Clinical Data Strategies for Navigating Complex Trials
Ebooks
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Clinical trial cycle times have seen little improvement over the last 30 years, despite huge advancements in technology. At the same time, these new technologies have increased the amount of clinical data that can be collected. While this data promises to yield richer insights and benefit patients, the sheer volume of data produced makes it challenging to realize its full value.
To address these challenges, adopting an interoperable clinical data infrastructure that drives collaboration between sponsors, trial sites, and third-party solution providers is mission critical. Having this foundation in place can transform how data is collected and utilized, ultimately improving productivity and accelerating cycle times. AI and ML technologies hold great potential to shape meaningful clinical data transformation, however, if there isn’t a modern data infrastructure in place, productivity gains promised with these emerging technologies will remain elusive.
In a recent panel discussion with several industry experts, we explored the continually evolving clinical trial landscape and how through strategic alignment of people, process, and technology, pharmaceutical organizations can lay the foundations for holistic, AI-driven clinical data transformation that boosts productivity and delivers meaningful return on investment (ROI).
Key Takeaways
- Increasing trial and data complexity is forcing companies to rethink their approach to clinical research.
- Sponsors, sites, and third-party vendors can foster efficiency by collaborating without compromising competitiveness.
- Advanced technologies can increase productivity and shorten cycle times, but must be carefully evaluated before adoption.
- Investing in AI technologies that quantifiably improve the patient experience is a path to getting long-term ROI.
