Microsoft Azure Gets Native FHIR Support to Aid Healthcare Research

The Azure API for FHIR lets developers and researchers manage healthcare data and quickly provision an enterprise-grade service. It also supports several compliance standards.

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Azure has become the first public provider to natively support FHIR, a standard of data formats and application programming for healthcare. Via a new API, developers, researchers, or device makers can quickly manage data and provision an FHIR environment. understands the unique value FHIR offers to enable management of Protected Health Information (PHI) in the cloud, so we're advancing Azure technology to enable our health customers the ability to ingest, manage, and persist PHI data across the Azure environment in the native FHIR format,” said Heather Jordan Cartwright, general manager, Microsoft Healthcare, in a blog post. The company says its goal was to craft a new PaaS to put the focus on development rather than resources and tech. The platform enables improved testing of AI, digital services, and cross-hospital collaboration, says London's Great Ormond Street Hospital. “We now have a unified API as a basis for designing, testing, and deploying the next generation of machine learning and digital services in the hospital for our young patients. This will also enable rapid and easier collaboration with our international pediatric hospital partners to share specialized tools to improve patient outcomes and experience,” said Professor Neil Sebire, Chief Information Officer at GOSH. Microsoft promises that interested parties can set up an enterprise-grade FHIR service in just a few minutes, with role-based access control and audit log tracking. The Azure API for FHIR is also compliant with a number of standards, including ISO 27001:2013, HIPAA, and GDPR. The service also sports multi-region failover to ensure data protection, as well as SMART on FHIR functionality. “While we're excited to light our cloud on FHIR, we're even more excited about the foundations FHIR is forging for the future of machine learning and life sciences in healthcare,” finishes Cartwright.