Hi Devs!

Recently I was impressed by @Dan Pasco's article where he shares also how he uses terminal aliases.

Terminal aliases is a very powerful tool for developers and sys admins if you often need to call some cumbersome terminal expressions and make it shorter and cleaner. Here is the documentation.
Yes!

But what about Docker environments? What if you are fan of Docker dev environments but also want to keep using your favorite aliases with Docker as well?

Turned out it is very possible.

In this article we are going to see how we can use the WhatsApp instant messaging service from InterSystems IRIS to send messages to different recipients. To do this we must create and configure an account in Meta and configure a Business Operation to send the messages we want.

Let's look at each of these steps in more detail.

Setting up an account on Meta

This is possibly the most complicated point of the entire configuration, since we will have to configure a series of accounts until we can have the messaging functionality.

Did you know that you can get JSON data directly from your SQL tables?

Let me introduce you to 2 useful SQL functions that are used to retrieve JSON data from SQL queries - JSON_ARRAY and JSON_OBJECT.
You can use those functions in the SELECT statement with other types of select items, and they can be specified in other locations where an SQL function can be used, such as in a WHERE clause

The JSON_ARRAY function takes a comma-separated list of expressions and returns a JSON array containing those values.

In a world where healthcare technology is rapidly evolving, the importance of efficient, reliable, and interoperable healthcare applications has never been greater.

Background

In 2021, I participated as an InterSystems mentor in a hackathon, where a newcomer to FHIR asked me if there was a tool to transform generic JSON data containing basic patient information into FHIR format. I informed her that I didn't know anything like that, unfortunately.

But that idea stays in my mind...

Several months later, in 2022, I came up with an idea to experiment: to train a named entity recognition (NER) to identify FHIR elements into generic texts. The training involved synthetic FHIR data generated by Synthea and the spaCy Python library.

Striking a Balance Between Health Analytics and Engaging Features

I created this application considering how to convert images such as prescription forms into FHIR messages

It recognizes the text in the image through OCR technology and extracts it, which is then transformed into fhir messages through AI (LLA language model).

Finally, sending the message to the fhir server of IntereSystems can verify whether the message meets the fhir requirements. If approved, it can be viewed on the select page.

Sometimes we need to convert FHIR message to HL7 V2, e.g. to register a patient to the PACS system.
In this article, I will explain the steps to achieve the desired by using IRIS FHIR Server production.

Below are the steps we need to follow:

1. Make sure FHIRServer production is started.
2. Register Business Service with FHIRServer endpoint.
3. Define Business Processes to convert FHIR message to SDA and then Convert SDA to HL7 v2.
4. Post JSON resource to FHIRServer endpoint and get HL7 V2 response.

Let's review the steps in detail.

Step 1. Make sure FHIRServer production is started

Open the production page and make sure Production is started. In the next step, we need to make sure business service HS.FHIRServer.Interop.Service is registered with FHIRServer

Considering new business interest in applying Generative-AI to local commercially sensitive private data and information, without exposure to public clouds. Like a match needs the energy of striking to ignite, the Tech lead new "activation energy" challenge is to reveal how investing in GPU hardware could support novel competitive capabilities. The capability can reveal the use-cases that provide new value and savings.

Sharpening this axe begins with a functional protocol for running LLMs on a local laptop.

InterSystems FAQ rubric

To enable Interoperability functionality after creating a namespace, use the EnableNamespace method of the %EnsembleMgr class:

do ##class(%EnsembleMgr).EnableNamespace()

An App that converts HL7 messages to JSON objects. About a year ago I started a GitHub repo for collecting stuff related to HL7. Recently my team added an HL7 interface to our Interoperability Production and we were asked to persist HL7 messages. We created a Kafka topic to receive HL7 messages. We use Kafka Bridges to send messages to Kafka topics. Kafka messages are sent to the Kafka Bridge in JSON format.

Django, a high-level web framework written in Python, has become a staple for developers seeking a robust, efficient, and easy-to-learn solution for building web applications. Its popularity stems from its versatility, offering developers an efficient toolkit for building web applications. Integrating Django with InterSystems IRIS introduces a dynamic synergy, providing developers with a comprehensive web development and database management solution. That's why on the Ideas Portal, @Evgeny Shvarov suggested that having Examples to work with IRIS from Django would be beneficial. In this article, we'll explore two projects created to answer the posted idea — Django-iris by @Dmitry Maslennikov and Iris-size-django by @Heloisa Paiva.

When defining a server connection in Atelier we are required to enter a username and password because these are mandatory fields in the dialog. However, if the /api/atelier web application definition on that server has only the "Unauthenticated" checkbox set in the section titled "Allowed Authentication Methods", then our Atelier connection will succeed even if we supply an invalid username and/or password.

We have a yummy dataset with recipes written by multiple Reddit users, however most of the information is free text as the title or description of a post. Let's find out how we can very easily load the dataset, extract some features and analyze it using features from OpenAI large language model within Embedded Python and the Langchain framework.