Development of Core Competencies in Clinical Data Science Graduate Education: A Follow-On Tutorial to From Clinical...

Area of Science:

• Clinical Data Science
• Translational Science Education

Background:

• The field of clinical data science demands a hybridized knowledge base integrating biostatistics, biomedical informatics, clinical medicine, and regulatory science.
• Existing educational models require updating to meet the interdisciplinary demands of modern clinical data science.

Purpose of the Study:

• To present a structured framework for designing a competency-based curriculum in clinical data science graduate education.
• To align graduate education with the evolving needs of translational scientists.

Main Methods:

• Identified core competencies through expert review and alignment with professional society standards.
• Utilized iterative validation from faculty and practitioners.
• Mapped 10 courses to professional standards and distilled them into five overarching competencies.

Main Results:

• Developed a novel curriculum framework for clinical data science graduate programs.
• Defined five key competencies integrating traditional and contemporary educational strategies.
• The framework integrates biostatistics, biomedical informatics, clinical medicine, and regulatory science.

Conclusions:

• The proposed framework provides a robust model for clinical data science graduate education.
• This competency-based approach prepares translational scientists for the future of data-driven healthcare.
• The framework is adaptable for other disciplines within translational science.