The rise of distributed systems and cloud-native architectures within large institutions has redefined hosting for InterSystems IRIS interoperability applications (productions). In modern environments, the standard layout involves at least two production instances operating on distinct nodes under a load-balancing configuration to handle requests.

The benefits are clear: you can now scale dynamically based on request volume, increasing the number of active productions during peak demand and going down when traffic subsides.

Overview 

This article explains how InterSystems IRIS Data Platform handles both transactional and analytic workloads without sacrificing performance. It also covers how the platform manages memory and scales under peak load, what its security model looks like, and how global banks and brokerages are using it in production today.

InterSystems IRIS Data Platform for Financial Services

InterSystems IRIS data platform is a technology for mission-critical financial services applications.

I would like to create a generic error handler defined somewhere in a class (or if not possible a routine that calls a class). 
This should not be page specific but for the whole app (as I have lots of CSP pages inside a legacy app). Obvisouly this will not be a single try catch block but rather something setup at system level (or web application level if not possible). The same way it's possible to handle sessions in a global way (by having aclass that inherit from %CSP.SessionEvents).

Hi everyone, 

I created a short demo and reference repository showing how to configure Auth0 as an OAuth provider for an InterSystems IRIS FHIR server, and how to call protected FHIR resources from a Python Flask application. 

The links below include a video walkthrough and a github repo with the sample application used in the video 

Links 

Video walkthrough: 

GitHub repository: https://github.com/MarshyKid/iris-fhir-oauth-demo

1. Introduction

Epidemiological surveillance is one of the foundational pillars of public health. Régis Júnior et al. (2026) define it as a continuous system of data collection, analysis, interpretation and dissemination of health events — a function whose effectiveness depends critically on the quality of information systems, data analysis capacity, and coordination between different levels of care.

I know IRIS has a built-in testing framework, but I'm not sure how to write tests that validate the full message flow through a Production without depending on external systems. Do you mock Business Operations? Is there a pattern you use to test integration scenarios in an isolated and repeatable way?

I'm trying to lock down access in our IRIS system.
I notice there is no current %DB_X resource (which I want to add to add a read-only role for it).
If I add resource %DB_X will there be an immediate effect or will it only be applicable to roles (and users linked to that role)?

I understand that Business Rules can be used to route or transform messages without touching code. But I'm not clear on when it's better to use a Business Rule versus handling the logic directly inside a BPL or a custom BP class. What criteria do you use to decide between them?

If I need to add or change properties in a message class that is already being used by a running Production, what is the safest way to do this? Are there risks with existing messages in the queue or in the message store? Is there a migration strategy the community recommends?

FHIR REST APIs are powerful and standardized, but they are not a general-purpose query engine.

They are great for operations such as reading resources, searching by supported parameters, following links, and working with Bundles. But many real-world questions require orchestration across multiple resources.

For example:

• Show diabetic patients with A1c above 9 in the last 6 months.
• Find diabetic patients whose HbA1c is getting worse despite active insulin therapy.
• Find patients with abnormal HbA1c and no follow-up encounter within 90 days.
• Find patients with overlapping anticoagulant and NSAID prescriptions.
• Build an evidence timeline for a patient.
• Explain why this patient matched the query.

Hi folks,

One of the things I've been thinking about recently: how strong AI is changing my work as an ObjectScript developer right now! Yeah, yeah, I know I'm not a very fast, it should have happened 2-3 years ago... But it's only now that I've realized that I am not writing the code all my workday like before.

Hospital readmissions cost the US healthcare system $17 billion annually. A significant portion of those readmissions are preventable — but only if the right information reaches the right clinician at the right moment: at discharge.

That's the problem I set out to solve with Smart Discharge Navigator, my entry for the InterSystems Programming Contest: AI Agents for FHIR 2026.

I want to thanks the InterSystems Global Masters Rewards.

I think better than reward, is the opportunity ti share knowledge, experience and help the community to increase.
Now I'll develop with more focus... ;)

Let's go share wit all.

