1,676 FHIR-related articles (latest first)
HL7 has released the specification for 'Data Types,' a code system that specifies the format of observation values used in segments like Observation Result (OBX). This document provides technical details on defining data types for the v2.4 version, crucial for healthcare information exchange.
This article technically explains how to search for FHIR Subscriptions resources within AWS HealthLake. Developers can search subscriptions and topics using not only standard parameters but also detailed custom parameters such as `contact`, `criteria`, `payload`, `status`, `type`, `url`, `filter-criteria`, `custom-channel`, `payload-type`, and `topic`.
HL7 has released a new specification detailing imaging reports for digital health solutions in Europe. This guide defines the structure and content of imaging studies and reports using a FHIR model, ensuring compatibility across various data exchange environments.
This article introduces Whitefox's AI converter, explaining the technology that automatically transforms custom or legacy JSON data into validated FHIR resources. This eliminates complex manual mapping for developers and achieves HL7-compatible data exchange.
HL7 Europe has opened a public review period for four HL7 FHIR Implementation Guides (IGs) designed to meet the requirements of the European Health Data Space (EHDS). This effort standardizes multiple high-priority health data categories, including those defined by eHealth Network and shared assets like the European Health Insurance Card.
On October 24th, Biomedica successfully passed two major international interoperability testsβDICOM SWF and FHIR MHDβat the Asia-Pacific IHE Connectathon 2025 held in Tokyo. This demonstrates that Taiwan has reached global standards in the fields of AI medical imaging and smart health information exchange.
Synthetic EHR offers a platform that accelerates healthcare innovation through strategic partnerships and developer ecosystem collaboration, built on privacy-first principles. By utilizing FHIR-native synthetic data, it removes regulatory barriers and achieves seamless integration via an API-first approach.
HL7 has published the ValueSet 'hl7VSpcaTypeV100' to specify types of Pain Control Analgesia (PCA). This resource can be used for classifying PCA in healthcare, supporting standardized data exchange.
Dr. Gustavo Ferrer explains that AI accelerates drug development and pharmaceutical manufacturing by transforming real-world clinical data into actionable insights. Platforms like Moxie-Link, which are FHIR-compliant, integrate care environments directly into research to reduce timelines and costs.
This service provides an automated tool to convert C-CDA, a clinical document format, into structured FHIR bundles. This resolves the difficulties associated with mapping and manual conversion from traditional XML-based data formats, enabling quick access to standards-compliant data necessary for analytics and system integration.
A new study demonstrated that combining CDISC standards with HL7 FHIR enhances data for Alzheimerβs Disease (ADRD) research. This synergy increases the usability of real-world data from electronic health records, accelerating the bridge between clinical care and research.
Google Cloud provides technical 'Reference Patterns' utilizing the Cloud Healthcare API to convert data, such as HL7v2 messages and FHIR resources, into standard models like OMOP. This allows structured organization of data obtained from Electronic Health Records (EHR).
This page provides information regarding the External Validation Service Front-end, promoted by IHE (Integrating the Heathcare Enterprise). The platform aims to enhance healthcare interoperability and connectivity, demonstrating continuous development efforts.
This tutorial guides researchers and data scientists on how to use Google Cloud's Cloud Healthcare API to remove or modify Personally Identifiable Information (PII) and Protected Health Information (PHI) from FHIR clinical data. This process protects patient privacy while preparing the data for research, data sharing, and machine learning.
FUME provides a real-time data interceptor that captures and transforms healthcare data streams. This ensures immediate compliance with FHIR standards, preventing interruptions in critical workflows like clinical alerts or monitoring updates that rely on rapid data exchange.
This service addresses the difficulty developers and clinical informaticists face when dealing with raw JSON payloads. By using the FHIR Viewer, complex FHIR JSON data is converted into a human-readable format, simplifying validation and sharing.
A new process has been established for clinicians and standards experts. This allows stakeholders to propose changes to the International Patient Summary (IPS) suite of standards, ensuring their continued accuracy and global applicability. Proposed changes can include corrections or improvements to existing standards, scope enhancements, or the addition of entirely new standards.
Outburn has introduced 'FUME,' a proprietary real-time data conversion engine. It enables non-intrusive, bidirectional data exchange between legacy formats like HL7 v2 and the modern FHIR standard.
Outburn has implemented a solution using embedded FUME Community Edition to establish seamless, standardized communication between healthcare organizations and insurance providers. This automates the payment authorization process that previously required patients to wait upon arrival, enabling real-time eligibility checks before medical care.
HL7 has released a code system for 'Religion' in the v2+ format. This CodeSystem includes identifiers for various denominations, such as Atheist, Catholic, and Hindu, helping to standardize faith information within medical data.