I have a large tree outside my window. Here in Phoenix, Arizona, USA it's still warm and sunny so our plants are still covered in lush foliage despite so many other places in the full throws of Autumn. My desk is oriented such that I can look out at the tree throughout the day.

This demo program is used to show how a custom FHIR profile can be employed to validate data compliance. The custom FHIR implementation guide was developed based on FHIR Version R4, and in this example implements the Organization resource extension to validating data compliance.

Anakin Skywalker challenged the high ground and has been terribly injured on Mustafar.

I have challenged to create a bot application using Azure Bot that can retrieve and post data to IRIS for Health.

A patient's data has already been registered in the FHIR repository of IRIS for Health.

The patient's MRN is 1001. His name is Taro Yamada. (in Japanese :山田 太郎)

This bot can post new pulse oximeter readings as an observation resource linked to the patient.

I have been using embedded python for more than 2 years now on a daily basis.
May be it's time to share some feedback about this journey.

Why write this feedback? Because, I guess, I'm like most of the people here, an ObjectScript developer, and I think that the community would benefit from this feedback and could better understand the pros & cons of chosing embedded python for developing stuff in IRIS. And also avoid some pitfalls.

• Feedback : Using embedded python daily for 2 years
• Introduction
• Starting with Python
  • Python is not ObjectScript
    • Pep8
    • Modules
    • Dunders
  • Conclusion
• Embedded Python
  • What is Embedded Python ?
  • How to use Embedded Python ?
  • How I use Embedded Python
    • Use Python libraries and code as they were ObjectScript classes
      • With the language tag
      • Without the language tag
      • Conclusion
    • Use a python first approach
      • Example : iris-fhir-python-strategy
      • Remarks
      • Where to find the code
      • How to implement a Strategy
      • Implementation of InteractionsStrategy
      • Implementation of Interactions
      • Interactions in Python
      • Implementation of the abstract python class
      • Too long, do a summary

Add a credential to login the FHIR REST interface - in this case only consider a basic authentication

The objective of the article is to provide the reader with the following informations:

• Configure and use the FHIR server
• Create an OAuth2 Authorization Server
• Bind the FHIR server to the OAuth2 Authorization Server for support of SMART on FHIR
• Use the interoperability capabilities of IRIS for Health to filter FHIR resources
• Create a custom operation on the FHIR server

Schema of the article:

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:

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.

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.

We return with our example of using the FHIR Adapter, in this article we are going to review how we can configure it in our IRIS instances and what the result of the installation is.

The steps taken to configure the project are the same as indicated in the official documentation, you can review them directly here. Well, let's get to work!

1、Background

When I first encountered FHIR, I encountered a problem with its message format. It was difficult for me to determine whether the message I created met the format, and it was also difficult to easily create an FHIR message from scratch.

So, through fhir server of IntereSystems’fhirserver API, I created this application for quickly generating/validating FHIR messages.