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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.

++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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In this post I show strategies for backing up Caché using External Backup with examples of integrating with snapshot based solutions. The majority of solutions I see today are deployed on Linux on VMware so a lot of the post shows how solutions integrate VMware snapshot technology as examples.

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This post will show you an approach to size shared memory requirements for database applications running on InterSystems data platforms including global and routine buffers, gmheap, and locksize as well as some performance tips you should consider when configuring servers and when virtualizing Caché applications. As ever when I talk about Caché I mean all the data platform (Ensemble, HealthShare, iKnow and Caché).

A list of other posts in this series is here

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I am often asked by customers, vendors or internal teams to explain CPU capacity planning for large production databases running on VMware vSphere.

In summary there are a few simple best practices to follow for sizing CPU for large production databases:

  • Plan for one vCPU per physical CPU core.
  • Consider NUMA and ideally size VMs to keep CPU and memory local to a NUMA node.
  • Right-size virtual machines. Add vCPUs only when needed.

Generally this leads to a couple of common questions:

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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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The Amazon Web Services (AWS) Cloud provides a broad set of infrastructure services, such as compute resources, storage options, and networking that are delivered as a utility: on-demand, available in seconds, with pay-as-you-go pricing. New services can be provisioned quickly, without upfront capital expense. This allows enterprises, start-ups, small and medium-sized businesses, and customers in the public sector to access the building blocks they need to respond quickly to changing business requirements.

Updated: 10-Jan, 2023

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** Revised Feb-12, 2018

While this article is about InterSystems IRIS, it also applies to Caché, Ensemble, and HealthShare distributions.

Introduction

Memory is managed in pages. The default page size is 4KB on Linux systems. Red Hat Enterprise Linux 6, SUSE Linux Enterprise Server 11, and Oracle Linux 6 introduced a method to provide an increased page size in 2MB or 1GB sizes depending on system configuration know as HugePages.

At first HugePages required to be assigned at boot time, and if not managed or calculated appropriately could result in wasted resources. As a result various Linux distributions introduced Transparent HugePages with the 2.6.38 kernel as enabled by default. This was meant as a means to automate creating, managing, and using HugePages. Prior kernel versions may have this feature as well however may not be marked as [always] and potentially set to [madvise].

Transparent Huge Pages (THP) is a Linux memory management system that reduces the overhead of Translation Lookaside Buffer (TLB) lookups on machines with large amounts of memory by using larger memory pages. However in current Linux releases THP can only map individual process heap and stack space.

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This week I am going to look at CPU, one of the primary hardware food groups :) A customer asked me to advise on the following scenario; Their production servers are approaching end of life and its time for a hardware refresh. They are also thinking of consolidating servers by virtualising and want to right-size capacity either bare-metal or virtualized. Today we will look at CPU, in later posts I will explain the approach for right-sizing other key food groups - memory and IO.

So the questions are:

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Your application is deployed and everything is running fine. Great, hi-five! Then out of the blue the phone starts to ring off the hook – it’s users complaining that the application is sometimes ‘slow’. But what does that mean? Sometimes? What tools do you have and what statistics should you be looking at to find and resolve this slowness? Is your system infrastructure up to the task of the user load? What infrastructure design questions should you have asked before you went into production? How can you capacity plan for new hardware with confidence and without over-spec'ing? How can you stop the phone ringing? How could you have stopped it ringing in the first place?

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In the previous article, we've discussed the origin of the standard HL7v2, the structure and the types of messages. Let's now look at one of the most used types of messages and an example of its structure. I'm talking about ADT.

HL7 ADT messages (Admit, Discharge, Transfer) are used to communicate basic patient information, visit information and patient state at a healthcare facility. ADT messages are one of the most widely-used and high volume HL7 message types, as it provides information for many trigger events including patient admissions, registrations, cancellations, updates, discharges, patient data merges, etc.

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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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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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Myself and the other Technology Architects often have to explain to customers and vendors Caché IO requirements and the way that Caché applications will use storage systems. The following tables are useful when explaining typical Caché IO profile and requirements for a transactional database application with customers and vendors. The original tables were created by Mark Bolinsky.

In future posts I will be discussing more about storage IO so am also posting these tables now as a reference for those articles.

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Database systems have very specific backup requirements that in enterprise deployments require forethought and planning. For database systems, the operational goal of a backup solution is to create a copy of the data in a state that is equivalent to when application is shut down gracefully. Application consistent backups meet these requirements and Caché provides a set of APIs that facilitate the integration with external solutions to achieve this level of backup consistency.

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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.

Open Exchange app
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The fhir-react project is a React UI framework based on Google Material Design, which covers almost all FHIR resources for versions DSTU2, STU3 and R4.

It design it's really friendly - there's just one component! As FHIR resource types are standards, the framework resolves internally what rendering class must be used.

To display your FHIR resource just write this component:

Open Exchange app
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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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Released with no formal announcement in IRIS preview release 2019.4 is the /api/monitor service exposing IRIS metrics in Prometheus format. Big news for anyone wanting to use IRIS metrics as part of their monitoring and alerting solution. The API is a component of the new IRIS System Alerting and Monitoring (SAM) solution that will be released in an upcoming version of IRIS.

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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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Over the past year or so, my team (Application Services at InterSystems - tasked with building and maintaining many of our internal applications, and providing tools and best practices for other departmental applications) has embarked on a journey toward building Angular/REST-based user interfaces to existing applications originally built using CSP and/or Zen. This has presented an interesting challenge that may be familiar to many of you - building out new REST APIs to existing data models and business logic.

Open Exchange app
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Article
· Jul 8, 2020 7m read
Tips for debugging with %Status

Introduction

If you're solving complex problems in ObjectScript, you probably have a lot of code that works with %Status values. If you have interacted with persistent classes from an object perspective (%Save, %OpenId, etc.), you have almost certainly seen them. A %Status provides a wrapper around a localizable error message in InterSystems' platforms. An OK status ($$$OK) is just equal to 1, whereas a bad status ($$$ERROR(errorcode,arguments...)) is represented as a 0 followed by a space followed by a $ListBuild list with structured information about the error. $System.Status (see class reference) provides several handy APIs for working with %Status values; the class reference is helpful and I won't bother duplicating it here. There have been a few other useful articles/questions on the topic as well (see links at the end). My focus in this article will be on a few debugging tricks techniques rather than coding best practices (again, if you're looking for those, see links at the end).

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Healthcare interoperability is instrumental in improving patient care, decreasing healthcare provider costs, and providing a more accurate picture to providers. However, with so many different systems, data is formatted in many different ways. There are many standards that have been created to try to solve this problem, including HL7v2, HL7v3, and CDA but each one has its drawbacks.

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Updated Jan 19th, 2023.

Hi all,

I want to share a quick little method you can use to enable ssl with a self signed certificate on your local development instance of IRIS/HealthShare. This enables you to test https-specific features such as OAuth without a huge lift.

1. Install OpenSSL

Windows     : Download from https://www.openssl.org or other built OpenSSL Binary. 

Debian Linux: $ sudo apt-get -y install openssl

RHEL        : $ sudo yum install openssl
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