myTomorrows Partners with Spanish Academic Hospital to Deploy AI-Powered Clinical Trial Matching System
核心洞察
myTomorrows (搜索) announced its first large-scale partnership in Spain with Clínica Universidad de Navarra to implement AI-assisted patient-trial matching technology integrated within the hospital's electronic health record system.
The AI system leverages large language models to analyze clinical variables and cross-check patient information against eligibility criteria across CUN (搜索)'s portfolio of more than 200 active clinical trials.
The collaboration introduces a phased deployment model that enables clinicians to access structured lists of potentially eligible trials directly within patient records during routine clinical workflows.
myTomorrows (搜索), a global health technology company, has announced a strategic partnership with Clínica Universidad de Navarra (CUN (搜索)), marking the company's first large-scale site collaboration in Spain. The partnership introduces AI-powered patient-trial matching technology integrated directly into CUN's electronic health record system, addressing the operational challenges of managing complex clinical trial portfolios.
AI-Driven Solution for Complex Trial Management
The collaboration addresses a critical operational challenge facing major research institutions. At CUN (搜索), where more than 200 clinical trials are currently active, maintaining systematic review across the full portfolio has become increasingly difficult as trial portfolios grow more complex with multiple active cohorts, biomarker-defined subgroups, and frequent recruitment updates.
The AI-powered eligibility support system leverages large language models to analyze both structured clinical variables—including diagnosis, staging, molecular profile, and prior therapies—and unstructured medical documentation in multiple languages. Patient information is systematically cross-checked against protocol-defined eligibility criteria and real-time cohort status across CUN (搜索)'s active trial portfolio.
"This collaboration enables us to integrate systems capable of automatically analyzing clinical information and matching it with the inclusion and exclusion criteria of active trials," said Dr. Eduardo Castañón, clinical coordinator of clinical trials for Oncology (搜索) at CUN (搜索). "It also reinforces CUN's commitment to adopting technologies that support decision making in an increasingly complex environment."
Integration Within Clinical Workflows
The system enables clinicians to access structured lists of potentially eligible trials directly within patient records during routine clinical review. This integration supports consistent, portfolio-level screening across tumor boards, pre-consultation review, and standard outpatient workflows, reducing reliance on manual cross-checking and minimizing the risk of overlooking specific trial arms or biomarker-defined cohorts.
The deployment follows a phased model designed to ensure alignment with clinical practice. CUN (搜索) clinicians initially evaluated the AI matching capabilities within a secure, GDPR-compliant web-based environment, using real clinical scenarios to assess performance and refine implementation. The solution has now been integrated via secure API into CUN's electronic health record and clinical trial management systems.
Enhanced Referral Coordination
The partnership extends beyond internal trial matching to enhance referral coordination between CUN (搜索) and external institutions. A CUN-branded website, powered by myTomorrows (搜索)' technology, provides referring physicians with visibility into CUN's active trials and real-time cohort status. Through this secure, GDPR-compliant environment, physicians can AI-pre-screen patients against CUN's portfolio and submit referrals with current eligibility criteria and cohort availability.
This referral system enables eligibility review prior to submission, supporting clearer communication, reducing administrative processes, and ensuring referrals reach CUN (搜索) aligned with current trial requirements.
Strategic Expansion and Clinical Impact
As myTomorrows (搜索)' first partner site in Spain, CUN (搜索) will provide continuous clinical and operational feedback to refine the model. The hospital joins a growing network of leading research institutions collaborating with myTomorrows to develop AI-enabled optimization of clinical trial operations.
"This partnership reflects the kind of collaboration we're looking to scale globally," said Michel van Harten, MD, CEO of myTomorrows (搜索). "CUN (搜索)'s established record in clinical research and technology adoption, combined with its expertise as a reference centre, makes it a trusted partner as we continue building more systematic and scalable solutions for clinical trial operations."
CUN (搜索) operates as an academic hospital with nearly 4,000 full-time professionals across campuses in Pamplona and Madrid. The institution is recognized as a national leader in personalized medicine and ranks among the world's top 120 hospitals in the World's Best Hospitals ranking and among the top 50 cancer (搜索) hospitals globally in specialized hospital rankings.
myTomorrows (搜索) has established a track record in connecting patients with treatment options, having helped more than 17,700 patients and 3,000 physicians across 440+ sites in over 135 countries through its proprietary technology platform that searches clinical trials and Expanded Access Programs.