The engine for interoperability
Cloud Healthcare API bridges the gap between care systems and applications built on Google Cloud. By supporting standards-based data formats and protocols of existing healthcare technologies, Cloud Healthcare API connects your data to advanced Google Cloud capabilities, including streaming data processing with Cloud Dataflow, scalable analytics with BigQuery, and machine learning with Cloud Machine Learning Engine. In addition, Cloud Healthcare API simplifies application development and device integration to accelerate digital transformation and enable real-time integration with care networks.
FHIR (Fast Healthcare Interoperability Resources) is the emerging standard for healthcare data interoperability. Using REST semantics, FHIR specifies a robust, extensible data model for interacting with clinical resources. Google Cloud can transform data from other formats into and out of FHIR resources to simplify data ingestion, making the data available for use with analytics and machine learning tools. The FHIR API provides full support for STU3 resources.
HL7v2 is an essential communication modality for any application seeking to connect to existing clinical systems. The HL7v2 API implements a REST interface for ingesting, sending, searching, and retrieving HL7v2 messages. The HL7v2 API has been integrated with an open source adapter to send and receive messages over Minimal Lower Layer Protocol (MLLP) as well as several common HL7v2 interface engines. The adapter runs within Google Kubernetes Engine to provide rapid provisioning, communicates over Cloud Pub/Sub to deliver horizontal scalability, and connects with Cloud VPN to enable transport security.
DICOM is the established standard for storing and exchanging medical images and their metadata across a wide range of modalities, including radiology, cardiology, ophthalmology, and dermatology. DICOMweb is a REST API used for storing, querying, and retrieving these images. The DICOMweb support in Cloud Healthcare API allows existing imaging devices, PACS solutions, and viewers to interact with the Cloud Healthcare API. This can be done either directly or via open source adapters designed to support existing DICOM DIMSE protocols. This allows customers to scalably store their medical imaging data and connect their data to powerful tools for analytics and machine learning.
De-identification is the process of removing or obfuscating identifying information from datasets so that the data cannot be linked back to specific individuals. De-identification is often a step in pre-processing healthcare datasets. It can be a critical step so that healthcare data can be made available for analysis, training, and evaluating machine learning models, and sharing with non-privileged parties, while protecting patient privacy. Cloud Healthcare API provides capabilities to de-identify several types of data stored in the service, facilitating these use cases and several others. This includes de-identifying structured medical records in FHIR format, as well as medical images in DICOM format (both metadata and pixel data).
Data is private, secure, and in your control
Data locality is a core component of Cloud Healthcare API. You choose the storage location for each dataset from current available locations that correspond to distinct geographic areas. Your organization controls where data is stored on Google Cloud via Cloud Healthcare API. Cloud Healthcare API services are integrated with Cloud Audit Logging, which allows your organization to track actions affecting your data. By default, administrative modifications to datasets, data stores, and IAM policies are logged. You can also enable audit logging of item creation, modification, and reads within each data store. Cloud Healthcare API is built using Google’s multi-layered security approach that leverages cutting-edge security capabilities, including data-loss prevention tools, precise policy controls, robust identity management, encryption, and many more.
Analytics and machine learning
Once data is brought to Google Cloud Platform, Cloud Healthcare API enables customers to integrate their data with powerful analytic tools such as BigQuery, visualization and machine learning tools such as Cloud Datalab, as well as third-party tools such as Tableau.
Customers can also use Cloud Healthcare API to connect their medical data to powerful machine learning solutions such as AutoML and Cloud ML Engine, which simplify custom machine learning model training. Once a model has been trained, customers can leverage Cloud Healthcare API's DICOM and FHIR support to deploy the model into existing clinical workflows.
Committed to compliance
Cloud Healthcare API supports HIPAA compliance. Google Cloud is HITRUST CSF certified. In addition, Cloud Healthcare API is in scope for Google Cloud Platform’s ISO 27001, ISO 27017, and ISO 27018 certifications. See Google Cloud Platform’s compliance page for more information.
Unlock the value of your data
Cloud Healthcare API allows you to unlock the true value of your healthcare data by bringing it to advanced analytics and machine learning solutions such as BigQuery, Cloud AutoML, and Cloud ML Engine.
Cloud Healthcare API provides web-native, serverless scaling optimized by Google’s infrastructure. Simply activate the API and start sending requests — no initial capacity configuration required. Although some limits exist (e.g., Cloud Pub/Sub quotas), capacity can expand to match usage patterns.
Cloud Healthcare API integrates with Apigee, recognized by Gartner as a leader in full lifecycle API management, to deliver app and service ecosystems around your data.
Cloud Healthcare API organizes your healthcare information into datasets with one or more modality-specific stores per set. Each store exposes both a REST and RPC interface. You can use Cloud IAM to set fine-grained access policies.
This product is in beta. For more information on our product launch stages, see here.