Leveraging Clinical Trial Data In Real Time To Effect Change And Mitigate Risk

By Nina Anderson, inSeption Group

Properly applied, data analytics can improve clinical trial data quality, as well as potentially reduce the costs and time investment associated with traditional trial monitoring. But advanced analytics are critically underutilized in the pharmaceutical industry, particularly in early-stage clinical trials, due to concerns over the investment, in-house expertise, or regulatory scrutiny, among other worries.[i]

However, pharmaceutical sponsors face a multitude of challenges that could be addressed through better understanding and application of analytics and data science. These obstacles include collecting high-quality data, managing the volume of data produced, and analyzing that data. Sponsors also are inhibited by the length of time needed to clean and monitor data, plus inconsistent access to meaningful, accurate data visualizations. Through better understanding of these challenges and the solutions available to overcome them, the industry can progress to more efficient and well-managed clinical trials whose results are trusted by regulatory authorities.