In this digital age, advanced analytics and AI have become a true necessity. When correctly applied to fit-for-purpose data sources, life science companies can better analyze clinical trial data and real-world data and build predictive models in ways that traditionally have not been possible. While many life science companies are making great strides in applying analytics to the data from their own trials, larger datasets are needed for many analytical applications.

These extensive cross-industry datasets play two key roles. When used as a complement to a sponsor or CRO’s own datasets, first, they form the engine that drives predictive models to provide life science companies the insight needed to gain a competitive edge, to lead, to succeed, and to outperform. From providing insight into patient populations, treatment outcomes, and disease understanding, to prioritizing sites and investigators based on trial needs, to rescuing studies that are in danger of not achieving required performance, to creating a more accurate inclusion-exclusion criteria based on patient availability and site performance, or to predicting trial dropout rates, the actionable insights generated are limitless.

Second, using insights generated by data from patients who participated in prior clinical trials while planning future trials allows sponsors to achieve their scientific goals and reduce the burden on study staff, investigators, and patients. Ultimately, this is one of best ways for life science companies to benchmark the success or failure rates of their clinical trials and improve the overall patient experience.

In this research, we get behind-the-scenes into this insight engine - the dataset - and explore the following topics:

• How deep and broad should these cross-industry datasets be? How can life science companies access such datasets?
• What are some of the qualities of a dataset that make it more suitable for generating actionable insights? How do you make it fit-for-purpose?
• When it comes to better predictive models, is more data better? When is the point of diminishing returns reached?
• What role does this data play in improving the experience of everyone working in clinical trials, especially patients?
• How do you shift the organizational mindset to become more data-driven and embrace cross-industry clinical trial datasets?

Together, we expect to hear compelling industry examples, use cases, and best practices. Based on past experience we expect you to leave with fresh ideas and takeaways, along with valuable new connections with industry leaders. Frost & Sullivan will produce an exclusive Industry Insight, capturing group thoughts and commentary from this event.