Search results for "FHIR": 3,717 articles
In 2026, digital health is moving from experimentation into execution. While technologies like AI and remote monitoring are becoming widespread, the critical importance of standards like FHIR and SNOMED CT for data exchange infrastructure is being reaffirmed.
Espeir introduces CLARYS, which continuously and reliably reads the patient journey to support medical decision-making. It integrates data from multiple sources and presents clinical information in an immediately usable format without modifying existing systems.
This paper introduces 'Auris,' an end-to-end pipeline that generates validated FHIR R4 resources directly from clinician-patient audio. The system moves beyond simple transcription to achieve structured data extraction, providing information in a format usable by Electronic Health Records (EHRs).
This article reviews terminology servers required to handle specialized vocabularies for genomics in the context of FHIR-aligned data processing. It introduces five specific solutions capable of supporting multiple standards like HGNC and LOINC, while also addressing practical clinical operational challenges.
The Peruvian Ministry of Health (Minsa) held the "Conectatón Perú 2026" to provide an innovation space aimed at improving interoperability across the entire digital healthcare system. This initiative aims to enable safe and efficient information sharing among medical facilities nationwide, thereby enhancing the quality of patient care.
This article analyzes the problem of data silos in healthcare IT, pointing to structural changes like payment models (1942) and lack of market forces as root causes. It ultimately argues that technical solutions like FHIR using RESTful JSON APIs are inevitable fixes.
VANNIN GROUP's Greencube facilitates the exchange of clinical data with systems already running in hospitals. It utilizes multiple international standards, including HL7 v2 and FHIR, to achieve seamless integration of diverse data such as EHR, lab results, and medication information.
Artificial intelligence (AI) is transforming healthcare across various fields, including diagnostic support and bed management. This transformation relies fundamentally on data linkage (interoperability) based on standards like HL7 FHIR.
OpenHealth Technologies provides an integrated information infrastructure that transforms fragmented clinical data from sources like EHRs and LIS into structured, actionable intelligence. This enables healthcare providers and insurers to gain a holistic view of patients, facilitating preventative care and cost reduction.
When deciding on data standards for system interoperability, the choice should not be based on whether a standard is old or new. Instead, it must be determined by identifying who the data consumer is and what format they require.
Simply installing sensors or collecting data does not make a hospital safer. This article argues that achieving successful healthcare IoT deployment requires more than technical interoperability (FHIR); it demands a fundamental 'care redesign.'
Health Samurai released an open-source FHIR server performance benchmark comparing four types: Aidbox, HAPI FHIR, Medplum, and Microsoft FHIR Server. The report provides a multi-faceted comparison covering CRUD throughput, search speed, and storage footprint, highlighting the unique characteristics of each engine.
インフォコムと大阪急性期・総合医療センターは、診療情報管理システム「Medi-Bank」にAI機能を実装し、退院サマリーの原案自動生成システムを開発した。これにより、医師や看護師の文書作成業務負担軽減と記録品質の向上を目指す。
The World Health Organization (WHO) established and publishes ICD-10 as an international standard for classifying diseases. This global system assigns alphanumeric codes to illnesses and injuries, used worldwide for statistics, billing, and public health surveillance.
The FHIR converter 'FUME' provides various functions ($count, $append, $sort, $distinct) designed to handle data arrays. These functions enable JSON-based data manipulation, offering capabilities such as duplicate removal and sorting.
This page displays the history and detailed XML data for a FHIR resource (Binary) retrieved from the HAPI FHIR server. Specifically, it shows metadata and link structures for a binary resource with a specific ID.
This article explains the operational principles of the FHIR API, which is foundational to modern healthcare interoperability. It details how structured medical resources, such as patient data, can be securely retrieved and utilized using standard web technologies like GET requests.
A FHIR API exposes healthcare data as standardized resources through RESTful endpoints to various systems like EHRs and analytics platforms. This eliminates the need for custom interfaces for every system, enabling efficient and secure data exchange.
AI technology addresses the challenge of 'information gaps' that occur when doctors and nurses manually search for data. This enables immediate, on-site data access, improving medical safety and efficiency.
OpenELIS Global offers a single platform for 'One Health' testing systems, covering human, animal, and environmental domains. This solves the problem of traditionally siloed record management, enabling national-level surveillance data sharing and analysis.