Enhancing Traceability in Clinical Research Data through a Metadata Framework

Area of Science:

• Biomedical Informatics
• Data Science
• Clinical Research Informatics

Background:

• Clinical research data lifecycle operates in silos, limiting traceability.
• Traceability is crucial for regulated and non-regulated studies.
• Existing tools offer limited metadata traceability and cross-phase querying.

Purpose of the Study:

• Develop a metadata traceability framework for querying and visualizing traceability.
• Identify and validate traceability gaps to improve data lineage and reproducibility.

Main Methods:

• Employed the design science research paradigm to create and evaluate an IT artifact.
• Developed Trace-XML, a framework extending metadata models and using graph traversal algorithms.
• Evaluated Trace-XML using analytical and qualitative methods.

Main Results:

• Trace-XML accurately and completely assesses metadata traceability.
• Qualitative analysis confirmed Trace-XML's utility for researchers.
• The framework enables querying, validation, and visualization of traceability metadata.

Conclusions:

• Trace-XML effectively addresses the problem of limited metadata traceability in clinical research.
• The framework enhances the ability to create and assess end-to-end study traceability.
• Trace-XML provides essential features for identifying gaps, validating, and visualizing metadata traceability.