AI Health Report Interrogation System for Medical Jargon Translation
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Solution Overview
Problem
Health reports often contain unfamiliar healthcare terms and numerical data, making it difficult for individuals to understand their health information, and existing reports lack relevant information on health conditions and treatment options, leading to potential health risks due to communication gaps between healthcare professionals and consumers.
Innovation Solution
A method utilizing a personal assistance device and a reporting system with a query-interrogation system, knowledge-graphing, and natural language understanding to extract and present relevant information from health reports, incorporating external databases and self-learning to provide clear and accurate responses to user queries.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If health reports contain detailed healthcare terms and numerical data, then the information completeness is improved, but the ease of understanding deteriorates
Solution Approach 1:
The patent introduces an AI-based natural language processing intermediary that translates complex healthcare terminology and numerical data into plain language explanations. The system acts as a mediator between the detailed health report and the consumer, converting difficult-to-understand medical jargon into accessible information while preserving the complete medical data for reference.
Solution Approach 2:
The patent segments the health report information into different levels of detail: a simplified summary for general understanding and the complete detailed report for reference. The system divides the information presentation into hierarchical layers, allowing consumers to first understand the overall health status through simplified explanations and then access specific detailed data when needed.
2Measurement precision
If health reports provide only raw data, then the information accuracy is improved, but the relevance to consumer needs deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the AI system analyzes the consumer's health data, identifies concerning patterns, and provides targeted explanations and recommendations. The system continuously adapts its responses based on the consumer's specific health conditions, asking follow-up questions and providing personalized guidance rather than generic information.
Solution Approach 2:
The patent applies local quality by providing different types of information explanations at different locations in the interface. Critical health indicators receive detailed contextual explanations, while normal values receive brief confirmations. The system tailors the depth and type of information provided to each specific health parameter and the consumer's individual health profile.
3Device complexity
If health reports lack contextual information, then the report simplicity is improved, but the ability to guide consumer action deteriorates
Solution Approach 1:
The patent performs preliminary actions by pre-analyzing the health data and preparing contextual information, risk assessments, and actionable recommendations before the consumer views the report. The AI system proactively identifies health concerns and prepares explanatory content, so when the consumer views the report, all necessary contextual information and guidance are already in place.
Solution Approach 2:
The patent enables self-service by allowing the consumer to interact with the AI system through natural language queries. The consumer can ask specific questions about their health data, and the system provides immediate contextual explanations and guidance without requiring professional interpretation. The system serves itself by automatically generating and updating contextual information based on the health data.
Data Source
AI summary
A method of retrieving information from a health report through a machine assisted interrogation process consists of a personal assistance device, a reporting system, a knowledge-graphing system, and a query-interrogation system. A user utilizes the personal assistance device to submit a query that retrieves information from the reporting system via the query-interrogation system. The knowledge-graphing system, which is generated using artificial intelligence modules, natural language understanding modules, and machine learning modules, is utilized to provide accurate results to the user. An administrative system monitors the validity of a plurality of health reports of the reporting system.