Abstract

Common Table Expressions (CTEs) provide a structured framework for defining reusable intermediate result sets within SQL statements. InterSystems IRIS implements CTEs via the WITH clause, enabling clearer query composition and modular analytical processing while remaining fully integrated with the IRIS cost-based optimizer.

This article explores the semantics of CTEs in InterSystems IRIS, explains their interaction with query optimization, discusses appropriate deployment scenarios, and presents executable examples illustrating practical patterns for production environments.

An engineering walkthrough of the IRIS-CardioFlow project architecture with real code for its AI, FHIR, and connectivity layers and the role of iris-agentic-dev in a modern ObjectScript workflow.

Introduction

Monitoring cardiovascular surgical flow in real time is a classic healthcare integration problem: data arrives from heterogeneous sources, must be persisted with clinical semantics, exposed through an API, and presented in a way the care team can act on. The CardioIris repository (internally named IRIS-CardioFlow) is a lean demonstration of that scenario, built on InterSystems IRIS 2026.

Hi Community,

Have you ever wished your EHR could think? Not just display data. Not just fire alerts. But actually read a patient record, reason over clinical guidelines, and write a structured referral order back to the system — in response to a single message from a clinician

In this article, I am going to show you how to create your own custom clinical AI agent.

🏥 About iris-fhir-agents App

iris-fhir-agents is a multi-agent clinical AI platform built entirely on InterSystems IRIS for Health.

When I started building Smart Discharge Navigator, I faced a common challenge: FHIR data is stored as JSON bundles, but the analysis I needed required joining multiple resources together, the kind of thing SQL was built for.

InterSystems FHIR SQL Builder solved this completely. Instead of parsing JSON bundles in application code, I was able to write plain SQL directly against FHIR resource projections inside IRIS for Health. This article shows exactly how I used it to build the population-level readmission risk scoring engine at the heart of the application.

Back when I was a Developer Support Engineer in the InterSystems Worldwide Response Center (WRC), I would occasionally receive cases involving large message backlogs in a production. The customer would discover that a subset of queued messages were no longer useful and would ask a question similar to the following:

Is there an established best practice for mass-aborting messages in a specific component's queue that match a particular set of criteria?

This article introduces SHAP explainability methods as an approach to understand the reasons behind predictions in machine learning black-box models. It also includes a simple Jupyter notebook that you can use and modify to gain hands-on experience with these concepts:

https://www.kaggle.com/code/jorgeivnjh/explainability-in-ml-models

https://github.com/JorgeIvanJH/Explainability-in-ML-models

We will leverage these concepts for a future implementation in our Continuous Training Pipeline: https://community.intersystems.com/post/complementing-iris-mlflow-continuous-training-ct-pipeline

I’m pleased to share a demo of FHIR Agent Studio, now available on YouTube.

FHIR Agent Studio Demo

https://www.youtube.com/watch?v=vktfq_kvNnk

FHIR Agent Studio brings AI agents together with FHIR, InterSystems IRIS, Vector Search, and large language models to demonstrate how developers can explore, build, and test agent-driven healthcare workflows.

You can also find more information here:

Introduction article: https://community.intersystems.com/post/introducing-fhir-agent-studio-ai-agents-fhir-intersystems-iris

GitHub repository: https://github.com/SeanConnelly/ai-studio-for-fhir

Hey! June Article Bounty is live on Global Masters! ☀️🚀

Submit a brand-new article on one of the announced topics and earn a bounty of 🏆 5,000 Global Masters points once it’s approved! 🎉

Here’s the list of June topics:

An AI agent reads the FHIR patient record before the consultation, asks the right questions based on what it already knows, detects critical red flags, and writes its findings back — so the physician arrives prepared.

This project was inspired in the suggested task #10 (Conversational FHIR Triage Assistant) for the InterSystems Programming Contest: AI Agents for FHIR

Healthcare Pain Points Addressed

Before a patient walks into a consultation room, a critical failure has already occurred: nobody read their medical history. The physician has 15 minutes.