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?

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.

Hey Community!

We're happy to share the first video in the "InterSystems Instruqt Walkthroughs" series on our InterSystems Developers YouTube:

⏯ Data Models of InterSystems IRIS - InterSystems Instruqt Walkthrough

 Hello Community,

I am learning more about InterSystems IRIS administration and would like to understand the recommended approach for monitoring performance in a production environment.

What tools, metrics, or dashboards do you typically use to monitor system health, database activity, and application performance?

I am particularly interested in learning which indicators are most useful for identifying potential bottlenecks before they affect users.

Thank you for sharing your experience and recommendations.

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.

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.

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.

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.

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