Drug development is in the midst of a digital transformation.  The pandemic has amplified existing trends towards decentralised trials, greater volume of data from multiple sources, and use of powerful technologies such as cloud computing and artificial intelligence. Clinical research analytics –which still relies heavily on traditional programming methods to extract insights and results from clinical trial data- is also ripe for change. 

Yet, as we work to improve how we analyse clinical trials, we must navigate two opposing forces. On the one hand, trial investigators and drug development leaders are driven to speed up ‘time to insight’ – by recruiting patients into trials swiftly and getting access to data more quickly to inform decisions. These stakeholders are rightly excited by the possibility of faster clinical research, leading to expedited approvals and improving patients’ lives. 

On the other side is the highly regulated nature of the industry. While there has been a concerted effort from regulatory agencies such as the FDA to be ‘innovation-enabling’ in recent years, the reality remains that the pharmaceutical industry depends on robust governance and ‘crossing the Is and dotting the Ts.

As an essential cornerstone in the operations of clinical trials, our analytics capability needs to grow and develop to meet these twin challenges.

In this guide, we’ll discuss how to get vital insights to investigators and medical stakeholders more swiftly, while at the same time ensuring adherence to due diligence requirements and high quality. We’ll touch on the following topics:

  1. Improvements in standardisation
  2. Automation offers potential for a step-change
  3. Expediting with expertise not workforce efforts

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