CLINICAL DATA SCIENCE (CDS)
Introduction
In simple words, Clinical data science is the data science in healthcare for improving the overall well-being of patients.
In SCDM point of view, clinical data science is the evolution of clinical data management (CDM).
Clinical data science revolves around 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 links the methods and insights of clinical data into data science.
Definition of clinical data science:
CDS is defined as 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.
What is clinical data?
The information gathered for the clinical research on micro-level (patient care) to the macro-level (broad applications within a health system).
Different ways for collecting the clinical data:
Differences between CDM and CDS
CDM: responsible for the life cycle of clinical data from collection to their delivery for statistical analysis in support of regulatory activities. Main focus of CDM includes data flow and data integrity.
CDS : Clinical Data Science broadens the focus by adding the data risk, data meaning and value dimensions for achieving data quality (i.e., data is credible and reliable).
Applications of CDS
Provide virtual assistance
Drug discovery
Genomics
Medical imaging
Track and prevent diseases
Monitor patient health
Predictive analysis.
How is clinical data science used in clinical trials?
It helps in expanding the criteria for patient selection.
It helps researcher in identifying the crucial patterns and potential trial complications.
More accurate and efficient clinical trials.
Safer drug production.
Conclusion:
Taking a new pharmaceutical drug or a medicinal product into market is a slow and costly process with frequent roadblocks, but with the help of data science we can reduce risks in the drug development. With the professional training, one can develop a data science team to suggest data science projects and facilitate collaboration and communication and to help create competitive advantages during the drug development.
References:
https://towardsdatascience.com/what-is-clinical-data-science-part-1-ebe135efb462.
https://scdm.org/clinical-data-science/#registration_topicbriefs.
https://www.google.com/search?q=data+science+in+simple+words&oq=data+science+in+simple+words&aqs=chrome .69i57j0i22i30l9.9942j0j7&sourceid=chrome&ie=UTF-8#imgrc=ZTqgLi-befGSfM&imgdii=TYQuczCc8ZGj-M.
https://www.propharmagroup.com/blog/clinical-data-and-clinical-data-science/
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