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Mapping the pathway

Real-world evidence is an incredibly powerful tool that our industry is only just beginning to understand. This data, which can come from registries, electronic health records, prescription and hospital records, and even social media, has the potential to drive improvements across the clinical research process – but only if we figure out how to integrate this information into the end-to-end decision making process.

If we can mine and analyze the data, we can generate meaningful insights that will deliver more efficient decision-making, speed trials, and reduce the waste and risk that plague these projects. But that is only the first step.

The effective application of real-world evidence requires a systemic approach that is achieved through three phases:

  • Step 1: Analyze the data, to find meaningful insights that can positively impact some aspect of the clinical process.
  • Step 2: Use those insights to create new rules and methods, or to adapt existing rules and methods in order to deliver better, faster and most cost effective results.
  • Step 3: Operationalize the new methods through process changes, new infrastructure, education and communication so that project teams and stakeholders can reap the full benefits of these changes, all while tracking and measuring results to feed the next iteration of the cycle.

It may sound like an obvious cycle of events, but the benefits of analytics are often thwarted by the siloed business models that prevent innovation and lessons learned from being shared across the organizational structure. In many biopharma organizations, the people responsible for gathering and analyzing data are isolated from those who develop trial methods and those who operationalize the project. This prevents these siloed teams from taking full advantage of real-world evidence to improve quality and cut the time and cost of trials. But when these teams work together they can improve aspects of the trial to deliver better, faster results.

With a “systems” approach, companies can apply real-world evidence to hone site selection, improve recruiting, reduce protocol amendments and capture the most powerful data to better understand the marketplace. For example, a clinical team might review data about the competitive landscape and regulatory approval of related products to determine whether to implement a comparator arm, or to gather additional data in the trial to better support commercialization strategies down the line. Their findings will feed back to the data and analytics teams to continuously improve the connectivity between both functions.

We are already incorporating real-world evidence in a systemic approach to positively impact many of our projects. In Europe, our teams are using real-world evidence to transform recruiting for a rare disease trial by using sales and diagnostic data, health records and site reports to better align site selection with local patient populations. This process led to new site evaluation methods that helped identify sites with large patient populations, which might have been dismissed from the selection process due to problems in the past, but with additional support could be valuable additions to the project. We are now operationalizing these new selection methods, and developing training and support tools for those sites to improve recruiting and more efficiently meet trial goals.

These are just a few examples of how real-world evidence and predictive algorithms can improve clinical research methods -- with the right implementation strategy.

Next generation clinical research

Success in developing the complex systems that drive the next generation of clinical development requires “systems thinking” which means focusing not just on the parts, but on the relationships between the parts, including the feedback loops where one function may generate feedback to one or more of the other parts. Analytics may drive new methods creation, but may also, if properly “systematized,” drive new ways of capturing or storing data (i.e., to make future analyses easier). Achieving this is bigger than tools and technology: it involves an intelligent organizational design that embodies customer focus, continuous improvement and teamwork.