Proteomic Health State Detection With EHR and Patient-Reported Data
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Solution Overview
Problem
Current digital health systems lack standardized data integration, user-friendly interfaces, and effective analysis capabilities to provide personalized medical advice, and face security risks, failing to leverage real-world evidence for treatment recommendations.
Innovation Solution
A system and method for collecting, analyzing, and reporting comprehensive medical information using a data management system, knowledge creation engine, and display to generate personalized medical advice, incorporating EHRs, PROs, wearable devices, and biological samples, with a rules engine for dynamic questionnaires and a phenotypic measurement subsystem for data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple data sources (EHR, PROs, wearable devices, biological samples) are integrated to provide comprehensive medical information, then the quality and personalization of medical advice is improved, but the system complexity and data management burden increase
Solution Approach 1:
The system divides data collection and management into separate modular components: EHR data module, PROs collection module, wearable device integration module, and biological sample analysis module. Each module handles specific data types independently, reducing overall system complexity while maintaining comprehensive integration capabilities.
Solution Approach 2:
A central data management server acts as an intermediary between diverse data sources and analysis engines. This mediator standardizes data formats, manages data flow, and coordinates between different components, simplifying the integration of multiple data sources without requiring direct complex connections between all components.
2Productivity
If advanced analysis capabilities are implemented to generate personalized medical advice, then the value and effectiveness of the health system is improved, but the computational resources and processing time required increase
Solution Approach 1:
The system performs preliminary data preprocessing, cleaning, and standardization as data is collected, before analysis is required. This advance preparation reduces the computational burden during actual analysis and advice generation, as raw data is already transformed into analysis-ready formats.
Solution Approach 2:
The analysis engine implements tiered processing: routine analyses are performed on all users, while more computationally intensive personalized analyses are performed only when clinically indicated or when sufficient data is available. This selective approach optimizes resource usage while maintaining high-value personalized advice capability.
3Ease of operation
If user-friendly interfaces and simple functional designs are implemented, then ease of operation is improved, but the system may lack advanced analytical capabilities
Solution Approach 1:
The system automatically performs data collection, integration, analysis, and report generation without requiring user intervention in complex processes. Users simply interact with simple interfaces to provide basic inputs, while the system handles sophisticated analysis and generates comprehensive reports automatically, maintaining both simplicity and advanced capability.
Solution Approach 2:
The backend system acts as an intermediary that handles complex analytical processing between simple user interfaces and advanced analysis engines. Users interact with simplified interfaces while the intermediary manages the complexity of data integration and analysis, shielding users from technical complexity while enabling advanced capabilities.
4Adaptability or versatility
If comprehensive patient data is collected and stored for analysis, then the ability to provide personalized treatment recommendations is improved, but security risks and data protection challenges increase
Solution Approach 1:
The system implements differential security measures: sensitive data elements receive enhanced encryption and access controls, while less sensitive data uses standard protection. Data is segmented into security zones based on sensitivity, with varying levels of protection applied locally to different data types, maintaining security while enabling comprehensive data collection for personalization.
Solution Approach 2:
A secure data management layer acts as an intermediary between data storage and access points, implementing authentication, authorization, and encryption protocols. This intermediary protects stored patient data while enabling authorized access for personalized analysis, balancing security requirements with the need to utilize comprehensive data for treatment recommendations.
Data Source
AI summary
Systems and methods for collecting, analyzing, and reporting information relating to comprehensive medical information from one or more users are disclosed. In some aspects, a system for collecting and analyzing medical data includes a data management system for collecting and storing medical information relating to a user, and a knowledge creation engine in communication with the data management system and configured to analyze the stored medical information for creating at least one of personalized medical advice for the user and general scientific information relating to a medical condition. A display in communication with the data management system and the knowledge creation engine can be configured to present a digital representation of the user based on the stored medical information including electronic health record (EHR), patient reported outcomes (PROs), biological samples, wearable devices, sensors, medical devices, and dynamic questionnaires to create a digital representation of the user.