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#Internal Tech Article Contest

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This group unites posts related to the Internal Technical Article Writing Contest.

>> More details about the current contest here. 

Contestant

10:47 AM — Jose Garcia's creatinine test results arrive at the hospital FHIR server.
2.1 mg/dL — a 35% increase from last month.

What happens next?

  • Most systems: ❌ The result sits in a queue until a clinician reviews it manually — hours or days later.
  • This system: 👍 An AI agent evaluates the trend, consults clinical guidelines, and generates evidence-based recommendations — in seconds, automatically.

No chatbot. No manual prompts. No black-box reasoning.

This is event-driven clinical decision support with full explainability:

image

✅ Triggered automatically by FHIR events
✅ Multi-agent reasoning (context, guidelines, recommendations)
✅ Complete audit trail in SQL (every decision, every evidence source)
✅ FHIR-native outputs (DiagnosticReport published to server)

Built with:
- InterSystems IRIS for Health — Orchestration, FHIR, persistence, vector search
- CrewAI — Multi-agent framework for structured reasoning

You'll learn: 🖋️ How to orchestrate agentic AI workflows within production-grade interoperability systems — and why explainability matters more than accuracy alone.

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How I Vibecoded a Backend (and Frontend) on InterSystems IRIS

I wanted to try vibecoding a real backend + frontend setup on InterSystems IRIS, ideally using something realistic rather than a toy example. The goal was simple: take an existing, well-known persistent package in IRIS and quickly build a usable UI and API around it — letting AI handle as much of the boilerplate as possible. Here is the result of the experiments.

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Vector search is a retrieval method that converts text, images, audio, and other data into numeric vectors using an AI model, and then searches for items that are semantically close. It enables “semantic similarity search” from free text, which is difficult with keyword search alone.

However, in real use, I encountered cases where results that are “close in meaning” but logically the opposite appeared near the top of the search results.

This is a serious issue in situations where affirmation vs. negation matters. If the system returns the wrong answer, the impact can be significant, so we cannot ignore this problem.

This article does not propose a new algorithm. I wrote it to share a practical way I found useful when semantic search fails due to negation.

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Introduction

The recent addition of FIFO groups allows First-In, First-Out (FIFO) message processing to be maintained in an interoperability production even when a Pool Size is greater than 1, enabling higher performance without sacrificing correctness. This feature first appears in InterSystems IRIS® data platform, InterSystems IRIS® for Health, and InterSystems Health Connect™ in version 2025.3.

First-In, First-Out message processing is critical in many integration scenarios, especially in healthcare. Traditionally, FIFO ordering is enforced by configuring each business host to process only one message at a time (Pool Size = 1). While effective, this approach can limit throughput and underutilize system resources. FIFO groups preserve FIFO ordering where needed without requiring a Pool Size of 1.

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Intersystems IRIS Productions provide a powerful framework for connecting disparate systems across various protocols and message formats in a reliable, observable, and scalable manner. intersystems_pyprod, short for InterSystems Python Productions, is a Python library that enables developers to build these interoperability components entirely in Python.

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Ever since I started using IRIS, I have wondered if we could create agents on IRIS. It seemed obvious: we have an Interoperability GUI that can trace messages, we have an underlying object database that can store SQL, Vectors and even Base64 images. We currently have a Python SDK that allows us to interface with the platform using Python, but not particularly optimized for developing agentic workflows. This was my attempt to create a Python SDK that can leverage several parts of IRIS to support development of agentic systems.

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There seems to be a generous use of ClassMethods in ObjectScript code generally. I hope my own experiences aren't representative, but I bet they are. Forgive me for giving away the ending of this article, but in short: don't use them. Unless you can make a pretty convincing case that you have to, just never use them.1

What is a ClassMethod? In an ObjectScript class, you can define methods in two different ways: in a Method, you must instantiate an instance of the class to call the method, and in a ClassMethod, you can call the method without instantiating the class. Of course, in a ClassMethod, you don't have access to any properties of the object (because there's no object), but you can access globals (they are global, after all) and Parameters (which are class constants).

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TL;DR: This article demonstrates how to run GraphRAG-style hybrid retrieval—combining vector similarity, graph traversal, and full-text search—entirely within InterSystems IRIS using the iris-vector-graph package. We use a fraud detection scenario to show how graph patterns reveal what vector search alone would miss.

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In InterSystems IRIS, when you compile a persistent class, you automatically get a SQL table. Sometimes, there are situations that require having a slightly (or not so slightly) different names/options in SQL compared to Object model. Here are some of the settings that you can change to make it happen.

Class-level settings

They define the "Face" of your class when it appears as a table in the SQL catalog.

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Welcome to the next chapter of my CI/CD series, where we discuss possible approaches toward software development with InterSystems technologies and GitLab.

Today I want to share a few approaches that will decrease your compilation time (especially if you're doing incremental/diff compilation rather than a full build).

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We were testing upgrades on our internal systems recently, and were initially surprised to see a change in some instances of SQL behavior. On further investigation, the changes make a lot of sense, and are easy to adapt to. But, to hopefully save you some time and provide additional information in case this is something you too find curious or interesting, we thought we would share our understanding of the changes and how we got around it to preserve previous behavior where desired.

