Search results for "FHIR": 1,661 articles
This content focuses on achieving interoperability of medical data using EHRs and APIs. Various presentations from experts are introduced, demonstrating that FHIR (Fast Healthcare Interoperability Resources) is crucial as a standard data exchange format in modern healthcare IT.
Medplumは、FHIR R4を開発者向けのバックエンドとして扱い、リソースタイプに基づいたRESTful APIを提供している。本記事では、単なるAPIの羅列ではなく、患者登録や観察記録の最終化といった意味のあるワークフローを「Arazzo」という形で定義し、その実装方法を解説した。
The author details a method for synthetically creating paired datasets of clinical notes and structured healthcare data in the FHIR format. This approach allows for the construction of necessary training data for fine-tuning small, private language models without relying on sensitive real patient records.
Modern healthcare AI is evolving beyond relying solely on Large Language Models (LLMs). By combining technologies like FHIR, Model Context Protocol (MCP), and Retrieval-Augmented Generation (RAG), it enables accurate and explainable medical reasoning based on real-time clinical data.
FHIR Operations provide functionality beyond basic CRUD actions, supporting complex clinical workflows such as validation, terminology management, and patient identity matching. This facilitates a transition from simple data exchange to safe and high-quality clinical processes.
In healthcare data exchange, intermediaries manage transactional functions and data flows between payers and providers. This content discusses common failure patterns observed in live operations and the necessity of testing for regulatory compliance, which cannot be captured by sandbox environments.
HL7は、DiagnosticReportリソースに関する「Europe Base and Core FHIR IG」v2.0.0を公開した。これは、HL7 Europe BaseおよびCore（STU 2）に基づき、FHIR R4を採用している。この仕様書は、診断レポートの構造化データ交換のための技術的なリファレンスを提供する。
Modern healthcare organizations are advised to adopt an architecture combining a Data Warehouse (DWH) and a Data Lake. This approach enables both reliable operational decision-making and advanced AI analytics by utilizing massive volumes of data from sources like EHRs and lab systems.
This article explains the implementation pipeline required to integrate an AI predictive model—specifically, one predicting length of stay (LOS)—into real-time hospital systems, utilizing the international standard HL7 FHIR. This aims to achieve healthcare efficiency through AI utilization.
A survey of 101 experts across 63 countries shows that FHIR has evolved from a mere standard into critical global infrastructure. Crucially, the advancement of AI is seen not as a threat, but as a catalyst driving deeper investment in structured data (FHIR), which is increasingly mandated by regulations and adopted nationally.
This article introduces a reference architecture built on Oracle Cloud Infrastructure (OCI) designed to handle bursty traffic while optimizing costs for HL7, EDI, and FHIR data processing. This approach aims to reduce risks associated with delays in healthcare data intake and transaction processing.
HL7 Europe has released the mapping specification for the 'Address model,' based on FHIR standards. This is provided as a technical reference within the Europe Base and Core FHIR IG, useful for developers.
Data engineering firm Sonra has released 'Flexter,' a free online tool designed to make legacy enterprise XML data usable by modern analytics platforms. The tool automatically converts complex, hierarchical XML files into flat structures like CSV, aiding in the utilization of historical data.
Google has integrated its Gemma 4 AI models and Medical Data Toolkit into the Aarogya Setu 2.0 app. This enables the extraction of crucial clinical information from unstructured medical records and converts it into the FHIR standard format, thereby enhancing interoperability in India's digital health sector.
FHIR Profiles define how base FHIR resources (like Patient, Observation) should be used in specific contexts or according to regional requirements. This process eliminates data exchange ambiguity and achieves reliable, testable interoperability.
In data exchange, using 'FHIR Documents' is recommended when a complete clinical context is needed, while utilizing 'FHIR APIs' is necessary for accessing specific, real-time operational data. Since the two have different purposes and technical characteristics, proper differentiation of use is required.
A FHIR Bundle groups multiple related healthcare resources (like patient data, lab results, etc.) into one structured package. This allows data to move safely and consistently between systems while preserving context.
This article explains that the FHIR (Fast Healthcare Interoperability Resources) server serves as the backbone for structured data exchange between various healthcare systems. It details how multiple components, such as API Gateways and Authorization Servers, collaborate to ensure security and reliability.
bonfireDB has announced a suite of development tools designed to extract and format only the necessary information from large FHIR records or search queries, optimizing them for AI agent input. This capability prevents context window overflow and enables data utilization based on clinical context.
InterSystems presented a video detailing how to combine HL7 FHIR and Agentic AI to build intelligent healthcare applications that operate directly on structured clinical data. The presentation covered specific technical elements, including the architecture of AI agents and the importance of terminology normalization.