One of the most important aspects of data collection and analysis in a clinical study is creating quality assurance sources which will ensure data integrity. As the globalization of clinical research increase, there is a need for a timely recording of events as they unveil without any unnecessary delays which may end up adding additional cost to a research study, in question. Hence, incorporating the electronic data capture (EDC) system to data management portfolio is a necessary means to overcoming the cumbersome workload in a clinical study. The “EDC/eClinical approach, shifts the burden of work to predeployment, and from reactive per instance to proactive per project”, which simply compared the burden of work in a traditional data collection system to a modern EDC system (Pratt, 2006). The comparison indicated that the burden of work in an EDC system increases significantly at the designing (planning stage) and development stage and then declines at the deployment, managing, closing and locking stage of data collection (Pratt, 2006). In contrast, in the traditional data collection setting, the burden of work significantly rest somewhere within the deployment, management and closing stage and then gradually declines at locking stage (Pratt, 2006).

Hence, one of the merit of the EDC system seems to enhance an easy and efficient data capturing in a proactive and systematic approach (systematically organized pre-trial data collection set up), and in re-engineering work process to attain an optimal effectiveness and investment return (Pratt, 2006). Furthermore, in clinical research time is of the essence and time are literally money. The more time it takes to collect and analyze data from a study, generates more and unexpected cost in the study and becomes an unavoidable burden to the approval timing of the investigational products in question, and may negatively affect or delay the approval process or timeline. For this reasons, EDC system introduces a significant cost effective measures by reducing the redundancy of data collection and analysis (Pratt, 2006).

Moreover, it is obvious that, with the traditional data collection system data, managing personnel’s work-load is lengthy and tedious. On the other hand, with the EDC setting, the work- load is dramatically reduced and create a more efficient environment than world be available in a traditional setting in terms of data and information flow. Ultimately, there is a short time frame to complete work-flow within an EDC setting than in a traditional data collection work-flow (Pratt, 2006).

Reference

Pratt, T. (2006). Data management: R.I.P. or brave new world? Applied Clinical Trials, 15(10), 58–64. Retrieved from http://proquest.umi.com.ezp.waldenulibrary.org/pqdweb? did=1165751941&sid=1&Fmt=3&clientId=70192&RQT=309&VName=PQD