All drug development procedures involve producing a large volume of documentation. Such documentation includes consistent, reliable and accurate data which is kept during and after the performance of a clinical trial. As explained in the ICH GCP Guideline 1.51, documentation (or source documentation) represents “all information in original records and certified copies of original records of clinical findings, observation, or other activities in a clinical trial necessary for the reconstruction and evaluation of the trial. Source data are contained in source documents (original records or certified copies).”
Good clinical data management depends on several factors: from having well-trained number of professionals, through conducting checkups upon the received data on a daily basis to, finally, having well-built and updated systems which also play a huge role in collecting and preserving clinical records. Results of appropriate clinical data management are: informative medical records of participants before the start of the study, during all of its phases and after its end-date; having data upon request about the progress and the reactions of the patient to the drug; and available information rewritten into a CRF and then formed as a clinical study report.
The main purpose of such Data Management Plan is to make you aware of all specifics which surround the process of data management itself. In addition to this, DMP also make you consider various significances of metadata generation, data preservation and data analysis before the very start of the clinical study. Taking all of these aspects into close consideration is vital as this will make sure that all the information collected is managed in the most efficient and purpose-driven way in the present time and in the future.
This way the collected data will be guaranteed to be:
– Organized
– In the correct format
– Comprehensive
– Appropriately annotated
Preparing DMP saves time, reduces additional resource-losses and unnecessary efforts as there will be no need for re-formatting, re-organizing or removing and adding details again. What is more, data management plans help researchers avoid duplication of scientific projects and reach their goals much faster.
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