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. 

Now I want to return a large amount of data to the front end. The string length has reached 40000 +, and the returned data needs to be encrypted by AES + Base64. I can convert the string into a stream. AES can use the AESCBCEncryptStream method to encrypt, but Base64 has no stream method。Anyone who get the solution  would you kindly share the solution please。

Any help would be appreciated. Thanks!

As an AI language model, ChatGPT is capable of performing a variety of tasks like language translation, writing songs, answering research questions, and even generating computer code. With its impressive abilities, ChatGPT has quickly become a popular tool for various applications, from chatbots to content creation.
But despite its advanced capabilities, ChatGPT is not able to access your personal data. So in this article, I will demonstrate below steps to build custom ChatGPT AI by using LangChain Framework:

++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).

Hi, this post was initially written for Caché. In June 2023, I finally updated it for IRIS. If you are revisiting the post since then, the only real change is substituting Caché for IRIS! I also updated the links for IRIS documentation and fixed a few typos and grammatical errors. Enjoy :)

In this post, I show strategies for backing up InterSystems IRIS using External Backup with examples of integrating with snapshot-based solutions. Most 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.

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).

[A list of other posts in this series is here](https://community.intersystems.

Another VSCode "Tips & Tricks" entry -

Do you want to see this option in VSCode?

This post provides guidelines for configuration, system sizing and capacity planning when deploying IRIS and IRIS on a VMware ESXi. This post is based on and replaces the earlier IRIS-era guidance and reflects current VMware and InterSystems recommendations.

Last update Jan 2026. These guidelines are a best effort, remember requirements and capabilities of VMware and IRIS can change.

I jump right in with recommendations assuming you already have an understanding of VMware vSphere virtualization platform.

Hi!

Suppose I have full access to InterSystems IRIS database instance A and want to export a consistent part of the data and import it into another InterSystems IRIS instance B. Classes are equal.

What are the most general and convenient options for me?

TIA!

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 

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.

 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.

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

This post was originally written in 2017, I am updating the post in February 2026. For context I have kept the original post, but highlighted changes. This post was originally written for ESXi 6.0. The core principles remain valid for vSphere 7.x and 8.x, though there have been improvements to vNUMA handling, CPU scheduling (particularly for AMD EPYC), and CPU Hot Add compatibility with vNUMA in vSphere 8.

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.

• Part 1 - Getting started on the Journey, collecting metrics.
• Part 2 - Looking at the metrics we collected.
• Part 3 - Focus on CPU.
• Part 4 - Looking at memory.
• Part 5 - Monitoring with SNMP.
• Part 6 - Caché storage IO profile.
• Part 7 - ECP for performance, scalability and availability.
• Part 8 - Hyper-Converged Infrastructure Capacity and Performance Planning
• Part 9 - Caché VMware Best Practice Guide
• Part 10 - VM Backups and IRIS freeze/thaw scripts
• Part 11 - Virtualizing large databases - VMware cpu capacity planning

** 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.

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.

If you're building solutions on IRIS and want to use Git, that's great! Just use VSCode with a local git repo and push your changes out to the server - it's that easy.

But what if:

• You're collaborating with other developers on a shared, remote development environment and want to avoid concurrent editing of the same file
• You're using editors based in the management portal for BPL, DTL, pivots, dashboards, etc.

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?

InterSystems Data Platforms and performance - Part 5 Monitoring with SNMP

In previous posts I have shown how it is possible to collect historical performance metrics using pButtons. I go to pButtons first because I know it is installed with every Data Platforms instance (Ensemble, Caché, …). However there are other ways to collect, process and display Caché performance metrics in real time either for simple monitoring or more importantly for much more sophisticated operational analytics and capacity planning.

InterSystems has been using a two-stream release cadence with InterSystems IRIS since 2018 (see the original announcement from when we started).   We provide:

• Continuous delivery (CD) releases — these releases provide access to new features quickly and are ideal for developing and deploying applications that are continuously being updated and can take immediate advantage of new features.
• Extended maintenance (EM) releases — these releases are less frequent than the continuous delivery releases but provide the increased stability of maintenance releases.

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.

InterSystems is pleased to announce a new Developer Download site providing full kit versions of InterSystems IRIS Community Edition and InterSystems IRIS for Health Community Edition.   These are available free of charge for application development use.

You can download directly from the InterSystems Developer Community by selecting Download InterSystems IRIS.

Hi !

I am getting below error in my .NET MVC project, I am IRIS Entity Framwork, in the database table filed and model having the same datatype int.

The specified cast from a materialized 'System.Int64' type to the 'System.Int32' type is not valid db Table creation Id field is created with [xDBC Type = BIGINT]

Please kindly advice me.

Thank you

Hi Community,
In this article, I will demonstrate below steps to create your own chatbot by using spaCy (spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython):

• Step1: Install required libraries

• Step2: Create patterns and responses file

• Step3: Train the Model

• Step4: Create ChatBot Application based on the trained model

So Let us start.

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.

ECP was used widely for many years in TrakCare deployments I was involved in. A decade ago a 'big' x86 server from one of the major vendors might only have a total of eight cores.

In the previous article, we've seen the structure of one of the most used types of HL7 message - ADT (Admit, Discharge, Transfer) and an example of ADT^A04 with the description of all its fields. Now let's look at another flow of data having to do with ordering and fulfilling the orders of tests. I'm talking about ORM (as of version 2.5 you should use specific messages to order tests, like OMG, OML, OMD, OMS, OMN, OMI, and OMP), ORL and ORU messages. In a very simplified case, the exchange of data may look like this.

Let's look at these messages in more detail.

Hi Community!

We'd like to invite you to join our next contest to share your FHIR knowledge:

🏆 InterSystems IRIS for Health Contest: FHIR for Women's Health 🏆

Submit an application that uses InterSystems FHIR or InterSystems Healthcare Interoperability!

Duration: November 14 - December 4, 2022

Prizes: $13,500!

>> Submit your application here <<

AWS launched their first generation of Amazon EC2 A1 instances last year, powered by Arm-based AWS Graviton processors. At AWS re:Invent 2019, Amazon announced the second-generation AWS Graviton2 processors and associated Amazon EC2 M6g instance type, boasting up to 40% better price performance over current generation Intel Xeon based M5 instances.

The AWS Graviton2-based M6g instances are currently in preview, and InterSystems jumped on the opportunity to measure their performance with the InterSystems IRIS Data Platform.

Trackcare 2021.2

Please may I know  how to access SQL parameter with string as datatype with NO reference to SQL Table and SQL Column.

Hi Experts,

I have these two clarifications to make:

1. I am struggling to understand the process and method of accessing the SQL table with no reference to SQL parameter or Table but string

This is what I tried: Tired to create ERD diagrams and try to extract Primary and Foreign keys to associate, but I cannot in this case

I would like to do something like this

Select * FROM

SQLUser.SS_User SSU

LEFT JOIN SQLUser.CT_Loc CTL ON CTL.CTLOC_Desc = ?

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.

What is FHIR?

FHIR is an acronym for Fast Healthcare Interoperability Resources and is defined as a standard for healthcare information exchange that can be implemented in a short period.

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.