Designing Feasibility Questionnaires Using Historical Site Data

The Importance of Historical Site Data in Feasibility Planning

Feasibility questionnaires are foundational tools in clinical trial planning. They help sponsors and CROs identify and select high-performing sites based on several factors like patient pool, investigator experience, infrastructure, and regulatory track record. However, when these questionnaires are designed without historical context, they can result in overly optimistic or inaccurate site responses. That’s where leveraging historical site data becomes critical.

Historical site data includes past enrollment rates, protocol deviation frequencies, screen failure rates, regulatory inspection outcomes, and adherence to visit schedules. Sponsors that fail to incorporate this data often face recruitment delays, budget overruns, and poor site compliance. Regulatory bodies including the FDA, EMA, and MHRA emphasize the use of evidence-based feasibility strategies during sponsor inspections.

In this article, we explore how to use historical site data to design smarter, more predictive feasibility questionnaires that improve site selection and study startup efficiency.

Types of Historical Data Relevant to Questionnaire Design

Historical site data spans multiple domains. The most useful categories include:

• Enrollment History: Number of subjects enrolled in similar trials within a specific timeframe
• Protocol Adherence: Frequency of deviations and their root causes
• Screen Failure Rates: Percentage of screened patients not meeting inclusion criteria
• Site Activation Timelines: Average time from contract finalization to first patient in (FPI)
• Regulatory Inspection Outcomes: FDA 483 observations, MHRA findings, or internal QA audits

Below is an example data summary from three sites in a cardiovascular trial:

From this table, it’s evident that Site A outperformed others in all key areas. Integrating this insight into a questionnaire helps to focus future feasibility assessments on parameters that matter.

Integrating Data into Feasibility Questionnaire Logic

Feasibility tools often consist of static checklists or self-reported site capabilities. When these are integrated with historical performance data, they become much more predictive. Here’s how historical data can enhance questionnaire sections:

• Recruitment Potential Section: Pre-fill enrollment numbers from past studies and ask the site to explain any changes
• Protocol Adherence Section: Highlight deviation patterns from previous trials and assess current mitigation measures
• Timeline Commitments: Use actual past activation data to validate new timeline estimates

For example, a dynamic form might display: “In your last three trials in this therapeutic area, your average enrollment was 20 patients over 6 months. What has changed to support your estimate of 60 patients in this protocol?”

This approach discourages over-promising and helps differentiate high-performing, realistic sites from aspirational responders.

Sources of Historical Site Data

Historical site data can be gathered from several internal and public sources:

• Clinical Trial Management Systems (CTMS): Capture site-level metrics from previous studies
• Electronic Data Capture (EDC) Platforms: Document protocol adherence and visit windows
• Trial Registries: Data from Be Part of Research (NIHR) and other registries to validate enrollment timelines
• Quality Management Systems (QMS): Archive audit outcomes, CAPA timelines, and deviations

Sponsors that maintain a structured site master file with past feasibility, audit reports, and performance summaries can extract this data with minimal effort. It’s also beneficial to include CRO partner databases and publicly available performance scores (e.g., from the TransCelerate Shared Investigator Platform).

Feasibility Questionnaire Elements That Benefit from Data Integration

Not all parts of a feasibility questionnaire require historical data, but certain sections benefit significantly from it:

By designing forms with auto-filled historical fields, sponsors can reduce bias and increase transparency. Some tools even allow scoring systems based on prior performance benchmarks.

Case Study: Data-Driven Feasibility Yields Better Enrollment

In a 2023 Phase II neurology study, the sponsor used historical site performance data to filter out low-recruiting sites from a previous epilepsy trial. By incorporating metrics such as “patients enrolled per FTE” and “visit adherence rate,” they excluded 30% of sites that had previously delayed timelines. The remaining sites achieved 95% of the recruitment target three months ahead of schedule.

This outcome illustrates how applying historical metrics during feasibility tool design directly impacts enrollment, cost, and data integrity.

Tools and Platforms That Support Data-Driven Questionnaire Design

Sponsors can use various platforms to operationalize this approach:

• CTMS Platforms: Veeva Vault CTMS, Medidata RAVE
• Feasibility Tools: SiteIQ, Clinscape Feasibility Module
• Analytics Dashboards: Tableau, Power BI connected to CTMS/EDC sources
• Risk-Based Monitoring Tools: RBM dashboards that include performance trend lines

These systems allow sponsors to design adaptive questionnaires, conduct real-time validation of site claims, and score site responses against benchmarks.

