Data Management

See how real-world evidence is enabling smaller, smarter, and more efficient trial designs through hybrid models, external comparators, and continuous patient monitoring—while preserving the role of traditional clinical trials.

As ESG expectations rise across clinical development, sponsors are finding that sustainability efforts gain traction only when embedded into existing vendor oversight and quality management processes rather than treated as a standalone reporting exercise.

Assess the data quality, linkage, transparency, and auditability challenges that sponsors must overcome to make de-identified real-world evidence fit for regulatory submissions.

Understand where real-world evidence most effectively complements or substitutes traditional trial data, from post-market surveillance and label expansion to challenging areas such as rare disease research.

Explore how large-scale, de-identified real-world datasets enable more representative trial design, improve site selection, and support patient identification beyond the limits of traditional clinical study populations.

Examine how the FDA’s acceptance of de-identified real-world evidence shifts clinical operations workflows and why understanding the difference between pseudonymized and anonymized data is now critical for privacy, compliance, and evidence generation.

Global clinical development has evolved into a technology-enabled, highly regulated, and geographically diversified enterprise, as sponsors adapt trial design, partnerships, and operations to meet rising demands for scale, speed, and patient-centricity.

Analysis of more than 880 clinical trials shows that while statistical data monitoring and key risk indicator signals close on similar timelines, durability—not speed—is the defining differentiator in effective, risk-based quality management.

As Parkinson’s disease cases surge worldwide, growing evidence gaps around women’s biology, care access, and outcomes are driving new data-driven and digital strategies to advance sex-specific research and precision medicine.

The clinical trial ecosystem is entering a phase of consolidation and reinvention driven by the collapse of boundaries between functions, data, and even companies themselves.

Persistent delays and inefficiencies in COA licensing and translation stem from limited pre-license access and fragmented processes, making a strong case for providing outcome assessment measures earlier to reduce risk and accelerate trial start-up.

Learn how real-time patient eligibility data is reshaping trial planning and site selection, allowing sponsors to design more inclusive studies based on current patient reality rather than past performance.

Unclear boundaries between investigator-controlled and sponsor-managed data in IRT systems are creating avoidable compliance risk, particularly around investigational product inventory and quality actions.

Only with recent advances in cloud computing, data standards, and interoperable platforms has it become feasible to realize the full potential of a digital thread.

New research finds that while eSource adoption is advancing through EHR-to-EDC workflows, scaling its impact will depend on integrating unstructured clinical data using AI, shared standards, and collaborative validation models across sites, sponsors, and vendors.

Discover how remote assessments, wearables, and digital technologies can decentralize data collection, reduce patient burden, and enable trials to reach more diverse and representative populations.

Strong relational governance between technology vendors and sponsor–CRO teams is becoming a critical foundation for eCOA trial success, enabling faster study launches, clearer communication, proactive issue management, and sustained quality across entire trial portfolios.

Learn how sponsors and CROs are adapting to evolving ICH GCP requirements while maintaining innovation in high-touch areas, ensuring patient protection, and preserving data integrity across global clinical trials.

AI-driven discovery, EHR-based real-world evidence, and synthetic patient modeling are rapidly reshaping drug repurposing, reducing development timelines, expanding therapeutic applications, and accelerating regulatory acceptance of computational approaches.

Emerging applications of AI/ML, automation, and digitization are helping sponsors cut clinical trial start-up times to as little as four weeks, reduce data errors, and enhance patient engagement—demonstrating how tech-enabled processes are reshaping trial efficiency and experience across the study lifecycle.

Webinar Date/Time: Wednesday, December 10th, 2025 at 11am EST | 8am PST | 4pm GMT | 5pm CET

Leveraging real-world data and AI-driven insights in clinical trial planning can reduce enrollment failures, improve retention, and prevent costly rescue studies by providing a more accurate view of patient populations and site feasibility.

Learn how organizing data assets, activating nonresearch HCPs, and building referral pathways with compliant remuneration can convert identified patients into enrolled participants.