3,756 FHIR-related articles (latest first)
As the importance of provider-payer data integration grows, Michael Westover highlights significant challenges in data sharing. He argues that relying on vendors for longitudinal data is complex and inefficient, emphasizing that standardized APIs (like FHIR) and strong partnerships are crucial for achieving value-based care.
The National Resource Center for EHR Standards has published a testing ValueSet for Body Measurement. This is part of the FHIR Implementation Guide for ABDM, contributing to the standardization of data structures in healthcare information exchange.
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.
Master Data Management (MDM) is crucial for integrating siloed healthcare data from systems like EHRs and billing platforms. It establishes a trusted single view of patient, provider, and payer data, enabling true interoperability.
CSIRO's Kate Ebrill et al. emphasized the critical importance of interoperability in digital health. They pointed out that global standards like FHIR and SNOMED CT provide the necessary data foundation to build high-quality, 'sovereign AI.'
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.
Google Cloud's Cloud Healthcare API allows for the batch retrieval of large volumes of HL7v2 messages. This capability resolves issues related to network costs and processing load associated with traditional single-message fetching, thereby improving data integration efficiency.
The modern healthcare ecosystem relies on seamless information exchange from diverse data sources. This article explains 'interoperability standards,' detailing how major specifications like HL7 and FHIR provide a common language for medical data.
2025年アジア太平洋永続医療論壇では、台湾と日本の医療専門家が集結。AIは人間の判断を補佐する「助攻者」であり、単なる代替物ではないという認識で一致した。台湾の健保署や日本大学病院など、各機関がデータ標準化（FHIR等）や低リスクからの導入を通じて人機協働を進める。
People Tech Group Inc offers a solution combining Azure Health Data Services and AI. This service modernizes revenue cycle management (RCM) for dental and outpatient clinics, automating processes from billing to insurance handling.
This article provides guidance on applying the FHIR standard to specific use cases, such as eCTD v4.0 and Veeva CRM/Epic EHR integration. It details solutions for international regulatory requirements and technical challenges in areas like pharmaceutical labeling (e-labeling) and clinical research data (eSource/ePRO).
This article presents the specific JSON format for a ServiceRequest resource within the MyHealth@Eu Laboratory Report. It explains how a blood test order (Hemoglobin and Hematocrit panel) is structured and utilized in electronic health record systems.
HL7 Korea has published a specific example of an FHIR Observation for respiratory rate, based on the KR Core Implementation Guide v2.0.0. This guide demonstrates how to structure and exchange vital sign data within medical records, adhering to the FHIR R4 standard.
The AI Assistant in Aidbox Forms automates the entire workflow, generating FHIR-compliant Questionnaires, extraction logic, and analytics-ready ViewDefinitions from plain language requests. This significantly shortens the cycle of clinical form creation and improves data utilization efficiency.
本記事は、ヘルスケア業界におけるデータ共有と相互運用性の課題を背景に、主要な標準規格（FHIR, HL7, IHEなど）について基本的な知識を提供する。特に、これらの技術がどのように医療データの構造化と交換を実現しているかを解説する。
HL7は、診断関連グループ（DRG）における臨床的複雑性レベル（CCL）を示すためのコードシステム「CCL Value」を公開した。これは、HL7 Version 2.xメッセージングのDG1セグメントで使用されるもので、米国での利用が想定されている。
Curiflow offers a platform that automatically converts diverse healthcare data, including eFax, PDFs, and CCDA files, into the FHIR format. This solution significantly reduces development cycles while ensuring improved data quality and compliance.
This article explains how to build a fast, privacy-respecting mobile clinical record system by utilizing the SMART on FHIR standard and implementing local-first storage (Hive). It presents architectural principles for stable data access in environments requiring multiple EHR integrations or weak network connectivity.
HL7 has published the technical specification for a ValueSet (hl7VSsegmentGroupV500) defining segment groups. This code set is used to specify optional segment groups that should be included in a response.
This article explains how to programmatically retrieve FHIR resources using the Google Cloud Healthcare API. It provides detailed Go and Java code samples, demonstrating the process of accessing specified FHIR resources by utilizing parameters such as project IDs and dataset IDs.