Get Ahead of the Curve with Early Access to AI and Data Analytics Capabilities in Clinical Trial Development
Early access to cutting-edge analytics platforms gives life sciences companies a leg up on the competition.
Clinical trials are the backbone of medical advancement, but their complexity, high costs and lengthy timelines pose significant challenges. In recent years, Artificial Intelligence (AI) and data analytics has emerged as a game-changer in addressing these hurdles by giving definitive insights to trial designers at every step of the trial process. In the CRO environment, AI analytics platforms and solutions for clinical trials are quickly evolving as technology and know-how sharpens, offering greater efficiency, better insights and greater scalability in many areas of clinical trial development and execution.
But AI and analytics products take time to ready for the market, including time to realize the promise of value at scale. That’s why it can be more beneficial to life sciences companies to have early access to these AI solutions before they are productized. This means real working and customizable models others do not yet have. Not only can early access give clinical trials the edge they need to succeed without the wait, but they can also provide proven value when it’s time to utilize these analytics solutions on a larger scale, setting forward-thinking companies way ahead of the competition.
Interested in learning more about how to implement early access analytics into your clinical trial?
Right now, developing critical AI and data analytics solutions for clinical trials is about expertise, innovation and speed—identifying what problems AI can solve for the greatest impact. AI and data teams are pushing every day to find the highest-performing algorithms, configurations and prompts that will help those designing and executing clinical trials make faster and smarter decisions that get their treatments to patients faster.
By applying early access analytics to clinical trials, analytics teams and the visionary companies who use them can lead the way by pioneering scalable models for:
- Accelerated patient enrollment
- High-performing site location targeting
- Real-time monitoring and adaptive trial design
- Efficient data management
- Predictive modeling and simulations for enhanced outcomes
Three Considerations When Gaining Early Access to AI Solutions
1. While early access can be extremely beneficial, it is not without its caveats. First, the implication of working with early access analytics is that many of these AI/ Machine Learning (ML) models and associated analysis are still in a proof-of-concept or MVP stage. That means that they are not fully baked, and there is usually some data processing, code configuration and customized build of visualization that needs to happen before they are ready for use. Life science companies need to understand that while the capabilities to accelerate and streamline decision-making in clinical trials exist, some experimentation is needed. For some, that’ll be a hard pill to swallow. But for those who can tolerate a little unknown, early access can mean shaping the solution while harvesting the results of cutting-edge technology with limited risk much earlier. This is especially important when competing trials are racing to meet goals before the competition does.
2. Experimentation and successful development in early access analytics is best achieved through collaboration. As with all AI and data analysis, early access analytics partnerships see the best outcomes when they embody the philosophy of having a “human in the loop.” That is to say, success often comes with partnering deeply with subject matter experts (SMEs), customer teams or analysts who have an incredible breadth and depth of operational and strategic experience. This helps focus developmental models and drill-down on pain points or areas that are critical to the success of a given study. Human collaboration is probably the most critical success factor, contributing not only to creating meaningful insights but also putting them into the context of broader decision making.
3. When companies test-drive early analytics, it gives them the opportunity to be part of shaping them, continuously improving them early and feeding the business context back into their development efforts. That means when companies move beyond the early access phase into at-scale enterprise adoption, they can confidently assume that the value will extrapolate, avoiding the POC trap where the majority of models fail to breakout from.
When looking for the right partner, determine if early access to analytics is embedded into every clinical trial and seamlessly integrated into their services. When a company has specific clinical trial needs that could benefit from early access to advanced AI insights, the experts will work with SMEs to develop bespoke solutions for their trials.
It’s an exciting and generative time that helps customers get ahead in their trial development. If your company is ready to roll up its sleeves and be part of the innovation process, using early access analytics can be the thing that takes your clinical trials to the next level, laying the foundations for you to adopt these ways of working when these AI solutions scale.
Contributors
Gowtham Sreenivaasan | Director of Data Science
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