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.

What is this?

This repository includes tools for transforming OpenMRS data into a FHIR based warehouse. There are two aspects to this transformation:

• Streaming mode: In this mode, there is an intermediate binary that continuously listens to changes in OpenMRS to translate new changes into FHIR resources and upload them to the target data warehouse.
• Bulk upload (a.k.a. batch mode): This is used for reading the whole content of OpenMRS MySQL database, transform it into FHIR resources, and upload to the target data warehouse.

Streaming mode

This is currently implemented as a stand alone app that sits between OpenMRS and the data warehouse. It currently depends on the Atom Feed module of OpenMRS. Note that this may change in near future by using MySQL binlog and Debezium.

The source of data is OpenMRS and the only sink currently implemented is GCP FHIR store and BigQuery but it should be easy to add other FHIR based sinks.

The steps for using this tool are:

• Add Atom Feed module to OpenMRS.
• Add FHIR2 module to OpenMRS and update Atom Feed config.
• Set up the Atom Feed client side database.
• Create the sink FHIR store and BigQuery dataset.
• Compile and run the streaming app.

Add Atom Feed module to OpenMRS

Assuming that you are using the Reference App of OpenMRS (for example, installed at http://localhost:9016), after login, go to "System Administration" > "Manage Modules" > "Search from Addons" and search for the Atom Feed module (e.g., install version 1.0.12).

For any changes in the OpenMRS database, this module creates entries with payloads related to that change, including URLs for FHIR resources if the change has corresponding FHIR resources.

Add FHIR2 module to OpenMRS and update Atom Feed config

The FHIR module in OpenMRS is being reimplemented in the fhir2 module. You need to compile this module from source and install the omod/target/fhir2-1.0.0-SNAPSHOT.omod module in OpenMRS (or copy that file to the modules directory of your OpenMRS installation).

The URLs for FHIR resources of this module have the form /ws/fhir2/R4/RESOURCE e.g., http://localhost:9016/openmrs/ws/fhir2/R4/Patient. Therefor we need to update the Atom Feed module config to produce these URLs. To do this, from the OpenMRS Ref. App home page, choose the "Atomfeed" option (this should appear once the Atom Feed module is installed) and click on "Load Configuration". From the top menu, choose the file provided in this repository at utils/fhir2_atom_feed_config.json and click "Import".

To check the above two steps, create a new Patient (or an Observation) and verify that you can see that Patient (or Observation) at the aforementioned FHIR URL. Then check the Atom Feed URL corresponding to your change, e.g.,

http://localhost:9016/openmrs/ws/atomfeed/patient/1

OR

http://localhost:9016/openmrs/ws/atomfeed/observation/1

Set up the Atom Feed client side database

Assuming that you have MySQL running on the default port 3306, simply run:

mysql --user=USER --password=PASSWORD < utils/create_db.sql

This will create a database called atomfeed_client with required tables (the USER should have permission to create databases). If you want to change the default database name atomfeed_client, you can edit utils/create_db.sql but then you need to change the database name in src/main/resources/hibernate.default.properties accordingly.

Create the sink FHIR store and BigQuery dataset

First you need to create a GCP project. Then create a Google Cloud Healthcare dataset and a BigQuery dataset with the same name in that project (for an overview of projects, datasets and data stores check this document). Once that is done, use the script utils/create_fhir_store.sh to create a FHIR store in this dataset which stream the changes to the BigQuery dataset as well:

./utils/create_fhir_store.sh PROJECT LOCATION DATASET FHIR-STORE-NAME

• PROJECT is your GCP project.
• LOCATION is GCP location where your dataset resides, e.g., us-central1.
• DATASET is the name of the dataset you created.
• FHIR-STORE-NAME is what it says.

You can run the script with no arguments to see a sample usage. After you create the FHIR store, its full URL would be:

https://healthcare.googleapis.com/v1/projects/PROJECT/locations/LOCATION/datasets/DATASET/fhirStores/FHIR-STORE-NAME

Compile and run the streaming app

From the root of your git repo, run:

mvn clean install

and then:

mvn exec:java -pl streaming \
  -Dexec.mainClass=org.openmrs.analytics.FhirStreaming \`
  -Dexec.args="http://localhost:9016/openmrs JSESSIONID GCP_FHIR_STORE"`

• JSESSIONID is the value of a browser cookie with the same name that is created by OpenMRS. After logging into your OpenMRS instance (e.g., http://localhost:9016/openmrs in this case), grab the value of JSESSIONID cookie to pass to FhirStreaming binary. It is a hexadecimal string like: 512F6DC48352022DBB8916CCB999B8A5.

• GCP_FHIR_STORE is the relative path of the FHIR store you set up in the previous step, i.e., something like: projects/PROJECT/locations/LOCATION/datasets/DATASET/fhirStores/FHIR-STORE-NAME where all-caps segments are based on what you set up above.

To test your changes, create a new patient (or observation) in OpenMRS and check that a corresponding Patient (or Observation) FHIR resource is created in the GCP FHIR store and corresponding rows added to the BigQuery tables.

Batch mode

The steps above for setting up a FHIR Store and a linked BigQuery dataset needs to be followed. Once it is done, and after mvn install, the pipeline can be run using a command like:

java -cp batch/target/fhir-batch-etl-bundled-0.1.0-SNAPSHOT.jar \
  org.openmrs.analytics.FhirEtl --serverUrl=http://localhost:9018 \
  --jsessionId=2950DA4C142EC44145978C02EDA0F311 \
  --searchList=Patient,Encounter,Observation --batchSize=20 \
 --targetParallelism=20 --gcpFhirStore=GCP_FHIR_STORE`

The searchList argument accepts a comma separated list of FHIR search URLs. For example, one can use Patient?given=Susan to extract only Patient resources that meet the given=Susan criteria.

TODO: Add details on how this works and caveats!