When IRIS 2023.2 reaches general availability, we’ll be making some improvements to how we tag and distribute IRIS & IRIS for Health containers.
IRIS containers have been tagged using the full build number format, for example 2023.1.0.235.1. Customers have been asking for more stable tags, so they don’t need to change their dockerfiles/Kubernetes files every time a new release is made. With that in mind, we’re making the following changes to how we tag container images.
Major.Minor Tags: Containers will be tagged with the year and release, but not the rest of the full build number.
For a variety of reasons, users may wish to mount a persistent volume on two or more pods spanning multiple availability zones. One such use case is to make data stored outside of IRIS available to both mirror members in case of failover.
What is the most efficient, memory-safe way to get the names of the corrupted indexes on very large tables for a rebuild. However, if an index has millions of corrupted rows, the .errors array in %ValidateIndices grows too large and throws a errorerror.
What's the most straight-forward way to install this on an offline server? I'm trying to set this up on an Azure DevOps server to support our CI/CD pipelines. I've tried using zpm installing the tgz from the local filesystem. I note zpm seems to need a repo configured to install but I can't work out how to setup a bare-bones Filesystem repo (please point me to some documentation on this). I have no idea what I'm doing...
zpm:%SYS>install f:/tmp/zpm-registry-1.3.4.tgz
ERROR! 'tmp' not found in any repository.
zpm:%SYS>install zpm-registry-1.3.4.tgz
ERROR! 'zpm-registry-1.3.4.tgz' not found in any repository.
zpm:%SYS>install zpm-registry
ERROR! 'zpm-registry' not found in any repository.
zpm:%SYS>install "f:\tmp\zpm-registry-1.3.4.tgz"
ERROR! 'f:\tmp\zpm-registry-1.3.4.tgz' not found in any repository.
zpm:%SYS>list
IPM (zpm) 0.10.6
zpm:%SYS>repo
1) Filesystem
2) ORAS
3) Remote Repository
Which sort of repository do you wish to configure? 1
Name: local
local
Source: F:\tmp\*
Enabled? Yes
Available? Yes
Use for Snapshots? Yes
Use for Prereleases? Yes
Is Read-Only? No
local
Source: F:\tmp\*
Enabled? Yes
Available? Yes
Use for Snapshots? Yes
Use for Prereleases? Yes
Is Read-Only? No
zpm:%SYS>install f:/tmp/zpm-registry-1.3.4.tgz
ERROR! 'tmp' not found in any repository.
zpm:%SYS>install zpm-registry-1.3.4.tgz
ERROR! 'zpm-registry-1.3.4.tgz' not found in any repository.
zpm:%SYS>install zpm-registry
ERROR! 'zpm-registry' not found in any repository.
zpm:%SYS>install "f:\tmp\zpm-registry-1.3.4.tgz"
ERROR! 'f:\tmp\zpm-registry-1.3.4.tgz' not found in any repository.
zpm:%SYS>info
Welcome to the Package Manager Shell (ZPM). Version: 0.10.6
Enter q/quit to exit the shell. Enter ?/help to view available commands
No registry configured
System Mode: <unset>
Mirror Status: NOTINIT
IRIS for Windows (x86-64) 2025.2 (Build 227U) Thu Jul 10 2025 11:01:07 EDT
Currently installed top-level modules are listed below:
IPM 0.10.6
This demo highlights how HL7 validation logs can be transformed into scalable, actionable data quality insights using a lightweight application built on top of Health Connect.
Batch ingestion and parsing of HL7 validation error logs into structured data
Aggregation of errors by segment, field, and type to reveal systemic issues
Interactive dashboard (Streamlit) for filtering, exploration, and root cause analysis
Rapid identification of top error patterns across entire data feeds
Natural language chatbot for intent-driven data investigation and querying
PDF report generation for sharing clear, evidence-based feedback during data source onboarding
Special thanks to @Henry Wojnicki for his contributions to designing and refining the application workflow.
🗣 Presenter: @Lynn Wu, Sales Engineer at InterSystems
The third developer previews of InterSystems IRIS® data platform, InterSystems IRIS® for Health, and InterSystemsHealth Connect™ 2026.1 have been posted to the WRC developer preview site. Containers can be found on our container registry and are tagged latest-preview.
These developer previews includes the dropping of Mac Intel support starting from 2026.1.0, and the adding back of Windows Server 2019 support to 2026.1.0.
Initial documentation can be found at these links below:
This demo shows how InterSystems Health Gateway can be used to pull in outside patient records from networks like Carequality, CommonWell, and eHealth Exchange, creating a more complete longitudinal view in a clinical viewer. That full record is then analyzed by AI to surface potential rare disease considerations with clear reasoning, helping clinicians see patterns they might otherwise miss.
Presenters: 🗣 @Jesse Reffsin, Senior Sales Engineer at InterSystems 🗣 @Georgia Gans, Sales Engineer at InterSystems 🗣 @Annie Tong, Sales Engineer at InterSystems
Health Galaxy creates an AI access point on top of any FHIR server, bringing healthcare into the AI future that has become a reality for many other industries.
