How to Enhance Urology Clinical Research with AI-Driven Data Curation

As clinical trials become more operationally complex and costly to organize, so too does the need for smarter, more efficient trial strategies. Nowhere is this more evident than in therapeutic areas like urology and uro-oncology, where dynamic treatment paradigms, aging populations, and cross-specialty management make patient identification and recruitment increasingly difficult.

The rising cost of clinical trials—often reaching hundreds of millions of dollars to bring a single treatment to market—underscores the importance of a more precise, data-driven approach in every aspect of the clinical development process, from discovery to development to postmarketing evidence generation. In oncology alone, average per-patient costs in clinical trials hover around $60,000. These operational challenges demand a better way forward, especially in uro-oncology, where the complexity of care coordination between urologists and oncologists further complicates traditional research models.

Rethinking Clinical Trial Site Selection and Patient Identification

A persistent challenge in clinical trials is the overreliance on historically used trial sites. This “comfort zone” approach often leads to site saturation and recruitment delays. A more refined, data-backed understanding of who is actually treating eligible patients is critical. Likewise, in the face of a shortage of qualified research staff, supporting sites by helping to more efficiently and accurately identify eligible patients is critical. 

Take bladder cancer, for instance. T stage—a common eligibility criterion—is notoriously difficult to accurately assess. It’s often buried in complex clinical data or inconsistently documented in electronic health records (EHRs). This makes it difficult for trial sponsors and contract research organizations (CROs) to assess which practices may truly be treating eligible patients and for sites to identify the right patients. Verana Health addresses this by curating T-stage data for bladder cancer, helping to streamline both site selection and patient recruitment.

Moreover, uro-oncology trials tend to come with long exclusion criteria lists, burdening site staff with complex pre-screening requirements. Verana Health can reduce this operational load by leveraging both structured and unstructured EHR data to pre-screen and eliminate ineligible patients—freeing site staff to focus on those most likely to enroll.

Designing Smarter Trials with Curated Real-World Data

A thoughtful trial design is both critical for the operational feasibility of a study and also foundational to the scientific integrity and regulatory success. Along these lines, identifying the right patient cohort to be included in a study is key. Verana Health’s access to real-world data (RWD) from the American Urological Association (AUA) Quality (AQUA)^® Registry, curated from urology EHRs with both clinical nuance and AI-driven intelligence, enables trial sponsors to define and understand complex cohorts with greater precision.

For example, Verana Health collaborated with experts to develop an intermediate-risk cohort for bladder cancer, grounded in International Bladder Cancer Group and AUA guidelines. This required mining EHRs for Transurethral Resection of Bladder Tumor (TURBT) procedure codes and parsing clinical notes for tumor size and focality. All data were validated against manually abstracted records to ensure accuracy.

This effort resulted in a high-quality cohort definition that not only reflects real-world practice, but also clarified expectations for identifying these patients for future clinical trials.  

A More Strategic Future for Urology Trials

To keep pace with the evolution of clinical research, sponsors and CROs must look beyond legacy approaches and embrace technology that turns complex clinical data into actionable insights. With AI-enhanced, expert-validated data curation capabilities, Verana Health offers a pathway to accelerate clinical development, reduce burden on sites, and ultimately bring new therapies to patients faster.