Key Takeaways from the 2023 SCDM AC Leadership Forum

2023 SCDM AC与领导力论坛的要点

By Eboni Russell, Executive Director and Global Head of Clinical Data Management FSP

To better understand how emerging artificial intelligence (AI) and machine learning (ML) capabilities can disrupt and empower change in clinical data science, the Society for Clinical Data Management (SCDM) recently held its annual conference including the strategic leadership event, the SCDM Leadership Forum.

Eboni Russell, Executive Director and Global Head of Clinical Data Management FSP at Fortrea, was invited as one of the thought leaders alongside regulators from the U.S. FDA and the MHRA to facilitate a panel discussion about current regulatory perspectives on emerging AI/ML capabilities. After the event, she shared her thoughts about the thought-provoking topics discussed.

What captured your attention at the SCDM Leadership Forum?

The event had a lot of focus on technology within our industry and discussions on how we can leverage it appropriately. There still needs to be human involvement, or as they call it, "human in the loop" to ensure that these tools provide outcomes that make sense, are relevant and able to be trusted. To work with these technologies and solutions requires employee education.

We also need to ensure that regulators have an awareness of how people are using these tools. Regulations are still going to be in place, but how we're executing studies will change. From a data management and industry perspective, we need to be able to explain the role of these tools and technologies as well as how we, as industry professionals, are assessing risk while producing outcomes on an ongoing basis.

How do you think AI/ML can help increase automation without compromising data integrity and reliability?

To me, it's about making sure that we are telling a story with our data, which includes the beginning, the middle and the end. By showing how you reached your results, you can enable transparency throughout the process and demonstrate quality delivery and data integrity. No matter what technology we're using, or what types of processes we're implementing, we still have to make sure the data tells the story and ultimately ensures patient safety. It's no different than the documentation that tells a story during regulatory audits and inspections.

How can AI/ML methodology support early signal and fraud detection in alignment with regulatory guidance?

I've heard a lot of conversations around these technologies helping us detect early trends and outliers. If applied correctly, these early signals can help us mitigate risks. We need to make sure the technologies are detecting true signals, and, if they are, have a plan throughout the course of a trial or program to assess the signals and address them.

In terms of these technologies, what do you envision for cross-functional opportunities to enhance collaboration—without duplicating efforts?

I think of cross-functional representation in the context of a data management study team. The impact of the team's decisions affects other functions, such as statistical analysis, medical writing, clinical operations and medical and safety. We all need to be thoughtful together.

What used to be very piecemeal, in the so-called "paper days," required a lot of handoffs. But now, with data at our fingertips, everybody's looking at the same data, at the same time, but for very different reasons. The last thing you want is a duplication of effort, or instances where we're not aligned on a piece of data. This requires being thoughtful to our approach to determine roles and responsibilities.

What does AI/ML mean for transforming job roles as well as attracting and retaining talent in clinical data management?

We need to be both intentional and thoughtful about how roles will evolve over time with the inclusion of more advanced technology. Some roles will either be eliminated, or they'll look vastly different than they do today. I believe we need to have an openness around this dialogue and expectations across the industry to determine where we need to go and ensure that we are not only retaining the talent we already have, but also continuing to attract the right talent. With these roles, we must also determine how we continually evolve as technology becomes a big player and still have an attractive career development pathway for our people in the industry.

Looking ahead, what’s most exciting for you in the AI/ML space as it relates to clinical research?

I'm most passionate about employee engagement and ensuring that people still see themselves as valued. Our responsibility goes beyond delivery, especially for those of us that are in leadership. We must take care of people and the opportunities ahead of them, even though it may look different than what it looks like today. It's about embracing the change.

Meet Eboni

Eboni Russell is leading Fortrea's evolution from Clinical Data Management into Clinical Data Science as we partner with pharma and biotech to deliver clinical trial data that benefits patients and communities around the globe. She has a bachelor's degree in Pharmaceutical Science from Purdue University and has been in the industry for over 20 years, primarily in roles related to Clinical Data Management and leadership.

Learn more about Fortrea’s tailored approach to Functional Service Provider (FSP) services and solutions spanning clinical and site operations, data management, biostatistics, programming and safety. Visit the Fortrea FSP website or contact us here.