Clinical Data Science

Clinical Data Science (CDS) is defined as the strategic discipline enabling data driven Clinical Research approaches and ensuring subject protection as well as the reliability and credibility of trial results. Clinical Data Science encompasses processes, domain expertise, technologies, data analytics and Good Clinical Data Management Practices essential to prompt decision making throughout the life cycle of Clinical Research.

Clinical Data Management (CDM) is responsible for the life cycle of clinical data from collection to delivery for statistical analysis in support of regulatory activities. CDM is primarily focusing on dataflows and data integrity (i.e. data is managed the right way). CDS expands the scope of CDM by adding the data meaning and value dimensions (i.e. data is credible and reliable). CDS also requires the ability to generate knowledge and insights from clinical data to support clinical research which requires different expertise, approaches and technologies.

Reflection Papers on the Evolution of Clinical Data Management to Clinical Data Science

  • Part 1 – The impact of the Clinical Research industry trends on Clinical Data Management (June 2019) – LINK
  • Part 2 – How technology will enable the evolution of CDM to Clinical Data Science (March 2020) – LINK
  • Part 3 – Evolution of CDM Role toward Clinical Data Science (August 2020) – LINK

Applied Clinical Applied (March 2020) – The Clinical Data Manager: A Roadmap for the FutureLINK (An article including insights from the SCDM Board)

We share the SCDM Vision:

“Leading innovative clinical data science to advance global health research and development”

Our Focus:

Clinical Data Science

Our Mission:

“Demonstrate SCDM Industry Thought-Leadership by defining SCDM roadmap toward Clinical Data Science”

Our Themes:

People, Process, Emerging Technologies, Regulations and Novel Clinical Research approaches

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