AI Medical Diagnostic Coding Automation
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Solution Overview
Problem
The current medical billing systems require physicians to manually enter complex service and diagnostic codes, consuming valuable time and increasing the risk of errors, which can lead to delayed or rejected claims and physician burnout.
Innovation Solution
A computer-implemented method and system that processes medical diagnostic information by identifying diagnostic terms within a text block, mapping these terms to corresponding service and diagnostic codes, and returning the matching codes, thereby automating the code identification process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physicians manually enter service and diagnostic codes into claim forms, then billing accuracy can be maintained, but time consumption increases significantly and error risk rises
Solution Approach 1:
The system enables self-service by automatically generating service and diagnostic codes through AI processing of clinical notes, eliminating the need for physicians to manually search and enter codes. The AI model autonomously extracts relevant information and maps it to appropriate billing codes, reducing time consumption while maintaining accuracy through automated validation.
Solution Approach 2:
The patent replaces the mechanical process of manual code entry with an automated AI-based system. The mechanical action of physicians typing and searching for codes is substituted by an electronic AI model that processes clinical text and generates codes automatically, significantly reducing time loss while maintaining billing accuracy through systematic processing.
2Manufacturing precision
If physicians spend more time on administrative billing tasks, then code accuracy may improve, but patient care time decreases
Solution Approach 1:
The billing system performs self-service by automatically generating accurate service and diagnostic codes without requiring physician intervention. This frees physicians from administrative tasks entirely, allowing them to dedicate more time to patient care while the AI system maintains code accuracy through its automated processing and validation mechanisms.
Solution Approach 2:
The system performs preliminary action by generating billing codes automatically during or immediately after the patient encounter, before the physician would otherwise need to spend time on billing tasks. This preliminary automation ensures code accuracy is achieved without consuming physician time that could be spent on patient care.
3Measurement precision
If complex rule-based code suggestions are provided, then billing precision may improve, but system complexity increases
Solution Approach 1:
The patent replaces complex mechanical rule-based systems with an AI-based semantic processing system. Instead of implementing numerous explicit billing rules and logic, the system uses natural language processing to understand clinical context and map it to appropriate codes, achieving billing precision while reducing system complexity by eliminating the need for explicit rule encoding.
Solution Approach 2:
The system changes the fundamental parameter of code generation from rule-based logic to AI-based semantic understanding. This parameter change allows the system to achieve billing precision through learned patterns and contextual understanding rather than explicit rules, thereby reducing system complexity while maintaining or improving billing accuracy.
4Loss of time
If physicians receive no formal training on medical billing, then educational time is saved, but billing error rate increases
Solution Approach 1:
The system performs self-service by automatically generating accurate billing codes without requiring physician knowledge or training in billing procedures. The AI model internally handles all billing logic and code selection, ensuring reliability and low error rates while eliminating the need for educational time investment in billing training.
Data Source
AI summary
Processing of medical diagnostic information returns comprises receiving a text block containing medical diagnostic information and returning corresponding tracking codes. Diagnostic terms may be identified within a text block containing medical diagnostic information, and the diagnostic terms may be mapped to corresponding respective tracking codes. At least one trained machine learning model, such as a large language model and/or a classifier, may be used to identify the tracking codes that correspond to the diagnostic terms.