Using cycle-time metrics to find activation bottlenecks

The Challenge

Leadership at an NCI-designated cancer center knew that study activation took too long. Investigators were frustrated. Sponsors were asking why sites at other institutions opened faster. But no one could point to exactly where time was being lost.

Was it the contract negotiation? The budget review? The IRB submission? Pharmacy setup? The answer was different depending on who you asked. Without data, every department could point to another department as the bottleneck. Fixing the problem required knowing, with specificity, which stages of startup consumed the most time and which ones varied the most from study to study.

What We Did

We built a measurement system from the ground up. We tracked every study in the activation portfolio from the moment it entered departmental intake through the day it was activated and ready to enroll.

For each study, we recorded timestamps at every major startup milestone:

• Departmental intake and feasibility assessment
• Protocol and budget receipt
• Contract negotiation start and execution
• IRB submission, approval, and any amendment cycles
• Ancillary committee reviews (radiation safety, biosafety, pharmacy)
• Regulatory document completion
• Site activation and enrollment readiness

With those timestamps, we calculated total cycle time and time spent at each individual stage. We then aggregated the data across the portfolio to identify patterns. Some stages were consistently fast. Others showed wide variation, signaling unpredictable dependencies or resource constraints.

We built dashboard-based reports that visualized the data for leadership. These dashboards showed median cycle times by stage, highlighted outlier studies, and tracked trends over time. The goal was to give decision-makers a clear, ongoing view of where startup time was actually going.

We presented findings and recommendations to clinical trial leadership. The data told a specific story: certain stages accounted for a disproportionate share of total cycle time, and targeted interventions at those stages would have the largest impact on overall activation speed.

The Result

Portfolio-level trends exposed bottlenecks that anecdotal feedback had missed. Leadership could see, backed by data, which stages needed attention and which were already performing well. That clarity replaced guesswork with targeted action.

The recommendations we made drove specific process changes at the stages where the data showed the most opportunity. Over time, the dashboards also served as an accountability tool. Teams could see whether changes they made actually moved the numbers, and departments stopped arguing about whose stage was the bottleneck because the timestamps answered the question for them.