A Modern Approach to Clinical Trial Feasibility and Site Selection

Clinical trial feasibility is a critical step in study planning, yet many sponsors and CROs still rely on fragmented data, manual workflows, and disconnected processes. As trials become more complex and timelines tighten, feasibility is evolving into a more data-driven, AI-enabled discipline that directly impacts site selection, enrollment performance, and overall trial success.

This guide outlines how sponsors assess clinical trial feasibility, how those insights inform site selection, and what tools are available to modernize the process.

Clinical Trial Feasibility: Key Questions Answered

How do I assess the feasibility of a clinical trial site?

Clinical trial feasibility is assessed by evaluating whether a site has the patient population, investigator experience, infrastructure, and operational capacity to successfully execute a specific protocol. Sponsors typically consider a combination of patient access, historical trial performance, site capabilities, and resource availability to determine fit. Leading sponsors are increasingly incorporating real-world patient data, site-validated inputs, and AI-driven analysis to make faster, more confident site selection decisions.

How do feasibility insights inform site selection decisions?

Feasibility insights help sponsors identify which sites are most likely to enroll patients efficiently and execute the study successfully. By comparing sites based on patient access, historical performance, and current site readiness, sponsors can prioritize high-performing sites and reduce the risk of delays. This leads to more targeted site selection and more predictable enrollment outcomes.

What tools are available for clinical trial site feasibility assessment?

Sponsors use a range of tools for clinical trial feasibility assessment, including historical trial databases, real-world evidence platforms, feasibility questionnaires, and AI-driven solutions. While these tools provide valuable inputs, they are often siloed. Modern AI-driven feasibility platforms integrate these data sources into a single workflow, enabling more accurate site identification, streamlined feasibility execution, and better decision-making.

How Do Sponsors Assess Clinical Trial Feasibility?

Clinical trial feasibility is the process of determining whether a study can be successfully executed at a given set of sites. For sponsors and CROs, this means evaluating both scientific fit (can the site support the protocol?) and operational readiness (can the site deliver results on time?).

Key Inputs to Feasibility Assessment

Most feasibility assessments rely on a combination of the following data points:

• Patient population access
  Does the site treat enough patients who meet the study’s inclusion and exclusion criteria? This is often the most critical factor, especially for complex or rare disease trials.
• Historical trial performance
  How has the site performed in past studies? Metrics such as enrollment rates, screen failure rates, and study completion timelines provide valuable signals.
• Investigator experience
  Does the principal investigator have experience with similar indications, protocols, or sponsors? Experienced investigators are more likely to navigate challenges effectively.
• Site infrastructure and resources
  Does the site have the staff, equipment, and operational capabilities required to run the trial?

AI is improving clinical trial feasibility by replacing manual, disconnected workflows with a more integrated approach. Sponsors can identify sites treating the right patients, validate site capabilities in real time, and make faster, more informed site selection decisions.

Solutions like H1’s Site Network Suite extend this approach by enabling sites to validate their capabilities, manage their profiles, and respond to feasibility workflows directly within the platform—creating a continuously updated source of truth.

How do feasibility insights inform site selection decisions?

Feasibility insights directly shape site selection, but their value depends on the quality and completeness of the data behind them. Sponsors need a clear, current view of both patient availability and site capabilities to confidently select the right sites.

Strong feasibility insights are built on:

• Comprehensive patient data
  Understanding where eligible patients are actively being treated, not just where trials have historically been run
• Historical site performance
  Evaluating past enrollment, execution, and study delivery to identify reliable sites
• Site-reported and validated capabilities
  Capturing current capacity, infrastructure, and interest directly from sites to reflect real-world readiness

While historical data provides important context, it is often incomplete on its own. Site-reported input ensures feasibility decisions are based on current, validated conditions—not assumptions.

When these data sources are combined, sponsors can:

• Prioritize sites most likely to meet enrollment targets
• Reduce the risk of underperforming or inactive sites
• Build more targeted, high-performing site networks

The result is more confident site selection decisions, leading to faster study startup, more predictable enrollment, and stronger overall trial execution.

What Tools Are Available for Clinical Trial Feasibility Assessment?

A variety of tools and methods are used to assess clinical trial feasibility, each with its own strengths and limitations.

Traditional Tools and Methods

• Feasibility questionnaires
  Widely used to collect site-specific information, but often time-consuming and inconsistent.
• Historical trial databases
  Provide insight into past performance, but may lack real-time updates or context.
• Internal sponsor data
  Includes previous site relationships and study outcomes, though often siloed.

Data-Driven Approaches

• Real-world evidence (RWE) platforms
  Offer visibility into patient populations, treatment patterns, and disease prevalence.
• Investigator and site intelligence platforms
  Aggregate historical data on investigators, sites, and clinical trial participation.

While these tools improve visibility, they are often disconnected from execution workflows.

AI-Driven Feasibility Platforms

AI-driven feasibility platforms represent the next evolution by combining real-world patient data and site and investigator intelligence into a single, connected system—while adding real-time, site-validated input and integrated workflows.

Modern AI-driven platforms like H1 enable:

• Protocol-aware site recommendations
  AI analyzes inclusion/exclusion criteria and historical data to identify sites treating relevant patients.
• Integrated feasibility workflows
  Sponsors can manage site identification, questionnaire distribution, and response analysis within one platform—eliminating fragmented processes
• Site-reported and validated capabilities
  Sites can manage and confirm their own profiles, ensuring feasibility decisions reflect current capacity, infrastructure, and interest
• Continuously updated, structured data
  Feasibility inputs are standardized and reusable, creating a more reliable and improved source of truth over time

By unifying these elements, sponsors move beyond static datasets toward a more dynamic, continuously improving approach to clinical trial feasibility.

Platforms like H1 illustrate this shift by combining AI-driven feasibility with a connected site network—enabling faster, more accurate, and more confident site selection decisions.

The Future of Clinical Trial Feasibility

As protocols become more complex and timelines tighten, sponsors need a more connected approach to feasibility. AI-driven platforms that bring site identification, feasibility execution, and site collaboration into a single workflow are enabling faster, more accurate site selection.

See how H1 can help you streamline clinical trial feasibility and make more confident site selection decisions.

Request a demo to learn more.