Hi folks!

Suppose a new version of your amazing software solution built on IRIS needs some persistence change: some data needs to be converted, some classes get new properties or even renamed, and some data should be prepopulated for newly introduced persistent classes to keep the whole application data consistent.

What are your approaches? 

SQL-based scripts? ObjectScript, or Python? for both directions of migration, up and down? 

Do you autogenerate it with Claude/Codex? What about data-consistency tests?

COuld you please share your thoughts? or libraries?

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.

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.

Hey All!

I wanted to reach out and get some advice on the best way to interact with a external SQL database, as I'm finding the documentation quite confusing to follow. I'm new to using SQL within InterSystem, and I want to make sure I'm following the best practices.

In essence, what I am trying to achieve, is extracting certain values from a HL7 message and then insert those message field values into an external database. The ODBC, DSN and Credentials details are all setup and working correctly.

Smart Discharge Navigator: An AI Agent for Hospital Readmission Risk — Built on IRIS for Health

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.

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.

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.

Hi Community!

The next addition to the "Code to Care" video series is in progress, and we’d like to hear from you. 

The goal of the project has always been to make complex healthcare and AI concepts easier to understand. In this discussion, we invite you to suggest fresh topics for a conversation.

💡 What could @Don Woodlock, the President of InterSystems, cover in future episodes?

Are there new AI concepts you'd like explained? Healthcare technology trends you're curious about? Architectural patterns, interoperability challenges, or emerging standards you think are worth exploring?

Hi Community,

It's voting time! Cast your votes for the best applications in our InterSystems Programming Contest: AI Agents for FHIR:

🔥 VOTE FOR THE BEST APPS 🔥

Voting period: June 8 to June 14, 2026

How to vote? Details below.

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.

Hey Community,

Round 1 of the Community Bounty Program "Idea to Application" is officially open. Three ideas from the Ideas Portal are ready to be implemented — pick one or more, build it, publish it on Open Exchange, and earn Global Masters points. 

🏅Every qualifying submission earns a Credly badge, Global Masters badge, and 10k+ points. Implement all ideas this round and unlock Tier 2 rewards.

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.

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. The patient repeats their conditions, medications, and allergies for the third time that year.

Has AI changed the way you learn and solve technical problems?

Do you still read technical blogs and community articles? If yes, which ones are still worth reading today?

Hi Community,

In this article, I will introduce my application iris-fhir-agents A multi-agent clinical AI platform powered by InterSystems IRIS for Health. Features agents for triage, specialist consultation, pharmacy safety, and FHIR server exploration — all grounded by IRIS Vector Search RAG. Includes a no-code Agent Builder that lets you design and deploy custom clinical agents without writing a single line of code.

I've seen that %JSON.Adaptor makes it easy to serialize and deserialize objects to JSON, but I've also seen people building custom serialization logic. Are there scenarios where %JSON.Adaptor falls short, such as handling nested objects, null values or custom field naming conventions? What has been your experience?

Sara is my entry for the InterSystems AI Agents for FHIR contest. Feel free to try out Sara and vote for Sara on the contest page.

https://youtu.be/UAjI9O848wU?si=BWTVk9gsV4xTOlUj

• Sara-1.5-4B on Hugging Face
• MedToolCalling on Hugging Face
• Sara on GitHub

Physicians spend only 47% of their work time on direct patient care [1]. The rest goes to EHR administrative tasks: order entry, documentation, prior authorizations, with 43% of their time spent on a clinical computer [2]

AI is going to change healthcare, but only if clinicians can trust it and developers can build it without reinventing the plumbing every time. FHIR Agent Studio is my attempt at both: a place to build AI agents for FHIR workflows from reusable building blocks, run them on a real InterSystems IRIS for Health backend, and inspect exactly what each one did at every step.

It is my entry in the InterSystems AI Agents for FHIR contest, and it ships with twelve working agents over a synthetic FHIR repository of around 1,000 patients and 20,000 resources.

Hi Developers,

We are happy to announce the new InterSystems online programming contest:

🏆 InterSystems Programming Contest: AI Agents for FHIR🏆

Duration: May 25 - June 14, 2026

Prize pool: $12,000

Healthcare billing isn’t a pipeline. It’s a conversation about trust. 

Every day, millions of clinical transactions flow from doctors’ keyboards to insurance ledgers. Standard health IT systems are built to count these transactions. They store them. They organize them. 

They route them. But standard systems don't notice them. 

We’ve built faster and faster databases to process claims, but we forgot to ask: Does this claim make sense? 

Because we don’t ask in real-time, we’ve accepted a compromise. We pay first, wait six months, and then hire forensic auditors to find the mistakes.