Digital Human Healthcare Platform for Private Predictive Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing medical services lack efficient, convenient, and accurate platforms for data processing, analysis, and prediction to assist medical staff in patient treatment and promote early recovery, while also ensuring data privacy and accuracy.
Innovation Solution
A medical and healthcare service platform utilizing a digital human system that integrates digital human replica, simuli, agent, and data acquisition systems, supported by a digital data currency system, for data processing, analysis, and prediction, including virtual representation, simulation, and modeling of health and disease processes, with data cleaning, anonymization, and incentivization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional data processing methods are used in medical platforms, then data privacy can be maintained, but data processing efficiency and analysis accuracy deteriorate
Solution Approach 1:
The patent creates digital twins (virtual copies) of patients, medical institutions, and medical devices that replicate their characteristics and behaviors. These digital copies enable data processing and analysis without exposing sensitive real patient information, thus maintaining privacy while improving processing efficiency. The digital twin technology allows comprehensive data analysis on virtual replicas without compromising the security of actual patient data.
Solution Approach 2:
The platform introduces an intermediary layer of digital twins that mediate between real medical data and analysis systems. This intermediary layer processes and transforms data before it reaches analysis algorithms, enabling efficient processing while protecting the confidentiality of original patient information. The digital twin acts as a buffer that decouples data privacy requirements from analysis efficiency needs.
2Measurement precision
If comprehensive medical data is collected for accurate analysis, then prediction accuracy improves, but data security risks and privacy concerns worsen
Solution Approach 1:
The system collects comprehensive medical data to create accurate digital twins, which then serve as the basis for analysis. By performing all analysis operations on these digital copies rather than original patient data, the system achieves high prediction accuracy while eliminating security risks associated with handling sensitive real patient information. The digital twin contains all necessary medical characteristics but is inherently safe to manipulate and share.
Solution Approach 2:
The patent extracts essential medical characteristics and parameters from real patient data to construct digital twins, separating the critical information needed for accurate analysis from the sensitive personal identifiers. This extraction process retains measurement precision by preserving key medical features while removing or protecting elements that pose security risks, enabling accurate predictions without compromising data security.
3Quantity of substance
If multiple participating parties are integrated into the platform, then data completeness and service quality improve, but system complexity and data management difficulty worsen
Solution Approach 1:
The digital twin technology provides a universal interface that can represent and integrate data from multiple different sources including patients, medical institutions, and devices. Each entity type is modeled using the same digital twin framework, which handles different data formats and structures uniformly. This multi-functional approach enables comprehensive data collection from diverse sources while maintaining consistent data management processes, thereby reducing system complexity despite increased data completeness.
Solution Approach 2:
The patent segments the complex multi-party system into independent digital twin modules, where each participant (patient, institution, device) is represented as a separate but interoperable digital entity. This segmentation allows each party's data to be managed independently through standardized interfaces, reducing the overall system complexity while enabling complete data integration. The modular digital twin architecture simplifies data management across multiple organizations by providing clear boundaries and standardized interaction protocols.
Data Source
AI summary
This disclosure provides a medical and healthcare service platform that is supported by a digital data currency system and provides medical and healthcare data processing, analyzing, and predicting based on a digital human system by integrating participating parties comprising individual persons, researchers, healthcare providers, and regulatory and public sectors.