Skip to content

A collection of tools for extracting OpenMRS data as FHIR resources and analytics services on top of that data. [NOTE: WIP/not production ready].

License

Apache-2.0, Unknown licenses found

Licenses found

Apache-2.0
LICENSE
Unknown
license-header.txt
Notifications You must be signed in to change notification settings

I-TECH-UW/openmrs-fhir-analytics

 
 

Repository files navigation

Build Status codecov

NOTE: This is a work in progress and the current version is only for demonstration purposes. Once these tools reach Alpha status, this note should be removed. This is a collaboration between Google and the OpenMRS community.

What is this?

This repository includes pipelines to transform data from an OpenMRS instance using the FHIR format into a data warehouse based on Apache Parquet files, or another FHIR server (e.g., a HAPI FHIR server or Google Cloud FHIR store). There is also a query library in Python to make working with FHIR based data warehouses simpler.

These tools are intended to be generic and eventually work with any FHIR-based data source and data warehouse. Here is the list of main directories with a brief description of their content:

  • pipelines/ Batch and streaming pipelines to transform data from a FHIR based source to an analytics friendly data warehouse or another FHIR store.

  • dwh/ Query library for working with distributed FHIR-based data warehouses.

  • bunsen/ A fork of a subset of the Bunsen project.

  • docker/ Docker configurations for various servers/pipelines.

  • doc/ Documentations

  • utils/ Various artifacts for setting up an initial database, running pipelines, etc.

  • e2e-tests/ Scripts for testing pipelines end-to-end.

About

A collection of tools for extracting OpenMRS data as FHIR resources and analytics services on top of that data. [NOTE: WIP/not production ready].

Resources

License

Apache-2.0, Unknown licenses found

Licenses found

Apache-2.0
LICENSE
Unknown
license-header.txt

Stars

Watchers

Forks

Packages

 
 
 

Languages

  • Java 45.4%
  • Jupyter Notebook 33.9%
  • Python 15.4%
  • Shell 4.5%
  • Dockerfile 0.8%