As you all know, the world of artificial intelligence is already here, and everyone wants to use it to their benefit.

There are many platforms that offer artificial intelligence services for free, by subscription or private ones. However, the one that stands out because of the amount of "noise" it made in the world of computing is Open AI, mainy thanks to its most renowned services: ChatGPT and DALL-E.

If you had the opportunity to change something in the IRIS Interoperability Message Viewer, what would you do?

Modern data architectures utilize real-time data capture, transformation, movement, and loading solutions to build data lakes, analytical warehouses, and big data repositories. It enables the analysis of data from various sources without impacting the operations that use them. To achieve this, establishing a continuous, scalable, elastic, and robust data flow is essential. The most prevalent method for that is through the CDC (Change Data Capture) technique. CDC monitors for small data set production, automatically captures this data, and delivers it to one or more recipients, including analytical data repositories. The major benefit is the elimination of the D+1 delay in analysis, as data is detected at the source as soon as it is produced, and later is replicated to the destination.

This article will demonstrate the two most common data sources for CDC scenarios, both as a source and a destination. For the data source (origin), we will explore the CDC in SQL databases and CSV files. For the data destination, we will use a columnar database (a typical high-performance analytical database scenario) and a Kafka topic (a standard approach for streaming data to the cloud and/or to multiple real-time data consumers).

Overview

This article will provide a sample for the following interoperability scenario:

Hi community,

This is another article about how to perform actions that you can do in the web portal but via code.

Today.... Add a business item in your production by code

Introduction

Say you have a receiving system that accepts HL7 and provides error messages in field ERR:3.9 in the ACK it returns. You require a different reply code action depending on the error message, however the Reply Code Actions settings for the operation do not provide this level of granularity. One option could be to create a process that takes the ACK and then completes the action you were expecting, however things can get a bit messy if the action is to retry the message, especially when trying to view a message trace.

When calling out to web services there are several settings of the Business Operation that play together in controlling what will happen when a response is not returned in the desired time.

EHR (Electronic Health Record) systems are modeled in a proprietary format/structure and are not based on market models such as FHIR or HL7. Some of these systems can interoperate data in a proprietary format for FHIR and other market models, but others can not. InterSystems has two platforms that can interoperate proprietary formats for market ones: InterSystems HealthShare Connect and InterSystems IRIS for Health.

Below is a simple alert processor based on the EnsLib.HTTP.OutboundAdapter to send text alerts via an SMS Gateway service. Typically, all that is needed to send an HTTP Post to the gateway service is the destination phone number, a source phone number, credentials, and the URL.

The code below is based on the Anveo gateway whose interface is as follows:

Introduction

There is a Link Procedure Wizard option within the Management Portal (System > SQL >Wizards > Link Procedure) which I had reliability issues with so I decided to use this solution instead.

Add a credential to login the FHIR REST interface - in this case only consider a basic authentication

Hi Community,

In the first part of this series, we examined the fundamentals of Interoperability on Python (IoP), specifically how it enables us to construct such interoperability elements as business services, processes, and operations using pure Python.

Now, we are ready to take things a step further. Real-world integration scenarios extend beyond simple message handoffs.They involve scheduled polling, custom message structures, decision logic, filtering, and configuration handling.In this article, we will delve into these more advanced IoP capabilities and demonstrate how to create and run a more complex interoperability flow using only Python.

To make it practical, we will build a comprehensive example: The Reddit Post Analyzer Production. The concept is straightforward: continuously retrieving the latest submissions from a chosen subreddit, filtering them based on popularity, adding extra tags to them, and sending them off for storage or further analysis.

The ultimate goal here is a reliable, self-running data ingestion pipeline. All major parts (the Business Service, Business Process, and Business Operation) are implemented in Python, showcasing how to use IoP as a Python-first integration methodology.

You might have encountered this and not known how to prevent it, or perhaps not even noticed it...

Framing refers to the characters that mark the start and end of an HL7 message (or other types of framed messages). Most HL7 services and operations have a Framing setting that allows the user to define this framing. The most common choices are available as defaults, but with the AsciiMM/NN setting, components can be configured to recognize any framing characters.