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Select Sites Faster: Feasibility Forms for Ops Using SASI & EHR Counts

October 4, 2026
Select Sites Faster: Feasibility Forms for Ops Using SASI & EHR Counts

The most effective way to optimize a feasibility questionnaire is to give sites the full protocol up front, pre-fill known historical site data, limit new questions to study-specific essentials, and apply a simple scoring rule to the responses. This combination shortens turnaround, cuts down on non-enrolling sites, and gives operations teams a defensible way to rank candidates. The sections below walk through the template, the scoring mechanics, and the rollout steps that make this work in practice.

TL;DR:

  • Pre-filling known site data, limiting questions to essentials, and applying straightforward scoring rules significantly speed up and improve feasibility assessments.
  • Using short, standardized questionnaires structured around core sections and validated questions helps sites respond quickly and accurately within about an hour.
  • Attaching clear metadata to EHR-derived patient counts and involving site staff in sign-off maintains data credibility and prevents inaccuracies.
  • Threshold gates for equipment, IRB status, and other protocol requirements can eliminate unsuitable sites early, while weighted scores predict enrollment likelihood effectively.
  • Piloting the questionnaire with a small group, refining based on feedback, and establishing clear ownership ensures scalable, efficient site feasibility processes.
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Table of Contents

Building a compact feasibility questionnaire template

A short, well-organized questionnaire gets better answers faster than a long one that tries to anticipate every possible objection. The goal is a form a coordinator can complete accurately in under an hour, not a document that sits in an inbox for two weeks.

Structure the form around these sections:

  • Protocol snapshot: confirmation the site received and reviewed the full protocol, not a synopsis.
  • Site profile: institution type, prior sponsor relationships, certifications.
  • Staff and experience: principal investigator history with the indication, sub-investigator availability, coordinator caseload.
  • EHR-derived patient counts: eligible population estimate with the method used to generate it.
  • Equipment and facilities: imaging, lab, or storage capabilities tied to protocol requirements.
  • Procedures and schedule feasibility: whether visit windows and procedures fit existing clinic flow.
  • Competing trials: active or planned studies drawing from the same patient pool.
  • IRB status: central or local review pathway and expected timeline.
  • Budget flags: known cost drivers or contracting concerns.
  • Timeline and interest: realistic activation date and site interest level.

Example closed-ended items work better than open prompts for most fields:

  1. "How many patients meeting the primary inclusion criteria were seen in the last 12 months?"
  2. "Is there a dedicated research pharmacy on site? Yes/No."
  3. "Rate current competing trial load: none, low, moderate, high."

Reserve free text for the rare field where a number or checkbox cannot capture nuance, such as a brief note on contracting constraints. Any prefilled field, whether it is a patient count or a staff roster, needs a visible source and date so the site can confirm or correct it rather than guess what changed.

Pro Tip: Lock prefilled fields as read-only with a "confirm or flag" toggle instead of an open edit box, so corrections are visible to the reviewer.

Why minimizing and standardizing questions improves response quality

Every extra question is a small tax on a coordinator's time, and long forms tend to get rushed answers or get set aside entirely. Clinical Research News has pointed out that minimizing and standardizing feasibility questions focused on site capacity and protocol fit reduces redundant data collection and speeds up responses.

Before adding any question, run it through a simple essentials test:

  • Does it map directly to a protocol requirement or inclusion criterion?
  • Does the answer actually change whether this site gets selected?
  • Can the answer be verified later without a site visit?

A question that fails all three belongs in a post-selection site initiation visit, not the feasibility form.

A well-designed standardized instrument can work even for busy sites. The Site Assessment Survey Instrument (SASI) found that 100% of sites rated it as collecting useful information, and Many completed it within one to two hours when it focused on verifiable, protocol-relevant items.

Shared field definitions and controlled vocabularies, rather than each sponsor inventing its own phrasing for "active site" or "screen fail rate," let a site reuse prior answers across studies instead of re-explaining the same facts every time.

Pro Tip: Keep a glossary of standard field definitions alongside the template so every study team asks for "eligible patient count" the same way.

Collecting credible patient counts and capacity data

A feasibility response is only as useful as the data behind it, and patient counts are the figure sponsors trust least when they arrive without context. Benchmarking from Applied Clinical Trials found that more than 70% of sites prefer receiving the full protocol rather than a synopsis at the feasibility stage, specifically because it lets them generate accurate, protocol-matched counts instead of rough estimates.

Every EHR-derived count needs the following metadata attached:

  1. The date window the query covers.
  2. The diagnosis, ICD, or CPT codes used to define the population.
  3. The denominator and any inclusion or exclusion filters applied.
  4. The name or role of the person who ran the query.

Prefilling these counts from a prior study or a standing site profile saves time, but the site's principal investigator or lead coordinator should always review and sign off before the number goes back to the sponsor. That sign-off step is what keeps a fast process from becoming a sloppy one.

Sponsors can request a few quick validation checks without adding real burden: a spot comparison against a second code set, a sanity check against the site's historical enrollment in similar studies, or a brief note on how the denominator was defined. Treating the underlying data as auditable, the kind of discipline covered in Qualitum's work on data integrity, protects both the site and the sponsor if a count is challenged later.

Illustration of validating patient counts

Scoring and ranking sites without overbuilding the model

Most feasibility programs do not need a predictive model. They need a scoring rule simple enough that a trial operations lead can explain it in one sentence and defend it in an audit.

