It is critical to acquire the right data to deliver the right insights to drive the right actions in order to shape the future of clinical monitoring.

Clinical trials are undergoing digital transformation in every aspect. Over the past few years and further accelerated by the COVID-19 pandemic, Risk Based Quality Management (RBQM) – in which trial management can adjust and allocate monitoring resources based on the level of risk identified for a specific trial or site – has utilized this digital evolution to emerge as a more efficient way to facilitate trial delivery. RBQM strategies optimize data flows, increase oversight, enhance data quality, and improve subject safety. 

Vast amounts of data are collected through clinical studies. A lot of time and effort is spent sourcing this data from disparate systems, as well as standardizing and cleaning it for analytics, rather than generating the wealth of insights this data could provide if companies have the time to comb through it. However, the focus always needs to be on quality and efficiency. Advanced analytics tools using artificial intelligence (AI) and machine learning (ML) enable research teams to make sense of this information. To keep up with this digital transformation, sponsors need better data management technologies that utilize AI and ML to not only accelerate data collection but also streamline the review and decision-making process. 

It is critical to acquire the right data to deliver the right insights to drive the right actions.

The Right Data 

One of the biggest challenges in the realm of clinical trials is data spread across disparate systems with minimal interoperability. Point solutions hinder centralized access to data and restrict efficiency in decision-making. This makes it difficult to scale when organizations grow. Trial data captured in disparate systems prevent a cohesive view of the subject. To fully review the critical data at a subject level, monitoring teams must have access to a single data profile of the subject – sourced from several systems.

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