Phastar and Medrio have been partnering together on trials for the past 3 years, culminating in a unique way to visualize clinical trial data. Through the use of Medrio’s API, Phastar has built a data visualization tool that reduces risk and allows for complete study oversight. In this podcast we’ll discuss the need for data visualization in clinical trials, how Phastar has continued their trials during a global pandemic, and what their thoughts are on the future of how we collect and view data.
Senior Director of Data Operations | Phastar
With more than 30 years of experience in clinical data management, Sheelagh has directed and delivered projects in all phases of clinical trials across numerous therapeutic areas and data collection platforms. Sheelagh holds a BSc in pharmacology and doctorate in pharmacokinetics from the University of Bath. She has led PHASTAR’s Data Operations group since 2016.
Chief Operating Officer | Medrio
Christina Hughes is the Chief Operating Officer of Medrio. Christina has over 20 years of experience in the clinical trials industry focused on delivering high quality solutions through the innovative use of technology. Prior to joining Medrio, Ms. Hughes held executive positions at Bracket (now Signant Health), AiCure, and ERT. Ms. Hughes holds a BS from Yale University and an MBA from University of Pennsylvania’s The Wharton School.
Why data visualization is so critical in clinical trials? [5:00]
There’s a variety of other visualizations. You can have progress charts, pie charts, frequency charts. They’re a great way of being able to quickly assimilate data and identify performance issues that allows us to act more quickly and on an ongoing basis. And I think people who are working now within clinical trials really appreciate that upfront, real time visualization of the progress of the trials. And if we think about the current situation with COVID, where there’s been a need to perform central and remote monitoring, having these visual representations allows that work to be carefully targeted and being able to proactively identify and perhaps monitoring at sites that are at risk and we can work with our clinical teams to target those corrective actions much more quickly.
How you have implemented your data visualization tool? [7:40]
From the start, we had to identify key elements for visualization. We didn’t want to overload the user with so much information that it becomes a self defeating. Our approach was to identify key metrics for those essential important items, relative to the study.
We hear a lot about once we come out of the pandemic and what the new normal will look like. What do you think will change in the way that we look at data and data collection? [14:05]
I think a lot has happened already. There are over 300,000 consumer grade health care apps out there. And approximately 100 of them are probably digital therapeutic technologies that can be used in studies. And we’ve seen a huge uptake in adoption of this since March, whereas it was a talking point back at the beginning of the year. It’s certainly an actionable point now.
Data security and regulatory requirements is an important issue. [16:00]
As we move forward with rapid data collection and AI and ML techniques, machine learning techniques, we do need to just make sure that we enhance the critical thinking elements within the clinical trials, and ensure that we have quality by design so that we’re asking good questions upfront, and we’re crucially making sure that we understand the answers. There’s a lot of data out there, and yes, visualization is a great tool to be able to represent that, but we still need those skills to be able to understand what’s in front of us.
If you look in your crystal ball, five years from now, where do you see the industry heading with regards to how we collect and visualize data? [17:45]
Oh, good question. I think there will probably be a consolidation of essential data collection… There is, and there will be a further shift to much more upfront design of studies. How we’re going to collect them, what we want to see from them, and getting that data in in real time will bring down the development time, the access to market times for drugs. So that’s a really exciting advance that I see happening in the next few years.