The system acts as an intermediary between clinical documentation and medical coding, using natural language processing and machine learning models as intermediate layers that process and interpret clinical data uniformly, thereby ensuring consistent coding practices while protecting data security through automated mediation
Data Source
AI summary
Automatically determining medical codes based on medical documents may be provided. Automatically determining medical codes can comprise processing and extracting data from a medical document. One or more medical codes can be determined for the medical document using a multi-tier plurality of models. The multi-tier plurality of models can comprise one or more first-level medical models for determining a medical code category, and a plurality of second-level medical models. One or more second-level models may be selected based on determined medical code categories, and the second-level models may be applied to determine medical codes. Confidence scores can be assigned to the determined medical codes, and a medical coding report can be generated comprising the one or more medical codes and the confidence scores.