AI Code Classification for Justified Medical Test Guidance
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Solution Overview
Problem
Providers often lack the knowledge or confidence to order relevant medical tests during intake, leading to inefficiencies in diagnosing medical issues due to the need for medical necessity justification and awareness of available tests.
Innovation Solution
A system utilizing a trained AI model, such as a generative AI (GenAI) model, generates support-based evidenced codes and recommended actions based on intake data, enabling real-time identification and execution of appropriate medical tests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a provider manually determines tests to order during intake, then medical necessity justification can be provided, but the provider lacks knowledge or confidence about available tests and applicable tests
Solution Approach 1:
The patent introduces an AI-based intermediary system that bridges the gap between providers and test ordering. The system automatically generates medical necessity justifications and identifies appropriate tests based on intake data, reducing the burden on providers while maintaining reliable justification documentation.
Solution Approach 2:
The system enables self-service by automatically generating test recommendations and medical necessity justifications without requiring extensive provider expertise. The AI model processes intake data and autonomously produces actionable test ordering guidance, allowing providers to focus on patient care rather than test selection.
2Measurement precision
If a provider manually identifies relevant tests, then appropriate tests can be ordered, but the process is time-consuming and reduces efficiency
Solution Approach 1:
The system performs preliminary action by pre-processing intake data and pre-identifying relevant tests before the provider completes the intake process. The AI model analyzes symptoms, complaints, and medical history in real-time and generates test recommendations that can be immediately acted upon, eliminating the need for manual test identification during the intake process.
Solution Approach 2:
The patent replaces the mechanical manual process of test identification with an automated AI-based system. The AI model processes medical data, identifies relevant tests, and generates justifications automatically, substituting the time-consuming manual search and selection process with rapid automated analysis.
3Reliability
If comprehensive tests are ordered to ensure accurate diagnosis, then patient care quality improves, but the timeline for obtaining correct care increases
Solution Approach 1:
The system applies partial action by identifying only the most relevant and necessary tests based on the specific intake data, rather than ordering comprehensive test panels. The AI model prioritizes tests that provide the highest diagnostic value for the given symptoms and medical history, avoiding unnecessary tests that would extend the care timeline.
Data Source
AI summary
Methods and systems for next-action recommendation and support interface generation are disclosed. A set of intake data is received and at least one support-based evidenced code is generated based on the set of intake data. At least one support-based recommended action is identified for the at least one support-based evidenced code and a selection of the at least one support-based recommended action is received. Instructions are transmitted to cause execution of the at least one support based recommended action.