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Advanced Interoperability. Unprecedented Speed.
Massive Scale.
Create information-rich, FHIR-based, healthcare applications faster
with InterSystems IRIS for Health™.

Data Services Built to Understand Healthcare and Clinical Research

Healthcare Interoperability and Data Standards

Deep support for FHIR, HL7 V2, IHE and other global healthcare information protocols and messaging formats enable the integration and interoperability of health applications.

Healthcare Data Management

An extensible FHIR repository and comprehensive REST APIs provide the foundation for modern healthcare application development and help you seamlessly handle multiple forms of data at high speed, with vertical and horizontal scalability.

Research Data Management

For clinical research, InterSystems IRIS for Health is a development platform that supports both i2b2 tools and the Observation Medical Outcomes Partnership (OMOP) Common Data Model (CDM), and is fully compatible with Observational Health Data Sciences and Informatics ( OHDSI) collaborative open-source tools.

InterSystems OMOP Platform provides a scalable, managed solution for populating your OMOP repository with real-world EHR data using standard bulk FHIR downloads and data transformations.

Healthcare Analytics Framework

Include analytics and AI in your solutions using our open analytics platform, with your choice of embedded, standards-based, and best-of-breed analytics technologies for exploration, analysis, and prediction.

Frequently Asked Questions

What development languages does InterSystems IRIS for Health support?
InterSystems IRIS for Health supports Python, Java, C# / .NET, Node.js, and ObjectScript.
What types of healthcare applications can I build--or connect--using InterSystems IRIS for Health?
Any app that needs to provide insights from diverse data.

  • Health information systems that deliver intelligent workflows with real-time analytics
  • Moving large, connected, healthcare data-intensive applications to the cloud
  • Building new connected health solutions that incorporate data from multiple sources using different standards
  • Clinical research applications examining large pools of data sources (e.g. clinical trials based on real-world evidence, population health initiatives)
  • Apps for the Internet of Healthy Things
  • Serving up massive quantities of clean data for machine learning or AI initiatives

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