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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.
Showing 7–12 of 81 posts
Everything healthcare data architects and engineers need to know about claims data — from EDI 837 transaction standards and ICD-10 coding to production-ready Star Schema design for Snowflake, Databricks, and BigQuery. Includes DDL examples, common analytics use cases, and data quality best practices.
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.
Most health plans have a data governance policy. What they often lack is the artifact governance committees produce: a working data dictionary engineers actually use. Here is how to build one — and make it stick across teams.
Three engineers at the same health plan. Same patient database. Three different column names for date of birth. This is the hidden cost of inconsistent data naming — and it is almost never a line item in the budget. Here is the ROI case for healthcare data dictionaries.
Every healthcare data warehouse has a provider dimension table with NPI numbers. Bad NPIs cause claims rejections, failed eligibility checks, and broken provider analytics. This guide shows you how to validate NPIs programmatically — from format checking to Luhn algorithm to bulk Snowflake UDF deployment.
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.
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