Phenotype Engine for Interoperable EHR Data and Sovereignty Control
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Solution Overview
Problem
Diverse EHR systems within organizations like DHS have different architectures, standards, and user interfaces, making it impractical to pool common-interest information across components or with third-parties.
Innovation Solution
An AI-enhanced, user-programmable, socially networked system for medical record exchange and health management, utilizing multi-source health data integration logic, sovereignty protective wrapping, and a collaboration platform to integrate, transform, and analyze health data across disparate systems while preserving data sovereignty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If EHR systems from different components maintain their own distinct architectures and standards, then each system maintains operational independence and data sovereignty, but the ability to pool and share common-interest information across components is rendered impractical
Solution Approach 1:
The patent introduces a data integration platform that acts as an intermediary between disparate EHR systems. This platform receives data from multiple source systems with different architectures, transforms them into a common representation, and makes them available to various consumers. The intermediary layer absorbs the complexity of different source systems while presenting a unified interface, thereby enabling data sharing without requiring changes to the underlying EHR system architectures.
Solution Approach 2:
The patent implements a universal data representation schema that can accommodate multiple EHR system formats. The integration platform provides multi-functional capabilities by accepting diverse input formats from different EHR systems and transforming them into a standardized common representation. This universal approach allows a single platform to handle data from various sources without requiring system-specific integration solutions for each component.
2Adaptability or versatility
If EHR systems use different record standards and user interfaces, then each system can be optimized for its specific operational needs, but pooling common-interest informational content becomes impractical
Solution Approach 1:
The data integration platform serves as an intermediary that handles the complexity of different record standards and user interfaces. It receives data in various formats from different EHR systems, performs transformation to a common representation, and delivers the data to consumers. This mediator approach allows systems to maintain their optimized local interfaces while enabling seamless information sharing across the organization.
Solution Approach 2:
The patent applies parameter changes by transforming data from different record standards into a unified common representation. The system changes the parameters and format of the data during the transformation process, converting diverse input formats into a standardized output format. This parameter transformation enables easy data exchange and pooling while preserving the operational optimizations of individual EHR systems.
3Reliability
If health data is transformed to a common representation for integration, then interoperability across EHR systems is achieved, but data sovereignty and ownership control become more difficult to maintain
Solution Approach 1:
The patent segments the data transformation process into distinct functional layers: data collection from source systems, transformation to common representation, and delivery to consumers. By segmenting these functions, the system can maintain clear boundaries around data sovereignty. Source systems retain ownership of their原始 data, while the integration platform handles transformation as a separate service. This segmentation allows interoperability to be achieved without consolidating data sovereignty control.
Solution Approach 2:
The data integration platform acts as an intermediary that facilitates transformation without claiming ownership of the data. It receives data from source systems, performs transformation to a common representation, and delivers it to consumers while maintaining clear provenance information. This intermediary role allows the system to achieve reliable interoperability through standardized transformation while preserving data sovereignty by acting as a service provider rather than data owner.
Data Source
AI summary
Systems and methods are described, and an example system includes an AI enhanced multi-source health data integration logic that receives a first source electronic health record (EHR) data from a first EHR system and a second source EHR data from a second EHR system, and transforms, according to a knowledge representation schema, health-related information content of the first source EHR data and the second source EHR data to a first source transformed health data and second source transformed health data. The system includes a collaboration platform, configured to host a multi-source transformed health data database, including the transformed first source health data and the transformed second source health data, and hosts AI-enhanced, multiple level telecollaborative analyses by a plurality of participants of the multi-source transformed health data database, generating health management data.