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The Evolution of Clinical Data Management

The “entropy” of clinical trial data has been continuously growing for years. To maintain efficiently in terms of quality and time in clinical trial, CDM organizations have evolved meaningfully to develop data leadership working within a digital environment with more strategic and extended functions

Nowadays, for coding and the reconciliation process during the study, along with patient data collection in case report form (CRF), there are huge volumes of data are also processed electronically by multiple data collection instruments such as Interactive Voice Response System (IVRS) and electronic patient-reported outcomes (ePRO) systems to make it unique and dynamic.

To cope up with this changing environment in clinical research, Clinical data management (CDM) organizations continuously evolve to response properly. With the aim of providing practical insights, the evolutionary role and function of CDM departments mainly focused on major role players. In this challenging scenario, data managers are forced to be adapt new skills with a more strategic view about what is relevant and what is not to avoid failures in data integrity.

Data managers are key players to find new, shortened procedures to get systems, vendors and study data ready for sharing. They knows how study data should be processed, how systems should be configured and validated to get true data that can fit the protocol endpoints. In this view, the role titles are classified into five principal areas of responsibilities, like Data Management, including data coding, capture and cleaning, Data Standards, including governance, Clinical Systems, software, management and programming systems, such as eCRF, ePRO and Wearables; Central Data Review/Analytics as data ready for interactive visualization, data oversight or risk-based monitoring strategies and Innovation.

Currently CDM departments have evolved by revising skills, functions to be ready for rapid adoption of new processes, standards, technologies, and regulatory demands. Advanced machine learning technology replaces mundane and error-prone practices of traditional data management.Current reality around CDM is managing non-CRF data, electronically captured in cloud systems with full or functional outsourced services. Now CDM is performed by professionals with the analytical and management skills able to lead, not only the internal, but the external resources contracted out which is strategically cover the entire project development data lifecycle. Lastly, this article will make importance in implement organizational changes in order to create new habits and capacities with limited resources within a strong competitive environment.

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The writer of this content is a talented writer. ICRI is a reputed educational institute that offers many useful courses to the students including clinical research courses.

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