Real-time data (RTD) offers exciting opportunities to empower clinical trial teams with the trusted, actionable insight that is vital for successful and meaningful research. It allows sponsors to react quickly to bottlenecks and potential issues like deviation from study protocol––before they jeopardize trial integrity.

But, with teams already grappling with data management and analysis challenges, there are significant hurdles which need to be overcome––including analysis capacity, data integration and consistency––if the true potential of RTD is unlocked.

In a modern data environment, sponsors need to move away from old, resource-intensive and inefficient processes and adopt unified, holistic approaches to data management and analytics. The most effective way to do this is via analytics platforms which harmonize data into stable, continuous models which support reusable, scalable oversight workflows. Use of such platforms can streamline previously resource-heavy processes, improve decision-making and help sponsors not just cope with the complex data landscape of today but thrive during the next evolution in clinical trials.

Data delays, fragmentation, and a lack of scalability

The volume of data collected in clinical trials is growing. Recent analysis found 5.9 million datapoints are now collected on average per phase III protocol––up 11% annually since 2020.¹ This is putting a strain on data managers––more than 6 in 10 pharma companies report struggling to keep up with data overload.² A large part of the problem is that traditional data management approaches are resource intensive and prone to user error even when carried out by highly skilled staff. With so much manual work required, periodic reviews can take place days, or even weeks, after data is collected, resulting in delayed detection of potential protocol deviations and adverse events.

At the same time, while access to RTD is crucial, not all data has the same level of relevance. Around a third of data points collected in Phase II and III trials are not needed for the study’s key analysis.¹ This, along with recent Good Clinical Practice (GCP) guidance, highlights the importance of tools which allow clinical teams to focus on critical data which has the greatest impact on data integrity, patient safety and overall trial success.³

A further challenge comes from a lack of integration of increasingly disparate data sources. While web-based, electronic data capture systems (EDCs) bring multiple advantages, they also make it harder to maintain data quality.⁴ Even when data is integrated, it can lack the consistency needed to ensure it is meaningful and decision ready. This challenge is exacerbated by variation across different systems and sites which results in data silos, inconsistencies and further inefficiencies. At the same time, traditional site monitoring approaches lack the flexibility needed to cope with the demands of decentralized, hybrid and emerging clinical trial models.

The power of real-time data analytics

Advanced analytics platforms harness innovations in AI and data science to enhance insights in clinical trials and overcome the challenges outlined above. Such platforms, which include AI-powered data review and predictive modelling, empower data teams to accurately assess and delve deeper into vast amounts of operational, clinical and safety data and take effective action more quickly. They offer a holistic approach to clinical operations, safety, medical monitoring and risk-based quality management (RBQM).

A key feature of advanced analytics platforms in an RTD era is the provision of data visualizations which combine fragmented, unstructured data sets into user-friendly representations. This solution has several advantages. Visualizations have been shown to help identify data and process issues early and aid cross-functional communication and review, reducing the time to database lock and improving overall data integrity.⁵ Interactive elements mean, rather than spending time trying to analyze data in sub-optimal formats, data management teams can instead explore and render rich information from a single view, gaining a deeper understanding of the data and addressing anomalies quickly and effectively. This ability to understand and act on data quickly will only become more important as RTD is increasingly utilized in clinical trials.

Another key feature of advanced analytics platforms is centralized dashboards which bring together critical data from multiple sites in real time. By providing clinical teams with a holistic view of the whole trial, such tools allow seamless management across clinical trial phases, reducing siloed processes and helping to prepare for a potential continuous model of drug development. A shared suite of tools also helps to increase cross-team collaboration and raise awareness of the work of the data management function.

Use case 1: Enhancing recruitment forecasting

Around 8 in 10 clinical trials fail to meet initial enrollment targets resulting in millions of dollars in lost revenue a day for pharma companies.⁶ Two common recruitment challenges are an inability to quickly identify bottlenecks and a lack of real-time, relevant information to inform effective solutions. Use of an advanced analytics platform which harnesses advanced statistical methods and includes data visualizations, can provide new insights into recruitment efforts and help pharma companies apply effective solutions.

For example, advanced statistical modeling can predict participant progression and treatment admins timelines, accounting for late site openings and RTD updates, to optimize site performance and avoid costly delays. Integration of a novel Kaplan-Meier model can enable different countries to optimize sites based on the probability of participant progression. Staff can be empowered with RTD updates, customized forecast filtering and the ability to actively reallocate participants based on the latest data.

Together, these capabilities demonstrate how RTD can help to overcome one of the earliest barriers to clinical trial success, overcome recruitment challenges and optimize participant distribution across global clinical trials.

Use case 2: Elevating medical monitoring

Medical monitoring is one of the most important aspects of any clinical trial. However, traditional approaches like 100% source data verification (SDV) are time-consuming, leave room for error, and provide only a limited data view. In contrast, advanced analytics platforms which incorporate RBQM tools use near-RTD to help clinical operations teams focus on high-risk areas, enhance data integrity and improve patient safety.⁷ Centralized monitoring and RBQM have been backed in GCP guidance which emphasizes the importance of adopting proportionate risk-based approaches in clinical trials.³

A key benefit of RBQM is its capacity for near real-time alerts on issues like protocol violations, data quality inconsistencies, persistent data entry delays or unusually low adverse event reporting. Automated risk assessments and heatmaps identify sites which deviate from expected performance, supporting adaptive site monitoring and allowing sponsors to focus resources on high-risk sites before they can cause delays or compromise overall data integrity.

Again, this ability to concentrate resources where they are needed most and react in near real-time is vital for sponsors looking to remain competitive in an evolving clinical trial landscape.

The next evolution of clinical trials

RTD has the potential to unlock a new era of discovery in clinical trials, ensuring treatments are safer and more effective for more diverse patients than ever before. However, with many sponsors still relying on outdated manual processes, it also presents a major data management challenge for the industry. As sponsors begin to adapt, it is important to remember that the successful use of RTD is not about speed alone. What will be crucial is access to standardized, clinically-meaningful data which supports informed decision making.

Advanced analytics platforms combine new technologies like AI with advanced statistical methodologies to ensure all data is consistent, trustworthy and meaningful. Sponsors looking to seize the opportunities of RTD should look for platforms which make data accessible, consumable and actionable in real time for users and are scalable across multiple phases and trials. Those that do will be able to harness the power of RTD in a meaningful way and give themselves the best chance of success in an evolving clinical trial landscape. Those that do not will be left behind.

Sources

1. https://link.springer.com/article/10.1007/s43441-025-00899-4
2. https://ccrps.org/clinical-research-blog/challenges-in-clinical-trial-data-management
3. https://database.ich.org/sites/default/files/ICH_E6%28R3%29_Step4_FinalGuideline_2025_0106.pdf
4. https://www.dovepress.com/bridging-the-past-and-future-of-clinical-data-management-the-transform-peer-reviewed-fulltext-article-OAJCT
5. https://link.springer.com/chapter/10.1007/978-1-4614-5329-1_19
6. https://pmc.ncbi.nlm.nih.gov/articles/PMC7673977/
7. https://link.springer.com/article/10.1007/s43441-021-00295-8