Insights
Data foundations, clinical workflows and practical evaluation of AI in healthcare.
An integration engineer’s walkthrough of a FHIR to OMOP pipeline: Bulk Data export, staging, vocabulary mapping with Athena, loading the CDM tables, and data quality checks, with the pitfalls that break studies.
How to build the pipelines behind a French hospital health data warehouse (EDS): CNIL framework, SNDS access through the Health Data Hub, pseudonymisation, OMOP standardisation, governance and EHDS readiness.
A practical evaluation framework for healthcare AI: workflow fit, data quality, human review, safety boundaries and operational measurement.
Classify the intended use, verify the applicable AI Act timeline and translate requirements into engineering evidence.
Explore how generative AI and machine learning are reshaping clinical workflows within EHR systems — from ambient documentation to predictive analytics and clinical decision support.
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