Hey Community!

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

 ⏯  Partnering For Success - Collaborative Innovations in Interoperability @ Ready 2025

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.

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.

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 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.

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.

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.

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.

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.

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.

Hey Community,

Enjoy the new video on InterSystems Developers YouTube:

⏯ Advancing Healthcare Interoperability - Strategy and Vision @ Ready 2025

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.

SET assigns value to the variable at RUNTIME.

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

#DIM name As %String
Set name = "Micheal Scott"
#DIM age As %Numeric
Set age = 36
#DIM employer As App.Employer               ; compile time
Set employer = ##class(App.Employer).%New() ; runtime 

SET or #DIM? Your design, your rules.

Hey Developers,

Enjoy the new video on InterSystems Developers YouTube

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

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:

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.

I am using IRIS for Windows (x86-64) 2022.1 (Build 209) Tue May 31 2022 12:27:55 EDT [Health:3.5.0]. I created Interoperability Production with a Service to read file from S3 bucket and an Operation to write files to a different S3 bucket. I specified AWS  ProviderCredentialsFile.

I see "Terminating Job 7096 / 'From S3 Bucket' with Status = ERROR #5023: Remote Gateway Error: Connection cannot be established, %QuitTask=

Do I need anything like Python libraries or AWS CLI to make this work?

The Interoperability user interface project has continued from 2025.1 and has incorporated many of the items that you – our customers and partners – have suggested and observed.  We are continuing to invest in feedback and updating this important user experience.   In the latest release, 2025.3 for IRIS, IRIS for Health, Health Connect and Health Connect Cloud, the DTL Editor and Production Configuration applications that are available for opt-in. 
NOTE:

• You can switch between the modernized and standard experiences. 
• All other Interoperability screens remain in the Standard user interface.

Hello Community,

Great news for developers who have just started working with InterSystems IRIS! We have hands‑on interactive tutorials available via the Instruqt platform! These are perfect for getting up to speed quickly, playing in real environments, and building confidence with IRIS‑based development.

Here is the list of available tutorials:

• Full Stack Tutorial - Experience how IRIS supports full-stack system architecture. 
• InterSystems Interoperability - Explore IRIS interoperability tools for connecting and integrating systems. 
• IRIS for Health Interoperability - Learn to use IRIS for Health features in application integration with data from an EHR (Electronic Health Record). 
• REST + Angular Application - Practice building applications with IRIS REST services. 
• RAG using IRIS Vector Search - Discover IRIS Vector Search capabilities for AI-powered applications. 

Hi Community,

In the first part of this series, we examined the fundamentals of Interoperability on Python (IoP), specifically how it enables us to construct such interoperability elements as business services, processes, and operations using pure Python.

Now, we are ready to take things a step further. Real-world integration scenarios extend beyond simple message handoffs.They involve scheduled polling, custom message structures, decision logic, filtering, and configuration handling.In this article, we will delve into these more advanced IoP capabilities and demonstrate how to create and run a more complex interoperability flow using only Python.

To make it practical, we will build a comprehensive example: The Reddit Post Analyzer Production. The concept is straightforward: continuously retrieving the latest submissions from a chosen subreddit, filtering them based on popularity, adding extra tags to them, and sending them off for storage or further analysis.

The ultimate goal here is a reliable, self-running data ingestion pipeline. All major parts (the Business Service, Business Process, and Business Operation) are implemented in Python, showcasing how to use IoP as a Python-first integration methodology.

The 2025.3 release of InterSystems IRIS® data platform, InterSystems IRIS^® for Health, and HealthShare^® Health Connect is now Generally Available (GA). This is a Continuous Delivery (CD) release.

Release Highlights:

• Secure Wallet: A new encrypted framework for managing sensitive data, built on the IRISSECURITY database, improving system security and access control.
• Expanded Observability & Cloud Integrations: Enhanced OpenTelemetry metrics, including new process and ECP metrics, plus journal archiving now supports Azure Blob Storage for cost-efficient retention.

Modern data architectures utilize real-time data capture, transformation, movement, and loading solutions to build data lakes, analytical warehouses, and big data repositories. It enables the analysis of data from various sources without impacting the operations that use them. To achieve this, establishing a continuous, scalable, elastic, and robust data flow is essential. The most prevalent method for that is through the CDC (Change Data Capture)  technique. CDC monitors for small data set production, automatically captures this data, and delivers it to one or more recipients, including analytical data repositories. The major benefit is the elimination of the D+1 delay in analysis, as data is detected at the source as soon as it is produced, and later is replicated to the destination.

This article will demonstrate the two most common data sources for CDC scenarios, both as a source and a destination. For the data source (origin), we will explore the CDC in SQL databases and CSV files. For the data destination, we will use a columnar database (a typical high-performance analytical database scenario) and a Kafka topic (a standard approach for streaming data to the cloud and/or to multiple real-time data consumers).

Overview

This article will provide a sample for the following interoperability scenario: