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#InterSystems IRIS for Health

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InterSystems IRIS for Health™ is the world’s first and only data platform engineered specifically for the rapid development of healthcare applications to manage the world’s most critical data. It includes powerful out-of-the-box features: transaction processing and analytics, an extensible healthcare data model, FHIR-based solution development, support for healthcare interoperability standards, and more. All enabling developers to realize value and build breakthrough applications, fast. Learn more.

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.

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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.

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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.

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Prefer not to read? Check out the demo video I created:

https://www.youtube.com/embed/-OwOAHC5b3s
[This is an embedded link, but you cannot view embedded content directly on the site because you have declined the cookies necessary to access it. To view embedded content, you would need to accept all cookies in your Cookies Settings]

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Introduction

This article aims to explore how the FHIR-PEX system operates and was developed, leveraging the capabilities of InterSystems IRIS.

Streamlining the identification and processing of medical examinations in clinical diagnostic centers, our system aims to enhance the efficiency and accuracy of healthcare workflows. By integrating FHIR standards with InterSystems IRIS database Java-PEX, the system help healthcare professionals with validation and routing capabilities, ultimately contributing to improved decision-making and patient care.

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This post will guide you through the process of sizing shared memory requirements for database applications running on InterSystems data platforms. It will cover key aspects such as global and routine buffers, gmheap, and locksize, providing you with a comprehensive understanding. Additionally, it will offer performance tips for configuring servers and virtualizing IRIS applications. Please note that when I refer to IRIS, I include all the data platforms (Ensemble, HealthShare, iKnow, Caché, and IRIS).

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Introduction

InterSystems has recently completed a performance and scalability benchmark of IRIS for Health 2020.1, focusing on HL7 version 2 interoperability. This article describes the observed throughput for various workloads, and also provides general configuration and sizing guidelines for systems where IRIS for Health is used as an interoperability engine for HL7v2 messaging.

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Python has become the most used programming language in the world (source: https://www.tiobe.com/tiobe-index/) and SQL continues to lead the way as a database language. Wouldn't it be great for Python and SQL to work together to deliver new functionality that SQL alone cannot? After all, Python has more than 380,000 published libraries (source: https://pypi.org/) with very interesting capabilities to extend your SQL queries within Python.

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Loading your IRIS Data to your Google Cloud Big Query Data Warehouse and keeping it current can be a hassle with bulky Commercial Third Party Off The Shelf ETL platforms, but made dead simple using the iris2bq utility.

Let's say IRIS is contributing to workload for a Hospital system, routing DICOM images, ingesting HL7 messages, posting FHIR resources, or pushing CCDA's to next provider in a transition of care. Natively, IRIS persists these objects in various stages of the pipeline via the nature of the business processes and anything you included along the way. Lets send that up to Google Big Query to augment and compliment the rest of our Data Warehouse data and ETL (Extract Transform Load) or ELT (Extract Load Transform) to our hearts desire.

A reference architecture diagram may be worth a thousand words, but 3 bullet points may work out a little bit better:

  • It exports the data from IRIS into DataFrames
  • It saves them into GCS as .avro to keep the schema along the data: this will avoid to specify/create the BigQuery table schema beforehands.
  • It starts BigQuery jobs to import those .avro into the respective BigQuery tables you specify.

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InterSystems FAQ rubric

Using the Config.Configuration class and SYS.Database class methods, you can create and register a namespace database from the terminal.

Below is a series of execution examples that create database file /CacheDB/AAA/cache.dat and register database AAA and namespace AAA in the configuration file (cache.cpf).
* Execute in the %SYS namespace. *

* Make sure that this script runs as the user that is used for all IRIS processes to ensure that the directory has appropriate ownership and permissions *

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InterSystems FAQ rubric

If the system does not stop for 24 hours, old journal files will be deleted at 0:30 according to the "Journal file deletion settings".

A possible cause of journal files remaining that are older than the "Journal file deletion settings" is that there are transactions that remain open.

In that case, you will be able to delete the journal file by searching for processes executing transactions and finalizing the transactions.

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I have just created a new Global Master Topic, "IRIS Cheatsheets". IRIS has introduced a lot of new functionality, especially in scripting languages, FHIR R4 support, enhanced Interoperability Tools, and IRIS Analytics. Having spent 35 years working on Windows-based PC's and Laptops, I have surprisingly little knowledge of Linux, Docker and Git. Furthermore, I have written almost every application and Interface in ObjectScript with splatterings of SQL, .Net, and Java Gateways and the most basic knowledge of WinSCP, Putty, SSH. All that changed when I received my first Raspberry Pi.

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In this GitHub we gather information from a csv, use a DataTransformation to make it into a FHIR object and then, save that information to a FHIR server all that using only Python.

The objective is to show how easy it is to manipulate data into the output we want, here a FHIR Bundle, in the IRIS full Python framework.

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The objective of the article is to provide the reader with the following informations:

  • Configure and use the FHIR server
  • Create an OAuth2 Authorization Server
  • Bind the FHIR server to the OAuth2 Authorization Server for support of SMART on FHIR
  • Use the interoperability capabilities of IRIS for Health to filter FHIR resources
  • Create a custom operation on the FHIR server

Schema of the article:

Schema

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Article
· May 2 3m read
Minify XML in IRIS

In a project I'm working on we need to store some arbitrary XML in the database. This XML does not have any corresponding class in IRIS, we just need to store it as a string (it's relatively small and can fit in a string).
Since there are MANY (millions!) of records in the database I decided to reduce as much as possible the size without compressing. I know that some XML to be stored is indented, some not, it varies.

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Hello, developers!

In this article, I will show you how to configure FHIR repository + OAuth2 authorization server/resource server on IRIS for Health following the previous article.

In Part 1, we introduced the preliminary preparations, configuring the OAuth2 authorization server, and obtaining the access token.
Part 2 will show you how to build an FHIR repository and configure an OAuth2 client/resource server.

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I am happy to share with you my first experience of using a docker container version of IRIS for Health to explore your interest in using or having a trial by taking the advantage of a docker container that is lightweight, and easy to deploy. This cookbook will go through the implementation steps using the GitHub repository called ENSDEMO written by Renan Lourenco.

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InterSystems FAQ rubric

To resolve the error <PROTECT>, remove the read-only attribute of the system-wide library database (IRISLIB for InterSystems IRIS, CACHELIB for Caché/Ensemble/HealthShare (Caché-based))

Once you have finished importing the routine, remember to change it back to read-only.

[Version 2013.1 and above]
[Management Portal] > [System Administration] > [Configuration] > [System Configuration] > [Local Database] Uncheck "Mount read-only" from the database name link.

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Based on a great sample and workshop built by @Luis Angel Pérez Ramos (see related articles and related Open Exchange app), which included a local InterSystems IRIS for Health container (and desired setup), this sample presented here, adapted the workshop for using the InterSystems Cloud FHIR Server, and it's related setup.

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