The Rising Need for Data on Demand in Clinical Trials

When you speak about data and clinical trials most people think it’s about the data you generate throughout the trial. It’s true that data collection and management of each study are crucial for ensuring the safety of participants but also the information on the endpoints of clinical trials is the real purpose of research. Thankfully technology exists that helps us get real-time access to this data in order to support key decision-making while running a clinical trial.

Yet, data has another important place in the clinical trials arena which is quite underserved at the moment. Each feasibility study is powered by numbers and statistics from previous experience, regulatory landscape, and demographical dynamics. This data is still fragmented and reliant on human resources which makes most feasibility and patient recruitment planning unscalable. Not having the full picture of patients, regulations, markets, and sites leads to protocol amendments, delays in patient recruitment, and challenging market access. 

How can access to data-on-demand fix this?

Clinical trials are a repeatable process if you look at the type of operations included. Protocols are what makes them unique and protocols have a lot of moving parts. These parts are what we need to assess prior to starting a clinical trial if we want to be on budget on time. 

Sometimes, you should focus more on some while others are straightforward. Translated into data: Sometimes you will need to have access to granular information about startup timelines, wherein other cases you will need to understand better the unmet need of patients in a country with high standards in healthcare. The source of these data points is different. This means your data needs will vary depending on the protocol you are working with. 

Imagine now you have access to all the experts and all the databases that provide you with a 360 degrees overview of the clinical trial landscape. Imagine you can get information in a fast and scalable way depending on your needs. What if you can build scenarios and contact the people you need without any extra efforts only when you request that?

This will help you foresee how your clinical trial might look once you start it in a given location, with a given population, and give you options to flexibly work around your protocol or plan for action in case you need to prevent delays. This is what we call data-on-demand.

Does data-on-demand exist?

Like the Germans say Jain, or otherwise translated yes and no. CROs and sponsors with years of experience in research and local offices can rely on their local experts in combination with their centralized databases powered by previous clinical trials (It’s another story how many of them really have structured and centralized their data in a scalable way). 

These organizations have feasibility and clinical operations teams that reach out to their local colleagues and excel files in order to get at least partly the data they need.

Why is this not enough?

First, like I mentioned earlier, centralized feasibility and patient recruitment databases is not something most companies have. Second, reaching out to local colleagues (especially if you are a CRO) is an extremely difficult process. 

On the surface, it’s just about sending an email and getting a response. But in practice you need to be very diligent with your communication as most of the people you will speak to don’t have feasibility research as a top priority. This means: it takes you time and double the effort to collect everything you need. 

I am not even mentioning the amount of time you are going to need to structure this information in order to put it into actionable insights to feed in your clinical trial planning algorithms.

Can this data-on-demand be automated and scalable?

We believe YES! My team and I have been working on algorithms for structuring clinical trial information and combining it with data about regulations, patient populations and communities, investigators’ background experience, etc. 

To the data and algorithms, we’ve included a powerful network of local experts, whose job and priority is to provide you with their insights on time so that you can have the data in time for the next projections and budget calculations. 

What we are working on next is to upgrade our capabilities with modeling algorithms to be able to support the analysis depending on the set of data you have and the types of challenges your protocol might come with. It’s all wrapped up in a system called TrialHub which we are already piloting with some of the best feasibility teams out there to automate their processes and help them be more agile and efficient when planning new clinical trials. 

The best part is that this system is not yet another platform that you need to upload or subscribe to and do the analysis and calculations on your own. It works on a demand basis and creates Reports ready to go to help you see the full picture and plan your trial in the best possible scenario. 

If you are one of the people trying to make clinical trials more efficient from the very beginning, I would love to share more about our future plans for TrialHub.

You can reach out to Maya on LinkedIn:


Scroll to Top