Let me introduce you to FHIRCraft, a lightweight tool to generate synthetic FHIR resources.

The objective of the article is to provide the reader with the following informations:

• Configure and use the FHIR server
• Create an OAuth2 Authorization Server
• Bind the FHIR server to the OAuth2 Authorization Server for support of SMART on FHIR
• Use the interoperability capabilities of IRIS for Health to filter FHIR resources
• Create a custom operation on the FHIR server

Schema of the article:

Programmatic Production Access

To Programmatically Edit Productions (interfaces) you can use a combination of the interoperability apis and SQL queries.

Current Namespace

At a high level, it is important to know the namespace and production you are working in at the moment.

My IRIS Haiku:

Store all things with care

Interoperability

IRIS unites us

Using embedded Python while building your InterSystems-based solution can add very powerful and deep capabilities to your toolbox.

I'd like to share one sample use-case I encountered - enabling a CDC (Change Data Capture) for a mongoDB Collection - capturing those changes, digesting them through an Interoperability flow, and eventually updating an EMR via a REST API.

Hey community, how are you doing?

How do you exercise your creativity? How do you test new ideas?

Over the years I have always thought about creating new things, modifying existing ones, experimenting, testing, breaking (it always happens), building again, starting over.

FHIR repositories, applications and servers typically serve clinical data in small quantities, whether to return data about a patient, their medications, vaccines, allergies, among other information. However, it is common for a large amount of data in FHIR/JSON format to be requested to be used to load into Data Lakes, identifying study cohorts, population health, or transferring data from one EHR to another. To meet these business scenarios that require large extractions and loads of data, it is recommended to use the FHIR Bulk Data Access feature provided by HL7 institution.

The OpenAPI Specification (OAS) defines a standard, language-agnostic interface to HTTP APIs which allows both humans and computers to discover and understand the capabilities of the service without access to source code, documentation, or through network traffic inspection. When properly defined, a consumer can understand and interact with the remote service with a minimal amount of implementation logic. While for SOAP based APIs there is a special wizard in InterSystems IRIS that cuts down orchestrations development time, not all APIs used in integrations are SOAP. That's why @Jaime Lerga suggested to add a wizard similar to the SOAP wizard to generate a REST client from OpenAPI specification. Implementation of this idea cuts down the development time of the REST API orchestrations with InterSystems IRIS. This idea is one of most popular ideas on the InterSystems ideas. This article, the third in the "Implemented Ideas" series, focuses on the OpenAPI Suite solution developed by @Lorenzo Scalese.

For my hundredth article on the Developer Community, I wanted to present something practical, so here's a comprehensive implementation of the GPG Interoperability Adapter for InterSystems IRIS.

Every so often, I would encounter a request for some GPG support, so I had several code samples written for a while, and I thought to combine all of them and add missing GPG functionality for a fairly complete coverage. That said, this Business Operation primarily covers data actions, skipping management actions such as key generation, export, and retrieval as they are usually one-off and performed manually anyways. However, this implementation does support key imports for obvious reasons. Well, let's get into it.

Problem

In a fast-paced clinical environment, where quick decision-making is crucial, the lack of streamlined document storage and access systems poses several obstacles. While storage solutions for documents exist (e.g, FHIR), accessing and effectively searching for specific patient data within those documents meaningfully can be a significant challenge.

I would like to demonstrate how you can create monitoring of Production with Node-RED, and get a dashboard like this.

In the previous article we have reviewed how to install our EMPI in standalone, so we are ready to start the basic configuration of our EMPI.

First of all we have to do an initial basic configuration, we can access to the configuration from the Configuration menu of our Registry.

Selecting that option will allow to us to edit the basic configuration table of the Registry:

Introduction

InterSystems has recently completed a performance and scalability benchmark of IRIS for Health 2020.1, focusing on HL7 version 2 interoperability. This article describes the observed throughput for various workloads, and also provides general configuration and sizing guidelines for systems where IRIS for Health is used as an interoperability engine for HL7v2 messaging.