ENROLLMENT PERFORMANCE TOOL
Model how referral conversion, intake quality, prescreening performance, site handoff, and coordinator capacity affect the path from referral to randomization.
Why It Matters
Randomization is only the final visible outcome. To understand clinical trial enrollment operations, sites need to see where candidates are lost before randomization, including intake, prescreening, site handoff, coordinator review, and scheduling. This calculator models each stage of the referral-to-randomization pathway so you can see where enrollment leakage occurs and which stage needs attention first.
Calculator
Adjust the inputs to model where candidates may be lost between referral and randomization.
Adjust each rate to reflect your site's current performance or planning assumptions.
Illustrative Reference: Default rates are examples for scenario planning, not validated industry benchmarks.
Total new referrals received each month before any intake or filtering.
Percentage of referrals where intake is completed with usable information.
Percentage of intake-completed candidates whose records are prepared for site review.
Percentage of prescreened candidates successfully handed off to the site.
Percentage of handed-off candidates scheduled for screening.
Percentage of scheduled candidates who meet protocol criteria at formal screening (site-controlled).
Percentage of candidates who complete informed consent (site-controlled). Map this rate to your protocol-approved sequence.
Percentage of candidates who complete randomization (site-controlled).
Map the site-controlled consent and formal-screening rates to the sequence required by the protocol, institutional review board (IRB) or independent ethics committee (IEC) approvals, and applicable requirements. The research site conducts informed consent, formal protocol screening, final eligibility review, and randomization in the order required by the protocol, IRB or IEC approvals, and applicable requirements.
Candidate movement through each stage of the enrollment pathway.
Results Dashboard
27.5
Modeled Randomizations per Month
Modeled candidates reaching randomization under the entered assumptions
27.5%
Referral-to-Randomization Conversion Rate
Percentage of referrals that reach randomization
72.5
Patient Loss
Candidates lost between referral and randomization
Intake Loss
-20
candidates / month
Prescreen Loss
Weakest-24
candidates / month
Capacity Loss
-5.6
candidates / month
Scheduling Loss
-7.6
candidates / month
Screen Failure Loss
-10.7
candidates / month
Consent Loss
-3.2
candidates / month
Randomization Loss
-1.4
candidates / month
Scorecard
Efficiency Rating
Scalable
Enrollment infrastructure may support consistent operations when referral volume increases, with lower proportional leakage.
Scenarios
The scenarios below show the modeled difference in monthly randomizations under a uniform rate adjustment across all stages. These are planning scenarios, not forecasts or promises. Stronger enrollment infrastructure and disciplined enrollment operations may further support these modeled outcomes.
+10% Uniform Rate Adjustment
+23.8
modeled difference / month
+20% Uniform Rate Adjustment
+45.1
modeled difference / month
+30% Uniform Rate Adjustment
+61.3
modeled difference / month
Priority Improvement
Based on your entered assumptions, the greatest modeled candidate loss occurs at the Prescreen stage. This stage creates the largest modeled change under the entered assumptions. Review this stage alongside local workflow, protocol, staffing, and site data.
Build a site-approved preliminary prescreening question set with the site
Review and update the question set with the site at each protocol amendment
Track prescreening completion rate and compare to site-recorded screen failure rate
Review site screen failure records with the site to identify information gaps
Insights
Candidates are lost at intake when referral information is incomplete, contact attempts fail, or the intake process lacks structure. Improving intake completion may help more referrals enter prescreening with usable data.
Clinical Trial Intake →Candidates are lost at prescreening when site-approved questions are unclear, the question set is outdated, or records advance without complete information. Strong prescreening may help protect coordinator time and reduce avoidable rework downstream.
Clinical Trial Prescreening →Candidates are lost during handoff when prescreened candidates are not delivered to the site cleanly or quickly. Weak handoff creates delays and lost momentum between prescreening and coordinator scheduling.
Referral to Randomization →Candidates are lost when coordinators lack the bandwidth to schedule and manage candidates promptly. Overloaded coordinators create scheduling delays that cause candidates to disengage before screening.
Coordinator Capacity →FAQ
If your calculator results reveal a performance gap, Consent2Randomize can help strengthen the intake, prescreening, handoff, and coordinator capacity structure behind your enrollment workflow.
