What the Future State Really Looks Like for Clinical Data Leaders

Accelerating trends in clinical data require a change of strategy.

Increasing industry-wide data complexity, digitization initiatives, and the volume of data sources, further propelled by the pandemic, require new approaches to clinical data management. A 2019 study found that 75% of life science organizations still use SAS and Excel to integrate and analyze data. More than 80% of respondents said data management activities were time-consuming and labor-intensive. The study also reported a 40% increase in Last Patient, Last Visit (LPLV) database lock cycle times for companies with five or more data sources and concluded that the fighting against disparate data sources was contributing to longer database lock cycle times.

Since 2019, trends impacting clinical data have only accelerated, in part due to the adoption of decentralized clinical trial (DCT) models that enable increased remote data collection and more great use of local laboratories. Amidst this disruption, modern digital and analytical data management is now an imperative. Here are three ways clinical development managers can approach this evolution of data management by aligning people, process, and technology.

The rise of the data steward

Few today would deny the importance of a data strategy. The current data environment is too complex not to have one. Most clinical data is generated from sources external to electronic data capture (EDC), and trials have an average of 8 or more data sources; many include 15+. The traditional siled data manager role, focused on cleaning and querying EDC data lists, is a thing of the past. But in its next iteration, data management has even more value to offer. The role of data management has continuously evolved, becoming more technically advanced and increasingly accountable and complex over the past 20 years. We heard the question, does the data manager become a data scientist? I would say no. Instead, the Data Steward has become a Data Steward, the confident leader who owns and guides modern data strategy.

This new paradigm also requires different skills. The Data Steward as data steward applies a wide range of knowledge across clinical development, from data management to quality and regulation. They collaborate with clinical operations, programming, and biostatistics with enough professional knowledge of each to enable cross-functional empathy, ensuring that all data stakeholders can get what they need from the data. This modern data manager looks at the whole picture that data tells across all data sources, connecting it to their knowledge of the data strategy for the trial. They apply their technical skills to the many different systems used in development to support changing protocol requirements and ensure technology aligns with process improvements to support accelerated timelines.

Strengthen data management with the right tools

The variety of acquisition technologies makes it possible to choose what best suits the trials and the participants. For infrastructure and clinical data analysis, this means interoperability is key. Centralized data platforms can increase the quality of data deliverables and reduce manual work, which positively impacts cycle times. Integrated clinical data platforms ingest and organize all data sources and structures. They should have the ability to serve all of their users, not just the data steward. Data managers are maximizing the use of these types of foundational tools that automate end-to-end data flows and enable greater collaboration and faster time to insight.

Optimize processes around a risk-based approach

It’s one thing to have technologies and another to drive their use, adoption and use. For the data steward, the development of team members and technology training are essential. It is important to view technology as organization-led and supported by a top-down approach. Involve teams from all functions in re-engineering processes around technology. It’s not about introducing the technology, but about maximizing its use for the greatest efficiency gains.

The value is recognized when teams adopt and apply these technology approaches within the context of their data strategy framework. Self-service analytics focus attention where it matters and create opportunities for colleagues to collaborate from a single source of truth. Teams can identify issues, see what has been reviewed, and spot trends and outliers that need discussion. This approach saves teams from waiting until the end of the trial to integrate data at risk of rework due to missing information or outliers.

In a hurry

Data is the end product delivered to the FDA, the result of research, and the news of the life science industry. We have reached an inflection point fueled by groundbreaking achievements in science, disruption in industry, and the intention to accelerate research. The data steward is the guardian of this most critical asset during this perfect storm. By developing their skills and embracing technology-enabled processes, they will lead the way, ensuring that modern data strategies produce sustainable efficiencies for the future of data management.

Katrina RiceHead of Delivery, Data Services at eClinical Solutions

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