Featured Editorial

31. Two Planning Mistakes That Quietly Sink Clinical Programs 9/22/2026

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

32. Token Costs Will Decide Clinical Supply AI 9/21/2026

    AI may make clinical supply forecasting faster, but cheaper tokens can drive higher costs. See how to measure AI at scale before a pilot becomes an expensive surprise.

33. Four Stages Of Clinical Research Technology Maturity 9/21/2026

    A 30-year clinical research veteran outlines four stages of technology maturity and explains why organizations often misjudge their own readiness.

34. Clinical Trial Sites Don't Need More Tech, They Need Fewer Silos 9/17/2026

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

35. A Biostatistician's Caution Around Digital Twins In Trials 9/15/2026

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

36. Who Owns Data Quality? The Authority Gap Slowing Trials 9/14/2026

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

37. 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.

38. What Will Life Science Orgs Do When The AI Bill Comes Through? 9/9/2026

    AI is transforming clinical development, but what happens when usage costs spike? Jon Newton explains how to govern it before the bill lands.

39. Clinical Trial Standards — What They Are And Why They're Indispensable 9/8/2026

    Clinical trial standards are essential. But to interpret changes, preserve meaning, and assess downstream impact, context is key.

40. 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.