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The Significance of Data Analytics in Clinical Trial Data Management

By CIOReview | Tuesday, June 25, 2019

Organizations are leveraging data analytics and data management tools to assess the available clinical data and enhance the quality as well as the accuracy of the trial outcomes.

FREMONT, CA – The emergence of big data and real-time analytics has revolutionized operations across various industries, including the healthcare sector. It has enabled medical organizations to enhance population health management and precision medicine initiatives. However, over the last few years, the pharmaceutical industry has witnessed a steady decline in success rates in the drug development sector.

To drive efficiency and cost-effectiveness, it has become imperative for organizations to adopt progressive strategies to augment the health systems. One such strategy that can assist the medical sector is data utilization. Clinical trials generate over three million data points. The organizations can leverage modern analytical tools to evaluate the troves of clinical data throughout the lifecycle.

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Often, the drug discovery and development stages end up in failure, with data as the only tangible output. Hence, leveraging this data is critical to increasing the success rate. The sophistication involved in the clinical trials has made data collection that much more difficult. Also, many tests fail to reach the patient recruitment milestones, resulting in monetary as well as research setbacks.

The significance of patient retention during the trials has forced sponsors to focus on enrollment. Organizations have also come to leverage data analytics and data management tools to assess the available clinical data and enhance the quality and accuracy of the trial outcomes.

To eliminate the preventable errors that can crop up during the trial phase, organizations have introduced data review strategies. The assessment of the drug application submissions for new molecular entities by the FDA showed that most of the failures could be attributed to preventable errors during drug dose selection, endpoint evaluation, and result drawing. 

The identification and prevention of errors can enable the organizations in passing the applications of more sponsors during the first stage instead of going through the expensive alterations. In this regard, data analytics can provide organizations with valuable insights to drive success rate and develop safe, effective drugs. The sophistication involved in targeted clinical trials has made it difficult to find and retain sponsors. Hence, organizations have to incorporate advanced analytical tools to analyze the clinical trial data and augment the process. 

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