Research Data Explorer: Lessons Learned in Design and Development of Context-based Cohort Definition and Selection

Area of Science:

• Clinical informatics
• Biomedical data science
• Health data management

Background:

• Clinical research databases require efficient data querying and cohort identification tools.
• Non-technical users often face challenges in accessing and interpreting complex clinical data.
• Existing tools may lack intuitive interfaces for understanding patient-level data context.

Purpose of the Study:

• To introduce Research Data eXplorer (RedX), a tool designed for self-service data queries and cohort identification.
• To highlight RedX's primary innovation: an electronic health record view for enhanced contextual understanding.
• To describe the development and refinement of RedX's core functionalities.

Main Methods:

• Development of a self-service query tool integrating individual patient views with population-based data.
• Iterative refinement of RedX components based on necessity and value during development.
• Focus on user-centric design to support non-technical users in complex data analysis.

Main Results:

• A functional self-service query and cohort identification tool was successfully developed.
• The electronic health record view proved valuable for contextualizing patient data.
• RedX demonstrated the utility of integrating patient-level and population-level data perspectives.

Conclusions:

• RedX successfully provided a self-service solution for clinical data exploration and cohort identification.
• The tool's design facilitated better understanding of complex clinical research data.
• Future plans involve consolidating RedX's enhanced functions into other existing data initiatives.