Development of a REDCap-based workflow for high-volume relational data analysis on real-time data in a medical...

Area of Science:

• Medical Informatics
• Clinical Data Management
• Health Data Science

Background:

• Increasing volumes of clinical data necessitate efficient storage and analysis strategies.
• Limited resources require technically feasible and economically sustainable data management solutions.
• The need for large-scale analyses in medical departments is growing.

Purpose of the Study:

• To develop a widely-usable workflow for data collection and analysis in medical departments.
• To enable high-volume relational data analysis on real-time clinical data.
• To create a cost-effective strategy using open-source software.

Main Methods:

• Developed a workflow using REDCap for collaborative data collection and Python with SQLite for data retrieval and storage.
• Selected software based on availability, reliability, cost, and team expertise.
• Utilized a prostate cancer patient database for a sample project.

Main Results:

• Successfully implemented a data strategy for collecting and analyzing large volumes of standardized relational data.
• REDCap facilitated secure, collaborative data collection with low technical difficulty.
• Python and SQLite enabled real-time data retrieval, complex analysis, and preservation of data relationships.
• All implemented software libraries were open-source and cost-free.

Conclusions:

• A REDCap-based workflow effectively supports high-volume relational data analysis on live data.
• The implemented strategy is suitable for medical departments with limited resources.
• Open-source software ensures cost-effectiveness and accessibility for the data workflow.