Clinical Research Code Generation Using Intermediate Data Mappings
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Solution Overview
Problem
The implementation of data standards for clinical research studies is burdensome and expensive, requiring significant programming efforts and resources to ensure regulatory compliance, and transformation of data to conform to regulatory standards is complex and time-consuming.
Innovation Solution
A system that digitizes clinical development by defining a data model with a metadata repository (MDR) to store study data, automatically generating transformations using intermediate concepts to conform to regulatory standards, reducing the need for manual programming and minimizing human error.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual programming is used to transform clinical study data to conform to regulatory data standards, then data consistency and regulatory compliance are ensured, but significant time and resources are consumed
Solution Approach 1:
The patent introduces an intermediary transformation framework that acts as a mediator between clinical study data systems and regulatory data standards. This framework includes standardized transformation templates, mapping rules, and validation mechanisms that automatically convert data while ensuring consistency with regulatory requirements, thereby reducing manual programming time while maintaining data reliability
Solution Approach 2:
The system employs parameter-based transformation configurations that allow flexible adjustment of transformation rules without requiring complete reprogramming. By changing parameters such as mapping relationships, data types, and validation criteria, the system adapts to different regulatory standards and study types efficiently, reducing the time needed for data transformation while maintaining consistency
2Reliability
If complex transformation programs are developed to ensure regulatory compliance, then data standards are met, but significant programming resources and costs are required
Solution Approach 1:
The patent implements preliminary action by pre-defining transformation templates, mapping rules, and validation logic that comply with regulatory standards before actual data transformation is needed. These pre-configured frameworks can be directly applied to clinical study data, eliminating the need to develop complex transformation programs from scratch and significantly improving programming efficiency while ensuring regulatory compliance
Solution Approach 2:
The transformation framework is designed with universal applicability across different regulatory standards and study types. A single framework can handle multiple data standards (e.g., CDISC, FDA, EMA) and various study configurations through configurable parameters and templates, reducing the need for separate programming efforts for each compliance scenario and improving overall productivity
3Adaptability or versatility
If data standards are updated or changed, then regulatory requirements are kept current, but significant programming efforts and expenses are required to update implementations
Solution Approach 1:
The system employs dynamic transformation configurations that can be easily updated to reflect changes in regulatory standards. The framework uses configurable parameters, flexible mapping rules, and modular transformation templates that can be adjusted without requiring complete reprogramming. This dynamic architecture allows rapid adaptation to new standards while maintaining high update efficiency and reducing programming expenses
4Reliability
If double programming is performed to validate transformation programs, then data consistency is ensured, but time and human resources are significantly consumed
Solution Approach 1:
The patent implements feedback mechanisms through automated validation processes that provide real-time verification of data transformation consistency. The system includes built-in validation rules, error checking, and consistency verification that automatically detect and report issues during transformation, eliminating the need for manual double programming and significantly reducing validation time while maintaining data consistency
Data Source
AI summary
In general, techniques are described by which to enable code generation for clinical research systems. A computing device comprising a memory and a processor may implement the techniques. The memory may store a code generator. The processor may execute the code generator. The code generator may determine data collected in support of a clinical research study, and obtain a first mapping between the collected data and an intermediate concept that uniformly classifies the collected data. The code generator may also obtain a second mapping between the intermediate concept and a standard model that conforms to a regulatory standard. The code generator may further generate, based on the first mapping and the second mapping, transformational data that defines a transformation between the data collected in support of the clinical research study and data that conforms to the standard model, and output the transformational data.