Hi, I would like to tell you how easy it is to spin up IRIS for Health docker container in compute engine(VPS) in google cloud.

I know that to run IRIS for Health in AWS is pretty simple and straightforward, but I wanted to tried if its same easy in GCP environment.

Create vm instance. 2GB RAM is more than enough.

I used Debian 11 as Linux distro.

Standart persistent disk is cheaper.

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I know that people who are completely new to VS Code, Git, Docker, FHIR, and other tools can sometimes struggle with setting up the environment. So I decided to write an article that walks through the entire setup process step by step to make it easier to get started.

I’d really appreciate it if you could leave a comment at the end - let me know if the instructions were clear, if anything was missing, or if there’s anything else you'd find helpful.

The setup includes:

✅ VS Code – Code editor
✅ Git – Version control system
✅ Docker – Runs an instance of IRIS for Health Community
✅ VS Code REST Client Extension – For running FHIR API queries
✅ Python – For writing FHIR-based scripts
✅ Jupyter Notebooks – For AI and FHIR assignments

Before you begin: Ensure you have administrator privileges on your system.

In addition to reading the guide, you can also follow the steps in the videos:

For Windows

https://www.youtube.com/embed/IyvuHbxCwCY
[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]

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Introduction

InterSystems has recently completed a performance and scalability benchmark of IRIS for Health 2020.1, focusing on HL7 version 2 interoperability. This article describes the observed throughput for various workloads, and also provides general configuration and sizing guidelines for systems where IRIS for Health is used as an interoperability engine for HL7v2 messaging.

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Article
· Jul 27, 2021 2m read
IRIS Interoperability Message Bank

I heard about Message Bank when we started redesigning a Health Connect production to run in containers in the cloud. Since there will be multiple IRIS containers, we were directed to utilize Message Bank as one place to view messages and logs from all containers.

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I am demonstrating a use case of how we can create an IRIS Interoperability Production for special use in an external language. InterSystems IRIS, within Interoperability has a framework called Production Extension (PEX), using which we can create productions and program them as per their purpose using external languages like Java, Python etc, and also develop custom inbound and outbound adapters to communicate with other applications.

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In this article, we'd like to describe Med.me's incubation experience and takeaways, the cross-organizational booking problem, and our approach to solving it.

This exciting story started during one of the regular medtech meetups, where I met @Evgeny Shvarov, senior InterSystems chief of the Developer Community. Following the good old startup rule “pitch wherever you can”, I briefly explained to Evgeny Med.me concept – EHR exchange and appointment scheduling platform to automate and simplify communication between clinics, pharma on one hand and insurance companies, doctors, and patients on the other.

Evgeny listened carefully and replied that InterSystems provides plenty of products to solve the interoperability problems amongst the vast variety of EMRs and kindly advised me to hurry up to apply to the FHIR incubator Caelestinus powered by InterSystems.

That led to a start of an amazing 9-months long road where we were able to ramp up our understanding of interoperability, participate at the InterSystems Global Summit, find new customers and partners, and where the new concept of Resource Availability Exchange was born.

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Article
· Jul 24, 2023 20m read
Local K8s Deployment of a FHIR server

k8s

This is a sample to deploy iris-oauth-fhir on k3d with IKO.
* iris-oauth-fhir is a sample to deploy a FHIR server with OAuth2 authentication with InterSystems IRIS for Health as a resource server and Google OpenId as an authorization server.
* k3d is a lightweight wrapper to run k3s (Rancher Lab's minimal Kubernetes distribution) in docker.
* IKO is a tool to deploy InterSystems IRIS for Health on Kubernetes.

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image

Hi Community,
In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

  • Step1: Install required libraries

  • Step2: Create patterns and responses file

  • Step3: Train the Model

  • Step4: Create ChatBot Application based on the trained model

So Let us start.

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Article
· Sep 12, 2024 7m read
Embedded python in InterSystems IRIS

Hello Community,

In this article, I will outline and illustrate the process of implementing ObjectScript within embedded Python. This discussion will also reference other articles related to embedded Python, as well as address questions that have been beneficial to my learning journey.

As you may know, the integration of Python features within IRIS has been possible for quite some time. This article will focus on how to seamlessly incorporate ObjectScript with embedded Python.

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While reviewing our documentation for our ^pButtons (in IRIS renamed as ^SystemPerformance) performance monitoring utility, a customer told me: "I understand all of this, but I wish it could be simpler… easier to define profiles, manage them etc.".

After this session I thought it would be a nice exercise to try and provide some easier human interface for this.

The first step in this was to wrap a class-based API to the existing pButtons routine.

I was also able to add some more "features" like showing what profiles are currently running, their time remaining to run, previously running processes and more.

The next step was to add on top of this API, a REST API class.

With this artifact (a pButtons REST API) in hand, one can go ahead and build a modern UI on top of that.

For example -

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Hi InterSystems Developers!

Recently I've updated the FHIR dev template so that it now publishes an IPM package fhir-server that makes the setup of InterSystems FHIR server a trivial manual or automatic or programmatic procedure one command long.

Please see below how you can benefit from it.

TLDR

USER>zpm "install fhir-server"

All the details below.

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I found myself in the not-so-comfortable situation of working with a Linux system on which someone had accidentally disabled user access to the Linux shell. HealthConnect was running, servicing hundreds of interfaces. To resolve the access issue, though, we needed to bring the host down for the application of a fix.

Without the shell, the iris command is not available to control the instance, so we were faced with the potential of shutting down the server ungracefully. We wanted to avoid that if possible ...

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InterSystems FAQ rubric

※Use this method if you want to compare databases that have been replicated using mirroring, shadowing, or some other mechanism.

You can use the DATACHECK utility to compare global variables. Please refer to the document below.
Overview of DataCheck [IRIS]

***

Routine comparisons use the system routine %RCMP or the Management Portal.

Below is how to use it in the Management Portal.

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In the previous article we presented the d[IA]gnosis application developed to support the coding of diagnoses in ICD-10. In this article we will see how InterSystems IRIS for Health provides us with the necessary tools for the generation of vectors from the ICD-10 code list using a pre-trained language model, its storage and the subsequent search for similarities on all these generated vectors.

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The rise of Big Data projects, real-time self-service analytics, online query services, and social networks, among others, have enabled scenarios for massive and high-performance data queries. In response to this challenge, MPP (massively parallel processing database) technology was created, and it quickly established itself. Among the open-source MPP options, Presto (https://prestodb.io/) is the best-known option. It originated in Facebook and was utilized for data analytics, but later became open-sourced.

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Introduction

We are in the age of the multiplatform economy and APIs are the "glue" in this digital scenario. Since they are so important, they are seen by developers as a service or product to be consumed. Therefore, usage experience is a crucial factor for its success.

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