Metriport provides tools to transform your data for analytics and pipe it into warehouses like Snowflake, Redshift, BigQuery, and more - enabling you to do population level analytics on your patient data. There are two primary ways to perform analytics on your patient population data: Managed and Manual.

​
Managed: Snowflake Data Warehouse

Metriport offers direct integration with Snowflake, allowing you to analyze your patient data in a powerful, scalable cloud data warehouse. This approach provides:
  • Managed data transformation: Metriport does the heavy lifting to get your data into an analytics ready data warehouse 🎉
  • Near Real-time Analytics: Query your patient data right after it’s processed
  • Scalable Performance: Leverage Snowflake’s elastic compute resources for complex analytics
  • Secure Data Sharing: Use Snowflake’s Secure Data Sharing feature for secure data collaboration

​
Setting Up Snowflake Integration

  1. Create a Snowflake Account: Sign up for a Business Critical Edition Snowflake account and share your account identification with us:
    • View account details:
    • Copy these fields and share them with your Metriport account manager:
      • Organization Name
      • Account Name
      • Account Locator
      • Cloud Provider (AWS, Azure, GCP)
      • Region
  2. Access the data: Metriport will share your patients’ medical data with your Snowflake instance:
    • It uses Snowflake’s Secure Data Sharing for secure, compliant, and zero-copy data sharing between Metriport and your Snowflake account.
    • Data will be visible through Secure Views, one for each resource type.

​
Manual: Tuva Data Model

To accomplish this, we standardize our data into the Tuva data model - another open source project that focuses on making healthcare data more standardized for analytics. The Tuva approach provides:
  • Open Source: Transparent, community-driven data modeling
  • Healthcare-Specific Metrics: Built-in calculations for common healthcare KPIs
  • Multi-Warehouse Support: Works with Snowflake, BigQuery, Redshift, and other warehouses
To get started with Tuva, check out our data warehouse connector documentation on the Tuva site.

​
Schema Documentation

The Metriport data model consists of several core tables that represent different aspects of healthcare data. Each table is designed to capture specific information in a standardized format for analytics:

​
Core Tables

  • Condition - Symptoms, problems, complaints, and diagnoses reported during encounters
  • Encounter - Unique patient interactions with the healthcare system
  • Immunization - Vaccines administered to patients
  • Lab Result - Laboratory test results with LOINC codes
  • Location - Practice and facility locations where care is provided
  • Medication - Medications ordered or administered during encounters
  • Observation - Clinical measurements like blood pressure, height, and weight
  • Patient - Patient demographic information and core patient data
  • Practitioner - Healthcare providers who deliver care
  • Procedure - Procedures performed on patients
Each schema page provides detailed information about the table structure, including primary keys, foreign keys, and column definitions.