Federated Biomedical Data Integration Across Secure Silos
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Solution Overview
Problem
Healthcare data is often stored in disparate silos, making it difficult to access, analyze, and integrate for precision medicine, which relies on comprehensive data sharing and analysis across various data sources with different formats and security protocols, leading to inefficiencies and increased computational run times.
Innovation Solution
A distributed data integration system that creates data source objects and data pools, allowing biomedical data from multiple sources to be accessed and analyzed uniformly across computational nodes, respecting data segregation constraints, and enabling the generation of causal models through federated computing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is stored in disparate locations in various formats across multiple hospital departments and providers, then data security and access control requirements are maintained, but data accessibility and analysis efficiency deteriorate
Solution Approach 1:
The patent introduces an intermediary layer (data integration system with standardized interfaces and protocols) that mediates between disparate data sources and analysis tools. This intermediary enables unified data access without compromising the security boundaries of individual data silos, resolving the contradiction between maintaining security and enabling accessibility.
Solution Approach 2:
The system implements universal data access protocols and standardized interfaces that allow multiple data sources with different security requirements to be accessed through a common framework. This multi-functional approach enables the system to handle diverse data types and security models while providing consistent access mechanisms.
2Adaptability or versatility
If heterogeneous biomedical data from multiple sources is integrated into a unified system, then precision medicine analysis capability is improved, but system complexity and integration difficulty increase
Solution Approach 1:
The patent transforms heterogeneous data by standardizing key parameters such as data formats, identification schemas, and access protocols. By changing the parameters of data representation to standardized forms, the system can integrate diverse biomedical data sources without proportionally increasing complexity, enabling precision medicine analyses.
Solution Approach 2:
The system segments the integration process into manageable components: data ingestion modules, transformation layers, and analysis interfaces. Each component handles specific data types or functions independently, reducing overall system complexity while maintaining comprehensive integration capability for precision medicine applications.
3Measurement precision
If manual processes are used to identify and compile patient data from multiple data sources, then data accuracy can be maintained, but time consumption and operational efficiency deteriorate
Solution Approach 1:
The system implements automated data retrieval and compilation mechanisms that self-serve by automatically querying multiple data sources, matching patient identifiers, and assembling complete patient records without manual intervention. This self-service capability maintains data accuracy through systematic verification while dramatically improving operational efficiency.
Solution Approach 2:
The system incorporates feedback mechanisms that automatically verify data completeness and accuracy during the automated compilation process. Validation rules and cross-checks provide real-time feedback to ensure data quality standards are met, maintaining accuracy while enabling automated high-speed data assembly.
Data Source
AI summary
Methods and systems are provided for a platform and language agnostic method for generating inter- and intra-data type aggregations of heterogeneous disparate data upon which various operations can be performed without altering the structure of the query or resulting distributed data set representation to account for which specific data sources are included in the query.