
Before you begin
- Labs create a Google Cloud project and resources for a fixed time
- Labs have a time limit and no pause feature. If you end the lab, you'll have to restart from the beginning.
- On the top left of your screen, click Start lab to begin
Create Healthcare Dataset and Data Store
/ 30
Data creation
/ 30
Exporting data to BigQuery
/ 40
Cloud Healthcare API provides a managed solution for storing and accessing healthcare data in Google Cloud, providing a critical bridge between existing care systems and applications hosted on Google Cloud. Using the API, you can unlock significant new capabilities for data analysis, machine learning and application development, and use these capabilities to build the next generation of healthcare solutions.
In this lab you will discover and use the basic functionality of Cloud Healthcare API using Fast Healthcare Interoperability Resources (FHIR) data model, how to export data to BigQuery, and how to access data in BigQuery via SQL.
In this lab, you will:
Cloud Healthcare API provides a managed solution for storing and accessing healthcare data in Google Cloud, providing a critical bridge between existing care systems and applications hosted on Google Cloud. Using the API, you can unlock significant new capabilities for data analysis, machine learning and application development, and use these capabilities to build the next generation of healthcare solutions.
The API is comprised of three modality-specific interfaces that implement key industry-wide standards for healthcare data:
Each interface is backed by a standards-compliant data store that provides read, write, search, and other operations on the data.
The Cloud Healthcare API provides a number of key features that are critical to bridging current technologies to the next generation of healthcare systems and applications:
For many applications, the Cloud Healthcare API can provide a modern alternative to legacy stacks implementing DICOM, HL7v2 or FHIR STU3 standards, simplifying data integration with existing systems and enabling the application developers to focus on their differentiating features such as UX and intelligence.
To get the most out of the Cloud Healthcare API, there are a few key concepts you'll want to understand. The information below should give you a good sense of Cloud Healthcare API capabilities, but you can find more details in the Cloud Healthcare API documentation.
The Cloud Healthcare API exposes interfaces that enable you to perform different types of functions:
These functions may vary slightly depending on the modality of data (FHIR, HL7 v2 or DICOM) being operated on. For example, data retrieval operations against an FHIR data store use an API that conforms to the FHIR standard, but data retrieval operations against an HL7 v2 store use operations better suited to operating on HL7v2-structured data.
All Cloud Healthcare API usage occurs within the context of a Google Cloud project. Projects form the basis for creating, enabling, and using all Google Cloud services including managing APIs, enabling billing, adding and removing collaborators, and managing permissions for Google Cloud resources. Cloud Healthcare API can be used in one or many Google Cloud projects, as appropriate; this flexibility allows you to separate production from non-production usage, for example, or to segregate applications and resources in order to better manage access or accommodate different development lifecycles.
Within a project, data ingested through Cloud Healthcare API is stored in a dataset, which resides in a geographic location corresponding to a specific Google Cloud region. You use the Cloud Healthcare API's administrative functions to create a dataset in a particular location; doing so facilitates implementation of data location requirements for the countries in which your applications provide services. For example, you can choose to create a dataset in Google Cloud's "us-central1" region for US-based applications, or in an EU or UK region for applications serving those customers. This level of location control is also available in other Google Cloud products, which can be combined with Cloud Healthcare API to create a complete application architecture. A list of generally available Google Cloud products and the regions in which they are implemented can be found on Google Cloud, Cloud locations.
Because each healthcare data modality has different structural and processing characteristics, datasets are split into modality-specific stores. A single dataset can contain one or many stores, and those stores can all service the same modality or different modalities as application needs dictate. Using multiple stores in the same dataset might be appropriate if a given application processes different types of data, for example, or if you'd like to be able to separate data according to its source hospital, clinic, department, etc. An application can access as many datasets or stores as its requirements dictate with no performance penalty, so it's important to design your overall dataset and store architecture to meet the organization's broad goals for locality, partitioning, access control, and so on.
The diagram below illustrates two datasets in a Google Cloud project, each of which contains multiple stores.
There are many ways to structure datasets and stores. As you design systems that use the Cloud Healthcare API, you may want to take the following into consideration:
The minimal lower layer protocol (MLLP) is the standard used for transmitting HL7v2 messages over TCP/IP connections within a network, such as a hospital.