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Senior engineering is defined not by the volume of code produced, but by the strategic avoidance of it. In complex integration environments, the tendency to utilize general-purpose libraries for every niche requirement introduces unnecessary overhead. True architectural maturity requires a commitment to "minimalist tooling"—prioritizing resilient, battle-tested system utilities over custom logic.

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A question that quickly arises when configuring IAM (aka Kong Gateway) is how many routes should be created to reach all the business objects in an IRIS API.

A common mistake is to create one route per business object, unnecessarily multiplying the number of routes.

Let's take the example of the Supply Chain Orchestrator Data Model API:

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This is the second part of an article pair where I walk you through:

  • Part I - Intro and Quick Tour (the previous article)
    • What is it?
    • Spinning up an InterSystems IRIS Cloud Document deployment
    • Taking a quick tour of the service via the service UI
  • Part II - Sample (Dockerized) Java App (this article)
    • Grabbing the connection details and TLS certificate
    • Reviewing a simple Java sample that creates a collection, inserts documents, and queries them
    • Setting up and running the Java (Dockerized) end‑to‑end sample

As mentioned the goal is to give you a smooth “first run” experience.

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If you already know Java (or .Net) and perhaps also have used other document databases (or looking for one), but you are new to the InterSystems world, this post should help you.

InterSystems IRIS Cloud Document is a fully managed document database that lets you store JSON documents and query them with familiar SQL syntax, delivered as a cloud service managed by InterSystems.

In this article pair I’ll walk you through:

  • Part I - Intro and Quick Tour (this article)
    • What is it?
    • Spinning up an InterSystems IRIS Cloud Document deployment
    • Taking a quick tour of the service via the service UI
  • Part II - Sample (Dockerized) Java App (the next article)
    • Grabbing the connection details and TLS certificate
    • Reviewing a simple Java sample that creates a collection, inserts documents, and queries them
    • Setting up and running the Java (Dockerized) end‑to‑end sample

The goal is to give you a smooth “first run” experience.

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This article is intended as a beginner level article for people that want to learn how to use OAuth2 in their web applications natively.

There is an accompanying video/demo that may be helpful here:

https://www.youtube.com/embed/4mfWQwcKcMI
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

and you can reproduce this locally with the Open Exchange application attached.

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Starting with InterSystems IRIS 2025.1, the way dependent cubes are handled in cube builds and cube synchronizes was changed.

This change may require modifying custom build/synchronize methods. If you are using the Cube Manager, these changes are already considered and handled, which means no action is needed.

Prior to this change, cubes were required to be built and synchronized in the proper order and account for any cube relationships/dependencies. With this change, dependent cubes are automatically updated as needed when using the %BuildCube or %SynchronizeCube APIs.

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Modern platforms usually treat observability as three core signals:

  • Metrics
  • Logs
  • Traces

OpenTelemetry (OTel) is the standard way to produce and ship all three signals. This article explains a practical setup for InterSystems IRIS running in Docker Compose, with a full local observability stack:

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Overview

Organizations that need to capture, store, and share patient-level quality or risk adjustment measure results via FHIR can do so by extending HealthShare SDA and enabling transformation to the FHIR MeasureReport resource.

This article describes how to configure HealthShare to support the Da Vinci Risk Adjustment (RA) Implementation Guide by enabling storage and FHIR-based exchange of MeasureReport resources for individual patients or members.

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The official InterSystems VS Code extensions are packed with features that make developing with InterSystems products a breeze. Today I'd like to highlight some small but useful features that you may have missed.

Show All Class Members

You can view all members of the current class, including inherited members, with the click of a button. Just click on the class icon in the top-right corner of the editor window and a filterable list of all members will appear. Clicking on one will reveal it in an editor tab.

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Introduction

Earlier this year, I set about creating kit to introduce young techy folk at a Health Tech hackathon to using InterSystems IRIS for health, particularly focusing on using FHIR and vector search.

I wanted to publish this to the developer community because the tutorials included in the kit make a great introduction to using FHIR and to building a basic RAG system in IRIS. Its an all inclusive set of tutorials to show in detail how to:

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How to set up RAG for OpenAI agents using IRIS Vector DB in Python

In this article, I’ll walk you through an example of using InterSystems IRIS Vector DB to store embeddings and integrate them with an OpenAI agent.

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One objective of vectorization is to render unstructured text more machine-usable. Vector embeddings accomplish this by encoding the semantics of text as high-dimensional numeric vectors, which can be employed by advanced search algorithms (normally an approximate nearest neighbor algorithm like Hierarchical Navigable Small World). This not only improves our ability to interact with unstructured text programmatically but makes it searchable by context and by meaning beyond what is captured literally by keyword.

In this article I will walk through a simple vector search implementation that Kwabena Ayim-Aboagye and I fleshed out using embedded python in InterSystems IRIS for Health. I'll also dive a bit into how to use embedded python and dynamic SQL generally, and how to take advantage of vector search features offered natively through IRIS.

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