A Touch of Hospitality for Clinical Trials

In today's ACT Brief, we examine FDA's expedited pathway for first-in-human trials, why operating model design matters more than sourcing strategy, and how early readiness signals predict trial risk.

This episode of The Human Side of Clinical Trials, hosted by Brian S. McGowan, PhD, FACEHP, chief learning officer and co-founder of ArcheMedX, Inc., and Kelly Ritch, chief operating officer of ArcheMedX, Inc., explores why training completion metrics like attendance and quiz scores fail to capture true study readiness, and how measuring both competence and confidence can reveal trial risks before enrollment even begins.

In today's ACT Brief, we examine what families with rare epilepsy teach about trial feasibility, how preclinical CRO partnerships are evolving beyond transactional services, and why cortisol dysregulation limits GLP-1 effectiveness in diabetes.

In today's ACT Brief, we examine how shifting patient engagement to endpoint selection improves trial data quality, why execution infrastructure must precede AI deployment, and addressing missing baseline data in real-world oncology research.

In today's ACT Brief, we examine why seizure reduction misses what families want from epilepsy trials, how lab coordination requires connecting data to intent, and why healthcare needs orchestration rather than more tools.

In today's ACT Brief, we examine genuine patient partnership in trial design, how AI enables informed recruitment while preserving clinician relationships, and moving patient engagement beyond performative gestures.

In today's ACT Brief, we examine why black-box AI fails in clinical trials, how participant understanding drives engagement more than gamification, and FDA permanent leadership appointments prioritizing regulatory predictability.

In this episode of Beyond Compliance, Otis Johnson, PhD, MPA, founder and principal consultant at Vantix Operations, speaks with Joseph Geraci, PhD, co-founder and chief scientific and technical officer at NetraMark, about why explainable AI, not black-box prediction, is needed to reveal clinically meaningful patient subgroups in regulated drug development.