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InterSystems IRIS for Health Technology Guide

Create Information-Rich Healthcare Applications, Faster

InterSystems IRIS for Health™, a comprehensive, cloud-first data platform, along with its predecessors, is the basis for healthcare software that manages over a billion health records globally

Its use is broad and deep across the healthcare industry, and our own healthcare product offerings are based on it. InterSystems IRIS for Health is at the heart of commercial healthcare solutions such as Epic’s EMR and 3M’s Encompass 360. Clinical laboratory companies such as Sonic, Labcorp, and Bioreference have based their internal management systems on our platform. It’s used by startups and established healthcare delivery and medical technology organizations, payers, pharmaceutical companies and others as a foundation for innovation through its connectivity, interoperability, and application development capabilities.

The Core of a Smart Data Fabric

Recently, smart data fabrics have emerged as an IT architectural approach that goes beyond previous designs used to gain access to data silos across an organization. Smart data fabrics combine technology to integrate, transform, and harmonize data from disparate sources on demand, and leverage that data in applications through a wide range of analytics capabilities.

For an increasing number of organizations and developers, InterSystems IRIS for Health forms the core of a smart data fabric that includes analytics and data exploration, business intelligence, natural language processing, complex business rules, and machine learning. Developers, researchers, and organizations across the healthcare continuum are using it to gain new insights from data and power a transformation where patients are at the center of every service provided.

InterSystems IRIS for Health

InterSystems IRIS for Health provides many of the capabilities required to implement real-time, smart data fabric architectures in a single product, eliminating the need to implement, integrate, and maintain dozens of different technologies. Key components of InterSystems IRIS for Health include:

  • A new approach to data and application integration
  • Seamless healthcare interoperability via HL7® FHIR® and other standards
  • Advanced FHIR capabilities
  • Embedded advanced analytics tools
  • Support for application development

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This article from UK Analyst Bloor Research recognises the capabilities and advantages of InterSystems IRIS data platform as an "innovative and powerful data management platform" and it’s Smart Data Fabric capabilities offering a market differentiator.
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How to get the most benefit from machine learning and AI How can healthcare organizations best leverage artificial intelligence (AI) tools, including generative artificial intelligence (GenAI)? Join Alex MacLeod, Director of Healthcare Solution Innovation at InterSystems, as she discusses key steps and challenges for organizations interested in adopting AI and other advanced technologies, as well as use cases that can improve care delivery and drive operational efficiencies.
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Migration Options for Application & Solution Providers
Migrating InterSystems IRIS® and InterSystems IRIS for Health™ from on-premises to the cloud offers many advantages for Application Providers and Solution Providers. These advantages include simplified operations, access to flexible resources, and enhanced resilience. Companies no longer need to worry about the physical constraints and expenses associated with maintaining on-prem infrastructure, such as power and space requirements and expensive computer hardware.
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TabbFORUM’s Market Structure Wave
In this edition of TabbFORUM’s Market Structure Wave video series, InterSystems Irene Galperin, Senior Advisor, Financial Services, discusses how firms in the capital markets have been investing and experimenting with AI, particularly most recently with the advent of ChatGPT. In this interview, conducted by TABB Group CEO Bruce Morris, Irene offers several use cases and explains that data is at the core of implementing AI, as data needs to be "high quality and consistent," essentially "AI-ready."
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