Clinical Trial Patient Technology Articles
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Clinical Research Isn't Even A Recognized Job. That's A Problem.
9/10/2026
The U.S. doesn’t have an official job code for clinical research professionals. David Vulcano explains why that gap is holding back the workforce.
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The Data Was Already There: RWE's Real Bottleneck Is People
9/7/2026
A pharma medical affairs veteran explains why real-world evidence’s biggest constraint isn’t data volume; it’s having enough people to interpret what AI finds.
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The Real Reason Clinical Trial Systems Don't Talk To Each Other
8/27/2026
Clinical trial technology isn’t the barrier to interoperability; the economics are. Two industry leaders explain why nothing forces systems to connect.
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The Vendor Questions Most Clinical Tech Buyers Forget To Ask
8/24/2026
A practical checklist for evaluating decentralized trial technology vendors, built from an RF engineer’s view of where data quality actually breaks.
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AI In Clinical Trials: Stop Hyping It, Start Proving It
8/20/2026
David Vulcano wants clinical research to report AI failures with the same rigor it applies to adverse events. Here’s why that call-to-action matters.
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Why Ophthalmology Trials Are Clinical Research's Toughest Tech Test
7/30/2026
A three-year ophthalmology coordinator on imaging integration, independent reading centers, and the one staffing problem no scheduling software has solved yet.
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Why Clinical Trial Coordinators Are Still The Human API
7/23/2026
A CRC who touches eight systems before lunch explains why clinical research’s biggest technology problem may be interoperability, not a lack of software.
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Designing eCOA Technology Patients Can Actually Use
7/15/2026
Why eCOA usability problems start upstream in trial design and workflow, not inside the app itself.
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Clinical Trial Technology Doesn't Fail – It Fails At The Handoff
7/15/2026
Why clinical trial technology breaks down at sponsor-CRO-site handoffs, not inside any single system.
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Inside Versiti's Data Strategy: Building AI-Ready Research Systems
7/10/2026
Versiti’s Banu Santebennur explains how clean data, semantic layers, and cautious tech adoption prepare clinical research for AI.