Challenges and Considerations

Despite the advantages, there are challenges to using historical data:

• Data inconsistency across CROs and systems
• Lack of access to complete legacy data for global sites
• Privacy and data protection regulations (e.g., GDPR)
• Misinterpretation of context (e.g., poor performance due to protocol flaws, not site issues)

Therefore, sponsors must contextualize historical data and allow sites to provide explanations for deviations or poor performance. Data should be used to initiate dialogue, not penalize sites without cause.

Conclusion

Designing feasibility questionnaires using historical site data enables evidence-based site selection, reduces trial risk, and improves regulatory compliance. Sponsors should move away from static, self-reported surveys and adopt dynamic, data-informed tools that consider past performance. Platforms such as CTMS, QMS, and analytics dashboards can help integrate these insights into feasibility tools, creating a predictive framework for identifying high-performing, inspection-ready sites. In doing so, the industry takes a meaningful step toward smarter, faster, and more reliable clinical trial execution.

Adapting Feasibility Tools to Specific Therapeutic Areas in Clinical Trials

Why Customization Matters in Feasibility Assessments

While feasibility questionnaires are a standard component of clinical trial planning, a “one-size-fits-all” approach often results in incomplete or misleading data. Different therapeutic areas present unique operational, regulatory, and recruitment challenges. Therefore, it is essential to adapt feasibility tools based on the specific clinical, procedural, and patient population characteristics of each therapeutic indication.

Regulatory agencies like the FDA and EMA expect feasibility efforts to align with study-specific complexities. For example, a Phase III oncology trial will have very different infrastructure and recruitment requirements compared to a vaccine study or a dermatology trial. Customization ensures that the sponsor gathers high-fidelity, indication-specific data, which reduces trial delays, improves protocol adherence, and enhances inspection readiness.

In this tutorial, we explore how sponsors and CROs can develop and deploy feasibility tools tailored to therapeutic areas including oncology, cardiology, infectious diseases, CNS disorders, and rare diseases.

Key Variables Differentiating Therapeutic Areas

Each therapeutic area involves unique variables that influence trial feasibility, including:

• Diagnostic criteria and screening processes
• Specialized equipment and lab tests
• Patient population size and disease prevalence
• Eligibility complexity and inclusion/exclusion criteria
• Site specialization and investigator qualifications

For example, an oncology trial may require immunohistochemistry, genetic sequencing, and radiologic assessments, while a vaccine trial may emphasize storage conditions for biologics and capacity for large-scale subject screening. Failing to account for these differences can lead to underperformance and protocol deviations.

Customizing Feasibility Tools in Oncology Trials

Oncology trials are often complex, with multiple arms, biomarker-based eligibility, and long treatment durations. Therefore, feasibility tools must address:

• Availability of tissue samples for biomarker testing
• Access to imaging facilities for RECIST-based assessments
• Experience in handling cytotoxic agents and managing SAE reporting
• Supportive care services like transfusion, nutrition, and palliative care

Below is a sample customization framework for oncology feasibility:

Oncology sites must also demonstrate access to multidisciplinary tumor boards, availability of radiology archiving systems, and electronic SAE tracking tools such as Argus Safety. To cross-reference recruitment and prior site experience, sponsors may consult the EU Clinical Trials Register.

Adapting Feasibility for Cardiovascular Trials

Cardiology studies may involve device implantation, ECG monitoring, and stress testing. In such cases, feasibility tools must capture:

• Availability of validated ECG and echocardiogram equipment
• GCP training in cardiovascular endpoints (e.g., MACE criteria)
• Presence of a catheterization lab or interventional cardiologist
• Patient adherence history in hypertension or dyslipidemia trials

Sample values might include:

• Validated ECG machine model: GE MAC 5500
• Calibration certificate date: June 2025
• Cardiology sub-investigator GCP completion: March 2024

Moreover, cardiology trials may need precise documentation of concomitant medications and lifestyle interventions. Questionnaires must be adapted to capture these site competencies.

Feasibility Tools for Infectious Disease Trials

Infectious disease trials—especially in vaccines or antimicrobial resistance studies—require a different set of site capabilities. Sponsors must customize feasibility questionnaires to capture:

• Cold-chain infrastructure for biologics (2–8°C and -20°C storage)
• Experience with biosafety level (BSL-2 or BSL-3) laboratory handling
• Regulatory familiarity with expedited review processes (e.g., EUA)
• Access to outbreak-prone communities or travel clinics

Feasibility templates for such trials often include verification of:

Sites that have participated in past epidemic response trials (e.g., COVID-19, H1N1) often score higher in feasibility assessments due to institutional readiness and protocol familiarity.