Start with mandatory gates: a site either meets protocol-required equipment and IRB pathway or it does not, and failing a gate removes it from the ranked list regardless of other strengths. Everything that passes the gates gets a weighted score across the criteria that actually predict enrollment:

  • Eligible patient volume, drawn from the validated EHR count.
  • PI and coordinator experience with the indication.
  • Competing trial load at the same site.
  • Historical performance on prior studies, where available.

Weightings should shift with the protocol: a rare-disease trial leans harder on patient volume, while a complex procedural study leans harder on staff experience and facilities. The SASI evaluation showed 90% of sites were comfortable supplying detailed information early, which suggests sites will tolerate a more thorough scoring input as long as the form itself stays short.

Predictive or machine-learning scoring can add value once a sponsor has enough historical enrollment data to train and validate a model honestly. Without that volume, or without checking for bias in which sites historically got selected, a predictive score just launders old assumptions into a new number.

Pro Tip: Document the weighting rationale once per protocol, not once per site, so reviewers can see why the criteria were set that way.

Rolling out the optimized questionnaire without overwhelming sites

A new questionnaire format succeeds or fails on how it is introduced, not on how well it is designed on paper.

  1. Pilot the template with a small subset of sites across one or two active protocols.
  2. Measure completion time and flag any question that caused confusion or delay.
  3. Refine the question set and prefill rules based on that feedback.
  4. Scale the revised template across the broader site network.

Assign clear owners before the pilot starts:

  • A requestor who owns the protocol-specific question list.
  • A data prefill owner who maintains the historical site profile database.
  • A site reviewer, typically the PI or lead coordinator, who signs off on prefilled data.
  • A scoring owner who applies the weighting consistently across sites.

Reusable site profiles stored centrally, a single portal rather than a new link for every study, and a modest acknowledgement of the time sites spend on feasibility work all reduce friction. The five-stage feasibility process built around staged automation shows how these pieces fit together operationally rather than as isolated fixes.

Pro Tip: Give sites a standing profile they can update quarterly instead of asking them to rebuild it from scratch for every new study.

How an operational partner approaches feasibility redesign

Feasibility optimization can be approached as one symptom of a broader operational mapping problem, not a standalone form-design exercise. The AI Velocity Diagnostic traces a sponsor's path from protocol finalization to first patient in, and feasibility bottlenecks usually show up as duplicated questions across studies, missing EHR methodology, or prefill data nobody owns.

The non-technical fixes matter as much as any tooling:

  • Align data owners before automating anything, so prefilled fields have one accountable source.
  • Build prefill rules around fields that genuinely do not change study to study.
  • Gate every prefilled count behind PI or coordinator sign-off, never auto-submit.

Readers can see how this plays out in site performance tracking on the site performance analytics piece, which covers the KPIs that feed back into scoring weightings over time.

Pragmatic trade-offs between sponsors and sites

The real bottleneck in feasibility work is rarely the questionnaire itself. It is the lack of sponsor and CRO agreement on a shared, flexible site profile that could eliminate half the redundant questions sites answer every year. Standardization only works when sponsors buy into it organizationally, not just technically, and that buy-in comes from piloting changes with real site feedback rather than mandating a new form from a committee. Prioritize the fields that actually affect selection quality, and let everything else go.

— John

Getting hands-on help implementing this

Most operations teams know what a better feasibility process looks like. The harder part is rebuilding the workflow, the data ownership, and the sign-off gates around it while studies are already in flight. An operational partnership can redesign prefill rules, scoring logic, and governed automation around actual protocols instead of a generic template after mapping where feasibility work currently breaks down.

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A typical starting engagement runs diagnostic first, pilots the redesigned questionnaire on a handful of active sites, then scales once completion time and site satisfaction hold up. Teams that want to see the solutions involved can review the full clinical and regulatory operations offering, or book a diagnostic to scope the fastest path for a specific trial.

FAQ

What is a feasibility questionnaire?

A feasibility questionnaire is the form sponsors send to prospective trial sites to assess whether a site can realistically conduct a given protocol. It typically covers patient population, staff experience, equipment, and competing trial load, and the response feeds directly into site selection.

What are the five feasibility factors sponsors typically assess?

Definitions vary by sponsor, but a common version covers patient population and recruitment potential, investigator and staff experience, facilities and equipment, competing trials, and timeline or budget fit. The FDA's guidance on operational feasibility frames these around investigator capability, patient population availability, and data integrity rather than a fixed numbered list.

What are good survey questions for a feasibility study?

Strong questions are closed-ended and protocol-specific, such as asking for an eligible patient count over a defined time window or a yes/no on whether a required piece of equipment is on site. Questions that cannot be verified or do not map to a protocol requirement add burden without improving the selection decision.

What are the major components of a feasibility study form?

Most forms include a protocol snapshot, site and staff profile, patient population counts, facility and equipment checks, competing trial disclosure, IRB status, and a timeline or interest section. Industry commentary from ACRP points to standardized, flexible profiles across these components as the fix for inconsistent sponsor requests.

How long should a feasibility questionnaire take to complete?

A well-designed, protocol-focused questionnaire can be completed in one to two hours, as seen with the SASI instrument in evaluation. Broader benchmarking shows the overall feasibility assessment cycle running close to a month, with sponsor and CRO response time, not site completion, driving most of that length.

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