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.
I was working on a DTL but kept getting ERROR #5002... MAXSTRING errors. The problem was that most of the DTL GUI action steps only support the string data type when working with the segments. A %String has a limit of 3,641,144 characters and my OBX5.1 was 5,242,952 characters long as the example provided. Of course PACS admin stated ultra high quality up to and including 4K resolution files were needed, so we could not get the vendor to compress or reformat these files to compressed jpg or something similar.
When developing a new Interoperability Production, it is quite natural that settings are initially added in the Production.
However, as soon as you want to move the Production from development to a test or staging environment, it becomes clear that some settings like HTTP Servers, IP addresses and/or ports need to be changed. In order to avoid these settings being overwritten during a redeployment later on, it is essential that you move these settings from the Production to the System Default settings.
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.
ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model (Hochbaum, Rosenstock, & Kegels, 1952) as a psychological framework to craft empathetic replies.
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.
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.
The InterSystems IRIS has a series of facilitators to capture, persist, interoperate, and generate analytical information from data in XML format. This article will demonstrate how to do the following:
Capture XML (via a file in our example);
Process the data captured in interoperability;
Persist XML in persistent entities/tables;
Create analytical views for the captured XML data.
Capture XML data
The InterSystems IRIS has many built-in adapters to capture data, including the next ones:
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.
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....
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.
Let's pretend for a moment that you're an international action spy who's dedicated your life to keeping the people of the world safe from danger. You recieve the following mission:
Good day, Agent IRIS,
We're sorry for interrupting your vacation in the Bahamas, but we just received word from our London agent that a "time bomb" is set to detonate in a highly populated area in Los Angeles. Our sources say that the "time bomb" is set to trigger at 3:14 PM this afternoon.
Current triage systems often rely on the experience of admitting physicians. This can lead to delays in care for some patients, especially when faced with inexperienced residents or non-critical symptoms. Additionally, it can result in unnecessary hospital admissions, straining resources and increasing healthcare costs.
In this article, I will introduce my application iris-HL7v2Gen .
IRIS-HL7v2Gen is a CSP application that facilitates the dynamic generation of HL7 test messages. This process is essential for testing, debugging, and integrating healthcare data systems. The application allows users to generate a wide variety of HL7 message types, validate their structure against HL7 specifications, explore the message hierarchy, and transmit messages over TCP/IP to production systems. These features are particularly useful in settings where compliance with HL7 standards is mandatory for interoperability between different healthcare organizations or systems.
Application Features
Dynamic HL7 Message Generation: Instantly create HL7 messages for a range of message types, facilitating comprehensive testing.
Message Structure Exploration: Visualize the structure of generated messages based on HL7 specifications.
Value Set Visualization View predefined sets of allowable coded values for specific fields.
Message Validation: Validate messages against HL7 standards to ensure compliance.
TCP/IP Communication: Easily transmit messages to production using TCP/IP settings.
Broad Message Type Support: Supports 184 different HL7 message types, ensuring versatility for various healthcare integration needs.
ClassMethod: Generate a Test Message by Invoking a Class Method
Version Support: Currently Supports HL7 Version 2.5
ChatIRIS Health Coach, a GPT-4 based agent that leverages the Health Belief Model as a psychological framework to craft empathetic replies. This article elaborates on the backend architecture and its components, focusing on how InterSystems IRIS supports the system's functionality.
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.
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.
In this post, we'll discuss our project that leverages Pulumi and Docker Compose to automate the deployment of InterSystems WSGI applications on AWS. The focus is on simplicity and efficiency, using pre-built infrastructure templates for provisioning and scaling AWS resources.
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.
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Open Exchange app
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