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Webinar

From Protocol Draft to Confident Feasibility: Democratizing Study Design with AI

ET
60 Minutes

Clinical feasibility is evolving. Instead of validating assumptions after a protocol is written, leading teams are embedding feasibility earlier—using real-world data and AI to iteratively pressure-test eligibility criteria, optimize study design, and build more recruitable protocols. In this session, TriNetX and Parexel share a modern feasibility workflow designed for clinical operations teams, including non-medical users empowered through natural language and AI translation. You’ll see how protocol criteria can be translated into executable queries, how “what-if” iteration improves protocol quality, and how feasibility insights can inform site and country strategies—faster and with more confidence.

Speakers

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Amy Spaziani

Senior Director, Clinical Feasibility
Parexel

Amy Spaziani is Director of Clinical Feasibility at Parexel, specializing in feasibility, site selection, and patient-centric trial design. She partners with sponsors to assess study viability, optimize recruitment strategies, and align protocols with real-world populations. Amy brings a decade of clinical research experience and contributes to advancing data-driven, inclusive trials.

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Jeffrey Brown

Chief Scientific Officer
TriNetX

Jeffrey Brown, PhD, is an internationally recognized expert in using real-world data to support regulatory and sponsor evidence needs and assess data quality. He brings over 25 years of research experience, including leadership roles at Harvard Medical School, and advises research groups and pharmaceutical companies on data-driven clinical and outcomes research.

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Sierra Luciano

Director, Clinical Study Feasibility & Analytics
TriNetX

Sierra is the Director of Clinical Feasibility & Analytics at TriNetX. She holds a bachelor's degree in chemistry, and joined TriNetX in February 2019 after briefly supporting intellectual property strategy in drug development. She is a subject matter expert in using the TriNetX LIVE platform to identify complex patient cohorts, conduct clinical trial feasibility, and run advanced analytics, and supports the creation of AI tools to enable users to create queries using natural language.

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