We recently uploaded to OpenExchange a small application that I developed a while ago (and that @Jose-Tomas Salvador improved and refined) that I often use when I need to generate large volumes of HL7 messaging.

We conclude this series of SMART On FHIR articles with Auth0 and InterSystems IRIS FHIR Repository by reviewing our application developed in Angular 16.

Let's remember what the architecture defined for our solution is like:

Our front-end application corresponds to the second column and as you can see it will be in charge of two things:

Introduction

I recently participated in a fantastically organized hands-on by @Patrick Jamieson in which an Angular application was configured together with an IRIS FHIR server following the protocols defined by SMART On FHIR and I found it really interesting, so I decided to develop my own Angular application and thus take advantage of what I learned to publish it in the Community.

SMART On FHIR

Let's see what Google tells us about SMART On FHIR:

It seems like yesterday when we did a small project in Java to test the performance of IRIS, PostgreSQL and MySQL (you can review the article we wrote back in June at the end of this article). If you remember, IRIS was superior to PostgreSQL and clearly superior to MySQL in insertions, with no big difference in queries.

I recently had the need to monitor from HealthConnect the records present in a NoSQL database in the Cloud, more specifically Cloud Firestore, deployed in Firebase. With a quick glance I could see how easy it would be to create an ad-hoc Adapter to make the connection taking advantage of the capabilities of Embedded Python, so I got to work.

We resume our series of articles on the FHIR Adapter tool available to HealthShare HealthConnect and InterSystems IRIS users.

In the previous articles we have presented the small application on which we set up our workshop and showed the architecture deployed in our IRIS instance after installing the FHIR Adapter. In today's article we will see an example of how we can perform one of the most common CRUD (Create - Read - Update - Delete) operations, the reading operation, and we will do it by recovering a Resource.

Surely you have all heard about FHIR as the panacea and solution to all interoperability and compatibility problems between systems. Right here we can see one of his classic defenders holding a FHIR resource in his hand and enjoying it immensely:

But for the rest of us mortals we are going to make a small introduction.

We continue with the series of articles related to the QuinielaML application. In this article we are going to cover how to prepare the raw data that we have captured using the Embedded Python functionality.

Welcome everybody!

Taking advantage of the Quiniela ML application and as we indicated in the previous article, we are going to explain how we can perform a JWT authentication between our frontend developed in Angular and our backend developed in InterSystems IRIS.

I remind you of the architecture of our QuinielaML project: