Fast Healthcare Interoperability Resources (FHIR, pronounced "fire") is a draft standard describing data formats and elements (known as "resources") and an application programming interface (API) for exchanging electronic health records
In a world where healthcare technology is rapidly evolving, the importance of efficient, reliable, and interoperable healthcare applications has never been greater.
In the past, reading information from a bar code was limited to a simple alphanumeric code. The creation of a bar code with more than one dimension (2D), especially the QR Code, allowed to increase the amount and variety of data stored in a bar code. While conventional bar codes are capable of storing a maximum of approximately 20 digits, the QR Code is capable of handling several tens to hundreds of times more information. This revolutionized the markets. Now QR codes are everywhere and can be very useful for storing textual, numeric, alphanumeric and even binary data.
Google has one intersting tool named Data Studio. This tool allows creating some interactive dashboards, based on your data, available from the internet. It already offers hundreds of connectors to any sort of data developed by the community. As well as some amount of community developed visualizing. And most importantly, Google offers a way to develop your own connector to your data.
FHIRaaS provides a REST API, and it's available from the internet. So I've decided to try to create some basic report on data stored there. And in the end, I got this.
In the previous article we saw how we could recover a resource stored in the database of our particular HIS, so today we will see how we can add new records in our HIS whose origin is an FHIR resource that we receive in our system.
In this article, I will introduce my application iris-fhir-bridge
IRIS-FHIR-Bridge is a robust interoperability engine built on InterSystems IRIS for Health, designed to transform healthcare data across multiple formats into FHIR and vice versa. It leverages the InterSystems FHIR Object Model (HS.FHIRModel.R4.*) to enable smooth data standardization and exchange across modern and legacy healthcare systems.
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.
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.
In the context of HL7 FHIR (Fast Healthcare Interoperability Resources), the terms "id" and "identifier" refer to specific elements used for identifying resources within the FHIR data model. For a newbie, these terms can be confusingly similar, but they serve distinct purposes.
Look at the below Patient resource for August T. Faulkner:
The resource has an id of “1” — generated by the FHIR server when the resource was created. Patient August T. Faulkner also has a identifier (Medical Record Number) — possibly provided by the hospital — of 78510398960
NLP stands for Natural Language Processing which is a field of Artificial Intelligence with a lot of complexity and
techniques to in short words "understand what are you talking about".
And FHIR is...???
FHIR stands for Fast Healthcare Interoperability Resources and is a standard to data structures for healthcare. There are
some good articles here explainig better how FHIR interact with Intersystems IRIS.
To achieve optimized AI performance, robust explainability, adaptability, and efficiency in healthcare solutions, InterSystems IRIS serves as the core foundation for a project within the x-rAI multi-agentic framework. This article provides an in-depth look at how InterSystems IRIS empowers the development of a real-time health data analytics platform, enabling advanced analytics and actionable insights. The solution leverages the strengths of InterSystems IRIS, including dynamic SQL, native vector search capabilities, distributed caching (ECP), and FHIR interoperability. This innovative approach directly aligns with the contest themes of "Using Dynamic SQL & Embedded SQL," "GenAI, Vector Search," and "FHIR, EHR," showcasing a practical application of InterSystems IRIS in a critical healthcare context.
Processing FHIR resources with FHIR SQL BUILDER to predict the probability of developing hepatitis C disease
With the development of technology, the medical industry is also constantly advancing, and humans often pay more attention to their own health, By learning and processing datasets through computers, diseases can be predicted.
Pre condition: Ability to use FHIR and ML Firstly, our dataset is obtained from kaggle and transformed into FHIR resources based on patient gender, age, ALP or ALT, and imported into the FHIR resource repository
Hello everyone, I’m a French student in academical exchange for my fifth year of engineering school and here is my participation in the FHIR for Women's Health contest.
This project is supposed to be seen as the backend of a bigger application. It can be plugged into a Front End app and help you gather information from your patients. It will read your data in local and use a Data Transformation to make it into a FHIR object before sending it to the included local FHIR server.
Wanted to have a FHIR Story in the back pocket to share with the participants on a dead simple series of calls against the FHIR Server for anybody in the mindset of emitting metrics from a device to FHIR.
This will be a very short article as in April 2025 with Lovable and other Prompt-to-UI tools it becomes possible to build the frontend with prompting. Even to the folks like me who is not familiar with modern UI techics at all.
Well, I know at least the words javascript, typescript and ReactJS, so in this very short article we will be building the ReactJS UI to InterSystems FHIR server with Lovable.ai.
When building a bundle from legacy data, I (and others) wanted to be able to control whether or not the resources were generated with a FHIR Request Method of PUT instead of the hard coded POST. I have extended the two classes responsible for transforming SDA to FHIR in an Interoperability Production to accomodate a setting that lets the user control the Request Method.
I participated in InterSystems Women’s Health FHIR contest, because I loved the challenge to learn a new-to-me technology. I wanted to develop an app that receives data from a mobile device like my Fitbit or a Smart Watch. I did not get access to such data except when I downloaded a spreadsheet (CSV file) showing my daily steps and sleep data. I saw in iris-fhir-template it imported some patient data to a FHIR server.
In previous articles we have seen how to configure and customize our EMPI, we have seen how we can include new patients in our system through HL7 messaging, but of course, not everything is HL7 v.2 in this life! How could we configure our EMPI instance to work with FHIR messaging?
In this article, I will show you how to configure FHIR repository + OAuth2 authorization server/resource server on IRIS for Health following the previous article.
In Part 1, we introduced the preliminary preparations, configuring the OAuth2 authorization server, and obtaining the access token.
Part 2 will show you how to build an FHIR repository and configure an OAuth2 client/resource server.
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!
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.
In this GitHub we gather information from a csv, use a DataTransformation to make it into a FHIR object and then, save that information to a FHIR server all that using only Python.
The objective is to show how easy it is to manipulate data into the output we want, here a FHIR Bundle, in the IRIS full Python framework.
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