1,676 FHIR-related articles (latest first)
InterSystems announced the details of technology bonuses for its programming contest focused on implementing AI agents using FHIR. The contest encourages participants to leverage advanced healthcare IT technologies such as FHIR server usage, vector search, and Large Language Models (LLMs).
Databricks and Health Samurai partnered to develop a FHIR-native health data platform. This initiative aims to unify fragmented healthcare data, making it accessible without traditional ETL processes.
This guide details the FUME server API for FHIR conversion. Developers must select appropriate API endpoints based on their workflow or goal (e.g., ad-hoc expression evaluation, mapping execution). It also clarifies the distinction between guide pages and OpenAPI contracts.
Researchers developed a new pipeline to leverage the medical information extraction capabilities of Large Language Models (LLMs) while addressing their unstructured output challenge. This method generates context-free grammars (CFGs) from FHIR JSON Schemas, demonstrating enhanced structural validity.
This article introduces services for developing scalable, HIPAA-compliant healthcare platforms. It emphasizes that enterprise system construction provides support for HL7/FHIR standards, integration with EHR systems like Epic, and advanced data security and compliance.
This document provides the structure and detailed specification for the FHIR resource, 'DeviceUseStatement,' used to record the usage status of medical devices. This resource helps track which patient used a device, when, and for what purpose.
This document provides the specification for the FHIR resource 'CoverageEligibilityResponse,' which is used to determine payment eligibility. This resource helps confirm whether services or products provided to a patient are covered by their specific insurance plan, incorporating details like the patient, requestor, and insurer.
FHIR APIs are a standard designed to simplify healthcare data exchange, solving interoperability challenges between various systems. This allows developers to build smarter, faster digital solutions that benefit both patients and healthcare providers.
Fire Arrow provides a backend for centralizing and managing data from various sources, including EHRs and legacy systems, through the FHIR standard. This enables diverse data utilization cases such as AI agents and remote patient monitoring.
Health Samurai and Databricks provide a unified foundation by standardizing diverse clinical data (e.g., HL7v2, C-CDA) into FHIR. This enables access to trusted data for all tools, including AI/ML, without requiring complex ETL processes, accelerating digital transformation in healthcare.
This article compares the technical differences between Australian (AU) and US FHIR implementations. While both use FHIR R4 as the base standard, significant variations exist at the Implementation Guide (IG) layer and in required data elements, indicating that simple configuration changes are insufficient.
Healthcare technology companies b.well Connected Health and myTomorrows have partnered to fundamentally improve how patients access clinical trials using AI-powered matching technology on unified health data. This collaboration streamlines the identification and enrollment of eligible patients by leveraging comprehensive, standardized health records.
Zintech offers cloud and AI support services utilizing the AWS FHIR API. The company deploys extensive technical capabilities to support infrastructure modernization and advanced data processing in the healthcare sector.
This study developed an information model using FHIR (Fast Healthcare Interoperability Resources) to enhance prenatal care. This allows multidisciplinary teams to seamlessly share clinical data, aiming to improve quality of care in primary healthcare.
FUME, a data transformation API factory with native FHIR support, provides mechanisms to convert various source data into FHIR resources and other structured formats. It enables flexible data processing using a mapping language that combines JSONata-based expressions and FLASH via the RESTful API.
This article details the specification for the 'MedicinalProductPharmaceutical' resource in FHIR R4. This resource provides a structured information model for pharmaceutical products, comprehensively defining elements such as identifiers, ingredients, dosage forms, and usage restrictions.
A FHIR Capability Statement is a machine-readable document published by a FHIR server or client that declares which resource types, operations, and profiles it supports. This allows users to programmatically confirm the API contract beforehand.
ASSYST offers 'FHIR®Break,' a zero-trust API solution enabling secure health information data exchange for enterprise users and subscribers. Built on an open API framework, it provides access to available FHIR resources and aggregate datasets.
A European data standardization project has published the specification for a logical model defining 'Health Professional' within electronic health records. This FHIR-based standard contributes to improving international healthcare data interoperability.
This study developed a standardized questionnaire based on HL7 FHIR to address the challenge of heterogeneous data collection for long-term follow-up of childhood cancer survivors. This provides a reusable foundation that supports not only primary clinical use but also secondary use within the European Health Data Space.