Kalyan Pamarthy, product management for the FHIR® and HL7 v2 data platform within Google Cloud AI. "Google Cloud healthcare data platform and FHIR® APIs" This presentation will provide a high-level view of the Google Cloud healthcare data platform, managed FHIR® APIs, and capabilities that support end-end customer use cases for healthcare data interoperability. It will also cover key themes around the adoption of FHIR and implementation patterns followed by Google Cloud's customers transitioning to FHIR®.

1. Cloud Healthcare FHIR APIs
FHIR Meetup
April 23, 2020

2. Kalyan Pamarthy,
Product Manager, Cloud Healthcare, Google
kpamarthy@google.com
https://www.linkedin.com/in/kpamarthy/

3. Topics for today
● Google Cloud Healthcare API & FHIR capabilities
● Google Cloud Perspectives on FHIR
● Example end-end solution on Google Cloud FHIR APIs
Cloud Healthcare API

4. Google Cloud Healthcare API

5. Accelerate your healthcare solution development
Healthcare API
● Primary API surface for healthcare specific data management
capabilities. Standards based
○ FHIR (DSTU2, STU3 and R4)
○ Hl7v2
○ DICOM
● Runs on Google Cloud Platform
● Serverless, REST interface
● Value add capabilities (e.g. De-Id)
● Stores PHI data in compliance with regulations (e.g. HIPAA)
● Integrated with Google Cloud applications (e.g. BigQuery)
● Integrated with Google Cloud components (e.g. PubSub)
Cloud Healthcare API - Capabilities

6. cloud.google.com/healthcare
Now Generally Available

7. Google Cloud Healthcare - FHIR APIs

8. FHIR as a data model FHIR as an API specification
FHIR serves two technical roles in Healthcare IT

9. Project
Cloud Healthcare API
Location
Dataset
Store (FHIR Datastore)
Organization: Project -> Location -> Dataset -> DataStore

10. Traditional database stores persist data to disk
and offer a API, like Structured Query Language
SQL HL7® FHIR
With Cloud Healthcare FHIR APIs, you
create FHIR data stores that
implement healthcare-native APIs
FHIR DataStores

11. Configuration on FHIR Store
{
"enableUpdateCreate": false,
"disableReferentialIntegrity": true,
"disableResourceVersioning": true,
"notificationConfig": {"pubsubTopic": "<topic>"}
}

12. REST Endpoints to manage FHIR Stores
* On Our Radar
Import Export
Streaming*
Bulk
Streaming
Bulk
Interact
Create Get List Delete Search
De Identify
FHIR
Config
Notify
Pub/Sub
Subscribe
FHIR Store

13. FHIR resource operations

14. https://healthcare.googleapis.com/<v>/projects/<P>/locations/<L>/datasets/<D>/<type>Stores/<S>
<V> Healthcare API version v1
<P> Project identifier general-hospital
<L> Location identifier us-central1
<D> Dataset identifier ehr-production
<type> Data type slug hl7v2, dicom, fhir
<S> Store identifier city-clinic
Accessing FHIR resources with REST

15. Apps & services
Example 1 : Get Patient data from a Cloud FHIR Store
$> curl -X GET
https://healthcare.googleapis.com/v1/projects/PROJECT_ID/locations/LOCATION/datasets/DATASET_ID/fhi
rStores/FHIR_STORE_ID/Patient/1
-H 'Authorization: Bearer <access-token>'
Cloud Healthcare
FHIR API

16. Cloud Healthcare API
curl -X POST
-H "Authorization: Bearer $(gcloud auth
print-access-token)"
-H "Content-Type: application/json;
charset=utf-8"
--data "{
'destinationDataset':
'projects/PROJECT_ID/locations/LOCATION/datasets
/DESTINATION_DATASET_ID',
'config': {
'fhir': {}
}
}"
"https://healthcare.googleapis.com/v1/projects/P
ROJECT_ID/locations/LOCATION/datasets/SOURCE_DAT
ASET_ID:deidentify"
Cloud Healthcare API
Example 2: De-identify an entire healthcare dataset

