A feasibility questionnaire can make almost any study look possible if every answer is based on best-case assumptions. The harder—and more useful—question is whether your site can run this specific protocol with the patients, people, systems, and time you actually have.
I have worked on feasibility from both the study-manager and site-leadership side. The most expensive feasibility mistakes are rarely dramatic. They are ordinary assumptions that survive long enough to become startup delays, enrollment misses, overtime, protocol deviations, or a study that quietly becomes a burden on the rest of the portfolio.
That is why I do not think feasibility should be treated as a form-completion exercise. It is a decision. And the decision should be defensible before your team spends months proving it was wrong.
What clinical trial site feasibility should actually answer
At its simplest, site feasibility asks whether a research site can execute a protocol safely, compliantly, and realistically. That includes more than investigator interest or an estimated patient count. A useful review looks at patient access, staffing, workflow, facilities, competing studies, startup dependencies, finances, and the fit between the protocol and standard clinical care.
That broader view is consistent with current industry thinking. ACRP's 2026 feasibility education frames trial selection around interest, accrual potential, capacity, and portfolio alignment, while recent research on feasibility checklists groups the work across domains such as participant recruitment, staffing, facilities, finances, trial management, and oversight.
10 questions I would answer before committing the site
Do we have the patient population—or only a diagnosis count?
A disease registry, clinic volume, or electronic health record count is only the top of the funnel. The protocol may narrow that population by stage, biomarker, line of therapy, prior treatment, laboratory values, washout windows, performance status, organ function, geography, or willingness to accept a demanding visit schedule.
Start with the protocol's real inclusion and exclusion logic. Then work toward a credible eligible population. If the estimate depends on several assumptions, write them down. “We see 300 patients a year” is not an enrollment forecast.
Where will eligible patients actually be identified?
Patient availability and patient identification are different capabilities. Who notices the patient? At tumor board? During clinic prep? Through an EHR query? By a treating physician? Who owns the prescreen list, and how often is it reviewed?
This matters because enrollment problems often begin before anyone formally screens. Our GU trial screening guide explains why the identification-to-approach transition deserves its own measurement.
Does the protocol fit the way care is delivered at our site?
Look closely at visit timing, required procedures, imaging, laboratory windows, pharmacy needs, specimen processing, inpatient or outpatient requirements, and who must be available on each visit day. A protocol can be clinically reasonable but operationally incompatible with the site's normal patient flow.
The test I use is simple: could a coordinator explain, step by step, how a real participant would move through the study at this institution? If the answer requires several “we'll figure that out later” statements, feasibility is not finished.
Do we have staff capacity at the time the study will actually open?
“We have coordinators” is not a capacity assessment. What else are those coordinators carrying? Which studies are opening at the same time? Does this protocol require unusually intensive screening, frequent visits, same-day data entry, complex safety reporting, central lab shipping, or high-volume query resolution?
Capacity should be assessed against the portfolio, not the org chart. A highly experienced team can still be overcommitted.
Is the principal investigator's interest matched by operational availability?
PI enthusiasm matters, but the study also needs predictable time for eligibility decisions, safety review, patient discussions, sponsor questions, signature requirements, protocol decisions, and escalation. If the investigator's availability is already a bottleneck, a complex new study will not make that bottleneck disappear.
What will startup depend on, and which dependencies can run in parallel?
Study startup is not one queue. Contracts, budgets, coverage analysis, IRB work, regulatory documents, system builds, pharmacy, laboratory setup, training, and internal approvals often have different owners. Recent ACRP guidance emphasizes that unclear responsibilities and serial processing can extend activation timelines.
Before saying yes, identify the likely critical path. If startup is already a known problem at your institution, review our start-up and activation readiness support.
Is the budget built around the work the protocol actually creates?
Feasibility and finance belong in the same conversation. Consider coordinator time, pharmacy, imaging, labs, specimen handling, screen failures, unscheduled visits, long-term follow-up, data entry, query management, monitoring visits, close-out, archiving, and institutional overhead.
A study can enroll well and still be financially unhealthy if the site underestimated the operating effort or failed to negotiate for recurring work that happens outside billable visits.
What competing studies or clinical options will affect enrollment?
A protocol does not enter an empty clinic. Competing trials, standard-of-care changes, physician preferences, other institutional priorities, and commercial treatment options can all shrink the realistic pool. Review what is open now and what is likely to open during the same enrollment period.
Can our systems support the study without creating a shadow workflow?
Think beyond “we have an EDC.” How will the study interact with the CTMS, EHR, eRegulatory/eTMF processes, source documentation, patient-reported outcomes, specimen tracking, scheduling, safety reporting, and local databases? Every duplicate entry point or manual handoff creates work and another place information can stall.
What would make us say no?
This may be the most important feasibility question because it forces the team to define boundaries before momentum takes over. Examples might include an unrealistic enrollment target, a required procedure the site cannot reliably deliver, insufficient coordinator capacity, an unresolved budget gap, or a startup date that conflicts with known institutional timelines.
A good site does not prove its value by saying yes to everything. It proves its value by selecting studies it can execute well.
A practical clinical trial feasibility checklist
If you need a shorter working version, I would put these items on one page and require an owner for each answer:
- Patient fit: realistic eligible population, not clinic volume.
- Identification path: where and how potential participants will be found.
- Protocol-to-clinic fit: visits, procedures, labs, pharmacy, imaging, specimens.
- Staff capacity: current workload plus planned openings.
- PI availability: actual decision and oversight bandwidth.
- Startup path: owners, dependencies, parallel work, expected bottlenecks.
- Financial fit: recurring operational work and hidden costs.
- Competitive landscape: trials and treatment options competing for the same participants.
- Systems fit: CTMS, EHR, EDC, regulatory, scheduling, data and specimen workflow.
- No-go criteria: conditions under which the site should decline or renegotiate.
My preferred feasibility output is a decision, not a score.
- Proceed: the study fits, and the operating assumptions are credible.
- Proceed with conditions: specific budget, staffing, startup, or workflow issues must be resolved first.
- Decline: the mismatch is material enough that optimism will not fix it.
Feasibility should make startup easier, not simply get the study selected
There is a reason feasibility and startup should be tightly connected. When feasibility captures real constraints, the startup team inherits useful information: the staffing plan, special procedures, financial pressure points, internal dependencies, and the participant-identification strategy.
When feasibility is mostly optimistic questionnaire completion, startup rediscovers those facts the expensive way.
That is also why I would track the quality of past feasibility decisions. Which studies met their enrollment assumptions? Which opened on the expected timeline? Which consumed more coordinator effort than predicted? Which struggled financially? Historical performance should improve the next decision.
The takeaway
Clinical trial site feasibility is not about proving that your site is capable. It is about deciding whether this study fits your site well enough to deserve the team's capacity.
If you can answer the ten questions above with evidence instead of optimism, you will enter startup with fewer surprises—and you will be more comfortable walking away from studies that were never a good operational fit.
If your organization needs a structured review of feasibility, capacity, startup dependencies, or portfolio fit, see our site performance diagnostic and start-up readiness work.