|Articles|July 1, 2020

67.3% of Trials Disrupted by COVID-19 are Due to Enrollment Suspension

GlobalData has found that the majority of disrupted clinical trials 67.3%, were due to the suspension of enrollment. The delayed initiation of planned trials follows at 18.4%, then, 14.4% of trials are currently being impacted due to slow enrollment.

For more information and findings, click here.

Stay current in clinical research with Applied Clinical Trials, providing expert insights, regulatory updates, and practical strategies for successful clinical trial design and execution.

Related to this article

© Murrstock - © Murrstock - stock.adobe.com

October 8th, 2026

Real-Time Data, Right-Time Decisions: Rethinking Analysis Frequency in RBQM

Real-time RBQM requires proportionate analysis frequency matched to data arrival and issue criticality, not continuous reanalysis, because signal churn from over-frequent runs creates alert fatigue and operational noise that obscures meaningful deterioration.

October 8th, 2026

What It Takes to Harmonize AI Across Fragmented Oncology Health Systems

In this video interview, Saamir Pasha and Carole Berini, PhD, of Ontada, explain how building trust into the data foundation—through source lineage, standardization, and reconciliation—is what makes reliable real-world evidence generation in oncology possible.

October 8th, 2026

ACT Brief: AI and Protocol Design, Site Training Readiness, and GLP-1 Unmet Needs

In today's ACT Brief, we examine how AI surfaces design flaws before enrollment, why site training readiness depends on learning science, and the priorities defining next-generation weight-loss therapies.

October 7th, 2026

Better Site Training Starts With Five Simple Questions

Site training effectiveness depends on readiness at first-patient-in, requiring learning-science design principles around performance focus, cognitive load reduction, progressive sequencing, and spaced retrieval practice rather than training completion metrics or content volume.

October 7th, 2026

Using AI to Design Better Trials Before the First Patient Enrolls: Q&A with Claire Riches, Citeline

In this Q&A, Claire Riches, VP of clinical solutions at Citeline, discusses how AI is transforming endpoint selection and protocol pressure-testing, what data inputs matter most for realistic enrollment planning, and why the tools are more accessible than most sponsors assume.

October 7th, 2026

ACT Brief: FDA Transparency Roadmap, Radiopharmaceutical Trial Operations, and AI-Powered Enrollment Prediction

In today's ACT Brief, we examine the FDA's multi-domain transparency priorities, why radiopharmaceutical trials need redesigned infrastructure, and how AI can make enrollment more predictable.

© photon_photo - © photon_photo - stock.adobe.com

October 6th, 2026

Beyond the Molecule: Why Radiopharmaceutical Trials Require a New Operating Model

Radiopharmaceutical therapy is exposing a broader challenge for clinical development: scientific innovation can advance only as fast as the infrastructure, data, and cross-functional decisions required to deliver it.

October 6th, 2026

FDA Releases Transparency Roadmap Outlining Disclosure Priorities Across Drug Development and Regulatory Review

A new FDA Voices post from the agency's acting chief of staff details completed milestones and upcoming priorities across complete response letter disclosure, application lifecycle transparency, new approach methodologies, labeling, and inspectional records.

67.3% of Trials Disrupted by COVID-19 are Due to Enrollment Suspension

Related to this article

Trending on Applied Clinical Trials Online

Using AI to Design Better Trials Before the First Patient Enrolls: Q&A with Claire Riches, Citeline

ACT Brief: FDA Transparency Roadmap, Radiopharmaceutical Trial Operations, and AI-Powered Enrollment Prediction

ACT Brief: AI and Protocol Design, Site Training Readiness, and GLP-1 Unmet Needs

FDA Releases Transparency Roadmap Outlining Disclosure Priorities Across Drug Development and Regulatory Review

What It Takes to Harmonize AI Across Fragmented Oncology Health Systems