Realistic expectations: where pharmaceutical sponsors should deploy human vs. technological resources in the digital age while running clinical trials
COVID-19 has highlighted the need for data-driven decision making and processes in both healthcare and clinical research. A.I. has tremendous potential to solve for predictive analytics, genomics, biomarker research, patient outcomes, and drug discovery. Can it meet the prescriptive and deterministic needs of running a clinical study? If not, what parts of the clinical operations process can be disrupted with technological advancement? What human-habit barriers exist and how can we change these predispositions that serve as barriers to technological advancement?
- Clinical trial criteria design: Designing clinical trial criteria with deterministic data attributes in mind to maximize algorithmically driven pre-screening
- Role of trial sites in adoption: Getting ahead of red tape, habits, and lack of familiarity on a given technology’s regulatory or IP risks
- What to expect from A.I.: Understanding what A.I. is really, understanding different types of applications, and identifying areas in the clinical trial value chain that can be expedited by certain types of A.I.
Senior Director, Clinical Development Operations, TA Lead Specialty Medicine
Reynold A. Panettieri, Jr, MD
Professor of Medicine, Vice Chancellor for Translational Medicine and Science
CEO & Chairman