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In-depth guides, tutorials, and best practices for database design, data architecture, and healthcare data modeling.
Platform comparisons, TCO analysis, and governance frameworks — written for the people who approve the budget.
13 articles
Showing 1–6 of 13 posts
Healthcare data architects spend days designing schemas from scratch — Medicare Advantage claims warehouses alone require 20+ tables, hundreds of columns, and platform-specific syntax. The mdatool AI Data Modeling tool generates a production-ready Star Schema for Snowflake, BigQuery, or Databricks in 30 seconds, with ISO-11179 standard column names built in.
Every healthcare data warehouse eventually develops naming drift — DOB in one model, birth_dt in another, member_birth_date in a third. The dbt-healthcare-standards package brings ISO-11179 column naming directly into your dbt project as installable macros and schema tests.
Healthcare data models take weeks to design manually — HIPAA constraints, CMS reporting requirements, and ISO-11179 naming standards all have to be applied correctly from the start. AI data modeling changes that. Here is how to generate production-ready schemas for Snowflake, BigQuery, and Databricks in seconds.
The complete SQL schema for a payer-side healthcare claims data warehouse — including claim header, claim line, adjudication, remittance, and provider tables with DDL for Snowflake and BigQuery.
HEDIS measure calculation is deceptively complex. NCQA technical specifications are hundreds of pages long, source data comes from four different systems, and one logic error can affect thousands of members. Here is how to build a pipeline that holds up to NCQA audit.
Prior authorization is one of the most operationally complex workflows in healthcare — and one of the most data-intensive. Here is the end-to-end data model, from PA request through appeal.
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