Live Webinar

Transforming Real-World Data to OMOP Faster with Trusted, Governed AI

Transform real-world data faster while strengthening trust, governance and research outcomes

Aug. 25 • 9 a.m. ET

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About the webinar

Real-world data (RWD) is becoming more important across health care and life sciences. Yet preparing data for analytics and research still takes significant time and effort. Teams often work across fragmented sources, evolving standards and increasing expectations for quality and trust.

As adoption of the OMOP Common Data Model grows, organizations are looking for more efficient ways to standardize and transform data at scale. Join this webinar to explore how generative AI, governed workflows and modern data architectures are helping accelerate RWD-to-OMOP conversion and support trusted analytics and evidence generation.

What you'll learn

  • How OMOP helps standardize and harmonize real-world data for analytics and research.
  • How generative AI can accelerate and simplify RWD-to-OMOP conversion.
  • How to improve data quality, governance and trust while reducing transformation effort.

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About the experts


Sherrine Eid

Global Head, Epidemiology, RWE & Observational Research

Sherrine Eid has over 25 years of experience. She has worked in global health development and served as the district epidemiologist for the city of Alexandria, VA. At Lehigh Valley Health Network, she co-authored outcomes research with health care providers. Eid joined Teva Pharmaceuticals, supporting regulatory, safety, late-phase and post-marketing activities across therapeutic areas using clinical data and RWE. She now leads epidemiology, real-world evidence and observational research efforts and contributes to trustworthy AI and responsible innovation.


Pankaj Attri

Data and AI Solutions Architect

Pankaj Attri is an AI/ML Solutions Architect focused on leveraging advanced data science and artificial intelligence to support customers in the Life Sciences and Health Care industries at SAS. He partners with these organizations to design and implement state-of-the-art custom solutions that accelerate innovation across research, development and clinical operations. This includes supporting critical workflows such as clinical trial data submission processes, as well as broader applications of AI and data management across drug discovery, diagnostics and health care delivery. Beyond solution development, Pankaj plays a key role in educating customers on SAS’ Data and AI Solutions capabilities, delivering impactful demonstrations, and translating complex technologies into clear business value. Pankaj brings together a background in Chemical Engineering (B.Tech) from IIT Bombay, India and a Master's in Computer Science from NC State University, USA.

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