7 Nov 2019 Blog
Everything you need to know about AI in Clinical Trials – Part 2

In Part-1 we discussed the problem statement and the focus areas for clinical development. We also concluded that quick access to relevant information decides the efficiency of a clinical trial. Let us now see how AI can help.

An end-to-end clinical data management platform powered by artificial intelligence is the right choice for streamlining, overseeing and managing trials in a coordinated way. With the help of a platform, study stakeholders can have defined role-based access and personalized views to data which makes it easy to monitor KPIs and stay on top of their tasks.

AI can predict risky sites

AI can predict risky sites by matching real-time data to historical benchmarks like drop-out rates and flag processes accordingly. Early red-flags makes it easy for Managers to keep the clinical trial on track and within budget. Such ability to find insights from big data would have been a tedious task for humans and would have consumed a lot of time but not for an AI platform. These insights would be on the dashboard by the time you make yourself a cup of coffee and this becomes instrumental in meeting the timelines and getting the drug faster to the market.

AI can tackle the need for Source Data Verification (SDV)

Source Data Verification (SDV) is one of the most important but time-consuming activities taken up during a trial. With all the constant manual checks to ensure compliance, it is also a major contributor to the cost of a trial. An AI-enabled platform can keep a tab on all types of inputs including data collected from patients, site performance data and completion metrics on Source Data Verification. The platform can run automated checks regularly to ensure that the processes comply with regulatory standards.

The platform makes it easy to manage data by standardizing the data from all sources into a singular regulatory format and storing them to be accessed as a singular source of truth. KPI’s can be predefined which will be tracked by the AI platform to identify potential protocol deviations and recommend course correction accordingly. It also records any deviations in the data and stores it to understand the pattern and detect much earlier in the future. After all, AI is a self-learner.

AI can collect and share information in real-time to improve the site and patient outcomes

AI provides users with access to data in real-time. This data is harmonized and is ready for analysis. The user can use a self-service visualization tool to keep a visual track on study milestones and checkpoints and immediately address any potential red flags. AI can also reduce patient dropouts by identifying the sites with access to the most relevant patient population.

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