A MedTech manufacturer needed to transform complex longitudinal healthcare data into reliable, regulatory-ready evidence while preserving confidence in how every data element was sourced, processed, and validated. This case study examines how an evidence-grade data pipeline was applied to support a real-world MedTech evidence workflow and establish the foundation for responsible Clinical AI.

This session follows the workflow from raw clinical data through evidence generation, demonstrating how persistent provenance was maintained across data extraction, transformation, calculation, classification, and adjudication. Each step was designed so that resulting evidence could be traced back to its original clinical source and reconstructed when required.

Auditability is embedded throughout the workflow by capturing source systems, transformation logic, processing dates, software and algorithm versions, reviewer actions, corrections, and exceptions. Validation rigor was applied based on clinical and regulatory significance, combining automated quality checks, plausibility testing, source reconciliation, discrepancy management, and expert review.

The case study also demonstrates how these controls enable AI-assisted extraction, classification, and adjudication – such as those provided by John Snow Labs’ clinical data abstraction pipelines – while keeping AI-generated outputs linked to source evidence and subject to human review when the risk warrants it. The result is an audit-ready data pipeline that enabled the customer to prove where data came from, how it was transformed, how it was validated, and why it could be trusted.