AI Health Data Management Engine for Real-Time Anomaly Detection
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Solution Overview
Problem
Current health data management systems struggle with processing vast volumes of data from various sources, lack real-time adaptation to changing health needs, require manual intervention, and fail to integrate with finance and insurance organizations, leading to inefficiencies and incomplete health profiles.
Innovation Solution
An AI-based system and method for personalized health data management that includes a health data management engine with data extraction, analysis, feature extraction, and machine learning models to identify anomalies and patterns, generating insights and recommendations, and integrating with finance and insurance organizations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If static processes are used for health data tracking, then system simplicity is maintained, but the ability to process and adapt to vast volumes of health data deteriorates
Solution Approach 1:
The patent replaces static mechanical processes with an AI-based system that uses machine learning models and natural language processing to automatically analyze health data. The system substitutes manual data processing with automated AI algorithms that can handle vast volumes of health data from multiple sources, thereby improving productivity while maintaining system manageability through software-based solutions.
2Measurement precision
If manual intervention is required for health data management, then data accuracy may be improved, but scalability and efficiency deteriorate
Solution Approach 1:
The patent implements a self-service system where the AI-based health data management engine automatically performs data extraction, analysis, and anomaly detection without requiring manual intervention. The system uses machine learning models to autonomously process health data, generate insights, and provide recommendations, thereby achieving both high accuracy through sophisticated algorithms and high scalability through automation.
Solution Approach 2:
The system incorporates feedback mechanisms where the AI model continuously learns from processed health data, improving its accuracy over time. The feedback loop allows the system to refine its anomaly detection capabilities and provide increasingly accurate health insights while maintaining automated operation, thus achieving both precision and scalability.
3Stability of the object's composition
If traditional health data management systems are used, then existing processes are maintained, but the ability to integrate data from various sources and provide comprehensive health profiles deteriorates
Solution Approach 1:
The patent creates a universal health data management system that can process and integrate multiple types of health data from diverse sources including electronic health records, wearable devices, and mobile applications. The AI-based engine is designed to handle various data formats and sources simultaneously, providing comprehensive health profiles while maintaining stable core processing functions through standardized algorithms.
4Quantity of substance
If current health data management systems are used, then basic data storage is achieved, but real-time adaptation to users' changing health needs and predictive capabilities deteriorate
Solution Approach 1:
The patent implements a dynamic system where the AI-based health data management engine continuously adapts to users' changing health needs in real-time. The machine learning models process incoming health data streams dynamically, adjusting analysis parameters and generating updated insights based on current health status. This enables the system to provide real-time adaptation and predictive capabilities while maintaining the ability to store and manage large quantities of historical health data.
Data Source
AI summary
An AI based system and a method for personalized health data management is provided. The invention provides for performing one or more data extraction operations on one or more data types to obtain processed data types. The data types are collected from multiple data sources. The processed data types are analyzed for detecting abnormalities and deviations in the collected data types by providing a sequence of prompts to AI models. One or more health features data is extracted from the analyzed data types by using feature extraction techniques. Machine learning models are employed to augment the extracted health features data in order to identify anomalies and patterns in the health features data. Insights and recommendations associated with health of a user are generated based on processing of the analyzed features data. Action items are triggered based on the generated insights and recommendations.