1,676 FHIR-related articles (latest first)
This article introduces the 'RareLink' framework, designed to solve the problem of interoperability among international rare disease registries. It aims to accelerate research by utilizing standardized data models like FHIR and Phenopackets for large-scale data sharing.
Tenasol offers solutions to convert various healthcare data formats into FHIR, the latest standard. This conversion is possible from a wide range of formats, including unstructured data such as images and PDFs, in addition to structured ones like X12/EDI, HL7v2, and HL7v3.
Trisotech announced that it can provide reliable, AI-powered clinical orchestration by combining the creativity of AI with governance based on BPM+ and HL7 standards. This utilizes industry standards like FHIR.
Healthcare data interoperability is the ability for different systems to seamlessly exchange and utilize patient data. This article explains FHIR, a next-generation standard that is key to solving this challenge.
A resource profile called PractitionerTeleconsulto has been published based on the HL7 FHIR standard. This defines data structures related to teleconsultation (remote care), providing concrete implementation guidelines for healthcare information exchange.
This article provides a technical guide on how to set up FHIR integration within the Cline tool. Users must use the provided JSON configuration example, tailored for operating systems like macOS, Windows, or Linux, and input it via the 'Add MCP Server' function. Crucially, restarting Cline is required after completing this setup.
The definition for a ValueSet titled 'Opiate Antagonists' has been published by NCQA PHEMUR. This ValueSet includes multiple drug codes related to opioid antagonists, such as buprenorphine and naltrexone, which aids in standardizing medical information.
The article compares Kong, a comprehensive API gateway, with Runbeam (Harmony), a secure data integration platform. Both platforms have distinct design philosophies and offer optimal solutions depending on the use case.
Google Cloud has enabled the centralized processing of electronic health records (FHIR), diagnostic images (DICOM), and clinical messages (HL7v2) through the Cloud Healthcare API. This allows healthcare institutions to manage and utilize diverse data formats in an efficient environment.
This guide explains how to export messages from an HL7v2 data store within Google Cloud Healthcare API to a Pub/Sub topic. This method addresses the lack of automatic notification mechanism when new HL7v2 messages are imported into a data warehouse.
This article provides detailed instructions and code samples for updating the configuration (Pub/sub topic name) of a DICOM store using the Google Cloud Healthcare API. Developers can modify the notification settings of a DICOM store via API calls using major languages like Go, Java, and Node.js.
This documentation provides technical procedures and code samples for patching (updating) FHIR resources using the Google Cloud Healthcare API. Developers can manage resource state by utilizing `application/json-patch+json` Content-Type to modify specific field values.
This document describes the structure for a specific FHIR resource that must conform to two identical extensions (cdl-ext-is-emergency). This demonstrates data consistency and rigorous definition in healthcare information exchange.
This article discusses the major challenges facing digital transformation in healthcare, providing specific solutions. It examines the future state of medical systems, focusing particularly on technical aspects such as data exchange and system integration.
Researchers introduced FHIR-AgentBench, a new benchmark to evaluate the performance of Large Language Model (LLM) agents designed for HL7 FHIR standard. This benchmark focuses on complex questions arising in real clinical settings, aiming to surpass the limitations of conventional structured data QA.
Waymark Systems offers comprehensive digital health solutions based on FHIR standards, aiming to improve clinical workflows and patient care. The company ensures interoperability while integrating global clinical terminologies like SNOMED CT and LOINC, thereby eliminating data silos and enabling real-time decision support.
ITC Infotech has launched an FHIR-based interoperability solution for the healthcare industry. This system utilizes MS Azure FHIR API to collate and interoperate clinical data from multiple disparate systems and standards.
OpenMRS has updated its FHIR2 module, evolving it to version 3.0.0. The primary change involves migrating the Data Access Object (DAO) layer from the Hibernate Criteria API to the more standardized JPA Criteria API. Consequently, developers must utilize a new custom context called `OpenmrsFhirCriteriaContext` for query construction.
This systematic review systematically evaluates Common Data Models (CDMs) and data standards necessary to integrate diverse data sources and enable federated analysis. The study concludes that OMOP CDM and FHIR scored best across various criteria, emphasizing that achieving seamless interoperability requires enabling transformations between different representations rather than relying on a single global model.
Google has published a guide detailing how developers can securely and efficiently connect various IDEs to the Cloud Healthcare API using Model Context Protocol (MCP). This enables LLMs to directly search and manipulate FHIR and DICOM data within healthcare datasets.