As we said yesterday. our EMPI can receive data from multiple sources, REST, HL7 messaging, etc. But it is possible that the standard fields are not enough and we want to expand the patient information to help discriminate and uniquely identify them. How could we customize patient data? Modifying the standard classes to our liking? NOOOOO!

As a former JAVA developer it has always been a challenge to decide which database was the most suitable for the project we were going to develop, one of the main criteria I used was their performance, as well as their HA configuration capabilities ( high availability). Well, now is the time to put IRIS to the test with respect to some of the most commonly used databases, so I've decided to create a small Java project based on SpringBoot that connects via JDBC with a MySQL database, another of PostgreSQL and finally with IRIS.

You may have already heard that, starting with IRIS and HealthShare HealthConnect 2023.2 versions, the internal Apache Server will be removed from the default installation, so it will be necessary to have an external application server such as Apache Server or NGINX.

In this article I am going to proceed to install a HealthShare HealthConnect 2023.1 so that it works with a pre-installed Apache Server. For this I will use a virtual machine on which I have installed an Ubuntu 22.04.

Here we are again with an article related to the Mirror!

In the previous article we saw how we could configure a Mirror between two IRIS instances, one acting as an active node and the other as a passive one. This mirroring system works on the transfer of a journal file that keeps the instance that works as a passive node continuously updated, but what happens if due to some communication failure or permissions of the journal file it is not transferred correctly?

Welcome, dear members of the community!

In this article we are going to demonstrate the great potential that IRIS/HealthConnect makes available to all its users with the use of Embedded Python and we are going to do it by developing a small production that will allow us to recognize and identify the faces present in a JPG file from some images that we will use as a reference.

Project configuration:

Let us begin! We have published the entire application in OpenExchange so that you only need to download and deploy it in Docker as you can read in the associated README file.

A common need for our customers is to configure both HealthShare HealthConnect and IRIS in high availability mode.

It's common for other integration engines on the market to be advertised as having "high availability" configurations, but that's not really true. In general, these solutions work with external databases and therefore, if these are not configured in high availability, when a database crash occurs or the connection to it is lost, the entire integration tool it becomes unusable.

We start this new article refreshing what we did in the previous EMPI configuration articles:

• Installation in Standalone mode the Patient Index on a HealthShare instance.
• Configuration of basic parameters to start working with the EMPI.
• Definition of indexes and weights for NICE process.

Very well, we are practically ready to start rolling our EMPI. We only have one detail left, to start the production created by the installation to be able to start working.

Here we can see the different components that we have available to work with our EMPI.

In our previous article we saw how to capture DICOM type files located in a folder in our server and how we could send them to a PACS software (in our case the ORTHANC open source solution) for storage and consultation. Well, in this article we are going to deal with the opposite movement.

In our example, we are going to configure our IRIS for Health production to receive images sent from our PACS via TCP/IP. To do this, we must include a Business Service of the standard EnsLib.DICOM.Service.TCP class that will allow us to configure receptions.

Welcome community members to a new article! this time we are going to test the interoperability capabilities of IRIS for Health to work with DICOM files.

Let's go to configure a short workshop using Docker. You'll find at the end of the article the URL to access to GitHub if you want to make it run in your own computer.

Previously to any configuration we are going to explain what is DICOM:

• DICOM is the acronym of Digital Imaging and Communication in Medicine and it's a images and medic data transmission standard.

In the previous article we have reviewed how to install our EMPI in standalone, so we are ready to start the basic configuration of our EMPI.

First of all we have to do an initial basic configuration, we can access to the configuration from the Configuration menu of our Registry.

Selecting that option will allow to us to edit the basic configuration table of the Registry:

In this menu we have to add the following parameters and update the value of one of them:

Hi community! I would like to show you how to install and configure one of the HealthShare products, the Enterprise Master Patient Index or EMPI.

The EMPI provides to any organization a master patient index to identify each patient of the organization univocally. You can find more information about the EMPI in the following URL: https://www.intersystems.com/interoperability-platform/patient-index/

We are going to start with a HealthShare instance installed in our server, this HealthShare should contains the EMPI functionality available.

I've been working for some days in the connectivity between NodeJS client applications and IRIS as server using web sockets.

You can get all the information in relation to the web socket connections using IRIS as a client or as a server from this URL: https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cl…

For this example we are going to configure an asynchronous server, that would be really usefull to create a subscription manager for our productions.

Dear community members!

A very common problem of our users is to use an external database as data source in an IRIS production. As many of you already know, we have two ways to connect directly to an external database, the first one is using an ODBC connection, the second is using JDBC.

In our example we are going to create a connection using JDBC, and we are going to build a simple Docker's project, in this way you will be able to modify the example as you wish.

The code is available from this URL: https://github.com/intersystems-ib/workshop-sql-jgw

In our docker-compose.