Using AI To Speed & Simplify Site Validation

The careful selection of clinical trial sites is crucial for sponsors, investigators, and patients alike. If sites lack the necessary skills and staff, it can lead to drug development delays, resulting in significant financial losses. Furthermore, overloaded or disinterested site staff may generate low-quality data, compromising the trial's validity and reliability.

This, in turn, can give rise to regulatory and audit issues, potentially leading to the approval of unsafe or ineffective drugs. Failure by site staff to adhere to proper protocols or commit errors can have severe consequences, including tarnishing the reputation and credibility of sponsors, triggering penalties, fines, lawsuits, or even harm to patients.

Given these risks, it is crucial to carefully choose high-performing clinical sites that possess the requisite experience, expertise, resources, and patient population. However, the process of site selection and validation often suffers from fragmentation and sluggishness. To overcome these shortfalls, we've found that standardizing data and streamlining processes can help by offering a centralized repository of data compiled from questionnaires and through cross-sponsor data sharing.

Artificial intelligence can then leverage this data to create an intelligent site profile. By combining, comparing, and deriving insights, discover how structured data and unstructured third-party data can offer a more comprehensive understanding of each site.