Feasibility Considerations in CNS Trials

CNS trials for indications like Alzheimer’s, Parkinson’s, or depression bring unique recruitment and assessment challenges. Key customization points include:

• Site capability for neurocognitive assessments (e.g., MMSE, MoCA)
• Investigator training in psychiatric or neurologic scales
• Caregiver consent handling for dementia patients
• Experience with long-term follow-up visits (≥12 months)

Example question: “Is your site trained in administering ADAS-Cog or CDR-SB assessments for Alzheimer’s patients?”

Feasibility tools must also factor in patient adherence barriers, comorbidities, and ability to comply with imaging and lab visit schedules. CNS studies often suffer from high dropout rates, so feasibility assessments should include questions on patient retention strategies.

Special Feasibility Approaches in Rare Disease Trials

Rare disease studies are constrained by extremely small patient populations. Feasibility tools in this context must go beyond traditional metrics and emphasize:

• Site access to patient registries or genetic databases
• Partnerships with advocacy groups or KOL networks
• Willingness to enroll non-local patients (e.g., travel support programs)
• Experience in adaptive trial designs and expanded access protocols

Due to ultra-orphan populations, sponsors may consider virtual or decentralized feasibility approaches, integrating telemedicine and remote monitoring tools. Additionally, feasibility questionnaires should include sections on protocol flexibility and site logistics for rare disease patients traveling long distances.

Best Practices for Implementing Customized Tools

To deploy customized feasibility tools effectively:

• Develop therapeutic area-specific templates reviewed by KOLs
• Pre-fill public domain data (e.g., IRB timelines) to reduce site burden
• Digitize questionnaires using secure platforms integrated with CTMS
• Score site responses using indication-weighted algorithms
• Train feasibility teams on therapeutic-specific nuances

Some organizations maintain a Feasibility SOP that includes annexures for oncology, cardiology, etc., ensuring consistency while allowing adaptation. For sponsors working with multiple CROs, standardizing customized tools via cross-functional working groups is recommended.

Conclusion

Feasibility tool customization is a regulatory, scientific, and operational imperative. Generic questionnaires can no longer capture the complexity of modern trials across diverse therapeutic areas. By developing indication-specific tools—grounded in real-world data, infrastructure requirements, and investigator qualifications—sponsors can enhance patient recruitment, ensure compliance, and minimize protocol deviations. With global trials becoming more complex, therapeutic customization of feasibility tools is essential for success in today’s regulatory environment.

Essential Questions for Designing an Effective Feasibility Questionnaire

Understanding the Role of Feasibility Questionnaires

Before selecting sites and investigators, sponsors and CROs must carefully evaluate a site’s ability to successfully execute a clinical trial. A feasibility questionnaire is one of the most important tools for this assessment. These documents collect structured information about a site’s resources, patient pool, regulatory experience, and infrastructure readiness. Regulatory agencies such as the FDA, EMA, and national authorities expect sponsors to document feasibility efforts as part of Good Clinical Practice (GCP) compliance. Without a robust feasibility process, sponsors risk delays, under-enrollment, and inspection findings during trial audits.

Feasibility questionnaires typically cover domains such as:

• Patient recruitment and retention potential
• Principal Investigator (PI) and sub-investigator experience
• Site infrastructure, including equipment and labs
• Previous performance in similar therapeutic areas
• Local regulatory and ethics committee processes

For example, in oncology studies, questionnaires often probe whether the site has access to pathology labs capable of immunohistochemistry testing, or whether genetic testing partnerships exist. In infectious disease studies, questions may focus on availability of biosafety level facilities. Thus, while core domains remain consistent, therapeutic area–specific tailoring is essential.

Critical Patient-Related Questions

Patient recruitment is one of the most common barriers to timely trial completion. Regulators, including the European Medicines Agency (EMA), emphasize that feasibility assessments should be realistic and data-driven. A questionnaire must therefore ask targeted questions about patient populations. Examples include:

Incorporating epidemiological data strengthens these questions. For example, if a site estimates 300 eligible patients annually but national disease burden data suggests fewer than 50 cases in the region, this discrepancy raises concerns about overestimation. Sponsors should cross-check questionnaire responses with external databases such as ClinicalTrials.gov to validate feasibility claims against trial recruitment histories.

Questions on Investigator and Staff Experience

A site’s human resources are equally critical. Regulators often highlight inadequate investigator oversight as a frequent finding in inspections. Questionnaires should evaluate whether the PI and supporting staff have the necessary experience. Key questions include:

• How many clinical trials has the PI conducted in the past five years, and in which therapeutic areas?
• Has the PI received any regulatory inspection findings related to GCP?
• What is the average turnover rate of study coordinators and research nurses?
• What GCP training and certification do staff currently hold?

For example, a PI with ten oncology trials completed but with multiple FDA Form 483 citations may be a higher risk compared to a less experienced PI with a clean regulatory record. Feasibility questionnaires should capture such nuances.

