Search results for "FHIR": 3,725 articles
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。
A new study demonstrated that combining CDISC standards with HL7 FHIR enhances data for Alzheimer’s Disease (ADRD) research. This synergy increases the usability of real-world data from electronic health records, accelerating the bridge between clinical care and research.
The Mount Sinai Tisch Cancer Center implemented an automated system that seamlessly transfers clinical data from electronic health records directly into clinical trial platforms. This eliminates manual entry errors and inefficiencies, accelerating cancer research.
Google Cloud provides technical 'Reference Patterns' utilizing the Cloud Healthcare API to convert data, such as HL7v2 messages and FHIR resources, into standard models like OMOP. This allows structured organization of data obtained from Electronic Health Records (EHR).
This tutorial guides researchers and data scientists on how to use Google Cloud's Cloud Healthcare API to remove or modify Personally Identifiable Information (PII) and Protected Health Information (PHI) from FHIR clinical data. This process protects patient privacy while preparing the data for research, data sharing, and machine learning.