Data Rationalization Engine for Adverse Event Harmonization
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Solution Overview
Problem
In pharmacovigilance, drug companies face difficulties in leveraging adverse event data due to its varied formats and coding standards, often requiring expensive custom rationalization or ignoring incompatible data sources, which hampers data utilization and compliance with regulatory requirements.
Innovation Solution
A data rationalization system that employs a web-based engine to convert raw adverse event data from diverse sources into a standardized XML format, using automated and manual rationalization rules to map non-preferred to preferred data instances, while maintaining an audit trail for regulatory compliance and data integrity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If custom rationalization is performed on each data repository, then data standardization is achieved, but cost and time increase significantly
Solution Approach 1:
The system performs preliminary rationalization by pre-processing and standardizing data from multiple repositories before analysis. The rationalization engine proactively transforms data into a standardized format, eliminating the need for time-consuming custom rationalization during actual analysis tasks.
Solution Approach 2:
The system enables self-service data rationalization through automated engines that independently process and standardize data from various repositories. The rationalization occurs automatically without requiring manual intervention for each data source, reducing both time and resource requirements.
2Manufacturing precision
If custom rationalization is performed on each data repository, then data standardization is achieved, but cost increases significantly
Solution Approach 1:
The system implements a universal rationalization engine that handles multiple data repository types through a single platform. This multi-functional approach eliminates the need for separate custom rationalization processes for each repository, significantly reducing overall costs while maintaining data standardization across all sources.
Solution Approach 2:
The system changes the parameters of data rationalization by transforming complex, repository-specific rationalization tasks into standardized parameter-based transformations. This allows consistent processing across different data sources using uniform rules and algorithms, reducing computational resources and costs.
3Reliability
If data is ignored due to format incompatibility, then analysis system compatibility is maintained, but data utilization decreases
Solution Approach 1:
The system introduces an intermediary rationalization layer between diverse data repositories and the analysis system. This mediator transforms incompatible data formats into a standardized intermediate format that the analysis system can process, enabling utilization of previously incompatible data sources without compromising system reliability.
Solution Approach 2:
The system performs preliminary format standardization on incompatible data sources before they enter the analysis system. By pre-processing data to match required formats, the system enables broader data utilization while maintaining compatibility requirements, turning previously unusable data into valuable analysis resources.
Data Source
AI summary
Systems, methods, and other embodiments associated with data rationalization are described. One example method includes receiving data from a primary data repository and automatically rationalizing the data by applying rationalization rules that map one or more non-preferred data instances to a preferred data instance. Any non-preferred data instances that have not been automatically rationalized into a preferred data instance are provided to an interface for manual rationalization. Automatically and manually rationalized data is stored in a rationalized data repository. In addition, rationalization rules based on the manual rationalization are extracted for use in subsequent automatic rationalization operations.