From Questionnaires to Conversations: Improving Clinical Trial Feasibility

Both Almenia and Christine shared perspectives that emphasized why it’s important to understand what sites can realistically deliver, identify operational issues earlier and keep the patient experience in view throughout clinical trial execution.

A major challenge facing clinical trial sponsors today is predictability in execution. Protocols are becoming more complex, competition for patients is increasing and sites are being asked to do more with the same or fewer resources. Because of this, even well-designed studies can struggle with activation timelines, patient identification and enrollment forecasting.

Sponsors can also underestimate the cumulative impact of these operational bottlenecks. Long before the first patient is enrolled, a trial can lose time through feasibility reviews, regulatory submissions, contracting and budget negotiations.

By the time enrollment begins to struggle, several underlying issues may already have occurred and the study may already be behind schedule.

In a recent Xtalks Spotlight discussion, Allucent’s Almenia Garvey and Christine Khoza explored how sponsors can bring more predictability to clinical trial execution. They shared how early site collaboration, stronger site intelligence and artificial intelligence (AI) could help teams identify operational risks, make more informed study decisions and build stronger relationships with sites.

Clinical Trial Delays Can Start Before Enrollment

Christine pointed out that clinical trial delays can build through a series of smaller operational bottlenecks. Feasibility reviews, regulatory submissions, contracts, budget negotiations and site readiness can each eat away at the timeline before recruitment even begins.

At the same time, protocols are becoming more complex, and sites are being asked to do more with the same or fewer resources.

This makes enrollment an incomplete measure of how well a study is progressing.

As Christine explained, “By the time enrollment is struggling, many of the underlying issues have already occurred.”

Almenia advocated for closer collaboration with sites, which often have years of firsthand experience with the patients a study hopes to recruit. That local knowledge may not be fully captured through standard feasibility assessments or historical site data.

“I’ve been at sites where the patients are coming in with cookies because they like their receptionist, and so they really know and understand these patients and these patient populations,” shared Almenia.

When study teams tap into this site familiarity early, they can develop more realistic feasibility assessments and stronger recruitment strategies. It can also help reduce repetitive administrative tasks, giving site staff more time to focus on patient care.

Moving From Feasibility Questionnaires to Feasibility Conversations

Traditional feasibility processes usually rely on a standardized questionnaire to assess a site’s capacity, staffing and equipment. But Almenia argued that standardized questions can miss important context about how a site operates.

“I don’t think we should be having feasibility questionnaires. I think we need to be having feasibility conversations because that way we can really get what’s under the hood,” she said.

A site might check “no” to a question about having a specific piece of equipment, but a conversation could reveal that it has direct access to that equipment at a hospital next door.

Almenia also highlighted the value of long-term strategic relationships with sites. Talking about Allucent’s IGNITE site network, she explained that knowing more about a site’s capabilities before a study starts can give sponsors a clearer picture of what may work and how quickly the site can get started.

Because these relationships and elements of collaboration are established beforehand, sites may be able to move more quickly once a study begins. This can also reduce administrative burden and delays that often appear during study start-up.

Sites can support enrollment planning by providing forecasts based on their experience, what they are seeing in their patient populations and what they are hearing directly from patients. Sponsors, therefore, have more insight into site capabilities and can predict enrollment more reliably.

“The best studies aren’t really built on contracts or work orders. It’s really built on trust, transparency, and a shared commitment between sponsors, CROs and the sites to deliver what’s best for the patients,” noted Almenia.

Looking at the Signals That Come Before Enrollment

Patient enrollment is a lagging indicator, meaning problems may only become visible after they have already developed.

Christine emphasized the need to monitor “leading indicators” throughout the entire study lifecycle. These signals include contract cycle times, activation timelines, data-entry timeliness, query resolution, screen-failure rates and protocol deviations.

“The real value comes when data and governance work together,” Christine said.

Governance can create structured discussions between sponsors, contract research organizations (CROs) and sites around performance, capacity and quality. When performance metrics are shared transparently, teams can respond as soon as a trend begins to change.

For example, if screen failures start to rise, teams can investigate the cause sooner instead of waiting until a study milestone is missed. This approach can also help identify delays in activation or changes in data quality while there is still time to respond.

Christine explained: “…when sponsors move from transactional engagement to strategy collaboration supported by the shared planning and the metrics and shared accountability as well as governance in place, they create a more predictable and efficient environment…”

Where AI and Site Intelligence Could Help

Almenia suggested that AI and site intelligence could improve site feasibility decisions by using historical site data, including recruitment trends and operational indicators, to identify risks sooner.

Questionnaires could be prepopulated with information already known about a site instead of asking staff to enter the same details again.

For clinical trials, AI could help research teams identify underserved patient populations and geographic areas with less access to studies, supporting more inclusive recruitment planning.

Site intelligence gives sponsors a more current view of a site’s capabilities, patient access and operational readiness when selecting and starting up sites.

These tools still come back to the people conducting and participating in clinical research, noted Almenia. Reducing repetitive work can give research teams more time for the judgement, creativity and relationships that depend on people.

“Patients place an enormous amount of trust in our research teams and our sites when they choose to participate in a clinical trial,” Almenia said. “And so when we invest in sites and reduce their burden, equip them with better tools and support, we’re ultimately improving the patient experience in our clinical trials.”

She closed our discussion with“…it isn’t the goal to simply run better study, the goal is to bring new therapies to patients faster, more efficiently.”

This article was created in collaboration with the sponsoring company and the Xtalks Editorial team.