Why take this course?
Digital quality metrics are becoming more important as life sciences companies adopt cloud services, SaaS platforms, AI, ML, and LLM-enabled tools across development, manufacturing, testing, and support activities. These technologies can improve speed and visibility, but they also introduce validation, Part 11, and data integrity concerns that cannot be handled through general IT oversight alone. When metrics pipelines, dashboards, and analytics influence quality decisions, companies need clear control over how data is generated, reviewed, and used.
This program focuses on building digital quality metrics that support continuous improvement while remaining aligned with FDA-regulated expectations. It addresses how risk-based metric selection, AI/ML analytics, and dashboard design connect to SDLC activities, GAMP®5 (Second Edition), CSV, and CSA. It also examines supplier-provided analytics, data integrity in aggregation layers, and the practical limits of AI, including hallucination, bias, and performance drift, where human judgment remains essential to sound quality and compliance decisions.
Key Areas Covered
Carolyn Troiano
Carolyn Troiano has more than 45 years of experience in computer system validation across FDA-regulated industries and advises companies on FDA compliance, CSV, and large-scale IT implementations. She also participated in the FDA/Industry Partnership that developed 21 CFR Part 11, bringing directly relevant experience in electronic records, data integrity, and validated digital systems.
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