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Clinical trials can shift to a better operating model based on dynamic approaches for monitoring studies, as well as sites and study participants.
The life sciences sector is at a unique inflection point. Advancements in medicine and emerging demands have opened new, potential markets. However, the absence of a centralized information repository with data present in diverse systems and formats and reliance on human memory in selecting clinical trial sites and the execution process impedes market deployment. There is a prevalent need for a data science-led solution that leverages next-generation technologies such as AI/ML to provide predictive use cases, like adaptive monitoring for quicker data-driven decisions and greater market success.
The pharma and contract research organizations (CRO) are facing multiple challenges and inefficiencies due to predetermined and fixed schedules of site monitoring. The COVID-19 pandemic and the disruptions caused by it have forced organizations to reimagine how they engage with and support investigator sites. Traditional site monitoring methods use lagging indicators that leverage historical data and fail to provide an accurate assessment of site performance. Manual and time-intensive resource allocation and planning add to the overall workload and inefficiencies.
The following paper details the utilization of AI (artificial intelligence)- based algorithms to assess participating site performance and perform predictive analytics for identifying site workload and site risks. Additionally, it discusses about key risk indicators (KRI) and key performance indicators (KPI) for clinical research professionals at the sponsor organization. It also describes how clinical operations can shift to a better operating model based on dynamic monitoring of clinical trials, sites, and study participants.
Please visit the page below to read the paper:
Swipe LeftSwipe RightLeveraging next-gen technologies such as artificial intelligence and machine learning can empower sponsors to make data-driven decisions in an expedited manner. When you actually use artificial intelligence to predict site and risk workload, this proactive and predictive approach clearly augments the way and empowers end users to perform effective site monitoring. This, in turn, can be instrumental in bringing drugs faster to the patients in need.”
SAURABH DAS
Head, TCS ADD™ Analytics and Insights, TCS
© Copyright [2024], Tata Consultancy Services Limited. All Rights Reserved. Document ID CGTC010674
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