In healthcare,interoperability is the ability of different information technology systems and software applications to communicate, exchange data, and use the information that has been exchanged.
IRIS supports CCDA and FHIR transformations out-of-the-box, yet the ability to access and view those features requires considerable setup time and product knowledge. The IRIS Interop DevTools application was designed to bridge that gap, allowing implementers to immediately jump in and view the built-in transformation capabilities of the product.
In addition to the IRIS XML, XPath, and CCDA Transformation environment, the Interop DevTools package now provides:
Profiling CCD Documents with LEAD North’s CCD Data Profiler Ever opened a CCD and been greeted by a wall of tangled XML? You’re not alone. Despite being a core format for clinical data exchange, CCD's are notoriously dense, verbose, and unfriendly to the human eye. For developers and analysts trying to validate their structure or extract meaningful insights, navigating these documents can feel more like archaeology than engineering.
Have you ever needed to change an IP or port before deploying an interface to production? Needed to remove items from an export? What about modifying the value(s) in a lookup table before deploying? Have you wanted to disable an interface before deploying? What about adding a comment, category, or alert setting to an interface before deploying to production?
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.
After we rolled out a new cointainer based on containers.intersystems.com/intersystems/irishealth:2023.1 this week, we suddenly noticed that our FHIR Repository started responding with an Error 500. This turns out to be caused by PROTECT violations on the new HSSYSLOCALTEMP namespace and database used by this version of the IRIS for Health FHIR components.
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.
One of the challenges of creating a DICOM message is how to implement putting data in the correct place. Part of it is by inserting the data in the specific DICOM tags, while the other is to insert binary data such as a picture - In this article I will explain both.
To create a DICOM message, you can either use the EnsLib.DICOM.File class (to create a DICOM file) or the EnsLib.DICOM.Document class (to create a message that can be sent to PACS directly). In either case, the SetValueAt method will allow you to add your data to the DICOM tags.
Every so often, I would encounter a request for some GPG support, so I had several code samples written for a while, and I thought to combine all of them and add missing GPG functionality for a fairly complete coverage. That said, this Business Operation primarily covers data actions, skipping management actions such as key generation, export, and retrieval as they are usually one-off and performed manually anyways. However, this implementation does support key imports for obvious reasons. Well, let's get into it.
The complex record mapper can help you process text file data consisting of various types of records to persistent messages in IRIS. To gain a basic understanding of the complex record mapper and see an example implemented in the production, check out the learning services video below:
As AI-driven automation becomes an essential part of modern information systems, integrating AI capabilities into existing platforms should be seamless and efficient. The IRIS Agent project showcases how generative AI can work effortlessly with InterSystems IRIS, leveraging its powerful interoperability framework—without the need to learn Python or build separate AI workflows from scratch.
The Interoperability user interface now includes modernized user experiences for the DTL Editor and Production Configuration applications that are available for opt-in in all interoperability products. You can switch between the modernized and standard views. All other Interoperability screens remain in the Standard user interface. Please note that changes are limited to these two applications and we identify below the functionality that is currently available.
In 2023, according to IDC, Salesforce's market share in CRM reached 21.7%. This company owns a substantial amount of critical corporate business processes and data, so the InterSystems IRIS must have an interoperability connector to fetch data from the Salesforce data catalog. This article will show you how to get any data hosted by Salesforce and create an interoperation production to get data and send it to such targets as files and relational databases.
High-Performance Message Searching in Health Connect
The Problem
Have you ever tried to do a search in Message Viewer on a busy interface and had the query time out? This can become quite a problem as the amount of data increases. For context, the instance of Health Connect I am working with does roughly 155 million Message Headers per day with 21 day message retention. To try and help with search performance, we extended the built-in SearchTable with commonly used fields in hopes that indexing these fields would result in faster query times. Despite this, we still couldn't get some of these queries to finish at all.
When we create a FHIR repository in IRIS, we have an endpoint to access information, create new resources, etc. But there are some resources in FHIR that probably we wont have in our repository, for example, Binary resource (this resource returns a document, like PDF for example).
I have created an example that when a Binary resource is requested, FHIR endpoint returns a response, like it exists in the repository.
TLS, the successor to SSL, stands for Transport Layer Security and provides security (i.e. encryption and authentication) over a TCP/IP connection. If you have ever noticed the "s" on "https" URLs, you have recognized an HTTP connection "secured" by SSL/TLS. In the past, only login/authorization pages on the web would use TLS, but in today's hostile internet environment, best practice indicates that we should secure all connections with TLS.
FHIR repositories, applications and servers typically serve clinical data in small quantities, whether to return data about a patient, their medications, vaccines, allergies, among other information. However, it is common for a large amount of data in FHIR/JSON format to be requested to be used to load into Data Lakes, identifying study cohorts, population health, or transferring data from one EHR to another. To meet these business scenarios that require large extractions and loads of data, it is recommended to use the FHIR Bulk Data Access feature provided by HL7 institution.
The world of Generative AI has been pretty inescapable for a while, commercial models running on paid Cloud instances are everywhere. With your data stored securely on-prem in IRIS, it might seem daunting to start getting the benefit of experimentation with Large Language Models without having to navigate a minefield of Governance and rapidly evolving API documentation. If only there was a way to bring an LLM to IRIS, preferably in a very small code footprint....
Interoperability of systems ensures smooth workflow and management of data in today's connected digital world. InterSystems IRIS extends interoperability a notch higher with its Embedded Python feature, which lets developers seamlessly integrate Python scripts into the IRIS components, like services, operations, and custom functions.
In your Interoperability Production you could always have a Business Operation that is an HTTP client, that uses OAuth 2.0 for authentication, but you had to customize the Operation for this authentication methodology. Since v2024.3, which was lately released, there is a new capability, providing new settings, to handle this more easily.
Using embedded Python while building your InterSystems-based solution can add very powerful and deep capabilities to your toolbox.
I'd like to share one sample use-case I encountered - enabling a CDC (Change Data Capture) for a mongoDB Collection - capturing those changes, digesting them through an Interoperability flow, and eventually updating an EMR via a REST API.
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