Infrastructure and Technology Questions

Infrastructure capability directly influences trial quality. For complex trials requiring bioanalytical testing, imaging, or cold-chain management, questionnaires must go beyond basic facilities inquiries. Sample questions include:

• Does the site have validated -80°C freezers with continuous temperature monitoring?
• Are backup power systems in place to safeguard sample integrity?
• Is the site equipped with validated software for electronic data capture (EDC)?
• Are laboratory instruments calibrated according to international standards (e.g., ISO 15189)?

Some questionnaires include sample validation parameters such as:

These details help sponsors differentiate between sites that claim readiness and those that are genuinely prepared for trial operations.

Regulatory and Ethics Questions

Finally, feasibility questionnaires must assess local regulatory and ethics environments. Delays in IRB/EC approvals are a common reason for missed trial timelines. Essential questions include:

• What is the average IRB/EC review timeline for clinical trials at your institution?
• Do you have prior experience submitting to regulatory authorities such as FDA, EMA, CDSCO, or PMDA?
• Are there institutional policies restricting enrollment of vulnerable populations?

For example, if a site reports an average of 45 days for ethics approvals, sponsors can plan activation timelines accordingly. Sites with extended timelines (e.g., >90 days) may not be suitable for fast-track studies.

Transition to Next Considerations

The above domains—patient recruitment, investigator experience, infrastructure, and regulatory landscape—form the backbone of feasibility questionnaires. However, sponsors must also evaluate validation of responses, data reliability, and strategies to prevent overpromising. These aspects will be explored in Part 2, with focus on case studies, pitfalls, and best practices for robust feasibility planning.

Validating Feasibility Questionnaire Responses

Feasibility questionnaires are only useful if responses are accurate. Regulators and sponsors increasingly emphasize data verification as part of trial oversight. Sponsors must apply validation strategies to ensure that sites are not inflating capabilities or patient pools to secure trial participation.

One approach is to cross-verify patient pool estimates with hospital records, referral databases, or national disease registries. For example, if a site reports 500 annual cases of Type 2 diabetes, but regional public health data suggests only 300 cases, the sponsor should investigate. Similarly, sponsors should request anonymized patient counts or ICD-10 code reports to substantiate claims.

Case Study: Inflated Patient Recruitment Claims

A multinational sponsor faced delays in an oncology trial when three sites overestimated recruitment potential. While questionnaires projected 50 patients per site annually, actual enrollment was less than 10. Upon review, it was found that sites included patients outside inclusion criteria. This case underscores the importance of rigorous validation, including review of electronic health records (EHRs) and prior recruitment histories from registries such as ISRCTN Registry.

Common Pitfalls in Questionnaire Design

Despite best intentions, poorly designed questionnaires often result in incomplete or misleading data. Common pitfalls include:

• Overly generic questions that do not capture therapeutic-specific nuances
• Yes/No questions without quantitative context (e.g., “Do you have lab facilities?” instead of “How many calibrated centrifuges are available?”)
• Failure to include data validation fields or request supporting documentation
• Excessive questionnaire length leading to incomplete responses

To avoid these issues, sponsors should pilot-test questionnaires with selected sites and adjust based on feedback. Regulatory authorities also recommend focusing on essential questions that directly impact trial feasibility, rather than exhaustive lists that burden sites unnecessarily.

Best Practices for Effective Questionnaires

Effective feasibility questionnaires balance comprehensiveness with clarity. Best practices include:

• Tailoring questionnaires by therapeutic area (oncology, cardiology, infectious disease)
• Using a mix of quantitative and qualitative questions
• Integrating electronic platforms to streamline completion and analysis
• Embedding mandatory data validation checks (e.g., requiring supporting documentation uploads)

Some sponsors now deploy digital feasibility tools integrated with Clinical Trial Management Systems (CTMS). These allow automated scoring, comparison across sites, and identification of red flags such as inconsistent patient data. For example, an AI-enabled feasibility tool might score sites based on patient pool adequacy, infrastructure readiness, and regulatory history, generating a composite feasibility index for decision-making.

Sample Feasibility Scoring Framework

This weighted approach ensures objective decision-making while allowing customization for specific trial needs. For instance, in rare disease studies with small populations, patient recruitment weight might increase to 60%.

Conclusion

Feasibility questionnaires are a cornerstone of site selection and clinical trial planning. By including targeted questions on patients, investigators, infrastructure, and regulatory environment—and by validating responses through data cross-checks—sponsors can mitigate risks of underperformance and regulatory non-compliance. Effective design not only accelerates trial start-up but also strengthens inspection readiness by demonstrating a structured feasibility process.