AI access: Health Galaxy gives AI agents a single gateway into any healthcare system, so they can pull patient data, schedule appointments, and check insurance automatically instead of a human doing it manually.
Ease of use: You point it at an existing FHIR endpoint, click a button, and it generates an MCP endpoint automatically from the capability statement.
FHIR: Since we are using FHIR, we can leverage both the storage and exchange capabilities of InterSystems IRIS.
🗣 Presenter: @Zelong Wang, Sales Engineer at InterSystems
At READY 2026, for the very first time, we held a customer hackathon — and it was an amazing experience! The hackathon took place on the pre-event day at 9 am. Some participants, like me, arrived early due to jet lag—but that actually helped us connect with fellow hackers before the event kicked off.
In our two preceding articles, we explored the fundamentals of the Interoperability Embedded Python (IoP) framework, including message handling, production setup, and Python-based business components.
In this third piece, we will examine advanced methodologies and practical patterns within the IoP framework that are pivotal for real-world interoperability implementations. We will explore the following topics:
✅ DTL (Data Transformation Language) in IoP ✅ JSON Schema Support ✅ Effective Debugging Techniques
Collectively, these features help us create maintainable, validated, and easily troubleshootable production-grade interoperability solutions.
This article presents a straightforward approach to automatically and efficiently tune hyperparameters for machine learning models using Optuna as the optimisation framework. We explore how to use both Optuna’s native storage options and InterSystems IRIS as a database backend to track the progress of hyperparameter searches. We also show how MLflow can be used to monitor experiments and manage models through its tracking and model registry UI.
This article is based on this Kaggle Notebook, which you can run and directly edit yourself.
If you ever wondered how to debug some requests that are being made to or from IRIS, well here is a little tutorial on how to do that.
During a complex project, usually you get the specifications and implement the communication between IRIS and other things based on that. But from the paper to the real world there's usually a huge gap and you need to know why you are receiving an error on a parameter, on a header, you are not receiving the data and so on.
In this demo, you will see how Gemini works directly with FHIR data, and how it leverages the harmonized dataset provided by InterSystems Unified Care Record. It also showcases multiple AI assistants helping multiple groups of users, e.g. clinicians, patients.
🗣 Presenter: @Simon Sha, Sales Architect at InterSystems
Data privacy regulations such as GDPR, LGPD, and HIPAA demand that organizations know exactly where Personally Identifiable Information (PII) lives inside their databases. Yet in practice, most teams rely on manual inventories, tribal knowledge, or external scanning tools that require data to leave the database engine — a process that itself creates privacy and security risks.
This article presents an MVP that takes a different approach: it runs PII detection inside InterSystems IRIS using Embedded Python, analyzing data where it lives and never exporting it to an external process.
We are using IRIS for Health to develop an agentic AI chatbot workflow that can interact with a patient using voice commands, reach out to an EHR or other system for context, and provide recommendations back.
Presenters: 🗣 @Vic Sun, Sales Engineer at InterSystems 🗣 @Brad Nissenbaum, Sales Engineer at InterSystems 🗣 Danielle Micciantuono, Clinical Solutions Specialist at InterSystems
ExplantIQ is an intelligent data application that tackles one of healthcare's most overlooked financial and regulatory risks: the management of explanted medical device warranty credits. When an implanted device is removed from a patient (due to failure or recall) hospitals are legally required to pursue manufacturer credits, refund payers if the credit exceeds 50% of the device's cost, and report to CMS. Miss that obligation and you're facing a reverse False Claims Act violation. Industry data shows hospitals miss 81% of eligible credits.
ExplantIQ, built entirely on InterSystems IRIS for Health and DeepSee, solves this by unifying clinical, supply chain, billing, and FDA recall data into a single real-time compliance dashboard, complete with KPI scorecards, trend analytics, and a Text-to-SQL AI Assistant that lets compliance officers query live operational data in plain English. No separate BI tool. No additional architecture. All questions can be answered without leaving your browser tab.
In case you're planning on deploying IRIS For Health, or any of our containerized products, via the IKO on OpenShift, I wanted to share some of the hurdles we had to overcome.
As with any IKO based installation, we first need to deploy the IKO itself. However we were getting this error:
Warning FailedCreate 75s (x16 over 3m59s) replicaset-controller Error creating: pods "intersystems-iris-operator-amd-f6757dcc-" is forbidden: unable to validate against any security context constraint:
proceeded by a list of all the security context constraints (SCCs) it could not validate against.
Create an operational data store using the data flowing through your production. Create user-defined analytics tables based on fields and paths to their data from incoming documents with varying standards (FHIR, CDA, HL7v2, etc.).
I have a daily service that consumes x number of files, each file name chronologically suffixed sequentially by _1, _2, etc. I only want to process the latest file.
How do I build a business rule so it will only process the last file?
InterSystems IRIS globals are one of the platform's core strengths: they store hierarchical data in a direct, ordered, and efficient structure. But when working from Python, manipulating globals can sometimes feel closer to a low-level API than to the natural habits of the language.
The iris-global-reference project provides a Python layer on top of IRIS globals. Its goal is simple: make access to globals more readable, more idiomatic, and easier to integrate into modern Python code, without hiding the underlying hierarchical model.
