An overview of clinical trial data management, its standards and recent trends




Natural Language Processing, Machine Learning, Text Classification, Language Modelling


Clinical trials help pharmaceutical/biomedical organisations to bring the drug/treatment/procedure from the development stage to the end user stage. Normally clinical trials consists of four stages Phase I – IV. During the Phase-III which involves human beings as the subject of trial, requires significant amount of data to be captured, cleaned, transformed, compiled, audited, analysed and reported. Traditional data management techniques may not be sufficient to manage the current clinical trials as they are conducted at different location results in high cost of collecting, storing and processing of data. Hence there is need for advanced technologies such as online/web based data management techniques to increase the efficiency in collecting, storing and processing of data and also to reduce the cost related to the clinical trial data.  This paper starts with a brief overview of clinical trials, regulatory requirement, guidelines, and its different phases. It further discusses the traditional tools, techniques and standards to handle clinical trials data, software available for clinical trial data management and recent trends.


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How to Cite

IJSMI, E. (2017). An overview of clinical trial data management, its standards and recent trends. International Journal of Statistics and Medical Informatics, 5(1), 1-6.