In this interview, Anca Copaescu, Founder and CEO at Strategikon, explores the complexities of clinical trial management. Anca provides insights into data blind spots in budgeting and vendor management, the revolutionary impact of AI and predictive analytics, and the shifting dynamics of vendor category management. This interview offers a strategic perspective on leveraging technology and data to enhance clinical trial efficiency, addressing both challenges and opportunities in the industry.
Moe: What are the most common data blind spots in clinical trial budgeting and vendor management?
The primary blind spot in planning and budgeting is the poor understanding of Sponsors’ historical data. Over 95% of pharma companies don’t fully grasp their internal cost structures or have control over their internal benchmarks, primarily due to how data is collected and stored. Proposals and contracts for outsourced clinical services, such as CRO proposals, are often saved as static documents, making them inaccessible for analytics. This results in companies not leveraging their historical cost data effectively, for example, for trend and impact analyses. For example, we often fail to revisit past budgets to understand line-item costs, such as project management fees with
specific CROs. Another significant blind spot is the industry’s unsteady nature, where complex data structures and differing costing languages between sponsors and CROs lead to mapping errors. It’s like relying on Google Translate to communicate in different languages instead of everyone learning a common language. This constant translation process creates inefficiencies and misalignments in budgeting and vendor management.
Moe: Why is it relevant to connect the planning and budgeting of clinical trials with the actual outsourcing process?
The disconnect between planning, budgeting, and outsourcing has been a persistent issue for decades. When these elements are not aligned, it leads to inefficiencies and missed opportunities for cost savings. For instance, preferred provider agreements should inform budgeting, but when disconnected from the actual RFP process, there’s no way to ensure these agreements are applied in the proposal budget. This lack of integration between what has been agreed and what is being proposed can result in change orders, lengthy negotiations, and frustration. An example is when a small company, lacking institutional knowledge, ends up at the mercy of service providers due to inadequate planning or overscoping the services needed for the study. By establishing a baseline through benchmarking, outsourcing managers, finance, or clinical operations stakeholders can negotiate with data rather than feelings, leading to more informed decisions. This approach is akin to knowing the market price of a car before entering negotiations, ensuring we don’t overpay or settle for less than we need.
Moe: How are AI and predictive analytics changing how pharma companies manage clinical business operations?
AI enhances what we were already doing with predictive analytics, but at a much larger scale and lower cost. One major use case is the ability to leverage AI to extract data from unstructured formats, bringing it into a common language with tremendous potential to unlock the analytics and correlate costs to cost drivers. For instance, AI can correlate thousands of data points from Excel, Word, or PDF documents sitting in SharePoint, transforming them into actionable insights. AI also aids in due diligence and comparison, allowing quick data interpretation to generate the right questions during negotiations. A great example is Clinical Maestro BOT, which demonstrated how AI can perform due diligence on a proposal in seconds, a task that took weeks. This efficiency is transformative, though we are still quantifying its full impact. The ability to quickly identify out-of-range items in a 50-page proposal or a 500-line Excel sheet is a game-changer, potentially saving weeks of manual labor and enabling faster decision-making. I like to say that AI makes outsourcing managers “super humans”.
Moe: What are some persistent misconceptions about using digital platforms for outsourcing and budgeting in clinical research?
A common misconception is that only large companies need digital platforms, which is far from true. Efficiency tools are essential from the start of clinical trials, especially as companies grow. For example, small companies often wait too long to implement processes and technology, relying instead on consultants’ opinions, leading to inefficiencies in handover and unscalable processes. Another misconception is that implementing technology is too complex for large companies, leading to paralysis by analysis. However, when companies overcome this mindset, they often see value quickly. Additionally, there’s a fear that technology will lead to job loss, but in reality, it enhances roles by making processes more efficient and attractive to younger talent. The next generation values efficiency tools, and failing to adopt them can make attracting and retaining talent challenging.
Moe: What do the most efficient clinical operations teams do differently regarding trial startup, vendor selection, or budget forecasting?
Efficient teams break silos, ensuring data is accessible across departments, which saves time and enhances communication. They make decisions based on data, acknowledging its importance and leveraging it to improve processes. For instance, teams integrating finance, outsourcing, and quality components can streamline operations and reduce miscommunication. Lastly, they embrace technology to collect and centralize data, simplifying processes and fostering collaboration. These teams avoid overcomplicating processes and instead focus on building strong partnerships with service providers, leading to more agile and successful operations. By fostering a culture of collaboration and data-driven decision-making, these teams can navigate the complexities of clinical trials more effectively, ultimately benefiting the entire ecosystem.
Moe Alsumidaie is Chief Editor of The Clinical Trial Vanguard. Moe holds decades of experience in the clinical trials industry. Moe also serves as Head of Research at CliniBiz and Chief Data Scientist at Annex Clinical Corporation.
