How to Optimize Site Selection & Feasibility with AI & Real-World Data

In the complex and time-sensitive world of clinical trials, selecting the right research sites and assessing their feasibility is pivotal for ensuring trial success. Yet, the traditional approach to site selection—often reliant on historic performance, spreadsheets, subjective assessments, and manual surveys—continues to be plagued with inefficiencies. Low patient enrollment, site dropouts, and protocol deviations are frequent outcomes of poor site feasibility planning.

Enter Artificial Intelligence (AI) and Real-World Data (RWD)—a powerful combination that is transforming how sponsors, CROs, and clinical operations teams approach site selection and feasibility. Together, they enable data-driven decisions, reduce risk, and improve the overall performance of clinical research programs.

Why Traditional Site Selection Is Broken

• Outdated Performance Metrics: Sponsors often rely on past trial participation or investigator experience, which may not reflect current site capabilities or available patient populations.

• Manual Feasibility Surveys: Static, time-consuming, and often inaccurate.

• Limited Visibility into Patient Access: There’s little real-time insight into whether the site has access to the desired patient population.

• Geographic & Demographic Blind Spots: Underserved populations and non-traditional research sites are frequently overlooked.

These limitations have led to significant inefficiencies:

• 11% of sites fail to enroll a single patient.

• 37% underperform and fail to meet enrollment targets.

• 50% of clinical trials are delayed due to poor site selection.

The AI & RWD Advantage

By leveraging AI with real-world data, clinical operations teams can reimagine site selection from being reactive and risk-prone to predictive and performance-driven.

1. Predictive Site Performance Modeling

AI algorithms can analyze historical trial data, performance KPIs (e.g., enrollment speed, data quality, protocol deviations), and investigator metrics to predict which sites are most likely to perform well in a specific therapeutic area or indication.

🔍 Example: A machine learning model trained on oncology trial data could predict that Site A has a 75% likelihood of exceeding enrollment targets within the first 60 days based on its performance in similar protocols.

2. Real-World Patient Data Integration

Accessing electronic health records (EHR), claims data, and patient registries allows feasibility teams to assess whether a site has access to a trial’s eligible patient population. AI can process this data to identify geographic "hotspots" with high concentrations of eligible patients.

🌍 Example: A site in Houston might have access to 2,000 patients with a rare mutation, identified by cross-referencing genomic EHR databases and claims data.

3. Automated Feasibility Assessments

Natural language processing (NLP) and AI agents can automate feasibility questionnaires, assess completeness, and cross-check responses against known performance metrics and RWD, reducing site burden and improving accuracy.

⚙️ Example: AI bots can pre-fill feasibility forms based on integrated data sources and flag inconsistencies, reducing turnaround time from weeks to hours.

4. Diversity and Inclusion Optimization

By mapping real-world data across underserved populations and community care settings, sponsors can expand site selection to include non-traditional sites and ensure better diversity in trials.

👩🏽‍⚕️ Example: AI can identify Federally Qualified Health Centers (FQHCs) that serve diverse communities and have a track record of clinical research participation, helping meet FDA’s diversity guidance.

Building the Future of AI-Powered Feasibility

To effectively implement AI and RWD in site selection and feasibility, organizations need:

• Access to High-Quality Data: Clinical trial data, RWD, lab data, and patient registries, all governed by strong data privacy practices.

• Interoperability Platforms: Unified data platforms that integrate RWD and CTMS/eTMF systems to support real-time insights.

• Configurable AI Models: AI tools that can be configured to specific therapeutic areas, geographies, and trial complexity.

• Collaborative Ecosystems: Partnerships with health systems, site networks, and data vendors to enable better data flow.

Cloudbyz: Powering AI-Driven Site Selection and Feasibility

At Cloudbyz, we are redefining clinical operations with our AI-powered feasibility and site selection engine—fully integrated into our unified eClinical platform.

Key capabilities include:

• Site Intelligence Dashboard: Real-time scoring of sites using AI models based on past trial KPIs and therapeutic alignment.

• Patient Population Heatmaps: AI-generated geographic overlays highlighting patient-rich areas based on RWD.

• Automated Feasibility Workflow: Embedded AI agents and smart forms streamline and validate feasibility assessments.

• Diversity Optimization Tools: Identify high-potential sites serving diverse and underserved populations.

Whether you're running early-phase trials or large-scale pivotal studies, Cloudbyz helps sponsors and CROs:

• Reduce time spent on site feasibility by up to 70%

• Improve site activation speed by 50%

• Increase enrollment success rate by 30%

Conclusion: Smarter, Faster, More Inclusive Trials

Optimizing site selection and feasibility with AI and real-world data is no longer optional—it’s essential for achieving operational excellence, regulatory compliance, and patient-centricity in clinical trials. By embracing data-driven methods, sponsors can build faster, smarter, and more inclusive trials that bring innovative therapies to patients sooner.

Let’s talk about how Cloudbyz can accelerate your site selection strategy with AI and RWD.