MLLP does not offer an exact mapping to the Cloud Healthcare API HL7v2 REST API], which uses HTTP. Therefore, an MLLP adapter must be used to convert messages transmitted over MLLP into a format that an HTTP/REST API can accept. To transmit messages over MLLP and then to the Cloud Healthcare API, use the Google Cloud MLLP adapter.
There are many ways to structure datasets and stores. As you design systems that use the Cloud Healthcare API, you may want to take the following into consideration:
Security and access control: Rules can be defined at both a dataset and store level, but you may choose to group all data for a particular application into the same dataset, and set access control rules such that only that application can access the dataset.
Application requirements: An application processing different types of data may have all of its data for all modalities in a single dataset.
Source systems: Often, the structure of healthcare data can vary according to the source system and modality. Separating data for different source systems into their own datasets may facilitate processing.
Intended use: Data from different systems can have different intended uses, such as research, analytics or machine learning predictions. Grouping data by intended use may facilitate ingestion into the target system.
Separating ePHI from de-identified data: Cloud Healthcare API data de-identification functions read from a source dataset and write the output into a new dataset that you specify. If you are preparing data to be used by researchers, for example, this approach to de-identifying data may be a consideration in how you use datasets to segregate data.
Data in the Cloud Healthcare API datasets and stores can be accessed and managed using a REST API that identifies each store using its project, location, dataset, store type and store name. This API implements modality-specific standards for access that are consistent with industry standards for that modality. For example, the Cloud Healthcare DICOM API natively provides operations for reading DICOM studies and series that are consistent with the DICOMweb standard, and supports the DICOM DIMSE C-STORE protocol via an open-source adapter. Similarly, the FHIR API provides operations for accessing or searching FHIR entity types that is based on the FHIR standard, and the HL7v2 API provides operations for reading and searching HL7v2 messages based on HL7v2 message or segment criteria.
Operations that access a modality-specific store use a request path that is comprised of two pieces: a base path, and a modality-specific request path. Administrative operations—which generally operate only on locations, datasets and stores—may only use the base path, but data modality-specific retrieval operations use both the base path (for identifying the store to be accessed) and request path (for identifying the actual data to be retrieved).
To reference a particular store within a Cloud Healthcare API dataset, you would use a base path structured like this:
/projects/<PROJECT>/locations/<LOCATION>/datasets/<DATASET>/<STORE-TYPE>/<STORE-NAME>
A concrete base path example might look like this:
/projects/myProj/locations/
which references a Cloud Healthcare HL7 v2 store in the Google Cloud project "myProj", in the "
To access a specific piece of data, the base path is used in combination with a request path that is formatted according to the appropriate modality standard. For example, a request to read a specific FHIR "Patient" entity using the entity ID might look like this:
<basePath>/resources/Patient/{patient_id}
with /Patient/{patient_id}
being a path—structured according to the FHIR standard—for the Patient resource whose identifier is specified by {patient_id}
. Similarly, DICOMweb requests to a DICOM store might look like this:
<basePath>/dicomWeb/studies/{study_id}/series?PatientName={patient_name}
where {study_id}
identifies a particular DICOM study, and the patient's name is specified by {patient_name}
. In this example, the path specification is consistent with the DICOMweb standard path structure.
Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources are made available to you.
This hands-on lab lets you do the lab activities in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials you use to sign in and access Google Cloud for the duration of the lab.
To complete this lab, you need:
Click the Start Lab button. If you need to pay for the lab, a dialog opens for you to select your payment method. On the left is the Lab Details pane with the following:
Click Open Google Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).
The lab spins up resources, and then opens another tab that shows the Sign in page.
Tip: Arrange the tabs in separate windows, side-by-side.
If necessary, copy the Username below and paste it into the Sign in dialog.
You can also find the Username in the Lab Details pane.
Click Next.
Copy the Password below and paste it into the Welcome dialog.
You can also find the Password in the Lab Details pane.
Click Next.
Click through the subsequent pages:
After a few moments, the Google Cloud console opens in this tab.
Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.
Click Activate Cloud Shell at the top of the Google Cloud console.
Click through the following windows:
When you are connected, you are already authenticated, and the project is set to your Project_ID,
gcloud
is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
Output:
Output:
gcloud
, in Google Cloud, refer to the gcloud CLI overview guide.
