From clinical data management to clinical data science: Time for a new educational model

Area of Science:

• Clinical Data Science
• Biomedical Informatics
• Health Data Analytics

Background:

• Growing need for specialized skills in managing and analyzing clinical research data.
• Lack of a standardized, comprehensive graduate program in clinical data science.
• Interdisciplinary nature of clinical research requiring integrated knowledge.

Purpose of the Study:

• To propose a blueprint for a graduate-level curriculum in clinical data science.
• To outline a structured program covering data measurement, acquisition, care, treatment, and inferencing.
• To provide a reproducible model for academic institutions.

Main Methods:

• Designing a curriculum with five core courses and five research courses.
• Incorporating foundational knowledge from biostatistics, clinical medicine, biomedical informatics, and regulatory affairs.
• Selecting elective courses to provide specialized training.

Main Results:

• A detailed curriculum structure is presented, including core and elective coursework.
• The program is designed to be adaptable and reproducible by other institutions.
• The curriculum aims to bridge foundational disciplines for a unified approach.

Conclusions:

• The proposed curriculum will equip graduates with essential skills for clinical data science roles.
• This program will prepare students for academic, industry, and government research positions.
• It establishes a foundational knowledge base for the emerging profession of clinical data science.