I am going to share some of the things I find very useful about VS Code, after many years developing in Studio.

The first and obvious thing to me is the fact that it does not crash in the middle of development when it disconnects from the network for any reason, forcing you to restart Studio and deal with the risk of losing valuable code. I also find VS Code a lot quicker than Studio, and the user interface far better with so many ways to customise it to your liking.

In France we use farm animal names for our dev and test environments.

1. The End of "Pay-and-Chase": A New Era of Payment Integrity

For decades, healthcare payers have been trapped in a reactive cycle known as "pay-and-chase." Traditional payment integrity relies on retrospective audits—reviewing claims months after disbursement to claw back overpayments. This inefficiency costs the industry billions in unrecovered fraud, waste, and abuse.

ClaimAuditAI introduces a fundamental paradigm shift by moving the audit to the point of submission.

Hi everyone, 

I created a short demo and reference repository showing how to configure Auth0 as an OAuth provider for an InterSystems IRIS FHIR server, and how to call protected FHIR resources from a Python Flask application. 

The links below include a video walkthrough and a github repo with the sample application used in the video 

Links 

Video walkthrough: 

GitHub repository: https://github.com/MarshyKid/iris-fhir-oauth-demo

Hi all,

I'm working on a DICOM router in InterSystems IRIS, based on the workshop-iris-dicom-interop (https://github.com/intersystems-ib/workshop-iris-dicom-interop) example on GitHub. I'm using EnsLib.DICOM.Process to route incoming images to different PACS destinations, where the target system is decided at runtime via a database lookup.

I've run into two issues I'd love some guidance on:

1. Dynamically changing the destination without disrupting transactions

Right now, if I want to send to a new PACS target, I'd need to add a new Business Operation and call

I want to develop a REST API for uploading PDF files. I started with OPENAPI spec file. When I test, I get error 8727 referencing missing required parameter file.

"{"$c(13,10,9)"""errors"":[ {"$c(13,10,9,9,9)"""code"":8727,"$c(13,10,9,9,9)"""domain"":""%ObjectErrors"","$c(13,10,9,9,9)"""error"":""ERROR #8727: Parameter required: file."","$c(13,10,9,9,9)"""id"":""RESTRequired"","$c(13,10,9,9,9)"""params"":[""file"""$c(13,10,9,9,9)"]"$c(13,10,9,9)"}"$c(13,10,9)"],"$c(13,10,9)"""summary"":""ERROR #8727: Parameter required: file.

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.

I created a new repo called ipm-module-budget from https://github.com/intersystems-community/intersystems-iris-dev-template template.

I cloned the repo to an EC2 instance in AWS. When I try to build image, I see this error:

#8 22.09 [IRISAPP|ipm-module-budget] Reload START (/home/irisowner/dev/)Segmentation fault (core dumped) #8 ERROR: process "/bin/sh -c pip3 install -r requirements.txt && iris start IRIS && \tiris session IRIS < iris.scri pt && iris stop IRIS quietly" did not complete successfully: exit code: 139

Why This Integration Matters

InterSystems continues to push AI capabilities forward natively in IRIS — vector search, MCP support, and Agentic AI capabilities. That roadmap is important, and there is no intention of stepping back from it.

But the AI landscape is also evolving in a way that makes ecosystem integration increasingly essential. Tools like Dify — an open-source, production-grade LLM orchestration platform — have become a serious part of enterprise AI stacks.

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.