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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.

Hi Community,

This post is a introduction of my open exchange iris-fhir-client application.

iris-fhir-client can connect to any open FHIR Server by using embedded python with the help of fhirpy Library.
Get Resource information by terminal and by using CSP web application.

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Hi developers! have you ever implemented a DICOM integration? Maybe you have searched for an example, or even a simulator. Here you can find a example that may be useful.

DICOM is a message standard widely used in healthcare for diagnostic images.You can use DICOM standard in interoperability productions using both IRIS For Health and Health Connect, here you can find the documentation.

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Hi Community,

This post is a introduction of my openexchange iris-python-apps application. Build by using Embedded Python and Python Flask Web Framework.
Application also demonstrates some of the Python functionalities like Data Science, Data Plotting, Data Visualization and QR Code generation.

image

Features

  • Responsive bootstrap IRIS Dashboard

  • View dashboard details along with interoperability events log and messages.

  • Use of Python plotting from IRIS

  • Use of Jupyter Notebook

  • Introduction to Data Science, Data Plotting and Data Visualization.

  • QR Code generator from python.

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Hello, developers!

In this series, I will not show you how to use IRIS for Health, but rather how to use SUSHI, a tool for creating FHIR profiles, as an associated technology.

With the right tools, the profile information (specifications, limitations, extensions, etc.) of a FHIR project can be well organized and published.

Before we begin, what is SUSHI? I will briefly explain it.

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Hi everyone,

I want to talk about our project and use the dataset theme for this contest.

Our intention never was to be a data curator, especially because sometimes my precious data means a lot for me, but not for the rest of the world.

My Precious

We want to go a step further and empower the user to find the perfect dataset for their needs.

Our project is a bridge between the data science community and the developer's community using InterSystems IRIS to achieve this mission.

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Article
· Jan 13, 2022 4m read
How to find the dataset you need?

Hey community! How are you doing?

I hope to find everyone well, and a happy 2022 to all of you!

Over the years, I've been working on a lot of different projects, and I've been able to find a lot of interesting data.

But, most of the time, the dataset that I used to work with was the customer data. When I started to join the contest in the past couple of years, I began to look for specific web datasets.

I've curated a few data by myself, but I was thinking, "This dataset is enough to help others?"

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For those that, at some point, need to test what means that of ECP for horizontal escalability (computing power and/or users and processes concurrency), but they're lazy o have no much time to build the environment, configure the server nodes, etc..., I've just published in Open Exchange the app/sample OPNEx-ECP Deployment .

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There are many ways to generate excel files using Intersystems, some of them are ZEN reports, IRIS reports ( Logi reports or formally known as JReports), or we can use third party Java libraries, the possibilities are almost endless.

But, what if you want to create a simple spreadsheet with only Caché ObjectScript? (no third party applications)

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I recently published the "appmsw-docbook" module for deploying the solution not by individual programs, but by the entire database. This solution allows you to deploy a project without source codes.

Having received feedback, I decided to improve it significantly. Replaced zip archive with tgz, which will allow not using external system-dependent archivers.

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In this article I will demonstrate the following :

  • Update ReferencesRange(OBX:7) against ObservationIdentifier(OBX:3.1)[TestCode] from database by using custom utility function
  • Update Abnormal Flag(OBX:8) against ObservationIdentifier(OBX:3.1)[TestCode] and ObservationValue(OBX:5)[Result] from database utility function
  • Route Message based on Abnormal Flag(OBX:8)

Below is the primary and transformed HL7 2.5 ORU_R01 message:

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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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Continuing the description of work on the use of the openhl python module in productive mode.

Since the version of iris with Embedded Python, does not yet have a final release, it is already necessary to use it in production now. We decided to back up the service for exporting requests to a xlsx file on a separate server, and save the query result in a global in a separate database.

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I have attached a document that describes the product I have developed called NiPaRobotica Pharmacy. This is an interface I developed that accepts Pharmacy Dispense Requests and converts the line items on the order into dispense dialogues which it sends to pharmacy robots. I deployed the interface into 3 Hospital pharmacies two of which had 6 robots that were arranged in such a way that the dispense chutes channelled medications to desks by the pharmacists sitting in windows serving 1200 patients a day. The robots cut the average waiting time from 2 hours down to one hour.

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