3,748 FHIR-related articles (latest first)
This article introduces Whitefox's AI converter, explaining the technology that automatically transforms custom or legacy JSON data into validated FHIR resources. This eliminates complex manual mapping for developers and achieves HL7-compatible data exchange.
HL7 Europe has opened a public review period for four HL7 FHIR Implementation Guides (IGs) designed to meet the requirements of the European Health Data Space (EHDS). This effort standardizes multiple high-priority health data categories, including those defined by eHealth Network and shared assets like the European Health Insurance Card.
iEHR has published the mapping specification for the 'Drugs Payment Scheme' data type, based on Ireland's core implementation guide. This specification details how to map this data across multiple standards like HL7 v2, v3, and ServD, aiming to improve interoperability in healthcare information exchange.
Qualys SSL Labs conducted an SSL security test on a specific FHIR-related server (hapi.fhir.tw). The report shows multiple IP addresses with statuses like 'In progress' or 'Pending,' indicating that the detailed security evaluation results are not yet finalized.
C-CDA, the backbone of electronic health information exchange, faces limitations in meeting modern real-time data needs. Consequently, industry efforts are advancing data conversion from C-CDA to HL7 FHIR, a format that is more flexible and cloud-ready, significantly improving analytics and patient access.
The Ministry of Health and Welfare (MOHW) in Taiwan announced plans, as part of the 'Taiwan Medical Information Standard Platform,' to integrate electronic medical records across major hospitals nationwide within two years. This initiative aims to standardize medical information and improve accessibility.
This technical documentation provides examples of how to programmatically update a FHIR resource using Google Cloud's Cloud Healthcare API. Developers can reference implementation examples in Go and Java to change the status (active/inactive) of a FHIR resource within a specific dataset.
マウントサイナイのティッシュがんセンターは、電子カルテから臨床試験プラットフォームへのデータ自動転送を可能にした。これにより、医師と研究者が情報をより正確かつ効率的に共有できるようになり、オンコロジー分野の研究加速に貢献する。
On October 24th, Biomedica successfully passed two international interoperability tests—DICOM SWF and FHIR MHD—at the Asia-Pacific IHE Connectathon 2025 held in Tokyo. This demonstrates the company's system integration capability and adherence to international standards, showing that Taiwan is highly advanced in AI medical imaging and smart healthcare information interoperability.
On October 24th, Biomedica successfully passed two major international interoperability tests—DICOM SWF and FHIR MHD—at the Asia-Pacific IHE Connectathon 2025 held in Tokyo. This demonstrates that Taiwan has reached global standards in the fields of AI medical imaging and smart health information exchange.
A FHIR ValueSet (jp-condition-severity-vs) for indicating Condition severity in Japan has been published. This provides a foundational structure for uniformly handling condition severity information across medical institutions and systems, promoting standardization in Japanese electronic health records and information exchange systems.
The Mount Sinai Tisch Cancer Center has implemented a system that automatically transfers clinical data from its electronic health record (EHR) into clinical trial platforms. This allows doctors and researchers to share information faster and more accurately during cancer clinical trials, significantly reducing manual effort and time.
The healthcare industry is leveraging Fast Healthcare Interoperability Resources (FHIR) standards and Artificial Intelligence (AI) to significantly improve utilization management processes. This automation streamlines prior authorization, enhances data exchange transparency, and promises faster patient access to care.
Synthetic EHR offers a platform that accelerates healthcare innovation through strategic partnerships and developer ecosystem collaboration, built on privacy-first principles. By utilizing FHIR-native synthetic data, it removes regulatory barriers and achieves seamless integration via an API-first approach.
HL7 has published the ValueSet 'hl7VSpcaTypeV100' to specify types of Pain Control Analgesia (PCA). This resource can be used for classifying PCA in healthcare, supporting standardized data exchange.
本記事は、FHIR規格を用いて特定の診療従事者（Practitioner）のデータを検索・取得する方法を解説している。具体的には、検索リクエストやリードリクエストを通じて、識別子に基づいた詳細な個人情報を取得できることが示されている。
Israeli tech company Outburn provides a national data linkage solution to solve the problem of fragmented electronic health records. This aims to improve continuity and safety in medical care.
Dr. Gustavo Ferrer explains that AI accelerates drug development and pharmaceutical manufacturing by transforming real-world clinical data into actionable insights. Platforms like Moxie-Link, which are FHIR-compliant, integrate care environments directly into research to reduce timelines and costs.
This service provides an automated tool to convert C-CDA, a clinical document format, into structured FHIR bundles. This resolves the difficulties associated with mapping and manual conversion from traditional XML-based data formats, enabling quick access to standards-compliant data necessary for analytics and system integration.
本記事は、電子カルテ標準であるFHIRを中心に、将来的な脅威となる量子コンピューティングのリスクを指摘。データの長期保存が必要な医療分野において、現在の暗号化技術では対応できない「収穫後解読」のリスクが深刻であり、今から対策を講じる必要性を訴えている。