Feasibility is supposed to tell you whether a trial will enroll, and where. Too often it’s a therapeutic-area average dressed up in a confident slide — and when the number is wrong, you find out six months in, when it costs the most. We answer it with a count, not a guess.
Most feasibility answers the first one with a shrug. All three deserve a number.
Will it enroll?
Not “probably.” The counted number of protocol-qualifying patients that actually exist for your study.
At which sites?
The specific investigators with the track record to enroll them, ranked, not the usual marquee names by reputation.
How fast?
A realistic enrollment picture from real access and measured track records, so the timeline holds up.
Feasibility starts as a wide, confident estimate. We hand it back as two numbers you can defend: how many patients qualify, and which sites to run.
Illustrative figures. The point is that the answer is counted from real patient data, not inferred from an average.
When feasibility is inferred, the bill comes due mid-trial — in four familiar ways.
Under-enrollment
Sites that looked good on paper never reach the patients, and the timeline slips.
The amendment tax
You rewrite criteria and re-file when the eligible pool turns out smaller than assumed.
Rescue sites
You bolt on new sites late, at premium cost, to catch up on enrollment.
Wasted activation
Startup budget spent on marquee sites that were never going to enroll this protocol.
We start from the most complete real-world patient data in the U.S. — national, longitudinal, every institution — count the patients your protocol actually qualifies, map them to the investigators who can enroll them, and hand back a ranked, defensible answer. You don’t need to follow the math to trust the result: it’s a number you can put in front of a sponsor.
In a recent rare-disease program, feasibility on real, patient-level data nearly tripled the eligible pool — then became a ranked list of the sites that could actually reach those patients, on one evidence base.
What is clinical trial feasibility?
It’s the assessment of whether a study can realistically enroll, and where. It should answer three questions, will it enroll, at which sites, and how fast, and answer them with counted, protocol-qualifying patients rather than a therapeutic-area average.
How is a feasibility study done?
Traditional feasibility leans on surveys, registry estimates, and historical trial counts. Real-data feasibility instead measures the actual qualifying patients across the country, maps them to investigators who can enroll them, and produces a ranked, defensible shortlist, in days.
Why do so many trials miss their enrollment targets?
Because feasibility is often a confident estimate rather than a count. When the number is inferred, the enrollment gap shows up months in, forcing amendments and rescue sites. Starting from what’s actually there reduces that risk.
What’s the difference between feasibility and site selection?
Feasibility asks whether and where a trial can enroll; site selection turns that into the ranked list you’ll actually use. The same real evidence base underpins both, which is why they shouldn’t run on different data.
Bring us your protocol. We’ll come back with the counted patients, the sites that can reach them, and a realistic enrollment picture — in days.