Professional team working on project; clinical trial feasibility concept

Patient Recruitment: The Role of Data Science in Clinical Trial Feasibility and Site Selection

Early efforts in clinical trial feasibility analysis and site selection can set a trial up for success — or burden it with challenges. 

The goals of clinical trial feasibility studies and site selection include “identify[ing] research sites that are most likely to recruit a sufficiently high number of subjects within trial timelines,” write Lars Hulstaert and fellow researchers. These early efforts form a foundation upon which all other clinical trial efforts are built. 

Clinical trial teams with access to a digital solution offering a single source of truth can more easily collaborate on these foundational steps. With their early efforts backed by deep, wide-ranging data access and focused analytical insights, these teams can better assess feasibility and choose the site that best supports clinical trial success. 

Trial Feasibility and Site Selection: Why Data Matters

Feasibility studies require data — and a lot of it. Both sponsors and contract research organizations may emphasize different elements, demanding different documentation and supporting evidence. “Feasibility questionnaires can be anywhere from 10 to 40 pages,” says Nancy Sacco, Head of Clinical Operations at SiteBridge Research Inc., citing her own experience working with clinical trial sponsors during the site selection process. 

Often, this paperwork includes “pie-in-the-sky” questions about general site features and capabilities — questions that can raise aspirations but that do not drill down into the available data or realities of a specific site, says Sacco. Without robust data regarding clinical trials, key opinion leader (KOL) involvement, and study site data, these questions and their answers do little to reveal the true opportunities or challenges presented by a potential trial site or study timeframe. 

Data is also crucial to trial feasibility and site selection efforts because, in many cases, clinical trial teams are pursuing many early goals at once. Efforts to gauge feasibility, identify optimal trial sites, and build a diverse, complete patient cohort may proceed in parallel. 

These efforts are closely related and benefit from access to substantially similar data sets. Yet they require tailored insights for each work group. 

“More than ever, companies are finding that managing these interdependent journeys in a holistic and integrated way is essential to their success in achieving change,” write IBM’s Julien Oleg Willard and Andrea Dobrindt, and Pfizer’s Jonathan Crowther. Giving feasibility and site selection teams access to a digital single source of truth can help integrate their respective efforts.

Patients seated in doctor's waiting room; clinical feasibility concept 

Boost Clinical Trial Feasibility Analysis and Site Selection With Better Data

Anju’s TA Scan helps improve clinical trial feasibility analysis and site selection. TA Scan accesses deep and far-ranging data sources and updates weekly to include the best available information. These search capabilities cover all disease indications on global, national, and local levels, allowing clinical trial researchers to assess available clinical trial information, KOL contributions, and study site data. 

Within TA Scan, clinical trial planning teams can: 

  • Aggregate and analyze data insights within a single source of truth that supports the entire clinical study project workflow.
  • Perform comprehensive trial feasibility analysis, conduct site selection comparisons, and understand site availability estimates. 
  • Work within a self-driven, intuitive user interface. 
  • Share interactive reports containing real-time data, either within TA Scan or by exporting reports to PDF to send information to a broader audience. 

TA Scan incorporates a cutting-edge feasibility algorithm to help trial teams plan when and where they’ll initiate clinical trials. By identifying and analyzing competing clinical studies occurring at preferred sites, TA Scan’s algorithm can provide better insight on when to start a clinical trial to avoid unnecessary competition for qualified trial participants. 

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Authored by Elke Ydens, Associate Director of Business Solutions, Data Division

Elke Ydens, Associate Director of Business Solutions at Anju’s Data Division, brings over a decade of life sciences experience and a PhD in Biochemistry and Biotechnology from the University of Antwerp. As a Subject Matter Expert in Data Science, she adeptly addresses customer needs, leveraging her background in neuro-immunology and biochemistry. Elke remains dedicated to professional growth, contributing to industry publications, and staying updated on industry trends, while also finding success in extracurricular pursuits, formerly competing in world and European bridge championships, and more recently active in beekeeping and coaching. Connect with Elke on LinkedIn to explore her achievements further.

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