In part I of this article, we have already learned more about HL7, its messages, structure, segments, and fields. In this part II, we will show you how to transform proprietary/custom data into HL7 using IRIS for Health. For our sample, we used the data generated by the SYNTHEA bulk data generation project (https://synthea.mitre.org/downloads). So, we will show you how to convert 1000 patients from a CSV file to HL7 v2, using the Interoperability features of IRIS for Health.

IRIS has the likes of SQL inbound adapters for use with SQL gateways such as EnsLib.SQL.InboundAdapter to repeatedly query SQL Gateway connections. A scenario appeared as that we wanted to query an Internal database for some data but did not see an out of the box service for this.

Have a Generic service that can poll internal SQL to work with downstream components.

InterSystems FAQ rubric

Class definitions created by users are stored in class definition classes. They can be used to obtain a list of class definitions from a program.

Note: Class definition classes refer to all classes contained in the %Dictionary package.

According to Databricks Apache Parquet is an open source, column-oriented data file format designed for efficient data storage and retrieval. It provides efficient data compression and encoding schemes with enhanced performance to handle complex data in bulk. Apache Parquet is designed to be a common interchange format for both batch and interactive workloads. It is similar to other columnar-storage file formats available in Hadoop, namely RCFile and ORC. (source: https://www.databricks.com/glossary/what-is-parquet).

Motivation

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.

How can IRIS productions be deployed more quickly and with greater peace of mind?

The aim of interoperability productions is to enable you to connect systems in order to transform and route messages between them. To connect systems, you develop, configure, deploy and manage productions that integrate several software systems.

In the world of Big Data, selecting the right file format is crucial for efficient data storage, processing, and analysis. With the massive amount of data generated every day, choosing the appropriate format can greatly impact the speed, cost, and accuracy of data processing tasks. There are several file formats available, each with its own set of advantages and disadvantages, making the decision of which one to use complex. Some of the popular Big Data file formats include CSV, JSON, Avro, ORC, and Parquet.

There are situations when your only access to a server is using a web browser,
and there is just no chance of a Terminal or Console access.
The Online Demo Server is such a case.

So you have WebTerminal. That's fine for ObjectScript and Embedded Python.

In my previous articles, I described my Command Line Extension to NativeAPI.
Of course, this is also available for any other NativeAPI package.
So I created this example as a demo for the actual Java Contest.

The package contains also an IRIS server in Docker for the demo
It is evident that it also works with any remote IRIS server.
You just have to provide it with my NativeAPI CommandLine Extension.

InterSystems FAQ rubric

To create a user-defined error you need to prepare the XML that describes the error code and corresponding message that you want to use as a user-defined error.

Please set the error code as a negative integer.

InterSystems FAQ rubric

Information on properties defined in a class can be obtained using the following system classes:

%Dictionary.ClassDefinetion

This article will cover turning over control of provisioning the InterSystems Kubernetes Operator, and starting your journey managing your own "Cloud" of InterSystems Solutions through Git Ops practices. This deployment pattern is also the fulfillment path for the PID^TOO||| FHIR Breathing Identity Resolution Engine.

We resume our series of articles on the FHIR Adapter tool available to HealthShare HealthConnect and InterSystems IRIS users.

In the previous articles we have presented the small application on which we set up our workshop and showed the architecture deployed in our IRIS instance after installing the FHIR Adapter. In today's article we will see an example of how we can perform one of the most common CRUD (Create - Read - Update - Delete) operations, the reading operation, and we will do it by recovering a Resource.

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!