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 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 a world where healthcare technology is rapidly evolving, the importance of efficient, reliable, and interoperable healthcare applications has never been greater.
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
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
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.
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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.
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.
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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.
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 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.
Watch this video to learn how to deploy your FHIR server using AWS, EKS, and Kubernetes: advantages and disadvantages of cloud deployment and demo a deployment:
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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.
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!
We'd like to highlight some outstanding projects created during the European Healthcare Hackathon 2023 in Prague.
Participants were presented with nine real-world healthcare challenges by IKEM and AstraZeneca. InterSystems introduced to the participants an opportunity to use the FHIR repository and perform FHIR availability into their solutions by providing FHIR cloud services on AWS.
Meet the winners of our challenge:
1st placeČarodějové (PathoSync)
"The PathoSync software is a solid base for complex pathologist platforms. With the use of custom mapping, any laboratory can project their data into FHIR7 standard, which soon will be mandatory worldwide. This makes the digitalization process smoother. The connection with InterSystems assures quality and implements many healthcare features. Furthermore, the GDPR norms are strictly followed using the FHIR server based in Europe, so the usage of software follows the European standards."
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