When I started building Smart Discharge Navigator, I faced a common challenge: FHIR data is stored as JSON bundles, but the analysis I needed required joining multiple resources together, the kind of thing SQL was built for.

InterSystems FHIR SQL Builder solved this completely. Instead of parsing JSON bundles in application code, I was able to write plain SQL directly against FHIR resource projections inside IRIS for Health. This article shows exactly how I used it to build the population-level readmission risk scoring engine at the heart of the application.

An AI agent reads the FHIR patient record before the consultation, asks the right questions based on what it already knows, detects critical red flags, and writes its findings back — so the physician arrives prepared.

Healthcare Pain Points Addressed

Before a patient walks into a consultation room, a critical failure has already occurred: nobody read their medical history. The physician has 15 minutes. The patient repeats their conditions, medications, and allergies for the third time that year.

Smart Discharge Navigator: An AI Agent for Hospital Readmission Risk — Built on IRIS for Health

Hospital readmissions cost the US healthcare system $17 billion annually. A significant portion of those readmissions are preventable — but only if the right information reaches the right clinician at the right moment: at discharge.

That's the problem I set out to solve with Smart Discharge Navigator, my entry for the InterSystems Programming Contest: AI Agents for FHIR 2026.

In the previous article, we used pyprod to create production components while relying on the UI for production configuration. That same production can now be defined entirely in Python:

from intersystems_pyprod import Production, ServiceItem, ProcessItem, OperationItem

iris_package_name = "HelloWorld"

class MyProduction(Production):
    services = [
        ServiceItem(
            "MyServiceName",
            "HelloWorld.MyService",
            host_settings={"target": "MyProcessName"},
        )
    ]
    processes = [
        ProcessItem(
            "MyProcessName",
            "HelloWorld.MyProcess",
            host_settings={"target": "MyOperationName"},
        )
    ]
    operations = [
        OperationItem("MyOperationName", "HelloWorld.MyOperation")
    ]

On save the ClassMethod:

ClassMethod create() [ Language = python ] {

 print("python") 

}

it has received the error : Compilation started on 06/03/2026 02:15:21 with qualifiers 'cuk /multicompile=0'

 ERROR #7802: Worker job/s '1078:17' unexpectedly shut down in group '#Default:(9119826733444):0'.

 ERROR #7802: Worker job/s '1078:17' unexpectedly shut down in group '#Default:(9119826733444):0'.  

with the compileFlags "cuk" the return is:

ERROR #7802: Worker job/s '1160:34' unexpectedly shut down in group '#Default:(6933525859080):0'.

In the previous IoP article, I showed how IoP can expose Python messages to DTL by generating JSON schemas. That is useful when the message is primarily a Python object and we want the IRIS tooling to understand its structure.

This time, the direction is a little different.

Starting with IoP 3.7.1, a PersistentMessage can now be a native IRIS message body class. The Python class is still the source code you write, but the generated IRIS class extends Ens.MessageBody

With Embedded Python and the Native API, it is becoming increasingly natural to write part of IRIS application logic in Python. But one question quickly comes up: how can you manipulate IRIS persistent objects from Python without losing the connection to the native object model, class dictionary, indexes, storage, and SQL projections?

iris-persistence explores that question. The project provides a Python object persistence layer for InterSystems IRIS, inspired by %Persistent

I am trying to connect to hive database and IRIS Intersystems Databases using jaydebeapi in python. I am able to connect to one database at a time. While trying to connect to other database, I am getting the below error

"Class org.apache.hive.jdbc.HiveDriver is not found"

or 

"Class com.intersystems.jdbc.IRISDriver is not found"

lin1 -

hive_con = jd.connect(java_driver_class, jdbc_conn_url, [hive_user, hive_pass],jarfile)

lin2 -

iris_con = jd.connect(iris_driver_class, iris_conn_url, [iris_user, iris_pass],jarfile)

Hello everyone.

After trying to run Python methods in the intersystemsdc/irishealth-community Docker container, which no longer allows any Python methods to be executed, I decided to abandon this version and start working with containers.intersystems.com/intersystems/iris-community.

In this environment, the Python methods work, which was a significant improvement.

I'm trying to import my libraries with pip install -r requirements.txt

InterSystems IRIS globals are one of the platform's core strengths: they store hierarchical data in a direct, ordered, and efficient structure. But when working from Python, manipulating globals can sometimes feel closer to a low-level API than to the natural habits of the language.

The iris-global-reference project provides a Python layer on top of IRIS globals. Its goal is simple: make access to globals more readable, more idiomatic, and easier to integrate into modern Python code, without hiding the underlying hierarchical model.

When developing Python applications with InterSystems IRIS, you can quickly end up with several execution contexts:

• Python launched directly by IRIS with Embedded Python;
• a regular python3 process that loads the Embedded Python libraries from a local IRIS installation;
• an external Python application that connects to IRIS through the official native driver.

These three cases are useful, but they do not behave exactly the same way for imports, system configuration, object APIs, and SQL access.

This article presents a straightforward approach to automatically and efficiently tune hyperparameters for machine learning models using Optuna as the optimisation framework. We explore how to use both Optuna’s native storage options and InterSystems IRIS as a database backend to track the progress of hyperparameter searches. We also show how MLflow can be used to monitor experiments and manage models through its tracking and model registry UI.

This article is based on this Kaggle Notebook, which you can run and directly edit yourself.

Data privacy regulations such as GDPR, LGPD, and HIPAA demand that organizations know exactly where Personally Identifiable Information (PII) lives inside their databases. Yet in practice, most teams rely on manual inventories, tribal knowledge, or external scanning tools that require data to leave the database engine — a process that itself creates privacy and security risks.

This article presents an MVP that takes a different approach: it runs PII detection inside InterSystems IRIS using Embedded Python, analyzing data where it lives and never exporting it to an external process.