Retrieval-Augmented Coding for Historical Healthcare Claim Analysis
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Solution Overview
Problem
Current systems for claim submission and tracking lack mechanisms for viewing historical data in context, integrating prior claim history, and optimizing coding based on patterns and behaviors, leading to inefficiencies and potential errors in healthcare documentation and billing processes.
Innovation Solution
A system integrating an Inference Engine and Retrieval Augmented Generation (RAG) Engine that utilizes a fine-tuned Large Language Model (LLM) to interpret unstructured healthcare data, generate optimal coding, and visualize claim vectors in reduced dimensions, along with databases for guideline, claim, and provision vectors, to enhance coding efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual documentation of patient encounters is used, then flexibility in recording observations is maintained, but labor intensity and human error increase
Solution Approach 1:
The patent replaces manual mechanical documentation processes with an automated system that uses natural language processing and machine learning algorithms to extract clinical observations, diagnostic details, and coding information from unstructured text, thereby reducing labor intensity while maintaining flexibility through configurable extraction rules
Solution Approach 2:
The system enables self-service by allowing the documentation system to automatically generate codes and extract information without requiring manual intervention, with the ability to learn from feedback and continuously improve its extraction accuracy autonomously
2Adaptability or versatility
If manual coding processes are used, then adaptability to different coding scenarios is maintained, but accuracy and consistency deteriorate
Solution Approach 1:
The system dynamically adjusts extraction parameters and coding rules based on the specific clinical scenario, document type, and identified patterns, allowing it to adapt to different coding situations while maintaining high accuracy through data-driven parameter optimization
Solution Approach 2:
The system incorporates feedback mechanisms where coding results are reviewed and corrected, with the feedback used to continuously refine the machine learning models and extraction algorithms, improving both accuracy and adaptability over time
3Ease of operation
If traditional claim submission systems are used, then simplicity of operation is maintained, but ability to integrate historical data and optimize coding is lost
Solution Approach 1:
The system performs preliminary analysis of historical claim data, coding patterns, and provider behavior before claim submission, pre-optimizing codes and identifying potential issues in advance, thereby maintaining operational simplicity while leveraging historical information
Solution Approach 2:
The system introduces an intermediary layer between the provider and the claim submission system that automatically analyzes historical data, suggests optimizations, and presents simplified options to the provider, maintaining ease of use while integrating complex historical analysis
4Adaptability or versatility
If multiple coding strategies are available, then flexibility in coding approaches is improved, but difficulty in selecting optimal coding increases
Solution Approach 1:
The system provides feedback to providers about the performance and outcomes of different coding strategies by analyzing historical data, allowing providers to make informed decisions about which coding approaches to use without having to manually evaluate multiple options
Solution Approach 2:
The system automatically adjusts coding parameters and strategy selection based on the specific claim characteristics, historical performance data, and identified patterns, simplifying the selection process by dynamically optimizing the choice of coding strategy
Data Source
AI summary
An apparatus, computer-readable medium, and computer-implemented method retrieval augmented generation of optimal coding, including encoding a current claim record comprising codes as a current claim vector, querying a claim vector database to identify claim vectors proximate to the current claim, querying a provision vector database to identify provision vectors corresponding to the codes, applying a predictive large language model (LLM) to the current claim vector, the claim vectors, the provision vectors, and a schedule corresponding to the provisioning structures to generate an optimal coding for the current claim record based on optimization criteria, and transforming the current claim record based at least in part on the determined optimal coding