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Building effective AI Agents for secondary use of clinical data requires a robust, structured data foundation.

This webinar outlines an end‑to‑end solution architecture framing the Patient Journeys Platform as an essential data preparation layer before AI Agents can effectively operate.

We will detail the end‑to‑end data flow: Ingestion of multimodal data from diverse sources, scalable processing to perform de‑identification, information extraction, coding to standard medical terminologies, and finally, transformation into an enriched OMOP data model.

The session will detail the Bronze, Silver, and Gold layer strategy and how it enables complete provenance of the system’s clinical reasoning and inference.

We will showcase how these clean, Agent-ready datasets (identified and de‑identified) enable high‑value use cases, such as cohort building and clinical trial matching, while differentiating our approach from primary use systems like EHRs and ambient listening.

David Cecchini
David Cecchini
Senior Data Scientist at John Snow Labs

David Cecchini is a Senior Data Scientist at John Snow Labs and a Professor based in São Paulo, with nearly a decade of experience applying NLP and machine learning in finance, legal, compliance, and healthcare. He leads GenAI and NLP consulting, maintains the open-source testing tool LangTest, and has developed multilingual models—particularly for CJK languages—that boosted client outcomes by ~20%. David co-founded Brazil’s first RegTech startup, Legalbot, and has taught deep learning with RNNs on DataCamp. His academic background spans institutions like Tsinghua-Berkeley Shenzhen Institute, USP, and Paris 1 Panthéon-Sorbonne University, with research focused on text classification and statistical learning. Fluent in Portuguese, English, and Spanish, and proficient in Mandarin, David combines cross-cultural fluency with production-grade engineering to deliver high-impact AI solutions./p>

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