Welcome community members to a new article! this time we are going to test the interoperability capabilities of IRIS for Health to work with DICOM files.
Let's go to configure a short workshop using Docker. You'll find at the end of the article the URL to access to GitHub if you want to make it run in your own computer.
Previously to any configuration we are going to explain what is DICOM:
These days the vast majority of applications are deployed on public cloud services. There are multiple advantages, including the reduction in human and material resources needed, the ability to grow quickly and cheaply, greater availability, reliability, elastic scalability, and options to improve the protection of digital assets. One of the most favored options is the Google Cloud. It lets us deploy our applications using virtual machines (Compute Engine), Docker containers (Cloud Run), or Kubernetes (Kubernetes Engine). The first one does not use Docker.
Recently I needed to restore a version of a production class, which was overwritten by compilation and running UpdateProduction. As the correct version was unavailable in the source control, I used journals to restore the data. Journals store a plethora of information about what's happening in the system and are quite a powerful tool. This article explains how to work with journals to extract the data you require.
As an AI language model, ChatGPT is capable of performing a variety of tasks like language translation, writing songs, answering research questions, and even generating computer code. With its impressive abilities, ChatGPT has quickly become a popular tool for various applications, from chatbots to content creation. But despite its advanced capabilities, ChatGPT is not able to access your personal data. So in this article, I will demonstrate below steps to build custom ChatGPT AI by using LangChain Framework:
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?
Record maps are used to efficiently map files containing delimited records or fixed-width records to message classes used by the interoperability function, and to map files from interoperability function message classes to text files.
Record map mapping definitions can be created using the Management Portal, and we also provide a CSV record wizard that allows you to define while reading a CSV file.
The OpenAPI Specification (OAS) defines a standard, language-agnostic interface to HTTP APIs which allows both humans and computers to discover and understand the capabilities of the service without access to source code, documentation, or through network traffic inspection. When properly defined, a consumer can understand and interact with the remote service with a minimal amount of implementation logic. While for SOAP based APIs there is a special wizard in InterSystems IRIS that cuts down orchestrations development time, not all APIs used in integrations are SOAP. That's why @Jaime Lerga suggested to add a wizard similar to the SOAP wizard to generate a REST client from OpenAPI specification. Implementation of this idea cuts down the development time of the REST API orchestrations with InterSystems IRIS. This idea is one of most popular ideas on the InterSystems ideas. This article, the third in the "Implemented Ideas" series, focuses on the OpenAPI Suite solution developed by @Lorenzo Scalese.
In this article, I will introduce my application iris-image-vector-search. The image vector retrieval demo uses IRIS Embedded Python and OpenAI CLIP model to convert images into 512 dimensional vector data. Through the new feature of Vector Search, VECTOR-COSINE is used to calculate similarity and display high similarity images.
Accessing an Azure cloud storage to upload/download blobs is quite easy using the designated %Net.Cloud.Storage.Client class API methods, or using the EnsLib.CloudStorage.* inbound/outbound adaptors.
Note that you'll need to have the %JavaServer External Language Server up and running to use the cloud storage API or adaptors, since they both use the PEX framework using the Java Server.
In the modern world, the most valuable asset for companies is their data. Everything from business processes and applications to transactions is based on data which defines the success of the organization's operations, analysis, and decisions. In this scenario, the data structures need to be ready for frequent changes, yet in a managed and governed way. Otherwise, we will inevitably lose money, time, and quality of corporate solutions.
The rise of Big Data projects, real-time self-service analytics, online query services, and social networks, among others, have enabled scenarios for massive and high-performance data queries. In response to this challenge, MPP (massively parallel processing database) technology was created, and it quickly established itself. Among the open-source MPP options, Presto (https://prestodb.io/) is the best-known option. It originated in Facebook and was utilized for data analytics, but later became open-sourced.
I recently had to refresh my knowledge of the HealthShare EMPI module and since I've been tinkering with IRIS's vector storage and search functionalities for a while I just had to add 1 + 1.
For those of you who are not familiar with EMPI functionality here's a little introduction:
Enterprise Master Patient Index
In general, all EMPIs work in a very similar way, ingesting information, normalizing it and comparing it with the data already present in their system. Well, in the case of HealthShare's EMPI, this process is known as NICE:
JWT (JSON Web Token) is an open standard (RFC 7519) that offers a lightweight, compact, and self-contained method for securely transmitting information between two parties. It is commonly used in web applications for authentication, authorization, and information exchange.
A JWT is typically composed of three parts:
1. JOSE (JSON Object Signing and Encryption) Header 2. Payload 3. Signature
RabbitMQ is a message broker that allows producers (those who send a data message) and consumers (those who receive a data message) to establish asynchronous, real-time, and high-performance massive data flows. RabbitMQ supports AMQP (Advanced Message Queuing Protocol), an open standard application layer protocol. The main reasons to employ RabbitMQ include the following:
You can improve the performance of the applications using an asynchronous approach.
It lets you decouple and reduce dependencies between services, microservices, and applications with the help of a data message mediator, meaning that there is no need for producers and consumers of exchanged data to know each other.
It allows the long-running processing of sent data (with the results) to be delivered after utilizing a response queue.
It helps you migrate from monolithic to microservices, where microservices exchange data via Rabbit in a decoupled and asynchronous way.
It offers reliability and resilience by making it possible for messages to be stored and forwarded. A message can be delivered multiple times until it is processed.
Message queueing is the key to scaling your application. As the workload increases, you will only have to add more workers to handle the queues faster.
While writing an article yesterday, I realized I was so busy with people who came to the Developer Community table at the Tech Exchange that I forgot to take photos for you. Luckily, I realized the error of my ways and corrected my behavior accordingly 😉
So, let's look at what happened on Tuesday at the InterSystems Ready 2025! It began with a speech of Scott Gnau about the approach and architecture of InterSystems Data Platform and how it is different from all other DBMSs:
This paper describes how Ensemble behaves after a system crash or similar uncontrolled shutdown or failover. Ensemble is normally configured to start processing messages automatically when the operating system restarts or when the system has failed over to a cluster member or mirror member. No manual intervention is required. But, in order for your system to robustly handle system failure and restart, you must understand how Ensemble handles these conditions and develop your productions using the rules and guidelines in this document.
Experts estimate that 85% of all data exists in unstructured formats – held in e-mails, documents (contracts, memos, clinical notes, legal briefs), social media feeds, etc. Where structured data typically accounts for quantitative facts, the more interesting and potentially more valuable expert opinions and conclusions are often hidden in these unstructured formats. And with massive volumes of text being generated at unprecedented speed, there’s very little chance this information can be made useful without some process of synthesis or automation.
The attached zip file contains a bunch of examples of Cache SQL Storage mappings that I have done over the years.
If you have existing globals and want to expose them via Objects or SQL you need to setup Cache SQL Storage mapping. If you do not see an example that helps with your case send me an example and I can help you out.
I am pleased to announce the next in the series of 2016.2 field test kits, 2016.2.0.585.0.
In the two weeks since the last field test posting Development has made over a hundred fixes and improvements in Atelier, Enterprise Manager, Ensemble and Caché.
In Atelier alone there were over a dozen changes including a fix for the incompatibility issue that Jamie Newton described in his posting of February 1.
I am pleased to announce that Caché and Ensemble 2015.2.3 are now available as maintenance releases. For a complete list of the corrections in 2015.2.3, please review the release notes.
Caché and Ensemble 2015.2.3 are available now for the same platforms as 2015.2.2
The complete set of supported platforms, including specific point releases and/or patches, is detailed in the Supported Platforms document.
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