17. {
“resourceType”: “Patient”,
"id": "1e712685-714f-6720-a23a-c90103f70be6",
"identifier":[
{
"use":"usual",
...,
"value":""
}
],
"name":[
{
"use":"official"
"family":"",
"given":["", ""]
}
],
"gender": “unknown”,
...
}
{
"resourceType":"Patient",
"id": "1e712685-714f-6720-a23a-c90103f70be6",
"identifier":[
{
"use":"usual",
...,
"value":"12345",
}
],
"name":[
{
"use":"official",
"family":"Chalmers",
"given":["Peter", "James"]
}
],
"gender": "male",
"birthDate":"1974-12-25",
...
}
Example 2: De-identify - Before/After

18. BigQuery
curl -X POST
-H "Authorization: Bearer $(gcloud auth print-access-token)"
-H "Content-Type: application/json; charset=utf-8"
--data "{
'bigqueryDestination': {
'datasetUri': 'bq://PROJECT_ID.DATASET_ID'
}
}"
"https://healthcare.googleapis.com/v1/projects/PROJECT_ID/locations/L
OCATION/datasets/DATASET_ID/fhirStores/FHIR_STORE_ID:export"
Example 3: Generate a SQL projection of your FHIR data

19. SELECT observation.subject.reference.value AS patient_id,
AVG(CAST(observation.value.quantity.value.value as FLOAT64)) AS avg_hr
FROM <D>.observation o,
UNNEST(observation.code.coding) c
GROUP BY observation.subject.reference.value
HAVING avg_hr > 130
ORDER BY avg_hr DESC
Example 3: Run SQL queries on the FHIR projection in BigQuery

20. Perspectives on FHIR

21. ● Have no existing infrastructure for managing clinical data
○ No on premises EHR to connect to
● Do not what to have to define a clinical semantic model
○ FHIR is a godsend as a primary CDR
● Want to write the minimum amount of new code
○ Expect standards & APIs to exist and leverage open source
● Do not want to have to operate or scale infrastructure
● Mobile first approach to applications
● Build machine learning in from day one
● Subject to the same regulatory controls
○ HIPAA, GDPR etc.
○ CMS & ONC interoperability proposals
Clinical data is
automatically in the
right format and
location to benefit
from the force
multiplication of
Google Cloud
Customer model 1 : “Born Cloud” Digital Healthcare Organizations

22. ● Have to address the EHR challenges
○ EHRs were not built to be a data platform servicing applications
○ Data is held in a proprietary format with low semantic interoperability
○ What FHIR APIs that do exist are “one way” and out of date
○ Can’t keep up with new data types (Omics, SDOH, Patient Generated)
○ Rarely only one EHR (average is 10)
○ Typically lots of decommissioned EHR data left after adoption
● BUT - EHRs are here to stay...
● Common answer is “Secondary Longitudinal CDR”
○ Hydrated from the on premises EHR data
○ Kept in near real time synch with EHR data
Customer model - 2 : :Traditional” Healthcare Organizations

23. Building “Serverless” apps on Google Cloud
FHIR APIs

24. Example : Putting The Pieces Together with Google Cloud
From Data To Prediction : Customer scenario
● Have configured Google Healthcare API in the Google Cloud
● Have trained a “Risk Assessment” ML model which requires
Heart Rate as one of its “features”
● Patient John Smith is having his vital signs monitored as an
inpatient
● The EHR generates an Observation as HL7 message
● We want to store this as a FHIR resource
● We want to run the ML model against this new data
● We want to feedback ML model results into the EHR

25. Cloud Pub/Sub
Healthcare API EHR Relay
4. Notify
Write Observation
Google Healthcare API- Putting The Pieces Together: Case Study
2. Write Observation
as FHIR resource
3. Stores
Observation
Observation
Heart Rate
1. HL7 message
5. Trigger
Cloud Function
6. Read Patient$everything
ML Engine
7. Predict
8. Prediction
9. Write RiskAssessment

26. cloud.google.com/healthcare/docs

27. Takeaways
1. Cloud Healthcare API :Managed service for FHIR
2. Accelerate healthcare solution development with Cloud
Healthcare API and Google Cloud Ecosystem
3. Import, FHIR Operations, Export, De-Identification,
AnalyticsCloud Healthcare API
Get Started at cloud.google.com/healthcare