AI Medical Coding System Using NLP and Deep Learning
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Solution Overview
Problem
The existing medical coding and billing systems are time-consuming, error-prone, and require multiple layers of audits and coding changes, leading to inconsistencies and inefficiencies.
Innovation Solution
A computer-implemented system using deep learning algorithms and natural language processing (NLP) to automate the conversion of medical procedure descriptions into standardized CPT and ICD-10 codes, with a feedback loop for continuous learning and improvement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual coding by employees is used, then coding can be performed with human judgment, but the process becomes time-consuming and error-prone
Solution Approach 1:
The patent replaces the manual mechanical coding process with an automated AI-based system that uses natural language processing and machine learning algorithms to convert medical procedure descriptions into CPT and ICD-10 codes, eliminating human manual intervention while maintaining or improving accuracy
Solution Approach 2:
The system enables self-service coding by allowing the AI model to autonomously perform code conversion without requiring human coders, with the model continuously improving through feedback loops and retraining on new data
2Measurement precision
If multiple layers of claim audits are implemented, then coding accuracy can be improved, but the process complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where the AI model's coding outputs are evaluated against actual billing outcomes and insurance decisions, with errors and denials fed back into the training data to continuously improve future coding accuracy, replacing multiple manual audit layers
Solution Approach 2:
The AI system performs multiple functions including code conversion, accuracy validation, and continuous learning within a single unified platform, eliminating the need for separate audit layers while maintaining high accuracy
3Measurement precision
If extensive training data and feedback loops are used, then coding accuracy is improved, but the implementation complexity increases
Solution Approach 1:
The patent performs preliminary training of the AI model on extensive historical coding data, medical literature, and billing outcomes before deployment, and continues pre-training through automated feedback loops after deployment, making the system highly accurate while managing implementation complexity through automated processes
Data Source
AI summary
A system and method for determining a medical code corresponding to a medical procedure description using artificial intelligence algorithms includes a Current Procedural Terminology (CPT) code set that includes records each including a code associated with a medical condition description, respectively. The system includes a deep learning neural network in communication with said CPT code set that is trained using medical data that is updated in real-time using at least medical literature obtained using natural language processing (NLP). The method includes using NLP to receive a medical procedure description and comparing the NLP-enhanced description to the code set until a match is made. That match represents the proper code associated with the NLP-enhanced description.