Resources

Discover how strategic decision-making across every trial phase — from clinical planning to patient enrollment — can drive faster timelines, improved outcomes, and stronger regulatory results.

Proprietary data ― on investigator performance, site feasibility and recruitment ― combined with historical data are crucial to success in clinical trial planning.

AI can optimize clinical trial design through tailored protocol designs as well as investigator and site selection. This ensures on-time patient recruitment and trial delivery.

It can be difficult to find patients for rare disease trials, given the limited population and complex protocol criteria. Still, there are ways to surmount these challenges.

Clinical trial enrollment remains challenging. The right solution can optimize recruitment so that you hit enrollment goals faster and gain a deeper understanding of trial performance.

Citeline's regulatory and compliance solutions give sponsors the information, tools, and services they need to meet clinical trial disclosure requirements.

By embracing AI strategically and ethically, the clinical research community can harness its power to drive efficiency, compliance, and patient centricity in clinical trial disclosure.

The Citeline Maturity Model for Clinical Trial Disclosure is a comprehensive framework designed to help organizations assess and improve their disclosure practices.

While countries have established various regulatory frameworks, and despite substantial penalties in many, actual enforcement of clinical trial disclosure compliance actions is often limited.

Examine the pros and cons of building vs. buying a clinical trial disclosure management system, incorporate insights from the Citeline Maturity Model for Clinical Trial Disclosure, learn about key industry trends, and explore real-world compliance challenges.