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This anthropic article made me think of several InterSystems presentations and articles on the topic of data quality for AI applications. InterSystems is right that data quality is crucial for AI, but I imagined there would be room for small errors, but this study suggests otherwise. That small errors can lead to big hallucinations. What do you think of this? And how can InterSystems technology help?

https://www.anthropic.com/research/small-samples-poison

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Hello and welcome to the September 2025 Developer Community YouTube Recap.
InterSystems Ready 2025
By Dean Andrews, David Reche
By Aldo Faisal
By Kari Hedges
By Scott Gnau, Peter Lesperance, Tom Woodfin, Gokhan Uluderya, Jeff Fried, Daniel Franco
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The ObjectScript language has incredible JSON support through classes like %DynamicObject and %JSON.Adaptor. This support is due to the JSON format's immense popularity over the previous dominance of XML. JSON brought less verbosity to data representation and increased readability for humans who needed to interpret JSON content. To further reduce verbosity and increase readability, the YAML format was created. The very easy-to-read YAML format quickly became the most popular format for representing configurations and parameterizations, due to its readability and minimal verbosity.

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Deploying new IRIS instances can be a time-consuming task, especially when setting up multiple environments with mirrored configurations.

I’ve encountered this issue many times and want to share my experience and recommendations for using Ansible to streamline the IRIS installation process. My approach also includes handling additional tasks typically performed before and after installing IRIS.

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To manage the accumulation of production data, InterSystems IRIS enables users to manage the database size by periodically purging the data. This purge can apply to messages, logs, business processes, and managed alerts.

Please check the documentation for more details on the settings of the purge task:
https://docs.intersystems.com/irislatest/csp/docbook/DocBook.UI.Page.cls?KEY=EGMG_purge#EGMG_purge_settings

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Introduction

Since InterSystems has recently announced the discontinuation of support for InterSystems Studio starting from version 2023.2 in favor of exclusive development of extensions for the Visual Studio Code (VSC) IDE, believing that the latter offers a superior experience compared to Studio, many of us developers have switched or are beginning to use VSC. Many may have wondered how to open the Terminal to perform operations, as VSC does not have an Output panel like Studio did, nor an integrated feature to open the IRIS terminal, except by downloading the plugins developed by InterSystems.

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Hello everybody,

I would like to export project contents using Visual Studio Code, as well as I do with InterSystems Studio.

However, while attempting the export through the InterSystems extension I get the following error:

- There are no folders in the current workspace that code can be exported to.

These are the step to reproduce my error:

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Hi Community,

It seems our Developer Community AI has decided to take a coffee break ☕️ (probably after answering one too many tricky ObjectScript questions).

The importance of the coffee break

For now, it’s gone mysteriously silent and refuses to generate answers. We suspect it might be rethinking its life choices after reading one too many deeply philosophical ObjectScript questions.

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Hi,

It's me again😁, recently I am working on generating some fake patient data for testing purpose with the help of Chat-GPT by using Python. And, at the same time I would like to share my learning curve.😑

1st of all for building a custom REST api service is easy by extending the %CSP.REST

Creating a REST Service Manually

Let's Start !😂

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I know the next ones:

1. Place all different settings in environment variables. You have a different .env file for each environment, and you must add some code to Production for reading and setting these values. It's good for deploying into containers, but challenging for management when we have a large production. I mean, we have many settings that can vary depending on the environment: active flag, pool size, timeouts, and so on. Not only endpoints.

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Introduction

In my previous article, I introduced the FHIR Data Explorer, a proof-of-concept application that connects InterSystems IRIS, Python, and Ollama to enable semantic search and visualization over healthcare data in FHIR format, a project currently participating in the InterSystems External Language Contest.

In this follow-up, we’ll see how I integrated Ollama for generating patient history summaries directly from structured FHIR data stored in IRIS, using lightweight local language models (LLMs) such as Llama 3.2:1B or Gemma 2:2B.

The goal was to build a completely local AI pipeline that can extract, format, and narrate patient histories while keeping data private and under full control.

All patient data used in this demo comes from FHIR bundles, which were parsed and loaded into IRIS via the IRIStool module. This approach makes it straightforward to query, transform, and vectorize healthcare data using familiar pandas operations in Python. If you’re curious about how I built this integration, check out my previous article Building a FHIR Vector Repository with InterSystems IRIS and Python through the IRIStool module.

Both IRIStool and FHIR Data Explorer are available on the InterSystems Open Exchange — and part of my contest submissions. If you find them useful, please consider voting for them!

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Introduction

In a previous article, I presented the IRIStool module, which seamlessly integrates the pandas Python library with the IRIS database. Now, I'm explaining how we can use IRIStool to leverage InterSystems IRIS as a foundation for intelligent, semantic search over healthcare data in FHIR format.

This article covers what I did to create the database for another of my projects, the FHIR Data Explorer. Both projects are candidates in the current InterSystems contest, so please vote for them if you find them useful.

You can find them at the Open Exchange:

In this article we'll cover:

  • Connecting to InterSystems IRIS database through Python
  • Creating a FHIR-ready database schema
  • Importing FHIR data with vector embeddings for semantic search

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With the rapid adoption of telemedicine, remote consultations, and digital dictation, healthcare professionals are communicating more through voice than ever before. Patients engaging in virtual conversations generate vast amounts of unstructured audio data, so how can clinicians or administrators search and extract information from hours of voice recordings?

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Hey Community,

The InterSystems team put on our monthly Developer Meetup with a triumphant return to CIC's Venture Café, the crowd including both new and familiar faces. Despite the shakeup in both location and topic, we had a full house of folks ready to listen, learn, and have discussions about health tech innovation!

https://www.youtube.com/embed/-ER8dW6ZtQw
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Hi, Community!

🌤 Wondering about the potential benefits of moving to the cloud? Hear from our experts about:

Considerations for Adopting Health Connect Cloud

https://www.youtube.com/embed/d1u2ouDxKjs?utm_source=youtube&utm_medium=social&utm_campaign=d1u2ouDxKjs
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