A recent Parkinson’s MFT reached a real patient population through targeted digital patient outreach and a short, protocol-informed eligibility survey. 214 people responded. Nine qualified.

The 205 who fell out were not short on interest. They responded because they wanted in.

But one untested requirement ruled them out before anyone spoke to them. Of the nine who qualified, every one asked for a call the moment a study opened near them.

Do the math on what more sites would have changed. Nine of 214 is the rate every clinical research site would draw from. 

Open five more sites and each one screens the same population against the same requirement, so you multiply the cost and the coordination, not the qualified patients.

Across five Market Feasibility Tests in different indications, the pattern held. Patient interest was rarely the barrier. A single criterion set the qualification number each time.

1. Parkinson’s. 214 responded, 9 qualified. A recent-device-use requirement, not weak interest, set the number. (83bar, 2026, n=214)
2. Heart failure (HFpEF). Most respondents reported the umbrella diagnosis, but only 44.4% met the specific clinical threshold the protocol required. (83bar, 2026, n=26)
3. Obesity. 35% completed the trial’s screening survey and interest ran high, yet 3% qualified once a treatment-history rule applied. (83bar, 2026, n=95)
4. Sleep apnea (OSA). A single BMI cutoff excluded 37% of respondents. One threshold decided the yield, not demand. (83bar, 2026, n=95)
5. Lung cancer. 25% had never had the clinician-ordered biomarker test the protocol required. A care-pathway gap, not a patient gap. (83bar, 2026, n=65)

Prevalence data and historical benchmarks make a population look ready, and everyone plans off the same optimistic default. Then a criterion that seems minor, a prior treatment, a recent test, a single threshold, removes a large share of the people the plan counted on. On paper the equation holds. In the clinic it does not, and the gap stays invisible until enrollment stalls.

Miss the number before sites open and the correction arrives at the most expensive moment, mid-study, when protocol and site changes cost the most. Sites open against patients who can never qualify. Enrollment stalls while the plan chases a supply that was never reachable. The fix becomes a protocol amendment, not a cheap edit.

The pattern is documented. Ineligibility is the leading reported reason patients fail screening, and screen failure averages 36.3% across therapeutic areas (Getz, 2019). More than 40% of protocols are amended before the first patient’s first visit, and about a third of those amendments are avoidable (Prelude EDC).

Electronic health record mining and historical benchmarks describe a population. They cannot tell you whether a real person will respond, engage, and qualify under your protocol. Most recruitment support does one of two things: it scales patient outreach, or it rescues a trial that is already behind schedule. Both happen once sites are open, after the protocol and budget are locked.

A feasibility test does the one thing neither can. It puts a draft protocol and its patient-facing messaging in front of real patients before sites open, and reports back the number who qualify.

It separates interest from restriction, so a low yield points to weak demand or over-narrow criteria while the fix is still a cheap edit. And it runs in days, not quarters.

See the qualification number for your protocol before you commit a site, a timeline, or a budget.

Tested. What’s your number?