Female doctor at computer

Advances in analytics technology are changing the way clinical research projects are planned and managed. With the ability to scan and analyze millions of pieces of data, and to develop algorithms that can adapt from trends in real time, we can more proactively identify risks, act upon these trends, and ultimately capture better information in a shorter amount of time.

The McKinsey Global Institute estimates that analytics tools and capabilities  could generate up to $100 billion in value annually across the US healthcare system by “optimizing innovation, improving the efficiency of research and clinical trials, and building new tools for physicians, consumers, insurers and regulators to meet the promise of more individualized approaches.”

That translates to real, measurable advantages for the biopharma companies that make analytics part of their project management process. But these advantages can only be achieved if research teams have the tools and the talent to take advantage of that data and processes in place to incorporate it into their project plan. 

The benefits of analytics don’t occur in a vacuum. They require talented professionals who understand how analytics technologies can be applied in a clinical research environment, and how to transform raw data into meaningful insights that can help to protect patients while bringing improved efficiency to the entire research program.

Best practice for project management

While many companies rely on some analytics, often overseen by the project leader, to run aspects of projects, QuintilesIMS saw the growth of analytics in clinical research as a catalyst for change in our project management dynamics. We believe this aspect of the research requires dedicated talent and a formal management process in order to deliver the most robust data-driven solutions for our clients.

Since 2015, we have started augmenting our project management support structure with expertise that help translate data and information from analytics into insight that complement the project management practices and leadership.

Support staff with such expertise are now embedded in our project teams where they are responsible for drawing insights from data analytics for the clinical research project. In this role they enable our teams to rapidly transform real-time data into actionable insights that enable our project managers to make faster, more-informed decisions throughout the study. This support team work in the background, harnessing our analytics platform to pull and aggregate data from multiple systems to identify trends and inform the project direction and support decision making.

The analyst has become a vital resource for the project leader, drawing their attention to data trends that require immediate attention, and helping them determine when a piece of information is indicative of a broader issue or merely an anomaly.

Better project control and outcome

Having analysts on our project teams not only ensures we have targeted technical talent on our teams to make the most of our analytics technology. They also free our project leaders to stay focused on big picture goals of the project, which includes overseeing the development of project staff, ensuring alignment across all functions involved in the project, making sure team members are provided with the required resources to deliver and communicating with clients.

Since implementing this we have already documented impressive results. Early measures show that project teams with these analysts deliver improvements in proactive risk based management, better upkeep of project schedules, and improved management of project management tools – all of which are factors that directly impact time and cost savings for trials.

We believe this model represents the future of clinical trial project management. As data analytics become an increasingly important part of managing trials and mitigating risks, having experts on the team dedicated to this specialty can give biopharma companies a competitive advantage, helping them improve the quality and efficiency of their research.