Ryan Helber

About Ryan Helber

For most of my career I’ve worked alongside research sites, coordinators, investigators, and research leadership, helping studies open faster, operate more smoothly, and stay financially sustainable. But over time I noticed a pattern. Some studies are difficult no matter how strong the site is. Amazing teams still struggle. Recruitment efforts increase. Timelines slip. The issue usually isn’t effort. It’s expectations.Enrollment challenges begin long before activation. Projections are often based on optimistic feasibility, incomplete referral assumptions, and historical comparisons that don’t truly match the real patient pathway. By the time a study opens, sites are trying to operationally solve a planning problem. My work now focuses on helping the industry think about enrollment earlier, before the first patient visit, and treating enrollment as something that can be analyzed, forecasted, and improved instead of reacted to.I still advise research sites and site networks on operations, budgets, and study startup. In addition, I serve as Chief Strategy Officer with CT SCAN, where I work on advancing predictive enrollment approaches aimed at setting more realistic timelines, reducing site burden, and improving patient access to clinical trials.The goal is simple:better expectations → healthier sites → faster trials → more patients receiving therapies.If you work anywhere in the clinical research ecosystem (site, sponsor, CRO, or partner), I’m always happy to exchange ideas.

Experience

1. Chief Strategy Officer

   CT SCAN

   Feb 2026 — Present

   Clinical trial enrollment has historically been treated as a downstream problem.Sites are selected based on historical performance, feasibility questionnaires, and investigator interest. Budgets and timelines are finalized. Then recruitment begins - and only after activation does the industry learn whether enrollment is actually achievable.I’ve spent my career working directly with clinical research sites, investigators, and study teams improving operations, finances, and startup execution. Across therapeutic areas and site networks, the same pattern consistently appeared: even well-run sites struggled when enrollment assumptions were wrong. Delays, amendments, and escalating recruitment costs were not operational failures - they were planning failures.CT Scan addresses that specific gap.The company applies Predictive Enrollment Engineering™, powered by DYNO Ai™, to model enrollment before a trial begins. By analyzing patient availability, response behavior, and site capacity, we quantify cost per enrollment, projected timelines, and execution risk prior to activation - and then operationalize recruitment against those projections.My role focuses on translating this framework into practical implementation across sponsors, CROs, and site networks. That includes study planning, feasibility strategy, site selection alignment, and resolving enrollment challenges during trial execution.The goal is simple: enrollment should be engineered during study design, not troubleshot after launch.

Education

• University of Colorado Boulder - Leeds School of Business

  Master of Business Administration (MBA), Entrepreneurship, Marketing

  2011 — 2013

• Westminster University

  B.S., Biology

  2003 — 2008

Skills

• Project Management
• Cardiology
• Regulatory Submissions
• Oncology
• Life Sciences
• Protocol
• Pharmaceutical Industry
• Sop
• Fda
• Clinical Monitoring
• Clinical Research
• Drug Development
• Cancer
• Gcp
• Edc
• Therapeutic Areas
• Pharmacology
• Ctms
• Research
• Pharmacovigilance
• Ich-Gcp
• Patient Recruitment
• Molecular Biology
• Oncology Clinical Research
• Validation
• Clinical Trials
• Start-Up Implementation
• Clinical Trial Marketing
• Management
• Biotechnology
• Electronic Data Capture
• Business Planning
• Irb
• CRO
• Regulatory Affairs
• Lifesciences
• Vaccines
• Infectious Diseases
• Clinical Development
• Clinical Data Management