In the world of clinical trial enrollment optimization, there is no shortage of subjective practices.  The scenario of two operations managers sitting in adjacent offices, running similar trials off of very dissimilar play books is all too common. By definition, best practices are standard procedures that, when executed appropriately, drive successful results. Problem is, when the use of subjective “best practices” occurs around enrollment planning and execution – the consequence is a scramble to rearrange milestone dates, re-negotiate vendor contracts, motivate and re-motivate investigators and justify the need for added budget.

The result of this ad hoc trial management is a lack of foundation, a difficulty translating subjective processes to upper management, and an inability to confirm initial planning assumptions to ensure execution of enrollment occurs on plan. Whether due to resource constraints, organizational pressures, lean technology to support the process or a combination of all the above, the re-work required to repair damage in this cycle is far more consuming than the time to implement a globalized set of best practices.

Current industry data reflects an enormous impact on the ability to get novel therapies to market as planned – with patient enrollment at the core of these delays:

• 34% of sites fail to attract a single subject

• 27% of screened subjects fail to randomize

• 76% of all Phase II & III studies are more than 90 days late, costing an estimated $5,000-$35,000 per day in daily operational expenses.

With globalization, outsourcing, resource reductions, there is now more than ever an incredible need to create automation of best practices around enrollment that can be implemented organization wide. The below illustration shows the 4 key focus areas where best practices when implemented not only across study teams, but across the organizational portfolio will result in ongoing, on-time enrollment.


1. Planning: It is during this critical planning phase that implementation of best practices through an automated planning tool ensures clarity around decision making and buy in from all parties prior to execution.  By building the most accurate and realistic plan upfront, whether it be through the ability to vet several assumptions with clear impact to timelines prior to implementation, or to support decision making with the use of historical performance data – once isolated global teams are now in alliance from the start.

In practice today by many companies: Study teams are sending around  excel spreadsheets filled with raw subjective planning data and analysis, or knocking on colleagues doors to identify someone with prior experience in a specific region or indication which is an inadequate approach in this critical period

2. Tracking: Automated upload of key performance indicators allow study teams to focus time on appropriate data, aggregated in an objective format and offering global team members a standard view of trial status from which to draw upon.

In practice today by many companies: Despite progress in technology our operations teams currently struggle to gather and analyze global and disparate data.  Often tracking and analyzing enrollment data is an operational resource drain, and identification of key performance indicators and trends causing delays are missed. 

3. Diagnose: Standardization of key drivers that impact enrollment offer teams guidance and focus on appropriate and actionable measures.  Standardized analysis of performance at the study level, country level and site level offer teams the ability to quickly identify critical elements of performance.

In practice today by many companies: Manual analysis of enrollment data from multiple source systems often leads to inaccurate diagnoses and inappropriate implementation of corrective measures.

4. Course Correct: The implementation of a process that ensures multiple data driven course correction strategies are pressure tested and reviewed collaboratively for accuracy prior to implementation drives appropriate action with measurable outcomes.


In practice today by many companies :  Adjustments to plans are done in a linear fashion, not accounting for variable trends in enrollment.   Managers are often unable to adjust plans with clarity and visibility when enrollment veers off course, leading study teams back to square 1 with a new, yet inaccurate plan of action in place. 

Implementation of the above set of standardized processes have resulted in significant performance improvement, and compelling cost reductions. Utilizing predictive modeling and optimization software solutions enables life sciences companies to target inefficiencies and the causes of inconsistent results that so often plague clinical trial enrollment.  In a recent case study with a top five global pharma company, the result of this streamlined implementation around enrollment best practices has impacted the entire portfolio and “doubled its efficiency in patient recruitment for clinical studies.” With 76% of trials delayed at least 3 months, the operational savings gained from implementation and automation of these best practices around enrollment have been a staggering $150,000 to $1 million per month per protocol. Technology solutions that seamlessly integrate business process automation, predictive modeling and simulations are becoming invaluable to life sciences companies seeking to improve the performance and predictability of their clinical enrollment efforts and overall clinical development operations.