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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 is the update on what are the new applications submitted on OpenExchange in March 2019

New Applications

isc-tar published by @Dmitry Maslennikov

Compact files as TAR or Extract files from TAR files

Light weight EXCEL download v.1.0 published by @Robert.Cemper

This is the working example of a light weight export to EXCEL based on data in SAMPLES namespace. Good old CSP is well equipped to produce HTML tables accepted from EXCEL as input. With modern Browsers you don't even need and tags. So the required code around your SQL result set is really slim. And you are free to add any formatting you need either by HTML or in SQL.

PythonGateway v.0.7 published by @Eduard Lebedyuk

Python Gateway for InterSystems Data Platforms.

Adopted Bitmaps v.1.0 published by @Robert.Cemper

This is a running example of the Bitmap Adoption

WebSockets Tutorial v.1.0 published by @Lily Taub

A short tutorial on WebSockets in InterSystems IRIS 2018.1+ and Caché 2016.2+

Sync Data with DSTIME v.1.0.0 published by @Robert.Cemper

Other Sync-Tools just work from Caché/IRIS to Caché/IRIS. Synchronizing your data to some external DB you requires some other solution. DSTIME can do it.

HL7 and SMS Interoperability Demo v.1.3 published by @Amir Samary

This demo shows how easy it is to integrate an Electronic Medical Record system that is sending HL7 messages with AWS.

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Breaking news!

InterSystems just announced the availability of the InterSystems IRIS for Health™ Data Platform across the Amazon Web Services, Google Cloud, and Microsoft Azure marketplaces.

With access to InterSystems unified data platform on all three major cloud providers, developers and customers have flexibility to rapidly build and scale the digital applications driving the future of care on the platform of their choice.

To learn more please follow this link.

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Hi Community!

I'm pleased to announce that we've just launched the new product in the family of InterSystems Data Platforms:

InterSystems IRIS for Health

IRIS for Health — is the world’s first and only data platform engineered specifically for healthcare. It empowers you to rapidly create and scale the industry’s next breakthrough applications.

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In the last post we scheduled 24-hour collections of performance metrics using pButtons. In this post we are going to be looking at a few of the key metrics that are being collected and how they relate to the underlying system hardware. We will also start to explore the relationship between Caché (or any of the InterSystems Data Platforms) metrics and system metrics. And how you can use these metrics to understand the daily beat rate of your systems and diagnose performance problems.

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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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One of the great availability and scaling features of Caché is Enterprise Cache Protocol (ECP). With consideration during application development distributed processing using ECP allows a scale out architecture for Caché applications. Application processing can scale to very high rates from a single application server to the processing power of up to 255 application servers with no application changes.

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Index

This is a list of all the posts in the Data Platforms’ capacity planning and performance series in order. Also a general list of my other posts. I will update as new posts in the series are added.

You will notice that I wrote some posts before IRIS was released and refer to Caché. I will revisit the posts over time, but in the meantime, Generally, the advice for configuration is the same for Caché and IRIS. Some command names may have changed; the most obvious example is that anywhere you see the ^pButtons command, you can replace it with ^SystemPerformance.

While some posts are updated to preserve links, others will be marked as strikethrough to indicate that the post is legacy. Generally, I will say, "See: some other post" if it is appropriate.

Capacity Planning and Performance Series

Generally, posts build on previous ones, but you can also just dive into subjects that look interesting.

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++Update: August 2, 2018

This article provides a reference architecture as a sample for providing robust performing and highly available applications based on InterSystems Technologies that are applicable to Caché, Ensemble, HealthShare, TrakCare, and associated embedded technologies such as DeepSee, iKnow, Zen and Zen Mojo.

Azure has two different deployment models for creating and working with resources: Azure Classic and Azure Resource Manager. The information detailed in this article is based on the Azure Resource Manager model (ARM).

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