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Cloud Healthcare FHIR APIs
FHIR Meetup
April 23, 2020
Kalyan Pamarthy,
Product Manager, Cloud Healthcare, Google
kpamarthy@google.com
https://www.linkedin.com/in/kpamarthy/
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
Google Cloud Healthcare API
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
cloud.google.com/healthcare
Now Generally Available
Google Cloud Healthcare - FHIR APIs
FHIR as a data model FHIR as an API specification
FHIR serves two technical roles in Healthcare IT
Project
Cloud Healthcare API
Location
Dataset
Store (FHIR Datastore)
Organization: Project -> Location -> Dataset -> DataStore
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
Configuration on FHIR Store
{
"enableUpdateCreate": false,
"disableReferentialIntegrity": true,
"disableResourceVersioning": true,
"notificationConfig": {"pubsubTopic": "<topic>"}
}
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
FHIR resource operations
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
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
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
{
“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
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
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
Perspectives on FHIR
● 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
● 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
Building “Serverless” apps on Google Cloud
FHIR APIs
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
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
cloud.google.com/healthcare/docs
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

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Google Cloud healthcare data platform and FHIR APIs by Kalyan Pamarthy

  • 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
  • 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
  • 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
  • 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
  • 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
  • 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