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:

Sample Question	Purpose
How many patients with the target condition were treated at your site in the past 12 months?	Estimate available patient pool using real-world data
What percentage of patients at your site are willing to participate in clinical trials?	Gauge cultural and demographic acceptance of trials
Do you have access to patient registries or referral networks?	Assess additional recruitment sources

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:

Parameter	Example Value
Limit of Detection (LOD)	0.05 ng/mL for biomarker assay
Limit of Quantitation (LOQ)	0.10 ng/mL for biomarker assay
Power backup duration	Minimum 8 hours for critical equipment

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

Domain	Weight	Example Metric
Patient Recruitment	40%	Number of eligible patients per year
Investigator Experience	25%	Number of prior GCP-compliant trials
Infrastructure Readiness	20%	Validated equipment and facilities
Regulatory/EC Environment	15%	Average ethics review timeline

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.