Clinical Research Blog
What Makes Real-World Data “Research-Ready”? A Fitness-for-Use Framework
Every real-world data source looks strong on a slide, yet patient count says nothing about whether the data fits your study. Learn the fitness-for-use framework of relevance and reliability behind research-ready real-world data.
Speed, Accuracy, and Trust in Real-World Evidence: A Researcher’s View
Speed, accuracy, and trust in real-world evidence are usually a trade-off. A researcher working with the BEKnetwork explains how to get all three without compromising rigor.
Real-World Evidence Doesn’t Need More Data. It Needs Better Reach.
Two decades of investment. Deeper claims databases. Wider EHR networks. And yet a landmark JAMA study found that none of 50 FDA-required confirmatory trials could be emulated using retrospective real-world data. The gap isn’t about volume. It’s about reach.
Why RWD-Informed Protocols Still Miss Enrollment Targets
The use of RWD in clinical trial design has never been higher. Enrollment performance has not improved in step. Nearly 80% of trials miss their original timelines, and one in ten sites enrolls zero patients. Three structural failure modes explain the gap, and what sponsors should demand to close it.
First Analysis: Industry Forums Highlight 5 Key Themes in Pharma IT
The latest Pharma IT outlook highlights that the future of clinical development will be defined by how well we operationalize data. AI, real-world evidence, and smarter infrastructure are no longer future concepts…
Why “Eligible Patients” Aren’t Always Enrollable Patients
Eligibility explains who could enroll. Enrollability determines who will. When trials confuse the two, feasibility looks strong and enrollment falls short.
Why Traditional Feasibility Fails (and What Actually Predicts Enrollment Success)
Traditional clinical trial feasibility often overestimates enrollment by relying on historical performance and surface-level patient counts. In this post, we break down why those methods fail, and what actually predicts enrollment success.
American Hospital Association: How AI Is Transforming Clinical Trials
Artificial intelligence is rapidly reshaping clinical research by reducing timelines, lowering costs, and improving trial efficiency. In the AHA Center for Health Innovation Market Scan, BEKhealth is highlighted for its AI-powered natural language processing platform that analyzes both structured and unstructured electronic health record data to accelerate the identification of protocol-eligible patients and strengthen feasibility analytics for sponsors and sites.
Top AI-Driven Clinical Trial Patient Identification Tools: What Research Teams Should Look for in 2026
AI is reshaping clinical trial enrollment by helping teams interpret complex medical data and surface eligible patients faster. This guide breaks down the essential capabilities that define the top AI-driven clinical trial patient identification tools and how they improve feasibility, accuracy, and workflow efficiency.
Beyond the Buzzword: What Real AI Looks Like in Clinical Research
The term “AI” is everywhere in clinical research. However, most of what’s called intelligence is really automation. True innovation lies in teaching machines to understand medicine the way humans do.
Human-in-the-Loop AI in Clinical Trials: Why the Future of Recruitment Isn’t Fully Automated
As AI reshapes clinical research, BEKhealth is proving that humans still belong in the loop. Discover how our hybrid approach combines AI precision with expert oversight to deliver faster, higher-quality recruitment and why the future of trial enrollment isn’t fully automated.
The Rise of the Digital-Ready Site: How Technology Readiness Wins More Studies
Most organizations use real-world data to recruit patients. Few use it to transform the entire research lifecycle. Learn how continuous clinical trial ecosystems connect feasibility, enrollment, monitoring, and outcomes to advance discovery and improve evidence reliability.
Beyond Recruitment: Leveraging RWD to Power a Continuous Clinical Trial Ecosystem
Most organizations use real-world data to recruit patients. Few use it to transform the entire research lifecycle. Learn how continuous clinical trial ecosystems connect feasibility, enrollment, monitoring, and outcomes to advance discovery and improve evidence reliability.
AI + Real-World Data: Accelerating What’s Possible in Clinical Research
Artificial intelligence is transforming how clinical researchers use real-world data. By unlocking insights from unstructured medical records, AI is accelerating trial recruitment, improving feasibility, and shaping regulatory evidence — moving healthcare closer to faster, smarter, and more inclusive clinical research.
Protocol Deviations and Patient Mismatch: The Silent Threat to Trial Success
Protocol deviations are one of the most costly and overlooked risks in clinical research—often stemming from mismatched patients at the very start of the trial. By replacing manual chart review with AI-driven patient matching, sites can prevent deviations before they occur, protect data integrity, and accelerate trial timelines.