Clinical Study Templates Using Metadata Parameterization
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Solution Overview
Problem
The implementation of data standards in clinical studies is burdensome and expensive, requiring significant programming efforts and updates, and there is a need for efficient and automated management of clinical research study data to facilitate regulatory compliance.
Innovation Solution
A digitized framework for clinical development is introduced, utilizing a data model with metadata repository (MDR) to manage data standards, generate study templates, and automate study planning, including graphical user interfaces for intuitive study design, thereby accelerating the clinical lifecycle and reducing errors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data standards are implemented for regulatory compliance, then data exchange quality and regulatory approval are improved, but implementation cost and programming effort increase significantly
Solution Approach 1:
The patent uses templates to copy and reuse standard data structures and protocols across multiple studies. Once a data standard template is created, it can be replicated and adapted for different studies without recreating the entire structure, significantly reducing programming effort while maintaining compliance quality
Solution Approach 2:
The system allows dynamic parameterization of data standards through configurable metadata. By changing parameters within the template framework rather than rewriting entire data structures, the system adapts to different regulatory requirements and study types efficiently, reducing implementation complexity while maintaining compliance
2Reliability
If data standards are updated or changed, then data quality and regulatory alignment are improved, but significant programming efforts and expenses are required for updates
Solution Approach 1:
The patent implements dynamic templates with configurable parameters that can be modified without structural changes. When data standards update, the system dynamically adjusts parameters within existing templates rather than requiring complete restructuring, enabling rapid adaptation to new regulations while maintaining data quality
Solution Approach 2:
The system prepares update mechanisms in advance by designing modular, parameterizable templates. This preliminary structuring allows future standard updates to be implemented by simply changing parameters rather than rewriting code, significantly improving update efficiency while maintaining data quality standards
3Adaptability or versatility
If manual study management processes are used, then flexibility in study design is maintained, but cycle times and administrative overhead increase
Solution Approach 1:
The patent applies preliminary action by creating pre-configured templates for common study designs and protocols. These templates contain pre-defined structures, endpoints, and analysis plans that can be quickly adapted to specific study needs, reducing cycle time while maintaining flexibility through parameter customization
Solution Approach 2:
The system segments study management into modular components (templates, protocols, endpoints, analysis plans) that can be independently configured and combined. This segmentation allows rapid assembly of study designs by selecting and customizing pre-built modules, reducing administrative overhead while maintaining design flexibility
Data Source
AI summary
Techniques are disclosed for digitizing a framework for clinical development. In one example, A computing device comprising a memory configured to store a regulatory database, and one or more processors may be configured to perform the techniques. The one or more processors may retrieve, from the regulatory database, a template based on a governance process, receive, via a user interface, input from a user, parameterize the template based on the governance process with the user input to create a parameterized template, and store the parameterized template in a database.