Medical Imaging Data Warehouse Integration via DICOM Standardization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current data warehouse systems fail to integrate medical imaging data and related metadata with other healthcare-related data sources, lacking the capability to analyze combined high-quality imaging metadata with clinical and administrative information.
Innovation Solution
A data warehouse management system that receives and stores medical imaging data, including metadata, from various sources, and performs data processing to integrate it with other healthcare-related data, using DICOM standards for data conversion and storage, and leveraging a logical data model to organize and analyze the integrated data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If medical imaging data is integrated with other healthcare data sources in a data warehouse, then the capability to analyze combined imaging metadata with clinical and administrative information is improved, but the system complexity and data integration challenges increase
Solution Approach 1:
The patent employs an intermediary layer (data warehouse management system) that mediates between diverse imaging data sources and analytical applications. This intermediary handles data standardization, integration, and management, thereby improving data integration capability while shielding the complexity from end users and simplifying the overall system architecture.
Solution Approach 2:
The system segments the complex data integration task into manageable components: data collection from multiple sources, data standardization using DICOM standards, metadata extraction and storage, and analytical processing. This segmentation reduces system complexity by breaking down the integration challenge into modular, independently manageable segments.
2Measurement precision
If comprehensive medical imaging metadata is stored and processed, then the quality and depth of healthcare analysis is improved, but the data storage requirements and processing time increase
Solution Approach 1:
The system extracts and stores only the essential imaging metadata and relevant features needed for analysis, rather than storing complete raw imaging datasets. This extraction approach maintains high analysis quality by preserving critical diagnostic and procedural information while significantly reducing data storage requirements and processing overhead.
Solution Approach 2:
The patent applies local quality by storing different types and levels of detail for different metadata elements based on their analytical importance. Critical metadata elements are stored with high precision and detail, while less critical elements are stored with reduced detail or aggregated, thereby optimizing the balance between analysis quality and storage efficiency.
3Adaptability or versatility
If multiple data sources are integrated into the data warehouse, then the comprehensiveness of healthcare information available for analysis is improved, but the difficulty of data standardization and compatibility increases
Solution Approach 1:
The system implements a universal data standardization framework based on DICOM standards that can handle multiple imaging data sources and formats. This universal approach enables the data warehouse to accept and process data from diverse sources (different modalities, vendors, and systems) while maintaining consistency, thereby improving compatibility without proportionally increasing standardization complexity.
Data Source
AI summary
Methods, systems, and computer readable media for integrating medical imaging data in a data warehouse are disclosed. According to one method, the method occurs at a data warehouse management server that manages a data warehouse system. The data warehouse management server includes at least one processor. The method includes receiving medical imaging data including imaging metadata from an imaging related data source. The method also includes storing the imaging metadata in the data warehouse. The method further includes performing data processing using the imaging metadata and other healthcare related data stored in the data warehouse, wherein the other healthcare related data is from one or more different data sources.