In the Cloud Console, go to Navigation menu () scroll down to the Analytics section, then choose Healthcare.
Click Enable.
You'll see a success message:
Output:
From the Datasets Browser screen, click the Refresh icon.
Then, click on dataset1.
Next, click Create Data Store.
Select the type: FHIR
Click in the ID field and name the data store fhirstore1.
Click Next.
Under Configure your FHIR Store, select R4.
Click Next.
Under Stream resource changes to BigQuery, make no changes and click Next.
In the Receive Cloud Pub/Sub notifications section, click Add a cloud Pub/Sub topic > Select a Cloud Pub/Sub topic > Create a topic.
Name the topic fhir-topic, then click Create.
Click Create.
Your first FHIR store is now created.
Create a second datastore by clicking Create Data Store.
Select FHIR in the Type dropdown.
Name the ID of the data store de_id.
Click Next.
Select R4 for the FHIR Store Configuration option.
Click Create. Your second FHIR store is now created.
You should now see the two FHIR stores listed on the Data stores view.
Click Check my progress to verify the objective.
Now you'll import sample data into the FHIR stores and stream to BigQuery.
This may take a couple of minutes to complete.
The CreateDataset
was a success and the ImportResources1
may still be running. Wait until the operation has been completed before moving on.
This may take a couple of minutes to complete.
You can view progress in the Operations tab in the Console.
Click on the Data Stores tab to view the datastores again once the operation is complete.
Click the Actions button for fhirstore1.
From the dropdown, select de-identify.
Select dataset1 as the dataset and de_id as the destination data store.
Click Append for the pop-up.
Click Next.
Click de-identify.
You can view progress in the Operations tab in the Console.
Click on the Data Stores tab to view the datastores again once the operation is complete.
Wait for this operation to complete before moving to the next step.
Click Check my progress to verify the objective.
In the Cloud Console, use the Navigation menu to open BigQuery.
In the left pane, under resources, select your Project ID and expand the drop-down. You should see the two recently created datasets named dataset1
, and de_id
.
Select dataset1 and expand the drop-down.
Navigate to the Patient table and preview the Schema.
Click the + icon to open a new Query Editor tab, then add the following SQL command to view patient data exported from the FHIR stores:
Click Check my progress to verify the objective.
See the difference in the data? In the query on the de-identified data, given_name and family name have been redacted, and the birth_date date shifted, while retaining the non-PHI PatientID.
In this section, you will create a new FHIR Patient resource in the FHIR store and export the newly created FHIR resource to BigQuery using streaming export.
This will not return any results. You will now stream this patient into the dataset and query again to demonstrate the newly created resource.
Patch
command in your Cloud Shell:In this API call, you are creating a new FHIR Patient resource in the FHIR store fhirstore1.
You should see a new patient created, the recently imported Darcy patient! This is a result of the streaming FHIR data export of the newly created FHIR Patient Resource to the BigQuery Dataset.
Cloud Healthcare API provides a comprehensive facility for ingesting, storing, managing, and securely exposing healthcare data in FHIR, DICOM, and HL7 v2 formats. Using Cloud Healthcare API, you can ingest and store data from electronic health records systems (EHRs), radiological information systems (RISs), and custom healthcare applications. You can then immediately make that data available to applications for analysis, machine learning prediction and inference, and consumer access.
Cloud Healthcare API enables application access to healthcare data via widely-accepted, standards-based interfaces such as FHIR STU3 and DICOMweb. These APIs allow data ingestion into modality-specific data stores, which support data retrieval, update, search and other functions using familiar standards-based interfaces.
Further, the API integrates with other capabilities in Google Cloud through two primary mechanisms:
Using Pub/Sub with Cloud Run functions enables you to invoke machine learning models on healthcare data, storing the resulting predictions back in Cloud Healthcare API data store. A similar integration with Cloud Dataflow supports transformation and cleansing of healthcare data prior to use by applications.
To support healthcare research, Cloud Healthcare API offers de-identification capabilities for FHIR and DICOM. This feature allows customers to share data with researchers working on new cutting-edge diagnostics and medicines.
In this lab you:
Manual Last Updated March 04, 2024
Lab Last Tested March 04, 2024
Copyright 2025 Google LLC All rights reserved. Google and the Google logo are trademarks of Google LLC. All other company and product names may be trademarks of the respective companies with which they are associated.