Why It’s Never Too Early for Small Biotechs to Harness Clinical Data with an Integrated Infrastructure
On March 24, 2022, Halloran hosted a Town Hall in partnership with CSL Behring on “Moving from Excel to Complete Integration for Better Clinical Trial Decisions,” bringing together Tony Ciliberto, Senior Director of Global Clinical Operations at CSL Behring, Charles Johnson, Director of eClinical Solutions at CSL Behring, and Halloran’s Meaghan Powers, Director of Clinical Practice Operations, and Karen Travers, Consultant at Halloran.
Here’s how the Town Hall started: the team at CSL Behring shared their data journey with us and how they were a heavily outsourced company using many disparate systems and data sources across their portfolio (and the issues they experienced) and how they successfully developed a streamlined, and integrated data approach with the right team. We knew their insight would be beneficial to the life science community, so partnering with them during our Town Hall was a great opportunity to elevate their case study.
In this article, we want to further expand the Town Hall insight and you’ll find two key takeaways from the discussion – why it’s never too early to start integrating clinical data sources regardless of company size and how to get buy-in across the organization on the process.
It’s Never Too Early to Start Integrating Clinical Data Sources
Due to the use of many disparate systems for data capture, CSL Behring was limited in their ability to manage their data and generate valuable insights. To operate more effectively, their team implemented a new data strategy for one of their trials. From this experience, they established four key pillars to address data quality across their portfolio. These pillars include ownership, interconnectivity, insights, and standardization. Focusing on each of these areas set the stage for the development of their centralized clinical data warehouse. More than just a repository, this warehouse enables their team to build out data standards, enhance their reporting capabilities, write programs for data transformations to support risk-based monitoring, and more.
While there is much to learn from this endeavor, perhaps the most important takeaway is that it is never too early to address data integrity and quality. As the ultimate product of studies, data should be prioritized no matter the size or stage of a company. This process may seem daunting or overwhelming for smaller companies, but the data system and strategy can be scaled back for a right-sized approach. While centralizing data sources is essential, there may be minimal integration required if starting small. Keeping that in mind, developing data standards is a great first initiative in the overall process because it demonstrates an ownership and control of the data, and it enables team members to speak confidently and consistently on study data.
Consider the following example: An executive asks three different team members how many patients are enrolled in a particular study. Person A answers with the number of patients who have been dosed. Person B answers with the number of patients who have been randomized. Person C answers with the number of patients who have signed informed consent. The executive has just received three different answers to a seemingly straightforward question. This variability results in a mistrust of data that hinders overall morale. For this same reason, it is also critical to work with your CROs to adhere to data standards that have been established. A lack of alignment around data inputs results in additional time and effort that should not be underestimated. By setting expectations upfront, it becomes much easier and quicker to organize, report, and leverage data to answer critical questions. Whether it’s for an internal conversation, a database lock, or an audit, teams will be grateful to have the data and insights at their fingertips.
In addition to achieving some shorter-term successes, data standardization prepares companies to effectively manage larger scale projects that may develop in the future, so take the time to set the foundation. Remember that data strategy is an ongoing commitment and should mature with an organization.
Getting Buy-In Across Leadership Levels
Of course, budgeting constraints are a reality for all companies, particularly for early-stage life science organizations carrying out their clinical trials. Scientific and business leaders, for all their strengths, may lack the appropriate insight into the benefits of centrally managed trial data and may be resistant to such a shift in process. CSL Behring’s team also experienced that dynamic. So, the question becomes: how do you convince the right people to pledge precious resources toward clinical data infrastructure? The answer, while not simple, starts with empowering the cross-functional data and study teams to bring insight to senior leadership around the value of a centralized and integrated data infrastructure.
CSL Behring established a critical takeaway that was foundational to their organizational change – the lack of knowledge around internally governed Clinical Data Management software, or CDMs, and the benefits they can provide – and used that insight to begin the process of organizational change. Their data had been historically outsourced across vendors – vendors often with inharmonious standards and reporting tools – and the ability to establish a clear end-to-end picture of clinical data was fraught with challenges. Their data and study teams aligned on key messaging, the value that would result from data control, connected with a variety of Subject Matter Experts (SMEs), and came together as a cross-functional team to bring that insight to senior leaders at the organization.
Up until that point, there had been prolonged resistance from both the leadership and the operational data teams to pivoting to such a massive development project, but because their leadership team was able to understand the proposed path forward would enable their study teams to become more confident and empowered with their clinical data, this proposal became a value add for the entire organization.
As we examine the case of CSL Behring and their larger scale trial (20,000 patient), the knowledge and expertise of their clinical team was allowed to shine. After carrying out their own CDMs implementation, they accomplished the creation of an ecosystem for complex data flows that would enable access to data at any time. As a major lesson learned, they shared that building interconnectivity between clinical and operational data teams is formative to synchronizing clinical data. As CSL Behring carries forward into 2022 and beyond, the change is paying dividends. Sources of data are well defined, and easily retrievable operational data have given senior leaders stronger insight into their trials.
The evolution of data infrastructure and management at CSL Behring makes a strong case for centralization and ownership. But you might be asking: how about the relative value of this kind of technology infrastructure on smaller scale studies? In short, starting small and selecting appropriately fitted data management software is advised. The extent of integration that CSL Behring has undergone may not be the answer for many, but the improvements to quality decision-making that flow from data management certainly can’t be missed.