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Careful and frequent monitoring can ensure that risks are detected early-on during the clinical trial. Traditionally, monitoring was done through full-scale Source Data Verification (SDV) that involved manual verification of all collected data from source documents and records. Later, algorithm-dependent approaches like the random SDV approach, declining SDV approach, three-tiered SDV approach, and mixed approaches were implemented to reduce the manual effort and time involved in SDV.

While these techniques were an improvement to the traditional SDV process, the associated manual effort, time, and costs were still significantly high, catalyzing the collective effort of the industry and regulatory participants to look out for alternative approaches.

Lately, cloud computing and advanced software-as-a-service (SAAS) platforms have led to breakthroughs in RBM applications. One of the key benefits of leveraging next-generation technologies is efficient centralized risk-based monitoring and analysis of clinical trial data in real-time. Know more in this whitepaper.

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