Have you ever needed to change an IP or port before deploying an interface to production? Needed to remove items from an export? What about modifying the value(s) in a lookup table before deploying? Have you wanted to disable an interface before deploying? What about adding a comment, category, or alert setting to an interface before deploying to production?

If you’ve ever needed to make any changes to an interface or lookup table before deploying to production, then Export Editor is for you!

Export Editoris a Python/WSGI web application that provides a method for editing exports before

Project Overview:

The Clinical Staff Master Data Management (CSMDM) system is a full-stack healthcare integration application built on InterSystems IRIS for Health. It centralizes and standardizes clinical staff metadata into a single authoritative repository, exposed through RESTful CRUD APIs and reusable backend methods.

The platform eliminates fragmented lookup tables and hardcoded mappings that commonly cause errors in HL7 and FHIR integration workflows, ensuring data consistency and interface reliability.

Application to Other Domains:

This approach can be applied to other domains such as

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.Designed for flexibility, it supports a hybrid approach: you can seamlessly mix new Python-based components with existing ObjectScript-based ones, leveraging your established IRIS infrastructure.

Welcome to the finale of our journey in building MAIS.

• In Part 1, we constructed the agnostic "Brain" using LiteLLM and IRIS.
• In Part 2, we designed the "Persona", mastering Dynamic Prompt Engineering and the ReAct theory.

Now, the stage is set. Our agents are ready, defined, and eager to work. However, they remain frozen in time. They require a mechanism to drive the conversation, execute their requested tools, and pass the baton to one another.

Today, we will assemble the Nervous System. We are going to implement the Orchestrator using InterSystems BPL, creating a living, breathing loop that

Hey everyone,

I'm just seeking some guidance and confirmation on what I'm doing for my production health monitoring.

We utilize Grafana to have reporting/monitoring dashboards and I have made a REST API to query the health of our productions. I believe I have everything figured out except for one thing that I'm uncertain about and that is the Production Item Color indicators:

Is there an easy way of being able to figure out the status of an item with the legend above?Ideally, I'd like to have this data in my JSON response.

If you work with Productions, highlighting connections between Business Hosts is a very convenient feature, allowing developers to get a visual representation of a data flow.

This feature works by default with all system Business Hosts. If a user writes their own Business Services, Processes, or Operations, they must implement the OnGetConnections method for this functionality to work with their custom Business Hosts (or use Ens.DataType.ConfigName properties for connections).
That said, the SMP shows only the first layer of connections of the selected Business Host. Sometimes, we need to get connections of connections recursively to build a complete data flow graph. Or we might need this connection information to check which downstream systems might be affected by a change upstream.

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:

✅ 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.

Hey Community!

We're happy to share a new video from our InterSystems Developers YouTube:

⏯  Taking the Fear out of FHIR - Fast Tracking Organizational FHIR Readiness @ Ready 2025

In Part 1, we laid the technical foundation of MAIS (Multi-Agent Interoperability Systems). We have successfully wired up the 'Brain', built a robust Adapter using LiteLLM, locked down our API keys with IRIS Credentials, and finally cracked the trick code on the Python interoperability puzzle.

However, right now our system is merely a raw pipe to an LLM. It processes text, but it lacks identity.

Today, in Part 2, we will define the Anatomy of an Agent. We will move from simple API calls to structured Personas.

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.

Job Title: InterSystems Developer / Specialist (IRIS / HealthShare)
Location: New York City, NY (Hybrid / Remote)
Duration: Contract (2+ years)
Experience: Min. 8 Years 

Job Summary

We are looking for an experienced InterSystems Developer / Specialist with expertise in InterSystems IRIS / HealthShare to support healthcare integration and interoperability initiatives. The ideal candidate should have hands-on experience in ObjectScript development, healthcare data standards, and integration protocols supporting EHR/HIE environments.

Key Skills Required

• InterSystems IRIS / HealthShare
• ObjectScript de

Some concepts make perfect sense on paper, whereas others require you to get your hands dirty. Take driving, for example. You can memorize every component of the engine mechanics, but that does not mean you can actually drive.

You cannot truly grasp it until you are in the driver's seat, physically feeling the friction point of the clutch and the vibration of the road beneath. While some computing concepts are intuitive, Intelligent Agents are different. To understand them, you have to get in the driver's seat.

In my previous articles regarding AI agents, we discussed such tools as CrewAI and La

What is a FHIR Profile?

A FHIR profile is a collection of rules and constraints used to customize and refine a base Fast Healthcare Interoperability Resources (FHIR) resource. Profiling is a vital process that adapts the base FHIR resource standard to satisfy the unique requirements of a specific use case, geographic region, medical institution, or clinical workflow.

While the base FHIR specification provides generic, flexible definitions for resources (such as Patient, Observation, or Medication), profiles transform these generic resources into more precise ones. This ensures consistent and interoperable data exchange tailored for a particular community or implementation.

FHIR is designed to cover various healthcare scenarios globally. Profiles allow implementers to adapt this general platform without losing the benefits of standardization.

