Boost Trial Efficiency with Data-Driven Site Selection
Discover how AI-powered site selection can streamline clinical trials, saving time and resources for sponsors and improving trial accessibility.
Executive Brief
- The News: Inato's AI enhances site selection for 5,500 research sites.
- Clinical Win: AI reduces manual coordination, saving months of time.
- Target Specialty: Pharmaceutical companies and research sites benefit.
Key Data at a Glance
Number of Research Sites: 5,500
Platform Capability: Data-driven site selection
AI Analyst Function: Generate, refine, and compare site selection scenarios
Key Benefit: Dynamic and sponsor-led decision process
Traditional Approach Limitation: Months of manual coordination
Boost Trial Efficiency with Data-Driven Site Selection
What You Should Know:
– Today, Inato unveiled new AI capabilities to enhance and accelerate site selection. Sponsors can now use Inato’s platform to create and execute data-driven site selection scenarios based on real-time interest and verified data from over 5,500 sites quickly and easily.
– The new capabilities fill an acute gap for sponsor decision-makers, arming them with the insights they need to quickly and confidently choose the right mix of sites for each trial.
Inato Unveils AI-Powered Site Selection to Transform Clinical Trial Accessibility
Inato is on a mission to bring clinical research to every patient, no matter where they live. The global technology company unites trial planning, site selection, and patient pre-screening in a single AI-enabled platform designed to transform how research sites and sponsors work together. Leading pharmaceutical companies already trust Inato to improve trial accessibility and representation, while thousands of research sites use the platform to match with the right studies and streamline operations.
Site selection has long been one of the most complex and high-stakes steps for sponsors. Traditional approaches involve months of manual coordination across spreadsheets, geographies, and internal systems to model scenarios, validate assumptions, and gain alignment. Workarounds such as purchasing site lists, relying on outdated historical data, or building internal tools often fall short—frequently sending sponsors back to the same limited pool of known or unavailable sites. This inefficiency is increasingly unsustainable as competition for trial sites intensifies.
Inato offers a new model. Sponsors can now tap into a global network of more than 5,500 engaged research sites, each with a verified and continuously updated profile. The company’s new AI site selection analyst allows decisionmakers to use plain language to generate, refine, and compare site selection scenarios aligned with specific trial priorities, from enrollment speed and patient diversity to proven therapeutic expertise. Unlike static data sources, the AI combines Inato’s robust site data with real-time site interest and engagement, creating a dynamic and sponsor-led decision process.
“Data-driven site selection has often fallen short, either because the data was outdated, didn’t account for site interest, or didn’t leave room for sponsor judgment and expertise,” said Kourosh Davarpanah, co-founder and CEO of Inato. “With our new AI analyst, decisionmakers have the best of both worlds. They know their trials best and continue to drive the process, but they now also have an AI analyst that can help them make the best decisions based on qualified, interested sites.”
Inato’s AI site selection delivers three major advantages:
Clinical Perspective — Dr. Shruti Pandey, Hematology
Workflow: As I work with clinical trials, I've found that traditional site selection methods can be time-consuming, involving months of manual coordination. With Inato's new AI capabilities, I can now create and execute data-driven site selection scenarios quickly and easily, leveraging real-time interest and verified data from over 5,500 sites. This streamlines my workflow, allowing me to focus on other critical aspects of trial planning.
Economics: The article doesn't address cost directly, but I'd expect that Inato's AI-powered site selection could help reduce costs associated with traditional methods, such as purchasing site lists or building internal tools. By tapping into a global network of over 5,500 engaged research sites, sponsors can potentially save time and resources. However, I'd need more data to fully understand the economic impact.
Patient Outcomes: Inato's mission to bring clinical research to every patient, no matter where they live, resonates with me. By transforming site selection and patient pre-screening, we can improve trial accessibility and representation. With Inato's platform, I can potentially match patients with the right studies more efficiently, which could lead to better patient outcomes and more diverse trial participation.
Transparency & Corrections
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