The Industry Challenge Traditional research organizations are built for scale. That scale creates friction.
What We've Seen
Traditional research organizations are built to deliver at scale across thousands of studies. That scale creates natural friction—handoffs, queueing, and layered governance—that can slow programs. These aren't failures; they're structural realities of operating at scale.
Study Start-Up Drags
The Problem: Average time from site identification to start-up completion is ~8 months. 35% of sites take 91+ days to activate; only 19% report activation in 30 days or less.
Why It Happens: More stakeholders, more handoffs (start-up, contracts, regulatory, vendors), and "queue time" across shared functions.
How Prana Is Different: Fast-cycle startup playbooks with 24-48h document turnaround. Every submission tracked daily. No queue time—your study is our priority.
Protocol Amendments Create Shocks
The Problem: 57% of protocols have at least one substantial amendment. Mean cost per amendment: $141K (Phase II) to $535K (Phase III). About 45% are considered avoidable.
Why It Happens: With multiple vendors and global governance, changes propagate slowly and create more change orders and rework loops.
How Prana Is Different: Protocols designed for execution, not just approval. Senior operators with operational expertise anticipate challenges before they become amendments.
Enrollment Underperformance
The Problem: ~80% of trials fail to meet initial enrollment targets or timelines. Delays can cost up to $8M per day in lost revenue for drug developers.
Why It Happens: Large portfolios compete for scarce CRA/PM/site bandwidth. "Average-case" operating cadence is too slow for rescue or priority execution.
How Prana Is Different: Dedicated teams with no competing priorities. 24-48h execution cadence keeps enrollment momentum. Performance dashboards surface issues before they become crises.
Uneven Site Productivity
The Problem: 11-40% of activated sites enroll zero patients, depending on the study. You're paying for sites that never deliver.
Why It Happens: Site selection and activation processes often optimize for coverage and scale, not predictive enrollment performance per site.
How Prana Is Different: Site selection based on performance history and realistic enrollment projections. RBQM signals identify underperforming sites early for intervention or replacement.
Staff Turnover Creates Inconsistency
The Problem: CRA turnover peaked at ~30% in 2022 and remains around ~22%. Each transition disrupts site relationships and introduces learning curves.
Why It Happens: Larger organizations have higher volume hiring and reallocation. Teams work in silos despite promises of end-to-end solutions.
How Prana Is Different: Senior-led delivery with executive operators attached to your program from kickoff to closeout. No layers, no handoffs, no learning curves mid-study.
Utilization vs. Outcome Incentives
The Problem: Large CROs report 21-24% adjusted EBITDA margins. Their operating systems optimize for capacity management and standardization—rational, but not always ideal for high-urgency programs.
Why It Happens: At scale, economic incentives can bias toward "utilization" (billable hours) over "outcome" (hitting milestones).
How Prana Is Different: Outcomes over hours. We commit to written delivery targets with governance to those targets. You pay for results, not utilization metrics.
The Bottom Line
These aren't failures—they're structural realities of operating at scale. Traditional organizations serve an important role in the industry. But for high-priority programs where speed, predictability, and senior attention matter most, you need a different model.
Prana is built for sponsors who can't afford average-case execution.