A Reference Architecture for AI-Driven Healthcare Data Engineering
For most of the last two decades, enterprise data engineering in healthcare has been built around a simple premise: move data reliably from source systems to a warehouse, clean it along the way, and let downstream teams build reports on top of it. That model worked when the goal was visibility. It struggles when the goal is action - flagging a compliance issue before it becomes a finding, catching a bad record before it corrupts a downstream model, or predicting an operational bottleneck before it delays care.
After more than a decade building data platforms across healthcare and insurance organizations, I've watched this shift happen from the inside. The pipelines that used to be judged purely on throughput and uptime are now expected to reason about the data flowing through them. This article lays out where that shift is happening, why it matters specifically in healthcare, and what a reference...
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