InterSystems IRIS for Health
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Create Information-Rich Healthcare Applications, Faster
InterSystems IRIS for Health is used by leading independent software vendors and healthcare organizations, the largest clinical laboratories, and the largest regional health information networks. It is an extension of the InterSystems IRIS Data Platform used by Epic to support healthcare organizations whose systems count 2.5 million concurrent users, processing roughly 1.8 billion database accesses per second across all Epic customers.
Our History Helps You Chart the Future
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.
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Connectivity for Creativity
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Try InterSystems IRIS for Health
Frequently Asked Questions
- 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