A Wits RHI sub-investigator explains why fragmented CRFs, labs, and randomization platforms – not bad software – undermine clinical trial technology.

A biostatistician on where real-world data changes outcomes, and where FDA still won’t accept it as a trial comparator.

How Virtua Health vets, trains, and re-engineers its imaging technology to safely anonymize data for clinical trial research.  

Strong data hygiene, formal governance, and human oversight are becoming essential to responsible, scalable AI adoption in clinical trials.

A Pfizer and Cytel veteran on why statisticians belong at trial design, and why fewer sponsors keep them in-house.

How one research administrator cut a universitys clinical trial disclosure noncompliance from 73% to 2.4% in two years, then built a second office.

AI and early digital twin models can help forecast trial performance, refine site feasibility, and anticipate operational needs before enrollment begins.

A veteran biostatistician on the planning document and the dose-selection shortcut that quietly decide whether trials succeed.

A 30-year clinical research veteran breaks down the four stages of technology maturity and why most organizations misjudge their own.

Disconnected systems – not a lack of technology – are slowing clinical trial execution and forcing site staff to bridge costly workflow gaps.

A veteran statistician explains why she’s unconvinced digital twins can replace randomization in clinical trials, at least for now.

A former Bayer medical affairs lead explains why data-quality decisions land with whoever controls the budget, not whoever understands the data.

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.

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.

Clinical trial sites aren’t overwhelmed by technology – they’re overwhelmed by entering the same data into disconnected systems again and again.

Clinical trial technology isn’t the barrier to interoperability; the economics are. Two industry leaders explain why nothing forces systems to connect.

A practical checklist for evaluating decentralized trial technology vendors, built from an RF engineer’s view of where data quality actually breaks.

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.

An RF and semiconductor engineer explains why AI can’t compensate for hardware and connectivity errors, and what questions to ask instead.

Wearables and other technology to facilitate decentralized trials were supposed to ease patient burden. Elisa Cascade explains why the results depend entirely on design.

An RF engineer explains why packet loss, multipath interference, and battery tradeoffs are scientific validity issues, not IT problems.

A peer-reviewed study on voice AI and depression screening shows why demographic portability testing matters before deploying clinical AI tools.

Why timestamp drift between wearable sensors quietly undermines decentralized trial data, and why almost no one is asking about it.

A three-year ophthalmology coordinator on imaging integration, independent reading centers, and the one staffing problem no scheduling software has solved yet.

An RF engineer explains why sensor physics and analog design, not software, determine whether wearable trial data can be trusted.

A CRC who touches eight systems before lunch explains why clinical research’s biggest technology problem may be interoperability, not a lack of software.

The right clinical trial tech stack isn’t the most complete one; it’s the one built with sites, funded fairly, and designed to remove friction, not add it.

Sponsors and sites both have technology, but integration only works when validation, economics, and site diversity are factored in from the start.