Clinical Data Aggregation Platform Using ELT and CDISC Mapping
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Solution Overview
Problem
The clinical data captured by various systems supporting clinical trials is highly fragmented due to regulatory requirements, study design, and different technology vendors, making it challenging to combine and build comprehensive business intelligence.
Innovation Solution
A clinical data aggregation platform (ClinDAP) that uses an innovative pipeline architecture to load and standardize data from disparate sources into a central data hub, leveraging the CDISC SDTM format, with advanced mapping and transformation engines to convert data into standardized sets, and supports auto-mapping through machine learning capabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data from various clinical systems is aggregated and standardized using traditional methods, then data integration capability is improved, but system complexity and processing time increase significantly
Solution Approach 1:
The patent transforms clinical data by changing its parameter representation from diverse source formats to a standardized target format. The transformation engine applies parameter mapping rules that convert source system parameters (e.g., patient_id, visit_date, lab_result) into standardized parameters, enabling data integration without proportionally increasing system complexity.
Solution Approach 2:
The patent introduces an intermediary transformation layer between diverse clinical data sources and the target data warehouse. This intermediary layer includes transformation rules, mapping templates, and validation logic that mediate between source system heterogeneity and target system standardization, reducing overall system complexity by encapsulating transformation logic in a dedicated intermediate component.
2Reliability
If comprehensive data standardization is performed across all clinical systems, then data quality and consistency are improved, but processing cost and time increase
Solution Approach 1:
The patent applies partial standardization by focusing transformation efforts on critical data elements that have the greatest impact on data consistency. Rather than uniformly processing all data fields, the system identifies and prioritizes transformation of key parameters (e.g., patient identifiers, timestamps, outcome measures) while applying lighter processing to less critical fields, reducing processing time while maintaining essential data consistency.
Solution Approach 2:
The patent performs preliminary data validation and formatting checks at the point of data ingestion, before full transformation processing. Source systems are equipped with preliminary validation rules that check data format, required fields, and basic consistency before data leaves the source system, reducing the processing burden and time required for comprehensive standardization later in the pipeline.
3Stability of the object's composition
If rigid data models are used for data standardization, then data structure control is improved, but flexibility to adapt to new clinical trial designs decreases
Solution Approach 1:
The patent implements a dynamic transformation framework where mapping rules and data models can be modified without requiring system redesign. The transformation engine supports runtime configuration of mapping templates, allowing the system to adapt to new clinical trial designs by loading updated transformation rules. This dynamic approach maintains data structure control through enforced schemas while enabling flexibility through configurable transformation logic.
Solution Approach 2:
The patent segments the data transformation process into independent, modular components: source system adapters, transformation rule engines, validation modules, and target system interfaces. Each segment can be independently configured and modified. This segmentation allows the system to maintain stable overall data structure control while enabling flexible adaptation in specific segments to accommodate new clinical trial designs or source systems.
Data Source
AI summary
A clinical data aggregation system and method, comprising ingesting, transforming and storing data in a clinical data lake. The present invention uses an Extract Load and Transform (ELT) rather than traditional Extract Transform and Load (ETL) design principle. The data hub platform leverages modern noSQL databases which makes the platform highly flexible to configure studies with any design complexity with relative ease.