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International Clinical Trials

Streamlining Data Management in Clinical Trials

No matter how sophisticated the techniques applied, clinical trials cannot be successful without good quality, accurate, and comprehensive clinical trial data. Lacking this information, companies cannot ensure the safety and efficacy standards that will pass regulatory review.

Today, challenges still exist in making sure that all data collected meet these standards. Clinical teams often spend hundreds (or more) of hours managing the data cleaning process rather than having the opportunity to focus on the analysis of the data. While manual efforts to assure quality standards are not a waste of time, they are costly and can possibly lead to significant delays in meeting set milestones on the clinical trial timeline.

With data and data sources, i.e., wearables, expanding at warp speed, it is critical for CROs and sponsors to investigate better ways of managing data. This is where artificial intelligence (AI) augmented by machine learning (ML) can make a difference, not only in time savings and the ability for teams to collaborate across trial sites, but also to ensure accuracy and quality, which can lead to timely and successful regulatory approvals.

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Jennifer Bradford, PhD, is Head of Data Science for the CRO PHASTAR. She previously worked for the Advanced Analytics Group at AstraZeneca, leading the development of the REACT clinical trial monitoring tool, which she later customised and delivered to other sponsors as part of Cancer Research UK (CRUK). Within CRUK, and in close collaboration with the Christie Hospital, she worked on electronic data capture, app development, and wearables data analytics in the context of clinical trials. Jennifer has a degree in Biomedical Sciences from Keele University, UK, and a bioinformatics Masters and PhD from Leeds University, UK. 60406b4b
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