1,676 FHIR-related articles (latest first)
This article explains how to integrate data for electronic health record exchange using the HL7 FHIR standard. By utilizing the 'FHIRValidate' node within IBM App Connect for Healthcare, users can validate if incoming messages are valid FHIR resources and perform conversions between XML and JSON.
This system helps healthcare professionals efficiently access and interpret patient data scattered across multiple clinical systems, such as Snowflake databases and Epic FHIR. Using natural language queries, the AI retrieves, analyzes, and compiles the results into a comprehensive report.
This study developed 'Wirachain,' a decentralized application integrating HL7 FHIR standard with blockchain technology, aiming to solve clinical data interoperability challenges. It demonstrated that secure and continuous clinical data exchange is possible while allowing patients to manage access permissions.
Black Book Research published a 2026-2027 report analyzing that healthcare procurement is shifting from applications to 'data itself.' OpenEHR, combined with HL7 FHIR, is emerging as a key platform for maintaining AI-usable longitudinal clinical records.
In HAPI FHIR, developers can use an Interceptor mechanism to 'intercept' messages during the process (pipeline) when an application sends a request to the FHIR server. This allows for adding custom functionality, such as enhancing authentication or logging.
While companies like OpenAI and Anthropic offer large language models (LLMs) for healthcare, the article argues that mere algorithms are insufficient. Achieving true clinical impact requires end-to-end systems incorporating regulatory compliance and comprehensive data integration.
Health Chain provides Centaur™, an FHIR-native health data platform, to support clinical quality improvement and reporting. This platform serves as the foundation for achieving real-time digital quality measures (dQM).
FHIR, developed by HL7, is a standard enabling secure and standardized exchange of electronic health data. It helps organizations meet regulatory requirements (e.g., 21st Century Cures Act) while driving innovation in data sharing and patient information access.
HL7 FHIR adoption is accelerating due to regulatory mandates and technological advancements, with U.S. federal initiatives like TEFCA driving progress. This article outlines the latest trends of the FHIR standard for 2026 and highlights key areas healthcare organizations must prepare for.
InterSystems Developers released a video detailing advancements in FHIR data management, focusing on patient identification, clinical data enrichment, and AI-powered decision support. The session explores how FHIR, MPI, and AI collaborate to enhance interoperability and improve care quality in healthcare settings.
This study developed and validated bidirectional data transformation rules between HL7 FHIR and OMOP CDM using the open-source platform TermX. This demonstrates a methodology for mapping clinical care data (FHIR) into standardized models suitable for secondary use, addressing interoperability challenges in digital health.
Healthcare data exchange relies on a complex mesh of systems. This article emphasizes that true interoperability requires more than just data exchange; it highlights the necessity of 'usable interoperability,' integrating technical standards (like HL7/FHIR) with organizational governance and workflow design.
Better Digital Health Platform has released 'FHIR Connect,' an open, vendor-neutral framework. It integrates FHIR applications with openEHR clinical data repositories, enabling the construction of bidirectional and reusable interfaces.
応急医療AI企業「달구」は、現場、救急車、病院データをリアルタイムで統合し、最適な患者搬送決定を支援する。これにより、不必要な移送の削減と安全な非緊急医療輸送（NEMT）への移行を目指す。
Global Health Connector announced 'FHIR Connect,' a new service featuring an open framework. This tool supports the creation of bidirectional interfaces for handling clinical data by integrating FHIR applications with an openEHR repository.
Furore is working on diverse international healthcare IT projects based on the HL7 FHIR standard, supporting client Firely. Consultants share experiences of collaborating across different cultures and stakeholders globally, discussing challenges related to technical complexity and resulting professional growth.
This study experimentally analyzed the interoperability performance of healthcare data based on HL7 FHIR using a RESTful API architecture. The results showed that performance varies depending on resource type and operation (GET vs SEARCH), indicating that the FHIR server has processing capacity limits.
This kit provides tutorials on developing advanced medical applications by integrating Electronic Health Record (FHIR) data with generative AI using Python. It enables the analysis of unstructured text, such as clinical notes and patient histories, through vector search and LLM processing to provide insights for healthcare professionals.
人工知能（AI）の進化や接続デバイスの増加に伴い、病院のITインフラは戦略的な役割を担うよう変化している。データ処理能力の向上に加え、FHIRなどの標準規格を活用した相互運用性の確保とサイバーセキュリティが喫緊の課題となっている。
Complex Event Processing (CEP) platforms analyze streaming data from sources like EHRs and wearables to detect critical patterns such as patient deterioration or medication non-adherence. This enables predictive care and resource optimization within hospitals and smart healthcare ecosystems.