1,676 FHIR-related articles (latest first)
Danusa Calixto introduced support for Swagger UI for the InterSystems IRIS FHIR model. This enhancement improves development efficiency by enabling quick, manual testing against the FHIR API R4.
Evgeny Shvarov introduced Swagger UI support to the InterSystems IRIS template utilizing the FHIR R4 API. This enhancement allows developers to easily perform manual tests against the REST API, significantly improving rapid validation processes.
This article provides a TypeScript code snippet for handling data structures related to FHIR. Specifically, it defines the class and constant declaration for representing a 'Date' type using the `io-ts` library.
This article presents a TypeScript definition for a data structure (`ITiming`) related to healthcare interoperability standards. The definition includes multiple attributes for handling time-based information, such as events and code concepts.
This article defines the `IotFhirDestinationCollection` interface, which handles FHIR-based destinations for IoT Connectors within an Azure environment. This interface provides a structure to manage multiple FHIR destinations for IoT data linkage, offering developers a technical reference point necessary for implementation.
This article details the implementation process for data conversion using the $convert-data endpoint of Azure Health Data Services. It provides concrete API calling methods and sample code for converting non-FHIR data (e.g., HL7v2) into FHIR format.
This page provides a practice test for the "HL7 FHIR STU3 Proficiency," offered by HL7. The test is designed to assess proficiency in knowledge based on a specific version (v2025-03-10).
This article addresses the challenges posed by siloed medical data, explaining how the standard FHIR (Fast Healthcare Interoperability Resources) serves as a solution. FHIR coexists with other standards like SNOMED CT and IHE to achieve efficient data exchange through integration with AI.
This systematic review comprehensively investigated applications using Fast Healthcare Interoperability Resources (FHIR) for non-direct healthcare provision activities. The findings revealed that FHIR is being utilized across three major domains: (1) clinical research, (2) public health, and (3) medical education.
HAPI FHIR, an open-source Java library implementing the HL7 FHIR standard, was developed and provided by UHN. This article explains its major components, from data models to REST clients, using practical code examples.
This article details the usage of the `Get-AzHealthcareIotConnectorFhirDestination` cmdlet in a Microsoft Azure environment. This function allows users to retrieve properties for a specified IoT Connector's FHIR destination.
This project aims to address the major healthcare challenge of hospital readmissions, which incur high costs. It utilizes a predictive solution leveraging AI and FHIR-based data interoperability to analyze patient health data, aiming to reduce readmission risks through early intervention and personalized care.
A comprehensive set of FHIR resource samples has been made available for download.
The HL7 Patient Care WG Steward has published a ValueSet defining Restless Leg Syndrome. This set includes multiple standard codes from SNOMED CT and ICD-10-CM, aiding in the definition and classification of the disorder.
This article addresses the challenges of data modeling in Electronic Health Records (EHRs), explaining that openEHR offers superior flexibility and extensibility. By comparing it with the data exchange standard FHIR, it argues that openEHR is superior for long-term clinical data management and adapting to new concepts.
Fast Healthcare Interoperability Resources (FHIR) is recognized as a global standard for healthcare data interoperability. However, existing market solutions often treat it merely as a 'connector,' failing to fully utilize its potential. INFOR addresses this challenge by offering 'FHIR Bridge,' which accelerates data exchange and system integration using over 150 pre-configured resources.
The article emphasizes the critical need for accurate and shareable healthcare data, especially with the rise of EHRs and predictive analytics. It argues that leveraging AI for preprocessing and utilizing standards like HL7/FHIR can improve data quality and system efficiency.
Participants at CNews Conferences 'Digital Healthcare 2025' pointed out that AI has the potential to go beyond mere diagnosis and even perform treatment. They aim to improve medical quality by utilizing electronic health records and research data.
Google provides repository information for the FHIR R4 specification. The page lists various version control details, including multiple branches and tags, serving as a technical reference resource.
This article explains how to manage the linkage settings between an IoT Connector and a FHIR target in a Microsoft Azure environment using PowerShell cmdlets. Developers can use the `Get-AzHealthcareIotConnectorFhirDestination` command to retrieve existing FHIR destination properties based on details like resource group or workspace name.