First, Clinical Data Science is not to be confused with the general discipline of Data Science which applies across multiple industries. From an SCDM point of view and as expressed in the third reflection paper, Clinical Data Science is an evolution of Clinical Data Management. 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 Science can be defined as the strategic discipline enabling the execution of complex protocol designs in a patient centric, data driven and risk-based approach ensuring subject protection as well as the reliability and credibility of trial results.
In contrast, Clinical Data Management is responsible for the life cycle of clinical data from collection to their delivery for statistical analysis in support of regulatory activities. Clinical Data Management is primarily focusing on dataflows and data integrity (i.e., data is managed the right way). Clinical Data Science broadens this focus by adding the data risk, data meaning and value dimensions for achieving data quality (i.e., data is credible and reliable). Clinical Data Science also expands the scope of Clinical Data Management beyond the study construct by requiring the ability to generate knowledge and insights from clinical data to support other clinical research activities which requires different expertise, approaches, and technologies.