Building a MedAdvantage RAF Engine with dbt & PostgreSQL
analytics•Jan 13, 2026

Building a MedAdvantage RAF Engine with dbt & PostgreSQL

Learn how to simulate a real-world healthcare analytics pipeline that transforms raw claims data into member-level risk scores using a step-by-step guide.

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Original source

Key summary

This article guides readers through building a mini Medicare Advantage Risk Adjustment Factor (RAF) engine using PostgreSQL, dbt Core, and synthetic healthcare data.

TL;DR

  • Built a mini Medicare Advantage RAF engine using PostgreSQL and dbt Core
  • Transformed raw claims data into member-level risk scores
  • Used synthetic healthcare data for simulation
  • Implemented a modern ELT workflow with dbt
  • Created a staging layer for data cleaning and standardization
  • Developed a core layer for analytics-ready entities
  • Produced a final analytics mart: member_risk_scores

This content is automatically summarized using AI. For full context, always refer to the original article.

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