The Industry is at a Crossroads
Clinical trials are the engine of medical advancement. But today, they are slower, riskier, and more expensive than ever. Sponsors, CROs, and site managers are under pressure to improve timelines, reduce costs, and increase predictability—without sacrificing data integrity or regulatory alignment.
And yet, the numbers highlight the challenges:
- Site performance can vary by up to 50%, impacting trial quality and consistency
- Poor site selection can increase costs by 20% or more
- Over 50% of Phase III trials fail, and 57% of those failures are due to ineffective design, not necessarily poor science
These are not surface-level inefficiencies—they’re root-cause obstacles to innovation.
The AI Hype vs. AI That Helps
In a landscape filled with AI hype, our approach is grounded in practicality. AI should solve real-world problems, not just serve as a marketing tagline. Mednet is taking a different route: one rooted in pragmatism, purpose, and client outcomes.
That’s why Mednet’s AI roadmap is focused on two critical areas: improving site analytics and streamlining study design through intelligent automation. We’ve created a dedicated AI taskforce, led by VP of Product Bill Mogg, to pursue a clear mission:
Build AI that solves real-world clinical trial challenges, without adding noise or complexity.
Our roadmap is strategic and iterative, starting with what matters most to sponsors and study teams today—and evolving as we learn. While we advance these initiatives, we remain committed to respecting client privacy and honoring preferences around data usage—ensuring no data is leveraged without proper consent or alignment with individual organizational policies.
The Path Forward: Three Strategic Pillars
1. AI-Driven Site Forecasting (in development)
One of the biggest challenges in clinical trials is evaluating site performance beyond anecdotal experience. With Mednet’s AI-driven Site Analytics, sponsors and CROs will gain deeper insights into site efficiency and quality. This initiative leverages AI frameworks to analyze key metrics such as:
Data lag: How quickly sites enter and update trial data
Query resolution speed: How efficiently issues are addressed
Enrollment pace: The rate at which participants are recruited
Data quality: Measured by patterns in data modifications
Monitoring effort: The cost and time required for oversight
What started as “Site Analytics” has evolved into something more powerful – evidence-based decisions. By tapping into study metadata and performance benchmarks, our platform will help users:
- Visualize site metrics (e.g., data lag, query resolution, enrollment speed)
- Forecast future site performance trends
- Flag potential risks early
- Make evidence-based site selection and monitoring decisions
This isn’t just a dashboard—it’s a learning model that will grow smarter with every study.
2. AI for Study Build & Business Logic (in development)
Mednet’s second AI initiative designed to assist study designers in streamlining their workflows. Additional information coming soon.
3. Adherence to Regulatory & Ethical Standards
While no formal regulations have been finalized, Mednet is committed to aligning with future guidance as it emerges. Transparency, auditability, and model explainability remain core principles in every tool we design, ensuring we’re prepared to meet evolving standards while delivering real-world value to clinical teams.
Why Mednet?
Mednet isn’t new to clinical trials. We’ve spent over 20 years building scalable, secure, and user-friendly eClinical technology that adapts to the complex demands of modern research.
Now, as we bring AI into that ecosystem, we do so with:
- A focus on value over novelty
- Client input baked into development cycles
- A measured release plan that evolves with learning
We’re not rushing AI to market. We’re designing it to last—and to help our customers succeed, now and into the future.