Cohort Selection and Management Application Leveraging Standards-based Semantic Interoperability and a Groovy DSL

Area of Science:

• Clinical Informatics
• Oncology Research
• Data Management

Background:

• Multi-centric clinical research requires precise patient cohort definition.
• Current methods for defining patient cohorts can be complex and time-consuming.
• Streamlining the cohort definition phase is crucial for efficient clinical trial initiation.

Purpose of the Study:

• To introduce a novel Cohort Selection application for oncology research.
• To enhance the ease of use and precision in defining patient selection criteria.
• To facilitate collaboration and data exploration in multi-site research settings.

Main Methods:

• Development of a Cohort Selection application using a Groovy DSL.
• Integration with a standards-based Semantic Interoperability Solution.
• Utilization of ontologies like SNOMED-CT for semantic data representation and filter definition.

Main Results:

• The application enables high expressiveness and flexibility in defining complex patient selection filters.
• It supports the composition of filters into intricate selection graphs with splits and merges.
• Facilitates patient data exploration and collaboration across heterogeneous, distributed data environments.

Conclusions:

• The Cohort Selection application effectively streamlines the definition phase of multi-centric oncology research.
• The use of semantic interoperability and ontologies enhances data sharing and collaborative research.
• The tool supports efficient patient data exploration and collaboration in complex research settings.