Real-World Data and Artificial Intelligence: Solving Key Clinical Trial Challenges - TriNetX

Real-World Data and Artificial Intelligence: Solving Key Clinical Trial Challenges

Thursday, May 08

Written by TriNetX

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Courtesy of TriNetX Experts

Bringing a new drug to market is expensive, complex, and slow. Clinical trials are a major bottleneck.

  • $314 million – $4.46 billion: Estimated cost for a new drug1
  • Up to 85%: Trials experience delays2
  • Up to $1 million per day: Cost of each delay3
  • 30% of patients: Drop out of Phase III trials4
  • 80-85% of trials: Struggle to recruit representative populations5

The result? Higher costs, longer timelines, and compromised outcomes. The need to solve these critical challenges has never been more urgent.

Why TriNetX? Intelligent Solutions That Drive Clinical Trial Success

RWD/RWE

Fueling clinical research and evidence generation with a globally federated network of deeply enriched, deidentified EHR data.

Countries

Healthcare Organizations

Sites

Patients

Speed

Accelerating clinical trials and decision-making through AI and real-world insights.

%

fewer protocol amendments, saving both time and money

Diversity

Empowering life sciences organizations to meet health equity goals by ensuring diverse clinical trials with representative data.

For patients who had a visit in the last year:

%

known race values

%

known ethnicity values

Collaboration

Fostering a global, collaborative research ecosystem that bridges the gap between healthcare providers and pharmaceutical companies.

Site identification and outreach outcomes for one large pharma:

%

Response Rate

average response time in days

%

response in 3 days

%

acceptance rate

Patient Centricity

Supporting the development of precision medicine and personalized therapies by leveraging comprehensive, de-identified patient data.

TriNetX non-US oncology RWD to fuel chimeric antigen receptor (CAR) T-cell therapy research to better understand personalized treatment pathways and follow-up protocols

Life sciences companies today can use RWD and AI to de-risk the clinical phase of drug development by understanding patient populations, creating better clinical trial protocols, predicting and avoiding potential problems, better identifying patients for enrollment, and more.
Akiko Shimamura

Senior Vice President, Trial Design and Optimization, TriNetX

Proven Impact: RWD & AI in Action at TriNetX

 

AI-Driven Trial Enrollment

  • TriNetX tackled the challenge of identifying inflammatory bowel disease patients at risk of treatment escalation by building and deploying a machine learning model.
  • Using real-time clinical data, the model predicts escalation risk within 7 days, allowing trial coordinators to prioritize high-risk patients.
  • This approach significantly improves trial enrollment, boosting conversion rates from 33% to 85% for Crohn’s disease and 70% for ulcerative colitis.

 

AI Model Testing in Live Clinical Environments

  • To ensure AI models perform in real-time clinical settings, researchers sought to validate them beyond retrospective evaluations and prepare them for deployment in healthcare organizations, enabling caregivers to act on patient risk scores.
  • Through the TriNetX LIVE™ platform—the healthcare industry’s largest global health research network for conducting real-world evidence studies—researchers facilitated real-time deployment and evaluation, integrating patient data, updating risk scores, and validating performance with diverse populations across two healthcare organizations. The models were also tested as tiered systems in clinical workflows.
  • Preliminary results confirm performance closely aligns with retrospective evaluations, demonstrating clinical potential. TriNetX’s diverse dataset enhanced model generalizability, while its unified platform streamlined development and deployment, paving the way for broader adoption.

By incorporating AI technologies, researchers can gain significantly deeper insights and unlock new possibilities for analysis, essentially creating a much more advanced and impactful way to utilize RWD compared to traditional methods.

Steve Kundrot

Chief Operating Officer, TriNetX

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