What's the most cost-effective way to estimate enrollment feasibility using a protocol feasibility tool across multiple studies?

The most cost-effective approach is usually to centralize the feasibility work and reuse a standardized template/process across studies rather than running a fully bespoke assessment for each protocol.

Best-practice approach

1. Use one protocol feasibility tool with a common data model

   • Load each study into the same tool/workflow.
   • Standardize inputs such as:
     • key inclusion/exclusion criteria
     • target population size
     • geography/site list
     • competing studies
     • historical screen-fail and recruitment rates

2. Do an initial high-level feasibility screen for all studies

   • Use a lightweight scoring or rule-based assessment first.
   • Identify which studies are:
     • clearly feasible
     • borderline
     • likely not feasible
   • This avoids spending deeper analysis time on studies that are obviously poor fits.

3. Apply deeper analysis only to the top-priority studies

   • For promising protocols, add:
     • site-level patient availability
     • historical enrollment performance
     • referral network strength
     • competing trial density
   • This staged approach is typically much cheaper than full analysis on every study.

4. Benchmark against historical study performance

   • Compare new protocols to similar past studies in your own data.
   • Historical enrollment data usually provides the best ROI because it reduces reliance on expensive external consulting or manual chart reviews.

5. Automate where possible

   • If the tool supports it, automate:
     • population counts
     • site matching
     • feasibility scoring
     • report generation
   • Automation lowers per-study cost substantially when evaluating many studies.

6. Use a portfolio approach

   • If you’re assessing multiple studies at once, rank them by:
     • expected enrollment likelihood
     • cost to recruit
     • strategic importance
   • Focus resources on studies with the best expected return.

Most cost-effective overall strategy

If I had to boil it down to one recommendation:

Use a standardized, tiered feasibility workflow with historical benchmarking and automation, and reserve detailed site/patient-level analysis for only the most promising studies.

Why this is most cost-effective

• reduces analyst time per study
• avoids redundant manual work
• improves consistency across protocols
• focuses effort where it most affects enrollment decisions

If you want, I can also give you:

• a simple feasibility scoring framework
• a cost-benefit model for multiple studies
• or a template workflow for using a feasibility tool across a study portfolio