NLP Medical Coding System for Vascular Interventional Radiology
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity and high cost of vascular interventional radiology (VasIR) procedures, combined with intricate coding rules, lead to human errors in medical report coding, necessitating an automated solution for efficient and consistent coding results.
Innovation Solution
A system utilizing natural language processing (NLP) to electronically assign medical billing codes, with a custom graphical user interface (GUI) for human coders to review, modify, and approve codes, providing training for the NLP engine and facilitating accurate and compliant coding.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If human coders manually code VasIR medical reports, then coding accuracy can be maintained through human judgment, but coding time is excessive and human errors occur
Solution Approach 1:
The patent replaces the manual mechanical coding process with an automated NLP-based electronic coding system. The system uses natural language processing to extract procedural information from medical reports and automatically assigns CPT codes, eliminating the need for manual human coding while maintaining accuracy through algorithmic consistency and rule-based validation.
Solution Approach 2:
The coding system performs self-service by automatically processing medical reports and generating codes without requiring human intervention for each individual report. The electronic coding engine independently analyzes report text, applies coding rules, and produces coded outputs, freeing human coders from repetitive manual tasks.
2Adaptability or versatility
If human coders manually code VasIR medical reports, then complex coding rules can be applied with human judgment, but human errors and inconsistency occur
Solution Approach 1:
The system transforms complex coding rules into programmable parameters and algorithms within the NLP engine. Coding guidelines are converted into structured decision trees and rule sets that the electronic system can execute consistently, changing the state of coding rules from human-interpretable text to machine-executable logic with consistent output.
3Productivity
If NLP is used to automate coding, then coding time is reduced and consistency is improved, but initial system complexity increases
Solution Approach 1:
The patent introduces an intermediary layer between the medical report and the coding output - the NLP processing engine. This intermediary automatically extracts entities, identifies procedures, and maps them to appropriate codes, serving as a mediator that handles the complexity of translating unstructured medical text into structured coding data without requiring direct human intervention.
4Productivity
If NLP automatically assigns codes, then coding speed increases, but accuracy may decrease without human review
Solution Approach 1:
The system implements feedback mechanisms where coding results are validated against established coding rules and guidelines. The NLP engine continuously learns from coded examples and feedback, refining its accuracy over time while maintaining high-speed automated processing. Human coder feedback on automated codes further improves system precision through iterative training.
Data Source
AI summary
A system and method for identifying medical procedure codes and medical diagnosis codes from physician reports that describe a vascular interventional radiology procedure using a combination of natural language processing (NLP) and human medical coders. In one embodiment, the system and method of the present invention creates billing results, and or other documents, that are compliant with applicable legal and policy instructions from the government or a medical institution. Medical billing codes are efficiently extracted from medical reports using a NLP engine and a graphical user interface optimized for understanding the VasIR medical procedure described in the report.