Introduction
InterSystems IRIS Adaptive Analytics is an optional extension that provides a business-oriented, virtual data model layer between InterSystems IRIS and popular Business Intelligence (BI) and Artificial Intelligence (AI) client tools. Adaptive Analytics is powered by AtScale, the AtScale documentation can be found at this link: https://documentation.intersystems.atscale.com

This article will showcase some AtScale features that can facilitate data analysis:

1. Cube creation
2. Excel visualization
3. Parallel Period
4. Queries
5. Snowflake
6. Security

7. Aggregates

Hi Community,

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.

Hi, every one. Again, it's me!!😀😀

Recently I was trying to organize some learning materials for InterSystems IRIS😆, and realized that the resources are actually quite scattered.🤐

So I put together a list here—grouped by categories—for anyone who:

• is new to InterSystems
• or wants to go deeper into specific areas

Also adding some of my own experience on what worked (and what didn’t, may be only not work for me🤫🤐).

1. For starters

If you don’t know where to begin, start here:

• https://developer.intersystems.com/
• https://developer.intersystems.com/resources/
• https://developer.

Since I started using Claude Code, my motivation to create things has skyrocketed.

Previously, even if I wanted to build something, actually doing the coding felt like a hassle, so unless there was a very strong need, I rarely went as far as programming. But now, if I just jot down the specifications, Claude Code handles the rest automatically, resulting in a dramatic improvement in productivity.

I come from a generation native to ObjectScript, so I used to feel some hesitation when it came to switching to Python.

Claude Code has a strong understanding of IRIS, but unexpected issues still occur.

The first issue is one that has already happened several times and is likely to continue occurring if not properly addressed.

In IRIS, the collation for string data (%String) is set to SQLUPPER by default. As a result, when data is retrieved via SQL, it may be returned in uppercase (for example, when sorting and aggregating with GROUP BY).

This time, it’s not really programming per se, but we are using React as a front-end development tool for IRIS. When using a web development framework—not limited to React—one key consideration is which CSS framework to use. Up until now, we’ve been using Bootstrap, which is the standard and the easiest to get started with.

However, while it’s easy to use, I felt that it offers limited flexibility for customization. That said, even if we were to switch to another CSS framework, learning it and rewriting everything from scratch would be a significant effort, so we left things as they were.

Is your source control treating your InterSystems IRIS environment like a simple collection of files? Most source control tools lack the native intelligence to handle InterSystems IRIS, which is why your code is treated as such. 

Deltanji is different. It understands how code and other artifacts are stored within IRIS and can manage them directly. This eliminates the friction that other tools introduce to the process. 

Join us for an InterSystems-hosted webinar to see how Deltanji provides an enterprise-grade source control solution that is tightly integrated with your IRIS environment.

A Continuous Training (CT) pipeline formalises a Machine Learning (ML) model developed through data science experimentation, using the data available at a given point in time. It prepares the model for deployment while enabling autonomous updates as new data becomes available, along with robust performance monitoring, logging, and model registry capabilities for auditing purposes.

InterSystems IRIS already provides nearly all the components required to support such a pipeline. However, one key element is missing: a standardised tool for model registry.

v2026.1 was just released as GA, and one of the features I'm looking forward to using is the DTL Explainer feature.

This allows you to take a Data Transformation, and with a click of a button get a human-readable description of the transformation (which you can also use as the basis for the DTL Description).

For complex DTLs, especially ones you didn't write yourself, or you did but a long time ago, this will allow you to get a clear quick understanding of what it's doing.

Introduction

FHIR (Fast Healthcare Interoperability Resources) is the modern standard for storing and exchanging clinical data. But once your data is in a FHIR server, how do you actually explore it? FHIR data is stored as JSON — powerful, but not practical to read directly. I wanted a tool where you could click on a patient, see their conditions, medications, lab results, and more — in a clean, readable format. So I built the FHIR Patient Viewer.

How It Works

The app runs entirely in Docker and connects directly to an InterSystems IRIS for Health FHIR server.

Working with files often starts off simple. open the file, read, and process. That approach works perfectly well, until the file happens to be an Excel file.

A Common Assumption

At first, an Excel file (.xlsx) looks like just another data file, rows, columns and values. nothing unusual. So it's natural to assume it can be read the same way as a .txt ot .csv file. But that's where things start to break.