RabbitMQ is a message broker that allows producers (those who send a data message) and consumers (those who receive a data message) to establish asynchronous, real-time, and high-performance massive data flows. RabbitMQ supports AMQP (Advanced Message Queuing Protocol), an open standard application layer protocol. 
The main reasons to employ RabbitMQ include the following:

• You can improve the performance of the applications using an asynchronous approach.
• It lets you decouple and reduce dependencies between services, microservices, and applications with the help of a data message mediator, meaning that there is no need for producers and consumers of exchanged data to know each other.
• It allows the long-running processing of sent data (with the results) to be delivered after utilizing a response queue.
• It helps you migrate from monolithic to microservices, where microservices exchange data via Rabbit in a decoupled and asynchronous way.
• It offers reliability and resilience by making it possible for messages to be stored and forwarded. A message can be delivered multiple times until it is processed.
• Message queueing is the key to scaling your application. As the workload increases, you will only have to add more workers to handle the queues faster.
• It works well with data streaming applications.
• It is beneficial for IoT applications.
• It is a must for Bots’ communication.

SETassigns value to the variable at RUNTIME.

#DIM declare the variable and it's Data Type at COMPILE TIME.

#DIM name As%StringSet name = "Micheal Scott"#DIM age As%NumericSet age = 36#DIM employer As App.Employer               ; compile timeSet employer = ##class(App.Employer).%New() ; runtime 

SETor #DIM? Your design, your rules.

Hey Community,

Enjoy the new video on InterSystems Developers YouTube:

⏯ Advancing Healthcare Interoperability - Strategy and Vision @ Ready 2025

Hello community!
I have a small challenge for you :)

One of customers is sending quite a lot of messages between interoperability components, which mix standard request content and streams representing serialized JSON objects. As these JSON streams can be quite long (but not too long, just couple hundreds of characters) they would appreciate if the JSON content of stream was displayed in a nice-to-read way, using syntax highlighting by message trace view.

This picture illustrates the challenge - we are talking about the text framed by a red rectangle:
 I'd love to hear from anyone that you were

Has anyone tried to authenticate IRIS client in Snowflake using Key Pair Auth as described in this doc?

I am looking for a way to capture Data Quality issues with the Source data that is populating HealthShare Provider Directory. 1 way is to use Managed Alerts, but since it could be multiple Providers and different messages it seems silly to alert on every message that has the error. Instead, I was thinking of using the Workflow Engine so it could populate a Worklist for someone to review and work.

Looking over the Demo.Workflow Engine example, I am not comprehending on how to send a task to the Workflow manager to populate the worklist from a DTL.

Does anyone have any examples on how this could

When working with InterSystems Interoperability (Iris / Health Connect / Ensemble), configuration data is often spread across many production items: services, processes, operations, adapters, and their settings.

A common operational or security need is to answer questions like:

• Which interfaces reference file system paths?
• Where are directories, network shares, or absolute paths configured?
• Can I quickly audit or document this information across all my productions?

The ObjectScript utility below solves exactly that problem by exporting selected configuration settings into a CSV file.

This

I'm a huge sci-fi fan, but while I'm fully onboard the Star Wars train (apologies to my fellow Trekkies!), but I've always appreciated the classic episodes of Star Trek from my childhood. The diverse crew of the USS Enterprise, each masterminding their unique roles, is a perfect metaphor for understanding AI agents and their power in projects like Facilis. So, let's embark on an intergalactic mission, leveraging AI as our ship's crew and  boldly go where no man has gone before!  This teamwork concept is a wonderful analogy to illustrate how AI agents work and how we use them in our DC-Facilis

Hey Developers,

Enjoy the new video on InterSystems Developers YouTube

⏯ First Customers Using Vector Search - Real World Experiences and Lessons Learned @ READY 2025

Introduction

As AI-driven automation becomes an essential part of modern information systems, integrating AI capabilities into existing platforms should be seamless and efficient. The IRIS Agent project showcases how generative AI can work effortlessly with InterSystems IRIS, leveraging its powerful interoperability framework—without the need to learn Python or build separate AI workflows from scratch.
This project explores how ChatGPT and Anthropic Claude, two of the most advanced AI models, can interact with IRIS using Custom GPTs and the Model Context Protocol (MCP).

Hi, Community!

See how to efficiently validate incoming CDA documents and comply with standards using the cloud-based CDA Validation Service!

Streamlining CDA Data Feed Setup in Health Connect Cloud

Supply Chain refers to a set of processes and activities performed by the company's business areas and its suppliers and partners (stakeholders), from the acquisition of raw materials, through production, to delivery to the end consumer. It can be better managed using SCM solutions with the orchestration of the InterSystems IRIS:

What are best practices for JSON transformation in IRIS interoperability? This is for a non-healthcare use case, so any tools we happen to have around FHIR might not be available. The motivating use case is trimming down a verbose and needlessly complex REST API response to feed to an LLM - trying to reduce token usage and maybe get better results from less noisy data.

Specifically, I'm imagining matching based on JSONPath expressions and simplifying structures based on the inferred type of elements rather than a full schema.

Hey everyone.

As part of an ongoing mission to track down orphaned HL7 Messages in our integration engine, I have been digging into our environment to track down the causes.

Having looked at various posts here (including one of my own) there are a few scenarios that can create orphaned messages resulting in excessive disk space usage. These scenarios are generally:

1. Purging an environment with the option "Bodies Too" deselected
2. Code creating/saving a Message but then not sending it
3. BPL errors similar to 2, or a scenario where the response to a Call component is not set.

These are certainly