How Tuva v0.17.0 preserves custom extension columns across the core data model with a configurable prefix-based pass-through pattern.

A practical architecture guide for building claims analytics platforms, covering ingestion, normalization, data quality, adjustment handling, and analytics-ready models. It outlines implementation tradeoffs and concrete design decisions for scaling payer and value-based care use cases.

An implementation-oriented introduction to dbt for healthcare data teams, focused on project structure, warehouse execution, Git workflows, and built-in testing/documentation. The post explains why these software engineering patterns materially improve analytics reliability and delivery speed.

A technical breakdown of the three core barriers to reliable healthcare analytics: normalization, data quality, and high-level concept generation. It frames why these issues persist across claims and clinical data and why they must be addressed in the data model itself.