Welcome, dear members of the Community!

In this article, we will present an example of a project implementing a FHIR-based solution. This project will be based on the national project (Spanish national project), known as ÚNICAS.

What is ÚNICAS?

In his own words:

A project whose objective is to create an ecosystem of partnerships to improve healthcare for pediatric patients with complex rare diseases (RMDs). This project is being implemented through the network within the National Health System (NHS) to improve the diagnosis and care of patients with rare diseases.

Another step in this implementation path, adding cross cloud, cross regional stretched IrisCluster with Mirroring + Disaster Recovery using the Intersystems Kubernetes Operator (IKO) and Tailscale

Though trivial, Id like to go multi-cloud with the stretched IrisCluster for a couple of reasons to socialize the power of Wireguard when it supplies the network for a properly zoned IrisCluster by adding another mirror role to Amazon Web Services in the Western United States based datacenter in Oregon.

InterSystems IRIS Data Platform is a comprehensive, multi-model, multi-workload data platform that is ideal for accommodating the challenging requirements of applications for the Internet of Things. It is a complete platform for developing, executing, and maintaining IoT applications in a single, consistent, unified environment. It features a distributed architecture to support massive data-ingest rates and data volumes, while providing the flexibility and durability of an enterprise-grade transactional multi-model database to ingest, process, and persist data from a wide range of devices in different formats. It features a complete set of integration, event-processing, and integrated analytics capabilities, including full SQL support and text processing, business process orchestration, and a standards-based development environment.

Connect to, ingest, and persist a wide range of disparate device data types and formats

The data types associated with IoT applications are often heterogeneous, as they may originate from various devices with diverse functions and manufactured by different vendors. The underlying data platform must be able to ingest and process a wide range of raw data types in their original formats. Many applications also require the data platform to persist all of the disparate source data to detect deviations from normal ranges, accommodate downstream ad hoc analytics, maintain regulatory compliance, and fulfill other purposes.

Introduction

HL7 messages often contain multiple repeating segments such as NTE, AL1, OBX, ZTX, DG1, and others. These segments sometimes require individual processing and routing to different downstream systems. This technical paper introduces a configurable template designed to automate the splitting of these repeating HL7 segments, improving message handling and integration efficiency.

What are the best practices for choosing a shard key in InterSystems IRIS to ensure long-term scalability without needing major refactoring later?

Hi, Community!

🔥Does your organization work with HL7® FHIR® resources and need training, now or in the future?

Learning Services is exploring whether to create a new instructor-led course on FHIR support in InterSystems IRIS® for Health and HealthShare® Health Connect.

Every thriving community has members whose energy, creativity, and commitment help shape it into something extraordinary. In the InterSystems Developer Community, one such member is @Henry Pereira — a developer, open-source contributor, contest winner, and true believer in the power of collaboration.

👏 Let’s take a closer look at Henry’s remarkable journey and his continuing legacy in the InterSystems ecosystem.

If one of your packages on OEX receives a review you get notified by OEX only of YOUR own package.
The rating reflects the experience of the reviewer with the status found at the time of review.
It is kind of a snapshot and might have changed meanwhile.
Reviews by other members of the community are marked by * in the last column.

Hi,

Just thought I'd share quite a handy hook that has helped me out when developing on Health Connect Cloud with VS Code and GitBash. When developing on Health Connect Cloud, if changes are made directly on the server such as routing rules or component deployments, they aren't automatically included in source control, therefore you must export from the server into your local files and push to your remote repo. I'm sure there are easier methods to deal with that which I'm in the process of testing, but as a quick solution I thought it would be handy have a pre-commit hook which triggers a reminder in GitBash - see below.

Background

For a variety of reasons, users may wish to mount a persistent volume on two or more pods spanning multiple availability zones. One such use case is to make data stored outside of IRIS available to both mirror members in case of failover.

Unfortunately the built-in storage classes in most Kubernetes implementations (whether cloud or on-prem) do not provide this capability:

Table of Contents

1. Purpose of the article
2. What containers are and why they make sense with IRIS
    2.1 Containers and images in a nutshell
    2.2 Why containers are useful for developers
    2.3 Why IRIS works well with Docker
3. Prerequisites
4. Installing the InterSystems IRIS image
    4.1 Using Docker Hub
    4.2 Pulling the image
5. Running the InterSystems IRIS image
    5.1 Starting an IRIS container
    5.2 Checking container status
    5.3 Executing code in the container terminal
    5.4 Accessing the IRIS Management Portal
    5.5 Connecting the container to VS Code
    5.6 Stopping or removing the container
    5.7 Setting a specific password with a bind mount
    5.8 Using durable %SYS volumes
     5.8.1 What gets stored with durable %SYS
     5.8.2 How to enable durable %SYS
6. Using Docker Compose
    6.1 Docker Compose example
    6.2 Running Docker Compose
7. Using a Dockerfile to run custom source code
    7.1 Dockerfile example
    7.2 Docker Compose example
    7.3 Understanding layers, image tagging and build vs. run time
    7.4 Source code and init script
    7.5 Building the image with Dockerfile
    7.6 Running instructions in the containerized IRIS terminal
8. Conclusion and what’s next

Finishing my previous example for multiple IRIS instances, I tried
to compose a local single instance version. The step from the external
Python app to a version using embedded Python seemed to be obvious.
This was a wrong assumption, as some Python libraries just refused installation
into my local Windows-based environment.

Hi all,

I needed to pick up the username from a set of credentials configured for a business operation in an interop production, and it turned out to take a few steps, so I'm sharing my method here, in the dual hope that someone has an easier way to do it, or failing that, that it is useful to someone.

Best,

Otto

InterSystems FAQ rubric

One way to optimize query performance is to use query parallelism on a per-query or system-wide basis (a standard feature).

This is a technique for dividing the execution of a particular query among processors on a multi-processor system. The query optimizer will execute parallel processing only if there is a possibility of benefiting from parallel processing. Parallel processing is only applicable to SELECT statements.