Jim Scullion
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Faced with regulatory uncertainty and an economy struggling to stabilize, pharmaceutical companies face increasing pressure
to bring new drugs to market on time and on budget. Meeting enrollment goals is one critical success factor for addressing
these pressures. But enrollment has its own challenges. A lack of a standardized enrollment business process and little transparency
of projected outcomes are some of the issues that contribute to enrollment difficulties. In addition, many organizations fail
to utilize predictive analytics technologies to identify problems before they rear their ugly head. The end result is a clinical
trial peppered with problems and delays.
Everyone in the industry is familiar with the data that indicate the average costs in bringing a new drug to market today
exceed $1 billion. And delays in clinical enrollment is a significant contributor to overall trial costs and delays.
To improve trial performance and transparency, life science companies need to stop relying on manual and inconsistent processes,
instinct, and last minute changes and utilize technologies that apply predictive modeling to help them stay on course during
the enrollment process.
Today's predictive modeling and optimization software applications allow organizations to apply a standardized approach to
run simulations and plot scenarios, to collaborate and share best practices, even on a global level.A realistic view
Many study managers still rely on spreadsheets and dashboards as opposed to predictive modeling and optimization software
solutions. As a result, study teams often experience inconsistent enrollment plans, inaccurate forecasts, and costly rescue
strategies.
For example, study teams that use Excel documents to generate enrollment forecasts usually estimate with a linear path of
"X" patients per week for "Y" months. This method can produce forecast errors of up to 40% because the reality is that enrollment
follows more of an "S-Curve," to reflect the initial ramp-up and subsequent slow down of enrollment by site investigators.
The result is an enrollment plan that is limited and disconnected from the business process. A good enrollment plan is key
to uncovering valuable insights and potential pitfalls and completing the patient enrollment process on time.
Sound solutions
Factors including subject availability and drug reactions can alter enrollment performance and require adjustments to even
the best laid plans. Solutions that can predict, simulate, and model different scenarios help study teams formulate better
resource allocation and intervention decisions.
Utilizing predictive modeling and optimization software enables life science companies to target inefficiencies and the causes
of inconsistent results. The best solutions help optimize enrollment by automating the process through planning, tracking,
diagnosing, and adjusting problems before they cost money. They also enable collaboration and real-time decision making across
global teams.
Solutions that seamlessly integrate business process automation, predictive modeling, and simulations are becoming invaluable
to life science companies seeking to improve the performance and predictability of their enrollment efforts and overall development
operations.
