Reducing Screen Failure Rates: The Economics and the Path Forward
Screen failures cost sponsors between $2,000 and $8,000 per failed screen. The path to reducing them runs through better pre-screening intelligence at the site level.
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Enrollment, EHR Data, and What the Numbers Actually Show
Practical analysis on clinical trial enrollment strategy, EHR phenotyping, AI matching, and site operations — written by the TrialVyx team in Boston for clinical operations professionals.
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AI-Driven Site Activation vs. Traditional CRM Approaches: A Practical Comparison
Many sites still rely on CRM-based outreach for trial activation. We compare the operational realities and outcomes of AI-signal-driven referral versus traditional CRM workflows.
Retention Starts at Recruitment: How Referral Quality Affects Trial Completion Rates
Data from site operations shows that referral quality at enrollment is the strongest predictor of retention through end-of-study.
Real-World Data in Clinical Trials: FDA's Framework and What It Means for Enrollment
The FDA's evolving framework for real-world data in clinical evidence opens new pathways for using EHR data in trial design and patient identification.
EHR Data Quality and Its Hidden Impact on Patient Matching Accuracy
The accuracy of AI-driven patient matching depends almost entirely on the quality of source EHR data. Here's what poor data quality actually costs in screen failure rates.
Boston's Clinical Trial Recruitment Landscape: Density, Competition, and What It Means for Sponsors
Greater Boston hosts more clinical trial activity per capita than almost any metro area in the US. For sponsors, that's both an opportunity and an enrollment competition challenge.
Diversity in Enrollment: How Site Selection and Referral Design Drive Representation
FDA guidance on diversity in clinical trials has pushed the conversation forward. But representation starts with where sites are selected and how patients are identified.
What Makes a Good Patient-Trial Match? A Look at Eligibility Criteria Mapping
Eligibility criteria in clinical protocols are written for regulators, not algorithms. Translating complex inclusion/exclusion logic into reliable matching signals is where most AI tools fall short.
Hybrid Recruitment in the Age of Decentralized Trials
Decentralized trials expanded access but introduced new recruitment complexity. Hybrid models that pair DCT tools with site-level EHR matching are showing the most promise.
Beyond Investigator CVs: AI Signals for Smarter Site Selection
Traditional site selection relies on investigator experience and historical performance. AI-driven signals from EHR and patient population data offer a sharper picture.
The Five Root Causes of Phase III Enrollment Delays (And What Data Shows)
Enrollment delays affect more than 80% of Phase III trials. We analyzed the most common root causes — and why most remediation efforts treat symptoms rather than causes.
How EHR Phenotyping Is Changing the Way Sponsors Find Eligible Patients
EHR phenotype signals offer a fundamentally different approach to patient identification — one that clinical trial sponsors are increasingly deploying for Phase II/III enrollment acceleration.
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