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OHS Analytics Concepts

The key concepts that underpin the OHS Analytics components are:

  1. ETL Pipelines: ETL Pipelines and Controller can be configured to continuously transform FHIR data into an analytics friendly Parquet on FHIR format.
  2. Deployment approaches: The pipelines are designed to accommodate various deployment approaches in terms of scalability; from a single machine to a distributed cluster environments.
  3. Query simplification approaches: Once the data is transformed into an analytics-friendly format, it should be queried. Multiple approaches are provided and/or showcased to flatten FHIR schema to make developing analytics solutions easier.

FHIR Data Pipes

FHIR Data Pipes is built on Apache Beam SDK for ease of scalability and has multiple deployment options from local, to on-prem clusters to public clouds.

FHIR Data Pipes is made up of the ETL Pipelines and Controller modules * that are designed to work together* to provide continuous transformation of FHIR data to Apache Parquet files (for data analysis) or another FHIR server ( for data integration).

FHIR Data Pipes Transform Step Image

ETL Pipelines

Java binaries to Extract, Transform and Load FHIR data from a FHIR source to Parquet format.

Extraction

FHIR Data Pipes is designed to fetch FHIR source data in various forms and APIs:

  • FHIR Search API: This should work with most FHIR servers, including those using FHIR data model like HAPI FHIR or FHIR facades like OpenMRS.
  • Direct database access: This is mostly a more efficient approach that works with HAPI FHIR only.
  • Bulk Export API: This will gradually become our main fetch API as more FHIR servers implement the Bulk Export API.
  • Files in json and ndjson formats.

Transformation

FHIR Resources are transformed into a "Parquet on FHIR" format:

  • Uses a forked version of Bunsen library ( currently supports STU3 and R4 versions of FHIR)
  • Configurable support for FHIR profiles and extensions
  • (Optional) In-pipeline 'flattening' of FHIR data using ViewDefinition resources - read more

Loading

FHIR Data Pipes supports different SQL Data Warehouse options depending on the needs of the project. These include:

  • Loading Parquet files into an OLAP query engine such as SparkSQL (which can be deployed on a single machine or a distributed cluster) or DuckDB (single machine in-process) or many other tools that understand Parquet.
  • Traditional relational databases such as PostgreSQL (when using FHIR ViewDefinition resources to generate materialized views)

Pipeline Controller Module

A user-interface wrapper for the FHIR Data Pipes Pipelines, integrating " full", "incremental", and "merger" pipelines together.

  • The Pipelines Controller is built on top of pipelines and shares many of the same settings
  • Using the controller module you can schedule periodic incremental updates or use the Web Control Panel to start the pipeline manually

Deployment Approaches

There are a number of different deployment approaches - see table below.

Choosing the right approach, comes down to a number of factors including the specific use-case, infrastructure constraints of the project, future scalability requirements, and expertise of the team.

Scenario Approach Considerations
Simple relational database to power dashboards or reporting Custom schema defined as ViewDefinition Resources - see Views By design, this will provide a subset of FHIR data in flat tables
Horizontally scalable query architecture with lossless FHIR data Parquet based DWH and distributed query engine (e.g. SparkSQL) - see tutorial This requires a distributed file system for Parquet files and a distributed query engine.
Lossless FHIR DWH with a single process query engine Parquet based DWH with non-distributed query engine set-up (e.g. single-node Spark or DuckDB) Scalability and performance (e.g. SparkSQL >> DuckDB)
Exploratory data science or ML use cases Use the generated Parquet files which as "near lossless" for enhanced data science workflows Can either use the Parquet or custom schema to power dashboards or reports
Push FHIR data to a central FHIR-store (e.g., for a Shared Health Record system) Use the Pipelines Controller to push from a FHIR source to a FHIR sink Management of the intermediate Parquet files created as part of the pipelines

Query Simplification with Flat Views

FHIR Data Pipes provides two approaches for flattening the FHIR resources into virtual or materialized views:

  1. SQL queries to generate virtual views (outside the pipeline)

  2. FHIR ViewDefinition resources to generate materialized views (within the pipeline)

For more information on both of these approaches, please check Schema and Flat Views.