Why Excel files behave differently

The key difference is how the data is stored:

-> .txt / .csv - plain text, line-by-line.

-> .

As you all probably know, Health Connect / IRIS for Health natively supports DICOM messaging, and many of you will have used it daily. Well, I recently discovered a rather insidious little issue that our beloved @Alberto Fuentes gave me the solution.

We (I) will call this phenomenon the Creosote effect (those of you who know Monty Python will understand the reference, for the rest you can see the sketch here )

Mr.

The problem

How many times have we migrated an IRIS Instance to another machine, maybe even another version, and after a few days realized we forgot that one SSL Configuration critical for a Business Operations to work? Or maybe a credential, or a lonely class in a package by itself?

The solution

The simple solution is to make a checklist¹. A checklist of the entities we have to move. But simple checklists on Word documents are often forgotten, or just ignored.

There are lessons we learned from developing and maintaining code for interoperability components within a healthcare institutional environment.

The plane is already flying

Be prepared to rebuild, improve, extend, and fix the plane mid-flight.
 

Maintenance windows for hospital systems are often very limited, with some required to be operational 24/7. While the critical health systems such as imaging modalities or registration kiosks — or at least their critical functions — must operate autonomously, efficiency and usability may be compromised when interoperability components malfunction.

As part of the new Interoperability UI wave (see what was new in 2025.1, and 2025.3 by @Aya Heshmat), v2026.1 which is available already as a Developer Preview, and this could be a reason for you to give it a ride - will be shipping the new UI for the Message viewing and searching, including Visual Trace (and other UI goodies).

He's a quick teaser:

As part of the new Interoperability UI wave (see what was new in 2025.1, and 2025.3 by @Aya Heshmat), v2026.1 which is available already as a Developer Preview, and this could be a reason for you to give it a ride - will be shipping the new UI for the BPL editor (and other UI goodies).

He's a quick teaser:

I may have mentioned this before: I believe the Visual Traces, these sequence diagrams with full content of each step, are a fantastic feature of the IRIS Data platform! Detailed information about how the API works internally, as a visual trace, can be very useful for projects on the IRIS platform. Of course, this applies when we are not developing a high-load solution, in which case we simply don't have time for saving/reading messages. For all other cases, welcome to this tutorial!

Background

Emergency Medical Services (EMS) teams often arrive at the emergency department with patients whose demographics are incomplete or unknown—no medical record number (MRN), no confirmed name, and sometimes no date of birth. Yet EMS transportation notes still need to land in the correct chart.

To support safe and reliable documentation, EMS agencies, third-party integration services, and hospital integration teams build secure interfaces that exchange identifiers and clinical messages.

Hi Community,
In this article, I will introduce my application iris-CliniNote .

CliniNote is a full-featured clinical notes application that combines classic CRUD operations with **real-time AI-assisted notes matching** powered by **InterSystems IRIS native vector search**. The standout feature: while a doctor is writing or editing a clinical note, a side panel shows the **top 5 most similar notes** based on the semantic content of the note being written — **excluding the current patient** to avoid trivial matches. This gives clinicians immediate access to "patients like this one" — helping with differential diagnosis, treatment pattern recognition, and rare presentation detection.

Online Demo

https://irisclininote.sandbox.developer.intersystems.com/csp/clininote/login.html

In the modern healthcare landscape, finding clinically similar patients often feels like looking for a needle in a haystack. Traditional keyword searches often fail because medical language is highly nuanced; a search for "Heart Failure" might miss a record containing "Congestive Cardiac Failure."

I am excited to share iris-medmatch, an AI-powered patient matching engine built on InterSystems IRIS for Health. By leveraging Vector Search, this tool understands clinical intent rather than just matching literal strings.

There are always those who say that their child looks more like their mother or their father. How about using math and Artificial Intelligence to be sure? The facial-matching application can answer you.

See these results:

1. Upload Father, Child and Mother photos:

The facial recognition has become the most popular method for validating people's identities, thus enabling access to systems, confirmation of personal and documentary data, and approval of actions and documents.
The challenges are related to performance when the database is very large, accuracy, and especially the privacy of biometric facial data. For these challenges, nothing is better than using InterSystems Vector Search, as it allows:

1. Performing vector searches in millions of records with much faster responses than traditional methods.