The Year Ahead: What Does EDC Look Like In 2024 and Beyond?

Over the past year, all of us working in clinical research have seen a significant increase in the number of sources we can pull data from. This rapid growth can be overwhelming – obviously we recognize that as the number of data sources increases, so does the volume and diversity of data. It’s perfectly okay to not know yet how to use all of this data, but we do need to reflect upon our ability to effectively collect and organize this data in ways that work for current trials and make it possible to discern potential new uses for all of this data moving forward.

The coming year, 2024, promises to be one of transformation. As an industry, it is important that we analyze the ways we work. Do our existing processes and workflows meet today’s increasingly complex data handling requirements? Do we have the right technologies in place to help? What kinds of training do our people need? The following are some thoughts from the pros behind TrialKit that are meant to provide a look into what we can expect in the year to come. 

The Traditional Definition and Role of EDC will Evolve

Modern studies will increasingly require EDC systems to move away from traditional, site-based data capture. Collecting data accurately and efficiently from sites will continue to be important, but systems must also be in place to integrate data from the broader range of data sources including wearables, mobile apps, electronic health records (EHRs), and more. This integration is critical for providing a comprehensive view of patient health and treatment outcomes.

Are We Ready for Real-Time Data Collection and Analysis?

Some data sources allow real-time data collection. As soon as the data is gathered from the source, it can be immediately available in the study database. We need to make sure we have tools that can integrate and format data in as close to real-time as possible so that it can be analyzed quickly as well. Whether utilizing human team members or technology that can speed up the detection of issues and identify important trends, getting the data fast isn’t providing maximum value for our studies if we cannot also quickly determine what that data is telling us.

Embracing Artificial Intelligence and Machine Learning

AI/ML are set to play a pivotal role in analyzing complex datasets. In fact, with the increasingly vast amounts of data that are coming into studies, AI/ML will be vital to keeping study timelines – there will just be too much data for humans to stay on top of with any level of thoroughness. These technologies will be crucial for quickly identifying patterns and insights that human analysts might miss or simply not have enough time to discover.

Enhanced Patient Engagement, Satisfaction, and Retention – and Better Data

Collecting data directly from patients through mobile apps and/or wearable devices, inherently, encourages a lot of patient engagement. While this may increase patient burden in some ways (e.g., asking them to enter patient-reported outcomes into a smartphone app), it can greatly reduce their burden in other key ways. For example, using a smartphone app to give eConsent, submit ePROs, and take part in video visits may mean that the patient no longer needs to commit the time and energy to make as many visits to the clinical trial site. These kinds of decentralized (DCT) approaches can be attractive to patients considering enrolling in your trial, and the ability to interact with clinical team members from a study app can provide a lot of comfort for participants over the course of a study. Researchers benefit as well, as these technologies allow for data collection much more frequently than just during site visits. Getting data directly from the patient results in more accurate data, and getting more data faster allows study teams to discover and solve problems before they can grow and jeopardize study outcomes.

Yes, It’s a Lot of Change, But With Growth Comes Improvement

It is time to embrace the growing number and diversity of data sources so that we can apply the necessary workflows and technologies to make real-time analysis possible. The way we work is changing, but these changes hold the promise to significantly reduce both time and costs associated with drug development. Huge volumes of data, backed by the technologies and approaches needed to quickly derive valuable insights from that data can help us deliver new, safe treatments to patients faster than ever before.  

For more information about clinical trial data management, AI/ML, DCTs, and other industry trends, or to find out how TrialKit can help you integrate new technologies, approaches, and data sources into your trials, visit

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