Metadata is critical to pharma organizations as it enables effective management and understanding of data. However, managing metadata can be a daunting task, especially for large organizations with vast amounts of data. This is where a metadata repository comes in handy. A metadata repository refers to a centralized database that holds information about data elements and their relationships. When integrated with artificial intelligence (AI) and automation, it can streamline metadata and set up clinical studies.
Metadata storage and management
Accurate and organized metadata storage is the first step toward ensuring a successful clinical trial. In the pharmaceutical industry, clinical trials generate a vast amount of metadata. Managing this metadata efficiently is essential for compliance, traceability, and quality.
A metadata repository provides centralized storage where metadata can be stored, organized, and accessed with ease throughout a trial’s lifecycle. This repository acts as the sole, authoritative location for all the clinical trial metadata. It ensures data consistency, accessibility, and version control, allowing all stakeholders to easily locate and retrieve the metadata when required.
After storing the metadata, it becomes necessary to validate it to ensure its accuracy and consistency. Validation can be tedious and time-consuming. However, a metadata repository can significantly reduce the validation time and effort. It provides validation rules and automated checks by creating standardized metadata definitions and schemas. This ensures the consistency of all metadata and facilitates its utilization across various studies. Metadata repositories can automatically apply industry- or sponsor-specific metadata standards to each data element. In addition, metadata repositories can be used to analyze the data and check for any errors.
In clinical trials, data is obtained from numerous sources and stored in diverse formats. Metadata mapping harmonizes data structures and establishes relationships between the elements to ensure consistency and interoperability. AI algorithms can automatically analyze collected metadata, identify commonalities, and enable efficient metadata mapping. This includes consolidating metadata from different clinical research organizations (CROs) or legacy studies whose data elements use different naming conventions or structures.
By automating metadata mapping using machine learning techniques, pharmaceutical companies can reduce the manual effort required for data transformation, minimize the risk of errors and inconsistencies, and achieve a unified view of data across an entire clinical trial lifecycle, especially when a company has multiple standards to follow.