At its core, the success of any clinical trial rests on clean, conclusive data. This creates challenges. Throughout a trial, the data must be properly aggregated, scrubbed, analyzed, acted on—and all those steps must be recorded, to assure regulatory authorities of their accuracy. As the industry continues to evolve, new solutions to these challenges have become available. The best of these solutions can be found by pairing cutting edge technology with the functional expertise afforded through a Functional Service Provider (FSP) model. Yet to properly assess the value of these solutions, it is important to first place the challenges in historical context.

Process-based challenges may undermine efficiency

Data management always rests on twin pillars: clinical management professionals and the technology that supports them. Each is equally critical to success. Brilliant data managers armed with inadequate technology will be less effective; cutting-edge technology run by an insufficiently trained team will not produce good results. This dichotomy is evident in three key historic data-management challenges.

Study Start-Up. Study protocols are among the first things study teams discuss. Although these protocols generally encompass database design and data-collection goals, the start-up team’s approach is often different from the approach a data manager would take. Each individual on the start-up team may have a specific vision for data collection and a unique protocol end-goal tied to their role; conversely, the data manager holds a broader overall understanding of the study’s ultimate data needs. When the data manager is not part of the protocol discussion, items can be missed, data collection efforts can be duplicated, or expectations for site data collection can be unclear, making it difficult for sites to understand priority data during each phase.

Trial Conduct. Likewise, when data managers are not fully folded into core study teams, problems emerge. With limited insight into study goals, data managers tend to take a straightforward approach to data cleaning; they treat all data as equal, spending equivalent time cleaning each piece. They approach datasets that have no impact on the study outcome with the same care as they approach endpoint data. Ironically, data management has become comfortable with this business-as-usual approach. So, when challenged to view things more analytically, they may hesitate. The requisite skill sets aren’t built into existing data management roles, which can make it difficult to evolve.

Tools and Technology. Yesterday’s tools don’t help any of these issues. Study teams often need targeted reports for data review and these reports may require manual setup unique to the study. These reports are time consuming to create, run, and compile—especially when they are needed for each review cycle throughout the entire trial. Indeed, data managers may actually spend more time creating reports for study teams than cleaning data. Adding additional complication, data managers must work in different formats with different configurations; some reports even overlap, making it complicated to remember what data exists where, while requiring manual cross-referencing of data points. Compounding the problem, these reports provide little-to-no value for future trials, so all these efforts must be manually replicated.

A holistic approach to data management holds the key

Each of these challenges can be overcome with the right people, the right technology, and the right approach. Start with the approach: Overall, pro-active attitudes to early data collection and study start-up enable teams to have a holistic view of the study’s needs, and to mitigate any issues before they become run-away problems. Then, with a combination of talent and technology, these historic challenges disappear.

New tools can easily answer the needs of study teams; properly trained data managers understand and embrace these tools. By bringing in the right data managers, equipping them with the right technology—and making them an integral part of the start-up team—sponsors can avoid missed data, duplicate data-collection, and unnecessary, time-wasting complexity.

Fortunately, this does not require hiring and training a new team from scratch. Using a FSP model combined with a centralized technology platform on which those FSPs are already trained, sponsors can harness a team of experts they can deploy as needed, operating under their SOPs and processes. They reap the full value of the technology, the full expertise of the data managers, and the full efficiencies that the two together can bring.