AI Medical Procedure Coding From Structured and Unstructured Records
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Solution Overview
Problem
The challenge of manually and tediously filling in missing medical procedure codes during the digitization of paper-based medical records, leading to time-consuming and impractical processes, is addressed by using generative AI to analyze medical records and predict appropriate codes, reducing effort and cost in healthcare systems.
Innovation Solution
A computer system utilizing a trained machine learning model processes structured and unstructured medical data to generate medical procedure codes, providing reasons for code predictions, thereby automating the code generation process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual coding is used to fill in missing medical procedure codes during digitization, then coding accuracy can be maintained, but the process becomes time-consuming and impractical
Solution Approach 1:
The patent replaces manual mechanical coding processes with an AI-based automated system. The machine learning model processes digital images of medical records and automatically generates procedure codes, eliminating the need for manual intervention while maintaining coding accuracy through sophisticated pattern recognition and classification algorithms.
Solution Approach 2:
The system enables self-service coding by allowing the AI model to independently analyze medical records and generate codes without human intervention. The automated system processes documents, extracts relevant information, and assigns procedure codes autonomously, freeing manual resources from time-consuming coding tasks.
2Reliability
If manual coding processes are used, then coding completeness can be ensured, but labor costs and operational complexity increase
Solution Approach 1:
The patent replaces complex manual operational processes with an automated AI system. The machine learning model handles document analysis, code generation, and verification automatically, reducing operational complexity while maintaining coding completeness through systematic processing of all medical record elements.
Solution Approach 2:
The AI system provides multi-functional capabilities including document image processing, information extraction, procedure identification, and code generation within a single integrated platform. This universal system handles various coding tasks simultaneously, reducing the need for multiple specialized manual processes and降低 overall operational complexity.
3Productivity
If automated AI coding is implemented, then time consumption is reduced, but system complexity and training requirements increase
Solution Approach 1:
The patent applies preliminary action by pre-training the machine learning model on extensive medical coding datasets and historical data before deployment. This preliminary training phase equips the system with the knowledge and patterns needed to perform accurate coding autonomously, reducing the need for complex real-time decision-making during actual coding operations and thereby lowering operational complexity.
Solution Approach 2:
The system uses copying by training the AI model on replicated and annotated versions of medical records and codes. The model learns from copied training data to recognize patterns and generate accurate codes, enabling automated coding without requiring complex real-time analysis capabilities during actual use.
4Ease of operation
If AI-based code generation is used, then manual effort is reduced, but data processing and training requirements increase
Solution Approach 1:
The patent applies preliminary action by conducting extensive pre-training of the AI model on large volumes of historical medical records and coded data before deployment. This preliminary data processing and training phase absorbs the bulk of data handling requirements upfront, enabling the system to operate with reduced manual effort during actual coding tasks without requiring continuous large-scale data processing.
Data Source
AI summary
Embodiments determine a medical procedure code. Embodiments receive a description of a medical procedure comprising unstructured data and structured data. Embodiments provide the description to a trained machine learning (“ML”) model, the ML model being trained with training data comprising a database of medical procedure codes and historical documentation and corresponding medical procedure codes for a category of patients. Embodiments generate, by the trained ML model, one or more predicted medical procedure codes corresponding to the description.