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Tuesday June 16, 2026 9:15am - 9:45am EDT
Component Type: Session

This session examines persistent scientific and operational challenges in eCOA implementation, particularly in studies using high frequency patient reported data. The discussion focuses on how advanced monitoring technologies and AI driven analytics can be used to detect emerging risks such as disengagement, response drift, and early signal degradation while data collection is ongoing. Through real world examples, the session highlights how predictive analytics and pattern recognition can support earlier, more informed decisions, helping teams distinguish true clinical change from measurement artifacts and strengthen confidence in longitudinal outcome data.

Learning Objectives

Identify common scientific and operational risk patterns in high-frequency eCOA data; Understand how technology-enabled monitoring and AI-driven analytics can surface early indicators of data quality risk while data collection is ongoing; Recognize how predictive, proactive oversight supports earlier and more targeted intervention, strengthening confidence in endpoint integrity without replacing scientific judgment.

Chair

IQVIA

Speaker

Speaker
Lindsay Hughes, PHD, MS


Speakers
avatar for Lindsay Hughes

Lindsay Hughes

Principal, RDS, IQVIA, United States
Lindsay Hughes, PhD is a Principal in IQVIA’s Patient Centered Solutions practice, with nearly two decades of experience in behavioral and life sciences. Her work focuses on understanding how people interact with technology in clinical research settings and applying that insight... Read More →
avatar for IQVIA

IQVIA

United States
Tuesday June 16, 2026 9:15am - 9:45am EDT
Innovation Theater 1 The Pennsylvania Convention Center 1101 Arch Street Philadelphia, PA 19107 USA

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