Database systems have very specific backup requirements that in enterprise deployments require forethought and planning. For database systems, the operational goal of a backup solution is to create a copy of the data in a state that is equivalent to when application is shut down gracefully. Application consistent backups meet these requirements and Caché provides a set of APIs that facilitate the integration with external solutions to achieve this level of backup consistency.

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I am often asked by customers, vendors or internal teams to explain CPU capacity planning for large production databases running on VMware vSphere.

In summary there are a few simple best practices to follow for sizing CPU for large production databases:

  • Plan for one vCPU per physical CPU core.
  • Consider NUMA and ideally size VMs to keep CPU and memory local to a NUMA node.
  • Right-size virtual machines. Add vCPUs only when needed.

Generally this leads to a couple of common questions:

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Article
· Oct 17, 2024 2m read
ORU to MDM with large HL7 v2 messages.

I was working on a DTL but kept getting ERROR #5002... MAXSTRING errors. The problem was that most of the DTL GUI action steps only support the string data type when working with the segments. A %String has a limit of 3,641,144 characters and my OBX5.1 was 5,242,952 characters long as the example provided. Of course PACS admin stated ultra high quality up to and including 4K resolution files were needed, so we could not get the vendor to compress or reformat these files to compressed jpg or something similar.

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One of the great availability and scaling features of Caché is Enterprise Cache Protocol (ECP). With consideration during application development distributed processing using ECP allows a scale out architecture for Caché applications. Application processing can scale to very high rates from a single application server to the processing power of up to 255 application servers with no application changes.

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Article
· Feb 1, 2024 2m read
Mind map your FHIR patient data

According Wikipedia a mind map is a diagram used to visually organize information into a hierarchy, showing relationships among pieces of the whole. It is often created around a single concept, drawn as an image in the center of a blank page, to which associated representations of ideas such as images, words and parts of words are added. Major ideas are connected directly to the central concept, and other ideas branch out from those major ideas.

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In this article I will demonstrate the following :

  • Update ReferencesRange(OBX:7) against ObservationIdentifier(OBX:3.1)[TestCode] from database by using custom utility function
  • Update Abnormal Flag(OBX:8) against ObservationIdentifier(OBX:3.1)[TestCode] and ObservationValue(OBX:5)[Result] from database utility function
  • Route Message based on Abnormal Flag(OBX:8)

Below is the primary and transformed HL7 2.5 ORU_R01 message:

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Article
· Nov 23, 2020 1m read
Iris key uploader

Introduction

This is iris-key-uploader a frontend in Angular with it's rest API.

The aim of this project is to easily import key file to Iris from a web ui.

Why this project

Unfortunatly the IRIS panel to change key doesn't give the opportunity to upload the license.

Panel

As you can see, you can only browse from the server side.

What if, you don't have a direct access to it ?

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Hi folks!

Recently I was in need to setup a local FHIR server using IRIS For Health and I think I found the easiest and simplest way ever.

Just run in terminal these two lines below :

docker run --rm --name my-iris -d --publish 9091:1972 --publish 9092:52773 intersystemsdc/irishealth-community

and

docker exec -it my-iris iris session iris -U "USER" '##class(%ZPM.PackageManager).Shell("install fhir-server")'

And you'll have FHIR server running locally at http://localhost:9092/fhir/r4.

That's it!

The FHIR server will use the latest build of InterSystems IRIS for Health Community Edition and will deploy FHIR server from this app via IPM package in FHIRSERVER namespace.

This is for Mac, so please add in comments how it works in Windows.

This is a very short article as it is really easy to setup a local FHIR server with InterSystems IRIS for Health and IPM Package Manager.

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InterSystems and Intel recently conducted a series of benchmarks combining InterSystems IRIS with 2nd Generation Intel® Xeon® Scalable Processors, also known as “Cascade Lake”, and Intel® Optane™ DC Persistent Memory (DCPMM). The goals of these benchmarks are to demonstrate the performance and scalability capabilities of InterSystems IRIS with Intel’s latest server technologies in various workload settings and server configurations. Along with various benchmark results, three different use-cases of Intel DCPMM with InterSystems IRIS are provided in this report.

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Hello Community,

This article aims to walk you through the process of setting up and utilizing the Flexible Python Runtime Feature for embedded Python. Prior to version 2024.2, Intersystems IRIS installer included a preinstalled version of Python, You can find the Python libraries and application files located in the \lib\python directory within your IRIS installation folder (for example, C:\InterSystems\IRIS20242\lib\python).

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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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Motivation

The motivation behind the InterLang project is rooted in the innovative integration of LangChain chatbot agents with the Fast Healthcare Interoperability Resources (FHIR) framework to revolutionize conversational social prescriptions in healthcare. This project aims to leverage the rich and standardized data available through FHIR, an emerging standard in healthcare data exchange, to inform and empower these advanced chatbot agents.

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Article
· Nov 27, 2023 2m read
Generative AI for image creation

Currently, many digital artists use generative AI technology as a support to accelerate the delivery of their work. Nowadays it is possible to generate a corresponding image from a text sentence. There are several market solutions for this, including some available to be used through APIs. See some at this link: https://www.analyticsvidhya.com/blog/2023/08/ai-image-generators/.

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Article
· Aug 22, 2020 2m read
My experience working with FHIR

Hi Community,

I want to share my experience creating the iris-fhir-portal with FHIR.

I decided to take a step forward and join the IRIS for Health FHIR contest, but I had never worked with FHIR before.

After the FHIR Contest Kick-Off Webinar, where we got an overview of how the IRIS for Health works with FHIR, I started to looking at the FHIR documentation to create my Patient Chart project.

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Hi guys.

Recently, I get interest in FHIR in order to run for the IRIS for Health FHIR
contest
. As a beginner on this topic, I've heard somewhat about it, but I didn't know how complex and powerful was FHIR. As pointed out by @Henrique.GonçalvesDias here, you can model several aspects of the patient history and other related entities.

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When we create a FHIR repository in IRIS, we have an endpoint to access information, create new resources, etc. But there are some resources in FHIR that probably we wont have in our repository, for example, Binary resource (this resource returns a document, like PDF for example).

I have created an example that when a Binary resource is requested, FHIR endpoint returns a response, like it exists in the repository.

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I'm always on the lookout for tools that make the development and testing of my interfaces more efficient. A couple of years ago I came across HL7 Spy, from Inner Harbour Software. It quickly became my go-to tool for running message comparison reports for interface engine migrations, message statistics gathering, and troubleshooting message receipt and delivery. It also offered enhanced functionality for things like fetching messages via sftp that other tools don't provide.

I've recently been working with HL7 Spy's author, Jon Reis, to enable support for fetching messages directly from the Ensemble message store. Its SQL Loader feature now has native Caché/IRIS support, and I've contributed a small server-side class to support the extraction of messages using it.

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